Modeling the value for money of changing clinical practice change: a stochastic application in diabetes care.

PubMed

Hoomans, Ties; Abrams, Keith R; Ament, Andre J H A; Evers, Silvia M A A; Severens, Johan L

2009-10-01

Decision making about resource allocation for guideline implementation to change clinical practice is inevitably undertaken in a context of uncertainty surrounding the cost-effectiveness of both clinical guidelines and implementation strategies. Adopting a total net benefit approach, a model was recently developed to overcome problems with the use of combined ratio statistics when analyzing decision uncertainty. To demonstrate the stochastic application of the model for informing decision making about the adoption of an audit and feedback strategy for implementing a guideline recommending intensive blood glucose control in type 2 diabetes in primary care in the Netherlands. An integrated Bayesian approach to decision modeling and evidence synthesis is adopted, using Markov Chain Monte Carlo simulation in WinBUGs. Data on model parameters is gathered from various sources, with effectiveness of implementation being estimated using pooled, random-effects meta-analysis. Decision uncertainty is illustrated using cost-effectiveness acceptability curves and frontier. Decisions about whether to adopt intensified glycemic control and whether to adopt audit and feedback alter for the maximum values that decision makers are willing to pay for health gain. Through simultaneously incorporating uncertain economic evidence on both guidance and implementation strategy, the cost-effectiveness acceptability curves and cost-effectiveness acceptability frontier show an increase in decision uncertainty concerning guideline implementation. The stochastic application in diabetes care demonstrates that the model provides a simple and useful tool for quantifying and exploring the (combined) uncertainty associated with decision making about adopting guidelines and implementation strategies and, therefore, for informing decisions about efficient resource allocation to change clinical practice.

Development of a model to guide decision making in amyotrophic lateral sclerosis multidisciplinary care.

PubMed

Hogden, Anne; Greenfield, David; Nugus, Peter; Kiernan, Matthew C

2015-10-01

Patients with amyotrophic lateral sclerosis (ALS) face numerous decisions for symptom management and quality of life. Models of decision making in chronic disease and cancer care are insufficient for the complex and changing needs of patients with ALS . The aim was to examine the question: how can decision making that is both effective and patient-centred be enacted in ALS multidisciplinary care? Fifty-four respondents (32 health professionals, 14 patients and eight carers) from two specialized ALS multidisciplinary clinics participated in semi-structured interviews. Interviews were transcribed, coded and analysed thematically. Comparison of stakeholder perspectives revealed six key themes of ALS decision making. These were the decision-making process; patient-centred focus; timing and planning; information sources; engagement with specialized ALS services; and access to non-specialized services. A model, embedded in the specialized ALS multidisciplinary clinic, was derived to guide patient decision making. The model is cyclic, with four stages: 'Participant Engagement'; 'Option Information'; 'Option Deliberation'; and 'Decision Implementation'. Effective and patient-centred decision making is enhanced by the structure of the specialized ALS clinic, which promotes patients' symptom management and quality of life goals. However, patient and carer engagement in ALS decision making is tested by the dynamic nature of ALS, and patient and family distress. Our model optimizes patient-centred decision making, by incorporating patients' cyclic decision-making patterns and facilitating carer inclusion in decision processes. The model captures the complexities of patient-centred decision making in ALS. The framework can assist patients and carers, health professionals, researchers and policymakers in this challenging disease environment. © 2013 John Wiley & Sons Ltd.

Clinical errors that can occur in the treatment decision-making process in psychotherapy.

PubMed

Park, Jake; Goode, Jonathan; Tompkins, Kelley A; Swift, Joshua K

2016-09-01

Clinical errors occur in the psychotherapy decision-making process whenever a less-than-optimal treatment or approach is chosen when working with clients. A less-than-optimal approach may be one that a client is unwilling to try or fully invest in based on his/her expectations and preferences, or one that may have little chance of success based on contraindications and/or limited research support. The doctor knows best and the independent choice models are two decision-making models that are frequently used within psychology, but both are associated with an increased likelihood of errors in the treatment decision-making process. In particular, these models fail to integrate all three components of the definition of evidence-based practice in psychology (American Psychological Association, 2006). In this article we describe both models and provide examples of clinical errors that can occur in each. We then introduce the shared decision-making model as an alternative that is less prone to clinical errors. PsycINFO Database Record (c) 2016 APA, all rights reserved

How shrinks think: decision making in psychiatry.

PubMed

Bhugra, Dinesh; Malliaris, Yanni; Gupta, Susham

2010-10-01

Psychiatrists use biopsychosocial models in identifying aetiological factors in assessing their patients and similar approaches in planning management. Models in decision making will be influenced by previous experience, training, age and gender, among other factors. Critical thinking and evidence base are both important components in the process of reaching clinical decisions. Expected outcome of treatment may be another factor. The way we think influences our decision making, clinical or otherwise. With patients expecting and taking larger roles in their own management, there needs to be a shift towards patient-centred care in decision making. Further exploration in how clinical decisions are made by psychiatrists is necessary. An understanding of the manner in which therapeutic alliances are formed between the clinician and the patient is necessary to understand decision making.

Prognostic and Prediction Tools in Bladder Cancer: A Comprehensive Review of the Literature.

PubMed

Kluth, Luis A; Black, Peter C; Bochner, Bernard H; Catto, James; Lerner, Seth P; Stenzl, Arnulf; Sylvester, Richard; Vickers, Andrew J; Xylinas, Evanguelos; Shariat, Shahrokh F

2015-08-01

This review focuses on risk assessment and prediction tools for bladder cancer (BCa). To review the current knowledge on risk assessment and prediction tools to enhance clinical decision making and counseling of patients with BCa. A literature search in English was performed using PubMed in July 2013. Relevant risk assessment and prediction tools for BCa were selected. More than 1600 publications were retrieved. Special attention was given to studies that investigated the clinical benefit of a prediction tool. Most prediction tools for BCa focus on the prediction of disease recurrence and progression in non-muscle-invasive bladder cancer or disease recurrence and survival after radical cystectomy. Although these tools are helpful, recent prediction tools aim to address a specific clinical problem, such as the prediction of organ-confined disease and lymph node metastasis to help identify patients who might benefit from neoadjuvant chemotherapy. Although a large number of prediction tools have been reported in recent years, many of them lack external validation. Few studies have investigated the clinical utility of any given model as measured by its ability to improve clinical decision making. There is a need for novel biomarkers to improve the accuracy and utility of prediction tools for BCa. Decision tools hold the promise of facilitating the shared decision process, potentially improving clinical outcomes for BCa patients. Prediction models need external validation and assessment of clinical utility before they can be incorporated into routine clinical care. We looked at models that aim to predict outcomes for patients with bladder cancer (BCa). We found a large number of prediction models that hold the promise of facilitating treatment decisions for patients with BCa. However, many models are missing confirmation in a different patient cohort, and only a few studies have tested the clinical utility of any given model as measured by its ability to improve clinical decision making. Copyright © 2015 European Association of Urology. Published by Elsevier B.V. All rights reserved.

Comparative-effectiveness research to aid population decision making by relating clinical outcomes and quality-adjusted life years.

PubMed

Campbell, Jonathan D; Zerzan, Judy; Garrison, Louis P; Libby, Anne M

2013-04-01

Comparative-effectiveness research (CER) at the population level is missing standardized approaches to quantify and weigh interventions in terms of their clinical risks, benefits, and uncertainty. We proposed an adapted CER framework for population decision making, provided example displays of the outputs, and discussed the implications for population decision makers. Building on decision-analytical modeling but excluding cost, we proposed a 2-step approach to CER that explicitly compared interventions in terms of clinical risks and benefits and linked this evidence to the quality-adjusted life year (QALY). The first step was a traditional intervention-specific evidence synthesis of risks and benefits. The second step was a decision-analytical model to simulate intervention-specific progression of disease over an appropriate time. The output was the ability to compare and quantitatively link clinical outcomes with QALYs. The outputs from these CER models include clinical risks, benefits, and QALYs over flexible and relevant time horizons. This approach yields an explicit, structured, and consistent quantitative framework to weigh all relevant clinical measures. Population decision makers can use this modeling framework and QALYs to aid in their judgment of the individual and collective risks and benefits of the alternatives over time. Future research should study effective communication of these domains for stakeholders. Copyright © 2013 Elsevier HS Journals, Inc. All rights reserved.

The professional medical ethics model of decision making under conditions of clinical uncertainty.

PubMed

McCullough, Laurence B

2013-02-01

The professional medical ethics model of decision making may be applied to decisions clinicians and patients make under the conditions of clinical uncertainty that exist when evidence is low or very low. This model uses the ethical concepts of medicine as a profession, the professional virtues of integrity and candor and the patient's virtue of prudence, the moral management of medical uncertainty, and trial of intervention. These features combine to justifiably constrain clinicians' and patients' autonomy with the goal of preventing nondeliberative decisions of patients and clinicians. To prevent biased recommendations by the clinician that promote such nondeliberative decisions, medically reasonable alternatives supported by low or very low evidence should be offered but not recommended. The professional medical ethics model of decision making aims to improve the quality of decisions by reducing the unacceptable variation that can result from nondeliberative decision making by patients and clinicians when evidence is low or very low.

Development of a Model of Interprofessional Shared Clinical Decision Making in the ICU: A Mixed-Methods Study.

PubMed

DeKeyser Ganz, Freda; Engelberg, Ruth; Torres, Nicole; Curtis, Jared Randall

2016-04-01

To develop a model to describe ICU interprofessional shared clinical decision making and the factors associated with its implementation. Ethnographic (observations and interviews) and survey designs. Three ICUs (two in Israel and one in the United States). A convenience sample of nurses and physicians. None. Observations and interviews were analyzed using ethnographic and grounded theory methodologies. Questionnaires included a demographic information sheet and the Jefferson Scale of Attitudes toward Physician-Nurse Collaboration. From observations and interviews, we developed a conceptual model of the process of shared clinical decision making that involves four stepped levels, proceeding from the lowest to the highest levels of collaboration: individual decision, information exchange, deliberation, and shared decision. This process is influenced by individual, dyadic, and system factors. Most decisions were made at the lower two levels. Levels of perceived collaboration were moderate with no statistically significant differences between physicians and nurses or between units. Both qualitative and quantitative data corroborated that physicians and nurses from all units were similarly and moderately satisfied with their level of collaboration and shared decision making. However, most ICU clinical decision making continues to take place independently, where there is some sharing of information but rarely are decisions made collectively. System factors, such as interdisciplinary rounds and unit culture, seem to have a strong impact on this process. This study provides a model for further study and improvement of interprofessional shared decision making.

Helping Health Care Providers and Clinical Scientists Understand Apparently Irrational Policy Decisions.

PubMed

Demeter, Sandor J

2016-12-21

Health care providers (HCP) and clinical scientists (CS) are generally most comfortable using evidence-based rational decision-making models. They become very frustrated when policymakers make decisions that, on the surface, seem irrational and unreasonable. However, such decisions usually make sense when analysed properly. The goal of this paper to provide a basic theoretical understanding of major policy models, to illustrate which models are most prevalent in publicly funded health care systems, and to propose a policy analysis framework to better understand the elements that drive policy decision-making. The proposed policy framework will also assist HCP and CS achieve greater success with their own proposals.

Comprehensible knowledge model creation for cancer treatment decision making.

PubMed

Afzal, Muhammad; Hussain, Maqbool; Ali Khan, Wajahat; Ali, Taqdir; Lee, Sungyoung; Huh, Eui-Nam; Farooq Ahmad, Hafiz; Jamshed, Arif; Iqbal, Hassan; Irfan, Muhammad; Abbas Hydari, Manzar

2017-03-01

A wealth of clinical data exists in clinical documents in the form of electronic health records (EHRs). This data can be used for developing knowledge-based recommendation systems that can assist clinicians in clinical decision making and education. One of the big hurdles in developing such systems is the lack of automated mechanisms for knowledge acquisition to enable and educate clinicians in informed decision making. An automated knowledge acquisition methodology with a comprehensible knowledge model for cancer treatment (CKM-CT) is proposed. With the CKM-CT, clinical data are acquired automatically from documents. Quality of data is ensured by correcting errors and transforming various formats into a standard data format. Data preprocessing involves dimensionality reduction and missing value imputation. Predictive algorithm selection is performed on the basis of the ranking score of the weighted sum model. The knowledge builder prepares knowledge for knowledge-based services: clinical decisions and education support. Data is acquired from 13,788 head and neck cancer (HNC) documents for 3447 patients, including 1526 patients of the oral cavity site. In the data quality task, 160 staging values are corrected. In the preprocessing task, 20 attributes and 106 records are eliminated from the dataset. The Classification and Regression Trees (CRT) algorithm is selected and provides 69.0% classification accuracy in predicting HNC treatment plans, consisting of 11 decision paths that yield 11 decision rules. Our proposed methodology, CKM-CT, is helpful to find hidden knowledge in clinical documents. In CKM-CT, the prediction models are developed to assist and educate clinicians for informed decision making. The proposed methodology is generalizable to apply to data of other domains such as breast cancer with a similar objective to assist clinicians in decision making and education. Copyright © 2017 Elsevier Ltd. All rights reserved.

Reflections in the clinical practice.

PubMed

Borrell-Carrió, F; Hernández-Clemente, J C

2014-03-01

The purpose of this article is to analyze some models of expert decision and their impact on the clinical practice. We have analyzed decision-making considering the cognitive aspects (explanatory models, perceptual skills, analysis of the variability of a phenomenon, creating habits and inertia of reasoning and declarative models based on criteria). We have added the importance of emotions in decision making within highly complex situations, such as those occurring within the clinical practice. The quality of the reflective act depends, among other factors, on the ability of metacognition (thinking about what we think). Finally, we propose an educational strategy based on having a task supervisor and rectification scenarios to improve the quality of medical decision making. Copyright © 2013 Elsevier España, S.L. All rights reserved.

Quantitative Systems Pharmacology: A Case for Disease Models.

PubMed

Musante, C J; Ramanujan, S; Schmidt, B J; Ghobrial, O G; Lu, J; Heatherington, A C

2017-01-01

Quantitative systems pharmacology (QSP) has emerged as an innovative approach in model-informed drug discovery and development, supporting program decisions from exploratory research through late-stage clinical trials. In this commentary, we discuss the unique value of disease-scale "platform" QSP models that are amenable to reuse and repurposing to support diverse clinical decisions in ways distinct from other pharmacometrics strategies. © 2016 The Authors Clinical Pharmacology & Therapeutics published by Wiley Periodicals, Inc. on behalf of The American Society for Clinical Pharmacology and Therapeutics.

Decision curve analysis: a novel method for evaluating prediction models.

PubMed

Vickers, Andrew J; Elkin, Elena B

2006-01-01

Diagnostic and prognostic models are typically evaluated with measures of accuracy that do not address clinical consequences. Decision-analytic techniques allow assessment of clinical outcomes but often require collection of additional information and may be cumbersome to apply to models that yield a continuous result. The authors sought a method for evaluating and comparing prediction models that incorporates clinical consequences,requires only the data set on which the models are tested,and can be applied to models that have either continuous or dichotomous results. The authors describe decision curve analysis, a simple, novel method of evaluating predictive models. They start by assuming that the threshold probability of a disease or event at which a patient would opt for treatment is informative of how the patient weighs the relative harms of a false-positive and a false-negative prediction. This theoretical relationship is then used to derive the net benefit of the model across different threshold probabilities. Plotting net benefit against threshold probability yields the "decision curve." The authors apply the method to models for the prediction of seminal vesicle invasion in prostate cancer patients. Decision curve analysis identified the range of threshold probabilities in which a model was of value, the magnitude of benefit, and which of several models was optimal. Decision curve analysis is a suitable method for evaluating alternative diagnostic and prognostic strategies that has advantages over other commonly used measures and techniques.

Semantic Clinical Guideline Documents

PubMed Central

Eriksson, Henrik; Tu, Samson W.; Musen, Mark

2005-01-01

Decision-support systems based on clinical practice guidelines can support physicians and other health-care personnel in the process of following best practice consistently. A knowledge-based approach to represent guidelines makes it possible to encode computer-interpretable guidelines in a formal manner, perform consistency checks, and use the guidelines directly in decision-support systems. Decision-support authors and guideline users require guidelines in human-readable formats in addition to computer-interpretable ones (e.g., for guideline review and quality assurance). We propose a new document-oriented information architecture that combines knowledge-representation models with electronic and paper documents. The approach integrates decision-support modes with standard document formats to create a combined clinical-guideline model that supports on-line viewing, printing, and decision support. PMID:16779037

[Modeling in value-based medicine].

PubMed

Neubauer, A S; Hirneiss, C; Kampik, A

2010-03-01

Modeling plays an important role in value-based medicine (VBM). It allows decision support by predicting potential clinical and economic consequences, frequently combining different sources of evidence. Based on relevant publications and examples focusing on ophthalmology the key economic modeling methods are explained and definitions are given. The most frequently applied model types are decision trees, Markov models, and discrete event simulation (DES) models. Model validation includes besides verifying internal validity comparison with other models (external validity) and ideally validation of its predictive properties. The existing uncertainty with any modeling should be clearly stated. This is true for economic modeling in VBM as well as when using disease risk models to support clinical decisions. In economic modeling uni- and multivariate sensitivity analyses are usually applied; the key concepts here are tornado plots and cost-effectiveness acceptability curves. Given the existing uncertainty, modeling helps to make better informed decisions than without this additional information.

Adoption of Clinical Information Systems in Health Services Organizations

PubMed Central

Austin, Charles J.; Holland, Gloria J.

1988-01-01

This paper presents a conceptual model of factors which influence organizational decisions to invest in the installation of clinical information systems. Using results of previous research as a framework, the relative influence of clinical, fiscal, and strategic-institutional decision structures are examined. These adoption decisions are important in health services organizations because clinical information is essential for managing demand and allocating resources, managing quality of care, and controlling costs.

Clinical, information and business process modeling to promote development of safe and flexible software.

PubMed

Liaw, Siaw-Teng; Deveny, Elizabeth; Morrison, Iain; Lewis, Bryn

2006-09-01

Using a factorial vignette survey and modeling methodology, we developed clinical and information models - incorporating evidence base, key concepts, relevant terms, decision-making and workflow needed to practice safely and effectively - to guide the development of an integrated rule-based knowledge module to support prescribing decisions in asthma. We identified workflows, decision-making factors, factor use, and clinician information requirements. The Unified Modeling Language (UML) and public domain software and knowledge engineering tools (e.g. Protégé) were used, with the Australian GP Data Model as the starting point for expressing information needs. A Web Services service-oriented architecture approach was adopted within which to express functional needs, and clinical processes and workflows were expressed in the Business Process Execution Language (BPEL). This formal analysis and modeling methodology to define and capture the process and logic of prescribing best practice in a reference implementation is fundamental to tackling deficiencies in prescribing decision support software.

Critical thinking in clinical nurse education: application of Paul's model of critical thinking.

PubMed

Andrea Sullivan, E

2012-11-01

Nurse educators recognize that many nursing students have difficulty in making decisions in clinical practice. The ability to make effective, informed decisions in clinical practice requires that nursing students know and apply the processes of critical thinking. Critical thinking is a skill that develops over time and requires the conscious application of this process. There are a number of models in the nursing literature to assist students in the critical thinking process; however, these models tend to focus solely on decision making in hospital settings and are often complex to actualize. In this paper, Paul's Model of Critical Thinking is examined for its application to nursing education. I will demonstrate how the model can be used by clinical nurse educators to assist students to develop critical thinking skills in all health care settings in a way that makes critical thinking skills accessible to students. Copyright © 2012 Elsevier Ltd. All rights reserved.

Integrating HL7 RIM and ontology for unified knowledge and data representation in clinical decision support systems.

PubMed

Zhang, Yi-Fan; Tian, Yu; Zhou, Tian-Shu; Araki, Kenji; Li, Jing-Song

2016-01-01

The broad adoption of clinical decision support systems within clinical practice has been hampered mainly by the difficulty in expressing domain knowledge and patient data in a unified formalism. This paper presents a semantic-based approach to the unified representation of healthcare domain knowledge and patient data for practical clinical decision making applications. A four-phase knowledge engineering cycle is implemented to develop a semantic healthcare knowledge base based on an HL7 reference information model, including an ontology to model domain knowledge and patient data and an expression repository to encode clinical decision making rules and queries. A semantic clinical decision support system is designed to provide patient-specific healthcare recommendations based on the knowledge base and patient data. The proposed solution is evaluated in the case study of type 2 diabetes mellitus inpatient management. The knowledge base is successfully instantiated with relevant domain knowledge and testing patient data. Ontology-level evaluation confirms model validity. Application-level evaluation of diagnostic accuracy reaches a sensitivity of 97.5%, a specificity of 100%, and a precision of 98%; an acceptance rate of 97.3% is given by domain experts for the recommended care plan orders. The proposed solution has been successfully validated in the case study as providing clinical decision support at a high accuracy and acceptance rate. The evaluation results demonstrate the technical feasibility and application prospect of our approach. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

A Knowledge-Modeling Approach to Integrate Multiple Clinical Practice Guidelines to Provide Evidence-Based Clinical Decision Support for Managing Comorbid Conditions.

PubMed

Abidi, Samina

2017-10-26

Clinical management of comorbidities is a challenge, especially in a clinical decision support setting, as it requires the safe and efficient reconciliation of multiple disease-specific clinical procedures to formulate a comorbid therapeutic plan that is both effective and safe for the patient. In this paper we pursue the integration of multiple disease-specific Clinical Practice Guidelines (CPG) in order to manage co-morbidities within a computerized Clinical Decision Support System (CDSS). We present a CPG integration framework-termed as COMET (Comorbidity Ontological Modeling & ExecuTion) that manifests a knowledge management approach to model, computerize and integrate multiple CPG to yield a comorbid CPG knowledge model that upon execution can provide evidence-based recommendations for handling comorbid patients. COMET exploits semantic web technologies to achieve (a) CPG knowledge synthesis to translate a paper-based CPG to disease-specific clinical pathways (CP) that include specialized co-morbidity management procedures based on input from domain experts; (b) CPG knowledge modeling to computerize the disease-specific CP using a Comorbidity CPG ontology; (c) CPG knowledge integration by aligning multiple ontologically-modeled CP to develop a unified comorbid CPG knowledge model; and (e) CPG knowledge execution using reasoning engines to derive CPG-mediated recommendations for managing patients with comorbidities. We present a web-accessible COMET CDSS that provides family physicians with CPG-mediated comorbidity decision support to manage Atrial Fibrillation and Chronic Heart Failure. We present our qualitative and quantitative analysis of the knowledge content and usability of COMET CDSS.

Clinical decision-making to facilitate appropriate patient management in chiropractic practice: 'the 3-questions model'.

PubMed

Amorin-Woods, Lyndon G; Parkin-Smith, Gregory F

2012-03-14

A definitive diagnosis in chiropractic clinical practice is frequently elusive, yet decisions around management are still necessary. Often, a clinical impression is made after the exclusion of serious illness or injury, and care provided within the context of diagnostic uncertainty. Rather than focussing on labelling the condition, the clinician may choose to develop a defendable management plan since the response to treatment often clarifies the diagnosis. This paper explores the concept and elements of defensive problem-solving practice, with a view to developing a model of agile, pragmatic decision-making amenable to real-world application. A theoretical framework that reflects the elements of this approach will be offered in order to validate the potential of a so called '3-Questions Model'; Clinical decision-making is considered to be a key characteristic of any modern healthcare practitioner. It is, thus, prudent for chiropractors to re-visit the concept of defensible practice with a view to facilitate capable clinical decision-making and competent patient examination skills. In turn, the perception of competence and trustworthiness of chiropractors within the wider healthcare community helps integration of chiropractic services into broader healthcare settings.

Implementation of workflow engine technology to deliver basic clinical decision support functionality.

PubMed

Huser, Vojtech; Rasmussen, Luke V; Oberg, Ryan; Starren, Justin B

2011-04-10

Workflow engine technology represents a new class of software with the ability to graphically model step-based knowledge. We present application of this novel technology to the domain of clinical decision support. Successful implementation of decision support within an electronic health record (EHR) remains an unsolved research challenge. Previous research efforts were mostly based on healthcare-specific representation standards and execution engines and did not reach wide adoption. We focus on two challenges in decision support systems: the ability to test decision logic on retrospective data prior prospective deployment and the challenge of user-friendly representation of clinical logic. We present our implementation of a workflow engine technology that addresses the two above-described challenges in delivering clinical decision support. Our system is based on a cross-industry standard of XML (extensible markup language) process definition language (XPDL). The core components of the system are a workflow editor for modeling clinical scenarios and a workflow engine for execution of those scenarios. We demonstrate, with an open-source and publicly available workflow suite, that clinical decision support logic can be executed on retrospective data. The same flowchart-based representation can also function in a prospective mode where the system can be integrated with an EHR system and respond to real-time clinical events. We limit the scope of our implementation to decision support content generation (which can be EHR system vendor independent). We do not focus on supporting complex decision support content delivery mechanisms due to lack of standardization of EHR systems in this area. We present results of our evaluation of the flowchart-based graphical notation as well as architectural evaluation of our implementation using an established evaluation framework for clinical decision support architecture. We describe an implementation of a free workflow technology software suite (available at http://code.google.com/p/healthflow) and its application in the domain of clinical decision support. Our implementation seamlessly supports clinical logic testing on retrospective data and offers a user-friendly knowledge representation paradigm. With the presented software implementation, we demonstrate that workflow engine technology can provide a decision support platform which evaluates well against an established clinical decision support architecture evaluation framework. Due to cross-industry usage of workflow engine technology, we can expect significant future functionality enhancements that will further improve the technology's capacity to serve as a clinical decision support platform.

Deriving the expected utility of a predictive model when the utilities are uncertain.

PubMed

Cooper, Gregory F; Visweswaran, Shyam

2005-01-01

Predictive models are often constructed from clinical databases with the goal of eventually helping make better clinical decisions. Evaluating models using decision theory is therefore natural. When constructing a model using statistical and machine learning methods, however, we are often uncertain about precisely how the model will be used. Thus, decision-independent measures of classification performance, such as the area under an ROC curve, are popular. As a complementary method of evaluation, we investigate techniques for deriving the expected utility of a model under uncertainty about the model's utilities. We demonstrate an example of the application of this approach to the evaluation of two models that diagnose coronary artery disease.

Decision making in asthma exacerbation: a clinical judgement analysis

PubMed Central

Jenkins, John; Shields, Mike; Patterson, Chris; Kee, Frank

2007-01-01

Background Clinical decisions which impact directly on patient safety and quality of care are made during acute asthma attacks by individual doctors based on their knowledge and experience. Decisions include administration of systemic corticosteroids (CS) and oral antibiotics, and admission to hospital. Clinical judgement analysis provides a methodology for comparing decisions between practitioners with different training and experience, and improving decision making. Methods Stepwise linear regression was used to select clinical cues based on visual analogue scale assessments of the propensity of 62 clinicians to prescribe a short course of oral CS (decision 1), a course of antibiotics (decision 2), and/or admit to hospital (decision 3) for 60 “paper” patients. Results When compared by specialty, paediatricians' models for decision 1 were more likely to include level of alertness as a cue (54% vs 16%); for decision 2 they were more likely to include presence of crepitations (49% vs 16%) and less likely to include inhaled CS (8% vs 40%), respiratory rate (0% vs 24%) and air entry (70% vs 100%). When compared to other grades, the models derived for decision 3 by consultants/general practitioners were more likely to include wheeze severity as a cue (39% vs 6%). Conclusions Clinicians differed in their use of individual cues and the number included in their models. Patient safety and quality of care will benefit from clarification of decision‐making strategies as general learning points during medical training, in the development of guidelines and care pathways, and by clinicians developing self‐awareness of their own preferences. PMID:17428817

Decision curve analysis assessing the clinical benefit of NMP22 in the detection of bladder cancer: secondary analysis of a prospective trial.

PubMed

Barbieri, Christopher E; Cha, Eugene K; Chromecki, Thomas F; Dunning, Allison; Lotan, Yair; Svatek, Robert S; Scherr, Douglas S; Karakiewicz, Pierre I; Sun, Maxine; Mazumdar, Madhu; Shariat, Shahrokh F

2012-03-01

• To employ decision curve analysis to determine the impact of nuclear matrix protein 22 (NMP22) on clinical decision making in the detection of bladder cancer using data from a prospective trial. • The study included 1303 patients at risk for bladder cancer who underwent cystoscopy, urine cytology and measurement of urinary NMP22 levels. • We constructed several prediction models to estimate risk of bladder cancer. The base model was generated using patient characteristics (age, gender, race, smoking and haematuria); cytology and NMP22 were added to the base model to determine effects on predictive accuracy. • Clinical net benefit was calculated by summing the benefits and subtracting the harms and weighting these by the threshold probability at which a patient or clinician would opt for cystoscopy. • In all, 72 patients were found to have bladder cancer (5.5%). In univariate analyses, NMP22 was the strongest predictor of bladder cancer presence (predictive accuracy 71.3%), followed by age (67.5%) and cytology (64.3%). • In multivariable prediction models, NMP22 improved the predictive accuracy of the base model by 8.2% (area under the curve 70.2-78.4%) and of the base model plus cytology by 4.2% (area under the curve 75.9-80.1%). • Decision curve analysis revealed that adding NMP22 to other models increased clinical benefit, particularly at higher threshold probabilities. • NMP22 is a strong, independent predictor of bladder cancer. • Addition of NMP22 improves the accuracy of standard predictors by a statistically and clinically significant margin. • Decision curve analysis suggests that integration of NMP22 into clinical decision making helps avoid unnecessary cystoscopies, with minimal increased risk of missing a cancer. © 2011 THE AUTHORS. BJU INTERNATIONAL © 2011 BJU INTERNATIONAL.

Modeling paradigms for medical diagnostic decision support: a survey and future directions.

PubMed

Wagholikar, Kavishwar B; Sundararajan, Vijayraghavan; Deshpande, Ashok W

2012-10-01

Use of computer based decision tools to aid clinical decision making, has been a primary goal of research in biomedical informatics. Research in the last five decades has led to the development of Medical Decision Support (MDS) applications using a variety of modeling techniques, for a diverse range of medical decision problems. This paper surveys literature on modeling techniques for diagnostic decision support, with a focus on decision accuracy. Trends and shortcomings of research in this area are discussed and future directions are provided. The authors suggest that-(i) Improvement in the accuracy of MDS application may be possible by modeling of vague and temporal data, research on inference algorithms, integration of patient information from diverse sources and improvement in gene profiling algorithms; (ii) MDS research would be facilitated by public release of de-identified medical datasets, and development of opensource data-mining tool kits; (iii) Comparative evaluations of different modeling techniques are required to understand characteristics of the techniques, which can guide developers in choice of technique for a particular medical decision problem; and (iv) Evaluations of MDS applications in clinical setting are necessary to foster physicians' utilization of these decision aids.

Weighing Clinical Evidence Using Patient Preferences: An Application of Probabilistic Multi-Criteria Decision Analysis.

PubMed

Broekhuizen, Henk; IJzerman, Maarten J; Hauber, A Brett; Groothuis-Oudshoorn, Catharina G M

2017-03-01

The need for patient engagement has been recognized by regulatory agencies, but there is no consensus about how to operationalize this. One approach is the formal elicitation and use of patient preferences for weighing clinical outcomes. The aim of this study was to demonstrate how patient preferences can be used to weigh clinical outcomes when both preferences and clinical outcomes are uncertain by applying a probabilistic value-based multi-criteria decision analysis (MCDA) method. Probability distributions were used to model random variation and parameter uncertainty in preferences, and parameter uncertainty in clinical outcomes. The posterior value distributions and rank probabilities for each treatment were obtained using Monte-Carlo simulations. The probability of achieving the first rank is the probability that a treatment represents the highest value to patients. We illustrated our methodology for a simplified case on six HIV treatments. Preferences were modeled with normal distributions and clinical outcomes were modeled with beta distributions. The treatment value distributions showed the rank order of treatments according to patients and illustrate the remaining decision uncertainty. This study demonstrated how patient preference data can be used to weigh clinical evidence using MCDA. The model takes into account uncertainty in preferences and clinical outcomes. The model can support decision makers during the aggregation step of the MCDA process and provides a first step toward preference-based personalized medicine, yet requires further testing regarding its appropriate use in real-world settings.

Classifying clinical decision making: a unifying approach.

PubMed

Buckingham, C D; Adams, A

2000-10-01

This is the first of two linked papers exploring decision making in nursing which integrate research evidence from different clinical and academic disciplines. Currently there are many decision-making theories, each with their own distinctive concepts and terminology, and there is a tendency for separate disciplines to view their own decision-making processes as unique. Identifying good nursing decisions and where improvements can be made is therefore problematic, and this can undermine clinical and organizational effectiveness, as well as nurses' professional status. Within the unifying framework of psychological classification, the overall aim of the two papers is to clarify and compare terms, concepts and processes identified in a diversity of decision-making theories, and to demonstrate their underlying similarities. It is argued that the range of explanations used across disciplines can usefully be re-conceptualized as classification behaviour. This paper explores problems arising from multiple theories of decision making being applied to separate clinical disciplines. Attention is given to detrimental effects on nursing practice within the context of multidisciplinary health-care organizations and the changing role of nurses. The different theories are outlined and difficulties in applying them to nursing decisions highlighted. An alternative approach based on a general model of classification is then presented in detail to introduce its terminology and the unifying framework for interpreting all types of decisions. The classification model is used to provide the context for relating alternative philosophical approaches and to define decision-making activities common to all clinical domains. This may benefit nurses by improving multidisciplinary collaboration and weakening clinical elitism.

Description and status update on GELLO: a proposed standardized object-oriented expression language for clinical decision support.

PubMed

Sordo, Margarita; Boxwala, Aziz A; Ogunyemi, Omolola; Greenes, Robert A

2004-01-01

A major obstacle to sharing computable clinical knowledge is the lack of a common language for specifying expressions and criteria. Such a language could be used to specify decision criteria, formulae, and constraints on data and action. Al-though the Arden Syntax addresses this problem for clinical rules, its generalization to HL7's object-oriented data model is limited. The GELLO Expression language is an object-oriented language used for expressing logical conditions and computations in the GLIF3 (GuideLine Interchange Format, v. 3) guideline modeling language. It has been further developed under the auspices of the HL7 Clinical Decision Support Technical Committee, as a proposed HL7 standard., GELLO is based on the Object Constraint Language (OCL), because it is vendor-independent, object-oriented, and side-effect-free. GELLO expects an object-oriented data model. Although choice of model is arbitrary, standardization is facilitated by ensuring that the data model is compatible with the HL7 Reference Information Model (RIM).

A Computational Model of Reasoning from the Clinical Literature

PubMed Central

Rennels, Glenn D.

1986-01-01

This paper explores the premise that a formalized representation of empirical studies can play a central role in computer-based decision support. The specific motivations underlying this research include the following propositions: 1. Reasoning from experimental evidence contained in the clinical literature is central to the decisions physicians make in patient care. 2. A computational model, based upon a declarative representation for published reports of clinical studies, can drive a computer program that selectively tailors knowledge of the clinical literature as it is applied to a particular case. 3. The development of such a computational model is an important first step toward filling a void in computer-based decision support systems. Furthermore, the model may help us better understand the general principles of reasoning from experimental evidence both in medicine and other domains. Roundsman is a developmental computer system which draws upon structured representations of the clinical literature in order to critique plans for the management of primary breast cancer. Roundsman is able to produce patient-specific analyses of breast cancer management options based on the 24 clinical studies currently encoded in its knowledge base. The Roundsman system is a first step in exploring how the computer can help to bring a critical analysis of the relevant literature to the physician, structured around a particular patient and treatment decision.

Factors and outcomes of decision making for cancer clinical trial participation.

PubMed

Biedrzycki, Barbara A

2011-09-01

To describe factors and outcomes related to the decision-making process regarding participation in a cancer clinical trial. Cross-sectional, descriptive. Urban, academic, National Cancer Institute-designated comprehensive cancer center in the mid-Atlantic United States. 197 patients with advanced gastrointestinal cancer. Mailed survey using one investigator-developed instrument, eight instruments used in published research, and a medical record review. disease context, sociodemographics, hope, quality of life, trust in healthcare system, trust in health professional, preference for research decision control, understanding risks, and information. decision to accept or decline research participation and satisfaction with this decision. All of the factors within the Research Decision Making Model together predicted cancer clinical trial participation and satisfaction with this decision. The most frequently preferred decision-making style for research participation was shared (collaborative) (83%). Multiple factors affect decision making for cancer clinical trial participation and satisfaction with this decision. Shared decision making previously was an unrecognized factor and requires further investigation. Enhancing the process of research decision making may facilitate an increase in cancer clinical trial enrollment rates. Oncology nurses have unique opportunities as educators and researchers to support shared decision making by those who prefer this method for deciding whether to accept or decline cancer clinical trial participation.

Lessons learned in detailed clinical modeling at Intermountain Healthcare

PubMed Central

Oniki, Thomas A; Coyle, Joseph F; Parker, Craig G; Huff, Stanley M

2014-01-01

Background and objective Intermountain Healthcare has a long history of using coded terminology and detailed clinical models (DCMs) to govern storage of clinical data to facilitate decision support and semantic interoperability. The latest iteration of DCMs at Intermountain is called the clinical element model (CEM). We describe the lessons learned from our CEM efforts with regard to subjective decisions a modeler frequently needs to make in creating a CEM. We present insights and guidelines, but also describe situations in which use cases conflict with the guidelines. We propose strategies that can help reconcile the conflicts. The hope is that these lessons will be helpful to others who are developing and maintaining DCMs in order to promote sharing and interoperability. Methods We have used the Clinical Element Modeling Language (CEML) to author approximately 5000 CEMs. Results Based on our experience, we have formulated guidelines to lead our modelers through the subjective decisions they need to make when authoring models. Reported here are guidelines regarding precoordination/postcoordination, dividing content between the model and the terminology, modeling logical attributes, and creating iso-semantic models. We place our lessons in context, exploring the potential benefits of an implementation layer, an iso-semantic modeling framework, and ontologic technologies. Conclusions We assert that detailed clinical models can advance interoperability and sharing, and that our guidelines, an implementation layer, and an iso-semantic framework will support our progress toward that goal. PMID:24993546

Decision models in the evaluation of psychotropic drugs : useful tool or useless toy?

PubMed

Barbui, Corrado; Lintas, Camilla

2006-09-01

A current contribution in the European Journal of Health Economics employs a decision model to compare health care costs of olanzapine and risperidone treatment for schizophrenia. The model suggests that a treatment strategy of first-line olanzapine is cost-saving over a 1-year period, with additional clinical benefits in the form of avoided relapses in the long-term. From a clinical perspective this finding is indubitably relevant, but can physicians and policy makers believe it? The study is presented in a balanced way, assumptions are based on data extracted from clinical trials published in major psychiatric journals, and the theoretical underpinnings of the model are reasonable. Despite these positive aspects, we believe that the methodology used in this study-the decision model approach-is an unsuitable and potentially misleading tool for evaluating psychotropic drugs. In this commentary, taking the olanzapine vs. risperidone model as an example, arguments are provided to support this statement.

Assessing clinical reasoning (ASCLIRE): Instrument development and validation.

PubMed

Kunina-Habenicht, Olga; Hautz, Wolf E; Knigge, Michel; Spies, Claudia; Ahlers, Olaf

2015-12-01

Clinical reasoning is an essential competency in medical education. This study aimed at developing and validating a test to assess diagnostic accuracy, collected information, and diagnostic decision time in clinical reasoning. A norm-referenced computer-based test for the assessment of clinical reasoning (ASCLIRE) was developed, integrating the entire clinical decision process. In a cross-sectional study participants were asked to choose as many diagnostic measures as they deemed necessary to diagnose the underlying disease of six different cases with acute or sub-acute dyspnea and provide a diagnosis. 283 students and 20 content experts participated. In addition to diagnostic accuracy, respective decision time and number of used relevant diagnostic measures were documented as distinct performance indicators. The empirical structure of the test was investigated using a structural equation modeling approach. Experts showed higher accuracy rates and lower decision times than students. In a cross-sectional comparison, the diagnostic accuracy of students improved with the year of study. Wrong diagnoses provided by our sample were comparable to wrong diagnoses in practice. We found an excellent fit for a model with three latent factors-diagnostic accuracy, decision time, and choice of relevant diagnostic information-with diagnostic accuracy showing no significant correlation with decision time. ASCLIRE considers decision time as an important performance indicator beneath diagnostic accuracy and provides evidence that clinical reasoning is a complex ability comprising diagnostic accuracy, decision time, and choice of relevant diagnostic information as three partly correlated but still distinct aspects.

Connecting clinical and actuarial prediction with rule-based methods.

PubMed

Fokkema, Marjolein; Smits, Niels; Kelderman, Henk; Penninx, Brenda W J H

2015-06-01

Meta-analyses comparing the accuracy of clinical versus actuarial prediction have shown actuarial methods to outperform clinical methods, on average. However, actuarial methods are still not widely used in clinical practice, and there has been a call for the development of actuarial prediction methods for clinical practice. We argue that rule-based methods may be more useful than the linear main effect models usually employed in prediction studies, from a data and decision analytic as well as a practical perspective. In addition, decision rules derived with rule-based methods can be represented as fast and frugal trees, which, unlike main effects models, can be used in a sequential fashion, reducing the number of cues that have to be evaluated before making a prediction. We illustrate the usability of rule-based methods by applying RuleFit, an algorithm for deriving decision rules for classification and regression problems, to a dataset on prediction of the course of depressive and anxiety disorders from Penninx et al. (2011). The RuleFit algorithm provided a model consisting of 2 simple decision rules, requiring evaluation of only 2 to 4 cues. Predictive accuracy of the 2-rule model was very similar to that of a logistic regression model incorporating 20 predictor variables, originally applied to the dataset. In addition, the 2-rule model required, on average, evaluation of only 3 cues. Therefore, the RuleFit algorithm appears to be a promising method for creating decision tools that are less time consuming and easier to apply in psychological practice, and with accuracy comparable to traditional actuarial methods. (c) 2015 APA, all rights reserved).

Next generation terminology infrastructure to support interprofessional care planning.

PubMed

Collins, Sarah; Klinkenberg-Ramirez, Stephanie; Tsivkin, Kira; Mar, Perry L; Iskhakova, Dina; Nandigam, Hari; Samal, Lipika; Rocha, Roberto A

2017-11-01

Develop a prototype of an interprofessional terminology and information model infrastructure that can enable care planning applications to facilitate patient-centered care, learn care plan linkages and associations, provide decision support, and enable automated, prospective analytics. The study steps included a 3 step approach: (1) Process model and clinical scenario development, and (2) Requirements analysis, and (3) Development and validation of information and terminology models. Components of the terminology model include: Health Concerns, Goals, Decisions, Interventions, Assessments, and Evaluations. A terminology infrastructure should: (A) Include discrete care plan concepts; (B) Include sets of profession-specific concerns, decisions, and interventions; (C) Communicate rationales, anticipatory guidance, and guidelines that inform decisions among the care team; (D) Define semantic linkages across clinical events and professions; (E) Define sets of shared patient goals and sub-goals, including patient stated goals; (F) Capture evaluation toward achievement of goals. These requirements were mapped to AHRQ Care Coordination Measures Framework. This study used a constrained set of clinician-validated clinical scenarios. Terminology models for goals and decisions are unavailable in SNOMED CT, limiting the ability to evaluate these aspects of the proposed infrastructure. Defining and linking subsets of care planning concepts appears to be feasible, but also essential to model interprofessional care planning for common co-occurring conditions and chronic diseases. We recommend the creation of goal dynamics and decision concepts in SNOMED CT to further enable the necessary models. Systems with flexible terminology management infrastructure may enable intelligent decision support to identify conflicting and aligned concerns, goals, decisions, and interventions in shared care plans, ultimately decreasing documentation effort and cognitive burden for clinicians and patients. Copyright © 2017 Elsevier Inc. All rights reserved.

Implementation of workflow engine technology to deliver basic clinical decision support functionality

PubMed Central

2011-01-01

Background Workflow engine technology represents a new class of software with the ability to graphically model step-based knowledge. We present application of this novel technology to the domain of clinical decision support. Successful implementation of decision support within an electronic health record (EHR) remains an unsolved research challenge. Previous research efforts were mostly based on healthcare-specific representation standards and execution engines and did not reach wide adoption. We focus on two challenges in decision support systems: the ability to test decision logic on retrospective data prior prospective deployment and the challenge of user-friendly representation of clinical logic. Results We present our implementation of a workflow engine technology that addresses the two above-described challenges in delivering clinical decision support. Our system is based on a cross-industry standard of XML (extensible markup language) process definition language (XPDL). The core components of the system are a workflow editor for modeling clinical scenarios and a workflow engine for execution of those scenarios. We demonstrate, with an open-source and publicly available workflow suite, that clinical decision support logic can be executed on retrospective data. The same flowchart-based representation can also function in a prospective mode where the system can be integrated with an EHR system and respond to real-time clinical events. We limit the scope of our implementation to decision support content generation (which can be EHR system vendor independent). We do not focus on supporting complex decision support content delivery mechanisms due to lack of standardization of EHR systems in this area. We present results of our evaluation of the flowchart-based graphical notation as well as architectural evaluation of our implementation using an established evaluation framework for clinical decision support architecture. Conclusions We describe an implementation of a free workflow technology software suite (available at http://code.google.com/p/healthflow) and its application in the domain of clinical decision support. Our implementation seamlessly supports clinical logic testing on retrospective data and offers a user-friendly knowledge representation paradigm. With the presented software implementation, we demonstrate that workflow engine technology can provide a decision support platform which evaluates well against an established clinical decision support architecture evaluation framework. Due to cross-industry usage of workflow engine technology, we can expect significant future functionality enhancements that will further improve the technology's capacity to serve as a clinical decision support platform. PMID:21477364

Development of a real-time clinical decision support system upon the web mvc-based architecture for prostate cancer treatment

PubMed Central

2011-01-01

Background A real-time clinical decision support system (RTCDSS) with interactive diagrams enables clinicians to instantly and efficiently track patients' clinical records (PCRs) and improve their quality of clinical care. We propose a RTCDSS to process online clinical informatics from multiple databases for clinical decision making in the treatment of prostate cancer based on Web Model-View-Controller (MVC) architecture, by which the system can easily be adapted to different diseases and applications. Methods We designed a framework upon the Web MVC-based architecture in which the reusable and extractable models can be conveniently adapted to other hospital information systems and which allows for efficient database integration. Then, we determined the clinical variables of the prostate cancer treatment based on participating clinicians' opinions and developed a computational model to determine the pretreatment parameters. Furthermore, the components of the RTCDSS integrated PCRs and decision factors for real-time analysis to provide evidence-based diagrams upon the clinician-oriented interface for visualization of treatment guidance and health risk assessment. Results The resulting system can improve quality of clinical treatment by allowing clinicians to concurrently analyze and evaluate the clinical markers of prostate cancer patients with instantaneous clinical data and evidence-based diagrams which can automatically identify pretreatment parameters. Moreover, the proposed RTCDSS can aid interactions between patients and clinicians. Conclusions Our proposed framework supports online clinical informatics, evaluates treatment risks, offers interactive guidance, and provides real-time reference for decision making in the treatment of prostate cancer. The developed clinician-oriented interface can assist clinicians in conveniently presenting evidence-based information to patients and can be readily adapted to an existing hospital information system and be easily applied in other chronic diseases. PMID:21385459

Development of a real-time clinical decision support system upon the Web MVC-based architecture for prostate cancer treatment.

PubMed

Lin, Hsueh-Chun; Wu, Hsi-Chin; Chang, Chih-Hung; Li, Tsai-Chung; Liang, Wen-Miin; Wang, Jong-Yi Wang

2011-03-08

A real-time clinical decision support system (RTCDSS) with interactive diagrams enables clinicians to instantly and efficiently track patients' clinical records (PCRs) and improve their quality of clinical care. We propose a RTCDSS to process online clinical informatics from multiple databases for clinical decision making in the treatment of prostate cancer based on Web Model-View-Controller (MVC) architecture, by which the system can easily be adapted to different diseases and applications. We designed a framework upon the Web MVC-based architecture in which the reusable and extractable models can be conveniently adapted to other hospital information systems and which allows for efficient database integration. Then, we determined the clinical variables of the prostate cancer treatment based on participating clinicians' opinions and developed a computational model to determine the pretreatment parameters. Furthermore, the components of the RTCDSS integrated PCRs and decision factors for real-time analysis to provide evidence-based diagrams upon the clinician-oriented interface for visualization of treatment guidance and health risk assessment. The resulting system can improve quality of clinical treatment by allowing clinicians to concurrently analyze and evaluate the clinical markers of prostate cancer patients with instantaneous clinical data and evidence-based diagrams which can automatically identify pretreatment parameters. Moreover, the proposed RTCDSS can aid interactions between patients and clinicians. Our proposed framework supports online clinical informatics, evaluates treatment risks, offers interactive guidance, and provides real-time reference for decision making in the treatment of prostate cancer. The developed clinician-oriented interface can assist clinicians in conveniently presenting evidence-based information to patients and can be readily adapted to an existing hospital information system and be easily applied in other chronic diseases.

A business planning model to identify new safety net clinic locations.

PubMed

Langabeer, James; Helton, Jeffrey; DelliFraine, Jami; Dotson, Ebbin; Watts, Carolyn; Love, Karen

2014-01-01

Community health clinics serving the poor and underserved are geographically expanding due to changes in U.S. health care policy. This paper describes the experience of a collaborative alliance of health care providers in a large metropolitan area who develop a conceptual and mathematical decision model to guide decisions on expanding its network of community health clinics. Community stakeholders participated in a collaborative process that defined constructs they deemed important in guiding decisions on the location of community health clinics. This collaboration also defined key variables within each construct. Scores for variables within each construct were then totaled and weighted into a community-specific optimal space planning equation. This analysis relied entirely on secondary data available from published sources. The model built from this collaboration revolved around the constructs of demand, sustainability, and competition. It used publicly available data defining variables within each construct to arrive at an optimal location that maximized demand and sustainability and minimized competition. This is a model that safety net clinic planners and community stakeholders can use to analyze demographic and utilization data to optimize capacity expansion to serve uninsured and Medicaid populations. Communities can use this innovative model to develop a locally relevant clinic location-planning framework.

Modelling and Decision Support of Clinical Pathways

NASA Astrophysics Data System (ADS)

Gabriel, Roland; Lux, Thomas

The German health care market is under a rapid rate of change, forcing especially hospitals to provide high-quality services at low costs. Appropriate measures for more effective and efficient service provision are process orientation and decision support by information technology of clinical pathway of a patient. The essential requirements are adequate modelling of clinical pathways as well as usage of adequate systems, which are capable of assisting the complete path of a patient within a hospital, and preferably also outside of it, in a digital way. To fulfil these specifications the authors present a suitable concept, which meets the challenges of well-structured clinical pathways as well as rather poorly structured diagnostic and therapeutic decisions, by interplay of process-oriented and knowledge-based hospital information systems.

Factors Predicting Oncology Care Providers' Behavioral Intention to Adopt Clinical Decision Support Systems

ERIC Educational Resources Information Center

Wolfenden, Andrew

2012-01-01

The purpose of this quantitative correlation study was to examine the predictors of user behavioral intention on the decision of oncology care providers to adopt or reject the clinical decision support system. The Unified Theory of Acceptance and Use of Technology (UTAUT) formed the foundation of the research model and survey instrument. The…

Validation workflow for a clinical Bayesian network model in multidisciplinary decision making in head and neck oncology treatment.

PubMed

Cypko, Mario A; Stoehr, Matthaeus; Kozniewski, Marcin; Druzdzel, Marek J; Dietz, Andreas; Berliner, Leonard; Lemke, Heinz U

2017-11-01

Oncological treatment is being increasingly complex, and therefore, decision making in multidisciplinary teams is becoming the key activity in the clinical pathways. The increased complexity is related to the number and variability of possible treatment decisions that may be relevant to a patient. In this paper, we describe validation of a multidisciplinary cancer treatment decision in the clinical domain of head and neck oncology. Probabilistic graphical models and corresponding inference algorithms, in the form of Bayesian networks, can support complex decision-making processes by providing a mathematically reproducible and transparent advice. The quality of BN-based advice depends on the quality of the model. Therefore, it is vital to validate the model before it is applied in practice. For an example BN subnetwork of laryngeal cancer with 303 variables, we evaluated 66 patient records. To validate the model on this dataset, a validation workflow was applied in combination with quantitative and qualitative analyses. In the subsequent analyses, we observed four sources of imprecise predictions: incorrect data, incomplete patient data, outvoting relevant observations, and incorrect model. Finally, the four problems were solved by modifying the data and the model. The presented validation effort is related to the model complexity. For simpler models, the validation workflow is the same, although it may require fewer validation methods. The validation success is related to the model's well-founded knowledge base. The remaining laryngeal cancer model may disclose additional sources of imprecise predictions.

Stakeholder perspectives on decision-analytic modeling frameworks to assess genetic services policy.

PubMed

Guzauskas, Gregory F; Garrison, Louis P; Stock, Jacquie; Au, Sylvia; Doyle, Debra Lochner; Veenstra, David L

2013-01-01

Genetic services policymakers and insurers often make coverage decisions in the absence of complete evidence of clinical utility and under budget constraints. We evaluated genetic services stakeholder opinions on the potential usefulness of decision-analytic modeling to inform coverage decisions, and asked them to identify genetic tests for decision-analytic modeling studies. We presented an overview of decision-analytic modeling to members of the Western States Genetic Services Collaborative Reimbursement Work Group and state Medicaid representatives and conducted directed content analysis and an anonymous survey to gauge their attitudes toward decision-analytic modeling. Participants also identified and prioritized genetic services for prospective decision-analytic evaluation. Participants expressed dissatisfaction with current processes for evaluating insurance coverage of genetic services. Some participants expressed uncertainty about their comprehension of decision-analytic modeling techniques. All stakeholders reported openness to using decision-analytic modeling for genetic services assessments. Participants were most interested in application of decision-analytic concepts to multiple-disorder testing platforms, such as next-generation sequencing and chromosomal microarray. Decision-analytic modeling approaches may provide a useful decision tool to genetic services stakeholders and Medicaid decision-makers.

Proposed Clinical Decision Rules to Diagnose Acute Rhinosinusitis Among Adults in Primary Care.

PubMed

Ebell, Mark H; Hansen, Jens Georg

2017-07-01

To reduce inappropriate antibiotic prescribing, we sought to develop a clinical decision rule for the diagnosis of acute rhinosinusitis and acute bacterial rhinosinusitis. Multivariate analysis and classification and regression tree (CART) analysis were used to develop clinical decision rules for the diagnosis of acute rhinosinusitis, defined using 3 different reference standards (purulent antral puncture fluid or abnormal finding on a computed tomographic (CT) scan; for acute bacterial rhinosinusitis, we used a positive bacterial culture of antral fluid). Signs, symptoms, C-reactive protein (CRP), and reference standard tests were prospectively recorded in 175 Danish patients aged 18 to 65 years seeking care for suspected acute rhinosinusitis. For each reference standard, we developed 2 clinical decision rules: a point score based on a logistic regression model and an algorithm based on a CART model. We identified low-, moderate-, and high-risk groups for acute rhinosinusitis or acute bacterial rhinosinusitis for each clinical decision rule. The point scores each had between 5 and 6 predictors, and an area under the receiver operating characteristic curve (AUROCC) between 0.721 and 0.767. For positive bacterial culture as the reference standard, low-, moderate-, and high-risk groups had a 16%, 49%, and 73% likelihood of acute bacterial rhinosinusitis, respectively. CART models had an AUROCC ranging from 0.783 to 0.827. For positive bacterial culture as the reference standard, low-, moderate-, and high-risk groups had a likelihood of acute bacterial rhinosinusitis of 6%, 31%, and 59% respectively. We have developed a series of clinical decision rules integrating signs, symptoms, and CRP to diagnose acute rhinosinusitis and acute bacterial rhinosinusitis with good accuracy. They now require prospective validation and an assessment of their effect on clinical and process outcomes. © 2017 Annals of Family Medicine, Inc.

Driving with roadmaps and dashboards: using information resources to structure the decision models in service organizations.

PubMed

Chorpita, Bruce F; Bernstein, Adam; Daleiden, Eric L

2008-03-01

This paper illustrates the application of design principles for tools that structure clinical decision-making. If the effort to implement evidence-based practices in community services organizations is to be effective, attention must be paid to the decision-making context in which such treatments are delivered. Clinical research trials commonly occur in an environment characterized by structured decision making and expert supports. Technology has great potential to serve mental health organizations by supporting these potentially important contextual features of the research environment, through organization and reporting of clinical data into interpretable information to support decisions and anchor decision-making procedures. This article describes one example of a behavioral health reporting system designed to facilitate clinical and administrative use of evidence-based practices. The design processes underlying this system-mapping of decision points and distillation of performance information at the individual, caseload, and organizational levels-can be implemented to support clinical practice in a wide variety of settings.

Exploring the clinical decision-making used by experienced cardiorespiratory physiotherapists: A mixed method qualitative design of simulation, video recording and think aloud techniques.

PubMed

Thackray, Debbie; Roberts, Lisa

2017-02-01

The ability of physiotherapists to make clinical decisions is a vital component of being an autonomous practitioner, yet this complex phenomenon has been under-researched in cardiorespiratory physiotherapy. The purpose of this study was to explore clinical decision-making (CDM) by experienced physiotherapists in a scenario of a simulated patient experiencing acute deterioration of their respiratory function. The main objective of this observational study was to identify the actions, thoughts, and behaviours used by experienced cardiorespiratory physiotherapists in their clinical decision-making processes. A mixed-methods (qualitative) design employing observation and think-aloud, was adopted using a computerised manikin in a simulated environment. The participants clinically assessed the manikin programmed with the same clinical signs, under standardised conditions in the clinical skills practice suite, which was set up as a ward environment. Experienced cardiorespiratory physiotherapists, recruited from clinical practice within a 50-mile radius of the University(*). Participants were video-recorded throughout the assessment and treatment and asked to verbalise their thought processes using the 'think-aloud' method. The recordings were transcribed verbatim and managed using a Framework approach. Eight cardiorespiratory physiotherapists participated (mean 7years clinical experience, range 3.5-16years. CDM was similar to the collaborative hypothetico-deductive model, five-rights nursing model, reasoning strategies, inductive reasoning and pattern recognition. However, the CDM demonstrated by the physiotherapists was complex, interactive and iterative. Information processing occurred continuously throughout the whole interaction with the patient, and the specific cognitive skills of recognition, matching, discriminating, relating, inferring, synthesising and prediction were identified as being used sequentially. The findings from this study were used to develop a new conceptual model of clinical decision-making for cardiorespiratory physiotherapy. This conceptual model can be used to inform future educational strategies to prepare physiotherapists and nurses for working in acute respiratory care. Crown Copyright © 2016. Published by Elsevier Ltd. All rights reserved.

Role of Positron Emission Tomography in the Treatment of Occult Disease in Head-and-Neck Cancer: A Modeling Approach

DOE Office of Scientific and Technical Information (OSTI.GOV)

Phillips, Mark H., E-mail: markp@u.washington.ed; Smith, Wade P.; Parvathaneni, Upendra

2011-03-15

Purpose: To determine under what conditions positron emission tomography (PET) imaging will be useful in decisions regarding the use of radiotherapy for the treatment of clinically occult lymph node metastases in head-and-neck cancer. Methods and Materials: A decision model of PET imaging and its downstream effects on radiotherapy outcomes was constructed using an influence diagram. This model included the sensitivity and specificity of PET, as well as the type and stage of the primary tumor. These parameters were varied to determine the optimal strategy for imaging and therapy for different clinical situations. Maximum expected utility was the metric by whichmore » different actions were ranked. Results: For primary tumors with a low probability of lymph node metastases, the sensitivity of PET should be maximized, and 50 Gy should be delivered if PET is positive and 0 Gy if negative. As the probability for lymph node metastases increases, PET imaging becomes unnecessary in some situations, and the optimal dose to the lymph nodes increases. The model needed to include the causes of certain health states to predict current clinical practice. Conclusion: The model demonstrated the ability to reproduce expected outcomes for a range of tumors and provided recommendations for different clinical situations. The differences between the optimal policies and current clinical practice are likely due to a disparity between stated clinical decision processes and actual decision making by clinicians.« less

A Data Model for Teleconsultation in Managing High-Risk Pregnancies: Design and Preliminary Evaluation

PubMed Central

Deldar, Kolsoum

2017-01-01

Background Teleconsultation is a guarantor for virtual supervision of clinical professors on clinical decisions made by medical residents in teaching hospitals. Type, format, volume, and quality of exchanged information have a great influence on the quality of remote clinical decisions or tele-decisions. Thus, it is necessary to develop a reliable and standard model for these clinical relationships. Objective The goal of this study was to design and evaluate a data model for teleconsultation in the management of high-risk pregnancies. Methods This study was implemented in three phases. In the first phase, a systematic review, a qualitative study, and a Delphi approach were done in selected teaching hospitals. Systematic extraction and localization of diagnostic items to develop the tele-decision clinical archetypes were performed as the second phase. Finally, the developed model was evaluated using predefined consultation scenarios. Results Our review study has shown that present medical consultations have no specific structure or template for patient information exchange. Furthermore, there are many challenges in the remote medical decision-making process, and some of them are related to the lack of the mentioned structure. The evaluation phase of our research has shown that data quality (P<.001), adequacy (P<.001), organization (P<.001), confidence (P<.001), and convenience (P<.001) had more scores in archetype-based consultation scenarios compared with routine-based ones. Conclusions Our archetype-based model could acquire better and higher scores in the data quality, adequacy, organization, confidence, and convenience dimensions than ones with routine scenarios. It is probable that the suggested archetype-based teleconsultation model may improve the quality of physician-physician remote medical consultations. PMID:29242181

An integrative model for in-silico clinical-genomics discovery science.

PubMed

Lussier, Yves A; Sarkar, Indra Nell; Cantor, Michael

2002-01-01

Human Genome discovery research has set the pace for Post-Genomic Discovery Research. While post-genomic fields focused at the molecular level are intensively pursued, little effort is being deployed in the later stages of molecular medicine discovery research, such as clinical-genomics. The objective of this study is to demonstrate the relevance and significance of integrating mainstream clinical informatics decision support systems to current bioinformatics genomic discovery science. This paper is a feasibility study of an original model enabling novel "in-silico" clinical-genomic discovery science and that demonstrates its feasibility. This model is designed to mediate queries among clinical and genomic knowledge bases with relevant bioinformatic analytic tools (e.g. gene clustering). Briefly, trait-disease-gene relationships were successfully illustrated using QMR, OMIM, SNOMED-RT, GeneCluster and TreeView. The analyses were visualized as two-dimensional dendrograms of clinical observations clustered around genes. To our knowledge, this is the first study using knowledge bases of clinical decision support systems for genomic discovery. Although this study is a proof of principle, it provides a framework for the development of clinical decision-support-system driven, high-throughput clinical-genomic technologies which could potentially unveil significant high-level functions of genes.

Factors influencing the clinical decision-making of midwives: a qualitative study.

PubMed

Daemers, Darie O A; van Limbeek, Evelien B M; Wijnen, Hennie A A; Nieuwenhuijze, Marianne J; de Vries, Raymond G

2017-10-06

Although midwives make clinical decisions that have an impact on the health and well-being of mothers and babies, little is known about how they make those decisions. Wide variation in intrapartum decisions to refer women to obstetrician-led care suggests that midwives' decisions are based on more than the evidence based medicine (EBM) model - i.e. clinical evidence, midwife's expertise, and woman's values - alone. With this study we aimed to explore the factors that influence clinical decision-making of midwives who work independently. We used a qualitative approach, conducting in-depth interviews with a purposive sample of 11 Dutch primary care midwives. Data collection took place between May and September 2015. The interviews were semi-structured, using written vignettes to solicit midwives' clinical decision-making processes (Think Aloud method). We performed thematic analysis on the transcripts. We identified five themes that influenced clinical decision-making: the pregnant woman as a whole person, sources of knowledge, the midwife as a whole person, the collaboration between maternity care professionals, and the organisation of care. Regarding the midwife, her decisions were shaped not only by her experience, intuition, and personal circumstances, but also by her attitudes about physiology, woman-centredness, shared decision-making, and collaboration with other professionals. The nature of the local collaboration between maternity care professionals and locally-developed protocols dominated midwives' clinical decision-making. When midwives and obstetricians had different philosophies of care and different practice styles, their collaborative efforts were challenged. Midwives' clinical decision-making is a more varied and complex process than the EBM framework suggests. If midwives are to succeed in their role as promoters and protectors of physiological pregnancy and birth, they need to understand how clinical decisions in a multidisciplinary context are actually made.

Clinic Workflow Simulations using Secondary EHR Data

PubMed Central

Hribar, Michelle R.; Biermann, David; Read-Brown, Sarah; Reznick, Leah; Lombardi, Lorinna; Parikh, Mansi; Chamberlain, Winston; Yackel, Thomas R.; Chiang, Michael F.

2016-01-01

Clinicians today face increased patient loads, decreased reimbursements and potential negative productivity impacts of using electronic health records (EHR), but have little guidance on how to improve clinic efficiency. Discrete event simulation models are powerful tools for evaluating clinical workflow and improving efficiency, particularly when they are built from secondary EHR timing data. The purpose of this study is to demonstrate that these simulation models can be used for resource allocation decision making as well as for evaluating novel scheduling strategies in outpatient ophthalmology clinics. Key findings from this study are that: 1) secondary use of EHR timestamp data in simulation models represents clinic workflow, 2) simulations provide insight into the best allocation of resources in a clinic, 3) simulations provide critical information for schedule creation and decision making by clinic managers, and 4) simulation models built from EHR data are potentially generalizable. PMID:28269861

Clinical-decision support based on medical literature: A complex network approach

NASA Astrophysics Data System (ADS)

Jiang, Jingchi; Zheng, Jichuan; Zhao, Chao; Su, Jia; Guan, Yi; Yu, Qiubin

2016-10-01

In making clinical decisions, clinicians often review medical literature to ensure the reliability of diagnosis, test, and treatment because the medical literature can answer clinical questions and assist clinicians making clinical decisions. Therefore, finding the appropriate literature is a critical problem for clinical-decision support (CDS). First, the present study employs search engines to retrieve relevant literature about patient records. However, the result of the traditional method is usually unsatisfactory. To improve the relevance of the retrieval result, a medical literature network (MLN) based on these retrieved papers is constructed. Then, we show that this MLN has small-world and scale-free properties of a complex network. According to the structural characteristics of the MLN, we adopt two methods to further identify the potential relevant literature in addition to the retrieved literature. By integrating these potential papers into the MLN, a more comprehensive MLN is built to answer the question of actual patient records. Furthermore, we propose a re-ranking model to sort all papers by relevance. We experimentally find that the re-ranking model can improve the normalized discounted cumulative gain of the results. As participants of the Text Retrieval Conference 2015, our clinical-decision method based on the MLN also yields higher scores than the medians in most topics and achieves the best scores for topics: #11 and #12. These research results indicate that our study can be used to effectively assist clinicians in making clinical decisions, and the MLN can facilitate the investigation of CDS.

An exploration of clinical decision making in mental health triage.

PubMed

Sands, Natisha

2009-08-01

Mental health (MH) triage is a specialist area of clinical nursing practice that involves complex decision making. The discussion in this article draws on the findings of a Ph.D. study that involved a statewide investigation of the scope of MH triage nursing practice in Victoria, Australia. Although the original Ph.D. study investigated a number of core practices in MH triage, the focus of the discussion in this article is specifically on the findings related to clinical decision making in MH triage, which have not previously been published. The study employed an exploratory descriptive research design that used mixed data collection methods including a survey questionnaire (n = 139) and semistructured interviews (n = 21). The study findings related to decision making revealed a lack of empirically tested evidence-based decision-making frameworks currently in use to support MH triage nursing practice. MH triage clinicians in Australia rely heavily on clinical experience to underpin decision making and have little of knowledge of theoretical models for practice, such as methodologies for rating urgency. A key recommendation arising from the study is the need to develop evidence-based decision-making frameworks such as clinical guidelines to inform and support MH triage clinical decision making.

Nurses' decision making in heart failure management based on heart failure certification status.

PubMed

Albert, Nancy M; Bena, James F; Buxbaum, Denise; Martensen, Linda; Morrison, Shannon L; Prasun, Marilyn A; Stamp, Kelly D

Research findings on the value of nurse certification were based on subjective perceptions or biased by correlations of certification status and global clinical factors. In heart failure, the value of certification is unknown. Examine the value of certification based nurses' decision-making. Cross-sectional study of nurses who completed heart failure clinical vignettes that reflected decision-making in clinical heart failure scenarios. Statistical tests included multivariable linear, logistic and proportional odds logistic regression models. Of nurses (N = 605), 29.1% were heart failure certified, 35.0% were certified in another specialty/job role and 35.9% were not certified. In multivariable modeling, nurses certified in heart failure (versus not heart failure certified) had higher clinical vignette scores (p = 0.002), reflecting higher evidence-based decision making; nurses with another specialty/role certification (versus no certification) did not (p = 0.62). Heart failure certification, but not in other specialty/job roles was associated with decisions that reflected delivery of high-quality care. Copyright © 2018 Elsevier Inc. All rights reserved.

Decision-support models for empiric antibiotic selection in Gram-negative bloodstream infections.

PubMed

MacFadden, D R; Coburn, B; Shah, N; Robicsek, A; Savage, R; Elligsen, M; Daneman, N

2018-04-25

Early empiric antibiotic therapy in patients can improve clinical outcomes in Gram-negative bacteraemia. However, the widespread prevalence of antibiotic-resistant pathogens compromises our ability to provide adequate therapy while minimizing use of broad antibiotics. We sought to determine whether readily available electronic medical record data could be used to develop predictive models for decision support in Gram-negative bacteraemia. We performed a multi-centre cohort study, in Canada and the USA, of hospitalized patients with Gram-negative bloodstream infection from April 2010 to March 2015. We analysed multivariable models for prediction of antibiotic susceptibility at two empiric windows: Gram-stain-guided and pathogen-guided treatment. Decision-support models for empiric antibiotic selection were developed based on three clinical decision thresholds of acceptable adequate coverage (80%, 90% and 95%). A total of 1832 patients with Gram-negative bacteraemia were evaluated. Multivariable models showed good discrimination across countries and at both Gram-stain-guided (12 models, areas under the curve (AUCs) 0.68-0.89, optimism-corrected AUCs 0.63-0.85) and pathogen-guided (12 models, AUCs 0.75-0.98, optimism-corrected AUCs 0.64-0.95) windows. Compared to antibiogram-guided therapy, decision-support models of antibiotic selection incorporating individual patient characteristics and prior culture results have the potential to increase use of narrower-spectrum antibiotics (in up to 78% of patients) while reducing inadequate therapy. Multivariable models using readily available epidemiologic factors can be used to predict antimicrobial susceptibility in infecting pathogens with reasonable discriminatory ability. Implementation of sequential predictive models for real-time individualized empiric antibiotic decision-making has the potential to both optimize adequate coverage for patients while minimizing overuse of broad-spectrum antibiotics, and therefore requires further prospective evaluation. Readily available epidemiologic risk factors can be used to predict susceptibility of Gram-negative organisms among patients with bacteraemia, using automated decision-making models. Copyright © 2018 European Society of Clinical Microbiology and Infectious Diseases. Published by Elsevier Ltd. All rights reserved.

External validation and clinical utility of a prediction model for 6-month mortality in patients undergoing hemodialysis for end-stage kidney disease.

PubMed

Forzley, Brian; Er, Lee; Chiu, Helen Hl; Djurdjev, Ognjenka; Martinusen, Dan; Carson, Rachel C; Hargrove, Gaylene; Levin, Adeera; Karim, Mohamud

2018-02-01

End-stage kidney disease is associated with poor prognosis. Health care professionals must be prepared to address end-of-life issues and identify those at high risk for dying. A 6-month mortality prediction model for patients on dialysis derived in the United States is used but has not been externally validated. We aimed to assess the external validity and clinical utility in an independent cohort in Canada. We examined the performance of the published 6-month mortality prediction model, using discrimination, calibration, and decision curve analyses. Data were derived from a cohort of 374 prevalent dialysis patients in two regions of British Columbia, Canada, which included serum albumin, age, peripheral vascular disease, dementia, and answers to the "the surprise question" ("Would I be surprised if this patient died within the next year?"). The observed mortality in the validation cohort was 11.5% at 6 months. The prediction model had reasonable discrimination (c-stat = 0.70) but poor calibration (calibration-in-the-large = -0.53 (95% confidence interval: -0.88, -0.18); calibration slope = 0.57 (95% confidence interval: 0.31, 0.83)) in our data. Decision curve analysis showed the model only has added value in guiding clinical decision in a small range of threshold probabilities: 8%-20%. Despite reasonable discrimination, the prediction model has poor calibration in this external study cohort; thus, it may have limited clinical utility in settings outside of where it was derived. Decision curve analysis clarifies limitations in clinical utility not apparent by receiver operating characteristic curve analysis. This study highlights the importance of external validation of prediction models prior to routine use in clinical practice.

Effect of experience on clinical decision making by cardiorespiratory physiotherapists in acute care settings.

PubMed

Smith, Megan; Higgs, Joy; Ellis, Elizabeth

2010-02-01

This article investigates clinical decision making in acute care hospitals by cardiorespiratory physiotherapists with differing degrees of clinical experience. Participants were observed as they engaged in their everyday practice and were interviewed about their decision making. Texts of the data were interpreted by using a hermeneutic approach that involved repeated reading and analysis of fieldnotes and interview transcripts to develop an understanding of the effect of experience on clinical decision making. Participants were classified into categories of cardiorespiratory physiotherapy experience: less experienced (<2 years), intermediate experience (2.5-4 years), and more experienced (>7 years). Four dimensions characteristic of increasing experience in cardiorespiratory physiotherapy clinical decision making were identified: 1) an individual practice model, 2) refined approaches to clinical decision making, 3) working in context, and 4) social and emotional capability. Underpinning these dimensions was evidence of reflection on practice, motivation to achieve best practice, critique of new knowledge, increasing confidence, and relationships with knowledgeable colleagues. These findings reflect characteristics of physiotherapy expertise that have been described in the literature. This study adds knowledge about the field of cardiorespiratory physiotherapy to the existing body of research on clinical decision making and broadens the existing understanding of characteristics of physiotherapy expertise.

Decision analysis to complete diagnostic research by closing the gap between test characteristics and cost-effectiveness.

PubMed

Schaafsma, Joanna D; van der Graaf, Yolanda; Rinkel, Gabriel J E; Buskens, Erik

2009-12-01

The lack of a standard methodology in diagnostic research impedes adequate evaluation before implementation of constantly developing diagnostic techniques. We discuss the methodology of diagnostic research and underscore the relevance of decision analysis in the process of evaluation of diagnostic tests. Overview and conceptual discussion. Diagnostic research requires a stepwise approach comprising assessment of test characteristics followed by evaluation of added value, clinical outcome, and cost-effectiveness. These multiple goals are generally incompatible with a randomized design. Decision-analytic models provide an important alternative through integration of the best available evidence. Thus, critical assessment of clinical value and efficient use of resources can be achieved. Decision-analytic models should be considered part of the standard methodology in diagnostic research. They can serve as a valid alternative to diagnostic randomized clinical trials (RCTs).

Quantitative Systems Pharmacology: A Case for Disease Models

PubMed Central

Ramanujan, S; Schmidt, BJ; Ghobrial, OG; Lu, J; Heatherington, AC

2016-01-01

Quantitative systems pharmacology (QSP) has emerged as an innovative approach in model‐informed drug discovery and development, supporting program decisions from exploratory research through late‐stage clinical trials. In this commentary, we discuss the unique value of disease‐scale “platform” QSP models that are amenable to reuse and repurposing to support diverse clinical decisions in ways distinct from other pharmacometrics strategies. PMID:27709613

An Organizational Informatics Analysis of Colorectal, Breast, and Cervical Cancer Screening Clinical Decision Support and Information Systems within Community Health Centers

ERIC Educational Resources Information Center

Carney, Timothy Jay

2012-01-01

A study design has been developed that employs a dual modeling approach to identify factors associated with facility-level cancer screening improvement and how this is mediated by the use of clinical decision support. This dual modeling approach combines principles of (1) Health Informatics, (2) Cancer Prevention and Control, (3) Health Services…

Enabling personalized cancer medicine decisions: The challenging pharmacological approach of PBPK models for nanomedicine and pharmacogenomics (Review).

PubMed

Vizirianakis, Ioannis S; Mystridis, George A; Avgoustakis, Konstantinos; Fatouros, Dimitrios G; Spanakis, Marios

2016-04-01

The existing tumor heterogeneity and the complexity of cancer cell biology critically demand powerful translational tools with which to support interdisciplinary efforts aiming to advance personalized cancer medicine decisions in drug development and clinical practice. The development of physiologically based pharmacokinetic (PBPK) models to predict the effects of drugs in the body facilitates the clinical translation of genomic knowledge and the implementation of in vivo pharmacology experience with pharmacogenomics. Such a direction unequivocally empowers our capacity to also make personalized drug dosage scheme decisions for drugs, including molecularly targeted agents and innovative nanoformulations, i.e. in establishing pharmacotyping in prescription. In this way, the applicability of PBPK models to guide individualized cancer therapeutic decisions of broad clinical utility in nanomedicine in real-time and in a cost-affordable manner will be discussed. The latter will be presented by emphasizing the need for combined efforts within the scientific borderlines of genomics with nanotechnology to ensure major benefits and productivity for nanomedicine and personalized medicine interventions.

Neonatal physical therapy. Part I: clinical competencies and neonatal intensive care unit clinical training models.

PubMed

Sweeney, Jane K; Heriza, Carolyn B; Blanchard, Yvette

2009-01-01

To describe clinical training models, delineate clinical competencies, and outline a clinical decision-making algorithm for neonatal physical therapy. In these updated practice guidelines, advanced clinical training models, including precepted practicum and residency or fellowship training, are presented to guide practitioners in organizing mentored, competency-based preparation for neonatal care. Clinical competencies in neonatal physical therapy are outlined with advanced clinical proficiencies and knowledge areas specific to each role. An algorithm for decision making on examination, evaluation, intervention, and re-examination processes provides a framework for clinical reasoning. Because of advanced-level competency requirements and the continuous examination, evaluation, and modification of procedures during each patient contact, the intensive care unit is a restricted practice area for physical therapist assistants, physical therapist generalists, and physical therapy students. Accountable, ethical physical therapy for neonates requires advanced, competency-based training with a preceptor in the pediatric subspecialty of neonatology.

Prescriptive models to support decision making in genetics.

PubMed

Pauker, S G; Pauker, S P

1987-01-01

Formal prescriptive models can help patients and clinicians better understand the risks and uncertainties they face and better formulate well-reasoned decisions. Using Bayes rule, the clinician can interpret pedigrees, historical data, physical findings and laboratory data, providing individualized probabilities of various diagnoses and outcomes of pregnancy. With the advent of screening programs for genetic disease, it becomes increasingly important to consider the prior probabilities of disease when interpreting an abnormal screening test result. Decision trees provide a convenient formalism for structuring diagnostic, therapeutic and reproductive decisions; such trees can also enhance communication between clinicians and patients. Utility theory provides a mechanism for patients to understand the choices they face and to communicate their attitudes about potential reproductive outcomes in a manner which encourages the integration of those attitudes into appropriate decisions. Using a decision tree, the relevant probabilities and the patients' utilities, physicians can estimate the relative worth of various medical and reproductive options by calculating the expected utility of each. By performing relevant sensitivity analyses, clinicians and patients can understand the impact of various soft data, including the patients' attitudes toward various health outcomes, on the decision making process. Formal clinical decision analytic models can provide deeper understanding and improved decision making in clinical genetics.

Empirically and Clinically Useful Decision Making in Psychotherapy: Differential Predictions with Treatment Response Models

ERIC Educational Resources Information Center

Lutz, Wolfgang; Saunders, Stephen M.; Leon, Scott C.; Martinovich, Zoran; Kosfelder, Joachim; Schulte, Dietmar; Grawe, Klaus; Tholen, Sven

2006-01-01

In the delivery of clinical services, outcomes monitoring (i.e., repeated assessments of a patient's response to treatment) can be used to support clinical decision making (i.e., recurrent revisions of outcome expectations on the basis of that response). Outcomes monitoring can be particularly useful in the context of established practice research…

Decision analysis in clinical cardiology: When is coronary angiography required in aortic stenosis

DOE Office of Scientific and Technical Information (OSTI.GOV)

Georgeson, S.; Meyer, K.B.; Pauker, S.G.

1990-03-15

Decision analysis offers a reproducible, explicit approach to complex clinical decisions. It consists of developing a model, typically a decision tree, that separates choices from chances and that specifies and assigns relative values to outcomes. Sensitivity analysis allows exploration of alternative assumptions. Cost-effectiveness analysis shows the relation between dollars spent and improved health outcomes achieved. In a tutorial format, this approach is applied to the decision whether to perform coronary angiography in a patient who requires aortic valve replacement for critical aortic stenosis.

Modular Architecture for Integrated Model-Based Decision Support.

PubMed

Gaebel, Jan; Schreiber, Erik; Oeser, Alexander; Oeltze-Jafra, Steffen

2018-01-01

Model-based decision support systems promise to be a valuable addition to oncological treatments and the implementation of personalized therapies. For the integration and sharing of decision models, the involved systems must be able to communicate with each other. In this paper, we propose a modularized architecture of dedicated systems for the integration of probabilistic decision models into existing hospital environments. These systems interconnect via web services and provide model sharing and processing capabilities for clinical information systems. Along the lines of IHE integration profiles from other disciplines and the meaningful reuse of routinely recorded patient data, our approach aims for the seamless integration of decision models into hospital infrastructure and the physicians' daily work.

Consultant psychiatrists’ experiences of and attitudes towards shared decision making in antipsychotic prescribing, a qualitative study

PubMed Central

2014-01-01

Background Shared decision making represents a clinical consultation model where both clinician and service user are conceptualised as experts; information is shared bilaterally and joint treatment decisions are reached. Little previous research has been conducted to assess experience of this model in psychiatric practice. The current project therefore sought to explore the attitudes and experiences of consultant psychiatrists relating to shared decision making in the prescribing of antipsychotic medications. Methods A qualitative research design allowed the experiences and beliefs of participants in relation to shared decision making to be elicited. Purposive sampling was used to recruit participants from a range of clinical backgrounds and with varying length of clinical experience. A semi-structured interview schedule was utilised and was adapted in subsequent interviews to reflect emergent themes. Data analysis was completed in parallel with interviews in order to guide interview topics and to inform recruitment. A directed analysis method was utilised for interview analysis with themes identified being fitted to a framework identified from the research literature as applicable to the practice of shared decision making. Examples of themes contradictory to, or not adequately explained by, the framework were sought. Results A total of 26 consultant psychiatrists were interviewed. Participants expressed support for the shared decision making model, but also acknowledged that it was necessary to be flexible as the clinical situation dictated. A number of potential barriers to the process were perceived however: The commonest barrier was the clinician’s beliefs regarding the service users’ insight into their mental disorder, presented in some cases as an absolute barrier to shared decision making. In addition factors external to the clinician - service user relationship were identified as impacting on the decision making process, including; environmental factors, financial constraints as well as societal perceptions of mental disorder in general and antipsychotic medication in particular. Conclusions This project has allowed identification of potential barriers to shared decision making in psychiatric practice. Further work is necessary to observe the decision making process in clinical practice and also to identify means in which the identified barriers, in particular ‘lack of insight’, may be more effectively managed. PMID:24886121

Consultant psychiatrists' experiences of and attitudes towards shared decision making in antipsychotic prescribing, a qualitative study.

PubMed

Shepherd, Andrew; Shorthouse, Oliver; Gask, Linda

2014-05-01

Shared decision making represents a clinical consultation model where both clinician and service user are conceptualised as experts; information is shared bilaterally and joint treatment decisions are reached. Little previous research has been conducted to assess experience of this model in psychiatric practice. The current project therefore sought to explore the attitudes and experiences of consultant psychiatrists relating to shared decision making in the prescribing of antipsychotic medications. A qualitative research design allowed the experiences and beliefs of participants in relation to shared decision making to be elicited. Purposive sampling was used to recruit participants from a range of clinical backgrounds and with varying length of clinical experience. A semi-structured interview schedule was utilised and was adapted in subsequent interviews to reflect emergent themes.Data analysis was completed in parallel with interviews in order to guide interview topics and to inform recruitment. A directed analysis method was utilised for interview analysis with themes identified being fitted to a framework identified from the research literature as applicable to the practice of shared decision making. Examples of themes contradictory to, or not adequately explained by, the framework were sought. A total of 26 consultant psychiatrists were interviewed. Participants expressed support for the shared decision making model, but also acknowledged that it was necessary to be flexible as the clinical situation dictated. A number of potential barriers to the process were perceived however: The commonest barrier was the clinician's beliefs regarding the service users' insight into their mental disorder, presented in some cases as an absolute barrier to shared decision making. In addition factors external to the clinician - service user relationship were identified as impacting on the decision making process, including; environmental factors, financial constraints as well as societal perceptions of mental disorder in general and antipsychotic medication in particular. This project has allowed identification of potential barriers to shared decision making in psychiatric practice. Further work is necessary to observe the decision making process in clinical practice and also to identify means in which the identified barriers, in particular 'lack of insight', may be more effectively managed.

[Clinical reasoning in undergraduate nursing education: a scoping review].

PubMed

Menezes, Sáskia Sampaio Cipriano de; Corrêa, Consuelo Garcia; Silva, Rita de Cássia Gengo E; Cruz, Diná de Almeida Monteiro Lopes da

2015-12-01

This study aimed at analyzing the current state of knowledge on clinical reasoning in undergraduate nursing education. A systematic scoping review through a search strategy applied to the MEDLINE database, and an analysis of the material recovered by extracting data done by two independent reviewers. The extracted data were analyzed and synthesized in a narrative manner. From the 1380 citations retrieved in the search, 23 were kept for review and their contents were summarized into five categories: 1) the experience of developing critical thinking/clinical reasoning/decision-making process; 2) teaching strategies related to the development of critical thinking/clinical reasoning/decision-making process; 3) measurement of variables related to the critical thinking/clinical reasoning/decision-making process; 4) relationship of variables involved in the critical thinking/clinical reasoning/decision-making process; and 5) theoretical development models of critical thinking/clinical reasoning/decision-making process for students. The biggest challenge for developing knowledge on teaching clinical reasoning seems to be finding consistency between theoretical perspectives on the development of clinical reasoning and methodologies, methods, and procedures in research initiatives in this field.

When is rational to order a diagnostic test, or prescribe treatment: the threshold model as an explanation of practice variation.

PubMed

Djulbegovic, Benjamin; van den Ende, Jef; Hamm, Robert M; Mayrhofer, Thomas; Hozo, Iztok; Pauker, Stephen G

2015-05-01

The threshold model represents an important advance in the field of medical decision-making. It is a linchpin between evidence (which exists on the continuum of credibility) and decision-making (which is a categorical exercise - we decide to act or not act). The threshold concept is closely related to the question of rational decision-making. When should the physician act, that is order a diagnostic test, or prescribe treatment? The threshold model embodies the decision theoretic rationality that says the most rational decision is to prescribe treatment when the expected treatment benefit outweighs its expected harms. However, the well-documented large variation in the way physicians order diagnostic tests or decide to administer treatments is consistent with a notion that physicians' individual action thresholds vary. We present a narrative review summarizing the existing literature on physicians' use of a threshold strategy for decision-making. We found that the observed variation in decision action thresholds is partially due to the way people integrate benefits and harms. That is, explanation of variation in clinical practice can be reduced to a consideration of thresholds. Limited evidence suggests that non-expected utility threshold (non-EUT) models, such as regret-based and dual-processing models, may explain current medical practice better. However, inclusion of costs and recognition of risk attitudes towards uncertain treatment effects and comorbidities may improve the explanatory and predictive value of the EUT-based threshold models. The decision when to act is closely related to the question of rational choice. We conclude that the medical community has not yet fully defined criteria for rational clinical decision-making. The traditional notion of rationality rooted in EUT may need to be supplemented by reflective rationality, which strives to integrate all aspects of medical practice - medical, humanistic and socio-economic - within a coherent reasoning system. © 2015 Stichting European Society for Clinical Investigation Journal Foundation.

Applying a family systems lens to proxy decision making in clinical practice and research.

PubMed

Rolland, John S; Emanuel, Linda L; Torke, Alexia M

2017-03-01

When patients are incapacitated and face serious illness, family members must make medical decisions for the patient. Medical decision sciences give only modest attention to the relationships among patients and their family members, including impact that these relationships have on the decision-making process. A review of the literature reveals little effort to systematically apply a theoretical framework to the role of family interactions in proxy decision making. A family systems perspective can provide a useful lens through which to understand the dynamics of proxy decision making. This article considers the mutual impact of family systems on the processes and outcomes of proxy decision making. The article first reviews medical decision science's evolution and focus on proxy decision making and then reviews a family systems approach, giving particular attention to Rolland's Family Systems Illness Model. A case illustrates how clinical practice and how research would benefit from bringing family systems thinking to proxy decisions. We recommend including a family systems approach in medical decision science research and clinical practices around proxy decisions making. We propose that clinical decisions could be less conflicted and less emotionally troubling for families and clinicians if family systems approaches were included. This perspective opens new directions for research and novel approaches to clinical care. (PsycINFO Database Record (c) 2017 APA, all rights reserved).

Multidisciplinary Modelling of Symptoms and Signs with Archetypes and SNOMED-CT for Clinical Decision Support.

PubMed

Marco-Ruiz, Luis; Maldonado, J Alberto; Karlsen, Randi; Bellika, Johan G

2015-01-01

Clinical Decision Support Systems (CDSS) help to improve health care and reduce costs. However, the lack of knowledge management and modelling hampers their maintenance and reuse. Current EHR standards and terminologies can allow the semantic representation of the data and knowledge of CDSS systems boosting their interoperability, reuse and maintenance. This paper presents the modelling process of respiratory conditions' symptoms and signs by a multidisciplinary team of clinicians and information architects with the help of openEHR, SNOMED and clinical information modelling tools for a CDSS. The information model of the CDSS was defined by means of an archetype and the knowledge model was implemented by means of an SNOMED-CT based ontology.

The Integrated Medical Model: A Decision Support Tool for In-flight Crew Health Care

NASA Technical Reports Server (NTRS)

Butler, Doug

2009-01-01

This viewgraph presentation reviews the development of an Integrated Medical Model (IMM) decision support tool for in-flight crew health care safety. Clinical methods, resources, and case scenarios are also addressed.

Clinical cognition and diagnostic error: applications of a dual process model of reasoning.

PubMed

Croskerry, Pat

2009-09-01

Both systemic and individual factors contribute to missed or delayed diagnoses. Among the multiple factors that impact clinical performance of the individual, the caliber of cognition is perhaps the most relevant and deserves our attention and understanding. In the last few decades, cognitive psychologists have gained substantial insights into the processes that underlie cognition, and a new, universal model of reasoning and decision making has emerged, Dual Process Theory. The theory has immediate application to medical decision making and provides an overall schema for understanding the variety of theoretical approaches that have been taken in the past. The model has important practical applications for decision making across the multiple domains of healthcare, and may be used as a template for teaching decision theory, as well as a platform for future research. Importantly, specific operating characteristics of the model explain how diagnostic failure occurs.

Lessons of War: Turning Data Into Decisions.

PubMed

Forsberg, Jonathan A; Potter, Benjamin K; Wagner, Matthew B; Vickers, Andrew; Dente, Christopher J; Kirk, Allan D; Elster, Eric A

2015-09-01

Recent conflicts in Afghanistan and Iraq produced a substantial number of critically wounded service-members. We collected biomarker and clinical information from 73 patients who sustained 116 life-threatening combat wounds, and sought to determine if the data could be used to predict the likelihood of wound failure. From each patient, we collected clinical information, serum, wound effluent, and tissue prior to and at each surgical débridement. Inflammatory cytokines were quantified in both the serum and effluent, as were gene expression targets. The primary outcome was successful wound healing. Computer intensive methods were used to derive prognostic models that were internally validated using target shuffling and cross-validation methods. A second cohort of eighteen critically injured civilian patients was evaluated to determine if similar inflammatory responses were observed. The best-performing models enhanced clinical observation with biomarker data from the serum and wound effluent, an indicator that systemic inflammatory conditions contribute to local wound failure. A Random Forest model containing ten variables demonstrated the highest accuracy (AUC 0.79). Decision Curve Analysis indicated that the use of this model would improve clinical outcomes and reduce unnecessary surgical procedures. Civilian trauma patients demonstrated similar inflammatory responses and an equivalent wound failure rate, indicating that the model may be generalizable to civilian settings. Using advanced analytics, we successfully codified clinical and biomarker data from combat patients into a potentially generalizable decision support tool. Analysis of inflammatory data from critically ill patients with acute injury may inform decision-making to improve clinical outcomes and reduce healthcare costs. United States Department of Defense Health Programs.

Reaching beyond the review of research evidence: a qualitative study of decision making during the development of clinical practice guidelines for disease prevention in healthcare.

PubMed

Richter Sundberg, Linda; Garvare, Rickard; Nyström, Monica Elisabeth

2017-05-11

The judgment and decision making process during guideline development is central for producing high-quality clinical practice guidelines, but the topic is relatively underexplored in the guideline research literature. We have studied the development process of national guidelines with a disease-prevention scope produced by the National board of Health and Welfare (NBHW) in Sweden. The NBHW formal guideline development model states that guideline recommendations should be based on five decision-criteria: research evidence; curative/preventive effect size, severity of the condition; cost-effectiveness; and ethical considerations. A group of health profession representatives (i.e. a prioritization group) was assigned the task of ranking condition-intervention pairs for guideline recommendations, taking into consideration the multiple decision criteria. The aim of this study was to investigate the decision making process during the two-year development of national guidelines for methods of preventing disease. A qualitative inductive longitudinal case study approach was used to investigate the decision making process. Questionnaires, non-participant observations of nine two-day group meetings, and documents provided data for the analysis. Conventional and summative qualitative content analysis was used to analyse data. The guideline development model was modified ad-hoc as the group encountered three main types of dilemmas: high quality evidence vs. low adoptability of recommendation; insufficient evidence vs. high urgency to act; and incoherence in assessment and prioritization within and between four different lifestyle areas. The formal guideline development model guided the decision-criteria used, but three new or revised criteria were added by the group: 'clinical knowledge and experience', 'potential guideline consequences' and 'needs of vulnerable groups'. The frequency of the use of various criteria in discussions varied over time. Gender, professional status, and interpersonal skills were perceived to affect individuals' relative influence on group discussions. The study shows that guideline development groups make compromises between rigour and pragmatism. The formal guideline development model incorporated multiple aspects, but offered few details on how the different criteria should be handled. The guideline development model devoted little attention to the role of the decision-model and group-related factors. Guideline development models could benefit from clarifying the role of the group-related factors and non-research evidence, such as clinical experience and ethical considerations, in decision-processes during guideline development.

Cognitive continuum theory in interprofessional healthcare: A critical analysis.

PubMed

Parker-Tomlin, Michelle; Boschen, Mark; Morrissey, Shirley; Glendon, Ian

2017-07-01

Effective clinical decision making is among the most important skills required by healthcare practitioners. Making sound decisions while working collaboratively in interprofessional healthcare teams is essential for modern healthcare planning, successful interventions, and patient care. The cognitive continuum theory (CCT) is a model of human judgement and decision making aimed at orienting decision-making processes. CCT has the potential to improve both individual health practitioner, and interprofessional team understanding about, and communication of, clinical decision-making processes. Examination of the current application of CCT indicates that this theory could strengthen interprofessional team clinical decision making (CDM). However, further research is needed before extending the use of this theoretical framework to a wider range of interprofessional healthcare team processes. Implications for research, education, practice, and policy are addressed.

Conceptual Models and Guidelines for Clinical Assessment of Financial Capacity

PubMed Central

Marson, Daniel

2016-01-01

The ability to manage financial affairs is a life skill of critical importance, and neuropsychologists are increasingly asked to assess financial capacity across a variety of settings. Sound clinical assessment of financial capacity requires knowledge and appreciation of applicable clinical conceptual models and principles. However, the literature has presented relatively little conceptual guidance for clinicians concerning financial capacity and its assessment. This article seeks to address this gap. The article presents six clinical models of financial capacity : (1) the early gerontological IADL model of Lawton, (2) the clinical skills model and (3) related cognitive psychological model developed by Marson and colleagues, (4) a financial decision-making model adapting earlier decisional capacity work of Appelbaum and Grisso, (5) a person-centered model of financial decision-making developed by Lichtenberg and colleagues, and (6) a recent model of financial capacity in the real world developed through the Institute of Medicine. Accompanying presentation of the models is discussion of conceptual and practical perspectives they represent for clinician assessment. Based on the models, the article concludes by presenting a series of conceptually oriented guidelines for clinical assessment of financial capacity. In summary, sound assessment of financial capacity requires knowledge and appreciation of clinical conceptual models and principles. Awareness of such models, principles and guidelines will strengthen and advance clinical assessment of financial capacity. PMID:27506235

Developing and testing a decision model for predicting influenza vaccination compliance.

PubMed Central

Carter, W B; Beach, L R; Inui, T S; Kirscht, J P; Prodzinski, J C

1986-01-01

Influenza vaccination has long been recommended for elderly high-risk patients, yet national surveys indicate that vaccination compliance rates are remarkably low (20 percent). We conducted a study to model prospectively the flu shot decisions and subsequent behavior of an elderly and/or chronically diseased (at high risk for complications of influenza) ambulatory care population at the Seattle VA Medical Center. Prior to the 1980-81 flu shot season, a random (stratified by disease) sample of 63 patients, drawn from the total population of high-risk patients in the general medicine clinic, was interviewed to identify patient-defined concerns regarding flu shots. Six potential consequences of influenza and nine of vaccination were emphasized by patients and provided the content for a weighted hierarchical utility model questionnaire. The utility model provides an operational framework for (1) obtaining subjective value and relative importance judgments from patients; (2) combining these judgments to obtain a prediction of behavioral intention and behavior for each patient; and, if the model is valid (predictive of behavior), (3) identifying those factors which are most salient to patient's decisions and subsequent behavior. Prior to the 1981-82 flu season, the decision model questionnaire was administered to 350 other high-risk patients from the same general medicine clinic population. The decision model correctly predicted behavioral intention for 87 percent and vaccination behavior for 82 percent of this population and, more importantly, differentiated shot "takers" and "nontakers" along several attitudinal dimensions that suggest specific content areas for clinical compliance intervention strategies. PMID:3949541

The relationship between patient data and pooled clinical management decisions.

PubMed

Ludbrook, G I; O'Loughlin, E J; Corcoran, T B; Grant, C

2013-01-01

A strong relationship between patient data and preoperative clinical decisions could potentially be used to support clinical decisions in preoperative management. The aim of this exploratory study was to determine the relationship between key patient data and pooled clinical opinions on management. In a previous study, panels of anaesthetists compared the quality of computer-assisted patient health assessments with outpatient consultations and made decisions on the need for preoperative tests, no preoperative outpatient assessment, possible postoperative intensive care unit/high dependency unit requirements and aspiration prophylaxis. In the current study, the relationship between patient data and these decisions was examined using binomial logistic regression analysis. Backward stepwise regression was used to identify independent predictors of each decision (at P >0.15), which were then incorporated into a predictive model. The number of factors related to each decision varied: blood picture (four factors), biochemistry (six factors), coagulation studies (three factors), electrocardiography (eight factors), chest X-ray (seven factors), preoperative outpatient assessment (17 factors), intensive care unit requirement (eight factors) and aspiration prophylaxis (one factor). The factor types also varied, but included surgical complexity, age, gender, number of medications or comorbidities, body mass index, hypertension, central nervous system condition, heart disease, sleep apnoea, smoking, persistent pain and stroke. Models based on these relationships usually demonstrated good sensitivity and specificity, with receiver operating characteristics in the following areas under curve: blood picture (0.75), biochemistry (0.86), coagulation studies (0.71), electrocardiography (0.90), chest X-ray (0.85), outpatient assessment (0.85), postoperative intensive care unit requirement (0.88) and aspiration prophylaxis (0.85). These initial results suggest modelling of patient data may have utility supporting clinicians' preoperative decisions.

Audit method suited for DSS in clinical environment.

PubMed

Vicente, Javier

2015-01-01

This chapter presents a novel online method to audit predictive models using a Bayesian perspective. The auditing model has been specifically designed for Decision Support Systems (DSSs) suited for clinical or research environments. Taking as starting point the working diagnosis supplied by the clinician, this method compares and evaluates the predictive skills of those models able to answer to that diagnosis. The approach consists in calculating the posterior odds of a model through the composition of a prior odds, a static odds, and a dynamic odds. To do so, this method estimates the posterior odds from the cases that the comparing models had in common during the design stage and from the cases already viewed by the DSS after deployment in the clinical site. In addition, if an ontology of the classes is available, this method can audit models answering related questions, which offers a reinforcement to the decisions the user already took and gives orientation on further diagnostic steps.The main technical novelty of this approach lies in the design of an audit model adapted to suit the decision workflow of a clinical environment. The audit model allows deciding what is the classifier that best suits each particular case under evaluation and allows the detection of possible misbehaviours due to population differences or data shifts in the clinical site. We show the efficacy of our method for the problem of brain tumor diagnosis with Magnetic Resonance Spectroscopy (MRS).

A Bridging Opportunities Work-frame to develop mobile applications for clinical decision making

PubMed Central

van Rooij, Tibor; Rix, Serena; Moore, James B; Marsh, Sharon

2015-01-01

Background: Mobile applications (apps) providing clinical decision support (CDS) may show the greatest promise when created by and for frontline clinicians. Our aim was to create a generic model enabling healthcare providers to direct the development of CDS apps. Methods: We combined Change Management with a three-tier information technology architecture to stimulate CDS app development. Results: A Bridging Opportunities Work-frame model was developed. A test case was used to successfully develop an app. Conclusion: Healthcare providers can re-use this globally applicable model to actively create and manage regional decision support applications to translate evidence-based medicine in the use of emerging medication or novel treatment regimens. PMID:28031883

Representation primitives, process models and patient data in computer-interpretable clinical practice guidelines: a literature review of guideline representation models.

PubMed

Wang, Dongwen; Peleg, Mor; Tu, Samson W; Boxwala, Aziz A; Greenes, Robert A; Patel, Vimla L; Shortliffe, Edward H

2002-12-18

Representation of clinical practice guidelines in a computer-interpretable format is a critical issue for guideline development, implementation, and evaluation. We studied 11 types of guideline representation models that can be used to encode guidelines in computer-interpretable formats. We have consistently found in all reviewed models that primitives for representation of actions and decisions are necessary components of a guideline representation model. Patient states and execution states are important concepts that closely relate to each other. Scheduling constraints on representation primitives can be modeled as sequences, concurrences, alternatives, and loops in a guideline's application process. Nesting of guidelines provides multiple views to a guideline with different granularities. Integration of guidelines with electronic medical records can be facilitated by the introduction of a formal model for patient data. Data collection, decision, patient state, and intervention constitute four basic types of primitives in a guideline's logic flow. Decisions clarify our understanding on a patient's clinical state, while interventions lead to the change from one patient state to another.

Exploratory Use of Decision Tree Analysis in Classification of Outcome in Hypoxic-Ischemic Brain Injury.

PubMed

Phan, Thanh G; Chen, Jian; Singhal, Shaloo; Ma, Henry; Clissold, Benjamin B; Ly, John; Beare, Richard

2018-01-01

Prognostication following hypoxic ischemic encephalopathy (brain injury) is important for clinical management. The aim of this exploratory study is to use a decision tree model to find clinical and MRI associates of severe disability and death in this condition. We evaluate clinical model and then the added value of MRI data. The inclusion criteria were as follows: age ≥17 years, cardio-respiratory arrest, and coma on admission (2003-2011). Decision tree analysis was used to find clinical [Glasgow Coma Score (GCS), features about cardiac arrest, therapeutic hypothermia, age, and sex] and MRI (infarct volume) associates of severe disability and death. We used the area under the ROC (auROC) to determine accuracy of model. There were 41 (63.7% males) patients having MRI imaging with the average age 51.5 ± 18.9 years old. The decision trees showed that infarct volume and age were important factors for discrimination between mild to moderate disability and severe disability and death at day 0 and day 2. The auROC for this model was 0.94 (95% CI 0.82-1.00). At day 7, GCS value was the only predictor; the auROC was 0.96 (95% CI 0.86-1.00). Our findings provide proof of concept for further exploration of the role of MR imaging and decision tree analysis in the early prognostication of hypoxic ischemic brain injury.

A Clinical Decision Support System for Breast Cancer Patients

NASA Astrophysics Data System (ADS)

Fernandes, Ana S.; Alves, Pedro; Jarman, Ian H.; Etchells, Terence A.; Fonseca, José M.; Lisboa, Paulo J. G.

This paper proposes a Web clinical decision support system for clinical oncologists and for breast cancer patients making prognostic assessments, using the particular characteristics of the individual patient. This system comprises three different prognostic modelling methodologies: the clinically widely used Nottingham prognostic index (NPI); the Cox regression modelling and a partial logistic artificial neural network with automatic relevance determination (PLANN-ARD). All three models yield a different prognostic index that can be analysed together in order to obtain a more accurate prognostic assessment of the patient. Missing data is incorporated in the mentioned models, a common issue in medical data that was overcome using multiple imputation techniques. Risk group assignments are also provided through a methodology based on regression trees, where Boolean rules can be obtained expressed with patient characteristics.

Is it the time to rethink clinical decision-making strategies? From a single clinical outcome evaluation to a Clinical Multi-criteria Decision Assessment (CMDA).

PubMed

Migliore, Alberto; Integlia, Davide; Bizzi, Emanuele; Piaggio, Tomaso

2015-10-01

There are plenty of different clinical, organizational and economic parameters to consider in order having a complete assessment of the total impact of a pharmaceutical treatment. In the attempt to follow, a holistic approach aimed to provide an evaluation embracing all clinical parameters in order to choose the best treatments, it is necessary to compare and weight multiple criteria. Therefore, a change is required: we need to move from a decision-making context based on the assessment of one single criteria towards a transparent and systematic framework enabling decision makers to assess all relevant parameters simultaneously in order to choose the best treatment to use. In order to apply the MCDA methodology to clinical decision making the best pharmaceutical treatment (or medical devices) to use to treat a specific pathology, we suggest a specific application of the Multiple Criteria Decision Analysis for the purpose, like a Clinical Multi-criteria Decision Assessment CMDA. In CMDA, results from both meta-analysis and observational studies are used by a clinical consensus after attributing weights to specific domains and related parameters. The decision will result from a related comparison of all consequences (i.e., efficacy, safety, adherence, administration route) existing behind the choice to use a specific pharmacological treatment. The match will yield a score (in absolute value) that link each parameter with a specific intervention, and then a final score for each treatment. The higher is the final score; the most appropriate is the intervention to treat disease considering all criteria (domain an parameters). The results will allow the physician to evaluate the best clinical treatment for his patients considering at the same time all relevant criteria such as clinical effectiveness for all parameters and administration route. The use of CMDA model will yield a clear and complete indication of the best pharmaceutical treatment to use for patients, helping physicians to choose drugs with a complete set of information, imputed in the model. Copyright © 2015 Elsevier Ltd. All rights reserved.

Financial Concerns About Participation in Clinical Trials Among Patients With Cancer.

PubMed

Wong, Yu-Ning; Schluchter, Mark D; Albrecht, Terrance L; Benson, Al Bowen; Buzaglo, Joanne; Collins, Michael; Flamm, Anne Lederman; Fleisher, Linda; Katz, Michael; Kinzy, Tyler G; Liu, Tasnuva M; Manne, Sharon; Margevicius, Seunghee; Miller, Dawn M; Miller, Suzanne M; Poole, David; Raivitch, Stephanie; Roach, Nancy; Ross, Eric; Meropol, Neal J

2016-02-10

The decision to enroll in a clinical trial is complex given the uncertain risks and benefits of new approaches. Many patients also have financial concerns. We sought to characterize the association between financial concerns and the quality of decision making about clinical trials. We conducted a secondary data analysis of a randomized trial of a Web-based educational tool (Preparatory Education About Clinical Trials) designed to improve the preparation of patients with cancer for making decisions about clinical trial enrollment. Patients completed a baseline questionnaire that included three questions related to financial concerns (five-point Likert scales): "How much of a burden on you is the cost of your medical care?," "I'm afraid that my health insurance won't pay for a clinical trial," and "I'm worried that I wouldn't be able to afford the costs of treatment on a clinical trial." Results were summed, with higher scores indicating greater concerns. We used multiple linear regressions to measure the association between concerns and self-reported measures of self-efficacy, preparation for decision making, distress, and decisional conflict in separate models, controlling for sociodemographic characteristics. One thousand two hundred eleven patients completed at least one financial concern question. Of these, 27% were 65 years or older, 58% were female, and 24% had a high school education or less. Greater financial concern was associated with lower self-efficacy and preparation for decision making, as well as with greater decisional conflict and distress, even after adjustment for age, race, sex, education, employment, and hospital location (P < .001 for all models). Financial concerns are associated with several psychological constructs that may negatively influence decision quality regarding clinical trials. Greater attention to patients' financial needs and concerns may reduce distress and improve patient decision making. © 2015 by American Society of Clinical Oncology.

Multi-scale Modeling of the Cardiovascular System: Disease Development, Progression, and Clinical Intervention.

PubMed

Zhang, Yanhang; Barocas, Victor H; Berceli, Scott A; Clancy, Colleen E; Eckmann, David M; Garbey, Marc; Kassab, Ghassan S; Lochner, Donna R; McCulloch, Andrew D; Tran-Son-Tay, Roger; Trayanova, Natalia A

2016-09-01

Cardiovascular diseases (CVDs) are the leading cause of death in the western world. With the current development of clinical diagnostics to more accurately measure the extent and specifics of CVDs, a laudable goal is a better understanding of the structure-function relation in the cardiovascular system. Much of this fundamental understanding comes from the development and study of models that integrate biology, medicine, imaging, and biomechanics. Information from these models provides guidance for developing diagnostics, and implementation of these diagnostics to the clinical setting, in turn, provides data for refining the models. In this review, we introduce multi-scale and multi-physical models for understanding disease development, progression, and designing clinical interventions. We begin with multi-scale models of cardiac electrophysiology and mechanics for diagnosis, clinical decision support, personalized and precision medicine in cardiology with examples in arrhythmia and heart failure. We then introduce computational models of vasculature mechanics and associated mechanical forces for understanding vascular disease progression, designing clinical interventions, and elucidating mechanisms that underlie diverse vascular conditions. We conclude with a discussion of barriers that must be overcome to provide enhanced insights, predictions, and decisions in pre-clinical and clinical applications.

Multi-scale Modeling of the Cardiovascular System: Disease Development, Progression, and Clinical Intervention

PubMed Central

Zhang, Yanhang; Barocas, Victor H.; Berceli, Scott A.; Clancy, Colleen E.; Eckmann, David M.; Garbey, Marc; Kassab, Ghassan S.; Lochner, Donna R.; McCulloch, Andrew D.; Tran-Son-Tay, Roger; Trayanova, Natalia A.

2016-01-01

Cardiovascular diseases (CVDs) are the leading cause of death in the western world. With the current development of clinical diagnostics to more accurately measure the extent and specifics of CVDs, a laudable goal is a better understanding of the structure-function relation in the cardiovascular system. Much of this fundamental understanding comes from the development and study of models that integrate biology, medicine, imaging, and biomechanics. Information from these models provides guidance for developing diagnostics, and implementation of these diagnostics to the clinical setting, in turn, provides data for refining the models. In this review, we introduce multi-scale and multi-physical models for understanding disease development, progression, and designing clinical interventions. We begin with multi-scale models of cardiac electrophysiology and mechanics for diagnosis, clinical decision support, personalized and precision medicine in cardiology with examples in arrhythmia and heart failure. We then introduce computational models of vasculature mechanics and associated mechanical forces for understanding vascular disease progression, designing clinical interventions, and elucidating mechanisms that underlie diverse vascular conditions. We conclude with a discussion of barriers that must be overcome to provide enhanced insights, predictions, and decisions in pre-clinical and clinical applications. PMID:27138523

Assessing the capacity of social determinants of health data to augment predictive models identifying patients in need of wraparound social services.

PubMed

Kasthurirathne, Suranga N; Vest, Joshua R; Menachemi, Nir; Halverson, Paul K; Grannis, Shaun J

2018-01-01

A growing variety of diverse data sources is emerging to better inform health care delivery and health outcomes. We sought to evaluate the capacity for clinical, socioeconomic, and public health data sources to predict the need for various social service referrals among patients at a safety-net hospital. We integrated patient clinical data and community-level data representing patients' social determinants of health (SDH) obtained from multiple sources to build random forest decision models to predict the need for any, mental health, dietitian, social work, or other SDH service referrals. To assess the impact of SDH on improving performance, we built separate decision models using clinical and SDH determinants and clinical data only. Decision models predicting the need for any, mental health, and dietitian referrals yielded sensitivity, specificity, and accuracy measures ranging between 60% and 75%. Specificity and accuracy scores for social work and other SDH services ranged between 67% and 77%, while sensitivity scores were between 50% and 63%. Area under the receiver operating characteristic curve values for the decision models ranged between 70% and 78%. Models for predicting the need for any services reported positive predictive values between 65% and 73%. Positive predictive values for predicting individual outcomes were below 40%. The need for various social service referrals can be predicted with considerable accuracy using a wide range of readily available clinical and community data that measure socioeconomic and public health conditions. While the use of SDH did not result in significant performance improvements, our approach represents a novel and important application of risk predictive modeling. © The Author 2017. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For Permissions, please email: journals.permissions@oup.com

Many faces of rationality: Implications of the great rationality debate for clinical decision-making.

PubMed

Djulbegovic, Benjamin; Elqayam, Shira

2017-10-01

Given that more than 30% of healthcare costs are wasted on inappropriate care, suboptimal care is increasingly connected to the quality of medical decisions. It has been argued that personal decisions are the leading cause of death, and 80% of healthcare expenditures result from physicians' decisions. Therefore, improving healthcare necessitates improving medical decisions, ie, making decisions (more) rational. Drawing on writings from The Great Rationality Debate from the fields of philosophy, economics, and psychology, we identify core ingredients of rationality commonly encountered across various theoretical models. Rationality is typically classified under umbrella of normative (addressing the question how people "should" or "ought to" make their decisions) and descriptive theories of decision-making (which portray how people actually make their decisions). Normative theories of rational thought of relevance to medicine include epistemic theories that direct practice of evidence-based medicine and expected utility theory, which provides the basis for widely used clinical decision analyses. Descriptive theories of rationality of direct relevance to medical decision-making include bounded rationality, argumentative theory of reasoning, adaptive rationality, dual processing model of rationality, regret-based rationality, pragmatic/substantive rationality, and meta-rationality. For the first time, we provide a review of wide range of theories and models of rationality. We showed that what is "rational" behaviour under one rationality theory may be irrational under the other theory. We also showed that context is of paramount importance to rationality and that no one model of rationality can possibly fit all contexts. We suggest that in context-poor situations, such as policy decision-making, normative theories based on expected utility informed by best research evidence may provide the optimal approach to medical decision-making, whereas in the context-rich circumstances other types of rationality, informed by human cognitive architecture and driven by intuition and emotions such as the aim to minimize regret, may provide better solution to the problem at hand. The choice of theory under which we operate is important as it determines both policy and our individual decision-making. © 2017 The Authors Journal of Evaluation in Clinical Practice Published by John Wiley & Sons Ltd.

Effects of computerized clinical decision support systems on practitioner performance and patient outcomes: methods of a decision-maker-researcher partnership systematic review.

PubMed

Haynes, R Brian; Wilczynski, Nancy L

2010-02-05

Computerized clinical decision support systems are information technology-based systems designed to improve clinical decision-making. As with any healthcare intervention with claims to improve process of care or patient outcomes, decision support systems should be rigorously evaluated before widespread dissemination into clinical practice. Engaging healthcare providers and managers in the review process may facilitate knowledge translation and uptake. The objective of this research was to form a partnership of healthcare providers, managers, and researchers to review randomized controlled trials assessing the effects of computerized decision support for six clinical application areas: primary preventive care, therapeutic drug monitoring and dosing, drug prescribing, chronic disease management, diagnostic test ordering and interpretation, and acute care management; and to identify study characteristics that predict benefit. The review was undertaken by the Health Information Research Unit, McMaster University, in partnership with Hamilton Health Sciences, the Hamilton, Niagara, Haldimand, and Brant Local Health Integration Network, and pertinent healthcare service teams. Following agreement on information needs and interests with decision-makers, our earlier systematic review was updated by searching Medline, EMBASE, EBM Review databases, and Inspec, and reviewing reference lists through 6 January 2010. Data extraction items were expanded according to input from decision-makers. Authors of primary studies were contacted to confirm data and to provide additional information. Eligible trials were organized according to clinical area of application. We included randomized controlled trials that evaluated the effect on practitioner performance or patient outcomes of patient care provided with a computerized clinical decision support system compared with patient care without such a system. Data will be summarized using descriptive summary measures, including proportions for categorical variables and means for continuous variables. Univariable and multivariable logistic regression models will be used to investigate associations between outcomes of interest and study specific covariates. When reporting results from individual studies, we will cite the measures of association and p-values reported in the studies. If appropriate for groups of studies with similar features, we will conduct meta-analyses. A decision-maker-researcher partnership provides a model for systematic reviews that may foster knowledge translation and uptake.

[Application of pharmacoeconomics in clinical management].

PubMed

Amat Díaz, M; Poveda Andrés, J L; Carrera-Hueso, F J

2011-05-01

The present article discusses the importance of clinical management in the transformation of organizations and its role in the daily activities of health professionals and, in particular, of hospital pharmacists. Because of social changes, healthcare models must make the shift from more rigid management models toward new organizational models based on clinical management. From this perspective, pharmacoeconomics is viewed as a useful tool to introduce the criteria of efficiency in all decisions subject to clinical management, including those on pharmacotherapeutics. Subsequently, the application of this discipline is discussed in real decision-making scenarios and settings for its use within the context of the work of hospital pharmacy are proposed. Copyright © 2011 Sociedad Española de Farmacia Hospitalaria. Published by Elsevier Espana. All rights reserved.

A First Step towards a Clinical Decision Support System for Post-traumatic Stress Disorders.

PubMed

Ma, Sisi; Galatzer-Levy, Isaac R; Wang, Xuya; Fenyö, David; Shalev, Arieh Y

2016-01-01

PTSD is distressful and debilitating, following a non-remitting course in about 10% to 20% of trauma survivors. Numerous risk indicators of PTSD have been identified, but individual level prediction remains elusive. As an effort to bridge the gap between scientific discovery and practical application, we designed and implemented a clinical decision support pipeline to provide clinically relevant recommendation for trauma survivors. To meet the specific challenge of early prediction, this work uses data obtained within ten days of a traumatic event. The pipeline creates personalized predictive model for each individual, and computes quality metrics for each predictive model. Clinical recommendations are made based on both the prediction of the model and its quality, thus avoiding making potentially detrimental recommendations based on insufficient information or suboptimal model. The current pipeline outperforms the acute stress disorder, a commonly used clinical risk factor for PTSD development, both in terms of sensitivity and specificity.

Knowledge bases, clinical decision support systems, and rapid learning in oncology.

PubMed

Yu, Peter Paul

2015-03-01

One of the most important benefits of health information technology is to assist the cognitive process of the human mind in the face of vast amounts of health data, limited time for decision making, and the complexity of the patient with cancer. Clinical decision support tools are frequently cited as a technologic solution to this problem, but to date useful clinical decision support systems (CDSS) have been limited in utility and implementation. This article describes three unique sources of health data that underlie fundamentally different types of knowledge bases which feed into CDSS. CDSS themselves comprise a variety of models which are discussed. The relationship of knowledge bases and CDSS to rapid learning health systems design is critical as CDSS are essential drivers of rapid learning in clinical care. Copyright © 2015 by American Society of Clinical Oncology.

Towards knowledge-based systems in clinical practice: development of an integrated clinical information and knowledge management support system.

PubMed

Kalogeropoulos, Dimitris A; Carson, Ewart R; Collinson, Paul O

2003-09-01

Given that clinicians presented with identical clinical information will act in different ways, there is a need to introduce into routine clinical practice methods and tools to support the scientific homogeneity and accountability of healthcare decisions and actions. The benefits expected from such action include an overall reduction in cost, improved quality of care, patient and public opinion satisfaction. Computer-based medical data processing has yielded methods and tools for managing the task away from the hospital management level and closer to the desired disease and patient management level. To this end, advanced applications of information and disease process modelling technologies have already demonstrated an ability to significantly augment clinical decision making as a by-product. The wide-spread acceptance of evidence-based medicine as the basis of cost-conscious and concurrently quality-wise accountable clinical practice suffices as evidence supporting this claim. Electronic libraries are one-step towards an online status of this key health-care delivery quality control environment. Nonetheless, to date, the underlying information and knowledge management technologies have failed to be integrated into any form of pragmatic or marketable online and real-time clinical decision making tool. One of the main obstacles that needs to be overcome is the development of systems that treat both information and knowledge as clinical objects with same modelling requirements. This paper describes the development of such a system in the form of an intelligent clinical information management system: a system which at the most fundamental level of clinical decision support facilitates both the organised acquisition of clinical information and knowledge and provides a test-bed for the development and evaluation of knowledge-based decision support functions.

Shared decision-making in epilepsy management.

PubMed

Pickrell, W O; Elwyn, G; Smith, P E M

2015-06-01

Policy makers, clinicians, and patients increasingly recognize the need for greater patient involvement in clinical decision-making. Shared decision-making helps address these concerns by providing a framework for clinicians and patients to make decisions together using the best evidence. Shared decision-making is applicable to situations where several acceptable options exist (clinical equipoise). Such situations occur commonly in epilepsy, for example, in decisions regarding the choice of medication, treatment in pregnancy, and medication withdrawal. A talk model is a way of implementing shared decision-making during consultations, and decision aids are useful tools to assist in the process. Although there is limited evidence available for shared decision-making in epilepsy, there are several benefits of shared decision-making in general including improved decision quality, more informed choices, and better treatment concordance. Copyright © 2015 Elsevier Inc. All rights reserved.

A multiple biomarker risk score for guiding clinical decisions using a decision curve approach.

PubMed

Hughes, Maria F; Saarela, Olli; Blankenberg, Stefan; Zeller, Tanja; Havulinna, Aki S; Kuulasmaa, Kari; Yarnell, John; Schnabel, Renate B; Tiret, Laurence; Salomaa, Veikko; Evans, Alun; Kee, Frank

2012-08-01

We assessed whether a cardiovascular risk model based on classic risk factors (e.g. cholesterol, blood pressure) could refine disease prediction if it included novel biomarkers (C-reactive protein, N-terminal pro-B-type natriuretic peptide, troponin I) using a decision curve approach which can incorporate clinical consequences. We evaluated whether a model including biomarkers and classic risk factors could improve prediction of 10 year risk of cardiovascular disease (CVD; chronic heart disease and ischaemic stroke) against a classic risk factor model using a decision curve approach in two prospective MORGAM cohorts. This included 7739 men and women with 457 CVD cases from the FINRISK97 cohort; and 2524 men with 259 CVD cases from PRIME Belfast. The biomarker model improved disease prediction in FINRISK across the high-risk group (20-40%) but not in the intermediate risk group, at the 23% risk threshold net benefit was 0.0033 (95% CI 0.0013-0.0052). However, in PRIME Belfast the net benefit of decisions guided by the decision curve was improved across intermediate risk thresholds (10-20%). At p(t) = 10% in PRIME, the net benefit was 0.0059 (95% CI 0.0007-0.0112) with a net increase in 6 true positive cases per 1000 people screened and net decrease of 53 false positive cases per 1000 potentially leading to 5% fewer treatments in patients not destined for an event. The biomarker model improves 10-year CVD prediction at intermediate and high-risk thresholds and in particular, could be clinically useful at advising middle-aged European males of their CVD risk.

A service oriented approach for guidelines-based clinical decision support using BPMN.

PubMed

Rodriguez-Loya, Salvador; Aziz, Ayesha; Chatwin, Chris

2014-01-01

Evidence-based medical practice requires that clinical guidelines need to be documented in such a way that they represent a clinical workflow in its most accessible form. In order to optimize clinical processes to improve clinical outcomes, we propose a Service Oriented Architecture (SOA) based approach for implementing clinical guidelines that can be accessed from an Electronic Health Record (EHR) application with a Web Services enabled communication mechanism with the Enterprise Service Bus. We have used Business Process Modelling Notation (BPMN) for modelling and presenting the clinical pathway in the form of a workflow. The aim of this study is to produce spontaneous alerts in the healthcare workflow in the diagnosis of Chronic Obstructive Pulmonary Disease (COPD). The use of BPMN as a tool to automate clinical guidelines has not been previously employed for providing Clinical Decision Support (CDS).

[Clinical everyday ethics-support in handling moral distress? : Evaluation of an ethical decision-making model for interprofessional clinical teams].

PubMed

Tanner, S; Albisser Schleger, H; Meyer-Zehnder, B; Schnurrer, V; Reiter-Theil, S; Pargger, H

2014-06-01

High-tech medicine and cost rationing provoke moral distress up to burnout syndromes. The consequences are severe, not only for those directly involved but also for the quality of patient care and the institutions. The multimodal model METAP (Modular, Ethical, Treatment, Allocation, Process) was developed as clinical everyday ethics to support the interprofessional ethical decision-making process. The distinctive feature of the model lays in education concerning ethics competence in dealing with difficult treatment decisions. METAP has been evaluated for quality testing. The research question of interest was whether METAP supports the handling of moral distress. The evaluation included 3 intensive care units and 3 geriatric units. In all, 33 single and 9 group interviews were held with 24 physicians, 44 nurses, and 9 persons from other disciplines. An additional questionnaire was completed by 122 persons (return rate 57%). Two-thirds of the interview answers and 55% of the questionnaire findings show that clinical everyday ethics supports the handling of moral distress, especially for interdisciplinary communication and collaboration and for the explanation and evaluation of treatment goals. METAP does not provide support for persons who are rarely confronted with ethical problems or have not applied the model long enough yet. To a certain degree, moral distress is unavoidable and must be addressed as an interprofessional problem. Herein, clinical everyday ethics may provide targeted support for ethical decision-making competence.

Rational clinical evaluation of suspected acute coronary syndromes: The value of more information.

PubMed

Hancock, David G; Chuang, Ming-Yu Anthony; Bystrom, Rebecca; Halabi, Amera; Jones, Rachel; Horsfall, Matthew; Cullen, Louise; Parsonage, William A; Chew, Derek P

2017-12-01

Many meta-analyses have provided synthesised likelihood ratio data to aid clinical decision-making. However, much less has been published on how to safely combine clinical information in practice. We aimed to explore the benefits and risks of pooling clinical information during the ED assessment of suspected acute coronary syndrome. Clinical information on 1776 patients was collected within a randomised trial conducted across five South Australian EDs between July 2011 and March 2013. Bayes theorem was used to calculate patient-specific post-test probabilities using age- and gender-specific pre-test probabilities and likelihood ratios corresponding to the presence or absence of 18 clinical factors. Model performance was assessed as the presence of adverse cardiac outcomes among patients theoretically discharged at a post-test probability less than 1%. Bayes theorem-based models containing high-sensitivity troponin T (hs-troponin) outperformed models excluding hs-troponin, as well as models utilising TIMI and GRACE scores. In models containing hs-troponin, a plateau in improving discharge safety was observed after the inclusion of four clinical factors. Models with fewer clinical factors better approximated the true event rate, tended to be safer and resulted in a smaller standard deviation in post-test probability estimates. We showed that there is a definable point where additional information becomes uninformative and may actually lead to less certainty. This evidence supports the concept that clinical decision-making in the assessment of suspected acute coronary syndrome should be focused on obtaining the least amount of information that provides the highest benefit for informing the decisions of admission or discharge. © 2017 Australasian College for Emergency Medicine and Australasian Society for Emergency Medicine.

Multi-criteria clinical decision support: A primer on the use of multiple criteria decision making methods to promote evidence-based, patient-centered healthcare.

PubMed

Dolan, James G

2010-01-01

Current models of healthcare quality recommend that patient management decisions be evidence-based and patient-centered. Evidence-based decisions require a thorough understanding of current information regarding the natural history of disease and the anticipated outcomes of different management options. Patient-centered decisions incorporate patient preferences, values, and unique personal circumstances into the decision making process and actively involve both patients along with health care providers as much as possible. Fundamentally, therefore, evidence-based, patient-centered decisions are multi-dimensional and typically involve multiple decision makers.Advances in the decision sciences have led to the development of a number of multiple criteria decision making methods. These multi-criteria methods are designed to help people make better choices when faced with complex decisions involving several dimensions. They are especially helpful when there is a need to combine "hard data" with subjective preferences, to make trade-offs between desired outcomes, and to involve multiple decision makers. Evidence-based, patient-centered clinical decision making has all of these characteristics. This close match suggests that clinical decision support systems based on multi-criteria decision making techniques have the potential to enable patients and providers to carry out the tasks required to implement evidence-based, patient-centered care effectively and efficiently in clinical settings.The goal of this paper is to give readers a general introduction to the range of multi-criteria methods available and show how they could be used to support clinical decision-making. Methods discussed include the balance sheet, the even swap method, ordinal ranking methods, direct weighting methods, multi-attribute decision analysis, and the analytic hierarchy process (AHP).

Multi-criteria clinical decision support: A primer on the use of multiple criteria decision making methods to promote evidence-based, patient-centered healthcare

PubMed Central

Dolan, James G.

2010-01-01

Current models of healthcare quality recommend that patient management decisions be evidence-based and patient-centered. Evidence-based decisions require a thorough understanding of current information regarding the natural history of disease and the anticipated outcomes of different management options. Patient-centered decisions incorporate patient preferences, values, and unique personal circumstances into the decision making process and actively involve both patients along with health care providers as much as possible. Fundamentally, therefore, evidence-based, patient-centered decisions are multi-dimensional and typically involve multiple decision makers. Advances in the decision sciences have led to the development of a number of multiple criteria decision making methods. These multi-criteria methods are designed to help people make better choices when faced with complex decisions involving several dimensions. They are especially helpful when there is a need to combine “hard data” with subjective preferences, to make trade-offs between desired outcomes, and to involve multiple decision makers. Evidence-based, patient-centered clinical decision making has all of these characteristics. This close match suggests that clinical decision support systems based on multi-criteria decision making techniques have the potential to enable patients and providers to carry out the tasks required to implement evidence-based, patient-centered care effectively and efficiently in clinical settings. The goal of this paper is to give readers a general introduction to the range of multi-criteria methods available and show how they could be used to support clinical decision-making. Methods discussed include the balance sheet, the even swap method, ordinal ranking methods, direct weighting methods, multi-attribute decision analysis, and the analytic hierarchy process (AHP) PMID:21394218

Using Computational Modeling to Assess the Impact of Clinical Decision Support on Cancer Screening within Community Health Centers

PubMed Central

Carney, Timothy Jay; Morgan, Geoffrey P.; Jones, Josette; McDaniel, Anna M.; Weaver, Michael; Weiner, Bryan; Haggstrom, David A.

2014-01-01

Our conceptual model demonstrates our goal to investigate the impact of clinical decision support (CDS) utilization on cancer screening improvement strategies in the community health care (CHC) setting. We employed a dual modeling technique using both statistical and computational modeling to evaluate impact. Our statistical model used the Spearman’s Rho test to evaluate the strength of relationship between our proximal outcome measures (CDS utilization) against our distal outcome measure (provider self-reported cancer screening improvement). Our computational model relied on network evolution theory and made use of a tool called Construct-TM to model the use of CDS measured by the rate of organizational learning. We employed the use of previously collected survey data from community health centers Cancer Health Disparities Collaborative (HDCC). Our intent is to demonstrate the added valued gained by using a computational modeling tool in conjunction with a statistical analysis when evaluating the impact a health information technology, in the form of CDS, on health care quality process outcomes such as facility-level screening improvement. Significant simulated disparities in organizational learning over time were observed between community health centers beginning the simulation with high and low clinical decision support capability. PMID:24953241

Clinical Decision Making and Mental Health Service Use Among Persons With Severe Mental Illness Across Europe.

PubMed

Cosh, Suzanne; Zenter, Nadja; Ay, Esra-Sultan; Loos, Sabine; Slade, Mike; De Rosa, Corrado; Luciano, Mario; Berecz, Roland; Glaub, Theodora; Munk-Jørgensen, Povl; Krogsgaard Bording, Malene; Rössler, Wulf; Kawohl, Wolfram; Puschner, Bernd

2017-09-01

The study explored relationships between preferences for and experiences of clinical decision making (CDM) with service use among persons with severe mental illness. Data from a prospective observational study in six European countries were examined. Associations of baseline staff-rated (N=213) and patient-rated (N=588) preferred and experienced decision making with service use were examined at baseline by using binomial regressions and at 12-month follow-up by using multilevel models. A preference by patients and staff for active patient involvement in decision making, rather than shared or passive decision making, was associated with longer hospital admissions and higher costs at baseline and with increases in admissions over 12 months (p=.043). Low patient-rated satisfaction with an experienced clinical decision was also related to increased costs over the study period (p=.005). A preference for shared decision making may reduce health care costs by reducing inpatient admissions. Patient satisfaction with decisions was a predictor of costs, and clinicians should maximize patient satisfaction with CDM.

Investigating the Heart Pump Implant Decision Process: Opportunities for Decision Support Tools to Help

PubMed Central

Yang, Qian; Zimmerman, John; Steinfeld, Aaron; Carey, Lisa; Antaki, James F.

2016-01-01

Clinical decision support tools (DSTs) are computational systems that aid healthcare decision-making. While effective in labs, almost all these systems failed when they moved into clinical practice. Healthcare researchers speculated it is most likely due to a lack of user-centered HCI considerations in the design of these systems. This paper describes a field study investigating how clinicians make a heart pump implant decision with a focus on how to best integrate an intelligent DST into their work process. Our findings reveal a lack of perceived need for and trust of machine intelligence, as well as many barriers to computer use at the point of clinical decision-making. These findings suggest an alternative perspective to the traditional use models, in which clinicians engage with DSTs at the point of making a decision. We identify situations across patients’ healthcare trajectories when decision supports would help, and we discuss new forms it might take in these situations. PMID:27833397

Decision-analytic modeling studies: An overview for clinicians using multiple myeloma as an example.

PubMed

Rochau, U; Jahn, B; Qerimi, V; Burger, E A; Kurzthaler, C; Kluibenschaedl, M; Willenbacher, E; Gastl, G; Willenbacher, W; Siebert, U

2015-05-01

The purpose of this study was to provide a clinician-friendly overview of decision-analytic models evaluating different treatment strategies for multiple myeloma (MM). We performed a systematic literature search to identify studies evaluating MM treatment strategies using mathematical decision-analytic models. We included studies that were published as full-text articles in English, and assessed relevant clinical endpoints, and summarized methodological characteristics (e.g., modeling approaches, simulation techniques, health outcomes, perspectives). Eleven decision-analytic modeling studies met our inclusion criteria. Five different modeling approaches were adopted: decision-tree modeling, Markov state-transition modeling, discrete event simulation, partitioned-survival analysis and area-under-the-curve modeling. Health outcomes included survival, number-needed-to-treat, life expectancy, and quality-adjusted life years. Evaluated treatment strategies included novel agent-based combination therapies, stem cell transplantation and supportive measures. Overall, our review provides a comprehensive summary of modeling studies assessing treatment of MM and highlights decision-analytic modeling as an important tool for health policy decision making. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

A review of the literature: midwifery decision-making and birth.

PubMed

Jefford, Elaine; Fahy, Kathleen; Sundin, Deborah

2010-12-01

Clinical decision-making was initially studied in medicine where hypothetico-deductive reasoning is the model for decision-making. The nursing perspective on clinical decision-making has largely been shaped by Patricia Benner's ground breaking work. Benner claimed expert nurses use humanistic-intuitive ways of making clinical decisions rather than the 'rational reasoning' as claimed by medicine. Clinical decision-making in midwifery is not the same as either nursing or medical decision-making because of the woman-midwife partnership where the woman is the ultimate decision-maker. CINHAL, Medline and Cochrane databases were systematically searched using key words derived from the guiding question. A review of the decision-making research literature in midwifery was undertaken where studies were published in English. The selection criteria for papers were: only research papers of direct relevance to the guiding research question were included in the review. Decision-making is under-researched in midwifery and more specifically birth, as only 4 research articles met the inclusion criteria in this review. Three of the studies involved qualified midwives, and one involved student midwives. Two studies were undertaken in England, one in Scotland and one in Sweden. The major findings synthesised from this review, are that; (1) midwifery decision-making during birth is socially negotiated involving hierarchies of surveillance and control; (2) the role of the woman in shared decision-making during birth has not been explored by midwifery research; (3) clinical decision-making encompasses clinical reasoning as essential but not sufficient for midwives to actually implement their preferred decision. We argue that existing research does not inform the discipline of the complexity of midwifery clinical decision-making during birth. A well-designed study would involve investigating the clinical reasoning skills of the midwife, her relationship with the woman, the context of the particular birthing unit and the employment status of the midwife. The role of the woman as decision-maker in her own care during birth also needs careful research attention. Copyright © 2010 Australian College of Midwives. All rights reserved.

[Diagnostic rationalism. Views of general practitioners on fibromyalgia].

PubMed

Daehli, B

1993-09-20

Clinical practice is characterized by having to make numerous important decisions, including the diagnosis. In this study, general practitioners were asked to agree or to disagree with statements of fibromyalgia. The main purpose was to test the usefulness of two well-known models for decision-making when studying diagnosis in cases of uncertainty and scepticism. The results show that the models are inadequate to explain the decisions.

Personalized treatment planning with a model of radiation therapy outcomes for use in multiobjective optimization of IMRT plans for prostate cancer.

PubMed

Smith, Wade P; Kim, Minsun; Holdsworth, Clay; Liao, Jay; Phillips, Mark H

2016-03-11

To build a new treatment planning approach that extends beyond radiation transport and IMRT optimization by modeling the radiation therapy process and prognostic indicators for more outcome-focused decision making. An in-house treatment planning system was modified to include multiobjective inverse planning, a probabilistic outcome model, and a multi-attribute decision aid. A genetic algorithm generated a set of plans embodying trade-offs between the separate objectives. An influence diagram network modeled the radiation therapy process of prostate cancer using expert opinion, results of clinical trials, and published research. A Markov model calculated a quality adjusted life expectancy (QALE), which was the endpoint for ranking plans. The Multiobjective Evolutionary Algorithm (MOEA) was designed to produce an approximation of the Pareto Front representing optimal tradeoffs for IMRT plans. Prognostic information from the dosimetrics of the plans, and from patient-specific clinical variables were combined by the influence diagram. QALEs were calculated for each plan for each set of patient characteristics. Sensitivity analyses were conducted to explore changes in outcomes for variations in patient characteristics and dosimetric variables. The model calculated life expectancies that were in agreement with an independent clinical study. The radiation therapy model proposed has integrated a number of different physical, biological and clinical models into a more comprehensive model. It illustrates a number of the critical aspects of treatment planning that can be improved and represents a more detailed description of the therapy process. A Markov model was implemented to provide a stronger connection between dosimetric variables and clinical outcomes and could provide a practical, quantitative method for making difficult clinical decisions.

Comprehensive process model of clinical information interaction in primary care: results of a "best-fit" framework synthesis.

PubMed

Veinot, Tiffany C; Senteio, Charles R; Hanauer, David; Lowery, Julie C

2018-06-01

To describe a new, comprehensive process model of clinical information interaction in primary care (Clinical Information Interaction Model, or CIIM) based on a systematic synthesis of published research. We used the "best fit" framework synthesis approach. Searches were performed in PubMed, Embase, the Cumulative Index to Nursing and Allied Health Literature (CINAHL), PsycINFO, Library and Information Science Abstracts, Library, Information Science and Technology Abstracts, and Engineering Village. Two authors reviewed articles according to inclusion and exclusion criteria. Data abstraction and content analysis of 443 published papers were used to create a model in which every element was supported by empirical research. The CIIM documents how primary care clinicians interact with information as they make point-of-care clinical decisions. The model highlights 3 major process components: (1) context, (2) activity (usual and contingent), and (3) influence. Usual activities include information processing, source-user interaction, information evaluation, selection of information, information use, clinical reasoning, and clinical decisions. Clinician characteristics, patient behaviors, and other professionals influence the process. The CIIM depicts the complete process of information interaction, enabling a grasp of relationships previously difficult to discern. The CIIM suggests potentially helpful functionality for clinical decision support systems (CDSSs) to support primary care, including a greater focus on information processing and use. The CIIM also documents the role of influence in clinical information interaction; influencers may affect the success of CDSS implementations. The CIIM offers a new framework for achieving CDSS workflow integration and new directions for CDSS design that can support the work of diverse primary care clinicians.

Models of clinical reasoning with a focus on general practice: A critical review.

PubMed

Yazdani, Shahram; Hosseinzadeh, Mohammad; Hosseini, Fakhrolsadat

2017-10-01

Diagnosis lies at the heart of general practice. Every day general practitioners (GPs) visit patients with a wide variety of complaints and concerns, with often minor but sometimes serious symptoms. General practice has many features which differentiate it from specialty care setting, but during the last four decades little attention was paid to clinical reasoning in general practice. Therefore, we aimed to critically review the clinical reasoning models with a focus on the clinical reasoning in general practice or clinical reasoning of general practitioners to find out to what extent the existing models explain the clinical reasoning specially in primary care and also identity the gaps of the model for use in primary care settings. A systematic search to find models of clinical reasoning were performed. To have more precision, we excluded the studies that focused on neurobiological aspects of reasoning, reasoning in disciplines other than medicine decision making or decision analysis on treatment or management plan. All the articles and documents were first scanned to see whether they include important relevant contents or any models. The selected studies which described a model of clinical reasoning in general practitioners or with a focus on general practice were then reviewed and appraisal or critics of other authors on these models were included. The reviewed documents on the model were synthesized. Six models of clinical reasoning were identified including hypothetic-deductive model, pattern recognition, a dual process diagnostic reasoning model, pathway for clinical reasoning, an integrative model of clinical reasoning, and model of diagnostic reasoning strategies in primary care. Only one model had specifically focused on general practitioners reasoning. A Model of clinical reasoning that included specific features of general practice to better help the general practitioners with the difficulties of clinical reasoning in this setting is needed.

Clinical decision support tool for Co-management signalling.

PubMed

Horta, Alexandra Bayão; Salgado, Cátia; Fernandes, Marta; Vieira, Susana; Sousa, João M; Papoila, Ana Luísa; Xavier, Miguel

2018-05-01

Co-management between internists and surgeons of selected patients is becoming one of the pillars of modern clinical management in large hospitals. Defining the patients to be co-managed is essential. The aim of this study is to create a decision tool using real-world patient data collected in the preoperative period, to support the decision on which patients should have the co-management service offered. Data was collected from the electronic clinical health records of patients who had an International Classification of Diseases, 9th edition (ICD-9) code of colorectal surgery during the period between January 2012 and October 2014 in a 200 bed private teaching hospital in Lisbon. ICD-9 codes of colorectal surgery [48.5 and 48.6 (anterior rectal resection and abdominoperineal resection), 45.7 (partial colectomy), 45.8 (Total Colectomy), and 45.9 (Bowel Anastomosis)] were used. Only patients above 18 years old were considered. Patients with more than one procedure were excluded from the study. From these data the authors investigated the construction of predictive models using logistic regression and Takagi-Sugeno fuzzy modelling. Data contains information obtained from the clinical records of a cohort of 344 adult patients. Data from 398 emergent and elective surgeries were collected, from which 54 were excluded because they were second procedures for the same patients. Four preoperative variables were identified as being the most predictive of co-management, in multivariable regression analysis. The final model performed well after being internally validated (0.81 AUC, 77% accuracy, 74% sensitivity, 78% specificity, 93% negative predictive value). The results indicate that the decision process can be more objective and potentially automated. The authors developed a prediction model based on preoperative characteristics, in order to support the decision for the co-management of surgical patients in the postoperative ward setting. The model is a simple bedside decision tool that uses only four numerical variables. Copyright © 2018 Elsevier B.V. All rights reserved.

Diffusion Decision Model: Current Issues and History

PubMed Central

Ratcliff, Roger; Smith, Philip L.; Brown, Scott D.; McKoon, Gail

2016-01-01

There is growing interest in diffusion models to represent the cognitive and neural processes of speeded decision making. Sequential-sampling models like the diffusion model have a long history in psychology. They view decision making as a process of noisy accumulation of evidence from a stimulus. The standard model assumes that evidence accumulates at a constant rate during the second or two it takes to make a decision. This process can be linked to the behaviors of populations of neurons and to theories of optimality. Diffusion models have been used successfully in a range of cognitive tasks and as psychometric tools in clinical research to examine individual differences. In this article, we relate the models to both earlier and more recent research in psychology. PMID:26952739

User-centered design to improve clinical decision support in primary care.

PubMed

Brunner, Julian; Chuang, Emmeline; Goldzweig, Caroline; Cain, Cindy L; Sugar, Catherine; Yano, Elizabeth M

2017-08-01

A growing literature has demonstrated the ability of user-centered design to make clinical decision support systems more effective and easier to use. However, studies of user-centered design have rarely examined more than a handful of sites at a time, and have frequently neglected the implementation climate and organizational resources that influence clinical decision support. The inclusion of such factors was identified by a systematic review as "the most important improvement that can be made in health IT evaluations." (1) Identify the prevalence of four user-centered design practices at United States Veterans Affairs (VA) primary care clinics and assess the perceived utility of clinical decision support at those clinics; (2) Evaluate the association between those user-centered design practices and the perceived utility of clinical decision support. We analyzed clinic-level survey data collected in 2006-2007 from 170 VA primary care clinics. We examined four user-centered design practices: 1) pilot testing, 2) provider satisfaction assessment, 3) formal usability assessment, and 4) analysis of impact on performance improvement. We used a regression model to evaluate the association between user-centered design practices and the perceived utility of clinical decision support, while accounting for other important factors at those clinics, including implementation climate, available resources, and structural characteristics. We also examined associations separately at community-based clinics and at hospital-based clinics. User-centered design practices for clinical decision support varied across clinics: 74% conducted pilot testing, 62% conducted provider satisfaction assessment, 36% conducted a formal usability assessment, and 79% conducted an analysis of impact on performance improvement. Overall perceived utility of clinical decision support was high, with a mean rating of 4.17 (±.67) out of 5 on a composite measure. "Analysis of impact on performance improvement" was the only user-centered design practice significantly associated with perceived utility of clinical decision support, b=.47 (p<.001). This association was present in hospital-based clinics, b=.34 (p<.05), but was stronger at community-based clinics, b=.61 (p<.001). Our findings are highly supportive of the practice of analyzing the impact of clinical decision support on performance metrics. This was the most common user-centered design practice in our study, and was the practice associated with higher perceived utility of clinical decision support. This practice may be particularly helpful at community-based clinics, which are typically less connected to VA medical center resources. Published by Elsevier B.V.

A Way to Approach the Strategic Decisions Facing Academic Health Centers.

ERIC Educational Resources Information Center

Capper, Stuart A.; Fargason, Crayton A., Jr.

1996-01-01

Three models of strategic management for academic health centers are detailed: the traditional model, which will become rare; a revised traditional model that offers "boutique" clinical services, biomedical research, and medical education; and the academic services model, focusing on competitive primary and secondary clinical services, health…

Improving Emergency Department Triage Classification with Computerized Clinical Decision Support at a Pediatric Hospital

ERIC Educational Resources Information Center

Kunisch, Joseph Martin

2012-01-01

Background: The Emergency Severity Index (ESI) is an emergency department (ED) triage classification system based on estimated patient-specific resource utilization. Rules for a computerized clinical decision support (CDS) system based on a patient's chief complaint were developed and tested using a stochastic model for predicting ESI scores.…

Applying Probabilistic Decision Models to Clinical Trial Design

PubMed Central

Smith, Wade P; Phillips, Mark H

2018-01-01

Clinical trial design most often focuses on a single or several related outcomes with corresponding calculations of statistical power. We consider a clinical trial to be a decision problem, often with competing outcomes. Using a current controversy in the treatment of HPV-positive head and neck cancer, we apply several different probabilistic methods to help define the range of outcomes given different possible trial designs. Our model incorporates the uncertainties in the disease process and treatment response and the inhomogeneities in the patient population. Instead of expected utility, we have used a Markov model to calculate quality adjusted life expectancy as a maximization objective. Monte Carlo simulations over realistic ranges of parameters are used to explore different trial scenarios given the possible ranges of parameters. This modeling approach can be used to better inform the initial trial design so that it will more likely achieve clinical relevance. PMID:29888075

Dual processing model of medical decision-making.

PubMed

Djulbegovic, Benjamin; Hozo, Iztok; Beckstead, Jason; Tsalatsanis, Athanasios; Pauker, Stephen G

2012-09-03

Dual processing theory of human cognition postulates that reasoning and decision-making can be described as a function of both an intuitive, experiential, affective system (system I) and/or an analytical, deliberative (system II) processing system. To date no formal descriptive model of medical decision-making based on dual processing theory has been developed. Here we postulate such a model and apply it to a common clinical situation: whether treatment should be administered to the patient who may or may not have a disease. We developed a mathematical model in which we linked a recently proposed descriptive psychological model of cognition with the threshold model of medical decision-making and show how this approach can be used to better understand decision-making at the bedside and explain the widespread variation in treatments observed in clinical practice. We show that physician's beliefs about whether to treat at higher (lower) probability levels compared to the prescriptive therapeutic thresholds obtained via system II processing is moderated by system I and the ratio of benefit and harms as evaluated by both system I and II. Under some conditions, the system I decision maker's threshold may dramatically drop below the expected utility threshold derived by system II. This can explain the overtreatment often seen in the contemporary practice. The opposite can also occur as in the situations where empirical evidence is considered unreliable, or when cognitive processes of decision-makers are biased through recent experience: the threshold will increase relative to the normative threshold value derived via system II using expected utility threshold. This inclination for the higher diagnostic certainty may, in turn, explain undertreatment that is also documented in the current medical practice. We have developed the first dual processing model of medical decision-making that has potential to enrich the current medical decision-making field, which is still to the large extent dominated by expected utility theory. The model also provides a platform for reconciling two groups of competing dual processing theories (parallel competitive with default-interventionalist theories).

Conceptual Models and Guidelines for Clinical Assessment of Financial Capacity.

PubMed

Marson, Daniel

2016-09-01

The ability to manage financial affairs is a life skill of critical importance, and neuropsychologists are increasingly asked to assess financial capacity across a variety of settings. Sound clinical assessment of financial capacity requires knowledge and appreciation of applicable clinical conceptual models and principles. However, the literature has presented relatively little conceptual guidance for clinicians concerning financial capacity and its assessment. This article seeks to address this gap. The article presents six clinical models of financial capacity : (1) the early gerontological IADL model of Lawton, (2) the clinical skills model and (3) related cognitive psychological model developed by Marson and colleagues, (4) a financial decision-making model adapting earlier decisional capacity work of Appelbaum and Grisso, (5) a person-centered model of financial decision-making developed by Lichtenberg and colleagues, and (6) a recent model of financial capacity in the real world developed through the Institute of Medicine. Accompanying presentation of the models is discussion of conceptual and practical perspectives they represent for clinician assessment. Based on the models, the article concludes by presenting a series of conceptually oriented guidelines for clinical assessment of financial capacity. In summary, sound assessment of financial capacity requires knowledge and appreciation of clinical conceptual models and principles. Awareness of such models, principles and guidelines will strengthen and advance clinical assessment of financial capacity. © The Author 2016. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Incorporation of expert variability into breast cancer treatment recommendation in designing clinical protocol guided fuzzy rule system models.

PubMed

Garibaldi, Jonathan M; Zhou, Shang-Ming; Wang, Xiao-Ying; John, Robert I; Ellis, Ian O

2012-06-01

It has been often demonstrated that clinicians exhibit both inter-expert and intra-expert variability when making difficult decisions. In contrast, the vast majority of computerized models that aim to provide automated support for such decisions do not explicitly recognize or replicate this variability. Furthermore, the perfect consistency of computerized models is often presented as a de facto benefit. In this paper, we describe a novel approach to incorporate variability within a fuzzy inference system using non-stationary fuzzy sets in order to replicate human variability. We apply our approach to a decision problem concerning the recommendation of post-operative breast cancer treatment; specifically, whether or not to administer chemotherapy based on assessment of five clinical variables: NPI (the Nottingham Prognostic Index), estrogen receptor status, vascular invasion, age and lymph node status. In doing so, we explore whether such explicit modeling of variability provides any performance advantage over a more conventional fuzzy approach, when tested on a set of 1310 unselected cases collected over a fourteen year period at the Nottingham University Hospitals NHS Trust, UK. The experimental results show that the standard fuzzy inference system (that does not model variability) achieves overall agreement to clinical practice around 84.6% (95% CI: 84.1-84.9%), while the non-stationary fuzzy model can significantly increase performance to around 88.1% (95% CI: 88.0-88.2%), p<0.001. We conclude that non-stationary fuzzy models provide a valuable new approach that may be applied to clinical decision support systems in any application domain. Copyright © 2012 Elsevier Inc. All rights reserved.

Patient-oriented Computerized Clinical Guidelines for Mobile Decision Support in Gestational Diabetes.

PubMed

García-Sáez, Gema; Rigla, Mercedes; Martínez-Sarriegui, Iñaki; Shalom, Erez; Peleg, Mor; Broens, Tom; Pons, Belén; Caballero-Ruíz, Estefanía; Gómez, Enrique J; Hernando, M Elena

2014-03-01

The risks associated with gestational diabetes (GD) can be reduced with an active treatment able to improve glycemic control. Advances in mobile health can provide new patient-centric models for GD to create personalized health care services, increase patient independence and improve patients' self-management capabilities, and potentially improve their treatment compliance. In these models, decision-support functions play an essential role. The telemedicine system MobiGuide provides personalized medical decision support for GD patients that is based on computerized clinical guidelines and adapted to a mobile environment. The patient's access to the system is supported by a smartphone-based application that enhances the efficiency and ease of use of the system. We formalized the GD guideline into a computer-interpretable guideline (CIG). We identified several workflows that provide decision-support functionalities to patients and 4 types of personalized advice to be delivered through a mobile application at home, which is a preliminary step to providing decision-support tools in a telemedicine system: (1) therapy, to help patients to comply with medical prescriptions; (2) monitoring, to help patients to comply with monitoring instructions; (3) clinical assessment, to inform patients about their health conditions; and (4) upcoming events, to deal with patients' personal context or special events. The whole process to specify patient-oriented decision support functionalities ensures that it is based on the knowledge contained in the GD clinical guideline and thus follows evidence-based recommendations but at the same time is patient-oriented, which could enhance clinical outcomes and patients' acceptance of the whole system. © 2014 Diabetes Technology Society.

Development of an evidence-based decision pathway for vestibular schwannoma treatment options.

PubMed

Linkov, Faina; Valappil, Benita; McAfee, Jacob; Goughnour, Sharon L; Hildrew, Douglas M; McCall, Andrew A; Linkov, Igor; Hirsch, Barry; Snyderman, Carl

To integrate multiple sources of clinical information with patient feedback to build evidence-based decision support model to facilitate treatment selection for patients suffering from vestibular schwannomas (VS). This was a mixed methods study utilizing focus group and survey methodology to solicit feedback on factors important for making treatment decisions among patients. Two 90-minute focus groups were conducted by an experienced facilitator. Previously diagnosed VS patients were recruited by clinical investigators at the University of Pittsburgh Medical Center (UPMC). Classical content analysis was used for focus group data analysis. Providers were recruited from practices within the UPMC system and were surveyed using Delphi methods. This information can provide a basis for multi-criteria decision analysis (MCDA) framework to develop a treatment decision support system for patients with VS. Eight themes were derived from these data (focus group + surveys): doctor/health care system, side effects, effectiveness of treatment, anxiety, mortality, family/other people, quality of life, and post-operative symptoms. These data, as well as feedback from physicians were utilized in building a multi-criteria decision model. The study illustrated steps involved in the development of a decision support model that integrates evidence-based data and patient values to select treatment alternatives. Studies focusing on the actual development of the decision support technology for this group of patients are needed, as decisions are highly multifactorial. Such tools have the potential to improve decision making for complex medical problems with alternate treatment pathways. Copyright © 2016 Elsevier Inc. All rights reserved.

Value judgements in the decision-making process for the elderly patient.

PubMed

Ubachs-Moust, J; Houtepen, R; Vos, R; ter Meulen, R

2008-12-01

The question of whether old age should or should not play a role in medical decision-making for the elderly patient is regularly debated in ethics and medicine. In this paper we investigate exactly how age influences the decision-making process. To explore the normative argumentation in the decisions regarding an elderly patient we make use of the argumentation model advanced by Toulmin. By expanding the model in order to identify normative components in the argumentation process it is possible to analyse the way that age-related value judgements influence the medical decision-making process. We apply the model to practice descriptions made by medical students after they had attended consultations and meetings in medical practice during their clinical training. Our results show the pervasive character of age-related value judgements. They influence the physician's decision in several ways and at several points in the decision-making process. Such explicit value judgements were not exclusively used for arguments against further diagnosis or treatment of older patients. We found no systematic "ageist" pattern in the clinical decisions by physicians. Since age plays such an important, yet hidden role in the medical decision-making process, we make a plea for revealing such normative argumentation in order to gain transparency and accountability in this process. An explicit deliberative approach will make the medical decision-making process more transparent and improve the physician-patient relationship, creating confidence and trust, which are at the heart of medical practice.

[Application of decision curve on evaluation of MRI predictive model for early assessing pathological complete response to neoadjuvant therapy in breast cancer].

PubMed

He, Y J; Li, X T; Fan, Z Q; Li, Y L; Cao, K; Sun, Y S; Ouyang, T

2018-01-23

Objective: To construct a dynamic enhanced MR based predictive model for early assessing pathological complete response (pCR) to neoadjuvant therapy in breast cancer, and to evaluate the clinical benefit of the model by using decision curve. Methods: From December 2005 to December 2007, 170 patients with breast cancer treated with neoadjuvant therapy were identified and their MR images before neoadjuvant therapy and at the end of the first cycle of neoadjuvant therapy were collected. Logistic regression model was used to detect independent factors for predicting pCR and construct the predictive model accordingly, then receiver operating characteristic (ROC) curve and decision curve were used to evaluate the predictive model. Results: ΔArea(max) and Δslope(max) were independent predictive factors for pCR, OR =0.942 (95% CI : 0.918-0.967) and 0.961 (95% CI : 0.940-0.987), respectively. The area under ROC curve (AUC) for the constructed model was 0.886 (95% CI : 0.820-0.951). Decision curve showed that in the range of the threshold probability above 0.4, the predictive model presented increased net benefit as the threshold probability increased. Conclusions: The constructed predictive model for pCR is of potential clinical value, with an AUC>0.85. Meanwhile, decision curve analysis indicates the constructed predictive model has net benefit from 3 to 8 percent in the likely range of probability threshold from 80% to 90%.

Use of statistical and pharmacokinetic-pharmacodynamic modeling and simulation to improve decision-making: A section summary report of the trends and innovations in clinical trial statistics conference.

PubMed

Kimko, Holly; Berry, Seth; O'Kelly, Michael; Mehrotra, Nitin; Hutmacher, Matthew; Sethuraman, Venkat

2017-01-01

The application of modeling and simulation (M&S) methods to improve decision-making was discussed during the Trends & Innovations in Clinical Trial Statistics Conference held in Durham, North Carolina, USA on May 1-4, 2016. Uses of both pharmacometric and statistical M&S were presented during the conference, highlighting the diversity of the methods employed by pharmacometricians and statisticians to address a broad range of quantitative issues in drug development. Five presentations are summarized herein, which cover the development strategy of employing M&S to drive decision-making; European initiatives on best practice in M&S; case studies of pharmacokinetic/pharmacodynamics modeling in regulatory decisions; estimation of exposure-response relationships in the presence of confounding; and the utility of estimating the probability of a correct decision for dose selection when prior information is limited. While M&S has been widely used during the last few decades, it is expected to play an essential role as more quantitative assessments are employed in the decision-making process. By integrating M&S as a tool to compile the totality of evidence collected throughout the drug development program, more informed decisions will be made.

Diffusion Decision Model: Current Issues and History.

PubMed

Ratcliff, Roger; Smith, Philip L; Brown, Scott D; McKoon, Gail

2016-04-01

There is growing interest in diffusion models to represent the cognitive and neural processes of speeded decision making. Sequential-sampling models like the diffusion model have a long history in psychology. They view decision making as a process of noisy accumulation of evidence from a stimulus. The standard model assumes that evidence accumulates at a constant rate during the second or two it takes to make a decision. This process can be linked to the behaviors of populations of neurons and to theories of optimality. Diffusion models have been used successfully in a range of cognitive tasks and as psychometric tools in clinical research to examine individual differences. In this review, we relate the models to both earlier and more recent research in psychology. Copyright © 2016. Published by Elsevier Ltd.

Can patients' preferences for involvement in decision-making regarding the use of medicines be predicted?

PubMed

Garfield, S; Smith, F; Francis, S A; Chalmers, C

2007-06-01

The current study aimed to develop a model of patients' preferences for involvement in decision-making concerning the use of medicines for chronic conditions in the UK and test it in a large representative sample of patients with one of two clinical conditions. Following a structured literature review, an instrument was developed which measured the variables that had been identified as predictors of patients' preferences for involvement in decision making in previous research. Five hundred and sixteen patients with rheumatoid arthritis or type 2 diabetes were recruited from outpatient and primary care clinics and asked to complete the instrument. Multivariate analysis revealed that age, social class and clinical condition were associated with preferences for involvement in decision-making concerning the use of medicines for chronic illness but gender, ethnic group, concerns about medicines, beliefs about necessity of medicines, health status, quality of life and time since diagnosis were not. In total, the fitted model explained only 14% of the variance. This study has demonstrated that current research does not provide a basis for predicting patients' preferences for involvement in decision-making. Building concordant relationships may depend on practitioners developing strategies to establish individuals' preferences for involvement in decision-making as part of the ongoing prescriber-patient relationship.

The Evolution of an Interprofessional Shared Decision-Making Research Program: Reflective Case Study of an Emerging Paradigm

PubMed Central

Menear, Matthew; Stacey, Dawn; Brière, Nathalie; Légaré, France

2016-01-01

Introduction: Healthcare research increasingly focuses on interprofessional collaboration and on shared decision making, but knowledge gaps remain about effective strategies for implementing interprofessional collaboration and shared decision-making together in clinical practice. We used Kuhn’s theory of scientific revolutions to reflect on how an integrated interprofessional shared decision-making approach was developed and implemented over time. Methods: In 2007, an interdisciplinary team initiated a new research program to promote the implementation of an interprofessional shared decision-making approach in clinical settings. For this reflective case study, two new team members analyzed the team’s four projects, six research publications, one unpublished and two published protocols and organized them into recognizable phases according to Kuhn’s theory. Results: The merging of two young disciplines led to challenges characteristic of emerging paradigms. Implementation of interprofessional shared-decision making was hindered by a lack of conceptual clarity, a dearth of theories and models, little methodological guidance, and insufficient evaluation instruments. The team developed a new model, identified new tools, and engaged knowledge users in a theory-based approach to implementation. However, several unresolved challenges remain. Discussion: This reflective case study sheds light on the evolution of interdisciplinary team science. It offers new approaches to implementing emerging knowledge in the clinical context. PMID:28435417

The Evolution of an Interprofessional Shared Decision-Making Research Program: Reflective Case Study of an Emerging Paradigm.

PubMed

Dogba, Maman Joyce; Menear, Matthew; Stacey, Dawn; Brière, Nathalie; Légaré, France

2016-07-19

Healthcare research increasingly focuses on interprofessional collaboration and on shared decision making, but knowledge gaps remain about effective strategies for implementing interprofessional collaboration and shared decision-making together in clinical practice. We used Kuhn's theory of scientific revolutions to reflect on how an integrated interprofessional shared decision-making approach was developed and implemented over time. In 2007, an interdisciplinary team initiated a new research program to promote the implementation of an interprofessional shared decision-making approach in clinical settings. For this reflective case study, two new team members analyzed the team's four projects, six research publications, one unpublished and two published protocols and organized them into recognizable phases according to Kuhn's theory. The merging of two young disciplines led to challenges characteristic of emerging paradigms. Implementation of interprofessional shared-decision making was hindered by a lack of conceptual clarity, a dearth of theories and models, little methodological guidance, and insufficient evaluation instruments. The team developed a new model, identified new tools, and engaged knowledge users in a theory-based approach to implementation. However, several unresolved challenges remain. This reflective case study sheds light on the evolution of interdisciplinary team science. It offers new approaches to implementing emerging knowledge in the clinical context.

A Representation for Gaining Insight into Clinical Decision Models

PubMed Central

Jimison, Holly B.

1988-01-01

For many medical domains uncertainty and patient preferences are important components of decision making. Decision theory is useful as a representation for such medical models in computer decision aids, but the methodology has typically had poor performance in the areas of explanation and user interface. The additional representation of probabilities and utilities as random variables serves to provide a framework for graphical and text insight into complicated decision models. The approach allows for efficient customization of a generic model that describes the general patient population of interest to a patient- specific model. Monte Carlo simulation is used to calculate the expected value of information and sensitivity for each model variable, thus providing a metric for deciding what to emphasize in the graphics and text summary. The computer-generated explanation includes variables that are sensitive with respect to the decision or that deviate significantly from what is typically observed. These techniques serve to keep the assessment and explanation of the patient's decision model concise, allowing the user to focus on the most important aspects for that patient.

Decision-Making Process Related to Participation in Phase I Clinical Trials: A Nonsystematic Review of the Existing Evidence.

PubMed

Gorini, Alessandra; Mazzocco, Ketti; Pravettoni, Gabriella

2015-01-01

Due to the lack of other treatment options, patient candidates for participation in phase I clinical trials are considered the most vulnerable, and many ethical concerns have emerged regarding the informed consent process used in the experimental design of such trials. Starting with these considerations, this nonsystematic review is aimed at analyzing the decision-making processes underlying patients' decision about whether to participate (or not) in phase I trials in order to clarify the cognitive and emotional aspects most strongly implicated in this decision. Considering that there is no uniform decision calculus and that many different variables other than the patient-physician relationship (including demographic, clinical, and personal characteristics) may influence patients' preferences for and processing of information, we conclude that patients' informed decision-making can be facilitated by creating a rigorously developed, calibrated, and validated computer tool modeled on each single patient's knowledge, values, and emotional and cognitive decisional skills. Such a tool will also help oncologists to provide tailored medical information that is useful to improve the shared decision-making process, thereby possibly increasing patient participation in clinical trials. © 2015 S. Karger AG, Basel.

Decision-Making in Audiology: Balancing Evidence-Based Practice and Patient-Centered Care.

PubMed

Boisvert, Isabelle; Clemesha, Jennifer; Lundmark, Erik; Crome, Erica; Barr, Caitlin; McMahon, Catherine M

2017-01-01

Health-care service delivery models have evolved from a practitioner-centered approach toward a patient-centered ideal. Concurrently, increasing emphasis has been placed on the use of empirical evidence in decision-making to increase clinical accountability. The way in which clinicians use empirical evidence and client preferences to inform decision-making provides an insight into health-care delivery models utilized in clinical practice. The present study aimed to investigate the sources of information audiologists use when discussing rehabilitation choices with clients, and discuss the findings within the context of evidence-based practice and patient-centered care. To assess the changes that may have occurred over time, this study uses a questionnaire based on one of the few studies of decision-making behavior in audiologists, published in 1989. The present questionnaire was completed by 96 audiologists who attended the World Congress of Audiology in 2014. The responses were analyzed using qualitative and quantitative approaches. Results suggest that audiologists rank clinical test results and client preferences as the most important factors for decision-making. Discussion with colleagues or experts was also frequently reported as an important source influencing decision-making. Approximately 20% of audiologists mentioned utilizing research evidence to inform decision-making when no clear solution was available. Information shared at conferences was ranked low in terms of importance and reliability. This study highlights an increase in awareness of concepts associated with evidence-based practice and patient-centered care within audiology settings, consistent with current research-to-practice dissemination pathways. It also highlights that these pathways may not be sufficient for an effective clinical implementation of these practices.

[The role of research-based evidence in health system policy decision-making].

PubMed

Patiño, Daniel; Lavis, John N; Moat, Kaelan

2013-01-01

Different models may be used for explaining how research-based evidence is used in healthcare system policy-making. It is argued that models arising from a clinical setting (i.e. evidence-based policy-making model) could be useful regarding some types of healthcare system decision-making. However, such models are "silent" concerning the influence of political contextual factors on healthcare policy-making and are thus inconsistent with decision-making regarding the modification of healthcare system arrangements. Other political science-based models would seem to be more useful for understanding that research is just one factor affecting decision-making and that different types of research-based evidence can be used instrumentally, conceptual or strategically during different policy-making stages.

The physician-patient encounter: the physician as a perfect agent for the patient versus the informed treatment decision-making model.

PubMed

Gafni, A; Charles, C; Whelan, T

1998-08-01

Assuming a goal of arriving at a treatment decision which is based on the physician's knowledge and the patient's preferences, we discuss the feasibility of implementing two treatment decision-making models: (1) the physician as a perfect agent for the patient, and (2) the informed treatment decision-making models. Both models fall under the rubric of agency models, however, the requirements from the physician and the patient are different. An important distinction between the two models is that in the former the patient delegates authority to her doctor to make medical decisions and thus the challenge is to encourage the physician to find out the patient's preferences. In the latter, the patient retains the authority to make medical decisions and the physician role is that of information transfer. The challenge here is to encourage the physician to transfer the knowledge in a clear and nonbiased way. We argue that the choice of model depends among other things on the ease of implementation (e.g., is it simpler to transfer patient's preferences to doctors or to transfer technical knowledge to patients?). Also the choice of treatment decision-making model is likely to have an impact on the type of incentives or regulations (i.e., contracts) needed to promote the chosen model. We show that in theory both models result in the same outcome. We argue that the approach of transferring information to the patient is easier (but not easy) and, hence, more feasible than transferring each patient's preferences to the physician in each medical encounter. We also argue that because better "technology" exists to transfer medical information to patients and time costs are involved in both tasks (i.e. transferring preferences or information), it is more feasible to design contracts to motivate physicians to transfer information to patients than to design contracts to motivate physicians to find out their patients' utility functions. We illustrate our arguments using a clinical example of the choice of adjuvant chemotherapy versus no adjuvant chemotherapy for women with early stage breast cancer. We also discuss issues relating to the current realities of clinical practice and their potential implications for the way that economists model physician-patient clinical encounters.

Bringing a humanistic approach to cancer clinical trials

PubMed Central

Arai, Roberto Jun; Longo, Elaine Santana; Sponton, Maria Helena; Del Pilar Estevez Diz, Maria

2017-01-01

In this article, we describe some practical aspects that promote the humanisation of clinical research. Actions are not limited to improving the communication skills of medical staff but also include maintenance of care continuity, accessible written information, and application of theoretic models such as shared decision-making and management of stress in decision-making under uncertainty. We believe that a comprehensive strategy will increase patients’ motivation to participate in and adhere to clinical research. PMID:28596804

THE IMPACT OF RACISM ON CLINICIAN COGNITION, BEHAVIOR, AND CLINICAL DECISION MAKING

PubMed Central

van Ryn, Michelle; Burgess, Diana J.; Dovidio, John F.; Phelan, Sean M.; Saha, Somnath; Malat, Jennifer; Griffin, Joan M.; Fu, Steven S.; Perry, Sylvia

2014-01-01

Over the past two decades, thousands of studies have demonstrated that Blacks receive lower quality medical care than Whites, independent of disease status, setting, insurance, and other clinically relevant factors. Despite this, there has been little progress towards eradicating these inequities. Almost a decade ago we proposed a conceptual model identifying mechanisms through which clinicians’ behavior, cognition, and decision making might be influenced by implicit racial biases and explicit racial stereotypes, and thereby contribute to racial inequities in care. Empirical evidence has supported many of these hypothesized mechanisms, demonstrating that White medical care clinicians: (1) hold negative implicit racial biases and explicit racial stereotypes, (2) have implicit racial biases that persist independently of and in contrast to their explicit (conscious) racial attitudes, and (3) can be influenced by racial bias in their clinical decision making and behavior during encounters with Black patients. This paper applies evidence from several disciplines to further specify our original model and elaborate on the ways racism can interact with cognitive biases to affect clinicians’ behavior and decisions and in turn, patient behavior and decisions. We then highlight avenues for intervention and make specific recommendations to medical care and grant-making organizations. PMID:24761152

Automating Guidelines for Clinical Decision Support: Knowledge Engineering and Implementation.

PubMed

Tso, Geoffrey J; Tu, Samson W; Oshiro, Connie; Martins, Susana; Ashcraft, Michael; Yuen, Kaeli W; Wang, Dan; Robinson, Amy; Heidenreich, Paul A; Goldstein, Mary K

2016-01-01

As utilization of clinical decision support (CDS) increases, it is important to continue the development and refinement of methods to accurately translate the intention of clinical practice guidelines (CPG) into a computable form. In this study, we validate and extend the 13 steps that Shiffman et al. 5 identified for translating CPG knowledge for use in CDS. During an implementation project of ATHENA-CDS, we encoded complex CPG recommendations for five common chronic conditions for integration into an existing clinical dashboard. Major decisions made during the implementation process were recorded and categorized according to the 13 steps. During the implementation period, we categorized 119 decisions and identified 8 new categories required to complete the project. We provide details on an updated model that outlines all of the steps used to translate CPG knowledge into a CDS integrated with existing health information technology.

S4HARA: System for HIV/AIDS resource allocation.

PubMed

Lasry, Arielle; Carter, Michael W; Zaric, Gregory S

2008-03-26

HIV/AIDS resource allocation decisions are influenced by political, social, ethical and other factors that are difficult to quantify. Consequently, quantitative models of HIV/AIDS resource allocation have had limited impact on actual spending decisions. We propose a decision-support System for HIV/AIDS Resource Allocation (S4HARA) that takes into consideration both principles of efficient resource allocation and the role of non-quantifiable influences on the decision-making process for resource allocation. S4HARA is a four-step spreadsheet-based model. The first step serves to identify the factors currently influencing HIV/AIDS allocation decisions. The second step consists of prioritizing HIV/AIDS interventions. The third step involves allocating the budget to the HIV/AIDS interventions using a rational approach. Decision-makers can select from several rational models of resource allocation depending on availability of data and level of complexity. The last step combines the results of the first and third steps to highlight the influencing factors that act as barriers or facilitators to the results suggested by the rational resource allocation approach. Actionable recommendations are then made to improve the allocation. We illustrate S4HARA in the context of a primary healthcare clinic in South Africa. The clinic offers six types of HIV/AIDS interventions and spends US$750,000 annually on these programs. Current allocation decisions are influenced by donors, NGOs and the government as well as by ethical and religious factors. Without additional funding, an optimal allocation of the total budget suggests that the portion allotted to condom distribution be increased from 1% to 15% and the portion allotted to prevention and treatment of opportunistic infections be increased from 43% to 71%, while allocation to other interventions should decrease. Condom uptake at the clinic should be increased by changing the condom distribution policy from a pull system to a push system. NGOs and donors promoting antiretroviral programs at the clinic should be sensitized to the results of the model and urged to invest in wellness programs aimed at the prevention and treatment of opportunistic infections. S4HARA differentiates itself from other decision support tools by providing rational HIV/AIDS resource allocation capabilities as well as consideration of the realities facing authorities in their decision-making process.

Dual Processing Model for Medical Decision-Making: An Extension to Diagnostic Testing

PubMed Central

Tsalatsanis, Athanasios; Hozo, Iztok; Kumar, Ambuj; Djulbegovic, Benjamin

2015-01-01

Dual Processing Theories (DPT) assume that human cognition is governed by two distinct types of processes typically referred to as type 1 (intuitive) and type 2 (deliberative). Based on DPT we have derived a Dual Processing Model (DPM) to describe and explain therapeutic medical decision-making. The DPM model indicates that doctors decide to treat when treatment benefits outweigh its harms, which occurs when the probability of the disease is greater than the so called “threshold probability” at which treatment benefits are equal to treatment harms. Here we extend our work to include a wider class of decision problems that involve diagnostic testing. We illustrate applicability of the proposed model in a typical clinical scenario considering the management of a patient with prostate cancer. To that end, we calculate and compare two types of decision-thresholds: one that adheres to expected utility theory (EUT) and the second according to DPM. Our results showed that the decisions to administer a diagnostic test could be better explained using the DPM threshold. This is because such decisions depend on objective evidence of test/treatment benefits and harms as well as type 1 cognition of benefits and harms, which are not considered under EUT. Given that type 1 processes are unique to each decision-maker, this means that the DPM threshold will vary among different individuals. We also showed that when type 1 processes exclusively dominate decisions, ordering a diagnostic test does not affect a decision; the decision is based on the assessment of benefits and harms of treatment. These findings could explain variations in the treatment and diagnostic patterns documented in today’s clinical practice. PMID:26244571

Dual Processing Model for Medical Decision-Making: An Extension to Diagnostic Testing.

PubMed

Tsalatsanis, Athanasios; Hozo, Iztok; Kumar, Ambuj; Djulbegovic, Benjamin

2015-01-01

Dual Processing Theories (DPT) assume that human cognition is governed by two distinct types of processes typically referred to as type 1 (intuitive) and type 2 (deliberative). Based on DPT we have derived a Dual Processing Model (DPM) to describe and explain therapeutic medical decision-making. The DPM model indicates that doctors decide to treat when treatment benefits outweigh its harms, which occurs when the probability of the disease is greater than the so called "threshold probability" at which treatment benefits are equal to treatment harms. Here we extend our work to include a wider class of decision problems that involve diagnostic testing. We illustrate applicability of the proposed model in a typical clinical scenario considering the management of a patient with prostate cancer. To that end, we calculate and compare two types of decision-thresholds: one that adheres to expected utility theory (EUT) and the second according to DPM. Our results showed that the decisions to administer a diagnostic test could be better explained using the DPM threshold. This is because such decisions depend on objective evidence of test/treatment benefits and harms as well as type 1 cognition of benefits and harms, which are not considered under EUT. Given that type 1 processes are unique to each decision-maker, this means that the DPM threshold will vary among different individuals. We also showed that when type 1 processes exclusively dominate decisions, ordering a diagnostic test does not affect a decision; the decision is based on the assessment of benefits and harms of treatment. These findings could explain variations in the treatment and diagnostic patterns documented in today's clinical practice.

Patterns of out-of-home placement decision-making in child welfare.

PubMed

Chor, Ka Ho Brian; McClelland, Gary M; Weiner, Dana A; Jordan, Neil; Lyons, John S

2013-10-01

Out-of-home placement decision-making in child welfare is founded on the best interest of the child in the least restrictive setting. After a child is removed from home, however, little is known about the mechanism of placement decision-making. This study aims to systematically examine the patterns of out-of-home placement decisions made in a state's child welfare system by comparing two models of placement decision-making: a multidisciplinary team decision-making model and a clinically based decision support algorithm. Based on records of 7816 placement decisions representing 6096 children over a 4-year period, hierarchical log-linear modeling characterized concordance or agreement, and discordance or disagreement when comparing the two models and accounting for age-appropriate placement options. Children aged below 16 had an overall concordance rate of 55.7%, most apparent in the least restrictive (20.4%) and the most restrictive placement (18.4%). Older youth showed greater discordant distributions (62.9%). Log-linear analysis confirmed the overall robustness of concordance (odd ratios [ORs] range: 2.9-442.0), though discordance was most evident from small deviations from the decision support algorithm, such as one-level under-placement in group home (OR=5.3) and one-level over-placement in residential treatment center (OR=4.8). Concordance should be further explored using child-level clinical and placement stability outcomes. Discordance might be explained by dynamic factors such as availability of placements, caregiver preferences, or policy changes and could be justified by positive child-level outcomes. Empirical placement decision-making is critical to a child's journey in child welfare and should be continuously improved to effect positive child welfare outcomes. Copyright © 2013 Elsevier Ltd. All rights reserved.

Mental Workload as a Key Factor in Clinical Decision Making

ERIC Educational Resources Information Center

Byrne, Aidan

2013-01-01

The decision making process is central to the practice of a clinician and has traditionally been described in terms of the hypothetico-deductive model. More recently, models adapted from cognitive psychology, such as the dual process and script theories have proved useful in explaining patterns of practice not consistent with purely cognitive…

Children Home Alone Unsupervised: Modeling Parental Decisions and Associated Factors in Botswana, Mexico, and Vietnam

ERIC Educational Resources Information Center

Ruiz-Casares, Monica; Heymann, Jody

2009-01-01

Objective: This paper examines different child care arrangements utilized by working families in countries undergoing major socio-economic transitions, with a focus on modeling parental decisions to leave children home alone. Method: The study interviewed 537 working caregivers attending government health clinics in Botswana, Mexico, and Vietnam.…

How do small groups make decisions? : A theoretical framework to inform the implementation and study of clinical competency committees.

PubMed

Chahine, Saad; Cristancho, Sayra; Padgett, Jessica; Lingard, Lorelei

2017-06-01

In the competency-based medical education (CBME) approach, clinical competency committees are responsible for making decisions about trainees' competence. However, we currently lack a theoretical model for group decision-making to inform this emerging assessment phenomenon. This paper proposes an organizing framework to study and guide the decision-making processes of clinical competency committees.This is an explanatory, non-exhaustive review, tailored to identify relevant theoretical and evidence-based papers related to small group decision-making. The search was conducted using Google Scholar, Web of Science, MEDLINE, ERIC, and PsycINFO for relevant literature. Using a thematic analysis, two researchers (SC & JP) met four times between April-June 2016 to consolidate the literature included in this review.Three theoretical orientations towards group decision-making emerged from the review: schema, constructivist, and social influence. Schema orientations focus on how groups use algorithms for decision-making. Constructivist orientations focus on how groups construct their shared understanding. Social influence orientations focus on how individual members influence the group's perspective on a decision. Moderators of decision-making relevant to all orientations include: guidelines, stressors, authority, and leadership.Clinical competency committees are the mechanisms by which groups of clinicians will be in charge of interpreting multiple assessment data points and coming to a shared decision about trainee competence. The way in which these committees make decisions can have huge implications for trainee progression and, ultimately, patient care. Therefore, there is a pressing need to build the science of how such group decision-making works in practice. This synthesis suggests a preliminary organizing framework that can be used in the implementation and study of clinical competency committees.

CorRECTreatment: A Web-based Decision Support Tool for Rectal Cancer Treatment that Uses the Analytic Hierarchy Process and Decision Tree

PubMed Central

Karakülah, G.; Dicle, O.; Sökmen, S.; Çelikoğlu, C.C.

2015-01-01

Summary Background The selection of appropriate rectal cancer treatment is a complex multi-criteria decision making process, in which clinical decision support systems might be used to assist and enrich physicians’ decision making. Objective The objective of the study was to develop a web-based clinical decision support tool for physicians in the selection of potentially beneficial treatment options for patients with rectal cancer. Methods The updated decision model contained 8 and 10 criteria in the first and second steps respectively. The decision support model, developed in our previous study by combining the Analytic Hierarchy Process (AHP) method which determines the priority of criteria and decision tree that formed using these priorities, was updated and applied to 388 patients data collected retrospectively. Later, a web-based decision support tool named corRECTreatment was developed. The compatibility of the treatment recommendations by the expert opinion and the decision support tool was examined for its consistency. Two surgeons were requested to recommend a treatment and an overall survival value for the treatment among 20 different cases that we selected and turned into a scenario among the most common and rare treatment options in the patient data set. Results In the AHP analyses of the criteria, it was found that the matrices, generated for both decision steps, were consistent (consistency ratio<0.1). Depending on the decisions of experts, the consistency value for the most frequent cases was found to be 80% for the first decision step and 100% for the second decision step. Similarly, for rare cases consistency was 50% for the first decision step and 80% for the second decision step. Conclusions The decision model and corRECTreatment, developed by applying these on real patient data, are expected to provide potential users with decision support in rectal cancer treatment processes and facilitate them in making projections about treatment options. PMID:25848413

CorRECTreatment: a web-based decision support tool for rectal cancer treatment that uses the analytic hierarchy process and decision tree.

PubMed

Suner, A; Karakülah, G; Dicle, O; Sökmen, S; Çelikoğlu, C C

2015-01-01

The selection of appropriate rectal cancer treatment is a complex multi-criteria decision making process, in which clinical decision support systems might be used to assist and enrich physicians' decision making. The objective of the study was to develop a web-based clinical decision support tool for physicians in the selection of potentially beneficial treatment options for patients with rectal cancer. The updated decision model contained 8 and 10 criteria in the first and second steps respectively. The decision support model, developed in our previous study by combining the Analytic Hierarchy Process (AHP) method which determines the priority of criteria and decision tree that formed using these priorities, was updated and applied to 388 patients data collected retrospectively. Later, a web-based decision support tool named corRECTreatment was developed. The compatibility of the treatment recommendations by the expert opinion and the decision support tool was examined for its consistency. Two surgeons were requested to recommend a treatment and an overall survival value for the treatment among 20 different cases that we selected and turned into a scenario among the most common and rare treatment options in the patient data set. In the AHP analyses of the criteria, it was found that the matrices, generated for both decision steps, were consistent (consistency ratio<0.1). Depending on the decisions of experts, the consistency value for the most frequent cases was found to be 80% for the first decision step and 100% for the second decision step. Similarly, for rare cases consistency was 50% for the first decision step and 80% for the second decision step. The decision model and corRECTreatment, developed by applying these on real patient data, are expected to provide potential users with decision support in rectal cancer treatment processes and facilitate them in making projections about treatment options.

Implementation science: a role for parallel dual processing models of reasoning?

PubMed Central

Sladek, Ruth M; Phillips, Paddy A; Bond, Malcolm J

2006-01-01

Background A better theoretical base for understanding professional behaviour change is needed to support evidence-based changes in medical practice. Traditionally strategies to encourage changes in clinical practices have been guided empirically, without explicit consideration of underlying theoretical rationales for such strategies. This paper considers a theoretical framework for reasoning from within psychology for identifying individual differences in cognitive processing between doctors that could moderate the decision to incorporate new evidence into their clinical decision-making. Discussion Parallel dual processing models of reasoning posit two cognitive modes of information processing that are in constant operation as humans reason. One mode has been described as experiential, fast and heuristic; the other as rational, conscious and rule based. Within such models, the uptake of new research evidence can be represented by the latter mode; it is reflective, explicit and intentional. On the other hand, well practiced clinical judgments can be positioned in the experiential mode, being automatic, reflexive and swift. Research suggests that individual differences between people in both cognitive capacity (e.g., intelligence) and cognitive processing (e.g., thinking styles) influence how both reasoning modes interact. This being so, it is proposed that these same differences between doctors may moderate the uptake of new research evidence. Such dispositional characteristics have largely been ignored in research investigating effective strategies in implementing research evidence. Whilst medical decision-making occurs in a complex social environment with multiple influences and decision makers, it remains true that an individual doctor's judgment still retains a key position in terms of diagnostic and treatment decisions for individual patients. This paper argues therefore, that individual differences between doctors in terms of reasoning are important considerations in any discussion relating to changing clinical practice. Summary It is imperative that change strategies in healthcare consider relevant theoretical frameworks from other disciplines such as psychology. Generic dual processing models of reasoning are proposed as potentially useful in identifying factors within doctors that may moderate their individual uptake of evidence into clinical decision-making. Such factors can then inform strategies to change practice. PMID:16725023

Implementation science: a role for parallel dual processing models of reasoning?

PubMed

Sladek, Ruth M; Phillips, Paddy A; Bond, Malcolm J

2006-05-25

A better theoretical base for understanding professional behaviour change is needed to support evidence-based changes in medical practice. Traditionally strategies to encourage changes in clinical practices have been guided empirically, without explicit consideration of underlying theoretical rationales for such strategies. This paper considers a theoretical framework for reasoning from within psychology for identifying individual differences in cognitive processing between doctors that could moderate the decision to incorporate new evidence into their clinical decision-making. Parallel dual processing models of reasoning posit two cognitive modes of information processing that are in constant operation as humans reason. One mode has been described as experiential, fast and heuristic; the other as rational, conscious and rule based. Within such models, the uptake of new research evidence can be represented by the latter mode; it is reflective, explicit and intentional. On the other hand, well practiced clinical judgments can be positioned in the experiential mode, being automatic, reflexive and swift. Research suggests that individual differences between people in both cognitive capacity (e.g., intelligence) and cognitive processing (e.g., thinking styles) influence how both reasoning modes interact. This being so, it is proposed that these same differences between doctors may moderate the uptake of new research evidence. Such dispositional characteristics have largely been ignored in research investigating effective strategies in implementing research evidence. Whilst medical decision-making occurs in a complex social environment with multiple influences and decision makers, it remains true that an individual doctor's judgment still retains a key position in terms of diagnostic and treatment decisions for individual patients. This paper argues therefore, that individual differences between doctors in terms of reasoning are important considerations in any discussion relating to changing clinical practice. It is imperative that change strategies in healthcare consider relevant theoretical frameworks from other disciplines such as psychology. Generic dual processing models of reasoning are proposed as potentially useful in identifying factors within doctors that may moderate their individual uptake of evidence into clinical decision-making. Such factors can then inform strategies to change practice.

Assessing Generalisability in Model-Based Economic Evaluation Studies: A Structured Review in Osteoporosis

PubMed Central

Urdahl, Hege; Manca, Andrea; Sculpher, Mark J

2008-01-01

Background To support decision making many countries have now introduced some formal assessment process to evaluate whether health technologies represent good ‘value for money’. These often take the form of decision models which can be used to explore elements of importance to generalisability of study results across clinical settings and jurisdictions. The objectives of the present review were to assess: (i) whether the published studies clearly defined the decision-making audience for the model; (ii) the transparency of the reporting in terms of study question, structure and data inputs; (iii) the relevance of the data inputs used in the model to the stated decision-maker or jurisdiction; and (iv) how fully the robustness of the model's results to variation in data inputs between locations was assessed. Methods Articles reporting decision-analytic models in the area of osteoporosis were assessed to establish the extent to which the information provided enabled decision makers in different countries/jurisdictions to fully appreciate the variability of results according to location, and the relevance to their own. Results Of the 18 articles included in the review, only three explicitly stated the decision-making audience. It was not possible to infer a decision-making audience in eight studies. Target population was well reported, as was resource and cost data, and clinical data used for estimates of relative risk reduction. However, baseline risk was rarely adapted to the relevant jurisdiction, and when no decision-maker was explicit it was difficult to assess whether the reported cost and resource use data was in fact relevant. A few studies used sensitivity analysis to explore elements of generalisability, such as compliance rates and baseline fracture risk rates, although such analyses were generally restricted to evaluating parameter uncertainty. Conclusion This review found that variability in cost-effectiveness across locations is addressed to a varying extent in modelling studies in the field of osteoporosis, limiting their use for decision-makers across different locations. Transparency of reporting is expected to increase as methodology develops, and decision-makers publish “reference case” type guidance. PMID:17129074

Shared decision making in mental health: the importance for current clinical practice.

PubMed

Alguera-Lara, Victoria; Dowsey, Michelle M; Ride, Jemimah; Kinder, Skye; Castle, David

2017-12-01

We reviewed the literature on shared decision making (regarding treatments in psychiatry), with a view to informing our understanding of the decision making process and the barriers that exist in clinical practice. Narrative review of published English-language articles. After culling, 18 relevant articles were included. Themes identified included models of psychiatric care, benefits for patients, and barriers. There is a paucity of published studies specifically related to antipsychotic medications. Shared decision making is a central part of the recovery paradigm and is of increasing importance in mental health service delivery. The field needs to better understand the basis on which decisions are reached regarding psychiatric treatments. Discrete choice experiments might be useful to inform the development of tools to assist shared decision making in psychiatry.

Distributive justice and infertility treatment in Canada.

PubMed

Nisker, Jeff

2008-05-01

An exploration of distributive justice in Canadian infertility treatment requires the integration of ethical, clinical, and economic principles. In 1971, American philosopher John Rawls proposed a theoretical model for fair decision-making in which "rational" and "self-interested" citizens are behind a "veil of ignorance" with respect to both their own position and the position of other decision-makers. Rawls proposed that these self-interested decision-makers, fearing that they are among the least advantaged persons who could be affected by the decision, will agree only upon rules that encode equality of opportunity and that bestow the greatest benefit on the least advantaged citizens. Regarding health policy decision-making, Rawls' model is best illustrated by Canadian philosopher Warren Bourgeois in his panel of "volunteers." These rational and self-interested volunteers receive an amnestic drug that renders them unaware of their health, social, and financial position, but they know that they are representative of diverse spheres of citizens whose well-being will be affected by their decision. After describing fair decision-making, Bourgeois considers the lack of a distributive justice imperative in Canada's Assisted Human Reproduction Act, in contrast to legislation in European nations and Australia, summarizes the economic and clinical considerations that must be provided to the decision-makers behind the "veil of ignorance" for fair decisions to occur, and considers altruism in relation to equality of access. He concludes by noting that among countries with legislation governing assisted reproduction Canada is alone in having legislation that is void of distributive justice in providing access to clinically appropriate infertility care.

From guideline modeling to guideline execution: defining guideline-based decision-support services.

PubMed Central

Tu, S. W.; Musen, M. A.

2000-01-01

We describe our task-based approach to defining the guideline-based decision-support services that the EON system provides. We categorize uses of guidelines in patient-specific decision support into a set of generic tasks--making of decisions, specification of work to be performed, interpretation of data, setting of goals, and issuance of alert and reminders--that can be solved using various techniques. Our model includes constructs required for representing the knowledge used by these techniques. These constructs form a toolkit from which developers can select modeling solutions for guideline task. Based on the tasks and the guideline model, we define a guideline-execution architecture and a model of interactions between a decision-support server and clients that invoke services provided by the server. These services use generic interfaces derived from guideline tasks and their associated modeling constructs. We describe two implementations of these decision-support services and discuss how this work can be generalized. We argue that a well-defined specification of guideline-based decision-support services will facilitate sharing of tools that implement computable clinical guidelines. PMID:11080007

Simulation as a preoperative planning approach in advanced heart failure patients. A retrospective clinical analysis.

PubMed

Capoccia, Massimo; Marconi, Silvia; Singh, Sanjeet Avtaar; Pisanelli, Domenico M; De Lazzari, Claudio

2018-05-02

Modelling and simulation may become clinically applicable tools for detailed evaluation of the cardiovascular system and clinical decision-making to guide therapeutic intervention. Models based on pressure-volume relationship and zero-dimensional representation of the cardiovascular system may be a suitable choice given their simplicity and versatility. This approach has great potential for application in heart failure where the impact of left ventricular assist devices has played a significant role as a bridge to transplant and more recently as a long-term solution for non eligible candidates. We sought to investigate the value of simulation in the context of three heart failure patients with a view to predict or guide further management. CARDIOSIM © was the software used for this purpose. The study was based on retrospective analysis of haemodynamic data previously discussed at a multidisciplinary meeting. The outcome of the simulations addressed the value of a more quantitative approach in the clinical decision process. Although previous experience, co-morbidities and the risk of potentially fatal complications play a role in clinical decision-making, patient-specific modelling may become a daily approach for selection and optimisation of device-based treatment for heart failure patients. Willingness to adopt this integrated approach may be the key to further progress.

Transforming clinical practice guidelines and clinical pathways into fast-and-frugal decision trees to improve clinical care strategies.

PubMed

Djulbegovic, Benjamin; Hozo, Iztok; Dale, William

2018-02-27

Contemporary delivery of health care is inappropriate in many ways, largely due to suboptimal Q5 decision-making. A typical approach to improve practitioners' decision-making is to develop evidence-based clinical practice guidelines (CPG) by guidelines panels, who are instructed to use their judgments to derive practice recommendations. However, mechanisms for the formulation of guideline judgments remains a "black-box" operation-a process with defined inputs and outputs but without sufficient knowledge of its internal workings. Increased explicitness and transparency in the process can be achieved by implementing CPG as clinical pathways (CPs) (also known as clinical algorithms or flow-charts). However, clinical recommendations thus derived are typically ad hoc and developed by experts in a theory-free environment. As any recommendation can be right (true positive or negative), or wrong (false positive or negative), the lack of theoretical structure precludes the quantitative assessment of the management strategies recommended by CPGs/CPs. To realize the full potential of CPGs/CPs, they need to be placed on more solid theoretical grounds. We believe this potential can be best realized by converting CPGs/CPs within the heuristic theory of decision-making, often implemented as fast-and-frugal (FFT) decision trees. This is possible because FFT heuristic strategy of decision-making can be linked to signal detection theory, evidence accumulation theory, and a threshold model of decision-making, which, in turn, allows quantitative analysis of the accuracy of clinical management strategies. Fast-and-frugal provides a simple and transparent, yet solid and robust, methodological framework connecting decision science to clinical care, a sorely needed missing link between CPGs/CPs and patient outcomes. We therefore advocate that all guidelines panels express their recommendations as CPs, which in turn should be converted into FFTs to guide clinical care. © 2018 John Wiley & Sons, Ltd.

Calculating when elective abdominal aortic aneurysm repair improves survival for individual patients: development of the Aneurysm Repair Decision Aid and economic evaluation.

PubMed

Grant, Stuart W; Sperrin, Matthew; Carlson, Eric; Chinai, Natasha; Ntais, Dionysios; Hamilton, Matthew; Dunn, Graham; Buchan, Iain; Davies, Linda; McCollum, Charles N

2015-04-01

Abdominal aortic aneurysm (AAA) repair aims to prevent premature death from AAA rupture. Elective repair is currently recommended when AAA diameter reaches 5.5 cm (men) and 5.0 cm (women). Applying population-based indications may not be appropriate for individual patient decisions, as the optimal indication is likely to differ between patients based on age and comorbidities. To develop an Aneurysm Repair Decision Aid (ARDA) to indicate when elective AAA repair optimises survival for individual patients and to assess the cost-effectiveness and associated uncertainty of elective repair at the aneurysm diameter recommended by the ARDA compared with current practice. The UK Vascular Governance North West and National Vascular Database provided individual patient data to develop predictive models for perioperative mortality and survival. Data from published literature were used to model AAA growth and risk of rupture. The cost-effectiveness analysis used data from published literature and from local and national databases. A combination of systematic review methods and clinical registries were used to provide data to populate models and inform the structure of the ARDA. Discrete event simulation (DES) was used to model the patient journey from diagnosis to death and synthesised data were used to estimate patient outcomes and costs for elective repair at alternative aneurysm diameters. Eight patient clinical scenarios (vignettes) were used as exemplars. The DES structure was validated by clinical and statistical experts. The economic evaluation estimated costs, quality-adjusted life-years (QALYs) and incremental cost-effectiveness ratios (ICERs) from the NHS, social care provider and patient perspective over a lifetime horizon. Cost-effectiveness acceptability analyses and probabilistic sensitivity analyses explored uncertainty in the data and the value for money of ARDA-based decisions. The ARDA outcome measures include perioperative mortality risk, annual risk of rupture, 1-, 5- and 10-year survival, postoperative long-term survival, median life expectancy and predicted time to current threshold for aneurysm repair. The primary economic measure was the ICER using the QALY as the measure of health benefit. The analysis demonstrated it is feasible to build and run a complex clinical decision aid using DES. The model results support current guidelines for most vignettes but suggest that earlier repair may be effective in younger, fitter patients and ongoing surveillance may be effective in elderly patients with comorbidities. The model adds information to support decisions for patients with aneurysms outside current indications. The economic evaluation suggests that using the ARDA compared with current guidelines could be cost-effective but there is a high level of uncertainty. Lack of high-quality long-term data to populate all sections of the model meant that there is high uncertainty about the long-term clinical and economic consequences of repair. Modelling assumptions were necessary and the developed survival models require external validation. The ARDA provides detailed information on the potential consequences of AAA repair or a decision not to repair that may be helpful to vascular surgeons and their patients in reaching informed decisions. Further research is required to reduce uncertainty about key data, including reintervention following AAA repair, and assess the acceptability and feasibility of the ARDA for use in routine clinical practice. The National Institute for Health Research Health Technology Assessment programme.

Models of clinical reasoning with a focus on general practice: A critical review

PubMed Central

YAZDANI, SHAHRAM; HOSSEINZADEH, MOHAMMAD; HOSSEINI, FAKHROLSADAT

2017-01-01

Introduction: Diagnosis lies at the heart of general practice. Every day general practitioners (GPs) visit patients with a wide variety of complaints and concerns, with often minor but sometimes serious symptoms. General practice has many features which differentiate it from specialty care setting, but during the last four decades little attention was paid to clinical reasoning in general practice. Therefore, we aimed to critically review the clinical reasoning models with a focus on the clinical reasoning in general practice or clinical reasoning of general practitioners to find out to what extent the existing models explain the clinical reasoning specially in primary care and also identity the gaps of the model for use in primary care settings. Methods: A systematic search to find models of clinical reasoning were performed. To have more precision, we excluded the studies that focused on neurobiological aspects of reasoning, reasoning in disciplines other than medicine decision making or decision analysis on treatment or management plan. All the articles and documents were first scanned to see whether they include important relevant contents or any models. The selected studies which described a model of clinical reasoning in general practitioners or with a focus on general practice were then reviewed and appraisal or critics of other authors on these models were included. The reviewed documents on the model were synthesized. Results: Six models of clinical reasoning were identified including hypothetic-deductive model, pattern recognition, a dual process diagnostic reasoning model, pathway for clinical reasoning, an integrative model of clinical reasoning, and model of diagnostic reasoning strategies in primary care. Only one model had specifically focused on general practitioners reasoning. Conclusion: A Model of clinical reasoning that included specific features of general practice to better help the general practitioners with the difficulties of clinical reasoning in this setting is needed. PMID:28979912

Information-sharing to promote informed choice in prenatal screening in the spirit of the SOGC clinical practice guideline: a proposal for an alternative model.

PubMed

Vanstone, Meredith; Kinsella, Elizabeth Anne; Nisker, Jeff

2012-03-01

The 2011 SOGC clinical practice guideline "Prenatal Screening for Fetal Aneuploidy in Singleton Pregnancies" recommends that clinicians offer prenatal screening to all pregnant women and provide counselling in a non-directive manner. Non-directive counselling is intended to facilitate autonomous decision-making and remove the clinician's views regarding a particular course of action. However, recent research in genetic counselling raises concerns that non-directive counselling is neither possible nor desirable, and that it may not be the best way to facilitate informed choice. We propose an alternative model of information-sharing specific to prenatal screening that combines attributes of the models of informative decision-making and shared decision-making. Our proposed model is intended to provide clinicians with a strategy to communicate information about prenatal screening in a way that facilitates a shared deliberative process and autonomous decision-making. Our proposed model may better prepare a pregnant woman to make an informed choice about participating in prenatal screening on the basis of her consideration of the medical information provided by her clinician and her particular circumstances and values.

Collaborative deliberation: a model for patient care.

PubMed

Elwyn, Glyn; Lloyd, Amy; May, Carl; van der Weijden, Trudy; Stiggelbout, Anne; Edwards, Adrian; Frosch, Dominick L; Rapley, Tim; Barr, Paul; Walsh, Thom; Grande, Stuart W; Montori, Victor; Epstein, Ronald

2014-11-01

Existing theoretical work in decision making and behavior change has focused on how individuals arrive at decisions or form intentions. Less attention has been given to theorizing the requirements that might be necessary for individuals to work collaboratively to address difficult decisions, consider new alternatives, or change behaviors. The goal of this work was to develop, as a forerunner to a middle range theory, a conceptual model that considers the process of supporting patients to consider alternative health care options, in collaboration with clinicians, and others. Theory building among researchers with experience and expertise in clinician-patient communication, using an iterative cycle of discussions. We developed a model composed of five inter-related propositions that serve as a foundation for clinical communication processes that honor the ethical principles of respecting individual agency, autonomy, and an empathic approach to practice. We named the model 'collaborative deliberation.' The propositions describe: (1) constructive interpersonal engagement, (2) recognition of alternative actions, (3) comparative learning, (4) preference construction and elicitation, and (5) preference integration. We believe the model underpins multiple suggested approaches to clinical practice that take the form of patient centered care, motivational interviewing, goal setting, action planning, and shared decision making. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

Classifying clinical decision making: interpreting nursing intuition, heuristics and medical diagnosis.

PubMed

Buckingham, C D; Adams, A

2000-10-01

This is the second of two linked papers exploring decision making in nursing. The first paper, 'Classifying clinical decision making: a unifying approach' investigated difficulties with applying a range of decision-making theories to nursing practice. This is due to the diversity of terminology and theoretical concepts used, which militate against nurses being able to compare the outcomes of decisions analysed within different frameworks. It is therefore problematic for nurses to assess how good their decisions are, and where improvements can be made. However, despite the range of nomenclature, it was argued that there are underlying similarities between all theories of decision processes and that these should be exposed through integration within a single explanatory framework. A proposed solution was to use a general model of psychological classification to clarify and compare terms, concepts and processes identified across the different theories. The unifying framework of classification was described and this paper operationalizes it to demonstrate how different approaches to clinical decision making can be re-interpreted as classification behaviour. Particular attention is focused on classification in nursing, and on re-evaluating heuristic reasoning, which has been particularly prone to theoretical and terminological confusion. Demonstrating similarities in how different disciplines make decisions should promote improved multidisciplinary collaboration and a weakening of clinical elitism, thereby enhancing organizational effectiveness in health care and nurses' professional status. This is particularly important as nurses' roles continue to expand to embrace elements of managerial, medical and therapeutic work. Analysing nurses' decisions as classification behaviour will also enhance clinical effectiveness, and assist in making nurses' expertise more visible. In addition, the classification framework explodes the myth that intuition, traditionally associated with nurses' decision making, is less rational and scientific than other approaches.

An Evidence-Based Practice Model across the Academic and Clinical Settings

ERIC Educational Resources Information Center

Wolter, Julie A.; Corbin-Lewis, Kim; Self, Trisha; Elsweiler, Anne

2011-01-01

This tutorial is designed to provide academic communication sciences and disorders (CSD) programs, at both the undergraduate and graduate levels, with a comprehensive instructional model on evidence-based practice (EBP). The model was designed to help students view EBP as an ongoing process needed in all clinical decision making. The three facets…

Toward an operational model of decision making, emotional regulation, and mental health impact.

PubMed

Collura, Thomas Francis; Zalaquett, Ronald P; Bonnstetter, Carlos Joyce; Chatters, Seria J

2014-01-01

Current brain research increasingly reveals the underlying mechanisms and processes of human behavior, cognition, and emotion. In addition to being of interest to a wide range of scientists, educators, and professionals, as well as laypeople, brain-based models are of particular value in a clinical setting. Psychiatrists, psychologists, counselors, and other mental health professionals are in need of operational models that integrate recent findings in the physical, cognitive, and emotional domains, and offer a common language for interdisciplinary understanding and communication. Based on individual traits, predispositions, and responses to stimuli, we can begin to identify emotional and behavioral pathways and mental processing patterns. The purpose of this article is to present a brain-path activation model to understand individual differences in decision making and psychopathology. The first section discusses the role of frontal lobe electroencephalography (EEG) asymmetry, summarizes state- and trait-based models of decision making, and provides a more complex analysis that supplements the traditional simple left-right brain model. Key components of the new model are the introduction of right hemisphere parallel and left hemisphere serial scanning in rendering decisions, and the proposition of pathways that incorporate both past experiences as well as future implications into the decision process. Main attributes of each decision-making mechanism are provided. The second section applies the model within the realm of clinical mental health as a tool to understand specific human behavior and pathology. Applications include general and chronic anxiety, depression, paranoia, risk taking, and the pathways employed when well-functioning operational integration is observed. Finally, specific applications such as meditation and mindfulness are offered to facilitate positive functioning.

Impact of Interprofessional Relationships from Nurses' Perspective on the Decision-Making Capacity of Patients in a Clinical Setting.

PubMed

Molina-Mula, Jesús; Gallo-Estrada, Julia; Perelló-Campaner, Catalina

2017-12-29

Interprofessional relationships may impact the decision making of patients in a clinical setting. The objective of this study was to analyse the decision-making capabilities of patients from nurses' perspectives of interprofessional relationships using Foucauldian ethics. This qualitative study was based on poststructuralist Foucault references with in-depth interviews of nurses working in internal medicine and specialties in a general hospital. The patients constantly appeared in the definition of teamwork, but also as a passive element used by every professional to communicate with others. Nurses continue modelling a type of patient passivity, or what Foucault called passive subjectivity in relation to oneself, because the patient is guided and directed to take charge of a truth provided by professionals. Nurses must break the rigid design of sections or professional skills, and adopt a model of teamwork that meets the needs of the patient and increases their decision-making power. The quality of care will increase to the extent that professionals establish a relationship of equality with the patient, allowing the patient to make real decisions about their care. An egalitarian model of teamwork is beneficial to the patient, abandoning the idea of a team where the patient and family are constantly excluded from decisions about their care.

Impact of Interprofessional Relationships from Nurses’ Perspective on the Decision-Making Capacity of Patients in a Clinical Setting

PubMed Central

Gallo-Estrada, Julia; Perelló-Campaner, Catalina

2017-01-01

Interprofessional relationships may impact the decision making of patients in a clinical setting. The objective of this study was to analyse the decision-making capabilities of patients from nurses’ perspectives of interprofessional relationships using Foucauldian ethics. This qualitative study was based on poststructuralist Foucault references with in-depth interviews of nurses working in internal medicine and specialties in a general hospital. The patients constantly appeared in the definition of teamwork, but also as a passive element used by every professional to communicate with others. Nurses continue modelling a type of patient passivity, or what Foucault called passive subjectivity in relation to oneself, because the patient is guided and directed to take charge of a truth provided by professionals. Nurses must break the rigid design of sections or professional skills, and adopt a model of teamwork that meets the needs of the patient and increases their decision-making power. The quality of care will increase to the extent that professionals establish a relationship of equality with the patient, allowing the patient to make real decisions about their care. An egalitarian model of teamwork is beneficial to the patient, abandoning the idea of a team where the patient and family are constantly excluded from decisions about their care. PMID:29286342

A novel computer based expert decision making model for prostate cancer disease management.

PubMed

Richman, Martin B; Forman, Ernest H; Bayazit, Yildirim; Einstein, Douglas B; Resnick, Martin I; Stovsky, Mark D

2005-12-01

We propose a strategic, computer based, prostate cancer decision making model based on the analytic hierarchy process. We developed a model that improves physician-patient joint decision making and enhances the treatment selection process by making this critical decision rational and evidence based. Two groups (patient and physician-expert) completed a clinical study comparing an initial disease management choice with the highest ranked option generated by the computer model. Participants made pairwise comparisons to derive priorities for the objectives and subobjectives related to the disease management decision. The weighted comparisons were then applied to treatment options to yield prioritized rank lists that reflect the likelihood that a given alternative will achieve the participant treatment goal. Aggregate data were evaluated by inconsistency ratio analysis and sensitivity analysis, which assessed the influence of individual objectives and subobjectives on the final rank list of treatment options. Inconsistency ratios less than 0.05 were reliably generated, indicating that judgments made within the model were mathematically rational. The aggregate prioritized list of treatment options was tabulated for the patient and physician groups with similar outcomes for the 2 groups. Analysis of the major defining objectives in the treatment selection decision demonstrated the same rank order for the patient and physician groups with cure, survival and quality of life being more important than controlling cancer, preventing major complications of treatment, preventing blood transfusion complications and limiting treatment cost. Analysis of subobjectives, including quality of life and sexual dysfunction, produced similar priority rankings for the patient and physician groups. Concordance between initial treatment choice and the highest weighted model option differed between the groups with the patient group having 59% concordance and the physician group having only 42% concordance. This study successfully validated the usefulness of a computer based prostate cancer management decision making model to produce individualized, rational, clinically appropriate disease management decisions without physician bias.

Decision-Making in Audiology: Balancing Evidence-Based Practice and Patient-Centered Care

PubMed Central

Clemesha, Jennifer; Lundmark, Erik; Crome, Erica; Barr, Caitlin; McMahon, Catherine M.

2017-01-01

Health-care service delivery models have evolved from a practitioner-centered approach toward a patient-centered ideal. Concurrently, increasing emphasis has been placed on the use of empirical evidence in decision-making to increase clinical accountability. The way in which clinicians use empirical evidence and client preferences to inform decision-making provides an insight into health-care delivery models utilized in clinical practice. The present study aimed to investigate the sources of information audiologists use when discussing rehabilitation choices with clients, and discuss the findings within the context of evidence-based practice and patient-centered care. To assess the changes that may have occurred over time, this study uses a questionnaire based on one of the few studies of decision-making behavior in audiologists, published in 1989. The present questionnaire was completed by 96 audiologists who attended the World Congress of Audiology in 2014. The responses were analyzed using qualitative and quantitative approaches. Results suggest that audiologists rank clinical test results and client preferences as the most important factors for decision-making. Discussion with colleagues or experts was also frequently reported as an important source influencing decision-making. Approximately 20% of audiologists mentioned utilizing research evidence to inform decision-making when no clear solution was available. Information shared at conferences was ranked low in terms of importance and reliability. This study highlights an increase in awareness of concepts associated with evidence-based practice and patient-centered care within audiology settings, consistent with current research-to-practice dissemination pathways. It also highlights that these pathways may not be sufficient for an effective clinical implementation of these practices. PMID:28752808

Healthy participants in phase I clinical trials: the quality of their decision to take part.

PubMed

Rabin, Cheryl; Tabak, Nili

2006-08-01

This study was set out to test the quality of the decision-making process of healthy volunteers in clinical trials. Researchers fear that the decision to volunteer for clinical trials is taken inadequately and that the signature on the consent forms, meant to affirm that consent was 'informed', is actually insubstantial. The study design was quasi-experimental, using a convenience quota sample. Over a period of a year, candidates were approached during their screening process for a proposed clinical trial, after concluding the required 'Informed Consent' procedure. In all, 100 participants in phase I trials filled out questionnaires based ultimately on the Janis and Mann model of vigilant information processing, during their stay in the research centre. Only 35% of the participants reached a 'quality decision'. There is a definite correlation between information processing and quality decision-making. However, many of the healthy research volunteers (58%) do not seek out information nor check alternatives before making a decision. Full disclosure is essential to a valid informed consent procedure but not sufficient; emphasis must be put on having the information understood and assimilated. Research nurses play a central role in achieving this objective.

Clinical leadership in contemporary clinical practice: implications for nursing in Australia.

PubMed

Davidson, P M; Elliott, D; Daly, J

2006-04-01

Leadership in the clinical practice environment is important to ensure both optimal patient outcomes and successive generations of motivated and enthusiastic clinicians. The present paper seeks to define and describe clinical leadership and identify the facilitators and barriers to clinical leadership. We also describe strategies to develop clinical leaders in Australia. Key drivers to the development of nursing leaders are strategies that recognize and value clinical expertise. These include models of care that highlight the importance of the nursing role; evidence-based practice and measurement of clinical outcomes; strategies to empower clinicians and mechanisms to ensure participation in clinical decision-making. Significant barriers to clinical leadership are organizational structures that preclude nurses from clinical decision making; the national shortage of nurses; fiscal constraints; absence of well evaluated models of care and trends towards less skilled clinicians. Systematic, strategic initiatives are required to nurture and develop clinical leaders. These strategies need to be collegial collaborations between the academic and health care sectors in order to provide a united voice for advancing the nursing profession.

Dual processing model of medical decision-making

PubMed Central

2012-01-01

Background Dual processing theory of human cognition postulates that reasoning and decision-making can be described as a function of both an intuitive, experiential, affective system (system I) and/or an analytical, deliberative (system II) processing system. To date no formal descriptive model of medical decision-making based on dual processing theory has been developed. Here we postulate such a model and apply it to a common clinical situation: whether treatment should be administered to the patient who may or may not have a disease. Methods We developed a mathematical model in which we linked a recently proposed descriptive psychological model of cognition with the threshold model of medical decision-making and show how this approach can be used to better understand decision-making at the bedside and explain the widespread variation in treatments observed in clinical practice. Results We show that physician’s beliefs about whether to treat at higher (lower) probability levels compared to the prescriptive therapeutic thresholds obtained via system II processing is moderated by system I and the ratio of benefit and harms as evaluated by both system I and II. Under some conditions, the system I decision maker’s threshold may dramatically drop below the expected utility threshold derived by system II. This can explain the overtreatment often seen in the contemporary practice. The opposite can also occur as in the situations where empirical evidence is considered unreliable, or when cognitive processes of decision-makers are biased through recent experience: the threshold will increase relative to the normative threshold value derived via system II using expected utility threshold. This inclination for the higher diagnostic certainty may, in turn, explain undertreatment that is also documented in the current medical practice. Conclusions We have developed the first dual processing model of medical decision-making that has potential to enrich the current medical decision-making field, which is still to the large extent dominated by expected utility theory. The model also provides a platform for reconciling two groups of competing dual processing theories (parallel competitive with default-interventionalist theories). PMID:22943520

[Evaluating the maturity of IT-supported clinical imaging and diagnosis using the Digital Imaging Adoption Model : Are your clinical imaging processes ready for the digital era?

PubMed

Studzinski, J

2017-06-01

The Digital Imaging Adoption Model (DIAM) has been jointly developed by HIMSS Analytics and the European Society of Radiology (ESR). It helps evaluate the maturity of IT-supported processes in medical imaging, particularly in radiology. This eight-stage maturity model drives your organisational, strategic and tactical alignment towards imaging-IT planning. The key audience for the model comprises hospitals with imaging centers, as well as external imaging centers that collaborate with hospitals. The assessment focuses on different dimensions relevant to digital imaging, such as software infrastructure and usage, workflow security, clinical documentation and decision support, data exchange and analytical capabilities. With its standardised approach, it enables regional, national and international benchmarking. All DIAM participants receive a structured report that can be used as a basis for presenting, e.g. budget planning and investment decisions at management level.

hs-CRP is strongly associated with coronary heart disease (CHD): A data mining approach using decision tree algorithm.

PubMed

Tayefi, Maryam; Tajfard, Mohammad; Saffar, Sara; Hanachi, Parichehr; Amirabadizadeh, Ali Reza; Esmaeily, Habibollah; Taghipour, Ali; Ferns, Gordon A; Moohebati, Mohsen; Ghayour-Mobarhan, Majid

2017-04-01

Coronary heart disease (CHD) is an important public health problem globally. Algorithms incorporating the assessment of clinical biomarkers together with several established traditional risk factors can help clinicians to predict CHD and support clinical decision making with respect to interventions. Decision tree (DT) is a data mining model for extracting hidden knowledge from large databases. We aimed to establish a predictive model for coronary heart disease using a decision tree algorithm. Here we used a dataset of 2346 individuals including 1159 healthy participants and 1187 participant who had undergone coronary angiography (405 participants with negative angiography and 782 participants with positive angiography). We entered 10 variables of a total 12 variables into the DT algorithm (including age, sex, FBG, TG, hs-CRP, TC, HDL, LDL, SBP and DBP). Our model could identify the associated risk factors of CHD with sensitivity, specificity, accuracy of 96%, 87%, 94% and respectively. Serum hs-CRP levels was at top of the tree in our model, following by FBG, gender and age. Our model appears to be an accurate, specific and sensitive model for identifying the presence of CHD, but will require validation in prospective studies. Copyright © 2017 Elsevier B.V. All rights reserved.

Registered nurses' decision-making regarding documentation in patients' progress notes.

PubMed

Tower, Marion; Chaboyer, Wendy; Green, Quentine; Dyer, Kirsten; Wallis, Marianne

2012-10-01

To examine registered nurses' decision-making when documenting care in patients' progress notes. What constitutes effective nursing documentation is supported by available guidelines. However, ineffective documentation continues to be cited as a major cause of adverse events for patients. Decision-making in clinical practice is a complex process. To make an effective decision, the decision-maker must be situationally aware. The concept of situation awareness and its implications for making safe decisions has been examined extensively in air safety and more recently is being applied to health. The study was situated in a naturalistic paradigm. Purposive sampling was used to recruit 17 registered nurses who used think-aloud research methods when making decisions about documenting information in patients' progress notes. Follow-up interviews were conducted to validate interpretations. Data were analysed systematically for evidence of cues that demonstrated situation awareness as nurses made decisions about documentation. Three distinct decision-making scenarios were illuminated from the analysis: the newly admitted patient, the patient whose condition was as expected and the discharging patient. Nurses used mental models for decision-making in documenting in progress notes, and the cues nurses used to direct their assessment of patients' needs demonstrated situation awareness at different levels. Nurses demonstrate situation awareness at different levels in their decision-making processes. While situation awareness is important, it is also important to use an appropriate decision-making framework. Cognitive continuum theory is suggested as a decision-making model that could support situation awareness when nurses made decisions about documenting patient care. Because nurses are key decision-makers, it is imperative that effective decisions are made that translate into safe clinical care. Including situation awareness training, combined with employing cognitive continuum theory as a decision-making framework, provides a powerful means of guiding nurses' decision-making. © 2012 Blackwell Publishing Ltd.

A Conceptual Framework for Decision-making Support in Uncertainty- and Risk-based Diagnosis of Rare Clinical Cases by Specialist Physicians.

PubMed

Santos, Adriano A; Moura, J Antão B; de Araújo, Joseana Macêdo Fechine Régis

2015-01-01

Mitigating uncertainty and risks faced by specialist physicians in analysis of rare clinical cases is something desired by anyone who needs health services. The number of clinical cases never seen by these experts, with little documentation, may introduce errors in decision-making. Such errors negatively affect well-being of patients, increase procedure costs, rework, health insurance premiums, and impair the reputation of specialists and medical systems involved. In this context, IT and Clinical Decision Support Systems (CDSS) play a fundamental role, supporting decision-making process, making it more efficient and effective, reducing a number of avoidable medical errors and enhancing quality of treatment given to patients. An investigation has been initiated to look into characteristics and solution requirements of this problem, model it, propose a general solution in terms of a conceptual risk-based, automated framework to support rare-case medical diagnostics and validate it by means of case studies. A preliminary validation study of the proposed framework has been carried out by interviews conducted with experts who are practicing professionals, academics, and researchers in health care. This paper summarizes the investigation and its positive results. These results motivate continuation of research towards development of the conceptual framework and of a software tool that implements the proposed model.

Studying the effect of clinical uncertainty on physicians' decision-making using ILIAD.

PubMed

Anderson, J D; Jay, S J; Weng, H C; Anderson, M M

1995-01-01

The influence of uncertainty on physicians' practice behavior is not well understood. In this research, ILIAD, a diagnostic expert system, has been used to study physicians' responses to uncertainty and how their responses affected clinical performance. The simulation mode of ILIAD was used to standardize the presentation and scoring of two cases to 46 residents in emergency medicine, internal medicine, family practice and transitional medicine at Methodist Hospital of Indiana. A questionnaire was used to collect additional data on how physicians respond to clinical uncertainty. A structural equation model was developed, estimated, and tested. The results indicate that stress that physicians experience in dealing with clinical uncertainty has a negative effect on their clinical performance. Moreover, the way that physicians respond to uncertainty has positive and negative effects on their performance. Open discussions with patients about clinical decisions and the use of practice guidelines improves performance. However, when the physician's clinical decisions are influenced by patient demands or their peers, their performance scores decline.

Using the weighted area under the net benefit curve for decision curve analysis.

PubMed

Talluri, Rajesh; Shete, Sanjay

2016-07-18

Risk prediction models have been proposed for various diseases and are being improved as new predictors are identified. A major challenge is to determine whether the newly discovered predictors improve risk prediction. Decision curve analysis has been proposed as an alternative to the area under the curve and net reclassification index to evaluate the performance of prediction models in clinical scenarios. The decision curve computed using the net benefit can evaluate the predictive performance of risk models at a given or range of threshold probabilities. However, when the decision curves for 2 competing models cross in the range of interest, it is difficult to identify the best model as there is no readily available summary measure for evaluating the predictive performance. The key deterrent for using simple measures such as the area under the net benefit curve is the assumption that the threshold probabilities are uniformly distributed among patients. We propose a novel measure for performing decision curve analysis. The approach estimates the distribution of threshold probabilities without the need of additional data. Using the estimated distribution of threshold probabilities, the weighted area under the net benefit curve serves as the summary measure to compare risk prediction models in a range of interest. We compared 3 different approaches, the standard method, the area under the net benefit curve, and the weighted area under the net benefit curve. Type 1 error and power comparisons demonstrate that the weighted area under the net benefit curve has higher power compared to the other methods. Several simulation studies are presented to demonstrate the improvement in model comparison using the weighted area under the net benefit curve compared to the standard method. The proposed measure improves decision curve analysis by using the weighted area under the curve and thereby improves the power of the decision curve analysis to compare risk prediction models in a clinical scenario.

Cost-effectiveness in Clostridium difficile treatment decision-making

PubMed Central

Nuijten, Mark JC; Keller, Josbert J; Visser, Caroline E; Redekop, Ken; Claassen, Eric; Speelman, Peter; Pronk, Marja H

2015-01-01

AIM: To develop a framework for the clinical and health economic assessment for management of Clostridium difficile infection (CDI). METHODS: CDI has vast economic consequences emphasizing the need for innovative and cost effective solutions, which were aim of this study. A guidance model was developed for coverage decisions and guideline development in CDI. The model included pharmacotherapy with oral metronidazole or oral vancomycin, which is the mainstay for pharmacological treatment of CDI and is recommended by most treatment guidelines. RESULTS: A design for a patient-based cost-effectiveness model was developed, which can be used to estimate the cost-effectiveness of current and future treatment strategies in CDI. Patient-based outcomes were extrapolated to the population by including factors like, e.g., person-to-person transmission, isolation precautions and closing and cleaning wards of hospitals. CONCLUSION: The proposed framework for a population-based CDI model may be used for clinical and health economic assessments of CDI guidelines and coverage decisions for emerging treatments for CDI. PMID:26601096

Cost-effectiveness in Clostridium difficile treatment decision-making.

PubMed

Nuijten, Mark Jc; Keller, Josbert J; Visser, Caroline E; Redekop, Ken; Claassen, Eric; Speelman, Peter; Pronk, Marja H

2015-11-16

To develop a framework for the clinical and health economic assessment for management of Clostridium difficile infection (CDI). CDI has vast economic consequences emphasizing the need for innovative and cost effective solutions, which were aim of this study. A guidance model was developed for coverage decisions and guideline development in CDI. The model included pharmacotherapy with oral metronidazole or oral vancomycin, which is the mainstay for pharmacological treatment of CDI and is recommended by most treatment guidelines. A design for a patient-based cost-effectiveness model was developed, which can be used to estimate the cost-effectiveness of current and future treatment strategies in CDI. Patient-based outcomes were extrapolated to the population by including factors like, e.g., person-to-person transmission, isolation precautions and closing and cleaning wards of hospitals. The proposed framework for a population-based CDI model may be used for clinical and health economic assessments of CDI guidelines and coverage decisions for emerging treatments for CDI.

Interoperability of clinical decision-support systems and electronic health records using archetypes: a case study in clinical trial eligibility.

PubMed

Marcos, Mar; Maldonado, Jose A; Martínez-Salvador, Begoña; Boscá, Diego; Robles, Montserrat

2013-08-01

Clinical decision-support systems (CDSSs) comprise systems as diverse as sophisticated platforms to store and manage clinical data, tools to alert clinicians of problematic situations, or decision-making tools to assist clinicians. Irrespective of the kind of decision-support task CDSSs should be smoothly integrated within the clinical information system, interacting with other components, in particular with the electronic health record (EHR). However, despite decades of developments, most CDSSs lack interoperability features. We deal with the interoperability problem of CDSSs and EHRs by exploiting the dual-model methodology. This methodology distinguishes a reference model and archetypes. A reference model is represented by a stable and small object-oriented model that describes the generic properties of health record information. For their part, archetypes are reusable and domain-specific definitions of clinical concepts in the form of structured and constrained combinations of the entities of the reference model. We rely on archetypes to make the CDSS compatible with EHRs from different institutions. Concretely, we use archetypes for modelling the clinical concepts that the CDSS requires, in conjunction with a series of knowledge-intensive mappings relating the archetypes to the data sources (EHR and/or other archetypes) they depend on. We introduce a comprehensive approach, including a set of tools as well as methodological guidelines, to deal with the interoperability of CDSSs and EHRs based on archetypes. Archetypes are used to build a conceptual layer of the kind of a virtual health record (VHR) over the EHR whose contents need to be integrated and used in the CDSS, associating them with structural and terminology-based semantics. Subsequently, the archetypes are mapped to the EHR by means of an expressive mapping language and specific-purpose tools. We also describe a case study where the tools and methodology have been employed in a CDSS to support patient recruitment in the framework of a clinical trial for colorectal cancer screening. The utilisation of archetypes not only has proved satisfactory to achieve interoperability between CDSSs and EHRs but also offers various advantages, in particular from a data model perspective. First, the VHR/data models we work with are of a high level of abstraction and can incorporate semantic descriptions. Second, archetypes can potentially deal with different EHR architectures, due to their deliberate independence of the reference model. Third, the archetype instances we obtain are valid instances of the underlying reference model, which would enable e.g. feeding back the EHR with data derived by abstraction mechanisms. Lastly, the medical and technical validity of archetype models would be assured, since in principle clinicians should be the main actors in their development. Copyright © 2013 Elsevier Inc. All rights reserved.

A quantitative benefit-risk assessment approach to improve decision making in drug development: Application of a multicriteria decision analysis model in the development of combination therapy for overactive bladder.

PubMed

de Greef-van der Sandt, I; Newgreen, D; Schaddelee, M; Dorrepaal, C; Martina, R; Ridder, A; van Maanen, R

2016-04-01

A multicriteria decision analysis (MCDA) approach was developed and used to estimate the benefit-risk of solifenacin and mirabegron and their combination in the treatment of overactive bladder (OAB). The objectives were 1) to develop an MCDA tool to compare drug effects in OAB quantitatively, 2) to establish transparency in the evaluation of the benefit-risk profile of various dose combinations, and 3) to quantify the added value of combination use compared to monotherapies. The MCDA model was developed using efficacy, safety, and tolerability attributes and the results of a phase II factorial design combination study were evaluated. Combinations of solifenacin 5 mg and mirabegron 25 mg and mirabegron 50 (5+25 and 5+50) scored the highest clinical utility and supported combination therapy development of solifenacin and mirabegron for phase III clinical development at these dose regimens. This case study underlines the benefit of using a quantitative approach in clinical drug development programs. © 2015 The American Society for Clinical Pharmacology and Therapeutics.

Analysis of the process of representing clinical statements for decision-support applications: a comparison of openEHR archetypes and HL7 virtual medical record.

PubMed

González-Ferrer, A; Peleg, M; Marcos, M; Maldonado, J A

2016-07-01

Delivering patient-specific decision-support based on computer-interpretable guidelines (CIGs) requires mapping CIG clinical statements (data items, clinical recommendations) into patients' data. This is most effectively done via intermediate data schemas, which enable querying the data according to the semantics of a shared standard intermediate schema. This study aims to evaluate the use of HL7 virtual medical record (vMR) and openEHR archetypes as intermediate schemas for capturing clinical statements from CIGs that are mappable to electronic health records (EHRs) containing patient data and patient-specific recommendations. Using qualitative research methods, we analyzed the encoding of ten representative clinical statements taken from two CIGs used in real decision-support systems into two health information models (openEHR archetypes and HL7 vMR instances) by four experienced informaticians. Discussion among the modelers about each case study example greatly increased our understanding of the capabilities of these standards, which we share in this educational paper. Differing in content and structure, the openEHR archetypes were found to contain a greater level of representational detail and structure while the vMR representations took fewer steps to complete. The use of openEHR in the encoding of CIG clinical statements could potentially facilitate applications other than decision-support, including intelligent data analysis and integration of additional properties of data items from existing EHRs. On the other hand, due to their smaller size and fewer details, the use of vMR potentially supports quicker mapping of EHR data into clinical statements.

Development of a digital clinical pathway for emergency medicine: Lessons from usability testing and implementation failure.

PubMed

Gutenstein, Marc; Pickering, John W; Than, Martin

2018-06-01

Clinical pathways are used to support the management of patients in emergency departments. An existing document-based clinical pathway was used as the foundation on which to design and build a digital clinical pathway for acute chest pain, with the aim of improving clinical calculations, clinician decision-making, documentation, and data collection. Established principles of decision support system design were used to build an application within the existing electronic health record, before testing with a multidisciplinary team of doctors using a think-aloud protocol. Technical authoring was successful, however, usability testing revealed that the user experience and the flexibility of workflow within the application were critical barriers to implementation. Emergency medicine and acute care decision support systems face particular challenges to existing models of linear workflow that should be deliberately addressed in digital pathway design. We make key recommendations regarding digital pathway design in emergency medicine.

On implementing clinical decision support: achieving scalability and maintainability by combining business rules and ontologies.

PubMed

Kashyap, Vipul; Morales, Alfredo; Hongsermeier, Tonya

2006-01-01

We present an approach and architecture for implementing scalable and maintainable clinical decision support at the Partners HealthCare System. The architecture integrates a business rules engine that executes declarative if-then rules stored in a rule-base referencing objects and methods in a business object model. The rules engine executes object methods by invoking services implemented on the clinical data repository. Specialized inferences that support classification of data and instances into classes are identified and an approach to implement these inferences using an OWL based ontology engine is presented. Alternative representations of these specialized inferences as if-then rules or OWL axioms are explored and their impact on the scalability and maintenance of the system is presented. Architectural alternatives for integration of clinical decision support functionality with the invoking application and the underlying clinical data repository; and their associated trade-offs are discussed and presented.

Using whole disease modeling to inform resource allocation decisions: economic evaluation of a clinical guideline for colorectal cancer using a single model.

PubMed

Tappenden, Paul; Chilcott, Jim; Brennan, Alan; Squires, Hazel; Glynne-Jones, Rob; Tappenden, Janine

2013-06-01

To assess the feasibility and value of simulating whole disease and treatment pathways within a single model to provide a common economic basis for informing resource allocation decisions. A patient-level simulation model was developed with the intention of being capable of evaluating multiple topics within National Institute for Health and Clinical Excellence's colorectal cancer clinical guideline. The model simulates disease and treatment pathways from preclinical disease through to detection, diagnosis, adjuvant/neoadjuvant treatments, follow-up, curative/palliative treatments for metastases, supportive care, and eventual death. The model parameters were informed by meta-analyses, randomized trials, observational studies, health utility studies, audit data, costing sources, and expert opinion. Unobservable natural history parameters were calibrated against external data using Bayesian Markov chain Monte Carlo methods. Economic analysis was undertaken using conventional cost-utility decision rules within each guideline topic and constrained maximization rules across multiple topics. Under usual processes for guideline development, piecewise economic modeling would have been used to evaluate between one and three topics. The Whole Disease Model was capable of evaluating 11 of 15 guideline topics, ranging from alternative diagnostic technologies through to treatments for metastatic disease. The constrained maximization analysis identified a configuration of colorectal services that is expected to maximize quality-adjusted life-year gains without exceeding current expenditure levels. This study indicates that Whole Disease Model development is feasible and can allow for the economic analysis of most interventions across a disease service within a consistent conceptual and mathematical infrastructure. This disease-level modeling approach may be of particular value in providing an economic basis to support other clinical guidelines. Copyright © 2013 International Society for Pharmacoeconomics and Outcomes Research (ISPOR). Published by Elsevier Inc. All rights reserved.

A Natural Language Processing-based Model to Automate MRI Brain Protocol Selection and Prioritization.

PubMed

Brown, Andrew D; Marotta, Thomas R

2017-02-01

Incorrect imaging protocol selection can contribute to increased healthcare cost and waste. To help healthcare providers improve the quality and safety of medical imaging services, we developed and evaluated three natural language processing (NLP) models to determine whether NLP techniques could be employed to aid in clinical decision support for protocoling and prioritization of magnetic resonance imaging (MRI) brain examinations. To test the feasibility of using an NLP model to support clinical decision making for MRI brain examinations, we designed three different medical imaging prediction tasks, each with a unique outcome: selecting an examination protocol, evaluating the need for contrast administration, and determining priority. We created three models for each prediction task, each using a different classification algorithm-random forest, support vector machine, or k-nearest neighbor-to predict outcomes based on the narrative clinical indications and demographic data associated with 13,982 MRI brain examinations performed from January 1, 2013 to June 30, 2015. Test datasets were used to calculate the accuracy, sensitivity and specificity, predictive values, and the area under the curve. Our optimal results show an accuracy of 82.9%, 83.0%, and 88.2% for the protocol selection, contrast administration, and prioritization tasks, respectively, demonstrating that predictive algorithms can be used to aid in clinical decision support for examination protocoling. NLP models developed from the narrative clinical information provided by referring clinicians and demographic data are feasible methods to predict the protocol and priority of MRI brain examinations. Copyright © 2017 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.

The drift diffusion model as the choice rule in reinforcement learning.

PubMed

Pedersen, Mads Lund; Frank, Michael J; Biele, Guido

2017-08-01

Current reinforcement-learning models often assume simplified decision processes that do not fully reflect the dynamic complexities of choice processes. Conversely, sequential-sampling models of decision making account for both choice accuracy and response time, but assume that decisions are based on static decision values. To combine these two computational models of decision making and learning, we implemented reinforcement-learning models in which the drift diffusion model describes the choice process, thereby capturing both within- and across-trial dynamics. To exemplify the utility of this approach, we quantitatively fit data from a common reinforcement-learning paradigm using hierarchical Bayesian parameter estimation, and compared model variants to determine whether they could capture the effects of stimulant medication in adult patients with attention-deficit hyperactivity disorder (ADHD). The model with the best relative fit provided a good description of the learning process, choices, and response times. A parameter recovery experiment showed that the hierarchical Bayesian modeling approach enabled accurate estimation of the model parameters. The model approach described here, using simultaneous estimation of reinforcement-learning and drift diffusion model parameters, shows promise for revealing new insights into the cognitive and neural mechanisms of learning and decision making, as well as the alteration of such processes in clinical groups.

The drift diffusion model as the choice rule in reinforcement learning

PubMed Central

Frank, Michael J.

2017-01-01

Current reinforcement-learning models often assume simplified decision processes that do not fully reflect the dynamic complexities of choice processes. Conversely, sequential-sampling models of decision making account for both choice accuracy and response time, but assume that decisions are based on static decision values. To combine these two computational models of decision making and learning, we implemented reinforcement-learning models in which the drift diffusion model describes the choice process, thereby capturing both within- and across-trial dynamics. To exemplify the utility of this approach, we quantitatively fit data from a common reinforcement-learning paradigm using hierarchical Bayesian parameter estimation, and compared model variants to determine whether they could capture the effects of stimulant medication in adult patients with attention-deficit hyper-activity disorder (ADHD). The model with the best relative fit provided a good description of the learning process, choices, and response times. A parameter recovery experiment showed that the hierarchical Bayesian modeling approach enabled accurate estimation of the model parameters. The model approach described here, using simultaneous estimation of reinforcement-learning and drift diffusion model parameters, shows promise for revealing new insights into the cognitive and neural mechanisms of learning and decision making, as well as the alteration of such processes in clinical groups. PMID:27966103

Characterizing Decision-Analysis Performances of Risk Prediction Models Using ADAPT Curves.

PubMed

Lee, Wen-Chung; Wu, Yun-Chun

2016-01-01

The area under the receiver operating characteristic curve is a widely used index to characterize the performance of diagnostic tests and prediction models. However, the index does not explicitly acknowledge the utilities of risk predictions. Moreover, for most clinical settings, what counts is whether a prediction model can guide therapeutic decisions in a way that improves patient outcomes, rather than to simply update probabilities.Based on decision theory, the authors propose an alternative index, the "average deviation about the probability threshold" (ADAPT).An ADAPT curve (a plot of ADAPT value against the probability threshold) neatly characterizes the decision-analysis performances of a risk prediction model.Several prediction models can be compared for their ADAPT values at a chosen probability threshold, for a range of plausible threshold values, or for the whole ADAPT curves. This should greatly facilitate the selection of diagnostic tests and prediction models.

Navigating Decisional Discord: The Pediatrician’s Role When Child and Parents Disagree

PubMed Central

DuBois, James; Kodish, Eric; Wolfe, Joanne; Feudtner, Chris

2017-01-01

From the time when children enter the preteen years onward, pediatric medical decision-making can entail a complex interaction between child, parents, and pediatrician. When the child and parents disagree regarding medical decisions, the pediatrician has the challenging task of guiding the family to a final decision. Unresolved discord can affect family cohesiveness, patient adherence, and patient self-management. In this article, we outline 3 models for the pediatrician’s role in the setting of decisional discord: deference, advocative, and arbitrative. In the deference model, the pediatrician prioritizes parental decision-making authority. In the advocative model, the pediatrician advocates for the child’s preference in decision-making so long as the child’s decision is medically reasonable. In the arbitrative model, the pediatrician works to resolve the conflict in a balanced fashion. Although each model has advantages and disadvantages, the arbitrative model should serve as the initial model in nearly all settings. The arbitrative model is likely to reach the most beneficial decision in a manner that maintains family cohesiveness by respecting the authority of parents and the developing autonomy of children. We also highlight, however, occasions when the deference or advocative models may be more appropriate. Physicians should keep all 3 models available in their professional toolkit and develop the wisdom to deploy the right model for each particular clinical situation. PMID:28562285

Navigating Decisional Discord: The Pediatrician's Role When Child and Parents Disagree.

PubMed

Sisk, Bryan A; DuBois, James; Kodish, Eric; Wolfe, Joanne; Feudtner, Chris

2017-06-01

From the time when children enter the preteen years onward, pediatric medical decision-making can entail a complex interaction between child, parents, and pediatrician. When the child and parents disagree regarding medical decisions, the pediatrician has the challenging task of guiding the family to a final decision. Unresolved discord can affect family cohesiveness, patient adherence, and patient self-management. In this article, we outline 3 models for the pediatrician's role in the setting of decisional discord: deference, advocative, and arbitrative. In the deference model, the pediatrician prioritizes parental decision-making authority. In the advocative model, the pediatrician advocates for the child's preference in decision-making so long as the child's decision is medically reasonable. In the arbitrative model, the pediatrician works to resolve the conflict in a balanced fashion. Although each model has advantages and disadvantages, the arbitrative model should serve as the initial model in nearly all settings. The arbitrative model is likely to reach the most beneficial decision in a manner that maintains family cohesiveness by respecting the authority of parents and the developing autonomy of children. We also highlight, however, occasions when the deference or advocative models may be more appropriate. Physicians should keep all 3 models available in their professional toolkit and develop the wisdom to deploy the right model for each particular clinical situation. Copyright © 2017 by the American Academy of Pediatrics.

Predicting inpatient clinical order patterns with probabilistic topic models vs conventional order sets.

PubMed

Chen, Jonathan H; Goldstein, Mary K; Asch, Steven M; Mackey, Lester; Altman, Russ B

2017-05-01

Build probabilistic topic model representations of hospital admissions processes and compare the ability of such models to predict clinical order patterns as compared to preconstructed order sets. The authors evaluated the first 24 hours of structured electronic health record data for > 10 K inpatients. Drawing an analogy between structured items (e.g., clinical orders) to words in a text document, the authors performed latent Dirichlet allocation probabilistic topic modeling. These topic models use initial clinical information to predict clinical orders for a separate validation set of > 4 K patients. The authors evaluated these topic model-based predictions vs existing human-authored order sets by area under the receiver operating characteristic curve, precision, and recall for subsequent clinical orders. Existing order sets predict clinical orders used within 24 hours with area under the receiver operating characteristic curve 0.81, precision 16%, and recall 35%. This can be improved to 0.90, 24%, and 47% ( P  < 10 -20 ) by using probabilistic topic models to summarize clinical data into up to 32 topics. Many of these latent topics yield natural clinical interpretations (e.g., "critical care," "pneumonia," "neurologic evaluation"). Existing order sets tend to provide nonspecific, process-oriented aid, with usability limitations impairing more precise, patient-focused support. Algorithmic summarization has the potential to breach this usability barrier by automatically inferring patient context, but with potential tradeoffs in interpretability. Probabilistic topic modeling provides an automated approach to detect thematic trends in patient care and generate decision support content. A potential use case finds related clinical orders for decision support. © The Author 2016. Published by Oxford University Press on behalf of the American Medical Informatics Association.

Predicting inpatient clinical order patterns with probabilistic topic models vs conventional order sets

PubMed Central

Goldstein, Mary K; Asch, Steven M; Mackey, Lester; Altman, Russ B

2017-01-01

Objective: Build probabilistic topic model representations of hospital admissions processes and compare the ability of such models to predict clinical order patterns as compared to preconstructed order sets. Materials and Methods: The authors evaluated the first 24 hours of structured electronic health record data for > 10 K inpatients. Drawing an analogy between structured items (e.g., clinical orders) to words in a text document, the authors performed latent Dirichlet allocation probabilistic topic modeling. These topic models use initial clinical information to predict clinical orders for a separate validation set of > 4 K patients. The authors evaluated these topic model-based predictions vs existing human-authored order sets by area under the receiver operating characteristic curve, precision, and recall for subsequent clinical orders. Results: Existing order sets predict clinical orders used within 24 hours with area under the receiver operating characteristic curve 0.81, precision 16%, and recall 35%. This can be improved to 0.90, 24%, and 47% (P < 10−20) by using probabilistic topic models to summarize clinical data into up to 32 topics. Many of these latent topics yield natural clinical interpretations (e.g., “critical care,” “pneumonia,” “neurologic evaluation”). Discussion: Existing order sets tend to provide nonspecific, process-oriented aid, with usability limitations impairing more precise, patient-focused support. Algorithmic summarization has the potential to breach this usability barrier by automatically inferring patient context, but with potential tradeoffs in interpretability. Conclusion: Probabilistic topic modeling provides an automated approach to detect thematic trends in patient care and generate decision support content. A potential use case finds related clinical orders for decision support. PMID:27655861

Parameter selection for and implementation of a web-based decision-support tool to predict extubation outcome in premature infants.

PubMed

Mueller, Martina; Wagner, Carol L; Annibale, David J; Knapp, Rebecca G; Hulsey, Thomas C; Almeida, Jonas S

2006-03-01

Approximately 30% of intubated preterm infants with respiratory distress syndrome (RDS) will fail attempted extubation, requiring reintubation and mechanical ventilation. Although ventilator technology and monitoring of premature infants have improved over time, optimal extubation remains challenging. Furthermore, extubation decisions for premature infants require complex informational processing, techniques implicitly learned through clinical practice. Computer-aided decision-support tools would benefit inexperienced clinicians, especially during peak neonatal intensive care unit (NICU) census. A five-step procedure was developed to identify predictive variables. Clinical expert (CE) thought processes comprised one model. Variables from that model were used to develop two mathematical models for the decision-support tool: an artificial neural network (ANN) and a multivariate logistic regression model (MLR). The ranking of the variables in the three models was compared using the Wilcoxon Signed Rank Test. The best performing model was used in a web-based decision-support tool with a user interface implemented in Hypertext Markup Language (HTML) and the mathematical model employing the ANN. CEs identified 51 potentially predictive variables for extubation decisions for an infant on mechanical ventilation. Comparisons of the three models showed a significant difference between the ANN and the CE (p = 0.0006). Of the original 51 potentially predictive variables, the 13 most predictive variables were used to develop an ANN as a web-based decision-tool. The ANN processes user-provided data and returns the prediction 0-1 score and a novelty index. The user then selects the most appropriate threshold for categorizing the prediction as a success or failure. Furthermore, the novelty index, indicating the similarity of the test case to the training case, allows the user to assess the confidence level of the prediction with regard to how much the new data differ from the data originally used for the development of the prediction tool. State-of-the-art, machine-learning methods can be employed for the development of sophisticated tools to aid clinicians' decisions. We identified numerous variables considered relevant for extubation decisions for mechanically ventilated premature infants with RDS. We then developed a web-based decision-support tool for clinicians which can be made widely available and potentially improve patient care world wide.

Clinical decision support of radiotherapy treatment planning: A data-driven machine learning strategy for patient-specific dosimetric decision making.

PubMed

Valdes, Gilmer; Simone, Charles B; Chen, Josephine; Lin, Alexander; Yom, Sue S; Pattison, Adam J; Carpenter, Colin M; Solberg, Timothy D

2017-12-01

Clinical decision support systems are a growing class of tools with the potential to impact healthcare. This study investigates the construction of a decision support system through which clinicians can efficiently identify which previously approved historical treatment plans are achievable for a new patient to aid in selection of therapy. Treatment data were collected for early-stage lung and postoperative oropharyngeal cancers treated using photon (lung and head and neck) and proton (head and neck) radiotherapy. Machine-learning classifiers were constructed using patient-specific feature-sets and a library of historical plans. Model accuracy was analyzed using learning curves, and historical treatment plan matching was investigated. Learning curves demonstrate that for these datasets, approximately 45, 60, and 30 patients are needed for a sufficiently accurate classification model for radiotherapy for early-stage lung, postoperative oropharyngeal photon, and postoperative oropharyngeal proton, respectively. The resulting classification model provides a database of previously approved treatment plans that are achievable for a new patient. An exemplary case, highlighting tradeoffs between the heart and chest wall dose while holding target dose constant in two historical plans is provided. We report on the first artificial-intelligence based clinical decision support system that connects patients to past discrete treatment plans in radiation oncology and demonstrate for the first time how this tool can enable clinicians to use past decisions to help inform current assessments. Clinicians can be informed of dose tradeoffs between critical structures early in the treatment process, enabling more time spent on finding the optimal course of treatment for individual patients. Copyright © 2017. Published by Elsevier B.V.

Acute care clinical pharmacy practice: unit- versus service-based models.

PubMed

Haas, Curtis E; Eckel, Stephen; Arif, Sally; Beringer, Paul M; Blake, Elizabeth W; Lardieri, Allison B; Lobo, Bob L; Mercer, Jessica M; Moye, Pamela; Orlando, Patricia L; Wargo, Kurt

2012-02-01

This commentary from the 2010 Task Force on Acute Care Practice Model of the American College of Clinical Pharmacy was developed to compare and contrast the "unit-based" and "service-based" orientation of the clinical pharmacist within an acute care pharmacy practice model and to offer an informed opinion concerning which should be preferred. The clinical pharmacy practice model must facilitate patient-centered care and therefore must position the pharmacist to be an active member of the interprofessional team focused on providing high-quality pharmaceutical care to the patient. Although both models may have advantages and disadvantages, the most important distinction pertains to the patient care role of the clinical pharmacist. The unit-based pharmacist is often in a position of reacting to an established order or decision and frequently is focused on task-oriented clinical services. By definition, the service-based clinical pharmacist functions as a member of the interprofessional team. As a team member, the pharmacist proactively contributes to the decision-making process and the development of patient-centered care plans. The service-based orientation of the pharmacist is consistent with both the practice vision embraced by ACCP and its definition of clinical pharmacy. The task force strongly recommends that institutions pursue a service-based pharmacy practice model to optimally deploy their clinical pharmacists. Those who elect to adopt this recommendation will face challenges in overcoming several resource, technologic, regulatory, and accreditation barriers. However, such challenges must be confronted if clinical pharmacists are to contribute fully to achieving optimal patient outcomes. © 2012 Pharmacotherapy Publications, Inc.

A dashboard-based system for supporting diabetes care.

PubMed

Dagliati, Arianna; Sacchi, Lucia; Tibollo, Valentina; Cogni, Giulia; Teliti, Marsida; Martinez-Millana, Antonio; Traver, Vicente; Segagni, Daniele; Posada, Jorge; Ottaviano, Manuel; Fico, Giuseppe; Arredondo, Maria Teresa; De Cata, Pasquale; Chiovato, Luca; Bellazzi, Riccardo

2018-05-01

To describe the development, as part of the European Union MOSAIC (Models and Simulation Techniques for Discovering Diabetes Influence Factors) project, of a dashboard-based system for the management of type 2 diabetes and assess its impact on clinical practice. The MOSAIC dashboard system is based on predictive modeling, longitudinal data analytics, and the reuse and integration of data from hospitals and public health repositories. Data are merged into an i2b2 data warehouse, which feeds a set of advanced temporal analytic models, including temporal abstractions, care-flow mining, drug exposure pattern detection, and risk-prediction models for type 2 diabetes complications. The dashboard has 2 components, designed for (1) clinical decision support during follow-up consultations and (2) outcome assessment on populations of interest. To assess the impact of the clinical decision support component, a pre-post study was conducted considering visit duration, number of screening examinations, and lifestyle interventions. A pilot sample of 700 Italian patients was investigated. Judgments on the outcome assessment component were obtained via focus groups with clinicians and health care managers. The use of the decision support component in clinical activities produced a reduction in visit duration (P ≪ .01) and an increase in the number of screening exams for complications (P < .01). We also observed a relevant, although nonstatistically significant, increase in the proportion of patients receiving lifestyle interventions (from 69% to 77%). Regarding the outcome assessment component, focus groups highlighted the system's capability of identifying and understanding the characteristics of patient subgroups treated at the center. Our study demonstrates that decision support tools based on the integration of multiple-source data and visual and predictive analytics do improve the management of a chronic disease such as type 2 diabetes by enacting a successful implementation of the learning health care system cycle.

Two approaches to incorporate clinical data uncertainty into multiple criteria decision analysis for benefit-risk assessment of medicinal products.

PubMed

Wen, Shihua; Zhang, Lanju; Yang, Bo

2014-07-01

The Problem formulation, Objectives, Alternatives, Consequences, Trade-offs, Uncertainties, Risk attitude, and Linked decisions (PrOACT-URL) framework and multiple criteria decision analysis (MCDA) have been recommended by the European Medicines Agency for structured benefit-risk assessment of medicinal products undergoing regulatory review. The objective of this article was to provide solutions to incorporate the uncertainty from clinical data into the MCDA model when evaluating the overall benefit-risk profiles among different treatment options. Two statistical approaches, the δ-method approach and the Monte-Carlo approach, were proposed to construct the confidence interval of the overall benefit-risk score from the MCDA model as well as other probabilistic measures for comparing the benefit-risk profiles between treatment options. Both approaches can incorporate the correlation structure between clinical parameters (criteria) in the MCDA model and are straightforward to implement. The two proposed approaches were applied to a case study to evaluate the benefit-risk profile of an add-on therapy for rheumatoid arthritis (drug X) relative to placebo. It demonstrated a straightforward way to quantify the impact of the uncertainty from clinical data to the benefit-risk assessment and enabled statistical inference on evaluating the overall benefit-risk profiles among different treatment options. The δ-method approach provides a closed form to quantify the variability of the overall benefit-risk score in the MCDA model, whereas the Monte-Carlo approach is more computationally intensive but can yield its true sampling distribution for statistical inference. The obtained confidence intervals and other probabilistic measures from the two approaches enhance the benefit-risk decision making of medicinal products. Copyright © 2014 International Society for Pharmacoeconomics and Outcomes Research (ISPOR). Published by Elsevier Inc. All rights reserved.

[Cost-conscious medical decisions. Normative guidance within the conflicting demands of ethics and economics].

PubMed

Marckmann, G; In der Schmitten, J

2014-05-01

Under the current conditions in the health care system, physicians inevitably have to take responsibility for the cost dimension of their decisions on the level of single cases. This article, therefore, discusses the question how physicians can integrate cost considerations into their clinical decisions at the microlevel in a medically rational and ethically justified way. We propose a four-step model for "ethical cost-consciousness": (1) forego ineffective interventions as required by good evidence-based medicine, (2) respect individual patient preferences, (3) minimize the diagnostic and therapeutic effort to achieve a certain treatment goal, and (4) forego expensive interventions that have only a small or unlikely (net) benefit for the patient. Steps 1-3 are ethically justified by the principles of beneficence, nonmaleficence, and respect for autonomy, step 4 by the principles of justice. For decisions on step 4, explicit cost-conscious guidelines should be developed locally or regionally. Following the four-step model can contribute to ethically defensible, cost-conscious decision-making at the microlevel. In addition, physicians' rationing decisions should meet basic standards of procedural fairness. Regular cost-case discussions and clinical ethics consultation should be available as decision support. Implementing step 4, however, requires first of all a clear political legitimation with the corresponding legal framework.

Allocation of resources for ambulatory care -a staffing model for outpatient clinics.

PubMed Central

Mansdorf, B D

1975-01-01

The enormous commitment of resources to ambulatory health care services requires that flexible and easily implementable management techniques be developed to improve the allocation of health manpower and funds. This article develops a feasible model for staffing outpatient clinics and thereby potentially provides an important analytical tool for allocating and monitoring the utilization of the most critical and expensive of ambulatory care resources-professional and nonprofessional clinic personnel. The model is simplistic, extremely flexible, and can be applied to many modes of delivering ambulatory care-from HMOs to traditional hospital outpatient clinics. To employ the model, certain decision variables must be specified so that the model can produce a least-cost staffing configuration to meet the demand for service in accordance with the desired mode and intensity of care. The key decision varables that require input from administrators and medical personnel include standards for physician-patient contact time, a desired ratio of staff time actually spent treating patients to total paid staff time, and the desired mix of various staff categories to achieve program objectives. Specific benefits of using the model include determining staffing for new, expanded, or existing outpatient clinics, determining budget requirements for such staffing needs, and providing quantitative productivity and utilization objectives and measurements. PMID:809787

What is a “good” treatment decision?: Decisional control, knowledge, treatment decision-making, and quality of life in men with clinically localized prostate cancer

PubMed Central

Orom, Heather; Biddle, Caitlin; Underwood, Willie; Nelson, Christian J.; Homish, D. Lynn

2016-01-01

Objective We explored whether active patient involvement in decision making and greater patient knowledge are associated with better treatment decision making experiences and better quality of life (QOL) among men with clinically localized prostate cancer. Localized prostate cancer treatment decision-making is an advantageous model for studying patient treatment decision-making dynamics as there are multiple treatment options and a lack of empirical evidence to recommend one over the other; consequently, it is recommended that patients be fully involved in making the decision. Methods Men with newly diagnosed clinically localized prostate cancer (N=1529) completed measures of decisional control, prostate cancer knowledge, and their decision-making experience (decisional conflict, and decision-making satisfaction and difficulty) shortly after they made their treatment decision. Prostate cancer-specific QOL was assessed 6-months after treatment. Results More active involvement in decision making and greater knowledge were associated with lower decisional conflict and higher decision-making satisfaction, but greater decision-making difficulty. An interaction between decisional control and knowledge revealed that greater knowledge was only associated with greater difficulty for men actively involved in making the decision (67% of sample). Greater knowledge, but not decisional control predicted better QOL 6-months post-treatment. Conclusion Although men who are actively involved in decision making and more knowledgeable may make more informed decisions, they could benefit from decisional support (e.g., decision-making aids, emotional support from providers, strategies for reducing emotional distress) to make the process easier. Men who were more knowledgeable about prostate cancer and treatment side effects at the time they made their treatment decision may have appraised their QOL as higher because they had realistic expectations about side effects. PMID:26957566

What Is a "Good" Treatment Decision? Decisional Control, Knowledge, Treatment Decision Making, and Quality of Life in Men with Clinically Localized Prostate Cancer.

PubMed

Orom, Heather; Biddle, Caitlin; Underwood, Willie; Nelson, Christian J; Homish, D Lynn

2016-08-01

We explored whether active patient involvement in decision making and greater patient knowledge are associated with better treatment decision-making experiences and better quality of life (QOL) among men with clinically localized prostate cancer. Localized prostate cancer treatment decision making is an advantageous model for studying patient treatment decision-making dynamics because there are multiple treatment options and a lack of empirical evidence to recommend one over the other; consequently, it is recommended that patients be fully involved in making the decision. Men with newly diagnosed clinically localized prostate cancer (N = 1529) completed measures of decisional control, prostate cancer knowledge, and decision-making experiences (decisional conflict and decision-making satisfaction and difficulty) shortly after they made their treatment decision. Prostate cancer-specific QOL was assessed at 6 months after treatment. More active involvement in decision making and greater knowledge were associated with lower decisional conflict and higher decision-making satisfaction but greater decision-making difficulty. An interaction between decisional control and knowledge revealed that greater knowledge was only associated with greater difficulty for men actively involved in making the decision (67% of sample). Greater knowledge, but not decisional control, predicted better QOL 6 months after treatment. Although men who are actively involved in decision making and more knowledgeable may make more informed decisions, they could benefit from decisional support (e.g., decision-making aids, emotional support from providers, strategies for reducing emotional distress) to make the process easier. Men who were more knowledgeable about prostate cancer and treatment side effects at the time that they made their treatment decision may have appraised their QOL as higher because they had realistic expectations about side effects. © The Author(s) 2016.

Method Development for Clinical Comprehensive Evaluation of Pediatric Drugs Based on Multi-Criteria Decision Analysis: Application to Inhaled Corticosteroids for Children with Asthma.

PubMed

Yu, Yuncui; Jia, Lulu; Meng, Yao; Hu, Lihua; Liu, Yiwei; Nie, Xiaolu; Zhang, Meng; Zhang, Xuan; Han, Sheng; Peng, Xiaoxia; Wang, Xiaoling

2018-04-01

Establishing a comprehensive clinical evaluation system is critical in enacting national drug policy and promoting rational drug use. In China, the 'Clinical Comprehensive Evaluation System for Pediatric Drugs' (CCES-P) project, which aims to compare drugs based on clinical efficacy and cost effectiveness to help decision makers, was recently proposed; therefore, a systematic and objective method is required to guide the process. An evidence-based multi-criteria decision analysis model that involved an analytic hierarchy process (AHP) was developed, consisting of nine steps: (1) select the drugs to be reviewed; (2) establish the evaluation criterion system; (3) determine the criterion weight based on the AHP; (4) construct the evidence body for each drug under evaluation; (5) select comparative measures and calculate the original utility score; (6) place a common utility scale and calculate the standardized utility score; (7) calculate the comprehensive utility score; (8) rank the drugs; and (9) perform a sensitivity analysis. The model was applied to the evaluation of three different inhaled corticosteroids (ICSs) used for asthma management in children (a total of 16 drugs with different dosage forms and strengths or different manufacturers). By applying the drug analysis model, the 16 ICSs under review were successfully scored and evaluated. Budesonide suspension for inhalation (drug ID number: 7) ranked the highest, with comprehensive utility score of 80.23, followed by fluticasone propionate inhaled aerosol (drug ID number: 16), with a score of 79.59, and budesonide inhalation powder (drug ID number: 6), with a score of 78.98. In the sensitivity analysis, the ranking of the top five and lowest five drugs remains unchanged, suggesting this model is generally robust. An evidence-based drug evaluation model based on AHP was successfully developed. The model incorporates sufficient utility and flexibility for aiding the decision-making process, and can be a useful tool for the CCES-P.

Bayesian model selection techniques as decision support for shaping a statistical analysis plan of a clinical trial: An example from a vertigo phase III study with longitudinal count data as primary endpoint

PubMed Central

2012-01-01

Background A statistical analysis plan (SAP) is a critical link between how a clinical trial is conducted and the clinical study report. To secure objective study results, regulatory bodies expect that the SAP will meet requirements in pre-specifying inferential analyses and other important statistical techniques. To write a good SAP for model-based sensitivity and ancillary analyses involves non-trivial decisions on and justification of many aspects of the chosen setting. In particular, trials with longitudinal count data as primary endpoints pose challenges for model choice and model validation. In the random effects setting, frequentist strategies for model assessment and model diagnosis are complex and not easily implemented and have several limitations. Therefore, it is of interest to explore Bayesian alternatives which provide the needed decision support to finalize a SAP. Methods We focus on generalized linear mixed models (GLMMs) for the analysis of longitudinal count data. A series of distributions with over- and under-dispersion is considered. Additionally, the structure of the variance components is modified. We perform a simulation study to investigate the discriminatory power of Bayesian tools for model criticism in different scenarios derived from the model setting. We apply the findings to the data from an open clinical trial on vertigo attacks. These data are seen as pilot data for an ongoing phase III trial. To fit GLMMs we use a novel Bayesian computational approach based on integrated nested Laplace approximations (INLAs). The INLA methodology enables the direct computation of leave-one-out predictive distributions. These distributions are crucial for Bayesian model assessment. We evaluate competing GLMMs for longitudinal count data according to the deviance information criterion (DIC) or probability integral transform (PIT), and by using proper scoring rules (e.g. the logarithmic score). Results The instruments under study provide excellent tools for preparing decisions within the SAP in a transparent way when structuring the primary analysis, sensitivity or ancillary analyses, and specific analyses for secondary endpoints. The mean logarithmic score and DIC discriminate well between different model scenarios. It becomes obvious that the naive choice of a conventional random effects Poisson model is often inappropriate for real-life count data. The findings are used to specify an appropriate mixed model employed in the sensitivity analyses of an ongoing phase III trial. Conclusions The proposed Bayesian methods are not only appealing for inference but notably provide a sophisticated insight into different aspects of model performance, such as forecast verification or calibration checks, and can be applied within the model selection process. The mean of the logarithmic score is a robust tool for model ranking and is not sensitive to sample size. Therefore, these Bayesian model selection techniques offer helpful decision support for shaping sensitivity and ancillary analyses in a statistical analysis plan of a clinical trial with longitudinal count data as the primary endpoint. PMID:22962944

Bayesian model selection techniques as decision support for shaping a statistical analysis plan of a clinical trial: an example from a vertigo phase III study with longitudinal count data as primary endpoint.

PubMed

Adrion, Christine; Mansmann, Ulrich

2012-09-10

A statistical analysis plan (SAP) is a critical link between how a clinical trial is conducted and the clinical study report. To secure objective study results, regulatory bodies expect that the SAP will meet requirements in pre-specifying inferential analyses and other important statistical techniques. To write a good SAP for model-based sensitivity and ancillary analyses involves non-trivial decisions on and justification of many aspects of the chosen setting. In particular, trials with longitudinal count data as primary endpoints pose challenges for model choice and model validation. In the random effects setting, frequentist strategies for model assessment and model diagnosis are complex and not easily implemented and have several limitations. Therefore, it is of interest to explore Bayesian alternatives which provide the needed decision support to finalize a SAP. We focus on generalized linear mixed models (GLMMs) for the analysis of longitudinal count data. A series of distributions with over- and under-dispersion is considered. Additionally, the structure of the variance components is modified. We perform a simulation study to investigate the discriminatory power of Bayesian tools for model criticism in different scenarios derived from the model setting. We apply the findings to the data from an open clinical trial on vertigo attacks. These data are seen as pilot data for an ongoing phase III trial. To fit GLMMs we use a novel Bayesian computational approach based on integrated nested Laplace approximations (INLAs). The INLA methodology enables the direct computation of leave-one-out predictive distributions. These distributions are crucial for Bayesian model assessment. We evaluate competing GLMMs for longitudinal count data according to the deviance information criterion (DIC) or probability integral transform (PIT), and by using proper scoring rules (e.g. the logarithmic score). The instruments under study provide excellent tools for preparing decisions within the SAP in a transparent way when structuring the primary analysis, sensitivity or ancillary analyses, and specific analyses for secondary endpoints. The mean logarithmic score and DIC discriminate well between different model scenarios. It becomes obvious that the naive choice of a conventional random effects Poisson model is often inappropriate for real-life count data. The findings are used to specify an appropriate mixed model employed in the sensitivity analyses of an ongoing phase III trial. The proposed Bayesian methods are not only appealing for inference but notably provide a sophisticated insight into different aspects of model performance, such as forecast verification or calibration checks, and can be applied within the model selection process. The mean of the logarithmic score is a robust tool for model ranking and is not sensitive to sample size. Therefore, these Bayesian model selection techniques offer helpful decision support for shaping sensitivity and ancillary analyses in a statistical analysis plan of a clinical trial with longitudinal count data as the primary endpoint.

Technological innovations in the development of cardiovascular clinical information systems.

PubMed

Hsieh, Nan-Chen; Chang, Chung-Yi; Lee, Kuo-Chen; Chen, Jeen-Chen; Chan, Chien-Hui

2012-04-01

Recent studies have shown that computerized clinical case management and decision support systems can be used to assist surgeons in the diagnosis of disease, optimize surgical operation, aid in drug therapy and decrease the cost of medical treatment. Therefore, medical informatics has become an extensive field of research and many of these approaches have demonstrated potential value for improving medical quality. The aim of this study was to develop a web-based cardiovascular clinical information system (CIS) based on innovative techniques, such as electronic medical records, electronic registries and automatic feature surveillance schemes, to provide effective tools and support for clinical care, decision-making, biomedical research and training activities. The CIS developed for this study contained monitoring, surveillance and model construction functions. The monitoring layer function provided a visual user interface. At the surveillance and model construction layers, we explored the application of model construction and intelligent prognosis to aid in making preoperative and postoperative predictions. With the use of the CIS, surgeons can provide reasonable conclusions and explanations in uncertain environments.

A study on specialist or special disease clinics based on big data.

PubMed

Fang, Zhuyuan; Fan, Xiaowei; Chen, Gong

2014-09-01

Correlation analysis and processing of massive medical information can be implemented through big data technology to find the relevance of different factors in the life cycle of a disease and to provide the basis for scientific research and clinical practice. This paper explores the concept of constructing a big medical data platform and introduces the clinical model construction. Medical data can be collected and consolidated by distributed computing technology. Through analysis technology, such as artificial neural network and grey model, a medical model can be built. Big data analysis, such as Hadoop, can be used to construct early prediction and intervention models as well as clinical decision-making model for specialist and special disease clinics. It establishes a new model for common clinical research for specialist and special disease clinics.

Shared decision making in chronic care in the context of evidence based practice in nursing.

PubMed

Friesen-Storms, Jolanda H H M; Bours, Gerrie J J W; van der Weijden, Trudy; Beurskens, Anna J H M

2015-01-01

In the decision-making environment of evidence-based practice, the following three sources of information must be integrated: research evidence of the intervention, clinical expertise, and the patient's values. In reality, evidence-based practice usually focuses on research evidence (which may be translated into clinical practice guidelines) and clinical expertise without considering the individual patient's values. The shared decision-making model seems to be helpful in the integration of the individual patient's values in evidence-based practice. We aim to discuss the relevance of shared decision making in chronic care and to suggest how it can be integrated with evidence-based practice in nursing. We start by describing the following three possible approaches to guide the decision-making process: the paternalistic approach, the informed approach, and the shared decision-making approach. Implementation of shared decision making has gained considerable interest in cases lacking a strong best-treatment recommendation, and when the available treatment options are equivalent to some extent. We discuss that in chronic care it is important to always invite the patient to participate in the decision-making process. We delineate the following six attributes of health care interventions in chronic care that influence the degree of shared decision making: the level of research evidence, the number of available intervention options, the burden of side effects, the impact on lifestyle, the patient group values, and the impact on resources. Furthermore, the patient's willingness to participate in shared decision making, the clinical expertise of the nurse, and the context in which the decision making takes place affect the shared decision-making process. A knowledgeable and skilled nurse with a positive attitude towards shared decision making—integrated with evidence-based practice—can facilitate the shared decision-making process. We conclude that nurses as well as other health care professionals in chronic care should integrate shared decision making with evidence-based practice to deliver patient-centred care. Copyright © 2014 Elsevier Ltd. All rights reserved.

Exploring the use of Option Grid™ patient decision aids in a sample of clinics in Poland.

PubMed

Scalia, Peter; Elwyn, Glyn; Barr, Paul; Song, Julia; Zisman-Ilani, Yaara; Lesniak, Monika; Mullin, Sarah; Kurek, Krzysztof; Bushell, Matt; Durand, Marie-Anne

2018-05-29

Research on the implementation of patient decision aids to facilitate shared decision making in clinical settings has steadily increased across Western countries. A study which implements decision aids and measures their impact on shared decision making has yet to be conducted in the Eastern part of Europe. To study the use of Option Grid TM patient decision aids in a sample of Grupa LUX MED clinics in Warsaw, Poland, and measure their impact on shared decision making. We conducted a pre-post interventional study. Following a three-month period of usual care, clinicians from three Grupa LUX MED clinics received a one-hour training session on how to use three Option Grid TM decision aids and were provided with copies for use for four months. Throughout the study, all eligible patients were asked to complete the three-item CollaboRATE patient-reported measure of shared decision making after their clinical encounter. CollaboRATE enables patients to assess the efforts clinicians make to: (i) inform them about their health issues; (ii) listen to 'what matters most'; (iii) integrate their treatment preference in future plans. A Hierarchical Logistic Regression model was performed to understand which variables had an effect on CollaboRATE. 2,048 patients participated in the baseline phase; 1,889 patients participated in the intervention phase. Five of the thirteen study clinicians had a statistically significant increase in their CollaboRATE scores (p<.05) when comparing baseline phase to intervention phase. All five clinicians were located at the same clinic, the only clinic where an overall increase (non-significant) in the mean CollaboRATE top score percentage occurred from baseline phase (M=60 %, SD=0.49; 95 % CI [57-63 %]) to intervention phase (M=62 %, SD=0.49; 95% CI [59-65%]). Only three of those five clinicians who had a statistically significant increase had a clinically significant difference. The implementation of Option Grid TM helped some clinicians practice shared decision making as reflected in CollaboRATE scores, but most clinicians did not have a significant increase in their scores. Our study indicates that the effect of these interventions may be dependent on clinic contexts and clinician engagement. Copyright © 2018. Published by Elsevier GmbH.

Using the Situated Clinical Decision-Making framework to guide analysis of nurses' clinical decision-making.

PubMed

Gillespie, Mary

2010-11-01

Nurses' clinical decision-making is a complex process that holds potential to influence the quality of care provided and patient outcomes. The evolution of nurses' decision-making that occurs with experience has been well documented. In addition, literature includes numerous strategies and approaches purported to support development of nurses' clinical decision-making. There has been, however, significantly less attention given to the process of assessing nurses' clinical decision-making and novice clinical educators are often challenged with knowing how to best support nurses and nursing students in developing their clinical decision-making capacity. The Situated Clinical Decision-Making framework is presented for use by clinical educators: it provides a structured approach to analyzing nursing students' and novice nurses' decision-making in clinical nursing practice, assists educators in identifying specific issues within nurses' clinical decision-making, and guides selection of relevant strategies to support development of clinical decision-making. A series of questions is offered as a guide for clinical educators when assessing nurses' clinical decision-making. The discussion presents key considerations related to analysis of various decision-making components, including common sources of challenge and errors that may occur within nurses' clinical decision-making. An exemplar illustrates use of the framework and guiding questions. Implications of this approach for selection of strategies that support development of clinical decision-making are highlighted. Copyright © 2010 Elsevier Ltd. All rights reserved.

Decision support systems and the healthcare strategic planning process: a case study.

PubMed

Lundquist, D L; Norris, R M

1991-01-01

The repertoire of applications that comprises health-care decision support systems (DSS) includes analyses of clinical, financial, and operational activities. As a whole, these applications facilitate developing comprehensive and interrelated business and medical models that support the complex decisions required to successfully manage today's health-care organizations. Kennestone Regional Health Care System's use of DSS to facilitate strategic planning has precipitated marked changes in the organization's method of determining capital allocations. This case study discusses Kennestone's use of DSS in the strategic planning process, including profiles of key DSS modeling components.

Multiscale mechanistic modeling in pharmaceutical research and development.

PubMed

Kuepfer, Lars; Lippert, Jörg; Eissing, Thomas

2012-01-01

Discontinuation of drug development projects due to lack of efficacy or adverse events is one of the main cost drivers in pharmaceutical research and development (R&D). Investments have to be written-off and contribute to the total costs of a successful drug candidate receiving marketing authorization and allowing return on invest. A vital risk for pharmaceutical innovator companies is late stage clinical failure since costs for individual clinical trials may exceed the one billion Euro threshold. To guide investment decisions and to safeguard maximum medical benefit and safety for patients recruited in clinical trials, it is therefore essential to understand the clinical consequences of all information and data generated. The complexity of the physiological and pathophysiological processes and the sheer amount of information available overcharge the mental capacity of any human being and prevent a prediction of the success in clinical development. A rigorous integration of knowledge, assumption, and experimental data into computational models promises a significant improvement of the rationalization of decision making in pharmaceutical industry. We here give an overview of the current status of modeling and simulation in pharmaceutical R&D and outline the perspectives of more recent developments in mechanistic modeling. Specific modeling approaches for different biological scales ranging from intracellular processes to whole organism physiology are introduced and an example for integrative multiscale modeling of therapeutic efficiency in clinical oncology trials is showcased.

Does [-2]Pro-Prostate Specific Antigen Meet the Criteria to Justify Its Inclusion in the Clinical Decision-Making Process?

PubMed

Sanchis-Bonet, Angeles; Barrionuevo-González, Marta; Bajo-Chueca, Ana; Morales-Palacios, Nelson; Sanchez-Chapado, Manuel

2018-01-01

To assess whether [-2]pro-prostate-specific antigen (p2PSA) meets the criteria to justify its inclusion in a predictive model of prostate cancer (PCa) diagnosis and in the clinical decision-making process. A total 172 men with total prostate-specific antigen of 2-10 ng/mL underwent measurement of free PSA and p2PSA before prostate biopsy in an observational and prospective study. From these measurements, the Prostate Health Index (PHI) was calculated. Clinical and analytical predictive models were created incorporating PHI. Of 172 men, 72 (42%) were diagnosed with PCa, 33 (46%) of whom were found to be with high-grade disease. PHI score was the most predictive of biopsy outcomes in terms of discriminative ability (area under the curve = 0.79), with an added gain in predictive accuracy of 17%. All the models that incorporated PHI worked better in terms of calibration close to 45° on the slope. In the decision curve analysis, at a threshold probability of 40% we could prevent 82 biopsies, missing only 16 tumors and 5 high-grade tumors. PHI score is a more discriminant biomarker, has superior calibration and superior net benefit, and provides a higher rate of avoided biopsies; thus, it can be useful for aiding in making a more informed decision for each patient. © 2018 S. Karger AG, Basel.

The anatomy of decision support during inpatient care provider order entry (CPOE): Empirical observations from a decade of CPOE experience at Vanderbilt

PubMed Central

Miller, Randolph A.; Waitman, Lemuel R.; Chen, Sutin; Rosenbloom, S. Trent

2006-01-01

The authors describe a pragmatic approach to the introduction of clinical decision support at the point of care, based on a decade of experience in developing and evolving Vanderbilt’s inpatient “WizOrder” care provider order entry (CPOE) system. The inpatient care setting provides a unique opportunity to interject CPOE-based decision support features that restructure clinical workflows, deliver focused relevant educational materials, and influence how care is delivered to patients. From their empirical observations, the authors have developed a generic model for decision support within inpatient CPOE systems. They believe that the model’s utility extends beyond Vanderbilt, because it is based on characteristics of end-user workflows and on decision support considerations that are common to a variety of inpatient settings and CPOE systems. The specific approach to implementing a given clinical decision support feature within a CPOE system should involve evaluation along three axes: what type of intervention to create (for which the authors describe 4 general categories); when to introduce the intervention into the user’s workflow (for which the authors present 7 categories), and how disruptive, during use of the system, the intervention might be to end-users’ workflows (for which the authors describe 6 categories). Framing decision support in this manner may help both developers and clinical end-users plan future alterations to their systems when needs for new decision support features arise. PMID:16290243

The predictive value of self-report questions in a clinical decision rule for pediatric lead poisoning screening.

PubMed

Kaplowitz, Stan A; Perlstadt, Harry; D'Onofrio, Gail; Melnick, Edward R; Baum, Carl R; Kirrane, Barbara M; Post, Lori A

2012-01-01

We derived a clinical decision rule for determining which young children need testing for lead poisoning. We developed an equation that combines lead exposure self-report questions with the child's census-block housing and socioeconomic characteristics, personal demographic characteristics, and Medicaid status. This equation better predicts elevated blood lead level (EBLL) than one using ZIP code and Medicaid status. A survey regarding potential lead exposure was administered from October 2001 to January 2003 to Michigan parents at pediatric clinics (n=3,396). These self-report survey data were linked to a statewide clinical registry of blood lead level (BLL) tests. Sensitivity and specificity were calculated and then used to estimate the cost-effectiveness of the equation. The census-block group prediction equation explained 18.1% of the variance in BLLs. Replacing block group characteristics with the self-report questions and dichotomized ZIP code risk explained only 12.6% of the variance. Adding three self-report questions to the census-block group model increased the variance explained to 19.9% and increased specificity with no loss in sensitivity in detecting EBLLs of ≥ 10 micrograms per deciliter. Relying solely on self-reports of lead exposure predicted BLL less effectively than the block group model. However, adding three of 13 self-report questions to our clinical decision rule significantly improved prediction of which children require a BLL test. Using the equation as the clinical decision rule would annually eliminate more than 7,200 unnecessary tests in Michigan and save more than $220,000.

The use of the Dutch Self-Sufficiency Matrix (SSM-D) to inform allocation decisions to public mental health care for homeless people.

PubMed

Lauriks, Steve; de Wit, Matty A S; Buster, Marcel C A; Fassaert, Thijs J L; van Wifferen, Ron; Klazinga, Niek S

2014-10-01

The current study set out to develop a decision support tool based on the Self-Sufficiency Matrix (Dutch version; SSM-D) for the clinical decision to allocate homeless people to the public mental health care system at the central access point of public mental health care in Amsterdam, The Netherlands. Logistic regression and receiver operating characteristic-curve analyses were used to model professional decisions and establish four decision categories based on SSM-D scores from half of the research population (Total n = 612). The model and decision categories were found to be accurate and reliable in predicting professional decisions in the second half of the population. Results indicate that the decision support tool based on the SSM-D is useful and feasible. The method to develop the SSM-D as a decision support tool could be applied to decision-making processes in other systems and services where the SSM-D has been implemented, to further increase the utility of the instrument.

A decision-supported outpatient practice system.

PubMed Central

Barrows, R. C.; Allen, B. A.; Smith, K. C.; Arni, V. V.; Sherman, E.

1996-01-01

We describe a Decision-supported Outpatient Practice (DOP) system developed and now in use at the Columbia-Presbyterian Medical Center. DOP is an automated ambulatory medical record system that integrates in-patient and ambulatory care data, and incorporates active and passive decision support mechanisms with a view towards improving the quality of primary care. Active decision support occurs in the form of event-driven reminders created within a remote clinical information system with its central data repository and decision support system (DSS). Novel features of DOP include patient specific health maintenance task lists calculated by the remote DSS. uses of a semantically structured controlled medical vocabulary to support clinical results review and provider data entry, and exploitation of an underlying ambulatory data model that provides for an explicit record of evolution of insight regarding patient management. Benefits, challenges, and plans are discussed. PMID:8947774

Different decision deficits impair response inhibition in progressive supranuclear palsy and Parkinson’s disease

PubMed Central

Rittman, Timothy; Nombela, Cristina; Fois, Alessandro; Coyle-Gilchrist, Ian; Barker, Roger A.; Hughes, Laura E.; Rowe, James B.

2016-01-01

Abstract Progressive supranuclear palsy and Parkinson’s disease have distinct underlying neuropathology, but both diseases affect cognitive function in addition to causing a movement disorder. They impair response inhibition and may lead to impulsivity, which can occur even in the presence of profound akinesia and rigidity. The current study examined the mechanisms of cognitive impairments underlying disinhibition, using horizontal saccadic latencies that obviate the impact of limb slowness on executing response decisions. Nineteen patients with clinically diagnosed progressive supranuclear palsy (Richardson’s syndrome), 24 patients with clinically diagnosed Parkinson’s disease and 26 healthy control subjects completed a saccadic Go/No-Go task with a head-mounted infrared saccadometer. Participants were cued on each trial to make a pro-saccade to a horizontal target or withhold their responses. Both patient groups had impaired behavioural performance, with more commission errors than controls. Mean saccadic latencies were similar between all three groups. We analysed behavioural responses as a binary decision between Go and No-Go choices. By using Bayesian parameter estimation, we fitted a hierarchical drift–diffusion model to individual participants’ single trial data. The model decomposes saccadic latencies into parameters for the decision process: decision boundary, drift rate of accumulation, decision bias, and non-decision time. In a leave-one-out three-way classification analysis, the model parameters provided better discrimination between patients and controls than raw behavioural measures. Furthermore, the model revealed disease-specific deficits in the Go/No-Go decision process. Both patient groups had slower drift rate of accumulation, and shorter non-decision time than controls. But patients with progressive supranuclear palsy were strongly biased towards a pro-saccade decision boundary compared to Parkinson’s patients and controls. This indicates a prepotency of responding in combination with a reduction in further accumulation of evidence, which provides a parsimonious explanation for the apparently paradoxical combination of disinhibition and severe akinesia. The combination of the well-tolerated oculomotor paradigm and the sensitivity of the model-based analysis provides a valuable approach for interrogating decision-making processes in neurodegenerative disorders. The mechanistic differences underlying participants’ poor performance were not observable from classical analysis of behavioural data, but were clearly revealed by modelling. These differences provide a rational basis on which to develop and assess new therapeutic strategies for cognition and behaviour in these disorders. PMID:26582559

Different decision deficits impair response inhibition in progressive supranuclear palsy and Parkinson's disease.

PubMed

Zhang, Jiaxiang; Rittman, Timothy; Nombela, Cristina; Fois, Alessandro; Coyle-Gilchrist, Ian; Barker, Roger A; Hughes, Laura E; Rowe, James B

2016-01-01

Progressive supranuclear palsy and Parkinson's disease have distinct underlying neuropathology, but both diseases affect cognitive function in addition to causing a movement disorder. They impair response inhibition and may lead to impulsivity, which can occur even in the presence of profound akinesia and rigidity. The current study examined the mechanisms of cognitive impairments underlying disinhibition, using horizontal saccadic latencies that obviate the impact of limb slowness on executing response decisions. Nineteen patients with clinically diagnosed progressive supranuclear palsy (Richardson's syndrome), 24 patients with clinically diagnosed Parkinson's disease and 26 healthy control subjects completed a saccadic Go/No-Go task with a head-mounted infrared saccadometer. Participants were cued on each trial to make a pro-saccade to a horizontal target or withhold their responses. Both patient groups had impaired behavioural performance, with more commission errors than controls. Mean saccadic latencies were similar between all three groups. We analysed behavioural responses as a binary decision between Go and No-Go choices. By using Bayesian parameter estimation, we fitted a hierarchical drift-diffusion model to individual participants' single trial data. The model decomposes saccadic latencies into parameters for the decision process: decision boundary, drift rate of accumulation, decision bias, and non-decision time. In a leave-one-out three-way classification analysis, the model parameters provided better discrimination between patients and controls than raw behavioural measures. Furthermore, the model revealed disease-specific deficits in the Go/No-Go decision process. Both patient groups had slower drift rate of accumulation, and shorter non-decision time than controls. But patients with progressive supranuclear palsy were strongly biased towards a pro-saccade decision boundary compared to Parkinson's patients and controls. This indicates a prepotency of responding in combination with a reduction in further accumulation of evidence, which provides a parsimonious explanation for the apparently paradoxical combination of disinhibition and severe akinesia. The combination of the well-tolerated oculomotor paradigm and the sensitivity of the model-based analysis provides a valuable approach for interrogating decision-making processes in neurodegenerative disorders. The mechanistic differences underlying participants' poor performance were not observable from classical analysis of behavioural data, but were clearly revealed by modelling. These differences provide a rational basis on which to develop and assess new therapeutic strategies for cognition and behaviour in these disorders. © The Author (2015). Published by Oxford University Press on behalf of the Guarantors of Brain.

Treating childhood pneumonia in hard-to-reach areas: a model-based comparison of mobile clinics and community-based care.

PubMed

Pitt, Catherine; Roberts, Bayard; Checchi, Francesco

2012-01-10

Where hard-to-access populations (such as those living in insecure areas) lack access to basic health services, relief agencies, donors, and ministries of health face a dilemma in selecting the most effective intervention strategy. This paper uses a decision mathematical model to estimate the relative effectiveness of two alternative strategies, mobile clinics and fixed community-based health services, for antibiotic treatment of childhood pneumonia, the world's leading cause of child mortality. A "Markov cycle tree" cohort model was developed in Excel with Visual Basic to compare the number of deaths from pneumonia in children aged 1 to 59 months expected under three scenarios: 1) No curative services available, 2) Curative services provided by a highly-skilled but intermittent mobile clinic, and 3) Curative services provided by a low-skilled community health post. Parameter values were informed by literature and expert interviews. Probabilistic sensitivity analyses were conducted for several plausible scenarios. We estimated median pneumonia-specific under-5 mortality rates of 0.51 (95% credible interval: 0.49 to 0.541) deaths per 10,000 child-days without treatment, 0.45 (95% CI: 0.43 to 0.48) with weekly mobile clinics, and 0.31 (95% CI: 0.29 to 0.32) with CHWs in fixed health posts. Sensitivity analyses found the fixed strategy superior, except when mobile clinics visited communities daily, where rates of care-seeking were substantially higher at mobile clinics than fixed posts, or where several variables simultaneously differed substantially from our baseline assumptions. Current evidence does not support the hypothesis that mobile clinics are more effective than CHWs. A CHW strategy therefore warrants consideration in high-mortality, hard-to-access areas. Uncertainty remains, and parameter values may vary across contexts, but the model allows preliminary findings to be updated as new or context-specific evidence becomes available. Decision analytic modelling can guide needed field-based research efforts in hard-to-access areas and offer evidence-based insights for decision-makers.

Design and Development of a Sharable Clinical Decision Support System Based on a Semantic Web Service Framework.

PubMed

Zhang, Yi-Fan; Gou, Ling; Tian, Yu; Li, Tian-Chang; Zhang, Mao; Li, Jing-Song

2016-05-01

Clinical decision support (CDS) systems provide clinicians and other health care stakeholders with patient-specific assessments or recommendations to aid in the clinical decision-making process. Despite their demonstrated potential for improving health care quality, the widespread availability of CDS systems has been limited mainly by the difficulty and cost of sharing CDS knowledge among heterogeneous healthcare information systems. The purpose of this study was to design and develop a sharable clinical decision support (S-CDS) system that meets this challenge. The fundamental knowledge base consists of independent and reusable knowledge modules (KMs) to meet core CDS needs, wherein each KM is semantically well defined based on the standard information model, terminologies, and representation formalisms. A semantic web service framework was developed to identify, access, and leverage these KMs across diverse CDS applications and care settings. The S-CDS system has been validated in two distinct client CDS applications. Model-level evaluation results confirmed coherent knowledge representation. Application-level evaluation results reached an overall accuracy of 98.66 % and a completeness of 96.98 %. The evaluation results demonstrated the technical feasibility and application prospect of our approach. Compared with other CDS engineering efforts, our approach facilitates system development and implementation and improves system maintainability, scalability and efficiency, which contribute to the widespread adoption of effective CDS within the healthcare domain.

Newly graduated nurses' use of knowledge sources in clinical decision-making: an ethnographic study.

PubMed

Voldbjerg, Siri Lygum; Grønkjaer, Mette; Wiechula, Rick; Sørensen, Erik Elgaard

2017-05-01

To explore which knowledge sources newly graduated nurses' use in clinical decision-making and why and how they are used. In spite of an increased educational focus on skills and competencies within evidence-based practice, newly graduated nurses' ability to use components within evidence-based practice with a conscious and reflective use of research evidence has been described as being poor. To understand why, it is relevant to explore which other knowledge sources are used. This may shed light on why research evidence is sparsely used and ultimately inform approaches to strengthen the knowledgebase used in clinical decision-making. Ethnographic study using participant-observation and individual semistructured interviews of nine Danish newly graduated nurses in medical and surgical hospital settings. Newly graduates use of knowledge sources was described within three main structures: 'other', 'oneself' and 'gut feeling'. Educational preparation, transition into clinical practice and the culture of the setting influenced the knowledge sources used. The sources ranged from overt easily articulated knowledge sources to covert sources that were difficult to articulate. The limited articulation of certain sources inhibited the critical reflection on the reasoning behind decisions. Reflection is a prerequisite for an evidence-based practice where decisions should be transparent in order to consider if other evidentiary sources could be used. Although there is a complexity and variety to knowledge sources used, there is an imbalance with the experienced nurse playing a key role, functioning both as predominant source and a role model as to which sources are valued and used in clinical decision-making. If newly graduates are to be supported in an articulate and reflective use of a variety of sources, they have to be allocated to experienced nurses who model a reflective, articulate and balanced use of knowledge sources. © 2016 John Wiley & Sons Ltd.

Three-class ROC analysis--the equal error utility assumption and the optimality of three-class ROC surface using the ideal observer.

PubMed

He, Xin; Frey, Eric C

2006-08-01

Previously, we have developed a decision model for three-class receiver operating characteristic (ROC) analysis based on decision theory. The proposed decision model maximizes the expected decision utility under the assumption that incorrect decisions have equal utilities under the same hypothesis (equal error utility assumption). This assumption reduced the dimensionality of the "general" three-class ROC analysis and provided a practical figure-of-merit to evaluate the three-class task performance. However, it also limits the generality of the resulting model because the equal error utility assumption will not apply for all clinical three-class decision tasks. The goal of this study was to investigate the optimality of the proposed three-class decision model with respect to several other decision criteria. In particular, besides the maximum expected utility (MEU) criterion used in the previous study, we investigated the maximum-correctness (MC) (or minimum-error), maximum likelihood (ML), and Nyman-Pearson (N-P) criteria. We found that by making assumptions for both MEU and N-P criteria, all decision criteria lead to the previously-proposed three-class decision model. As a result, this model maximizes the expected utility under the equal error utility assumption, maximizes the probability of making correct decisions, satisfies the N-P criterion in the sense that it maximizes the sensitivity of one class given the sensitivities of the other two classes, and the resulting ROC surface contains the maximum likelihood decision operating point. While the proposed three-class ROC analysis model is not optimal in the general sense due to the use of the equal error utility assumption, the range of criteria for which it is optimal increases its applicability for evaluating and comparing a range of diagnostic systems.

Workshop on using natural language processing applications for enhancing clinical decision making: an executive summary

PubMed Central

Pai, Vinay M; Rodgers, Mary; Conroy, Richard; Luo, James; Zhou, Ruixia; Seto, Belinda

2014-01-01

In April 2012, the National Institutes of Health organized a two-day workshop entitled ‘Natural Language Processing: State of the Art, Future Directions and Applications for Enhancing Clinical Decision-Making’ (NLP-CDS). This report is a summary of the discussions during the second day of the workshop. Collectively, the workshop presenters and participants emphasized the need for unstructured clinical notes to be included in the decision making workflow and the need for individualized longitudinal data tracking. The workshop also discussed the need to: (1) combine evidence-based literature and patient records with machine-learning and prediction models; (2) provide trusted and reproducible clinical advice; (3) prioritize evidence and test results; and (4) engage healthcare professionals, caregivers, and patients. The overall consensus of the NLP-CDS workshop was that there are promising opportunities for NLP and CDS to deliver cognitive support for healthcare professionals, caregivers, and patients. PMID:23921193

Clinical reasoning in nursing, a think-aloud study using virtual patients - a base for an innovative assessment.

PubMed

Forsberg, Elenita; Ziegert, Kristina; Hult, Håkan; Fors, Uno

2014-04-01

In health-care education, it is important to assess the competencies that are essential for the professional role. To develop clinical reasoning skills is crucial for nursing practice and therefore an important learning outcome in nursing education programmes. Virtual patients (VPs) are interactive computer simulations of real-life clinical scenarios and have been suggested for use not only for learning, but also for assessment of clinical reasoning. The aim of this study was to investigate how experienced paediatric nurses reason regarding complex VP cases and how they make clinical decisions. The study was also aimed to give information about possible issues that should be assessed in clinical reasoning exams for post-graduate students in diploma specialist paediatric nursing education. The information from this study is believed to be of high value when developing scoring and grading models for a VP-based examination for the specialist diploma in paediatric nursing education. Using the think-aloud method, data were collected from 30 RNs working in Swedish paediatric departments, and child or school health-care centres. Content analysis was used to analyse the data. The results indicate that experienced nurses try to consolidate their hypotheses by seeing a pattern and judging the value of signs, symptoms, physical examinations, laboratory tests and radiology. They show high specific competence but earlier experience of similar cases was also of importance for the decision making. The nurses thought it was an innovative assessment focusing on clinical reasoning and clinical decision making. They thought it was an enjoyable way to be assessed and that all three main issues could be assessed using VPs. In conclusion, VPs seem to be a possible model for assessing the clinical reasoning process and clinical decision making, but how to score and grade such exams needs further research. © 2013.

An analysis of clinical reasoning through a recent and comprehensive approach: the dual-process theory.

PubMed

Pelaccia, Thierry; Tardif, Jacques; Triby, Emmanuel; Charlin, Bernard

2011-03-14

Clinical reasoning plays a major role in the ability of doctors to make diagnoses and decisions. It is considered as the physician's most critical competence, and has been widely studied by physicians, educationalists, psychologists and sociologists. Since the 1970s, many theories about clinical reasoning in medicine have been put forward. This paper aims at exploring a comprehensive approach: the "dual-process theory", a model developed by cognitive psychologists over the last few years. After 40 years of sometimes contradictory studies on clinical reasoning, the dual-process theory gives us many answers on how doctors think while making diagnoses and decisions. It highlights the importance of physicians' intuition and the high level of interaction between analytical and non-analytical processes. However, it has not received much attention in the medical education literature. The implications of dual-process models of reasoning in terms of medical education will be discussed.

Clinical Assistant Diagnosis for Electronic Medical Record Based on Convolutional Neural Network.

PubMed

Yang, Zhongliang; Huang, Yongfeng; Jiang, Yiran; Sun, Yuxi; Zhang, Yu-Jin; Luo, Pengcheng

2018-04-20

Automatically extracting useful information from electronic medical records along with conducting disease diagnoses is a promising task for both clinical decision support(CDS) and neural language processing(NLP). Most of the existing systems are based on artificially constructed knowledge bases, and then auxiliary diagnosis is done by rule matching. In this study, we present a clinical intelligent decision approach based on Convolutional Neural Networks(CNN), which can automatically extract high-level semantic information of electronic medical records and then perform automatic diagnosis without artificial construction of rules or knowledge bases. We use collected 18,590 copies of the real-world clinical electronic medical records to train and test the proposed model. Experimental results show that the proposed model can achieve 98.67% accuracy and 96.02% recall, which strongly supports that using convolutional neural network to automatically learn high-level semantic features of electronic medical records and then conduct assist diagnosis is feasible and effective.

Cost-effectiveness modelling in diagnostic imaging: a stepwise approach.

PubMed

Sailer, Anna M; van Zwam, Wim H; Wildberger, Joachim E; Grutters, Janneke P C

2015-12-01

Diagnostic imaging (DI) is the fastest growing sector in medical expenditures and takes a central role in medical decision-making. The increasing number of various and new imaging technologies induces a growing demand for cost-effectiveness analysis (CEA) in imaging technology assessment. In this article we provide a comprehensive framework of direct and indirect effects that should be considered for CEA in DI, suitable for all imaging modalities. We describe and explain the methodology of decision analytic modelling in six steps aiming to transfer theory of CEA to clinical research by demonstrating key principles of CEA in a practical approach. We thereby provide radiologists with an introduction to the tools necessary to perform and interpret CEA as part of their research and clinical practice. • DI influences medical decision making, affecting both costs and health outcome. • This article provides a comprehensive framework for CEA in DI. • A six-step methodology for conducting and interpreting cost-effectiveness modelling is proposed.

Comparing decision making between cancer patients and the general population: thoughts, emotions, or social influence?

PubMed

Yang, Z Janet; McComas, Katherine A; Gay, Geri K; Leonard, John P; Dannenberg, Andrew J; Dillon, Hildy

2012-01-01

This study extends a risk information seeking and processing model to explore the relative effect of cognitive processing strategies, positive and negative emotions, and normative beliefs on individuals' decision making about potential health risks. Most previous research based on this theoretical framework has examined environmental risks. Applying this risk communication model to study health decision making presents an opportunity to explore theoretical boundaries of the model, while also bringing this research to bear on a pressing medical issue: low enrollment in clinical trials. Comparative analysis of data gathered from 2 telephone surveys of a representative national sample (n = 500) and a random sample of cancer patients (n = 411) indicated that emotions played a more substantive role in cancer patients' decisions to enroll in a potential trial, whereas cognitive processing strategies and normative beliefs had greater influences on the decisions of respondents from the national sample.

Patient involvement in decision-making: a cross-sectional study in a Malaysian primary care clinic

PubMed Central

Ambigapathy, Ranjini; Ng, Chirk Jenn

2016-01-01

Objective Shared decision-making has been advocated as a useful model for patient management. In developing Asian countries such as Malaysia, there is a common belief that patients prefer a passive role in clinical consultation. As such, the objective of this study was to determine Malaysian patients’ role preference in decision-making and the associated factors. Design A cross-sectional study. Setting Study was conducted at an urban primary care clinic in Malaysia in 2012. Participants Patients aged >21 years were chosen using systematic random sampling. Methods Consenting patients answered a self-administered questionnaire, which included demographic data and their preferred and actual role before and after consultation. Doctors were asked to determine patients’ role preference. The Control Preference Scale was used to assess patients’ role preference. Primary outcome Prevalence of patients’ preferred role in decision-making. Secondary outcomes (1) Actual role played by the patient in decision-making. (2) Sociodemographic factors associated with patients’ preferred role in decision-making. (3) Doctors’ perception of patients’ involvement in decision-making. Results The response rate was 95.1% (470/494). Shared decision-making was preferred by 51.9% of patients, followed by passive (26.3%) and active (21.8%) roles in decision-making. Higher household income was significantly associated with autonomous role preference (p=0.018). Doctors’ perception did not concur with patients’ preferred role. Among patients whom doctors perceived to prefer a passive role, 73.5% preferred an autonomous role (p=0.900, κ=0.006). Conclusions The majority of patients attending the primary care clinic preferred and played an autonomous role in decision-making. Doctors underestimated patients’ preference to play an autonomous role. PMID:26729393

Toward better public health reporting using existing off the shelf approaches: The value of medical dictionaries in automated cancer detection using plaintext medical data.

PubMed

Kasthurirathne, Suranga N; Dixon, Brian E; Gichoya, Judy; Xu, Huiping; Xia, Yuni; Mamlin, Burke; Grannis, Shaun J

2017-05-01

Existing approaches to derive decision models from plaintext clinical data frequently depend on medical dictionaries as the sources of potential features. Prior research suggests that decision models developed using non-dictionary based feature sourcing approaches and "off the shelf" tools could predict cancer with performance metrics between 80% and 90%. We sought to compare non-dictionary based models to models built using features derived from medical dictionaries. We evaluated the detection of cancer cases from free text pathology reports using decision models built with combinations of dictionary or non-dictionary based feature sourcing approaches, 4 feature subset sizes, and 5 classification algorithms. Each decision model was evaluated using the following performance metrics: sensitivity, specificity, accuracy, positive predictive value, and area under the receiver operating characteristics (ROC) curve. Decision models parameterized using dictionary and non-dictionary feature sourcing approaches produced performance metrics between 70 and 90%. The source of features and feature subset size had no impact on the performance of a decision model. Our study suggests there is little value in leveraging medical dictionaries for extracting features for decision model building. Decision models built using features extracted from the plaintext reports themselves achieve comparable results to those built using medical dictionaries. Overall, this suggests that existing "off the shelf" approaches can be leveraged to perform accurate cancer detection using less complex Named Entity Recognition (NER) based feature extraction, automated feature selection and modeling approaches. Copyright © 2017 Elsevier Inc. All rights reserved.

Decision science and cervical cancer.

PubMed

Cantor, Scott B; Fahs, Marianne C; Mandelblatt, Jeanne S; Myers, Evan R; Sanders, Gillian D

2003-11-01

Mathematical modeling is an effective tool for guiding cervical cancer screening, diagnosis, and treatment decisions for patients and policymakers. This article describes the use of mathematical modeling as outlined in five presentations from the Decision Science and Cervical Cancer session of the Second International Conference on Cervical Cancer held at The University of Texas M. D. Anderson Cancer Center, April 11-14, 2002. The authors provide an overview of mathematical modeling, especially decision analysis and cost-effectiveness analysis, and examples of how it can be used for clinical decision making regarding the prevention, diagnosis, and treatment of cervical cancer. Included are applications as well as theory regarding decision science and cervical cancer. Mathematical modeling can answer such questions as the optimal frequency for screening, the optimal age to stop screening, and the optimal way to diagnose cervical cancer. Results from one mathematical model demonstrated that a vaccine against high-risk strains of human papillomavirus was a cost-effective use of resources, and discussion of another model demonstrated the importance of collecting direct non-health care costs and time costs for cost-effectiveness analysis. Research presented indicated that care must be taken when applying the results of population-wide, cost-effectiveness analyses to reduce health disparities. Mathematical modeling can encompass a variety of theoretical and applied issues regarding decision science and cervical cancer. The ultimate objective of using decision-analytic and cost-effectiveness models is to identify ways to improve women's health at an economically reasonable cost. Copyright 2003 American Cancer Society.

Recovery and decision-making involvement in people with severe mental illness from six countries: a prospective observational study.

PubMed

Loos, Sabine; Clarke, Eleanor; Jordan, Harriet; Puschner, Bernd; Fiorillo, Andrea; Luciano, Mario; Ivánka, Tibor; Magyar, Erzsébet; Krogsgaard-Bording, Malene; Østermark-Sørensen, Helle; Rössler, Wulf; Kawohl, Wolfram; Mayer, Benjamin; Slade, Mike

2017-01-23

Clinical decision-making is the vehicle of health care provision, and level of involvement predicts implementation and satisfaction. The aim of this study was to investigate the impact of decision-making experience on recovery. Data derived from an observational cohort study "Clinical decision making and outcome in routine care for people with severe mental illness" (CEDAR). Adults (aged 18-60) meeting standardised criteria for severe mental illness were recruited from caseloads of outpatient and community mental health services in six European countries. After consenting, they were assessed using standardised measures of decision-making, clinical outcome and stage of recovery at baseline and 1 year later. Latent class analysis was used to identify course of recovery, and proportional odds models to investigate predictors of recovery stage and change. Participants (n = 581) clustered into three stages of recovery at baseline: Moratorium (N = 115; 19.8%), Awareness/Preparation (N = 145; 25.0%) and Rebuilding/Growth (N = 321; 55.2%). Higher stage was cross-sectionally associated with being male, married, living alone or with parents, and having better patient-rated therapeutic alliance and fewer symptoms. The model accounted for 40% of the variance in stage of recovery. An increased chance of worse outcome (change over 1 year to lower stage of recovery) was found for patients with active involvement compared with either shared (OR = 1.84, 95% CI 1.15-2.94) or passive (OR = 1.71, 95% CI = 1.00-2.95) involvement. Overall, both process (therapeutic relationship) and outcome (symptomatology) are cross-sectionally associated with stage of recovery. Patient-rated decision-making involvement and change in stage of recovery are associated. Joint consideration of decision practise within the recovery process between patient and clinician is supposed to be a useful strategy to improve clinical practice (ISRCTN registry: ISRCTN75841675. Retrospectively registered 15 September 2010).

Using sense-making theory to aid understanding of the recognition, assessment and management of pain in patients with dementia in acute hospital settings.

PubMed

Dowding, Dawn; Lichtner, Valentina; Allcock, Nick; Briggs, Michelle; James, Kirstin; Keady, John; Lasrado, Reena; Sampson, Elizabeth L; Swarbrick, Caroline; José Closs, S

2016-01-01

The recognition, assessment and management of pain in hospital settings is suboptimal, and is a particular challenge in patients with dementia. The existing process guiding pain assessment and management in clinical settings is based on the assumption that nurses follow a sequential linear approach to decision making. In this paper we re-evaluate this theoretical assumption drawing on findings from a study of pain recognition, assessment and management in patients with dementia. To provide a revised conceptual model of pain recognition, assessment and management based on sense-making theories of decision making. The research we refer to is an exploratory ethnographic study using nested case sites. Patients with dementia (n=31) were the unit of data collection, nested in 11 wards (vascular, continuing care, stroke rehabilitation, orthopaedic, acute medicine, care of the elderly, elective and emergency surgery), located in four NHS hospital organizations in the UK. Data consisted of observations of patients at bedside (170h in total); observations of the context of care; audits of patient hospital records; documentary analysis of artefacts; semi-structured interviews (n=56) and informal open conversations with staff and carers (family members). Existing conceptualizations of pain recognition, assessment and management do not fully explain how the decision process occurs in clinical practice. Our research indicates that pain recognition, assessment and management is not an individual cognitive activity; rather it is carried out by groups of individuals over time and within a specific organizational culture or climate, which influences both health care professional and patient behaviour. We propose a revised theoretical model of decision making related to pain assessment and management for patients with dementia based on theories of sense-making, which is reflective of the reality of clinical decision making in acute hospital wards. The revised model recognizes the salience of individual cognition as well as acknowledging that decisions are constructed through social interaction and organizational context. The model will be used in further research to develop decision support interventions to assist with the assessment and management of patients with dementia in acute hospital settings. Copyright © 2015. Published by Elsevier Ltd.

A proposed model for economic evaluations of major depressive disorder.

PubMed

Haji Ali Afzali, Hossein; Karnon, Jonathan; Gray, Jodi

2012-08-01

In countries like UK and Australia, the comparability of model-based analyses is an essential aspect of reimbursement decisions for new pharmaceuticals, medical services and technologies. Within disease areas, the use of models with alternative structures, type of modelling techniques and/or data sources for common parameters reduces the comparability of evaluations of alternative technologies for the same condition. The aim of this paper is to propose a decision analytic model to evaluate long-term costs and benefits of alternative management options in patients with depression. The structure of the proposed model is based on the natural history of depression and includes clinical events that are important from both clinical and economic perspectives. Considering its greater flexibility with respect to handling time, discrete event simulation (DES) is an appropriate simulation platform for modelling studies of depression. We argue that the proposed model can be used as a reference model in model-based studies of depression improving the quality and comparability of studies.

Deciding about fast and slow decisions.

PubMed

Croskerry, Pat; Petrie, David A; Reilly, James B; Tait, Gordon

2014-02-01

Two reports in this issue address the important topic of clinical decision making. Dual process theory has emerged as the dominant model for understanding the complex processes that underlie human decision making. This theory distinguishes between the reflexive, autonomous processes that characterize intuitive decision making and the deliberate reasoning of an analytical approach. In this commentary, the authors address the polarization of viewpoints that has developed around the relative merits of the two systems. Although intuitive processes are typically fast and analytical processes slow, speed alone does not distinguish them. In any event, the majority of decisions in clinical medicine are not dependent on very short response times. What does appear relevant to diagnostic ease and accuracy is the degree to which the symptoms of the disease being diagnosed are characteristic ones. There are also concerns around some methodological issues related to research design in this area of enquiry. Reductionist approaches that attempt to isolate dependent variables may create such artificial experimental conditions that both external and ecological validity are sacrificed. Clinical decision making is a complex process with many independent (and interdependent) variables that need to be separated out in a discrete fashion and then reflected on in real time to preserve the fidelity of clinical practice. With these caveats in mind, the authors believe that research in this area should promote a better understanding of clinical practice and teaching by focusing less on the deficiencies of intuitive and analytical systems and more on their adaptive strengths.

The role of clinician emotion in clinical reasoning: Balancing the analytical process.

PubMed

Langridge, Neil; Roberts, Lisa; Pope, Catherine

2016-02-01

This review paper identifies and describes the role of clinicians' memory, emotions and physical responses in clinical reasoning processes. Clinical reasoning is complex and multi-factorial and key models of clinical reasoning within musculoskeletal physiotherapy are discussed, highlighting the omission of emotion and subsequent physical responses and how these can impact upon a clinician when making a decision. It is proposed that clinicians should consider the emotions associated with decision-making, especially when there is concern surrounding a presentation. Reflecting on practice in the clinical environment and subsequently applying this to a patient presentation should involve some acknowledgement of clinicians' physical responses, emotions and how they may play a part in any decision made. Presenting intuition and gut-feeling as separate reasoning methods and how these processes co-exist with other more accepted reasoning such as hypothetico-deductive is also discussed. Musculoskeletal physiotherapy should consider the elements of feelings, emotions and physical responses when applying reflective practice principles. Furthermore, clinicians dealing with difficult and challenging presentations should look at the emotional as well as the analytical experience when justifying decisions and learning from practice. Copyright © 2015 Elsevier Ltd. All rights reserved.

Deciding to Come Out to Parents: Toward a Model of Sexual Orientation Disclosure Decisions.

PubMed

Grafsky, Erika L

2017-08-16

The purpose of this study was to conduct research to understand nonheterosexual youths' decision to disclose their sexual orientation information to their parents. The sample for this study includes 22 youth between the ages of 14 and 21. Constructivist grounded theory guided the qualitative methodology and data analysis. The findings from this study posit an emerging model of sexual orientation disclosure decisions comprised of four interrelated factors that influence the decision to disclose or not disclose, as well as a description of the mechanism through which disclosure either does or does not occur. Clinical implications and recommendations for further research are provided. © 2017 Family Process Institute.

Combining the Generic Entity-Attribute-Value Model and Terminological Models into a Common Ontology to Enable Data Integration and Decision Support.

PubMed

Bouaud, Jacques; Guézennec, Gilles; Séroussi, Brigitte

2018-01-01

The integration of clinical information models and termino-ontological models into a unique ontological framework is highly desirable for it facilitates data integration and management using the same formal mechanisms for both data concepts and information model components. This is particularly true for knowledge-based decision support tools that aim to take advantage of all facets of semantic web technologies in merging ontological reasoning, concept classification, and rule-based inferences. We present an ontology template that combines generic data model components with (parts of) existing termino-ontological resources. The approach is developed for the guideline-based decision support module on breast cancer management within the DESIREE European project. The approach is based on the entity attribute value model and could be extended to other domains.

Evaluating the decision accuracy and speed of clinical data visualizations.

PubMed

Pieczkiewicz, David S; Finkelstein, Stanley M

2010-01-01

Clinicians face an increasing volume of biomedical data. Assessing the efficacy of systems that enable accurate and timely clinical decision making merits corresponding attention. This paper discusses the multiple-reader multiple-case (MRMC) experimental design and linear mixed models as means of assessing and comparing decision accuracy and latency (time) for decision tasks in which clinician readers must interpret visual displays of data. These tools can assess and compare decision accuracy and latency (time). These experimental and statistical techniques, used extensively in radiology imaging studies, offer a number of practical and analytic advantages over more traditional quantitative methods such as percent-correct measurements and ANOVAs, and are recommended for their statistical efficiency and generalizability. An example analysis using readily available, free, and commercial statistical software is provided as an appendix. While these techniques are not appropriate for all evaluation questions, they can provide a valuable addition to the evaluative toolkit of medical informatics research.

Decision Making in Health and Medicine

NASA Astrophysics Data System (ADS)

Hunink, Myriam; Glasziou, Paul; Siegel, Joanna; Weeks, Jane; Pliskin, Joseph; Elstein, Arthur; Weinstein, Milton C.

2001-11-01

Decision making in health care means navigating through a complex and tangled web of diagnostic and therapeutic uncertainties, patient preferences and values, and costs. In addition, medical therapies may include side effects, surgery may lead to undesirable complications, and diagnostic technologies may produce inconclusive results. In many clinical and health policy decisions it is necessary to counterbalance benefits and risks, and to trade off competing objectives such as maximizing life expectancy vs optimizing quality of life vs minimizing the required resources. This textbook plots a clear course through these complex and conflicting variables. It clearly explains and illustrates tools for integrating quantitative evidence-based data and subjective outcome values in making clinical and health policy decisions. An accompanying CD-ROM features solutions to the exercises, PowerPoint® presentations of the illustrations, and sample models and tables.

Understanding surgeon decision making in the use of radiotherapy as neoadjuvant treatment in rectal cancer.

PubMed

Ansari, Nabila; Young, Christopher J; Schlub, Timothy E; Dhillon, Haryana M; Solomon, Michael J

2015-12-01

Strong evidence supports the use of neoadjuvant radiotherapy in rectal cancer to improve local control. This randomised controlled trial aimed to determine the effect of clinical and non-clinical factors on decision making by colorectal surgeons in patients with rectal cancer. Two surveys comprising vignettes of alternating short (4) and long (12) cues identified previously as important in rectal cancer, were randomly assigned to all members of the CSSANZ. Respondents chose from three possible treatments: long course chemoradiotherapy (LC), short course radiotherapy (SC) or surgery alone to investigate the effects on surgeon decision and confidence in decisions. Choice data were analysed using multinomial logistic regression models. 106 of 165 (64%) surgeons responded. LC was the preferred treatment choice in 73% of vignettes. Surgeons were more likely to recommend LC over SC (OR 1.79) or surgery alone (OR 1.99) when presented with the shorter, four-cue scenarios. There was no significant difference in confidence in decisions made when surgeons were presented with long cue vignettes (P = 0.57). Significant effects on the choice between LC, SC and surgery alone were tumour stage (P < 0.001), nodal status (P < 0.001), tumour position in the rectum (P < 0.001) and the circumferential location of the tumour (P < 0.001). A T4 tumour was the factor most likely associated with a recommendation against surgery alone (OR 335.96) or SC (OR 61.73). This study shows that clinical factors exert the greatest influence on surgeon decision making, which follows a "fast and frugal" heuristic decision making model. Copyright © 2015 IJS Publishing Group Limited. Published by Elsevier Ltd. All rights reserved.

Motivation for health information seeking and processing about clinical trial enrollment.

PubMed

Yang, Z Janet; McComas, Katherine; Gay, Geri; Leonard, John P; Dannenberg, Andrew J; Dillon, Hildy

2010-07-01

Low patient accrual in clinical trials poses serious concerns for the advancement of medical science in the United States. Past research has identified health communication as a crucial step in overcoming barriers to enrollment. However, few communication scholars have studied this problem from a sociopsychological perspective to understand what motivates people to look for or pay attention to information about clinical trial enrollment. This study applies the model of Risk Information Seeking and Processing (RISP) to this context of health decision making. By recognizing the uncertainties embedded in clinical trials, we view clinical trial enrollment as a case study of risk. With data from a random-digit-dial telephone survey of 500 adults living in the United States, we used structural equation modeling to test the central part of the RISP model. In particular, we examined the role of optimistic feelings, as a type of positive affect, in motivating information seeking and processing. Our results indicated that rather than exerting an indirect influence on information seeking through motivating a psychological need for more information, optimistic feelings have more direct relationships with information seeking and processing. Similarly, informational subjective norms also exhibit a more direct relationship with information seeking and processing. These results suggest merit in applying the RISP model to study health decision making related to clinical trial enrollment. Our findings also render practical implications on how to improve communication about clinical trial enrollment.

Thinking Together: Modeling Clinical Decision-Support as a Sociotechnical System

PubMed Central

Hussain, Mustafa I.; Reynolds, Tera L.; Mousavi, Fatemeh E.; Chen, Yunan; Zheng, Kai

2017-01-01

Computerized clinical decision-support systems are members of larger sociotechnical systems, composed of human and automated actors, who send, receive, and manipulate artifacts. Sociotechnical consideration is rare in the literature. This makes it difficult to comparatively evaluate the success of CDS implementations, and it may also indicate that sociotechnical context receives inadequate consideration in practice. To facilitate sociotechnical consideration, we developed the Thinking Together model, a flexible diagrammatical means of representing CDS systems as sociotechnical systems. To develop this model, we examined the literature with the lens of Distributed Cognition (DCog) theory. We then present two case studies of vastly different CDSSs, one almost fully automated and the other with minimal automation, to illustrate the flexibility of the Thinking Together model. We show that this model, informed by DCog and the CDS literature, are capable of supporting both research, by enabling comparative evaluation, and practice, by facilitating explicit sociotechnical planning and communication. PMID:29854164

Clinical decision making: how surgeons do it.

PubMed

Crebbin, Wendy; Beasley, Spencer W; Watters, David A K

2013-06-01

Clinical decision making is a core competency of surgical practice. It involves two distinct types of mental process best considered as the ends of a continuum, ranging from intuitive and subconscious to analytical and conscious. In practice, individual decisions are usually reached by a combination of each, according to the complexity of the situation and the experience/expertise of the surgeon. An expert moves effortlessly along this continuum, according to need, able to apply learned rules or algorithms to specific presentations, choosing these as a result of either pattern recognition or analytical thinking. The expert recognizes and responds quickly to any mismatch between what is observed and what was expected, coping with gaps in information and making decisions even where critical data may be uncertain or unknown. Even for experts, the cognitive processes involved are difficult to articulate as they tend to be very complex. However, if surgeons are to assist trainees in developing their decision-making skills, the processes need to be identified and defined, and the competency needs to be measurable. This paper examines the processes of clinical decision making in three contexts: making a decision about how to manage a patient; preparing for an operative procedure; and reviewing progress during an operative procedure. The models represented here are an exploration of the complexity of the processes, designed to assist surgeons understand how expert clinical decision making occurs and to highlight the challenge of teaching these skills to surgical trainees. © 2013 The Authors. ANZ Journal of Surgery © 2013 Royal Australasian College of Surgeons.

Decision Aid Use in Primary Care: An Overview and Theory-Based Framework.

PubMed

Shultz, Cameron G; Jimbo, Masahito

2015-10-01

Increasing patients' participation in health care is a commonly cited goal. While patient decision aids can promote participation, they remain underutilized. Theory-based models that assess barriers and facilitators to sustained decision aid use are needed. The ready, willing, and able model specifies three preconditions for behavioral change. We present a descriptive analysis of the uptake of patient decision aids in the primary care setting and show how the ready, willing, and able model can be used to identify potential barriers and facilitators. An Ovid Medline literature search from January 2004 to November 2014 was used; additional sources were identified from reference lists and through peer consultations. Barriers and facilitators to decision aid use were identified and grouped into salient themes. The ready, willing, and able model provided a simple yet practical framework for identifying the mechanisms that facilitate (or work against) the adoption of patient decision aids within primary care. While time was a prominent barrier, additional barriers such as perceived legitimacy, clinic capacity, processes of care, and the overarching health care environment were also noted. The ready, willing, and able model posits that several preconditions must first be satisfied before sustained use of patient decision aids can take hold. By pinpointing bottlenecks, the model can inform policies and tailored interventions to target identified problems. Using the model to troubleshoot for bottlenecks prior to the implementation of a decision aid could help to improve uptake and sustained use within the primary care setting.

The Value of Decision Analytical Modeling in Surgical Research: An Example of Laparoscopic Versus Open Distal Pancreatectomy.

PubMed

Tax, Casper; Govaert, Paulien H M; Stommel, Martijn W J; Besselink, Marc G H; Gooszen, Hein G; Rovers, Maroeska M

2017-11-02

To illustrate how decision modeling may identify relevant uncertainty and can preclude or identify areas of future research in surgery. To optimize use of research resources, a tool is needed that assists in identifying relevant uncertainties and the added value of reducing these uncertainties. The clinical pathway for laparoscopic distal pancreatectomy (LDP) versus open (ODP) for nonmalignant lesions was modeled in a decision tree. Cost-effectiveness based on complications, hospital stay, costs, quality of life, and survival was analyzed. The effect of existing uncertainty on the cost-effectiveness was addressed, as well as the expected value of eliminating uncertainties. Based on 29 nonrandomized studies (3.701 patients) the model shows that LDP is more cost-effective compared with ODP. Scenarios in which LDP does not outperform ODP for cost-effectiveness seem unrealistic, e.g., a 30-day mortality rate of 1.79 times higher after LDP as compared with ODP, conversion in 62.2%, surgically repair of incisional hernias in 21% after LDP, or an average 2.3 days longer hospital stay after LDP than after ODP. Taking all uncertainty into account, LDP remained more cost-effective. Minimizing these uncertainties did not change the outcome. The results show how decision analytical modeling can help to identify relevant uncertainty and guide decisions for future research in surgery. Based on the current available evidence, a randomized clinical trial on complications, hospital stay, costs, quality of life, and survival is highly unlikely to change the conclusion that LDP is more cost-effective than ODP.

Reconciling the principle of patient autonomy with the practice of informed consent: decision-making about prognostication in uveal melanoma.

PubMed

Cook, Sharon A; Damato, Bertil; Marshall, Ernie; Salmon, Peter

2011-12-01

Influential views on how to protect patient autonomy in clinical care have been greatly shaped by rational and deliberative models of decision-making. Our aim was to understand how the general principle of respecting autonomy can be reconciled with the local reality of obtaining consent in a clinical situation that precludes extended deliberation. We interviewed 22 patients with intraocular melanoma who had been offered cytogenetic tumour typing to indicate whether the tumour was likely to shorten life considerably. They were interviewed before and/or up to 36 months after receiving cytogenetic results. Patients described their decision-making about the test and how they anticipated and used the results. Their accounts were analysed qualitatively, using inconsistencies at a descriptive level to guide interpretative analysis. Patients did not see a decision to be made. For those who accepted testing, their choice reflected trust of what the clinicians offered them. Patients anticipated that a good prognosis would be reassuring, but this response was not evident. Although they anticipated that a poor prognosis would enable end-of-life planning, adverse results were interpreted hopefully. In general, the meaning of the test for patients was not separable from ongoing care. Models of decision-making and associated consent procedures that emphasize patients' active consideration of isolated decision-making opportunities are invalid for clinical situations such as this. Hence, responsibility for ensuring that a procedure protects patients' interests rests with practitioners who offer it and cannot be delegated to patients. © 2010 Blackwell Publishing Ltd.

Clinical engineering and risk management in healthcare technological process using architecture framework.

PubMed

Signori, Marcos R; Garcia, Renato

2010-01-01

This paper presents a model that aids the Clinical Engineering to deal with Risk Management in the Healthcare Technological Process. The healthcare technological setting is complex and supported by three basics entities: infrastructure (IS), healthcare technology (HT), and human resource (HR). Was used an Enterprise Architecture - MODAF (Ministry of Defence Architecture Framework) - to model this process for risk management. Thus, was created a new model to contribute to the risk management in the HT process, through the Clinical Engineering viewpoint. This architecture model can support and improve the decision making process of the Clinical Engineering to the Risk Management in the Healthcare Technological process.

Balancing the benefits and costs of antibiotic drugs: the TREAT model.

PubMed

Leibovici, L; Paul, M; Andreassen, S

2010-12-01

TREAT is a computerized decision support system aimed at improving empirical antibiotic treatment of inpatients with suspected bacterial infections. It contains a model that balances, for each antibiotic choice (including 'no antibiotics'), expected benefit and expected costs. The main benefit afforded by appropriate, empirical, early antibiotic treatment in moderate to severe infections is a better chance of survival. Each antibiotic drug was consigned three cost components: cost of the drug and administration; cost of side effects; and costs of future resistance. 'No treatment' incurs no costs. The model worked well for decision support. Its analysis showed, yet again, that for moderate to severe infections, a model that does not include costs of resistance to future patients will always return maximum antibiotic treatment. Two major moral decisions are hidden in the model: how to take into account the limited life-expectancy and limited quality of life of old or very sick patients; and how to assign a value for a life-year of a future, unnamed patient vs. the present, individual patient. © 2010 The Authors. Clinical Microbiology and Infection © 2010 European Society of Clinical Microbiology and Infectious Diseases.

Estimation of Survival Probabilities for Use in Cost-effectiveness Analyses: A Comparison of a Multi-state Modeling Survival Analysis Approach with Partitioned Survival and Markov Decision-Analytic Modeling

PubMed Central

Williams, Claire; Lewsey, James D.; Mackay, Daniel F.; Briggs, Andrew H.

2016-01-01

Modeling of clinical-effectiveness in a cost-effectiveness analysis typically involves some form of partitioned survival or Markov decision-analytic modeling. The health states progression-free, progression and death and the transitions between them are frequently of interest. With partitioned survival, progression is not modeled directly as a state; instead, time in that state is derived from the difference in area between the overall survival and the progression-free survival curves. With Markov decision-analytic modeling, a priori assumptions are often made with regard to the transitions rather than using the individual patient data directly to model them. This article compares a multi-state modeling survival regression approach to these two common methods. As a case study, we use a trial comparing rituximab in combination with fludarabine and cyclophosphamide v. fludarabine and cyclophosphamide alone for the first-line treatment of chronic lymphocytic leukemia. We calculated mean Life Years and QALYs that involved extrapolation of survival outcomes in the trial. We adapted an existing multi-state modeling approach to incorporate parametric distributions for transition hazards, to allow extrapolation. The comparison showed that, due to the different assumptions used in the different approaches, a discrepancy in results was evident. The partitioned survival and Markov decision-analytic modeling deemed the treatment cost-effective with ICERs of just over £16,000 and £13,000, respectively. However, the results with the multi-state modeling were less conclusive, with an ICER of just over £29,000. This work has illustrated that it is imperative to check whether assumptions are realistic, as different model choices can influence clinical and cost-effectiveness results. PMID:27698003

Estimation of Survival Probabilities for Use in Cost-effectiveness Analyses: A Comparison of a Multi-state Modeling Survival Analysis Approach with Partitioned Survival and Markov Decision-Analytic Modeling.

PubMed

Williams, Claire; Lewsey, James D; Mackay, Daniel F; Briggs, Andrew H

2017-05-01

Modeling of clinical-effectiveness in a cost-effectiveness analysis typically involves some form of partitioned survival or Markov decision-analytic modeling. The health states progression-free, progression and death and the transitions between them are frequently of interest. With partitioned survival, progression is not modeled directly as a state; instead, time in that state is derived from the difference in area between the overall survival and the progression-free survival curves. With Markov decision-analytic modeling, a priori assumptions are often made with regard to the transitions rather than using the individual patient data directly to model them. This article compares a multi-state modeling survival regression approach to these two common methods. As a case study, we use a trial comparing rituximab in combination with fludarabine and cyclophosphamide v. fludarabine and cyclophosphamide alone for the first-line treatment of chronic lymphocytic leukemia. We calculated mean Life Years and QALYs that involved extrapolation of survival outcomes in the trial. We adapted an existing multi-state modeling approach to incorporate parametric distributions for transition hazards, to allow extrapolation. The comparison showed that, due to the different assumptions used in the different approaches, a discrepancy in results was evident. The partitioned survival and Markov decision-analytic modeling deemed the treatment cost-effective with ICERs of just over £16,000 and £13,000, respectively. However, the results with the multi-state modeling were less conclusive, with an ICER of just over £29,000. This work has illustrated that it is imperative to check whether assumptions are realistic, as different model choices can influence clinical and cost-effectiveness results.

What perceptions do patients have of decision making (DM)? Toward an integrative patient-centered care model. A qualitative study using focus-group interviews.

PubMed

Moreau, Alain; Carol, Laurent; Dedianne, Marie Cécile; Dupraz, Christian; Perdrix, Corinne; Lainé, Xavier; Souweine, Gilbert

2012-05-01

To understand patients' perceptions of decision making and identify relationships among decision-making models. This qualitative study was made up of four focus group interviews (elderly persons, users of health support groups, students, and rural inhabitants). Participants were asked to report their perceptions of decision making in three written clinical scenarios (hypertension, breast cancer, prostate cancer). The analysis was based on the principles of grounded theory. Most patients perceived decision making as shared decision making, a deliberative question-response interaction with the physician that allowed patients to be experts in obtaining clearer information, participating in the care process, and negotiating compromises with physician preferences. Requesting second opinions allowed patients to maintain control, even within the paternalistic model preferred by elderly persons. Facilitating factors (trust, qualitative non-verbal communication, time to think) and obstacles (serious/emergency situations, perceived inadequate scientific competence, problems making requests, fear of knowing) were also part of shared decision making. In the global concept of patient-centered care, shared decision making can be flexible and can integrate paternalistic and informative models. Physicians' expertise should be associated with biomedical and relational skills through listening to, informing, and advising patients, and by supporting patients' choices. Copyright © 2011 Elsevier Ireland Ltd. All rights reserved.

Analyzing the effectiveness of teaching and factors in clinical decision-making.

PubMed

Hsieh, Ming-Chen; Lee, Ming-Shinn; Chen, Tsung-Ying; Tsai, Tsuen-Chiuan; Pai, Yi-Fong; Sheu, Min-Muh

2017-01-01

The aim of this study is to prepare junior physicians, clinical education should focus on the teaching of clinical decision-making. This research is designed to explore teaching of clinical decision-making and to analyze the benefits of an "Analogy guide clinical decision-making" as a learning intervention for junior doctors. This study had a "quasi-experimental design" and was conducted in a medical center in eastern Taiwan. Participants and Program Description: Thirty junior doctors and three clinical teachers were involved in the study. The experimental group (15) received 1 h of instruction from the "Analogy guide for teaching clinical decision-making" every day for 3 months. Program Evaluation: A "Clinical decision-making self-evaluation form" was used as the assessment tool to evaluate participant learning efficiency before and after the teaching program. Semi-structured qualitative research interviews were also conducted. We found using the analogy guide for teaching clinical decision-making could help enhance junior doctors' self-confidence. Important factors influencing clinical decision-making included workload, decision-making, and past experience. Clinical teaching using the analogy guide for clinical decision-making may be a helpful tool for training and can contribute to a more comprehensive understanding of decision-making.

Modeling the Innovation-Decision Process: Dissemination and Adoption of a Motivational Interviewing Preparatory Procedure in Addiction Outpatient Clinics

PubMed Central

Walitzer, Kimberly S.; Dermen, Kurt H.; Barrick, Christopher; Shyhalla, Kathleen

2015-01-01

Widespread adoption of empirically-supported treatment innovations has the potential to improve effectiveness of treatment received by individuals with substance use disorders. However, the process of disseminating such innovations has been complex, slow, and difficult. We empirically describe the dissemination and adoption of a treatment innovation – an alcohol-treatment preparatory therapeutic procedure based on motivational interviewing (MI) – in the context of Rogers’ (2003) five stages of innovation-decision process (knowledge, persuasion, decision, implementation and confirmation). To this end, 145 randomly-chosen outpatient addiction treatment clinics in New York State received an onsite visit from a project trainer delivering one of three randomly-assigned dissemination intensities: a 15-minute, a half-day or a full-day presentation. Across these clinics, 141 primary administrators and 837 clinicians completed questionnaires assessing aspects of five innovation-decision stages. At each clinic, questionnaire administration occurred immediately pre- and post-dissemination, as well as one and six months after dissemination. Consistent with Rogers’ theory, earlier stages of the innovation-decision process predicted later stages. As hypothesized, dissemination intensity predicted clinicians’ post-dissemination knowledge. Clinician baseline characteristics (including gender, pre-dissemination knowledge regarding the MI preparatory technique, education, case load, beliefs regarding the nature of alcohol problems, and beliefs and behavior with regard to therapeutic style) predicted knowledge and persuasion stage variables. One baseline clinic characteristic (i.e., clinic mean beliefs and behavior regarding an MI-consistent therapeutic style) predicted implementation stage variables. Findings suggest that dissemination strategies should accommodate clinician and clinic characteristics. PMID:25934460

A Model for Developing Clinical Analytics Capacity: Closing the Loops on Outcomes to Optimize Quality.

PubMed

Eggert, Corinne; Moselle, Kenneth; Protti, Denis; Sanders, Dale

2017-01-01

Closed Loop Analytics© is receiving growing interest in healthcare as a term referring to information technology, local data and clinical analytics working together to generate evidence for improvement. The Closed Loop Analytics model consists of three loops corresponding to the decision-making levels of an organization and the associated data within each loop - Patients, Protocols, and Populations. The authors propose that each of these levels should utilize the same ecosystem of electronic health record (EHR) and enterprise data warehouse (EDW) enabled data, in a closed-loop fashion, with that data being repackaged and delivered to suit the analytic and decision support needs of each level, in support of better outcomes.

How modeling and simulation have enhanced decision making in new drug development.

PubMed

Miller, Raymond; Ewy, Wayne; Corrigan, Brian W; Ouellet, Daniele; Hermann, David; Kowalski, Kenneth G; Lockwood, Peter; Koup, Jeffrey R; Donevan, Sean; El-Kattan, Ayman; Li, Cheryl S W; Werth, John L; Feltner, Douglas E; Lalonde, Richard L

2005-04-01

The idea of model-based drug development championed by Lewis Sheiner, in which pharmacostatistical models of drug efficacy and safety are developed from preclinical and available clinical data, offers a quantitative approach to improving drug development and development decision-making. Examples are presented that support this paradigm. The first example describes a preclinical model of behavioral activity to predict potency and time-course of response in humans and assess the potential for differentiation between compounds. This example illustrates how modeling procedures expounded by Lewis Sheiner provided the means to differentiate potency and the lag time between drug exposure and response and allow for rapid decision making and dose selection. The second example involves planning a Phase 2a dose-ranging and proof of concept trial in Alzheimer's disease (AD). The issue was how to proceed with the study and what criteria to use for a go/no go decision. The combined knowledge of AD disease progression, and preclinical and clinical information about the drug were used to simulate various clinical trial scenarios to identify an efficient and effective Phase 2 study. A design was selected and carried out resulting in a number of important learning experiences as well as extensive financial savings. The motivation for this case in point was the "Learn-Confirm" paradigm described by Lewis Sheiner. The final example describes the use of Pharmacokinetic and Pharmacodynamic (PK/PD) modeling and simulation to confirm efficacy across doses. In the New Drug Application for gabapentin, data from two adequate and well-controlled clinical trials was submitted to the Food and Drug Administration (FDA) in support of the approval of the indication for the treatment of post-herpetic neuralgia. The clinical trial data was not replicated for each of the sought dose levels in the drug application presenting a regulatory dilemma. Exposure response analysis submitted in the New Drug Application was applied to confirm the evidence of efficacy across these dose levels. Modeling and simulation analyses showed that the two studies corroborate each other with respect to the pain relief profiles. The use of PK/PD information confirmed evidence of efficacy across the three studied doses, eliminating the need for additional clinical trials and thus supporting the approval of the product. It can be speculated that the work by Lewis Sheiner reflected in the FDA document titled "Innovation or Stagnation: Challenge and Opportunity on the Critical Path to New Medical Products" made this scientific approach to the drug approval process possible.

Multimorbidity, service organization and clinical decision making in primary care: a qualitative study.

PubMed

Bower, Peter; Macdonald, Wendy; Harkness, Elaine; Gask, Linda; Kendrick, Tony; Valderas, Jose M; Dickens, Chris; Blakeman, Tom; Sibbald, Bonnie

2011-10-01

Primary care professionals often manage patients with multiple long-term health conditions, but managing multimorbidity is challenging given time and resource constraints and interactions between conditions. To explore GP and nurse perceptions of multimorbidity and the influence on service organization and clinical decision making. A qualitative interview study with primary care professionals in practices in Greater Manchester, U.K. Interviews were conducted with 15 GPs and 10 practice nurses. Primary care professionals identified tensions between delivering care to meet quality targets and fulfilling the patient's agenda, tensions which are exacerbated in multimorbidity. They were aware of the inconvenience suffered by patients through attendance at multiple clinic appointments when care was structured around individual conditions. They reported difficulties managing patients with multimorbidity in limited consultation time, which led to adoption of an 'additive-sequential' decision-making model which dealt with problems in priority order until consultation resources were exhausted, when further management was deferred. Other challenges included the need for patients to co-ordinate their care, the difficulties of self-management support in multimorbidity and problems of making sense of the relationships between physical and mental health. Doctor and nurse accounts included limited consideration of multimorbidity in terms of the interactions between conditions or synergies between management of different conditions. Primary care professionals identify a number of challenges in care for multimorbidity and adopt a particular model of decision making to deliver care for multiple individual conditions. However, they did not describe specific decision making around managing multimorbidity per se.

Dashboard systems: implementing pharmacometrics from bench to bedside.

PubMed

Mould, Diane R; Upton, Richard N; Wojciechowski, Jessica

2014-09-01

In recent years, there has been increasing interest in the development of medical decision-support tools, including dashboard systems. Dashboard systems are software packages that integrate information and calculations about therapeutics from multiple components into a single interface for use in the clinical environment. Given the high cost of medical care, and the increasing need to demonstrate positive clinical outcomes for reimbursement, dashboard systems may become an important tool for improving patient outcome, improving clinical efficiency and containing healthcare costs. Similarly the costs associated with drug development are also rising. The use of model-based drug development (MBDD) has been proposed as a tool to streamline this process, facilitating the selection of appropriate doses and making informed go/no-go decisions. However, complete implementation of MBDD has not always been successful owing to a variety of factors, including the resources required to provide timely modeling and simulation updates. The application of dashboard systems in drug development reduces the resource requirement and may expedite updating models as new data are collected, allowing modeling results to be available in a timely fashion. In this paper, we present some background information on dashboard systems and propose the use of these systems both in the clinic and during drug development.

Evidence-based dentistry: a model for clinical practice.

PubMed

Faggion, Clóvis M; Tu, Yu-Kang

2007-06-01

Making decisions in dentistry should be based on the best evidence available. The objective of this study was to demonstrate a practical procedure and model that clinicians can use to apply the results of well-conducted studies to patient care by critically appraising the evidence with checklists and letter grade scales. To demonstrate application of this model for critically appraising the quality of research evidence, a hypothetical case involving an adult male with chronic periodontitis is used as an example. To determine the best clinical approach for this patient, a four-step, evidence-based model is demonstrated, consisting of the following: definition of a research question using the PICO format, search and selection of relevant literature, critical appraisal of identified research reports using checklists, and the application of evidence. In this model, the quality of research evidence was assessed quantitatively based on different levels of quality that are assigned letter grades of A, B, and C by evaluating the studies against the QUOROM (Quality of Reporting Meta-Analyses) and CONSORT (Consolidated Standards of Reporting Trials) checklists in a tabular format. For this hypothetical periodontics case, application of the model identified the best available evidence for clinical decision making, i.e., one randomized controlled trial and one systematic review of randomized controlled trials. Both studies showed similar answers for the research question. The use of a letter grade scale allowed an objective analysis of the quality of evidence. A checklist-driven model that assesses and applies evidence to dental practice may substantially improve dentists' decision making skill.

Classification and regression tree (CART) model to predict pulmonary tuberculosis in hospitalized patients.

PubMed

Aguiar, Fabio S; Almeida, Luciana L; Ruffino-Netto, Antonio; Kritski, Afranio Lineu; Mello, Fernanda Cq; Werneck, Guilherme L

2012-08-07

Tuberculosis (TB) remains a public health issue worldwide. The lack of specific clinical symptoms to diagnose TB makes the correct decision to admit patients to respiratory isolation a difficult task for the clinician. Isolation of patients without the disease is common and increases health costs. Decision models for the diagnosis of TB in patients attending hospitals can increase the quality of care and decrease costs, without the risk of hospital transmission. We present a predictive model for predicting pulmonary TB in hospitalized patients in a high prevalence area in order to contribute to a more rational use of isolation rooms without increasing the risk of transmission. Cross sectional study of patients admitted to CFFH from March 2003 to December 2004. A classification and regression tree (CART) model was generated and validated. The area under the ROC curve (AUC), sensitivity, specificity, positive and negative predictive values were used to evaluate the performance of model. Validation of the model was performed with a different sample of patients admitted to the same hospital from January to December 2005. We studied 290 patients admitted with clinical suspicion of TB. Diagnosis was confirmed in 26.5% of them. Pulmonary TB was present in 83.7% of the patients with TB (62.3% with positive sputum smear) and HIV/AIDS was present in 56.9% of patients. The validated CART model showed sensitivity, specificity, positive predictive value and negative predictive value of 60.00%, 76.16%, 33.33%, and 90.55%, respectively. The AUC was 79.70%. The CART model developed for these hospitalized patients with clinical suspicion of TB had fair to good predictive performance for pulmonary TB. The most important variable for prediction of TB diagnosis was chest radiograph results. Prospective validation is still necessary, but our model offer an alternative for decision making in whether to isolate patients with clinical suspicion of TB in tertiary health facilities in countries with limited resources.

Decaying relevance of clinical data towards future decisions in data-driven inpatient clinical order sets.

PubMed

Chen, Jonathan H; Alagappan, Muthuraman; Goldstein, Mary K; Asch, Steven M; Altman, Russ B

2017-06-01

Determine how varying longitudinal historical training data can impact prediction of future clinical decisions. Estimate the "decay rate" of clinical data source relevance. We trained a clinical order recommender system, analogous to Netflix or Amazon's "Customers who bought A also bought B..." product recommenders, based on a tertiary academic hospital's structured electronic health record data. We used this system to predict future (2013) admission orders based on different subsets of historical training data (2009 through 2012), relative to existing human-authored order sets. Predicting future (2013) inpatient orders is more accurate with models trained on just one month of recent (2012) data than with 12 months of older (2009) data (ROC AUC 0.91 vs. 0.88, precision 27% vs. 22%, recall 52% vs. 43%, all P<10 -10 ). Algorithmically learned models from even the older (2009) data was still more effective than existing human-authored order sets (ROC AUC 0.81, precision 16% recall 35%). Training with more longitudinal data (2009-2012) was no better than using only the most recent (2012) data, unless applying a decaying weighting scheme with a "half-life" of data relevance about 4 months. Clinical practice patterns (automatically) learned from electronic health record data can vary substantially across years. Gold standards for clinical decision support are elusive moving targets, reinforcing the need for automated methods that can adapt to evolving information. Prioritizing small amounts of recent data is more effective than using larger amounts of older data towards future clinical predictions. Copyright © 2017 The Authors. Published by Elsevier B.V. All rights reserved.

Animated-simulation modeling facilitates clinical-process costing.

PubMed

Zelman, W N; Glick, N D; Blackmore, C C

2001-09-01

Traditionally, the finance department has assumed responsibility for assessing process costs in healthcare organizations. To enhance process-improvement efforts, however, many healthcare providers need to include clinical staff in process cost analysis. Although clinical staff often use electronic spreadsheets to model the cost of specific processes, PC-based animated-simulation tools offer two major advantages over spreadsheets: they allow clinicians to interact more easily with the costing model so that it more closely represents the process being modeled, and they represent cost output as a cost range rather than as a single cost estimate, thereby providing more useful information for decision making.

Nurse supervisors' actions in relation to their decision-making style and ethical approach to clinical supervision.

PubMed

Berggren, Ingela; Severinsson, Elisabeth

2003-03-01

The aim of the study was to explore the decision-making style and ethical approach of nurse supervisors by focusing on their priorities and interventions in the supervision process. Clinical supervision promotes ethical awareness and behaviour in the nursing profession. A focus group comprised of four clinical nurse supervisors with considerable experience was studied using qualitative hermeneutic content analysis. The essence of the nurse supervisors' decision-making style is deliberations and priorities. The nurse supervisors' willingness, preparedness, knowledge and awareness constitute and form their way of creating a relationship. The nurse supervisors' ethical approach focused on patient situations and ethical principles. The core components of nursing supervision interventions, as demonstrated in supervision sessions, are: guilt, reconciliation, integrity, responsibility, conscience and challenge. The nurse supervisors' interventions involved sharing knowledge and values with the supervisees and recognizing them as nurses and human beings. Nurse supervisors frequently reflected upon the ethical principle of autonomy and the concept and substance of integrity. The nurse supervisors used an ethical approach that focused on caring situations in order to enhance the provision of patient care. They acted as role models, shared nursing knowledge and ethical codes, and focused on patient related situations. This type of decision-making can strengthen the supervisees' professional identity. The clinical nurse supervisors in the study were experienced and used evaluation decisions as their form of clinical decision-making activity. The findings underline the need for further research and greater knowledge in order to improve the understanding of the ethical approach to supervision.

Extensions to decision curve analysis, a novel method for evaluating diagnostic tests, prediction models and molecular markers

PubMed Central

Vickers, Andrew J; Cronin, Angel M; Elkin, Elena B; Gonen, Mithat

2008-01-01

Background Decision curve analysis is a novel method for evaluating diagnostic tests, prediction models and molecular markers. It combines the mathematical simplicity of accuracy measures, such as sensitivity and specificity, with the clinical applicability of decision analytic approaches. Most critically, decision curve analysis can be applied directly to a data set, and does not require the sort of external data on costs, benefits and preferences typically required by traditional decision analytic techniques. Methods In this paper we present several extensions to decision curve analysis including correction for overfit, confidence intervals, application to censored data (including competing risk) and calculation of decision curves directly from predicted probabilities. All of these extensions are based on straightforward methods that have previously been described in the literature for application to analogous statistical techniques. Results Simulation studies showed that repeated 10-fold crossvalidation provided the best method for correcting a decision curve for overfit. The method for applying decision curves to censored data had little bias and coverage was excellent; for competing risk, decision curves were appropriately affected by the incidence of the competing risk and the association between the competing risk and the predictor of interest. Calculation of decision curves directly from predicted probabilities led to a smoothing of the decision curve. Conclusion Decision curve analysis can be easily extended to many of the applications common to performance measures for prediction models. Software to implement decision curve analysis is provided. PMID:19036144

Extensions to decision curve analysis, a novel method for evaluating diagnostic tests, prediction models and molecular markers.

PubMed

Vickers, Andrew J; Cronin, Angel M; Elkin, Elena B; Gonen, Mithat

2008-11-26

Decision curve analysis is a novel method for evaluating diagnostic tests, prediction models and molecular markers. It combines the mathematical simplicity of accuracy measures, such as sensitivity and specificity, with the clinical applicability of decision analytic approaches. Most critically, decision curve analysis can be applied directly to a data set, and does not require the sort of external data on costs, benefits and preferences typically required by traditional decision analytic techniques. In this paper we present several extensions to decision curve analysis including correction for overfit, confidence intervals, application to censored data (including competing risk) and calculation of decision curves directly from predicted probabilities. All of these extensions are based on straightforward methods that have previously been described in the literature for application to analogous statistical techniques. Simulation studies showed that repeated 10-fold crossvalidation provided the best method for correcting a decision curve for overfit. The method for applying decision curves to censored data had little bias and coverage was excellent; for competing risk, decision curves were appropriately affected by the incidence of the competing risk and the association between the competing risk and the predictor of interest. Calculation of decision curves directly from predicted probabilities led to a smoothing of the decision curve. Decision curve analysis can be easily extended to many of the applications common to performance measures for prediction models. Software to implement decision curve analysis is provided.

Integrating Decision Making and Mental Health Interventions Research: Research Directions

PubMed Central

Wills, Celia E.; Holmes-Rovner, Margaret

2006-01-01

The importance of incorporating patient and provider decision-making processes is in the forefront of the National Institute of Mental Health (NIMH) agenda for improving mental health interventions and services. Key concepts in patient decision making are highlighted within a simplified model of patient decision making that links patient-level/“micro” variables to services-level/“macro” variables via the decision-making process that is a target for interventions. The prospective agenda for incorporating decision-making concepts in mental health research includes (a) improved measures for characterizing decision-making processes that are matched to study populations, complexity, and types of decision making; (b) testing decision aids in effectiveness research for diverse populations and clinical settings; and (c) improving the understanding and incorporation of preference concepts in enhanced intervention designs. PMID:16724158

Methodological issues in evaluating cost effectiveness of adjuvant aromatase inhibitors in early breast cancer: a need for improved modelling to aid decision making.

PubMed

Annemans, Lieven

2008-01-01

The optimal adjuvant hormonal strategy in post-menopausal women with early breast cancer is a subject of ongoing debate. Aromatase inhibitors (AIs) have been successfully evaluated in clinical trials that have compared them with a standard treatment of 5 years of tamoxifen. However, several options are available in terms of treatment schedule and selected drug. Systematic reviews of clinical trials and health economic evaluations attempt to contribute to the debate. The objective of this paper is to provide a critical review of existing health economic evaluations with a focus on those parameters and assumptions with the largest impact on final outcomes.A wide range of different inputs and assumptions exist, which make a comparison of results difficult, if not impossible. In particular, the modelling of recurrence rates over longer time horizons than those observed in clinical trials, a cornerstone of health economic modelling, is subject to quite different approaches. The practice of indirect comparison of different AIs without sufficiently acknowledging population differences is also bothersome. A list of key features (related to time horizon, clinical data input, patient subtypes, budget impact and model calibration) that an ideal model should have in order to better assist decision makers in this field is proposed.

Spontaneous Cerebellar Hematoma: Decision Making in Conscious Adults.

PubMed

Alkosha, Hazem M; Ali, Nabil Mansour

2017-06-01

To detect predictors of the clinical course and outcome of cerebellar hematoma in conscious patients that may help in decision making. This study entails retrospective and prospective review and collection of the demographic, clinical, and radiologic data of 92 patients with cerebellar hematoma presented conscious and initially treated conservatively. Primary outcome was deterioration lower than a Glasgow Coma Scale score of 14 and secondary outcome was Glasgow Outcome Scale score at discharge and 3 months later. Relevant data to primary outcome were used to create a prediction model and derive a risk score. The model was validated using a bootstrap technique and performance measures of the score were presented. Surgical interventions and secondary outcomes were correlated to the score to explore its use in future decision making. Demographic and clinical data showed no relevance to outcome. The relevant initial computed tomography criteria were used to build up the prediction model. A score was derived after the model proved to be valid using internal validation with bootstrapping technique. The score (0-6) had a cutoff value of ≥2, with sensitivity of 93.3% and specificity of 88.0%. It was found to have a significant negative association with the onset of neurologic deterioration, end point Glasgow Coma Scale scores and the Glasgow Outcome Scale scores at discharge. The score was positively correlated to the aggressiveness of surgical interventions and the length of hospital stay. Early definitive management is critical in conscious patients with cerebellar hematomas and can improve outcome. Our proposed score is a simple tool with high discrimination power that may help in timely decision making in those patients. Copyright © 2017 Elsevier Inc. All rights reserved.

Relational autonomy: moving beyond the limits of isolated individualism.

PubMed

Walter, Jennifer K; Ross, Lainie Friedman

2014-02-01

Although clinicians may value respecting a patient's or surrogate's autonomy in decision-making, it is not always clear how to proceed in clinical practice. The confusion results, in part, from which conception of autonomy is used to guide ethical practice. Reliance on an individualistic conception such as the "in-control agent" model prioritizes self-sufficiency in decision-making and highlights a decision-maker's capacity to have reason transcend one's emotional experience. An alternative model of autonomy, relational autonomy, highlights the social context within which all individuals exist and acknowledges the emotional and embodied aspects of decision-makers. These 2 conceptions of autonomy lead to different interpretations of several aspects of ethical decision-making. The in-control agent model believes patients or surrogates should avoid both the influence of others and emotional persuasion in decision-making. As a result, providers have a limited role to play and are expected to provide medical expertise but not interfere with the individual's decision-making process. In contrast, a relational autonomy approach acknowledges the central role of others in decision-making, including clinicians, who have a responsibility to engage patients' and surrogates' emotional experiences and offer clear guidance when patients are confronting serious illness. In the pediatric setting, in which decision-making is complicated by having a surrogate decision-maker in addition to a patient, these conceptions of autonomy also may influence expectations about the role that adolescents can play in decision-making.

[A study on participation in clinical decision making by home healthcare nurses].

PubMed

Kim, Se Young

2010-12-01

This study was done to identify participation by home healthcare nurses in clinical decision making and factors influencing clinical decision making. A descriptive survey was used to collect data from 68 home healthcare nurses in 22 hospital-based home healthcare services in Korea. To investigate participation, the researcher developed 3 scenarios through interviews with 5 home healthcare nurses. A self-report questionnaire composed of tools for characteristics, factors of clinical decision making, and participation was used. Participation was relatively high, but significantly lower in the design phase (F=3.51, p=.032). Competency in clinical decision making (r=.45, p<.001), perception of the decision maker role (r=.47, p<.001), and perception of the utility of clinical practice guidelines (r=.25, p=.043) were significantly correlated with participation. Competency in clinical decision making (Odds ratio [OR]=41.79, p=.007) and perception of the decision maker role (OR=15.09, p=.007) were significant factors predicting participation in clinical decision making by home healthcare nurses. In order to encourage participation in clinical decision making, education programs should be provided to home healthcare nurses. Official clinical practice guidelines should be used to support home healthcare nurses' participation in clinical decision making in cases where they can identify and solve the patient health problems.

A multimedia electronic patient record (ePR) system to improve decision support in pre- and rehabilitation through clinical and movement analysis

NASA Astrophysics Data System (ADS)

Liu, Brent; Documet, Jorge; McNitt-Gray, Sarah; Requejo, Phil; McNitt-Gray, Jill

2011-03-01

Clinical decisions for improving motor function in patients both with disability as well as improving an athlete's performance are made through clinical and movement analysis. Currently, this analysis facilitates identifying abnormalities in a patient's motor function for a large amount of neuro-musculoskeletal pathologies. However definitively identifying the underlying cause or long-term consequences of a specific abnormality in the patient's movement pattern is difficult since this requires information from multiple sources and formats across different times and currently relies on the experience and intuition of the expert clinician. In addition, this data must be persistent for longitudinal outcomes studies. Therefore a multimedia ePR system integrating imaging informatics data could have a significant impact on decision support within this clinical workflow. We present the design and architecture of such an ePR system as well as the data types that need integration in order to develop relevant decision support tools. Specifically, we will present two data model examples: 1) A performance improvement project involving volleyball athletes and 2) Wheelchair propulsion evaluation of patients with disabilities. The end result is a new frontier area of imaging informatics research within rehabilitation engineering and biomechanics.

Assessing the utility of the willingness/prototype model in predicting help-seeking decisions.

PubMed

Hammer, Joseph H; Vogel, David L

2013-01-01

Prior research on professional psychological help-seeking behavior has operated on the assumption that the decision to seek help is based on intentional and reasoned processes. However, research on the dual-process prototype/willingness model (PWM; Gerrard, Gibbons, Houlihan, Stock, & Pomery, 2008) suggests health-related decisions may also involve social reaction processes that influence one's spontaneous willingness (rather than planned intention) to seek help, given conducive circumstances. The present study used structural equation modeling to evaluate the ability of these 2 information-processing pathways (i.e., the reasoned pathway and the social reaction pathway) to predict help-seeking decisions among 182 college students currently experiencing clinical levels of psychological distress. Results indicated that when both pathways were modeled simultaneously, only the social reaction pathway independently accounted for significant variance in help-seeking decisions. These findings argue for the utility of the PWM framework in the context of professional psychological help seeking and hold implications for future counseling psychology research, prevention, and practice. PsycINFO Database Record (c) 2013 APA, all rights reserved.

Use of a computerized medication shared decision making tool in community mental health settings: impact on psychotropic medication adherence.

PubMed

Stein, Bradley D; Kogan, Jane N; Mihalyo, Mark J; Schuster, James; Deegan, Patricia E; Sorbero, Mark J; Drake, Robert E

2013-04-01

Healthcare reform emphasizes patient-centered care and shared decision-making. This study examined the impact on psychotropic adherence of a decision support center and computerized tool designed to empower and activate consumers prior to an outpatient medication management visit. Administrative data were used to identify 1,122 Medicaid-enrolled adults receiving psychotropic medication from community mental health centers over a two-year period from community mental health centers. Multivariate linear regression models were used to examine if tool users had higher rates of 180-day medication adherence than non-users. Older clients, Caucasian clients, those without recent hospitalizations, and those who were Medicaid-eligible due to disability had higher rates of 180-day medication adherence. After controlling for sociodemographics, clinical characteristics, baseline adherence, and secular changes over time, using the computerized tool did not affect adherence to psychotropic medications. The computerized decision tool did not affect medication adherence among clients in outpatient mental health clinics. Additional research should clarify the impact of decision-making tools on other important outcomes such as engagement, patient-prescriber communication, quality of care, self-management, and long-term clinical and functional outcomes.

Design, Development, and Initial Evaluation of a Terminology for Clinical Decision Support and Electronic Clinical Quality Measurement.

PubMed

Lin, Yanhua; Staes, Catherine J; Shields, David E; Kandula, Vijay; Welch, Brandon M; Kawamoto, Kensaku

2015-01-01

When coupled with a common information model, a common terminology for clinical decision support (CDS) and electronic clinical quality measurement (eCQM) could greatly facilitate the distributed development and sharing of CDS and eCQM knowledge resources. To enable such scalable knowledge authoring and sharing, we systematically developed an extensible and standards-based terminology for CDS and eCQM in the context of the HL7 Virtual Medical Record (vMR) information model. The development of this terminology entailed three steps: (1) systematic, physician-curated concept identification from sources such as the Health Information Technology Standards Panel (HITSP) and the SNOMED-CT CORE problem list; (2) concept de-duplication leveraging the Unified Medical Language System (UMLS) MetaMap and Metathesaurus; and (3) systematic concept naming using standard terminologies and heuristic algorithms. This process generated 3,046 concepts spanning 68 domains. Evaluation against representative CDS and eCQM resources revealed approximately 50-70% concept coverage, indicating the need for continued expansion of the terminology.

An analysis of clinical reasoning through a recent and comprehensive approach: the dual-process theory

PubMed Central

Pelaccia, Thierry; Tardif, Jacques; Triby, Emmanuel; Charlin, Bernard

2011-01-01

Context Clinical reasoning plays a major role in the ability of doctors to make diagnoses and decisions. It is considered as the physician's most critical competence, and has been widely studied by physicians, educationalists, psychologists and sociologists. Since the 1970s, many theories about clinical reasoning in medicine have been put forward. Purpose This paper aims at exploring a comprehensive approach: the “dual-process theory”, a model developed by cognitive psychologists over the last few years. Discussion After 40 years of sometimes contradictory studies on clinical reasoning, the dual-process theory gives us many answers on how doctors think while making diagnoses and decisions. It highlights the importance of physicians’ intuition and the high level of interaction between analytical and non-analytical processes. However, it has not received much attention in the medical education literature. The implications of dual-process models of reasoning in terms of medical education will be discussed. PMID:21430797

Design, Development, and Initial Evaluation of a Terminology for Clinical Decision Support and Electronic Clinical Quality Measurement

PubMed Central

Lin, Yanhua; Staes, Catherine J; Shields, David E; Kandula, Vijay; Welch, Brandon M; Kawamoto, Kensaku

2015-01-01

When coupled with a common information model, a common terminology for clinical decision support (CDS) and electronic clinical quality measurement (eCQM) could greatly facilitate the distributed development and sharing of CDS and eCQM knowledge resources. To enable such scalable knowledge authoring and sharing, we systematically developed an extensible and standards-based terminology for CDS and eCQM in the context of the HL7 Virtual Medical Record (vMR) information model. The development of this terminology entailed three steps: (1) systematic, physician-curated concept identification from sources such as the Health Information Technology Standards Panel (HITSP) and the SNOMED-CT CORE problem list; (2) concept de-duplication leveraging the Unified Medical Language System (UMLS) MetaMap and Metathesaurus; and (3) systematic concept naming using standard terminologies and heuristic algorithms. This process generated 3,046 concepts spanning 68 domains. Evaluation against representative CDS and eCQM resources revealed approximately 50–70% concept coverage, indicating the need for continued expansion of the terminology. PMID:26958220

Value of innovation for hematologic malignancies.

PubMed

Monia, Marchetti

2016-01-01

Several novel drugs are dramatically improving both lifespan and quality-of-life of patients with blood cancers. Prolonged disease duration and increased treatment costs for hematologic malignancies impose a relevant economic burden onto healthcare services, despite the low incidence of blood cancers. Therefore, an appropriate paradigm for valuing 'innovation' is urgently required in order to refine pricing and reimbursement decisions. Cost-per-QALY-gained is still the standard metric for assessing the 'incremental' value of new drugs; however, the high number of 'comparator' therapies and the huge variety of treatment sequences make plain two-treatment comparisons sub-optimal, while multiple-treatment and multiple-sequence comparisons require complex and less-transparent decision models. A repository of standard backbones for decision models might allow benchmarking and comparability among cost-effectiveness analyses; however, an international effort is required to build it up. Deontology recommends that hematologists act in optimizing healthcare resources while preserving patient-physician alliance, but clinical practice guidelines do not support doctors in balancing cost against clinical outcomes. Decision models of chronic blood cancers unexpectedly proved that cost might be an appropriate value for innovation if treatments avoided severe toxicity and further lines of treatments, despite the eventually long duration of treatment and the competing risk of death due to comorbidity and old age. The improved transparency of decision models allows sharing of relevant structural and analytic parameters (i.e., time horizon, comparator treatments, hierarchy of end-point, assumptions, source of data, sub-group analyses) by stakeholders, physicians and patients, making health economics a noble 'translator' of values for innovation.

A clinical decision rule to prioritize polysomnography in patients with suspected sleep apnea.

PubMed

Rodsutti, Julvit; Hensley, Michael; Thakkinstian, Ammarin; D'Este, Catherine; Attia, John

2004-06-15

To derive and validate a clinical decision rule that can help to prioritize patients who are on waiting lists for polysomnography, Prospective data collection on consecutive patients referred to a sleep center. The Newcastle Sleep Disorders Centre, University of Newcastle, NSW, Australia. Consecutive adult patients who had been scheduled for initial diagnostic polysomnography. Eight hundred and thirty-seven patients were used for derivation of the decision rule. An apnea-hypopnoea index of at least 5 was used as the cutoff point to diagnose sleep apnea. Fifteen clinical features were included in the analyses using logistic regression to construct a model from the derivation data set. Only 5 variables--age, sex, body mass index, snoring, and stopping breathing during sleep--were significantly associated with sleep apnea. A scoring scheme based on regression coefficients was developed, and the total score was trichotomized into low-, moderate-, and high-risk groups with prevalence of sleep apnea of 8%, 51%, and 82%, respectively. Color-coded tables were developed for ease of use. The clinical decision rule was validated on a separate set of 243 patients. Receiver operating characteristic analysis confirmed that the decision rule performed well, with the area under the curve being similar for both the derivation and validation sets: 0.81 and 0.79, P =.612. We conclude that this decision rule was able to accurately classify the risk of sleep apnea and will be useful for prioritizing patients with suspected sleep apnea who are on waiting lists for polysomnography.

Shared clinical decision making

PubMed Central

AlHaqwi, Ali I.; AlDrees, Turki M.; AlRumayyan, Ahmad; AlFarhan, Ali I.; Alotaibi, Sultan S.; AlKhashan, Hesham I.; Badri, Motasim

2015-01-01

Objectives: To determine preferences of patients regarding their involvement in the clinical decision making process and the related factors in Saudi Arabia. Methods: This cross-sectional study was conducted in a major family practice center in King Abdulaziz Medical City, Riyadh, Saudi Arabia, between March and May 2012. Multivariate multinomial regression models were fitted to identify factors associated with patients preferences. Results: The study included 236 participants. The most preferred decision-making style was shared decision-making (57%), followed by paternalistic (28%), and informed consumerism (14%). The preference for shared clinical decision making was significantly higher among male patients and those with higher level of education, whereas paternalism was significantly higher among older patients and those with chronic health conditions, and consumerism was significantly higher in younger age groups. In multivariate multinomial regression analysis, compared with the shared group, the consumerism group were more likely to be female [adjusted odds ratio (AOR) =2.87, 95% confidence interval [CI] 1.31-6.27, p=0.008] and non-dyslipidemic (AOR=2.90, 95% CI: 1.03-8.09, p=0.04), and the paternalism group were more likely to be older (AOR=1.03, 95% CI: 1.01-1.05, p=0.04), and female (AOR=2.47, 95% CI: 1.32-4.06, p=0.008). Conclusion: Preferences of patients for involvement in the clinical decision-making varied considerably. In our setting, underlying factors that influence these preferences identified in this study should be considered and tailored individually to achieve optimal treatment outcomes. PMID:26620990

Measurement-based care for refractory depression: a clinical decision support model for clinical research and practice.

PubMed

Trivedi, Madhukar H; Daly, Ella J

2007-05-01

Despite years of antidepressant drug development and patient and provider education, suboptimal medication dosing and duration of exposure resulting in incomplete remission of symptoms remains the norm in the treatment of depression. Additionally, since no one treatment is effective for all patients, optimal implementation focusing on the measurement of symptoms, side effects, and function is essential to determine effective sequential treatment approaches. There is a need for a paradigm shift in how clinical decision making is incorporated into clinical practice and for a move away from the trial-and-error approach that currently determines the "next best" treatment. This paper describes how our experience with the Texas Medication Algorithm Project (TMAP) and the Sequenced Treatment Alternatives to Relieve Depression (STAR*D) trial has confirmed the need for easy-to-use clinical support systems to ensure fidelity to guidelines. To further enhance guideline fidelity, we have developed an electronic decision support system that provides critical feedback and guidance at the point of patient care. We believe that a measurement-based care (MBC) approach is essential to any decision support system, allowing physicians to individualize and adapt decisions about patient care based on symptom progress, tolerability of medication, and dose optimization. We also believe that successful integration of sequential algorithms with MBC into real-world clinics will facilitate change that will endure and improve patient outcomes. Although we use major depression to illustrate our approach, the issues addressed are applicable to other chronic psychiatric conditions including comorbid depression and substance use disorder as well as other medical illnesses.

Measurement-Based Care for Refractory Depression: A Clinical Decision Support Model for Clinical Research and Practice

PubMed Central

Trivedi, Madhukar H.; Daly, Ella J.

2009-01-01

Despite years of antidepressant drug development and patient and provider education, suboptimal medication dosing and duration of exposure resulting in incomplete remission of symptoms remains the norm in the treatment of depression. Additionally, since no one treatment is effective for all patients, optimal implementation focusing on the measurement of symptoms, side effects, and function is essential to determine effective sequential treatment approaches. There is a need for a paradigm shift in how clinical decision making is incorporated into clinical practice and for a move away from the trial-and-error approach that currently determines the “next best” treatment. This paper describes how our experience with the Texas Medication Algorithm Project (TMAP) and the Sequenced Treatment Alternatives to Relieve Depression (STAR*D) trial has confirmed the need for easy-to-use clinical support systems to ensure fidelity to guidelines. To further enhance guideline fidelity, we have developed an electronic decision support system that provides critical feedback and guidance at the point of patient care. We believe that a measurement-based care (MBC) approach is essential to any decision support system, allowing physicians to individualize and adapt decisions about patient care based on symptom progress, tolerability of medication, and dose optimization. We also believe that successful integration of sequential algorithms with MBC into real-world clinics will facilitate change that will endure and improve patient outcomes. Although we use major depression to illustrate our approach, the issues addressed are applicable to other chronic psychiatric conditions including comorbid depression and substance use disorder as well as other medical illnesses. PMID:17320312

Standards for detailed clinical models as the basis for medical data exchange and decision support.

PubMed

Coyle, Joseph F; Mori, Angelo Rossi; Huff, Stanley M

2003-03-01

Detailed clinical models are necessary to exchange medical data between heterogeneous computer systems and to maintain consistency in a longitudinal electronic medical record system. At Intermountain Health Care (IHC), we have a history of designing detailed clinical models. The purpose of this paper is to share our experience and the lessons we have learned over the last 5 years. IHC's newest model is implemented using eXtensible Markup Language (XML) Schema as the formalism, and conforms to the Health Level Seven (HL7) version 3 data types. The centerpiece of the new strategy is the Clinical Event Model, which is a flexible name-value pair data structure that is tightly linked to a coded terminology. We describe IHC's third-generation strategy for representing and implementing detailed clinical models, and discuss the reasons for this design.

An IT Architecture for Systems Medicine.

PubMed

Ganzinger, Matthias; Gietzelt, Matthias; Karmen, Christian; Firnkorn, Daniel; Knaup, Petra

2015-01-01

Systems medicine aims to support treatment of complex diseases like cancer by integrating all available data for the disease. To provide such a decision support in clinical practice, a suitable IT architecture is necessary. We suggest a generic architecture comprised of the following three layers: data representation, decision support, and user interface. For the systems medicine research project "Clinically-applicable, omics-based assessment of survival, side effects, and targets in multiple myeloma" (CLIOMMICS) we developed a concrete instance of the generic architecture. We use i2b2 for representing the harmonized data. Since no deterministic model exists for multiple myeloma we use case-based reasoning for decision support. For clinical practice, visualizations of the results must be intuitive and clear. At the same time, they must communicate the uncertainty immanent in stochastic processes. Thus, we develop a specific user interface for systems medicine based on the web portal software Liferay.

Investigating a self-scoring interview simulation for learning and assessment in the medical consultation.

PubMed

Bruen, Catherine; Kreiter, Clarence; Wade, Vincent; Pawlikowska, Teresa

2017-01-01

Experience with simulated patients supports undergraduate learning of medical consultation skills. Adaptive simulations are being introduced into this environment. The authors investigate whether it can underpin valid and reliable assessment by conducting a generalizability analysis using IT data analytics from the interaction of medical students (in psychiatry) with adaptive simulations to explore the feasibility of adaptive simulations for supporting automated learning and assessment. The generalizability (G) study was focused on two clinically relevant variables: clinical decision points and communication skills. While the G study on the communication skills score yielded low levels of true score variance, the results produced by the decision points, indicating clinical decision-making and confirming user knowledge of the process of the Calgary-Cambridge model of consultation, produced reliability levels similar to what might be expected with rater-based scoring. The findings indicate that adaptive simulations have potential as a teaching and assessment tool for medical consultations.

Modelling emergency decisions: recognition-primed decision making. The literature in relation to an ophthalmic critical incident.

PubMed

Bond, Susan; Cooper, Simon

2006-08-01

To review and reflect on the literature on recognition-primed decision (RPD) making and influences on emergency decisions with particular reference to an ophthalmic critical incident involving the sub-arachnoid spread of local anaesthesia following the peribulbar injection. This paper critics the literature on recognition-primed decision making, with particular reference to emergency situations. It illustrates the findings by focussing on an ophthalmic critical incident. Systematic literature review with critical incident reflection. Medline, CINAHL and PsychINFO databases were searched for papers on recognition-primed decision making (1996-2004) followed by the 'snowball method'. Studies were selected in accordance with preset criteria. A total of 12 papers were included identifying the recognition-primed decision making as a good theoretical description of acute emergency decisions. In addition, cognitive resources, situational awareness, stress, team support and task complexity were identified as influences on the decision process. Recognition-primed decision-making theory describes the decision processes of experts in time-bound emergency situations and is the foundation for a model of emergency decision making (Fig. 2). Decision theory and models, in this case related to emergency situations, inform practice and enhance clinical effectiveness. The critical incident described highlights the need for nurses to have a comprehensive and in-depth understanding of anaesthetic techniques as well as an ability to manage and resuscitate patients autonomously. In addition, it illustrates how the critical incidents should influence the audit cycle with improvements in patient safety.

Using evidence-based algorithms to improve clinical decision making: the case of a first-time anterior shoulder dislocation.

PubMed

Federer, Andrew E; Taylor, Dean C; Mather, Richard C

2013-09-01

Decision making in health care has evolved substantially over the last century. Up until the late 1970s, medical decision making was predominantly intuitive and anecdotal. It was based on trial and error and involved high levels of problem solving. The 1980s gave way to empirical medicine, which was evidence based probabilistic, and involved pattern recognition and less problem solving. Although this represented a major advance in the quality of medical decision making, limitations existed. The advantages of the gold standard of the randomized controlled clinical trial (RCT) are well-known and this technique is irreplaceable in its ability to answer critical clinical questions. However, the RCT does have drawbacks. RCTs are expensive and can only capture a snapshot in time. As treatments change and new technologies emerge, new expensive clinical trials must be undertaken to reevaluate them. Furthermore, in order to best evaluate a single intervention, other factors must be controlled. In addition, the study population may not match that of another organization or provider. Although evidence-based medicine has provided powerful data for clinicians, effectively and efficiently tailoring it to the individual has not yet evolved. We are now in a period of transition from this evidence-based era to one dominated by the personalization and customization of care. It will be fueled by policy decisions to shift financial responsibility to the patient, creating a powerful and sophisticated consumer, unlike any patient we have known before. The challenge will be to apply medical evidence and personal preferences to medical decisions and deliver it efficiently in the increasingly busy clinical setting. In this article, we provide a robust review of the concepts of customized care and some of techniques to deliver it. We will illustrate this through a personalized decision model for the treatment decision after a first-time anterior shoulder dislocation.

Heuristics in Managing Complex Clinical Decision Tasks in Experts’ Decision Making

PubMed Central

Islam, Roosan; Weir, Charlene; Del Fiol, Guilherme

2016-01-01

Background Clinical decision support is a tool to help experts make optimal and efficient decisions. However, little is known about the high level of abstractions in the thinking process for the experts. Objective The objective of the study is to understand how clinicians manage complexity while dealing with complex clinical decision tasks. Method After approval from the Institutional Review Board (IRB), three clinical experts were interviewed the transcripts from these interviews were analyzed. Results We found five broad categories of strategies by experts for managing complex clinical decision tasks: decision conflict, mental projection, decision trade-offs, managing uncertainty and generating rule of thumb. Conclusion Complexity is created by decision conflicts, mental projection, limited options and treatment uncertainty. Experts cope with complexity in a variety of ways, including using efficient and fast decision strategies to simplify complex decision tasks, mentally simulating outcomes and focusing on only the most relevant information. Application Understanding complex decision making processes can help design allocation based on the complexity of task for clinical decision support design. PMID:27275019

Heuristics in Managing Complex Clinical Decision Tasks in Experts' Decision Making.

PubMed

Islam, Roosan; Weir, Charlene; Del Fiol, Guilherme

2014-09-01

Clinical decision support is a tool to help experts make optimal and efficient decisions. However, little is known about the high level of abstractions in the thinking process for the experts. The objective of the study is to understand how clinicians manage complexity while dealing with complex clinical decision tasks. After approval from the Institutional Review Board (IRB), three clinical experts were interviewed the transcripts from these interviews were analyzed. We found five broad categories of strategies by experts for managing complex clinical decision tasks: decision conflict, mental projection, decision trade-offs, managing uncertainty and generating rule of thumb. Complexity is created by decision conflicts, mental projection, limited options and treatment uncertainty. Experts cope with complexity in a variety of ways, including using efficient and fast decision strategies to simplify complex decision tasks, mentally simulating outcomes and focusing on only the most relevant information. Understanding complex decision making processes can help design allocation based on the complexity of task for clinical decision support design.

Complex contexts and relationships affect clinical decisions in group therapy.

PubMed

Tasca, Giorgio A; Mcquaid, Nancy; Balfour, Louise

2016-09-01

Clinical errors tend to be underreported even though examining them can provide important training and professional development opportunities. The group therapy context may be prone to clinician errors because of the added complexity within which therapists work and patients receive treatment. We discuss clinical errors that occurred within a group therapy in which a patient for whom group was not appropriate was admitted to the treatment and then was not removed by the clinicians. This was countertherapeutic for both patient and group. Two clinicians were involved: a clinical supervisor who initially assessed and admitted the patient to the group, and a group therapist. To complicate matters, the group therapy occurred within the context of a clinical research trial. The errors, possible solutions, and recommendations are discussed within Reason's Organizational Accident Model (Reason, 2000). In particular, we discuss clinician errors in the context of countertransference and clinician heuristics, group therapy as a local work condition that complicates clinical decision-making, and the impact of the research context as a latent organizational factor. We also present clinical vignettes from the pregroup preparation, group therapy, and supervision. Group therapists are more likely to avoid errors in clinical decisions if they engage in reflective practice about their internal experiences and about the impact of the context in which they work. Therapists must keep in mind the various levels of group functioning, especially related to the group-as-a-whole (i.e., group composition, cohesion, group climate, and safety) when making complex clinical decisions in order to optimize patient outcomes. (PsycINFO Database Record (c) 2016 APA, all rights reserved).

“Entering a Clinical Trial: Is it Right For You?"-- A Randomized Study of The Clinical Trials Video and Its Impact on the Informed Consent Process

PubMed Central

Hoffner, Brianna; Bauer-Wu, Susan; Hitchcock-Bryan, Suzanne; Powell, Mark; Wolanski, Andrew; Joffe, Steven

2011-01-01

PURPOSE This randomized study was designed to assess the utility of an educational video in preparing cancer patients for decisions about clinical trial participation. The study assessed the effect of the video on patients’ understanding and perceptions of clinical trials, its impact on decision making and patient-provider communication, and patients’ satisfaction with the video. METHODS Ninety adults considering cancer clinical trials were randomized to receive (n=45) or not receive (n=45) the video. Using the validated Quality of Informed Consent (QuIC), respondents’ knowledge about clinical trial participation was assessed. All subjects completed additional questions about satisfaction with the video, decision making, and patient-provider communication. Data were analyzed using the Wilcoxon rank-sum test, regression model and descriptive statistics. RESULTS Although intent-to-treat analysis found no significant group differences in objective understanding between those randomized to view or not view the video, the majority of participants reported favorable experiences with regard to watching the video: 85% found the video was an important source of information about clinical trials; 81% felt better prepared to discuss the trial with their physician; 89% of those who watched the video with family indicated that it helped family better understand clinical trials; and 73% indicated it helped family accept their decision about participation. CONCLUSIONS Although the video did not measurably improve patients’ knowledge about clinical trials, it was an important source of information, helped educate families, and enhanced patient communication with their oncology providers. PMID:22009665

Defining the optimal therapy sequence in synchronous resectable liver metastases from colorectal cancer: a decision analysis approach.

PubMed

Van Dessel, E; Fierens, K; Pattyn, P; Van Nieuwenhove, Y; Berrevoet, F; Troisi, R; Ceelen, W

2009-01-01

Approximately 5%-20% of colorectal cancer (CRC) patients present with synchronous potentially resectable liver metastatic disease. Preclinical and clinical studies suggest a benefit of the 'liver first' approach, i.e. resection of the liver metastasis followed by resection of the primary tumour. A formal decision analysis may support a rational choice between several therapy options. Survival and morbidity data were retrieved from relevant clinical studies identified by a Web of Science search. Data were entered into decision analysis software (TreeAge Pro 2009, Williamstown, MA, USA). Transition probabilities including the risk of death from complications or disease progression associated with individual therapy options were entered into the model. Sensitivity analysis was performed to evaluate the model's validity under a variety of assumptions. The result of the decision analysis confirms the superiority of the 'liver first' approach. Sensitivity analysis demonstrated that this assumption is valid on condition that the mortality associated with the hepatectomy first is < 4.5%, and that the mortality of colectomy performed after hepatectomy is < 3.2%. The results of this decision analysis suggest that, in patients with synchronous resectable colorectal liver metastases, the 'liver first' approach is to be preferred. Randomized trials will be needed to confirm the results of this simulation based outcome.

The functional neuroanatomy of decision-making.

PubMed

Rosenbloom, Michael H; Schmahmann, Jeremy D; Price, Bruce H

2012-01-01

Decision-making is a complex executive function that draws on past experience, present goals, and anticipation of outcome, and which is influenced by prevailing and predicted emotional tone and cultural context. Functional imaging investigations and focal lesion studies identify the orbitofrontal, anterior cingulate, and dorsolateral prefrontal cortices as critical to decision-making. The authors review the connections of these prefrontal regions with the neocortex, limbic system, basal ganglia, and cerebellum, highlight current ideas regarding the cognitive processes of decision-making that these networks subserve, and present a novel integrated neuroanatomical model for decision-making. Finally, clinical relevance of this circuitry is illustrated through a discussion of frontotemporal dementia, traumatic brain injury, and sociopathy.

[Clinical bioethics for primary health care].

PubMed

González-de Paz, L

2013-01-01

The clinical decision making process with ethical implications in the area of primary healthcare differs from other healthcare areas. From the ethical perspective it is important to include these issues in the decision making model. This dissertation explains the need for a process of bioethical deliberation for Primary Healthcare, as well as proposing a method for doing so. The decision process method, adapted to this healthcare area, is flexible and requires a more participative Healthcare System. This proposal involves professionals and the patient population equally, is intended to facilitate the acquisition of responsibility for personal and community health. Copyright © 2012 Sociedad Española de Médicos de Atención Primaria (SEMERGEN). Publicado por Elsevier España. All rights reserved.

Combining macula clinical signs and patient characteristics for age-related macular degeneration diagnosis: a machine learning approach.

PubMed

Fraccaro, Paolo; Nicolo, Massimo; Bonetto, Monica; Giacomini, Mauro; Weller, Peter; Traverso, Carlo Enrico; Prosperi, Mattia; OSullivan, Dympna

2015-01-27

To investigate machine learning methods, ranging from simpler interpretable techniques to complex (non-linear) "black-box" approaches, for automated diagnosis of Age-related Macular Degeneration (AMD). Data from healthy subjects and patients diagnosed with AMD or other retinal diseases were collected during routine visits via an Electronic Health Record (EHR) system. Patients' attributes included demographics and, for each eye, presence/absence of major AMD-related clinical signs (soft drusen, retinal pigment epitelium, defects/pigment mottling, depigmentation area, subretinal haemorrhage, subretinal fluid, macula thickness, macular scar, subretinal fibrosis). Interpretable techniques known as white box methods including logistic regression and decision trees as well as less interpreitable techniques known as black box methods, such as support vector machines (SVM), random forests and AdaBoost, were used to develop models (trained and validated on unseen data) to diagnose AMD. The gold standard was confirmed diagnosis of AMD by physicians. Sensitivity, specificity and area under the receiver operating characteristic (AUC) were used to assess performance. Study population included 487 patients (912 eyes). In terms of AUC, random forests, logistic regression and adaboost showed a mean performance of (0.92), followed by SVM and decision trees (0.90). All machine learning models identified soft drusen and age as the most discriminating variables in clinicians' decision pathways to diagnose AMD. Both black-box and white box methods performed well in identifying diagnoses of AMD and their decision pathways. Machine learning models developed through the proposed approach, relying on clinical signs identified by retinal specialists, could be embedded into EHR to provide physicians with real time (interpretable) support.

[Clinical-pharmacological aspects to accelerate the development process from the preclinical to the clinical phase/1st communication: The contribution of clinical pharmacology].

PubMed

Kuhlmann, Jochen

2004-01-01

To improve the transition from research to development a critical evaluation of the individual project by research and disease area teams is required to include input from pharmacology, toxicology, pharmacokinetics, galenics, clinical pharmacology, clinical as well as regulatory experts and marketing. Decisions on the individual development strategy should be made prior to the start of development and all projects should be reviewed at predefined stages throughout the product development life cycle. This ensures consistency of decision-making not only during the development of individual products but throughout the entire development pipeline. Studies in the exploratory stage of drug development should be designed for decision making in contrast to later clinical trials in the confirmatory stage that require power for proof-of-safety and proof-of-efficacy. The more thorough and profound studies have been carried out during this exploratory stage of drug development, the earlier a decision can be made on the continuation or discontinuation of further development, thus saving development time and money and assessing and considerably reducing the risk for the patients and increasing the success rate of the project in the later confirmatory effectiveness trial with an adequate number of subjects receiving the new therapy under typical conditions of use. Strategies which may be helpful to improve the quality of decisions in drug discovery and drug development are: discovery experiments should be done to critically evaluate the compound, the "killer" experiments should be done as early as possible, continuous effort on preclinical disease models is necessary to improve predictability of efficacy in patients ("humanized" research): genomic technology should be used to identify novel, disease-related targets and to characterise preclinical test systems, improvement of knowledge and experience concerning the relevance of new technologies for the clinical picture; genotyping of clinical trial patients to select patient groups which are likely to respond to treatment (pharmacogenomics), modelling and simulation of preclinical and clinical trials, integration of pharmacokinetic and pharmacodynamic principles into drug development, assessment of the interaction potential (CYP-450, trasporter proteins and others), increasing use of biomarker/surrogate marker for rapid clinical feedback, involvement of the target population as soon as possible, applying statistical data analysis techniques for proving effectiveness, co-operation with high quality centers. To reach this goal clinical pharmacology must be fully integrated in the whole process from the candidate selection to its positioning within the market.

Characteristics Associated With Preferences for Parent-Centered Decision Making in Neonatal Intensive Care.

PubMed

Weiss, Elliott Mark; Xie, Dawei; Cook, Noah; Coughlin, Katherine; Joffe, Steven

2018-05-01

Little is known about how characteristics of particular clinical decisions influence decision-making preferences by patients or their surrogates. A better understanding of the factors underlying preferences is essential to improve the quality of shared decision making. To identify the characteristics of particular decisions that are associated with parents' preferences for family- vs medical team-centered decision making across the spectrum of clinical decisions that arise in the neonatal intensive care unit (NICU). This cross-sectional survey assessed parents' preferences for parent- vs medical team-centered decision making across 16 clinical decisions, along with parents' assessments of 7 characteristics of those decisions. Respondents included 136 parents of infants in 1 of 3 academically affiliated hospital NICUs in Philadelphia, Pennsylvania, from January 7 to July 8, 2016. Respondents represented a wide range of educational levels, employment status, and household income but were predominantly female (109 [80.1%]), white (68 [50.0%]) or African American (53 [39.0%]), and married (81 of 132 responding [61.4%]). Preferences for parent-centered decision making. For each decision characteristic (eg, urgency), multivariable analyses tested whether middle and high levels of that characteristic (compared with low levels) were associated with a preference for parent-centered decision making, resulting in 2 odds ratios (ORs) per decision characteristic. Among the 136 respondents (109 women [80.1%] and 27 men [19.9%]; median age, 30 years [range, 18-43 years]), preferences for parent-centered decision making were positively associated with decisions that involved big-picture goals (middle OR, 2.01 [99% CI, 0.83-4.86]; high OR, 3.38 [99% CI, 1.48-7.75]) and that had the potential to harm the infant (middle OR, 1.32 [99% CI, 0.84-2.08]; high OR, 2.62 [99% CI, 1.67-4.11]). In contrast, preferences for parent-centered decision making were inversely associated with the following 4 decision characteristics: technical decisions (middle OR, 0.82 [99% CI, 0.45-1.52]; high OR, 0.48 [99% CI, 0.25-0.93]), the potential to benefit the infant (middle OR, 0.42 [99% CI, 0.16-1.05]; high OR, 0.21 [99% CI, 0.08-0.52]), requires medical expertise (middle OR, 0.48 [99% CI, 0.22-1.05]; high OR, 0.21 [99% CI, 0.10-0.48]), and a high level of urgency (middle OR, 0.47 [99% CI, 0.24-0.92]; high OR, 0.42 [99% CI, 0.22-0.83]). Preferences for parent-centered vs medical team-centered decision making among parents of infants in the NICU may vary systematically by the characteristics of particular clinical decisions. Incorporating this variation into shared decision making and endorsing models that allow parents to cede control to physicians in appropriate clinical circumstances might improve the quality and outcomes of medical decisions.

Use of declarative statements in creating and maintaining computer-interpretable knowledge bases for guideline-based care.

PubMed

Tu, Samson W; Hrabak, Karen M; Campbell, James R; Glasgow, Julie; Nyman, Mark A; McClure, Robert; McClay, James; Abarbanel, Robert; Mansfield, James G; Martins, Susana M; Goldstein, Mary K; Musen, Mark A

2006-01-01

Developing computer-interpretable clinical practice guidelines (CPGs) to provide decision support for guideline-based care is an extremely labor-intensive task. In the EON/ATHENA and SAGE projects, we formulated substantial portions of CPGs as computable statements that express declarative relationships between patient conditions and possible interventions. We developed query and expression languages that allow a decision-support system (DSS) to evaluate these statements in specific patient situations. A DSS can use these guideline statements in multiple ways, including: (1) as inputs for determining preferred alternatives in decision-making, and (2) as a way to provide targeted commentaries in the clinical information system. The use of these declarative statements significantly reduces the modeling expertise and effort required to create and maintain computer-interpretable knowledge bases for decision-support purpose. We discuss possible implications for sharing of such knowledge bases.

Linking guidelines to Electronic Health Record design for improved chronic disease management.

PubMed

Barretto, Sistine A; Warren, Jim; Goodchild, Andrew; Bird, Linda; Heard, Sam; Stumptner, Markus

2003-01-01

The promise of electronic decision support to promote evidence based practice remains elusive in the context of chronic disease management. We examine the problem of achieving a close relationship of Electronic Health Record (EHR) content to other components of a clinical information system (guidelines, decision support and workflow), particularly linking the decisions made by providers back to the guidelines. We use the openEHR architecture, which allows extension of a core Reference Model via Archetypes to refine the detailed information recording options for specific classes of encounter. We illustrate the use of openEHR for tracking the relationship of a series of clinical encounters to a guideline via a case study of guideline-compliant treatment of hypertension in diabetes. This case study shows the contribution guideline content can have on problem-specific EHR structure and demonstrates the potential for a constructive interaction of electronic decision support and the EHR.

Linking Guidelines to Electronic Health Record Design for Improved Chronic Disease Management

PubMed Central

Barretto, Sistine A.; Warren, Jim; Goodchild, Andrew; Bird, Linda; Heard, Sam; Stumptner, Markus

2003-01-01

The promise of electronic decision support to promote evidence based practice remains elusive in the context of chronic disease management. We examine the problem of achieving a close relationship of Electronic Health Record (EHR) content to other components of a clinical information system (guidelines, decision support and work-flow), particularly linking the decisions made by providers back to the guidelines. We use the openEHR architecture, which allows extension of a core Reference Model via Archetypes to refine the detailed information recording options for specific classes of encounter. We illustrate the use of openEHR for tracking the relationship of a series of clinical encounters to a guideline via a case study of guideline-compliant treatment of hypertension in diabetes. This case study shows the contribution guideline content can have on problem-specific EHR structure and demonstrates the potential for a constructive interaction of electronic decision support and the EHR. PMID:14728135

Decision modeling in donation after circulatory death liver transplantation.

PubMed

McLean, Kenneth A; Camilleri-Brennan, Julian; Knight, Stephen R; Drake, Thomas M; Ots, Riinu; Shaw, Catherine A; Wigmore, Stephen J; Harrison, Ewen M

2017-05-01

Donation after circulatory death (DCD) liver allografts are increasingly used for transplantation. However, the posttransplantation clinical and quality of life outcomes of DCD recipients are traditionally considered to be inferior compared with donation after brain death (DBD) allograft recipients. Decision making for such marginal organs can be difficult. This study investigated the optimal decision to accept or decline a DCD liver allograft for a patient based on their current health. A Markov decision process model was constructed to predict the 5-year clinical course of patients on the liver transplant waiting list. Clinical outcomes were determined from the UK transplant registry or appropriate literature. Quality-adjusted life years (QALYs) were determined using the condition-specific short form of liver disease quality of life (SF-LDQoL) questionnaire. There were 293/374 (78.3%) eligible patients who completed the SF-LDQoL questionnaire. A total of 73 respondents (24.9%) were before transplant and 220 were after transplant (DBD recipient, 56.3%; DCD recipient, 8.5%; ischemic cholangiopathy patient, 2.4%; retransplant recipient, 7.9%). Predictive modeling indicated that QALYs gained at 5 years were significantly higher in DCD recipients (3.77; 95% confidence interval [CI], 3.44-4.10) compared with those who remained on the waiting list for a DBD transplant with Model for End-Stage Liver Disease (MELD) scores of 15-20 (3.36; 95% CI, 3.28-3.43), or >20 (3.07; 95% CI, 3.00-3.14). There was no significant advantage for individuals with MELD scores <15 (3.55; 95% CI, 3.47-3.63). In conclusion, this model predicts that patients on the UK liver transplant waiting list with MELD scores >15 should receive an offered DCD allograft based on the QALYs gained at 5 years. This analysis only accounts for donor-recipient risk pairings seen in current practice. The optimal decision for patients with MELD scores <15 remains unclear. However, a survival benefit was observed when a DCD organ was accepted. Liver Transplantation 23 594-603 2017 AASLD. © 2016 by the American Association for the Study of Liver Diseases.

The evolution of integration: innovations in clinical skills and ethics in first year medicine.

PubMed

Brunger, Fern; Duke, Pauline S

2012-01-01

Critical self-reflection, medical ethics and clinical skills are each important components of medical education but are seldom linked in curriculum development. We developed a curriculum that builds on the existing integration of ethics education into the clinical skills course to more explicitly link these three skills. The curriculum builds on the existing integration of clinical skills and ethics in first year medicine. It refines the integration through scheduling changes; adds case studies that emphasise the social, economic and political context of our province's patient population; and introduces reflection on the "culture of medicine" as a way to have students articulate and understand their own values and moral decision making frameworks. This structured Clinical Skills course is a model for successfully integrating critical self-reflection, reflection on the political, economic and cultural contexts shaping health and healthcare, and moral decision making into clinical skills training.

Proposal of a clinical response score and predictors of clinical response to 2 years of GH replacement therapy in adult GH deficiency.

PubMed

Schneider, Harald J; Buchfelder, Michael; Wallaschofski, Henri; Luger, Anton; Johannsson, Gudmundur; Kann, Peter H; Mattsson, Anders

2015-12-01

There is no single clinical marker to reliably assess the clinical response to growth hormone replacement therapy (GHRT) in adults with growth hormone deficiency (GHD). The objective of this study was to propose a clinical response score to GHRT in adult GHD and to establish clinical factors that predict clinical response. This was a prospective observational cohort study from the international KIMS database (Pfizer International Metabolic Database). We included 3612 adult patients with GHD for proposing the response score and 844 patients for assessing predictors of response. We propose a clinical response score based on changes in total cholesterol, waist circumference and QoL-AGHDA quality of life measurements after 2 years of GHRT. A score point was added for each quintile of change in each variable, resulting in a sum score ranging from 3 to 15. For clinical response at 2 years, we analysed predictors at baseline and after 6 months using logistic regression analyses. In a baseline prediction model, IGF1, QoL-AGHDA, total cholesterol and waist circumference predicted response, with worse baseline parameters being associated with a favourable response (AUC 0.736). In a combined baseline and 6-month prediction model, baseline QoL-AGHDA, total cholesterol and waist circumference, and 6-month change in waist circumference were significant predictors of response (AUC 0.815). A simple clinical response score might be helpful in evaluating the success of GHRT. The baseline prediction model may aid in the decision to initiate GHRT and the combined prediction model may be helpful in the decision to continue GHRT. © 2015 European Society of Endocrinology.

New technology continues to invade healthcare. What are the strategic implications/outcomes?

PubMed

Smith, Coy

2004-01-01

Healthcare technology continues to advance and be implemented in healthcare organizations. Nurse executives must strategically evaluate the effectiveness of each proposed system or device using a strategic planning process. Clinical information systems, computer-chip-based clinical monitoring devices, advanced Web-based applications with remote, wireless communication devices, clinical decision support software--all compete for capital and registered nurse salary dollars. The concept of clinical transformation is developed with new models of care delivery being supported by technology rather than driving care delivery. Senior nursing leadership's role in clinical transformation and healthcare technology implementation is developed. Proposed standards, expert group action, business and consumer groups, and legislation are reviewed as strategic drivers in the development of an electronic health record and healthcare technology. A matrix of advancing technology and strategic decision-making parameters are outlined.

A text-based data mining and toxicity prediction modeling system for a clinical decision support in radiation oncology: A preliminary study

NASA Astrophysics Data System (ADS)

Kim, Kwang Hyeon; Lee, Suk; Shim, Jang Bo; Chang, Kyung Hwan; Yang, Dae Sik; Yoon, Won Sup; Park, Young Je; Kim, Chul Yong; Cao, Yuan Jie

2017-08-01

The aim of this study is an integrated research for text-based data mining and toxicity prediction modeling system for clinical decision support system based on big data in radiation oncology as a preliminary research. The structured and unstructured data were prepared by treatment plans and the unstructured data were extracted by dose-volume data image pattern recognition of prostate cancer for research articles crawling through the internet. We modeled an artificial neural network to build a predictor model system for toxicity prediction of organs at risk. We used a text-based data mining approach to build the artificial neural network model for bladder and rectum complication predictions. The pattern recognition method was used to mine the unstructured toxicity data for dose-volume at the detection accuracy of 97.9%. The confusion matrix and training model of the neural network were achieved with 50 modeled plans (n = 50) for validation. The toxicity level was analyzed and the risk factors for 25% bladder, 50% bladder, 20% rectum, and 50% rectum were calculated by the artificial neural network algorithm. As a result, 32 plans could cause complication but 18 plans were designed as non-complication among 50 modeled plans. We integrated data mining and a toxicity modeling method for toxicity prediction using prostate cancer cases. It is shown that a preprocessing analysis using text-based data mining and prediction modeling can be expanded to personalized patient treatment decision support based on big data.

A qualitative evaluation of the crucial attributes of contextual information necessary in EHR design to support patient-centered medical home care.

PubMed

Weir, Charlene R; Staggers, Nancy; Gibson, Bryan; Doing-Harris, Kristina; Barrus, Robyn; Dunlea, Robert

2015-04-16

Effective implementation of a Primary Care Medical Home model of care (PCMH) requires integration of patients' contextual information (physical, mental, social and financial status) into an easily retrievable information source for the healthcare team and clinical decision-making. This project explored clinicians' perceptions about important attributes of contextual information for clinical decision-making, how contextual information is expressed in CPRS clinical documentation as well as how clinicians in a highly computerized environment manage information flow related to these areas. A qualitative design using Cognitive Task Analyses and a modified Critical Incident Technique were used. The study was conducted in a large VA with a fully implemented EHR located in the western United States. Seventeen providers working in a PCMH model of care in Primary Care, Home Based Care and Geriatrics reported on a recent difficult transition requiring contextual information for decision-making. The transcribed interviews were qualitatively analyzed for thematic development related to contextual information using an iterative process and multiple reviewers with ATLAS@ti software. Six overarching themes emerged as attributes of contextual information: Informativeness, goal language, temporality, source attribution, retrieval effort, and information quality. These results indicate that specific attributes are needed to in order for contextual information to fully support clinical decision-making in a Medical Home care delivery environment. Improved EHR designs are needed for ease of contextual information access, displaying linkages across time and settings, and explicit linkages to both clinician and patient goals. Implications relevant to providers' information needs, team functioning and EHR design are discussed.

Prostate cancer gene 3 and multiparametric magnetic resonance can reduce unnecessary biopsies: decision curve analysis to evaluate predictive models.

PubMed

Busetto, Gian Maria; De Berardinis, Ettore; Sciarra, Alessandro; Panebianco, Valeria; Giovannone, Riccardo; Rosato, Stefano; D'Errigo, Paola; Di Silverio, Franco; Gentile, Vincenzo; Salciccia, Stefano

2013-12-01

To overcome the well-known prostate-specific antigen limits, several new biomarkers have been proposed. Since its introduction in clinical practice, the urinary prostate cancer gene 3 (PCA3) assay has shown promising results for prostate cancer (PC) detection. Furthermore, multiparametric magnetic resonance imaging (mMRI) has the ability to better describe several aspects of PC. A prospective study of 171 patients with negative prostate biopsy findings and a persistent high prostate-specific antigen level was conducted to assess the role of mMRI and PCA3 in identifying PC. All patients underwent the PCA3 test and mMRI before a second transrectal ultrasound-guided prostate biopsy. The accuracy and reliability of PCA3 (3 different cutoff points) and mMRI were evaluated. Four multivariate logistic regression models were analyzed, in terms of discrimination and the cost benefit, to assess the clinical role of PCA3 and mMRI in predicting the biopsy outcome. A decision curve analysis was also plotted. Repeated transrectal ultrasound-guided biopsy identified 68 new cases (41.7%) of PC. The sensitivity and specificity of the PCA3 test and mMRI was 68% and 49% and 74% and 90%, respectively. Evaluating the regression models, the best discrimination (area under the curve 0.808) was obtained using the full model (base clinical model plus mMRI and PCA3). The decision curve analysis, to evaluate the cost/benefit ratio, showed good performance in predicting PC with the model that included mMRI and PCA3. mMRI increased the accuracy and sensitivity of the PCA3 test, and the use of the full model significantly improved the cost/benefit ratio, avoiding unnecessary biopsies. Copyright © 2013 Elsevier Inc. All rights reserved.

Medical students, clinical preventive services, and shared decision-making.

PubMed

Keefe, Carole W; Thompson, Margaret E; Noel, Mary Margaret

2002-11-01

Improving access to preventive care requires addressing patient, provider, and systems barriers. Patients often lack knowledge or are skeptical about the importance of prevention. Physicians feel that they have too little time, are not trained to deliver preventive services, and are concerned about the effectiveness of prevention. We have implemented an educational module in the required family practice clerkship (1) to enhance medical student learning about common clinical preventive services and (2) to teach students how to inform and involve patients in shared decision making about those services. Students are asked to examine available evidence-based information for preventive screening services. They are encouraged to look at the recommendations of various organizations and use such resources as reports from the U.S. Preventive Services Task Force to determine recommendations they want to be knowledgeable about in talking with their patients. For learning shared decision making, students are trained to use a model adapted from Braddock and colleagues(1) to discuss specific screening services and to engage patients in the process of making informed decisions about what is best for their own health. The shared decision making is presented and modeled by faculty, discussed in small groups, and students practice using Web-based cases and simulations. The students are evaluated using formative and summative performance-based assessments as they interact with simulated patients about (1) screening for high blood cholesterol and other lipid abnormalities, (2) screening for colorectal cancer, (3) screening for prostate cancer, and (4) screening for breast cancer. The final student evaluation is a ten-minute, videotaped discussion with a simulated patient about screening for colorectal cancer that is graded against a checklist that focuses primarily on the elements of shared decision making. Our medical students appear quite willing to accept shared decision making as a skill that they should have in working with patients, and this was the primary focus of the newly implemented module. However, we have learned that students need to deepen their understanding of screening services in order to help patients understand the associated benefits and risks. The final videotaped interaction with a simulated patient about colorectal cancer screening has been very helpful in making it more obvious to faculty what students believe and know about screening for colorectal cancer. As the students are asked to discuss clinical issues with patients and discuss the pros and cons of screening tests as part of the shared decision-making process, their thinking becomes transparent and it is evident where curricular changes and enhancements are required. We have found that an explicit model that allows students to demonstrate a process for shared decision making is a good introductory tool. We think it would be helpful to provide students with more formative feedback. We would like to develop faculty development programs around shared decision making so that more of our clinical faculty would model such a process with patients. Performance-based assessments are resource-intensive, but they appear to be worth the added effort in terms of enhanced skills development and a more comprehensive appraisal of student learning.

Clinical Decision Support Systems (CDSS) for preventive management of COPD patients.

PubMed

Velickovski, Filip; Ceccaroni, Luigi; Roca, Josep; Burgos, Felip; Galdiz, Juan B; Marina, Nuria; Lluch-Ariet, Magí

2014-11-28

The use of information and communication technologies to manage chronic diseases allows the application of integrated care pathways, and the optimization and standardization of care processes. Decision support tools can assist in the adherence to best-practice medicine in critical decision points during the execution of a care pathway. The objectives are to design, develop, and assess a clinical decision support system (CDSS) offering a suite of services for the early detection and assessment of chronic obstructive pulmonary disease (COPD), which can be easily integrated into a healthcare providers' work-flow. The software architecture model for the CDSS, interoperable clinical-knowledge representation, and inference engine were designed and implemented to form a base CDSS framework. The CDSS functionalities were iteratively developed through requirement-adjustment/development/validation cycles using enterprise-grade software-engineering methodologies and technologies. Within each cycle, clinical-knowledge acquisition was performed by a health-informatics engineer and a clinical-expert team. A suite of decision-support web services for (i) COPD early detection and diagnosis, (ii) spirometry quality-control support, (iii) patient stratification, was deployed in a secured environment on-line. The CDSS diagnostic performance was assessed using a validation set of 323 cases with 90% specificity, and 96% sensitivity. Web services were integrated in existing health information system platforms. Specialized decision support can be offered as a complementary service to existing policies of integrated care for chronic-disease management. The CDSS was able to issue recommendations that have a high degree of accuracy to support COPD case-finding. Integration into healthcare providers' work-flow can be achieved seamlessly through the use of a modular design and service-oriented architecture that connect to existing health information systems.

Clinical Decision Support Systems (CDSS) for preventive management of COPD patients

PubMed Central

2014-01-01

Background The use of information and communication technologies to manage chronic diseases allows the application of integrated care pathways, and the optimization and standardization of care processes. Decision support tools can assist in the adherence to best-practice medicine in critical decision points during the execution of a care pathway. Objectives The objectives are to design, develop, and assess a clinical decision support system (CDSS) offering a suite of services for the early detection and assessment of chronic obstructive pulmonary disease (COPD), which can be easily integrated into a healthcare providers' work-flow. Methods The software architecture model for the CDSS, interoperable clinical-knowledge representation, and inference engine were designed and implemented to form a base CDSS framework. The CDSS functionalities were iteratively developed through requirement-adjustment/development/validation cycles using enterprise-grade software-engineering methodologies and technologies. Within each cycle, clinical-knowledge acquisition was performed by a health-informatics engineer and a clinical-expert team. Results A suite of decision-support web services for (i) COPD early detection and diagnosis, (ii) spirometry quality-control support, (iii) patient stratification, was deployed in a secured environment on-line. The CDSS diagnostic performance was assessed using a validation set of 323 cases with 90% specificity, and 96% sensitivity. Web services were integrated in existing health information system platforms. Conclusions Specialized decision support can be offered as a complementary service to existing policies of integrated care for chronic-disease management. The CDSS was able to issue recommendations that have a high degree of accuracy to support COPD case-finding. Integration into healthcare providers' work-flow can be achieved seamlessly through the use of a modular design and service-oriented architecture that connect to existing health information systems. PMID:25471545

Facilitating healthcare decisions by assessing the certainty in the evidence from preclinical animal studies

PubMed Central

Hooijmans, Carlijn R.; de Vries, Rob B. M.; Ritskes-Hoitinga, Merel; Rovers, Maroeska M.; Leeflang, Mariska M.; IntHout, Joanna; Wever, Kimberley E.; Hooft, Lotty; de Beer, Hans; Kuijpers, Ton; Macleod, Malcolm R.; Sena, Emily S.; ter Riet, Gerben; Morgan, Rebecca L.; Thayer, Kristina A.; Rooney, Andrew A.; Guyatt, Gordon H.; Schünemann, Holger J.

2018-01-01

Laboratory animal studies are used in a wide range of human health related research areas, such as basic biomedical research, drug research, experimental surgery and environmental health. The results of these studies can be used to inform decisions regarding clinical research in humans, for example the decision to proceed to clinical trials. If the research question relates to potential harms with no expectation of benefit (e.g., toxicology), studies in experimental animals may provide the only relevant or controlled data and directly inform clinical management decisions. Systematic reviews and meta-analyses are important tools to provide robust and informative evidence summaries of these animal studies. Rating how certain we are about the evidence could provide important information about the translational probability of findings in experimental animal studies to clinical practice and probably improve it. Evidence summaries and certainty in the evidence ratings could also be used (1) to support selection of interventions with best therapeutic potential to be tested in clinical trials, (2) to justify a regulatory decision limiting human exposure (to drug or toxin), or to (3) support decisions on the utility of further animal experiments. The Grading of Recommendations, Assessment, Development, and Evaluation (GRADE) approach is the most widely used framework to rate the certainty in the evidence and strength of health care recommendations. Here we present how the GRADE approach could be used to rate the certainty in the evidence of preclinical animal studies in the context of therapeutic interventions. We also discuss the methodological challenges that we identified, and for which further work is needed. Examples are defining the importance of consistency within and across animal species and using GRADE’s indirectness domain as a tool to predict translation from animal models to humans. PMID:29324741

Facilitating healthcare decisions by assessing the certainty in the evidence from preclinical animal studies.

PubMed

Hooijmans, Carlijn R; de Vries, Rob B M; Ritskes-Hoitinga, Merel; Rovers, Maroeska M; Leeflang, Mariska M; IntHout, Joanna; Wever, Kimberley E; Hooft, Lotty; de Beer, Hans; Kuijpers, Ton; Macleod, Malcolm R; Sena, Emily S; Ter Riet, Gerben; Morgan, Rebecca L; Thayer, Kristina A; Rooney, Andrew A; Guyatt, Gordon H; Schünemann, Holger J; Langendam, Miranda W

2018-01-01

Laboratory animal studies are used in a wide range of human health related research areas, such as basic biomedical research, drug research, experimental surgery and environmental health. The results of these studies can be used to inform decisions regarding clinical research in humans, for example the decision to proceed to clinical trials. If the research question relates to potential harms with no expectation of benefit (e.g., toxicology), studies in experimental animals may provide the only relevant or controlled data and directly inform clinical management decisions. Systematic reviews and meta-analyses are important tools to provide robust and informative evidence summaries of these animal studies. Rating how certain we are about the evidence could provide important information about the translational probability of findings in experimental animal studies to clinical practice and probably improve it. Evidence summaries and certainty in the evidence ratings could also be used (1) to support selection of interventions with best therapeutic potential to be tested in clinical trials, (2) to justify a regulatory decision limiting human exposure (to drug or toxin), or to (3) support decisions on the utility of further animal experiments. The Grading of Recommendations, Assessment, Development, and Evaluation (GRADE) approach is the most widely used framework to rate the certainty in the evidence and strength of health care recommendations. Here we present how the GRADE approach could be used to rate the certainty in the evidence of preclinical animal studies in the context of therapeutic interventions. We also discuss the methodological challenges that we identified, and for which further work is needed. Examples are defining the importance of consistency within and across animal species and using GRADE's indirectness domain as a tool to predict translation from animal models to humans.

Decaying Relevance of Clinical Data Towards Future Decisions in Data-Driven Inpatient Clinical Order Sets

PubMed Central

Chen, Jonathan H; Alagappan, Muthuraman; Goldstein, Mary K; Asch, Steven M; Altman, Russ B

2017-01-01

Objective Determine how varying longitudinal historical training data can impact prediction of future clinical decisions. Estimate the “decay rate” of clinical data source relevance. Materials and Methods We trained a clinical order recommender system, analogous to Netflix or Amazon’s “Customers who bought A also bought B…” product recommenders, based on a tertiary academic hospital’s structured electronic health record data. We used this system to predict future (2013) admission orders based on different subsets of historical training data (2009 through 2012), relative to existing human-authored order sets. Results Predicting future (2013) inpatient orders is more accurate with models trained on just one month of recent (2012) data than with 12 months of older (2009) data (ROC AUC 0.91 vs. 0.88, precision 27% vs. 22%, recall 52% vs. 43%, all P<10−10). Algorithmically learned models from even the older (2009) data was still more effective than existing human-authored order sets (ROC AUC 0.81, precision 16% recall 35%). Training with more longitudinal data (2009–2012) was no better than using only the most recent (2012) data, unless applying a decaying weighting scheme with a “half-life” of data relevance about 4 months. Discussion Clinical practice patterns (automatically) learned from electronic health record data can vary substantially across years. Gold standards for clinical decision support are elusive moving targets, reinforcing the need for automated methods that can adapt to evolving information. Conclusions and Relevance Prioritizing small amounts of recent data is more effective than using larger amounts of older data towards future clinical predictions. PMID:28495350

Bayesian Decision Support for Adaptive Lung Treatments

NASA Astrophysics Data System (ADS)

McShan, Daniel; Luo, Yi; Schipper, Matt; TenHaken, Randall

2014-03-01

Purpose: A Bayesian Decision Network will be demonstrated to provide clinical decision support for adaptive lung response-driven treatment management based on evidence that physiologic metrics may correlate better with individual patient response than traditional (population-based) dose and volume-based metrics. Further, there is evidence that information obtained during the course of radiation therapy may further improve response predictions. Methods: Clinical factors were gathered for 58 patients including planned mean lung dose, and the bio-markers IL-8 and TGF-β1 obtained prior to treatment and two weeks into treatment along with complication outcomes for these patients. A Bayesian Decision Network was constructed using Netica 5.0.2 from Norsys linking these clinical factors to obtain a prediction of radiation induced lung disese (RILD) complication. A decision node was added to the network to provide a plan adaption recommendation based on the trade-off between the RILD prediction and complexity of replanning. A utility node provides the weighting cost between the competing factors. Results: The decision node predictions were optimized against the data for the 58 cases. With this decision network solution, one can consider the decision result for a new patient with specific findings to obtain a recommendation to adaptively modify the originally planned treatment course. Conclusions: A Bayesian approach allows handling and propagating probabilistic data in a logical and principled manner. Decision networks provide the further ability to provide utility-based trade-offs, reflecting non-medical but practical cost/benefit analysis. The network demonstrated illustrates the basic concept, but many other factors may affect these decisions and work on building better models are being designed and tested. Acknowledgement: Supported by NIH-P01-CA59827

Examining chronic care patient preferences for involvement in health-care decision making: the case of Parkinson's disease patients in a patient-centred clinic.

PubMed

Zizzo, Natalie; Bell, Emily; Lafontaine, Anne-Louise; Racine, Eric

2017-08-01

Patient-centred care is a recommended model of care for Parkinson's disease (PD). It aims to provide care that is respectful and responsive to patient preferences, values and perspectives. Provision of patient-centred care should entail considering how patients want to be involved in their care. To understand the participation preferences of patients with PD from a patient-centred care clinic in health-care decision-making processes. Mixed-methods study with early-stage Parkinson's disease patients from a patient-centred care clinic. Study involved a modified Autonomy Preference Index survey (N=65) and qualitative, semi-structured in-depth interviews, analysed using thematic qualitative content analysis (N=20, purposefully selected from survey participants). Interviews examined (i) the patient preferences for involvement in health-care decision making; (ii) patient perspectives on the patient-physician relationship; and (iii) patient preferences for communication of information relevant to decision making. Preferences for participation in decision making varied between individuals and also within individuals depending on decision type, relational and contextual factors. Patients had high preferences for communication of information, but with acknowledged limits. The importance of communication in the patient-physician relationship was emphasized. Patient preferences for involvement in decision making are dynamic and support shared decision making. Relational autonomy corresponds to how patients envision their participation in decision making. Clinicians may need to assess patient preferences on an on-going basis. Our results highlight the complexities of decision-making processes. Improved understanding of individual preferences could enhance respect for persons and make for patient-centred care that is truly respectful of individual patients' wants, needs and values. © 2016 The Authors. Health Expectations Published by John Wiley & Sons Ltd.

Clinical decision rules, spinal pain classification and prediction of treatment outcome: A discussion of recent reports in the rehabilitation literature

PubMed Central

2012-01-01

Clinical decision rules are an increasingly common presence in the biomedical literature and represent one strategy of enhancing clinical-decision making with the goal of improving the efficiency and effectiveness of healthcare delivery. In the context of rehabilitation research, clinical decision rules have been predominantly aimed at classifying patients by predicting their treatment response to specific therapies. Traditionally, recommendations for developing clinical decision rules propose a multistep process (derivation, validation, impact analysis) using defined methodology. Research efforts aimed at developing a “diagnosis-based clinical decision rule” have departed from this convention. Recent publications in this line of research have used the modified terminology “diagnosis-based clinical decision guide.” Modifications to terminology and methodology surrounding clinical decision rules can make it more difficult for clinicians to recognize the level of evidence associated with a decision rule and understand how this evidence should be implemented to inform patient care. We provide a brief overview of clinical decision rule development in the context of the rehabilitation literature and two specific papers recently published in Chiropractic and Manual Therapies. PMID:22726639

Joint Models of Longitudinal and Time-to-Event Data with More Than One Event Time Outcome: A Review.

PubMed

Hickey, Graeme L; Philipson, Pete; Jorgensen, Andrea; Kolamunnage-Dona, Ruwanthi

2018-01-31

Methodological development and clinical application of joint models of longitudinal and time-to-event outcomes have grown substantially over the past two decades. However, much of this research has concentrated on a single longitudinal outcome and a single event time outcome. In clinical and public health research, patients who are followed up over time may often experience multiple, recurrent, or a succession of clinical events. Models that utilise such multivariate event time outcomes are quite valuable in clinical decision-making. We comprehensively review the literature for implementation of joint models involving more than a single event time per subject. We consider the distributional and modelling assumptions, including the association structure, estimation approaches, software implementations, and clinical applications. Research into this area is proving highly promising, but to-date remains in its infancy.

Applicability and accuracy of pretest probability calculations implemented in the NICE clinical guideline for decision making about imaging in patients with chest pain of recent onset.

PubMed

Roehle, Robert; Wieske, Viktoria; Schuetz, Georg M; Gueret, Pascal; Andreini, Daniele; Meijboom, Willem Bob; Pontone, Gianluca; Garcia, Mario; Alkadhi, Hatem; Honoris, Lily; Hausleiter, Jörg; Bettencourt, Nuno; Zimmermann, Elke; Leschka, Sebastian; Gerber, Bernhard; Rochitte, Carlos; Schoepf, U Joseph; Shabestari, Abbas Arjmand; Nørgaard, Bjarne; Sato, Akira; Knuuti, Juhani; Meijs, Matthijs F L; Brodoefel, Harald; Jenkins, Shona M M; Øvrehus, Kristian Altern; Diederichsen, Axel Cosmus Pyndt; Hamdan, Ashraf; Halvorsen, Bjørn Arild; Mendoza Rodriguez, Vladimir; Wan, Yung Liang; Rixe, Johannes; Sheikh, Mehraj; Langer, Christoph; Ghostine, Said; Martuscelli, Eugenio; Niinuma, Hiroyuki; Scholte, Arthur; Nikolaou, Konstantin; Ulimoen, Geir; Zhang, Zhaoqi; Mickley, Hans; Nieman, Koen; Kaufmann, Philipp A; Buechel, Ronny Ralf; Herzog, Bernhard A; Clouse, Melvin; Halon, David A; Leipsic, Jonathan; Bush, David; Jakamy, Reda; Sun, Kai; Yang, Lin; Johnson, Thorsten; Laissy, Jean-Pierre; Marcus, Roy; Muraglia, Simone; Tardif, Jean-Claude; Chow, Benjamin; Paul, Narinder; Maintz, David; Hoe, John; de Roos, Albert; Haase, Robert; Laule, Michael; Schlattmann, Peter; Dewey, Marc

2018-03-19

To analyse the implementation, applicability and accuracy of the pretest probability calculation provided by NICE clinical guideline 95 for decision making about imaging in patients with chest pain of recent onset. The definitions for pretest probability calculation in the original Duke clinical score and the NICE guideline were compared. We also calculated the agreement and disagreement in pretest probability and the resulting imaging and management groups based on individual patient data from the Collaborative Meta-Analysis of Cardiac CT (CoMe-CCT). 4,673 individual patient data from the CoMe-CCT Consortium were analysed. Major differences in definitions in the Duke clinical score and NICE guideline were found for the predictors age and number of risk factors. Pretest probability calculation using guideline criteria was only possible for 30.8 % (1,439/4,673) of patients despite availability of all required data due to ambiguity in guideline definitions for risk factors and age groups. Agreement regarding patient management groups was found in only 70 % (366/523) of patients in whom pretest probability calculation was possible according to both models. Our results suggest that pretest probability calculation for clinical decision making about cardiac imaging as implemented in the NICE clinical guideline for patients has relevant limitations. • Duke clinical score is not implemented correctly in NICE guideline 95. • Pretest probability assessment in NICE guideline 95 is impossible for most patients. • Improved clinical decision making requires accurate pretest probability calculation. • These refinements are essential for appropriate use of cardiac CT.

A clinical decision support system for diagnosis of Allergic Rhinitis based on intradermal skin tests.

PubMed

Jabez Christopher, J; Khanna Nehemiah, H; Kannan, A

2015-10-01

Allergic Rhinitis is a universal common disease, especially in populated cities and urban areas. Diagnosis and treatment of Allergic Rhinitis will improve the quality of life of allergic patients. Though skin tests remain the gold standard test for diagnosis of allergic disorders, clinical experts are required for accurate interpretation of test outcomes. This work presents a clinical decision support system (CDSS) to assist junior clinicians in the diagnosis of Allergic Rhinitis. Intradermal Skin tests were performed on patients who had plausible allergic symptoms. Based on patient׳s history, 40 clinically relevant allergens were tested. 872 patients who had allergic symptoms were considered for this study. The rule based classification approach and the clinical test results were used to develop and validate the CDSS. Clinical relevance of the CDSS was compared with the Score for Allergic Rhinitis (SFAR). Tests were conducted for junior clinicians to assess their diagnostic capability in the absence of an expert. The class based Association rule generation approach provides a concise set of rules that is further validated by clinical experts. The interpretations of the experts are considered as the gold standard. The CDSS diagnoses the presence or absence of rhinitis with an accuracy of 88.31%. The allergy specialist and the junior clinicians prefer the rule based approach for its comprehendible knowledge model. The Clinical Decision Support Systems with rule based classification approach assists junior doctors and clinicians in the diagnosis of Allergic Rhinitis to make reliable decisions based on the reports of intradermal skin tests. Copyright © 2015 Elsevier Ltd. All rights reserved.

Is risk assessment the new clinical model in public mental health?

PubMed

Holmes, Alex

2013-12-01

Australian public mental health services have seen a rapid adoption of risk assessment into clinical practice over the past decade. It is timely to review the role of risk assessment in clinical practice, evidence for its validity and to explore its role in clinical decision-making. There is little evidence to support the current form of risk assessment used in public mental health. The continued focus in risk may lead public psychiatrists into a bind where their specialist role is defined by a capacity that they do not fully possess. Further work is required to find ways of demonstrating our attention to the possibility of adverse outcomes whilst maintaining our skills and capacity to manage mental illness with complexity and balance within the limitations of rational decision-making.

Community-Acquired Pneumonia: 2012 History, Mythology, and Science

PubMed Central

Donowitz, Gerald R.

2013-01-01

Pneumonia remains one of the major disease entities practicing physicians must manage. It is a leading cause of infection-related morbidity and mortality in all age groups, and a leading cause of death in those older than 65 years of age. Despite its frequency and importance, clinical questions have remained in the therapy of community-acquired pneumonia including when to start antibiotics, when to stop them, who to treat, and what agents to use. Answers to these questions have involved historical practice, mythology, and science—sometimes good science, and sometimes better science. How clinical decisions are made for patients with community-acquired pneumonia serves as an illustrative model for other problem areas of medicine and allows for insight as to how clinical decisions have been made and clinical practice established. PMID:23874036

Community-acquired pneumonia: 2012 history, mythology, and science.

PubMed

Donowitz, Gerald R

2013-01-01

Pneumonia remains one of the major disease entities practicing physicians must manage. It is a leading cause of infection-related morbidity and mortality in all age groups, and a leading cause of death in those older than 65 years of age. Despite its frequency and importance, clinical questions have remained in the therapy of community-acquired pneumonia including when to start antibiotics, when to stop them, who to treat, and what agents to use. Answers to these questions have involved historical practice, mythology, and science-sometimes good science, and sometimes better science. How clinical decisions are made for patients with community-acquired pneumonia serves as an illustrative model for other problem areas of medicine and allows for insight as to how clinical decisions have been made and clinical practice established.

Artificial intelligence framework for simulating clinical decision-making: a Markov decision process approach.

PubMed

Bennett, Casey C; Hauser, Kris

2013-01-01

In the modern healthcare system, rapidly expanding costs/complexity, the growing myriad of treatment options, and exploding information streams that often do not effectively reach the front lines hinder the ability to choose optimal treatment decisions over time. The goal in this paper is to develop a general purpose (non-disease-specific) computational/artificial intelligence (AI) framework to address these challenges. This framework serves two potential functions: (1) a simulation environment for exploring various healthcare policies, payment methodologies, etc., and (2) the basis for clinical artificial intelligence - an AI that can "think like a doctor". This approach combines Markov decision processes and dynamic decision networks to learn from clinical data and develop complex plans via simulation of alternative sequential decision paths while capturing the sometimes conflicting, sometimes synergistic interactions of various components in the healthcare system. It can operate in partially observable environments (in the case of missing observations or data) by maintaining belief states about patient health status and functions as an online agent that plans and re-plans as actions are performed and new observations are obtained. This framework was evaluated using real patient data from an electronic health record. The results demonstrate the feasibility of this approach; such an AI framework easily outperforms the current treatment-as-usual (TAU) case-rate/fee-for-service models of healthcare. The cost per unit of outcome change (CPUC) was $189 vs. $497 for AI vs. TAU (where lower is considered optimal) - while at the same time the AI approach could obtain a 30-35% increase in patient outcomes. Tweaking certain AI model parameters could further enhance this advantage, obtaining approximately 50% more improvement (outcome change) for roughly half the costs. Given careful design and problem formulation, an AI simulation framework can approximate optimal decisions even in complex and uncertain environments. Future work is described that outlines potential lines of research and integration of machine learning algorithms for personalized medicine. Copyright © 2012 Elsevier B.V. All rights reserved.

Improving clinical models based on knowledge extracted from current datasets: a new approach.

PubMed

Mendes, D; Paredes, S; Rocha, T; Carvalho, P; Henriques, J; Morais, J

2016-08-01

The Cardiovascular Diseases (CVD) are the leading cause of death in the world, being prevention recognized to be a key intervention able to contradict this reality. In this context, although there are several models and scores currently used in clinical practice to assess the risk of a new cardiovascular event, they present some limitations. The goal of this paper is to improve the CVD risk prediction taking into account the current models as well as information extracted from real and recent datasets. This approach is based on a decision tree scheme in order to assure the clinical interpretability of the model. An innovative optimization strategy is developed in order to adjust the decision tree thresholds (rule structure is fixed) based on recent clinical datasets. A real dataset collected in the ambit of the National Registry on Acute Coronary Syndromes, Portuguese Society of Cardiology is applied to validate this work. In order to assess the performance of the new approach, the metrics sensitivity, specificity and accuracy are used. This new approach achieves sensitivity, a specificity and an accuracy values of, 80.52%, 74.19% and 77.27% respectively, which represents an improvement of about 26% in relation to the accuracy of the original score.

External model validation of binary clinical risk prediction models in cardiovascular and thoracic surgery.

PubMed

Hickey, Graeme L; Blackstone, Eugene H

2016-08-01

Clinical risk-prediction models serve an important role in healthcare. They are used for clinical decision-making and measuring the performance of healthcare providers. To establish confidence in a model, external model validation is imperative. When designing such an external model validation study, thought must be given to patient selection, risk factor and outcome definitions, missing data, and the transparent reporting of the analysis. In addition, there are a number of statistical methods available for external model validation. Execution of a rigorous external validation study rests in proper study design, application of suitable statistical methods, and transparent reporting. Copyright © 2016 The American Association for Thoracic Surgery. Published by Elsevier Inc. All rights reserved.

Predictive models in cancer management: A guide for clinicians.

PubMed

Kazem, Mohammed Ali

2017-04-01

Predictive tools in cancer management are used to predict different outcomes including survival probability or risk of recurrence. The uptake of these tools by clinicians involved in cancer management has not been as common as other clinical tools, which may be due to the complexity of some of these tools or a lack of understanding of how they can aid decision-making in particular clinical situations. The aim of this article is to improve clinicians' knowledge and understanding of predictive tools used in cancer management, including how they are built, how they can be applied to medical practice, and what their limitations may be. Literature review was conducted to investigate the role of predictive tools in cancer management. All predictive models share similar characteristics, but depending on the type of the tool its ability to predict an outcome will differ. Each type has its own pros and cons, and its generalisability will depend on the cohort used to build the tool. These factors will affect the clinician's decision whether to apply the model to their cohort or not. Before a model is used in clinical practice, it is important to appreciate how the model is constructed, what its use may add over and above traditional decision-making tools, and what problems or limitations may be associated with it. Understanding all the above is an important step for any clinician who wants to decide whether or not use predictive tools in their practice. Copyright © 2016 Royal College of Surgeons of Edinburgh (Scottish charity number SC005317) and Royal College of Surgeons in Ireland. Published by Elsevier Ltd. All rights reserved.

A Temporal Mining Framework for Classifying Un-Evenly Spaced Clinical Data: An Approach for Building Effective Clinical Decision-Making System.

PubMed

Jane, Nancy Yesudhas; Nehemiah, Khanna Harichandran; Arputharaj, Kannan

2016-01-01

Clinical time-series data acquired from electronic health records (EHR) are liable to temporal complexities such as irregular observations, missing values and time constrained attributes that make the knowledge discovery process challenging. This paper presents a temporal rough set induced neuro-fuzzy (TRiNF) mining framework that handles these complexities and builds an effective clinical decision-making system. TRiNF provides two functionalities namely temporal data acquisition (TDA) and temporal classification. In TDA, a time-series forecasting model is constructed by adopting an improved double exponential smoothing method. The forecasting model is used in missing value imputation and temporal pattern extraction. The relevant attributes are selected using a temporal pattern based rough set approach. In temporal classification, a classification model is built with the selected attributes using a temporal pattern induced neuro-fuzzy classifier. For experimentation, this work uses two clinical time series dataset of hepatitis and thrombosis patients. The experimental result shows that with the proposed TRiNF framework, there is a significant reduction in the error rate, thereby obtaining the classification accuracy on an average of 92.59% for hepatitis and 91.69% for thrombosis dataset. The obtained classification results prove the efficiency of the proposed framework in terms of its improved classification accuracy.

Lightweight Expression of Granular Objects (LEGO) Content Modeling Using the SNOMED CT Observables Model to Represent Nursing Assessment Data.

PubMed

Johnson, Christie

2016-01-01

This poster presentation presents a content modeling strategy using the SNOMED CT Observable Model to represent large amounts of detailed clinical data in a consistent and computable manner that can support multiple use cases. Lightweight Expression of Granular Objects (LEGOs) represent question/answer pairs on clinical data collection forms, where a question is modeled by a (usually) post-coordinated SNOMED CT expression. LEGOs transform electronic patient data into a normalized consumable, which means that the expressions can be treated as extensions of the SNOMED CT hierarchies for the purpose of performing subsumption queries and other analytics. Utilizing the LEGO approach for modeling clinical data obtained from a nursing admission assessment provides a foundation for data exchange across disparate information systems and software applications. Clinical data exchange of computable LEGO patient information enables the development of more refined data analytics, data storage and clinical decision support.

Evaluating the predictive accuracy and the clinical benefit of a nomogram aimed to predict survival in node-positive prostate cancer patients: External validation on a multi-institutional database.

PubMed

Bianchi, Lorenzo; Schiavina, Riccardo; Borghesi, Marco; Bianchi, Federico Mineo; Briganti, Alberto; Carini, Marco; Terrone, Carlo; Mottrie, Alex; Gacci, Mauro; Gontero, Paolo; Imbimbo, Ciro; Marchioro, Giansilvio; Milanese, Giulio; Mirone, Vincenzo; Montorsi, Francesco; Morgia, Giuseppe; Novara, Giacomo; Porreca, Angelo; Volpe, Alessandro; Brunocilla, Eugenio

2018-04-06

To assess the predictive accuracy and the clinical value of a recent nomogram predicting cancer-specific mortality-free survival after surgery in pN1 prostate cancer patients through an external validation. We evaluated 518 prostate cancer patients treated with radical prostatectomy and pelvic lymph node dissection with evidence of nodal metastases at final pathology, at 10 tertiary centers. External validation was carried out using regression coefficients of the previously published nomogram. The performance characteristics of the model were assessed by quantifying predictive accuracy, according to the area under the curve in the receiver operating characteristic curve and model calibration. Furthermore, we systematically analyzed the specificity, sensitivity, positive predictive value and negative predictive value for each nomogram-derived probability cut-off. Finally, we implemented decision curve analysis, in order to quantify the nomogram's clinical value in routine practice. External validation showed inferior predictive accuracy as referred to in the internal validation (65.8% vs 83.3%, respectively). The discrimination (area under the curve) of the multivariable model was 66.7% (95% CI 60.1-73.0%) by testing with receiver operating characteristic curve analysis. The calibration plot showed an overestimation throughout the range of predicted cancer-specific mortality-free survival rates probabilities. However, in decision curve analysis, the nomogram's use showed a net benefit when compared with the scenarios of treating all patients or none. In an external setting, the nomogram showed inferior predictive accuracy and suboptimal calibration characteristics as compared to that reported in the original population. However, decision curve analysis showed a clinical net benefit, suggesting a clinical implication to correctly manage pN1 prostate cancer patients after surgery. © 2018 The Japanese Urological Association.

[Clinical-pharmacological aspects to accelerate the development process from the preclinical to the clinical phase/2nd communication: promising strategies].

PubMed

Kuhlmann, Jochen

2004-01-01

To improve the transition from research to development a critical evaluation of the individual project by research and disease area teams is required to include input from pharmacology, toxicology, pharmacokinetics, galenics, clinical pharmacology, clinical as well as regulatory experts and marketing. Decisions on the individual development strategy should be made prior to the start of development and all projects should be reviewed at predefined stages throughout the product development life cycle. This ensures consistency of decision-making not only during the development of individual products but throughout the entire development pipeline. Studies in the exploratory stage of drug development should be designed for decision making in contrast to later clinical trials in the confirmatory stage that require power for proof-of-safety and proof-of-efficacy. The more thorough and profound studies have been carried out during this exploratory stage of drug development, the earlier a decision can be made on the continuation or discontinuation of further development, thus saving development time and money and assessing and considerably reducing the risk for the patients and increasing the success rate of the project in the later confirmatory effectiveness trial with an adequate number of subjects receiving the new therapy under typical conditions of use. Strategies which may be helpful to improve the quality of decisions in drug discovery and drug development are: discovery experiments should be done to critically evaluate the compound, the "killer" experiments should be done as early as possible, continuous effort on preclinical disease models is necessary to improve predictability of efficacy in patients ("humanized" research): genomic technology should be used to identify novel, disease-related targets and to characterise preclinical test systems, improvement of knowledge and experience concerning the relevance of new technologies for the clinical picture, genotyping of clinical trial patients to select patient groups which are likely to respond to treatment (pharmacogenomics), modelling and simulation of preclinical and clinical trials, integration of pharmacokinetic and pharmacodynamic principles into drug development, assessment of the interaction potential (CYP-450, trasporter proteins and others), increasing use of biomarker/surrogate marker for rapid clinical feedback, involvement of the target population as soon as possible, applying statistical data analysis techniques for proving effectiveness, co-operation with high quality centers. To reach this goal clinical pharmacology must be fully integrated in the whole process from the candidate selection to its positioning within the market.

Predicting Appropriate Admission of Bronchiolitis Patients in the Emergency Department: Rationale and Methods.

PubMed

Luo, Gang; Stone, Bryan L; Johnson, Michael D; Nkoy, Flory L

2016-03-07

In young children, bronchiolitis is the most common illness resulting in hospitalization. For children less than age 2, bronchiolitis incurs an annual total inpatient cost of $1.73 billion. Each year in the United States, 287,000 emergency department (ED) visits occur because of bronchiolitis, with a hospital admission rate of 32%-40%. Due to a lack of evidence and objective criteria for managing bronchiolitis, ED disposition decisions (hospital admission or discharge to home) are often made subjectively, resulting in significant practice variation. Studies reviewing admission need suggest that up to 29% of admissions from the ED are unnecessary. About 6% of ED discharges for bronchiolitis result in ED returns with admission. These inappropriate dispositions waste limited health care resources, increase patient and parental distress, expose patients to iatrogenic risks, and worsen outcomes. Existing clinical guidelines for bronchiolitis offer limited improvement in patient outcomes. Methodological shortcomings include that the guidelines provide no specific thresholds for ED decisions to admit or to discharge, have an insufficient level of detail, and do not account for differences in patient and illness characteristics including co-morbidities. Predictive models are frequently used to complement clinical guidelines, reduce practice variation, and improve clinicians' decision making. Used in real time, predictive models can present objective criteria supported by historical data for an individualized disease management plan and guide admission decisions. However, existing predictive models for ED patients with bronchiolitis have limitations, including low accuracy and the assumption that the actual ED disposition decision was appropriate. To date, no operational definition of appropriate admission exists. No model has been built based on appropriate admissions, which include both actual admissions that were necessary and actual ED discharges that were unsafe. The goal of this study is to develop a predictive model to guide appropriate hospital admission for ED patients with bronchiolitis. This study will: (1) develop an operational definition of appropriate hospital admission for ED patients with bronchiolitis, (2) develop and test the accuracy of a new model to predict appropriate hospital admission for an ED patient with bronchiolitis, and (3) conduct simulations to estimate the impact of using the model on bronchiolitis outcomes. We are currently extracting administrative and clinical data from the enterprise data warehouse of an integrated health care system. Our goal is to finish this study by the end of 2019. This study will produce a new predictive model that can be operationalized to guide and improve disposition decisions for ED patients with bronchiolitis. Broad use of the model would reduce iatrogenic risk, patient and parental distress, health care use, and costs and improve outcomes for bronchiolitis patients.

The IGNITE network: a model for genomic medicine implementation and research.

PubMed

Weitzel, Kristin Wiisanen; Alexander, Madeline; Bernhardt, Barbara A; Calman, Neil; Carey, David J; Cavallari, Larisa H; Field, Julie R; Hauser, Diane; Junkins, Heather A; Levin, Phillip A; Levy, Kenneth; Madden, Ebony B; Manolio, Teri A; Odgis, Jacqueline; Orlando, Lori A; Pyeritz, Reed; Wu, R Ryanne; Shuldiner, Alan R; Bottinger, Erwin P; Denny, Joshua C; Dexter, Paul R; Flockhart, David A; Horowitz, Carol R; Johnson, Julie A; Kimmel, Stephen E; Levy, Mia A; Pollin, Toni I; Ginsburg, Geoffrey S

2016-01-05

Patients, clinicians, researchers and payers are seeking to understand the value of using genomic information (as reflected by genotyping, sequencing, family history or other data) to inform clinical decision-making. However, challenges exist to widespread clinical implementation of genomic medicine, a prerequisite for developing evidence of its real-world utility. To address these challenges, the National Institutes of Health-funded IGNITE (Implementing GeNomics In pracTicE; www.ignite-genomics.org ) Network, comprised of six projects and a coordinating center, was established in 2013 to support the development, investigation and dissemination of genomic medicine practice models that seamlessly integrate genomic data into the electronic health record and that deploy tools for point of care decision making. IGNITE site projects are aligned in their purpose of testing these models, but individual projects vary in scope and design, including exploring genetic markers for disease risk prediction and prevention, developing tools for using family history data, incorporating pharmacogenomic data into clinical care, refining disease diagnosis using sequence-based mutation discovery, and creating novel educational approaches. This paper describes the IGNITE Network and member projects, including network structure, collaborative initiatives, clinical decision support strategies, methods for return of genomic test results, and educational initiatives for patients and providers. Clinical and outcomes data from individual sites and network-wide projects are anticipated to begin being published over the next few years. The IGNITE Network is an innovative series of projects and pilot demonstrations aiming to enhance translation of validated actionable genomic information into clinical settings and develop and use measures of outcome in response to genome-based clinical interventions using a pragmatic framework to provide early data and proofs of concept on the utility of these interventions. Through these efforts and collaboration with other stakeholders, IGNITE is poised to have a significant impact on the acceleration of genomic information into medical practice.

Many faces of rationality: Implications of the great rationality debate for clinical decision‐making

PubMed Central

Elqayam, Shira

2017-01-01

Abstract Given that more than 30% of healthcare costs are wasted on inappropriate care, suboptimal care is increasingly connected to the quality of medical decisions. It has been argued that personal decisions are the leading cause of death, and 80% of healthcare expenditures result from physicians' decisions. Therefore, improving healthcare necessitates improving medical decisions, ie, making decisions (more) rational. Drawing on writings from The Great Rationality Debate from the fields of philosophy, economics, and psychology, we identify core ingredients of rationality commonly encountered across various theoretical models. Rationality is typically classified under umbrella of normative (addressing the question how people “should” or “ought to” make their decisions) and descriptive theories of decision‐making (which portray how people actually make their decisions). Normative theories of rational thought of relevance to medicine include epistemic theories that direct practice of evidence‐based medicine and expected utility theory, which provides the basis for widely used clinical decision analyses. Descriptive theories of rationality of direct relevance to medical decision‐making include bounded rationality, argumentative theory of reasoning, adaptive rationality, dual processing model of rationality, regret‐based rationality, pragmatic/substantive rationality, and meta‐rationality. For the first time, we provide a review of wide range of theories and models of rationality. We showed that what is “rational” behaviour under one rationality theory may be irrational under the other theory. We also showed that context is of paramount importance to rationality and that no one model of rationality can possibly fit all contexts. We suggest that in context‐poor situations, such as policy decision‐making, normative theories based on expected utility informed by best research evidence may provide the optimal approach to medical decision‐making, whereas in the context‐rich circumstances other types of rationality, informed by human cognitive architecture and driven by intuition and emotions such as the aim to minimize regret, may provide better solution to the problem at hand. The choice of theory under which we operate is important as it determines both policy and our individual decision‐making. PMID:28730671

Accuracy of dengue clinical diagnosis with and without NS1 antigen rapid test: Comparison between human and Bayesian network model decision.

PubMed

Sa-Ngamuang, Chaitawat; Haddawy, Peter; Luvira, Viravarn; Piyaphanee, Watcharapong; Iamsirithaworn, Sopon; Lawpoolsri, Saranath

2018-06-18

Differentiating dengue patients from other acute febrile illness patients is a great challenge among physicians. Several dengue diagnosis methods are recommended by WHO. The application of specific laboratory tests is still limited due to high cost, lack of equipment, and uncertain validity. Therefore, clinical diagnosis remains a common practice especially in resource limited settings. Bayesian networks have been shown to be a useful tool for diagnostic decision support. This study aimed to construct Bayesian network models using basic demographic, clinical, and laboratory profiles of acute febrile illness patients to diagnose dengue. Data of 397 acute undifferentiated febrile illness patients who visited the fever clinic of the Bangkok Hospital for Tropical Diseases, Thailand, were used for model construction and validation. The two best final models were selected: one with and one without NS1 rapid test result. The diagnostic accuracy of the models was compared with that of physicians on the same set of patients. The Bayesian network models provided good diagnostic accuracy of dengue infection, with ROC AUC of 0.80 and 0.75 for models with and without NS1 rapid test result, respectively. The models had approximately 80% specificity and 70% sensitivity, similar to the diagnostic accuracy of the hospital's fellows in infectious disease. Including information on NS1 rapid test improved the specificity, but reduced the sensitivity, both in model and physician diagnoses. The Bayesian network model developed in this study could be useful to assist physicians in diagnosing dengue, particularly in regions where experienced physicians and laboratory confirmation tests are limited.

Development of Validated Computer-based Preoperative Predictive Model for Proximal Junction Failure (PJF) or Clinically Significant PJK With 86% Accuracy Based on 510 ASD Patients With 2-year Follow-up.

PubMed

Scheer, Justin K; Osorio, Joseph A; Smith, Justin S; Schwab, Frank; Lafage, Virginie; Hart, Robert A; Bess, Shay; Line, Breton; Diebo, Bassel G; Protopsaltis, Themistocles S; Jain, Amit; Ailon, Tamir; Burton, Douglas C; Shaffrey, Christopher I; Klineberg, Eric; Ames, Christopher P

2016-11-15

A retrospective review of large, multicenter adult spinal deformity (ASD) database. The aim of this study was to build a model based on baseline demographic, radiographic, and surgical factors that can predict clinically significant proximal junctional kyphosis (PJK) and proximal junctional failure (PJF). PJF and PJK are significant complications and it remains unclear what are the specific drivers behind the development of either. There exists no predictive model that could potentially aid in the clinical decision making for adult patients undergoing deformity correction. Inclusion criteria: age ≥18 years, ASD, at least four levels fused. Variables included in the model were demographics, primary/revision, use of three-column osteotomy, upper-most instrumented vertebra (UIV)/lower-most instrumented vertebra (LIV) levels and UIV implant type (screw, hooks), number of levels fused, and baseline sagittal radiographs [pelvic tilt (PT), pelvic incidence and lumbar lordosis (PI-LL), thoracic kyphosis (TK), and sagittal vertical axis (SVA)]. PJK was defined as an increase from baseline of proximal junctional angle ≥20° with concomitant deterioration of at least one SRS-Schwab sagittal modifier grade from 6 weeks postop. PJF was defined as requiring revision for PJK. An ensemble of decision trees were constructed using the C5.0 algorithm with five different bootstrapped models, and internally validated via a 70 : 30 data split for training and testing. Accuracy and the area under a receiver operator characteristic curve (AUC) were calculated. Five hundred ten patients were included, with 357 for model training and 153 as testing targets (PJF: 37, PJK: 102). The overall model accuracy was 86.3% with an AUC of 0.89 indicating a good model fit. The seven strongest (importance ≥0.95) predictors were age, LIV, pre-operative SVA, UIV implant type, UIV, pre-operative PT, and pre-operative PI-LL. A successful model (86% accuracy, 0.89 AUC) was built predicting either PJF or clinically significant PJK. This model can set the groundwork for preop point of care decision making, risk stratification, and need for prophylactic strategies for patients undergoing ASD surgery. 3.

The Montana State University Conceptual Model of Complementary and Alternative Medicine Health Literacy

PubMed Central

Shreffler-Grant, Jean; Nichols, Elizabeth; Weinert, Clarann; Ide, Bette

2016-01-01

This article aims to present and describe a model of complementary and alternative medicine (CAM) health literacy. The model is the conceptual basis for CAM health literacy, which is operationally defined as the information about CAM needed to make informed self-management decisions regarding health. Improving health literacy is a national priority, and widespread use of CAM has added to the complexity of this task. There are no currently available models or measures of health literacy regarding CAM. The authors developed the model using an iterative process of deriving concepts, constructs, and empirical indicators from the literature and the author’s prior work, review and critique by experts, and revision. The model of CAM health literacy can serve as the basis for future research on the use and efficacy of CAM and the constructs and concepts within it can be used to identify points of intervention for research or for clinical practice. It is anticipated that the model will have scientific and clinical application for assessing health literacy in other self care decision-making situations. PMID:23889542

The Montana State University conceptual model of complementary and alternative medicine health literacy.

PubMed

Shreffler-Grant, Jean; Nichols, Elizabeth; Weinert, Clarann; Ide, Bette

2013-01-01

This article aims to present and describe a model of complementary and alternative medicine (CAM) health literacy. The model is the conceptual basis for CAM health literacy, which is operationally defined as the information about CAM needed to make informed self-management decisions regarding health. Improving health literacy is a national priority, and widespread use of CAM has added to the complexity of this task. There are no currently available models or measures of health literacy regarding CAM. The authors developed the model using an iterative process of deriving concepts, constructs, and empirical indicators from the literature and the author's prior work, review and critique by experts, and revision. The model of CAM health literacy can serve as the basis for future research on the use and efficacy of CAM and the constructs and concepts within it can be used to identify points of intervention for research or for clinical practice. It is anticipated that the model will have scientific and clinical application for assessing health literacy in other self care decision-making situations.

Constructing a clinical decision-making framework for image-guided radiotherapy using a Bayesian Network

NASA Astrophysics Data System (ADS)

Hargrave, C.; Moores, M.; Deegan, T.; Gibbs, A.; Poulsen, M.; Harden, F.; Mengersen, K.

2014-03-01

A decision-making framework for image-guided radiotherapy (IGRT) is being developed using a Bayesian Network (BN) to graphically describe, and probabilistically quantify, the many interacting factors that are involved in this complex clinical process. Outputs of the BN will provide decision-support for radiation therapists to assist them to make correct inferences relating to the likelihood of treatment delivery accuracy for a given image-guided set-up correction. The framework is being developed as a dynamic object-oriented BN, allowing for complex modelling with specific subregions, as well as representation of the sequential decision-making and belief updating associated with IGRT. A prototype graphic structure for the BN was developed by analysing IGRT practices at a local radiotherapy department and incorporating results obtained from a literature review. Clinical stakeholders reviewed the BN to validate its structure. The BN consists of a sub-network for evaluating the accuracy of IGRT practices and technology. The directed acyclic graph (DAG) contains nodes and directional arcs representing the causal relationship between the many interacting factors such as tumour site and its associated critical organs, technology and technique, and inter-user variability. The BN was extended to support on-line and off-line decision-making with respect to treatment plan compliance. Following conceptualisation of the framework, the BN will be quantified. It is anticipated that the finalised decision-making framework will provide a foundation to develop better decision-support strategies and automated correction algorithms for IGRT.

Paediatricians' decision making about prescribing stimulant medications for children with attention-deficit/hyperactivity disorder.

PubMed

Chow, S-J; Sciberras, E; Gillam, L H; Green, J; Efron, D

2014-05-01

Attention-deficit/hyperactivity disorder (ADHD) is now the most common reason for a child to present to a paediatrician in Australia. Stimulant medications are commonly prescribed for children with ADHD, to reduce symptoms and improve function. In this study we investigated the factors that influence paediatricians' decisions about prescribing stimulant medications. In-depth, semi-structured interviews were conducted with paediatricians (n = 13) who were purposively recruited so as to sample a broad demographic of paediatricians working in diverse clinical settings. Paediatricians were recruited from public outpatient and private paediatrician clinics in Victoria, Australia. The interviews were audio-recorded and transcribed verbatim for thematic analysis. Paediatricians also completed a questionnaire describing their demographic and practice characteristics. Our findings showed that the decision to prescribe is a dynamic process involving two key domains: (1) weighing up clinical factors; and (2) interacting with parents and the patient along the journey to prescribing. Five themes relating to this process emerged from data analysis: comprehensive assessments that include history, examination and information from others; influencing factors such as functional impairment and social inclusion; previous success; facilitating parental understanding including addressing myths and parental confusion; and decision-making model. Paediatricians' decisions to prescribe stimulant medications are influenced by multiple factors that operate concurrently and interdependently. Paediatricians do not make decisions about prescribing in isolation; rather, they actively involve parents, teachers and patients, to arrive at a collective, well-informed decision. © 2013 John Wiley & Sons Ltd.

A diagnostic process extended in time as a fuzzy model

NASA Astrophysics Data System (ADS)

Rakus-Andersson, Elisabeth; Gerstenkorn, Tadeusz

1999-03-01

The paper refers to earlier results obtained by the authors and constitutes their essential complement and extension by introducing to a diagnostic model the assumption that the decision concerning the diagnosis is based on observations of symptoms carried out repeatedly, by stages, which may have effect in a change of these symptoms in increasing time. The model concerns the observations of symptoms at an individual patient at a time interval. The changes of the symptoms give some additional information, sometimes very important in the diagnostic process when the clinical picture of a patient in a certain interval of time differs from that one which has been received from the beginning of the disease. It may occur that the change in the intensity of a symptom decides an acceptance of another diagnosis after some time when the patient does not feel better. The aim is to fix an optimal diagnosis on the basis of clinical symptoms typical of several morbid units with respect to the changes of these symptoms in time. In order to solve such a posed problem the authors apply the method of fuzzy relation equations which are modelled by means of logical rules of inference. Moreover, in the final decision concerning the choice of a proper diagnosis, a normed Euclidean distance is introduced as a measure between a real decision and an "ideal" decision. A simple example presents the practical action of the method to show its relevance to a possible user.

A Decision Tree to Identify Children Affected by Prenatal Alcohol Exposure

PubMed Central

Goh, Patrick K.; Doyle, Lauren R.; Glass, Leila; Jones, Kenneth L.; Riley, Edward P.; Coles, Claire D.; Hoyme, H. Eugene; Kable, Julie A.; May, Philip A.; Kalberg, Wendy O.; Elizabeth, R. Sowell; Wozniak, Jeffrey R.; Mattson, Sarah N.

2017-01-01

Objective To develop and validate a hierarchical decision tree model, combining neurobehavioral and physical measures, for identification of children affected by prenatal alcohol exposure even when facial dysmorphology is not present. Study design Data were collected as part of a multisite study across the United States. The model was developed after evaluating over 1000 neurobehavioral and dysmorphology variables collected from 434 children (8–16y) with prenatal alcohol exposure, with and without fetal alcohol syndrome (FAS), and non-exposed controls, with and without other clinically-relevant behavioral or cognitive concerns. The model was subsequently validated in an independent sample of 454 children in two age ranges (5–7y or 10–16y). In all analyses, the discriminatory ability of each model step was tested with logistic regression. Classification accuracies and positive and negative predictive values were calculated. Results The model consisted of variables from 4 measures (2 parent questionnaires, an IQ score, and a physical examination). Overall accuracy rates for both the development and validation samples met or exceeded our goal of 80% overall accuracy. Conclusions The decision tree model distinguished children affected by prenatal alcohol exposure from non-exposed controls, including those with other behavioral concerns or conditions. Improving identification of this population will streamline access to clinical services, including multidisciplinary evaluation and treatment. PMID:27476634

Dying cancer patients talk about physician and patient roles in DNR decision making.

PubMed

Eliott, Jaklin A; Olver, Ian

2011-06-01

Within medical and bioethical discourse, there are many models depicting the relationships between, and roles of, physician and patient in medical decision making. Contestation similarly exists over the roles of physician and patient with regard to the decision not to provide cardiopulmonary resuscitation (CPR) following cardiac arrest [the do-not-resuscitate or do-not-resuscitate (DNR) decision], but there is little analysis of patient perspectives. Analyse what patients with cancer within weeks before dying say about the decision to forego CPR and the roles of patient and physician in this decision. Discursive analysis of qualitative data gathered during semi-structured interviews with 28 adult cancer patients close to death and attending palliative or oncology clinics of an Australian teaching hospital. Participants' descriptions of appropriate patient or physician roles in decisions about CPR appeared related to how they conceptualized the decision: as a personal or a medical issue, with patient and doctor respectively identified as appropriate decision makers; or alternatively, both medical and personal, with various roles assigned embodying different versions of a shared decision-making process. Participants' endorsement of physicians as decision makers rested upon physicians' enactment of the rational, knowledgeable and compassionate expert, which legitimized entrusting them to make the DNR decision. Where this was called into question, physicians were positioned as inappropriate decision makers. When patients' and physicians' understandings of the best decision, or of the preferred role of either party, diverge, conflict may ensue. In order to elicit and negotiate with patient preferences, flexibility is required during clinical interactions about decision making. © 2010 Blackwell Publishing Ltd.

The Impact of Alternative Payment Models on Oncology Innovation and Patient Care.

PubMed

Miller, Amy M; Omenn, Gilbert S; Kean, Marcia A

2016-05-15

Oncology care is in a time of major transformation. Scientific discovery is driving breakthroughs in prevention, diagnostics, and treatment, resulting in tremendous gains for patients as the number of cancer survivors continues to grow on an annual basis. At the same time, there is mounting pressure across the healthcare system to contain costs while improving the quality of cancer care. In response to this pressure, private and government payers are increasingly turning to tools such as alternative payment models (APM) and clinical pathways to improve the efficiency of care, inform coverage decisions, and support shared decision-making. As APMs, clinical pathways and other tools are utilized more broadly, it will be critical that these models support the evidence-based use of innovative biomedical advances, including personalized medicine, and deliver patient-centered, high-value care. Clin Cancer Res; 22(10); 2335-41. ©2016 AACR. ©2016 American Association for Cancer Research.

SU-E-T-23: A Developing Australian Network for Datamining and Modelling Routine Radiotherapy Clinical Data and Radiomics Information for Rapid Learning and Clinical Decision Support

DOE Office of Scientific and Technical Information (OSTI.GOV)

Thwaites, D; Holloway, L; Bailey, M

2015-06-15

Purpose: Large amounts of routine radiotherapy (RT) data are available, which can potentially add clinical evidence to support better decisions. A developing collaborative Australian network, with a leading European partner, aims to validate, implement and extend European predictive models (PMs) for Australian practice and assess their impact on future patient decisions. Wider objectives include: developing multi-institutional rapid learning, using distributed learning approaches; and assessing and incorporating radiomics information into PMs. Methods: Two initial standalone pilots were conducted; one on NSCLC, the other on larynx, patient datasets in two different centres. Open-source rapid learning systems were installed, for data extraction andmore » mining to collect relevant clinical parameters from the centres’ databases. The European DSSs were learned (“training cohort”) and validated against local data sets (“clinical cohort”). Further NSCLC studies are underway in three more centres to pilot a wider distributed learning network. Initial radiomics work is underway. Results: For the NSCLC pilot, 159/419 patient datasets were identified meeting the PM criteria, and hence eligible for inclusion in the curative clinical cohort (for the larynx pilot, 109/125). Some missing data were imputed using Bayesian methods. For both, the European PMs successfully predicted prognosis groups, but with some differences in practice reflected. For example, the PM-predicted good prognosis NSCLC group was differentiated from a combined medium/poor prognosis group (2YOS 69% vs. 27%, p<0.001). Stage was less discriminatory in identifying prognostic groups. In the good prognosis group two-year overall survival was 65% in curatively and 18% in palliatively treated patients. Conclusion: The technical infrastructure and basic European PMs support prognosis prediction for these Australian patient groups, showing promise for supporting future personalized treatment decisions, improved treatment quality and potential practice changes. The early indications from the distributed learning and radiomics pilots strengthen this. Improved routine patient data quality should strengthen such rapid learning systems.« less

Understanding information synthesis in oral surgery for the design of systems for clinical information technology.

PubMed

Suebnukarn, Siriwan; Chanakarn, Piyawadee; Phisutphatthana, Sirada; Pongpatarat, Kanchala; Wongwaithongdee, Udom; Oupadissakoon, Chanekrid

2015-12-01

An understanding of the processes of clinical decision-making is essential for the development of health information technology. In this study we have analysed the acquisition of information during decision-making in oral surgery, and analysed cognitive tasks using a "think-aloud" protocol. We studied the techniques of processing information that were used by novices and experts as they completed 4 oral surgical cases modelled from data obtained from electronic hospital records. We studied 2 phases of an oral surgeon's preoperative practice including the "diagnosis and planning of treatment" and "preparing for a procedure". A framework analysis approach was used to analyse the qualitative data, and a descriptive statistical analysis was made of the quantitative data. The results showed that novice surgeons used hypotheticodeductive reasoning, whereas experts recognised patterns to diagnose and manage patients. Novices provided less detail when they prepared for a procedure. Concepts regarding "signs", "importance", "decisions", and "process" occurred most often during acquisition of information by both novices and experts. Based on these results, we formulated recommendations for the design of clinical information technology that would help to improve the acquisition of clinical information required by oral surgeons at all levels of expertise in their clinical decision-making. Copyright © 2015 The British Association of Oral and Maxillofacial Surgeons. Published by Elsevier Ltd. All rights reserved.

The development and validation of the clinicians' awareness towards cognitive errors (CATChES) in clinical decision making questionnaire tool.

PubMed

Chew, Keng Sheng; Kueh, Yee Cheng; Abdul Aziz, Adlihafizi

2017-03-21

Despite their importance on diagnostic accuracy, there is a paucity of literature on questionnaire tools to assess clinicians' awareness toward cognitive errors. A validation study was conducted to develop a questionnaire tool to evaluate the Clinician's Awareness Towards Cognitive Errors (CATChES) in clinical decision making. This questionnaire is divided into two parts. Part A is to evaluate the clinicians' awareness towards cognitive errors in clinical decision making while Part B is to evaluate their perception towards specific cognitive errors. Content validation for both parts was first determined followed by construct validation for Part A. Construct validation for Part B was not determined as the responses were set in a dichotomous format. For content validation, all items in both Part A and Part B were rated as "excellent" in terms of their relevance in clinical settings. For construct validation using exploratory factor analysis (EFA) for Part A, a two-factor model with total variance extraction of 60% was determined. Two items were deleted. Then, the EFA was repeated showing that all factor loadings are above the cut-off value of >0.5. The Cronbach's alpha for both factors are above 0.6. The CATChES questionnaire tool is a valid questionnaire tool aimed to evaluate the awareness among clinicians toward cognitive errors in clinical decision making.

What can paper-based clinical information systems tell us about the design of computerized clinical information systems (CIS) in the ICU?

PubMed

Miller, A; Pilcher, D; Mercaldo, N; Leong, T; Scheinkestel, C; Schildcrout, J

2010-08-01

Screen designs in computerized clinical information systems (CIS) have been modeled on their paper predecessors. However, limited understanding about how paper forms support clinical work means that we risk repeating old mistakes and creating new opportunities for error and inefficiency as illustrated by problems associated with computerized provider order entry systems. This study was designed to elucidate principles underlying a successful ICU paper-based CIS. The research was guided by two exploratory hypotheses: (1) paper-based artefacts (charts, notes, equipment, order forms) are used differently by nurses, doctors and other healthcare professionals in different (formal and informal) conversation contexts and (2) different artefacts support different decision processes that are distributed across role-based conversations. All conversations undertaken at the bedsides of five patients were recorded with any supporting artefacts for five days per patient. Data was coded according to conversational role-holders, clinical decision process, conversational context and artefacts. 2133 data points were analyzed using Poisson logistic regression analyses. Results show significant interactions between artefacts used during different professional conversations in different contexts (chi(2)((df=16))=55.8, p<0.0001). The interaction between artefacts used during different professional conversations for different clinical decision processes was not statistically significant although all two-way interactions were statistically significant. Paper-based CIS have evolved to support complex interdisciplinary decision processes. The translation of two design principles - support interdisciplinary perspectives and integrate decision processes - from paper to computerized CIS may minimize the risks associated with computerization. 2010 Australian College of Critical Care Nurses Ltd. Published by Elsevier Ltd. All rights reserved.

Self-Stigma and Consumer Participation in Shared Decision Making in Mental Health Services.

PubMed

Hamann, Johannes; Bühner, Markus; Rüsch, Nicolas

2017-08-01

People with mental illness struggle with symptoms and with public stigma. Some accept common prejudices and lose self-esteem, resulting in shame and self-stigma, which may affect their interactions with mental health professionals. This study explored whether self-stigma and shame are associated with consumers' preferences for participation in medical decision making and their behavior in psychiatric consultations. In a cross-sectional study conducted in Germany, 329 individuals with a diagnosis of a schizophrenia spectrum disorder or an affective disorder and their psychiatrists provided sociodemographic and illness-related information. Self-stigma, shame, locus of control, and views about clinical decision making were assessed by self-report. Psychiatrists rated their impression of the decision-making behavior of consumers. Regression analyses and structural equation modeling were used to determine the association of self-stigma and shame with clinical decision making. Self-stigma was not related to consumers' participation preferences, but it was associated with some aspects of communicative behavior. Active and critical behavior (for example, expressing views, daring to challenge the doctor's opinion, and openly speaking out about disagreements with the doctor) was associated with less shame, less self-stigma, more self-responsibility, less attribution of external control to powerful others, and more years of education. Self-stigma and shame were associated with less participative and critical behavior, which probably leads to clinical encounters that involve less shared decision making and more paternalistic decision making. Paternalistic decision making may reinforce self-stigma and lead to poorer health outcomes. Therefore, interventions that reduce self-stigma and increase consumers' critical and participative communication may improve health outcomes.

Free and open source enabling technologies for patient-centric, guideline-based clinical decision support: a survey.

PubMed

Leong, T Y; Kaiser, K; Miksch, S

2007-01-01

Guideline-based clinical decision support is an emerging paradigm to help reduce error, lower cost, and improve quality in evidence-based medicine. The free and open source (FOS) approach is a promising alternative for delivering cost-effective information technology (IT) solutions in health care. In this paper, we survey the current FOS enabling technologies for patient-centric, guideline-based care, and discuss the current trends and future directions of their role in clinical decision support. We searched PubMed, major biomedical informatics websites, and the web in general for papers and links related to FOS health care IT systems. We also relied on our background and knowledge for specific subtopics. We focused on the functionalities of guideline modeling tools, and briefly examined the supporting technologies for terminology, data exchange and electronic health record (EHR) standards. To effectively support patient-centric, guideline-based care, the computerized guidelines and protocols need to be integrated with existing clinical information systems or EHRs. Technologies that enable such integration should be accessible, interoperable, and scalable. A plethora of FOS tools and techniques for supporting different knowledge management and quality assurance tasks involved are available. Many challenges, however, remain in their implementation. There are active and growing trends of deploying FOS enabling technologies for integrating clinical guidelines, protocols, and pathways into the main care processes. The continuing development and maturation of such technologies are likely to make increasingly significant contributions to patient-centric, guideline-based clinical decision support.

The value and cost of complexity in predictive modelling: role of tissue anisotropic conductivity and fibre tracts in neuromodulation

NASA Astrophysics Data System (ADS)

Salman Shahid, Syed; Bikson, Marom; Salman, Humaira; Wen, Peng; Ahfock, Tony

2014-06-01

Objectives. Computational methods are increasingly used to optimize transcranial direct current stimulation (tDCS) dose strategies and yet complexities of existing approaches limit their clinical access. Since predictive modelling indicates the relevance of subject/pathology based data and hence the need for subject specific modelling, the incremental clinical value of increasingly complex modelling methods must be balanced against the computational and clinical time and costs. For example, the incorporation of multiple tissue layers and measured diffusion tensor (DTI) based conductivity estimates increase model precision but at the cost of clinical and computational resources. Costs related to such complexities aggregate when considering individual optimization and the myriad of potential montages. Here, rather than considering if additional details change current-flow prediction, we consider when added complexities influence clinical decisions. Approach. Towards developing quantitative and qualitative metrics of value/cost associated with computational model complexity, we considered field distributions generated by two 4 × 1 high-definition montages (m1 = 4 × 1 HD montage with anode at C3 and m2 = 4 × 1 HD montage with anode at C1) and a single conventional (m3 = C3-Fp2) tDCS electrode montage. We evaluated statistical methods, including residual error (RE) and relative difference measure (RDM), to consider the clinical impact and utility of increased complexities, namely the influence of skull, muscle and brain anisotropic conductivities in a volume conductor model. Main results. Anisotropy modulated current-flow in a montage and region dependent manner. However, significant statistical changes, produced within montage by anisotropy, did not change qualitative peak and topographic comparisons across montages. Thus for the examples analysed, clinical decision on which dose to select would not be altered by the omission of anisotropic brain conductivity. Significance. Results illustrate the need to rationally balance the role of model complexity, such as anisotropy in detailed current flow analysis versus value in clinical dose design. However, when extending our analysis to include axonal polarization, the results provide presumably clinically meaningful information. Hence the importance of model complexity may be more relevant with cellular level predictions of neuromodulation.

Psychopathology, psychopharmacological properties, decision-making capacity to consent to clinical research and the willingness to participate among long-term hospitalized patients with schizophrenia.

PubMed

Wu, Bo-Jian; Liao, Hsun-Yi; Chen, Hsing-Kang; Lan, Tsuo-Hung

2016-03-30

Many studies discuss factors related to the decision-making capacity to consent to clinical research (DMC) of patients with schizophrenia. However, these studies rarely approached willingness to participate and the association between psychopharmacological properties (e.g., antipsychotic-induced side effects) and DMC. This study aimed to explore factors related to DMC and willingness to participate in patients with schizophrenia. All 139 patients with schizophrenia were assessed with the MacArthur Competence Assessment Tool for Clinical Research (MacCAT-CR) and other measures. A linear regression model was used to find the predictors of MacCAT-CR scores. A logistic regression model was used for exploring the predictors of willingness to participate. Patients with more severe negative symptoms performed poorly in DMC outcomes. In addition, females, those with fewer years of education and reduced cognitive function are more likely to experience difficulties in decision-making. Forty-three subjects (30.4%) chose to participate. Patients with higher level of positive symptoms, longer length of stay, higher burden of anticholinergics and users of atypical antipsychotics were more likely to participate in a clinical study which aimed to "enhance cognition". These finding suggest that research investigators should consider many variables for patients who require more intensive screening for impaired DMC. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Rapid Accurate Identification of Tuberculous Meningitis Among South African Children Using a Novel Clinical Decision Tool.

PubMed

Goenka, Anu; Jeena, Prakash M; Mlisana, Koleka; Solomon, Tom; Spicer, Kevin; Stephenson, Rebecca; Verma, Arpana; Dhada, Barnesh; Griffiths, Michael J

2018-03-01

Early diagnosis of tuberculous meningitis (TBM) is crucial to achieve optimum outcomes. There is no effective rapid diagnostic test for use in children. We aimed to develop a clinical decision tool to facilitate the early diagnosis of childhood TBM. Retrospective case-control study was performed across 7 hospitals in KwaZulu-Natal, South Africa (2010-2014). We identified the variables most predictive of microbiologically confirmed TBM in children (3 months to 15 years) by univariate analysis. These variables were modelled into a clinical decision tool and performance tested on an independent sample group. Of 865 children with suspected TBM, 3% (25) were identified with microbiologically confirmed TBM. Clinical information was retrieved for 22 microbiologically confirmed cases of TBM and compared with 66 controls matched for age, ethnicity, sex and geographical origin. The 9 most predictive variables among the confirmed cases were used to develop a clinical decision tool (CHILD TB LP): altered Consciousness; caregiver HIV infected; Illness length >7 days; Lethargy; focal neurologic Deficit; failure to Thrive; Blood/serum sodium <132 mmol/L; CSF >10 Lymphocytes ×10/L; CSF Protein >0.65 g/L. This tool successfully classified an independent sample of 7 cases and 21 controls with a sensitivity of 100% and specificity of 90%. The CHILD TB LP decision tool accurately classified microbiologically confirmed TBM. We propose that CHILD TB LP is prospectively evaluated as a novel rapid diagnostic tool for use in the initial evaluation of children with suspected neurologic infection presenting to hospitals in similar settings.

Temporal characteristics of decisions in hospital encounters: a threshold for shared decision making? A qualitative study.

PubMed

Ofstad, Eirik H; Frich, Jan C; Schei, Edvin; Frankel, Richard M; Gulbrandsen, Pål

2014-11-01

To identify and characterize physicians' statements that contained evidence of clinically relevant decisions in encounters with patients in different hospital settings. Qualitative analysis of 50 videotaped encounters from wards, the emergency room (ER) and outpatient clinics in a department of internal medicine at a Norwegian university hospital. Clinical decisions could be grouped in a temporal order: decisions which had already been made, and were brought into the encounter by the physician (preformed decisions), decisions made in the present (here-and-now decisions), and decisions prescribing future actions given a certain course of events (conditional decisions). Preformed decisions were a hallmark in the ward and conditional decisions a main feature of ER encounters. Clinical decisions related to a patient-physician encounter spanned a time frame exceeding the duration of the encounter. While a distribution of decisions over time and space fosters sharing and dilution of responsibility between providers, it makes the decision making process hard to access for patients. In order to plan when and how to involve patients in decisions, physicians need increased awareness of when clinical decisions are made, who usually makes them, and who should make them. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

Shared decision making: empowering the bedside nurse.

PubMed

Slack, Stephanie M; Boguslawski, Jean M; Eickhoff, Rachel M; Klein, Kristi A; Pepin, Teresa M; Schrandt, Kevin; Wise, Carrie A; Zylstra, Jody A

2005-12-01

Shared decision making is a process that has empowered specialty nurses at the Mayo Clinic in Rochester, MN, to solve a practice concern. Staff nurses recognized a lack of concise, collated information available that described what nurses need to know when caring for patients receiving chemotherapy. Many aspects of the administration process were knowledge and experience based and not easily retrievable. The Hematology/Oncology/Blood and Marrow Transplant Clinical Practice Committee identified this as a significant practice issue. Ideas were brainstormed regarding how to make the information available to nursing colleagues. The Chemotherapy Yellow Pages is a resource that was developed to facilitate the rapid retrieval of pertinent information for bedside nurses. The content of this article outlines a'model of shared decision making and the processes used to address and resolve the practice concern.

Behavioral modeling of human choices reveals dissociable effects of physical effort and temporal delay on reward devaluation.

PubMed

Klein-Flügge, Miriam C; Kennerley, Steven W; Saraiva, Ana C; Penny, Will D; Bestmann, Sven

2015-03-01

There has been considerable interest from the fields of biology, economics, psychology, and ecology about how decision costs decrease the value of rewarding outcomes. For example, formal descriptions of how reward value changes with increasing temporal delays allow for quantifying individual decision preferences, as in animal species populating different habitats, or normal and clinical human populations. Strikingly, it remains largely unclear how humans evaluate rewards when these are tied to energetic costs, despite the surge of interest in the neural basis of effort-guided decision-making and the prevalence of disorders showing a diminished willingness to exert effort (e.g., depression). One common assumption is that effort discounts reward in a similar way to delay. Here we challenge this assumption by formally comparing competing hypotheses about effort and delay discounting. We used a design specifically optimized to compare discounting behavior for both effort and delay over a wide range of decision costs (Experiment 1). We then additionally characterized the profile of effort discounting free of model assumptions (Experiment 2). Contrary to previous reports, in both experiments effort costs devalued reward in a manner opposite to delay, with small devaluations for lower efforts, and progressively larger devaluations for higher effort-levels (concave shape). Bayesian model comparison confirmed that delay-choices were best predicted by a hyperbolic model, with the largest reward devaluations occurring at shorter delays. In contrast, an altogether different relationship was observed for effort-choices, which were best described by a model of inverse sigmoidal shape that is initially concave. Our results provide a novel characterization of human effort discounting behavior and its first dissociation from delay discounting. This enables accurate modelling of cost-benefit decisions, a prerequisite for the investigation of the neural underpinnings of effort-guided choice and for understanding the deficits in clinical disorders characterized by behavioral inactivity.

Behavioral Modeling of Human Choices Reveals Dissociable Effects of Physical Effort and Temporal Delay on Reward Devaluation

PubMed Central

Klein-Flügge, Miriam C.; Kennerley, Steven W.; Saraiva, Ana C.; Penny, Will D.; Bestmann, Sven

2015-01-01

There has been considerable interest from the fields of biology, economics, psychology, and ecology about how decision costs decrease the value of rewarding outcomes. For example, formal descriptions of how reward value changes with increasing temporal delays allow for quantifying individual decision preferences, as in animal species populating different habitats, or normal and clinical human populations. Strikingly, it remains largely unclear how humans evaluate rewards when these are tied to energetic costs, despite the surge of interest in the neural basis of effort-guided decision-making and the prevalence of disorders showing a diminished willingness to exert effort (e.g., depression). One common assumption is that effort discounts reward in a similar way to delay. Here we challenge this assumption by formally comparing competing hypotheses about effort and delay discounting. We used a design specifically optimized to compare discounting behavior for both effort and delay over a wide range of decision costs (Experiment 1). We then additionally characterized the profile of effort discounting free of model assumptions (Experiment 2). Contrary to previous reports, in both experiments effort costs devalued reward in a manner opposite to delay, with small devaluations for lower efforts, and progressively larger devaluations for higher effort-levels (concave shape). Bayesian model comparison confirmed that delay-choices were best predicted by a hyperbolic model, with the largest reward devaluations occurring at shorter delays. In contrast, an altogether different relationship was observed for effort-choices, which were best described by a model of inverse sigmoidal shape that is initially concave. Our results provide a novel characterization of human effort discounting behavior and its first dissociation from delay discounting. This enables accurate modelling of cost-benefit decisions, a prerequisite for the investigation of the neural underpinnings of effort-guided choice and for understanding the deficits in clinical disorders characterized by behavioral inactivity. PMID:25816114

A three-talk model for shared decision making: multistage consultation process

PubMed Central

Durand, Marie Anne; Song, Julia; Aarts, Johanna; Barr, Paul J; Berger, Zackary; Cochran, Nan; Frosch, Dominick; Galasiński, Dariusz; Gulbrandsen, Pål; Han, Paul K J; Härter, Martin; Kinnersley, Paul; Lloyd, Amy; Mishra, Manish; Perestelo-Perez, Lilisbeth; Scholl, Isabelle; Tomori, Kounosuke; Trevena, Lyndal; Witteman, Holly O; Van der Weijden, Trudy

2017-01-01

Objectives To revise an existing three-talk model for learning how to achieve shared decision making, and to consult with relevant stakeholders to update and obtain wider engagement. Design Multistage consultation process. Setting Key informant group, communities of interest, and survey of clinical specialties. Participants 19 key informants, 153 member responses from multiple communities of interest, and 316 responses to an online survey from medically qualified clinicians from six specialties. Results After extended consultation over three iterations, we revised the three-talk model by making changes to one talk category, adding the need to elicit patient goals, providing a clear set of tasks for each talk category, and adding suggested scripts to illustrate each step. A new three-talk model of shared decision making is proposed, based on “team talk,” “option talk,” and “decision talk,” to depict a process of collaboration and deliberation. Team talk places emphasis on the need to provide support to patients when they are made aware of choices, and to elicit their goals as a means of guiding decision making processes. Option talk refers to the task of comparing alternatives, using risk communication principles. Decision talk refers to the task of arriving at decisions that reflect the informed preferences of patients, guided by the experience and expertise of health professionals. Conclusions The revised three-talk model of shared decision making depicts conversational steps, initiated by providing support when introducing options, followed by strategies to compare and discuss trade-offs, before deliberation based on informed preferences. PMID:29109079

Lung Cancer Assistant: a hybrid clinical decision support application for lung cancer care.

PubMed

Sesen, M Berkan; Peake, Michael D; Banares-Alcantara, Rene; Tse, Donald; Kadir, Timor; Stanley, Roz; Gleeson, Fergus; Brady, Michael

2014-09-06

Multidisciplinary team (MDT) meetings are becoming the model of care for cancer patients worldwide. While MDTs have improved the quality of cancer care, the meetings impose substantial time pressure on the members, who generally attend several such MDTs. We describe Lung Cancer Assistant (LCA), a clinical decision support (CDS) prototype designed to assist the experts in the treatment selection decisions in the lung cancer MDTs. A novel feature of LCA is its ability to provide rule-based and probabilistic decision support within a single platform. The guideline-based CDS is based on clinical guideline rules, while the probabilistic CDS is based on a Bayesian network trained on the English Lung Cancer Audit Database (LUCADA). We assess rule-based and probabilistic recommendations based on their concordances with the treatments recorded in LUCADA. Our results reveal that the guideline rule-based recommendations perform well in simulating the recorded treatments with exact and partial concordance rates of 0.57 and 0.79, respectively. On the other hand, the exact and partial concordance rates achieved with probabilistic results are relatively poorer with 0.27 and 0.76. However, probabilistic decision support fulfils a complementary role in providing accurate survival estimations. Compared to recorded treatments, both CDS approaches promote higher resection rates and multimodality treatments.

Decision making and senior management: the implementation of change projects covering clinical management in SUS hospitals.

PubMed

Pacheco, José Márcio da Cunha; Gomes, Romeu

2016-08-01

This paper analyses the decision making process for senior management in public hospitals that are a part of the National Health Service in Brazil (hereafter SUS) in relation to projects aimed at changing clinical management. The methodological design of this study is qualitative in nature taking a hermeneutics-dialectics perspective in terms of results. Hospital directors noted that clinical management projects changed the state of hospitals through: improving their organizations, mobilizing their staff in order to increase a sense of order and systemizing actions and available resources. Technical rationality was the principal basis used in the decision making process for managers. Due to the reality of many hospitals having fragmented organizations, this fact impeded the use of aspects related to rationality, such as economic and financial factors in the decision making process. The incremental model and general politics also play a role in this area. We concluded that the decision making process embraces a large array of factors including rational aspects such as the use of management techniques and the ability to analyze, interpret and summarize. It also incorporates subjective elements such as how to select values and dealing with people's working experiences. We recognized that management problems are wide in scope, ambiguous, complex and do not come with a lot of structure in practice.

Which breast cancer decisions remain non-compliant with guidelines despite the use of computerised decision support?

PubMed Central

Séroussi, B; Laouénan, C; Gligorov, J; Uzan, S; Mentré, F; Bouaud, J

2013-01-01

Background: Despite multidisciplinary tumour boards (MTBs), non-compliance with clinical practice guidelines is still observed for breast cancer patients. Computerised clinical decision support systems (CDSSs) may improve the implementation of guidelines, but cases of non-compliance persist. Methods: OncoDoc2, a guideline-based decision support system, has been routinely used to remind MTB physicians of patient-specific recommended care plans. Non-compliant MTB decisions were analysed using a multivariate adjusted logistic regression model. Results: Between 2007 and 2009, 1624 decisions for invasive breast cancers with a global non-compliance rate of 8.3% were analysed. Patient factors associated with non-compliance were age>80 years (odds ratio (OR): 7.7; 95% confidence interval (CI): 3.7–15.7) in pre-surgical decisions; microinvasive tumour (OR: 5.2; 95% CI: 1.5–17.5), surgical discovery of microinvasion in addition to a unique invasive tumour (OR: 4.2; 95% CI: 1.4–12.5), and prior neoadjuvant treatment (OR: 4.2; 95% CI: 1.1–15.1) in decisions with recommendation of re-excision; age<35 years (OR: 4.7; 95% CI: 1.9–11.4), positive hormonal receptors with human epidermal growth factor receptor 2 overexpression (OR: 15.7; 95% CI: 3.1–78.7), and the absence of prior axillary surgery (OR: 17.2; 95% CI: 5.1–58.1) in adjuvant decisions. Conclusion: Residual non-compliance despite the use of OncoDoc2 illustrates the need to question the clinical profiles where evidence is missing. These findings challenge the weaknesses of guideline content rather than the use of CDSSs. PMID:23942076

Dying cancer patients talk about physician and patient roles in DNR decision making

PubMed Central

Eliott, Jaklin A.; Olver, Ian

2011-01-01

Abstract Background  Within medical and bioethical discourse, there are many models depicting the relationships between, and roles of, physician and patient in medical decision making. Contestation similarly exists over the roles of physician and patient with regard to the decision not to provide cardiopulmonary resuscitation (CPR) following cardiac arrest [the do‐not‐resuscitate or do‐not‐resuscitate (DNR) decision], but there is little analysis of patient perspectives. Objective  Analyse what patients with cancer within weeks before dying say about the decision to forego CPR and the roles of patient and physician in this decision. Design and participants  Discursive analysis of qualitative data gathered during semi‐structured interviews with 28 adult cancer patients close to death and attending palliative or oncology clinics of an Australian teaching hospital. Results  Participants’ descriptions of appropriate patient or physician roles in decisions about CPR appeared related to how they conceptualized the decision: as a personal or a medical issue, with patient and doctor respectively identified as appropriate decision makers; or alternatively, both medical and personal, with various roles assigned embodying different versions of a shared decision‐making process. Participants’ endorsement of physicians as decision makers rested upon physicians’ enactment of the rational, knowledgeable and compassionate expert, which legitimized entrusting them to make the DNR decision. Where this was called into question, physicians were positioned as inappropriate decision makers. Conclusion  When patients’ and physicians’ understandings of the best decision, or of the preferred role of either party, diverge, conflict may ensue. In order to elicit and negotiate with patient preferences, flexibility is required during clinical interactions about decision making. PMID:20860782

Clinical reasoning in the real world is mediated by bounded rationality: implications for diagnostic clinical practice guidelines.

PubMed

Bonilauri Ferreira, Ana Paula Ribeiro; Ferreira, Rodrigo Fernando; Rajgor, Dimple; Shah, Jatin; Menezes, Andrea; Pietrobon, Ricardo

2010-04-20

Little is known about the reasoning mechanisms used by physicians in decision-making and how this compares to diagnostic clinical practice guidelines. We explored the clinical reasoning process in a real life environment. This is a qualitative study evaluating transcriptions of sixteen physicians' reasoning during appointments with patients, clinical discussions between specialists, and personal interviews with physicians affiliated to a hospital in Brazil. FOUR MAIN THEMES WERE IDENTIFIED: simple and robust heuristics, extensive use of social environment rationality, attempts to prove diagnostic and therapeutic hypothesis while refuting potential contradictions using positive test strategy, and reaching the saturation point. Physicians constantly attempted to prove their initial hypothesis while trying to refute any contradictions. While social environment rationality was the main factor in the determination of all steps of the clinical reasoning process, factors such as referral letters and number of contradictions associated with the initial hypothesis had influence on physicians' confidence and determination of the threshold to reach a final decision. Physicians rely on simple heuristics associated with environmental factors. This model allows for robustness, simplicity, and cognitive energy saving. Since this model does not fit into current diagnostic clinical practice guidelines, we make some propositions to help its integration.

Clinical Reasoning in the Real World Is Mediated by Bounded Rationality: Implications for Diagnostic Clinical Practice Guidelines

PubMed Central

Bonilauri Ferreira, Ana Paula Ribeiro; Ferreira, Rodrigo Fernando; Rajgor, Dimple; Shah, Jatin; Menezes, Andrea; Pietrobon, Ricardo

2010-01-01

Background Little is known about the reasoning mechanisms used by physicians in decision-making and how this compares to diagnostic clinical practice guidelines. We explored the clinical reasoning process in a real life environment. Method This is a qualitative study evaluating transcriptions of sixteen physicians' reasoning during appointments with patients, clinical discussions between specialists, and personal interviews with physicians affiliated to a hospital in Brazil. Results Four main themes were identified: simple and robust heuristics, extensive use of social environment rationality, attempts to prove diagnostic and therapeutic hypothesis while refuting potential contradictions using positive test strategy, and reaching the saturation point. Physicians constantly attempted to prove their initial hypothesis while trying to refute any contradictions. While social environment rationality was the main factor in the determination of all steps of the clinical reasoning process, factors such as referral letters and number of contradictions associated with the initial hypothesis had influence on physicians' confidence and determination of the threshold to reach a final decision. Discussion Physicians rely on simple heuristics associated with environmental factors. This model allows for robustness, simplicity, and cognitive energy saving. Since this model does not fit into current diagnostic clinical practice guidelines, we make some propositions to help its integration. PMID:20421920

Computer simulation models of pre-diabetes populations: a systematic review protocol

PubMed Central

Khurshid, Waqar; Pagano, Eva; Feenstra, Talitha

2017-01-01

Introduction Diabetes is a major public health problem and prediabetes (intermediate hyperglycaemia) is associated with a high risk of developing diabetes. With evidence supporting the use of preventive interventions for prediabetes populations and the discovery of novel biomarkers stratifying the risk of progression, there is a need to evaluate their cost-effectiveness across jurisdictions. In diabetes and prediabetes, it is relevant to inform cost-effectiveness analysis using decision models due to their ability to forecast long-term health outcomes and costs beyond the time frame of clinical trials. To support good implementation and reimbursement decisions of interventions in these populations, models should be clinically credible, based on best available evidence, reproducible and validated against clinical data. Our aim is to identify recent studies on computer simulation models and model-based economic evaluations of populations of individuals with prediabetes, qualify them and discuss the knowledge gaps, challenges and opportunities that need to be addressed for future evaluations. Methods and analysis A systematic review will be conducted in MEDLINE, Embase, EconLit and National Health Service Economic Evaluation Database. We will extract peer-reviewed studies published between 2000 and 2016 that describe computer simulation models of the natural history of individuals with prediabetes and/or decision models to evaluate the impact of interventions, risk stratification and/or screening on these populations. Two reviewers will independently assess each study for inclusion. Data will be extracted using a predefined pro forma developed using best practice. Study quality will be assessed using a modelling checklist. A narrative synthesis of all studies will be presented, focussing on model structure, quality of models and input data, and validation status. Ethics and dissemination This systematic review is exempt from ethics approval because the work is carried out on published documents. The findings of the review will be disseminated in a related peer-reviewed journal and presented at conferences. Reviewregistration number CRD42016047228. PMID:28982807

A stochastic multicriteria model for evidence-based decision making in drug benefit-risk analysis.

PubMed

Tervonen, Tommi; van Valkenhoef, Gert; Buskens, Erik; Hillege, Hans L; Postmus, Douwe

2011-05-30

Drug benefit-risk (BR) analysis is based on firm clinical evidence regarding various safety and efficacy outcomes. In this paper, we propose a new and more formal approach for constructing a supporting multi-criteria model that fully takes into account the evidence on efficacy and adverse drug reactions. Our approach is based on the stochastic multi-criteria acceptability analysis methodology, which allows us to compute the typical value judgments that support a decision, to quantify decision uncertainty, and to compute a comprehensive BR profile. We construct a multi-criteria model for the therapeutic group of second-generation antidepressants. We assess fluoxetine and venlafaxine together with placebo according to incidence of treatment response and three common adverse drug reactions by using data from a published study. Our model shows that there are clear trade-offs among the treatment alternatives. Copyright © 2011 John Wiley & Sons, Ltd.

Shared governance in a clinic system.

PubMed

Meyers, Michelle M; Costanzo, Cindy

2015-01-01

Shared governance in health care empowers nurses to share in the decision-making process, which results in decentralized management and collective accountability. Share governance practices have been present in hospitals since the late 1970s. However, shared governance in ambulatory care clinics has not been well established. The subjects of this quality project included staff and administrative nurses in a clinic system. The stakeholder committee chose what model of shared governance to implement and educated clinic staff. The Index of Professional Nursing Governance measured a shared governance score pre- and postimplementation of the Clinic Nursing Council. The Clinic Nursing Council met bimonthly for 3 months during this project to discuss issues and make decisions related to nursing staff. The Index of Professional Nursing Governance scores indicated traditional governance pre- and postimplementation of the Clinic Nursing Council, which is to be expected. The stakeholder committee was beneficial to the initial implementation process and facilitated staff nurse involvement. Shared governance is an evolutionary process that develops empowered nurses and nurse leaders.

Titrating guidance: a model to guide physicians in assisting patients and family members who are facing complex decisions.

PubMed

Goldstein, Nathan E; Back, Anthony L; Morrison, R Sean

2008-09-08

Over the last century, developments in new medical treatments have led to an exponential increase in longevity, but, as a consequence, patients may be left with chronic illness associated with long-term severe functional and cognitive disability. Patients and their families are often forced to make a difficult and complex choice between death and long-term debility, neither of which is an acceptable outcome. Traditional models of medical decision making, however, do not fully address how clinicians should best assist with these decisions. Herein, we present a new paradigm that demonstrates how the role of the physician changes over time in response to the curved relationship between the predictability of a patient's outcome and the chance of returning to an acceptable quality of life. To translate this model into clinical practice, we propose a 5-step model for physicians with which they can (1) determine at which point the patient is on our model; (2) identify the cognitive factors and preferences for outcomes that affect the decision-making process of the patient and his or her family; (3) reflect on their own reaction to the decision at hand; (4) acknowledge how these factors can be addressed in conversation; and (5) guide the patient and his or her family in creating a plan of care. This model can help improve patient-physician communication and decision making so that complex and difficult decisions can be turned into ones that yield to medical expertise, good communication, and personal caring.

Clinical reasoning and population health: decision making for an emerging paradigm of health care.

PubMed

Edwards, Ian; Richardson, Barbara

2008-01-01

Chronic conditions now provide the major disease and disability burden facing humanity. This development has necessitated a reorientation in the practice skills of health care professions away from hospital-based inpatient and outpatient care toward community-based management of patients with chronic conditions. Part of this reorientation toward community-based management of chronic conditions involves practitioners' understanding and adoption of a concept of population health management based on appropriate theoretical models of health care. Drawing on recent studies of expertise in physiotherapy, this article proposes a clinical reasoning and decision-making framework to meet these challenges. The challenge of population and community-based management of chronic conditions also provides an opportunity for physiotherapists to further clarify a professional epistemology of practice that embraces the kinds of knowledge and clinical reasoning processes used in physiotherapy practice. Three case studies related to the management of chronic musculoskeletal pain in different populations are used to exemplify the range of epistemological perspectives that underpin community-based practice. They illustrate the link between conceptualizations of practice problems and knowledge sources that are used as a basis for clinical reasoning and decision making as practitioners are increasingly required to move between the clinic and the community.

Four principles for user interface design of computerised clinical decision support systems.

PubMed

Kanstrup, Anne Marie; Christiansen, Marion Berg; Nøhr, Christian

2011-01-01

The paper presents results from a design research project of a user interface (UI) for a Computerised Clinical Decision Support System (CDSS). The ambition has been to design Human-Computer Interaction (HCI) that can minimise medication errors. Through an iterative design process a digital prototype for prescription of medicine has been developed. This paper presents results from the formative evaluation of the prototype conducted in a simulation laboratory with ten participating physicians. Data from the simulation is analysed by use of theory on how users perceive information. The conclusion is a model, which sum up four principles of interaction for design of CDSS. The four principles for design of user interfaces for CDSS are summarised as four A's: All in one, At a glance, At hand and Attention. The model emphasises integration of all four interaction principles in the design of user interfaces for CDSS, i.e. the model is an integrated model which we suggest as a guide for interaction design when working with preventing medication errors.

Toward better public health reporting using existing off the shelf approaches: A comparison of alternative cancer detection approaches using plaintext medical data and non-dictionary based feature selection.

PubMed

Kasthurirathne, Suranga N; Dixon, Brian E; Gichoya, Judy; Xu, Huiping; Xia, Yuni; Mamlin, Burke; Grannis, Shaun J

2016-04-01

Increased adoption of electronic health records has resulted in increased availability of free text clinical data for secondary use. A variety of approaches to obtain actionable information from unstructured free text data exist. These approaches are resource intensive, inherently complex and rely on structured clinical data and dictionary-based approaches. We sought to evaluate the potential to obtain actionable information from free text pathology reports using routinely available tools and approaches that do not depend on dictionary-based approaches. We obtained pathology reports from a large health information exchange and evaluated the capacity to detect cancer cases from these reports using 3 non-dictionary feature selection approaches, 4 feature subset sizes, and 5 clinical decision models: simple logistic regression, naïve bayes, k-nearest neighbor, random forest, and J48 decision tree. The performance of each decision model was evaluated using sensitivity, specificity, accuracy, positive predictive value, and area under the receiver operating characteristics (ROC) curve. Decision models parameterized using automated, informed, and manual feature selection approaches yielded similar results. Furthermore, non-dictionary classification approaches identified cancer cases present in free text reports with evaluation measures approaching and exceeding 80-90% for most metrics. Our methods are feasible and practical approaches for extracting substantial information value from free text medical data, and the results suggest that these methods can perform on par, if not better, than existing dictionary-based approaches. Given that public health agencies are often under-resourced and lack the technical capacity for more complex methodologies, these results represent potentially significant value to the public health field. Copyright © 2016 Elsevier Inc. All rights reserved.

Machine Learning Techniques for Prediction of Early Childhood Obesity.

PubMed

Dugan, T M; Mukhopadhyay, S; Carroll, A; Downs, S

2015-01-01

This paper aims to predict childhood obesity after age two, using only data collected prior to the second birthday by a clinical decision support system called CHICA. Analyses of six different machine learning methods: RandomTree, RandomForest, J48, ID3, Naïve Bayes, and Bayes trained on CHICA data show that an accurate, sensitive model can be created. Of the methods analyzed, the ID3 model trained on the CHICA dataset proved the best overall performance with accuracy of 85% and sensitivity of 89%. Additionally, the ID3 model had a positive predictive value of 84% and a negative predictive value of 88%. The structure of the tree also gives insight into the strongest predictors of future obesity in children. Many of the strongest predictors seen in the ID3 modeling of the CHICA dataset have been independently validated in the literature as correlated with obesity, thereby supporting the validity of the model. This study demonstrated that data from a production clinical decision support system can be used to build an accurate machine learning model to predict obesity in children after age two.

Bayesian imperfect information analysis for clinical recurrent data

PubMed Central

Chang, Chih-Kuang; Chang, Chi-Chang

2015-01-01

In medical research, clinical practice must often be undertaken with imperfect information from limited resources. This study applied Bayesian imperfect information-value analysis to realistic situations to produce likelihood functions and posterior distributions, to a clinical decision-making problem for recurrent events. In this study, three kinds of failure models are considered, and our methods illustrated with an analysis of imperfect information from a trial of immunotherapy in the treatment of chronic granulomatous disease. In addition, we present evidence toward a better understanding of the differing behaviors along with concomitant variables. Based on the results of simulations, the imperfect information value of the concomitant variables was evaluated and different realistic situations were compared to see which could yield more accurate results for medical decision-making. PMID:25565853

SU-D-BRB-05: Quantum Learning for Knowledge-Based Response-Adaptive Radiotherapy

DOE Office of Scientific and Technical Information (OSTI.GOV)

El Naqa, I; Ten, R

Purpose: There is tremendous excitement in radiotherapy about applying data-driven methods to develop personalized clinical decisions for real-time response-based adaptation. However, classical statistical learning methods lack in terms of efficiency and ability to predict outcomes under conditions of uncertainty and incomplete information. Therefore, we are investigating physics-inspired machine learning approaches by utilizing quantum principles for developing a robust framework to dynamically adapt treatments to individual patient’s characteristics and optimize outcomes. Methods: We studied 88 liver SBRT patients with 35 on non-adaptive and 53 on adaptive protocols. Adaptation was based on liver function using a split-course of 3+2 fractions with amore » month break. The radiotherapy environment was modeled as a Markov decision process (MDP) of baseline and one month into treatment states. The patient environment was modeled by a 5-variable state represented by patient’s clinical and dosimetric covariates. For comparison of classical and quantum learning methods, decision-making to adapt at one month was considered. The MDP objective was defined by the complication-free tumor control (P{sup +}=TCPx(1-NTCP)). A simple regression model represented state-action mapping. Single bit in classical MDP and a qubit of 2-superimposed states in quantum MDP represented the decision actions. Classical decision selection was done using reinforcement Q-learning and quantum searching was performed using Grover’s algorithm, which applies uniform superposition over possible states and yields quadratic speed-up. Results: Classical/quantum MDPs suggested adaptation (probability amplitude ≥0.5) 79% of the time for splitcourses and 100% for continuous-courses. However, the classical MDP had an average adaptation probability of 0.5±0.22 while the quantum algorithm reached 0.76±0.28. In cases where adaptation failed, classical MDP yielded 0.31±0.26 average amplitude while the quantum approach averaged a more optimistic 0.57±0.4, but with high phase fluctuations. Conclusion: Our results demonstrate that quantum machine learning approaches provide a feasible and promising framework for real-time and sequential clinical decision-making in adaptive radiotherapy.« less

Resisting Prescribed Opioids: A Qualitative Study of Decision Making in Patients Taking Opioids for Chronic Noncancer Pain.

PubMed

Paterson, Charlotte; Ledgerwood, Kay; Arnold, Carolyn; Hogg, Malcolm; Xue, Charlie; Zheng, Zhen

2016-04-01

Opioids are increasingly prescribed for chronic noncancer pain across the developed world. Clinical guidelines for management of these patients focus on over-use. However, research into other types of long-term medication indicates that many patients minimize drug use whenever possible. To identify the varying influences on patients' decisions about their use of prescribed opioids and explore whether concepts of resistance and minimization of intake apply to these patients. A multiprofessional team performed a qualitative interview study using the constant-comparative method. Patient's decision making was explored in depth and with a thematic analysis utilizing a published "Model of medicine-taking." A purposive sample of 20 participants drawn from two pain clinics in Melbourne, Australia. The sample was biased toward patients interested in nonmedication pain management options. Patients' needs to obtain relief from severe pain, maintain function, and minimize side effects could lead to under-use as well as over-use of prescribed opioids. In keeping with the published Model of medicine-taking, resistance to taking opioids was a common and important influence on behavior. In the face of severe chronic pain, many participants used a variety of strategies to evaluate, avoid, reduce, self-regulate, and replace opioids. Furthermore, participants perceived a resistance to opioids within the system and among some healthcare professionals. This sometimes adversely affected their adherence. Both patients and doctors exhibit aspects of resistance to the use of prescribed opioids for chronic noncancer pain, suggesting that this shared concern could be the basis of a productive therapeutic alliance to improve communication and shared decision making. Clinical guidelines for opioids use for chronic noncancer pain focus on over-use. Our qualitative interview study found that many patients resisted and minimized the use of opioids. Using a published "Model of medicine-taking," we identified various influences on patient decision making. Both patients and doctors had concerns about using opioids for chronic noncancer pain. These could be the basis of a productive therapeutic alliance to improve communication and shared decision making. © 2015 American Academy of Pain Medicine. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Undertaking an information-needs analysis of the emergency-care physician to inform the role of the clinical librarian: a Greek perspective.

PubMed

Lappa, E

2005-06-01

The primary focus of this pilot study was to gain a better understanding of the information needs of emergency-care clinicians. The secondary focus was to compare the traditional current practice of information provision within other emergency departments in Greek hospitals, with the new model of clinical librarianship (CL). Clinical staff in the emergency department deal with a variety of cases, they have no time to visit the library, but need information instantly in their place of work. Clinical decision making in the emergency department setting frequently requires the clinician to obtain additional sources of information and clinical librarians may facilitate this. The present study focused on two professional groups: medical librarians (group A) and clinicians (consultants, senior registrars, registrars, nurses (group B), working in the emergency departments of two Greek hospitals. The study was organized through a questionnaire survey and some in-depth interviews. This study showed that, for 100% of the clinicians in daily practice, the main information needs arise while treating patients, and that information would help in making patient-care decisions. Clinicians made little use of hospital libraries because they are usually under tremendous time pressure. The main outcome of the study was the use of a clinical librarian as an information provider. Clinical librarians supply information to assist decisions, based on this model. This extends the librarian's role in evidence-based medicine, giving much stronger attention to the relevant evidence in clinical practice. Nowadays, health services are facing organizational change. The introduction of new technology, and rapid growth of medical knowledge creates a demand for new ways of providing information. Clinical librarian programmes may deliver patients specific information in a timely manner. The mission of the clinical librarian is to facilitate access to quality information which is necessary for improving health, and to act as an informationist in the emergency department.

Borderline Personality Traits and Disorder: Predicting Prospective Patient Functioning

ERIC Educational Resources Information Center

Hopwood, Christopher J.; Zanarini, Mary C.

2010-01-01

Objective: Decisions about the composition of personality assessment in the "Diagnostic and Statistical Manual of Mental Disorders" (5th ed.; DSM-V) will be heavily influenced by the clinical utility of candidate constructs. In this study, we addressed 1 aspect of clinical utility by testing the incremental validity of 5-factor model (FFM)…

Principles of health economic evaluations of lipid-lowering strategies.

PubMed

Ara, Roberta; Basarir, Hasan; Ward, Sue Elizabeth

2012-08-01

Policy decision-making in cardiovascular disease is increasingly informed by the results generated from decision-analytic models (DAMs). The methodological approaches and assumptions used in these DAMs impact on the results generated and can influence a policy decision based on a cost per quality-adjusted life year (QALY) threshold. Decision makers need to be provided with a clear understanding of the key sources of evidence and how they are used in the DAM to make an informed judgement on the quality and appropriateness of the results generated. Our review identified 12 studies exploring the cost-effectiveness of pharmaceutical lipid-lowering interventions published since January 2010. All studies used Markov models with annual cycles to represent the long-term clinical pathway. Important differences in the model structures and evidence base used within the DAMs were identified. Whereas the reporting standards were reasonably good, there were many instances when reporting of methods could be improved, particularly relating to baseline risk levels, long-term benefit of treatment and health state utility values. There is a scope for improvement in the reporting of evidence and modelling approaches used within DAMs to provide decision makers with a clearer understanding of the quality and validity of the results generated. This would be assisted by fuller publication of models, perhaps through detailed web appendices.

Clinical decision making-a functional medicine perspective.

PubMed

Pizzorno, Joseph E

2012-09-01

As 21st century health care moves from a disease-based approach to a more patient-centric system that can address biochemical individuality to improve health and function, clinical decision making becomes more complex. Accentuating the problem is the lack of a clear standard for this more complex functional medicine approach. While there is relatively broad agreement in Western medicine for what constitutes competent assessment of disease and identification of related treatment approaches, the complex functional medicine model posits multiple and individualized diagnostic and therapeutic approaches, most or many of which have reasonable underlying science and principles, but which have not been rigorously tested in a research or clinical setting. This has led to non-rigorous thinking and sometimes to uncritical acceptance of both poorly documented diagnostic procedures and ineffective therapies, resulting in less than optimal clinical care.

Clinical Decision Making—A Functional Medicine Perspective

PubMed Central

2012-01-01

As 21st century health care moves from a disease-based approach to a more patient-centric system that can address biochemical individuality to improve health and function, clinical decision making becomes more complex. Accentuating the problem is the lack of a clear standard for this more complex functional medicine approach. While there is relatively broad agreement in Western medicine for what constitutes competent assessment of disease and identification of related treatment approaches, the complex functional medicine model posits multiple and individualized diagnostic and therapeutic approaches, most or many of which have reasonable underlying science and principles, but which have not been rigorously tested in a research or clinical setting. This has led to non-rigorous thinking and sometimes to uncritical acceptance of both poorly documented diagnostic procedures and ineffective therapies, resulting in less than optimal clinical care. PMID:24278827

[Parameter of evidence-based medicine in health care economics].

PubMed

Wasem, J; Siebert, U

1999-08-01

In the view of scarcity of resources, economic evaluations in health care, in which not only effects but also costs related to a medical intervention are examined and a incremental cost-outcome-ratio is build, are an important supplement to the program of evidence based medicine. Outcomes of a medical intervention can be measured by clinical effectiveness, quality-adjusted life years, and monetary evaluation of benefits. As far as costs are concerned, direct medical costs, direct non-medical costs and indirect costs have to be considered in an economic evaluation. Data can be used from primary studies or secondary analysis; metaanalysis for synthesizing of data may be adequate. For calculation of incremental cost-benefit-ratios, models of decision analysis (decision tree models, Markov-models) often are necessary. Methodological and ethical limits for application of the results of economic evaluation in resource allocation decision in health care have to be regarded: Economic evaluations and the calculation of cost-outcome-rations should only support decision making but cannot replace it.

Future of electronic health records: implications for decision support.

PubMed

Rothman, Brian; Leonard, Joan C; Vigoda, Michael M

2012-01-01

The potential benefits of the electronic health record over traditional paper are many, including cost containment, reductions in errors, and improved compliance by utilizing real-time data. The highest functional level of the electronic health record (EHR) is clinical decision support (CDS) and process automation, which are expected to enhance patient health and healthcare. The authors provide an overview of the progress in using patient data more efficiently and effectively through clinical decision support to improve health care delivery, how decision support impacts anesthesia practice, and how some are leading the way using these systems to solve need-specific issues. Clinical decision support uses passive or active decision support to modify clinician behavior through recommendations of specific actions. Recommendations may reduce medication errors, which would result in considerable savings by avoiding adverse drug events. In selected studies, clinical decision support has been shown to decrease the time to follow-up actions, and prediction has proved useful in forecasting patient outcomes, avoiding costs, and correctly prompting treatment plan modifications by clinicians before engaging in decision-making. Clinical documentation accuracy and completeness is improved by an electronic health record and greater relevance of care data is delivered. Clinical decision support may increase clinician adherence to clinical guidelines, but educational workshops may be equally effective. Unintentional consequences of clinical decision support, such as alert desensitization, can decrease the effectiveness of a system. Current anesthesia clinical decision support use includes antibiotic administration timing, improved documentation, more timely billing, and postoperative nausea and vomiting prophylaxis. Electronic health record implementation offers data-mining opportunities to improve operational, financial, and clinical processes. Using electronic health record data in real-time for decision support and process automation has the potential to both reduce costs and improve the quality of patient care. © 2012 Mount Sinai School of Medicine.

Risk factors for invasive fungal disease in critically ill adult patients: a systematic review.

PubMed

Muskett, Hannah; Shahin, Jason; Eyres, Gavin; Harvey, Sheila; Rowan, Kathy; Harrison, David

2011-01-01

Over 5,000 cases of invasive Candida species infections occur in the United Kingdom each year, and around 40% of these cases occur in critical care units. Invasive fungal disease (IFD) in critically ill patients is associated with increased morbidity and mortality at a cost to both the individual and the National Health Service. In this paper, we report the results of a systematic review performed to identify and summarise the important risk factors derived from published multivariable analyses, risk prediction models and clinical decision rules for IFD in critically ill adult patients to inform the primary data collection for the Fungal Infection Risk Evaluation Study. An internet search was performed to identify articles which investigated risk factors, risk prediction models or clinical decisions rules for IFD in critically ill adult patients. Eligible articles were identified in a staged process and were assessed by two investigators independently. The methodological quality of the reporting of the eligible articles was assessed using a set of questions addressing both general and statistical methodologies. Thirteen articles met the inclusion criteria, of which eight articles examined risk factors, four developed a risk prediction model or clinical decision rule and one evaluated a clinical decision rule. Studies varied in terms of objectives, risk factors, definitions and outcomes. The following risk factors were found in multiple studies to be significantly associated with IFD: surgery, total parenteral nutrition, fungal colonisation, renal replacement therapy, infection and/or sepsis, mechanical ventilation, diabetes, and Acute Physiology and Chronic Health Evaluation II (APACHE II) or APACHE III score. Several other risk factors were also found to be statistically significant in single studies only. Risk factor selection process and modelling strategy also varied across studies, and sample sizes were inadequate for obtaining reliable estimates. This review shows a number of risk factors to be significantly associated with the development of IFD in critically ill adults. Methodological limitations were identified in the design and conduct of studies in this area, and caution should be used in their interpretation.

How the dual process model of human cognition can inform efforts to de-implement ineffective and harmful clinical practices: A preliminary model of unlearning and substitution.

PubMed

Helfrich, Christian D; Rose, Adam J; Hartmann, Christine W; van Bodegom-Vos, Leti; Graham, Ian D; Wood, Suzanne J; Majerczyk, Barbara R; Good, Chester B; Pogach, Leonard M; Ball, Sherry L; Au, David H; Aron, David C

2018-02-01

One way to understand medical overuse at the clinician level is in terms of clinical decision-making processes that are normally adaptive but become maladaptive. In psychology, dual process models of cognition propose 2 decision-making processes. Reflective cognition is a conscious process of evaluating options based on some combination of utility, risk, capabilities, and/or social influences. Automatic cognition is a largely unconscious process occurring in response to environmental or emotive cues based on previously learned, ingrained heuristics. De-implementation strategies directed at clinicians may be conceptualized as corresponding to cognition: (1) a process of unlearning based on reflective cognition and (2) a process of substitution based on automatic cognition. We define unlearning as a process in which clinicians consciously change their knowledge, beliefs, and intentions about an ineffective practice and alter their behaviour accordingly. Unlearning has been described as "the questioning of established knowledge, habits, beliefs and assumptions as a prerequisite to identifying inappropriate or obsolete knowledge underpinning and/or embedded in existing practices and routines." We hypothesize that as an unintended consequence of unlearning strategies clinicians may experience "reactance," ie, feel their professional prerogative is being violated and, consequently, increase their commitment to the ineffective practice. We define substitution as replacing the ineffective practice with one or more alternatives. A substitute is a specific alternative action or decision that either precludes the ineffective practice or makes it less likely to occur. Both approaches may work independently, eg, a substitute could displace an ineffective practice without changing clinicians' knowledge, and unlearning could occur even if no alternative exists. For some clinical practice, unlearning and substitution strategies may be most effectively used together. By taking into account the dual process model of cognition, we may be able to design de-implementation strategies matched to clinicians' decision-making processes and avoid unintended consequence. © 2018 The Authors. Journal of Evaluation in Clinical Practice published by John Wiley & Sons, Ltd.

Risk factors for invasive fungal disease in critically ill adult patients: a systematic review

PubMed Central

2011-01-01

Introduction Over 5,000 cases of invasive Candida species infections occur in the United Kingdom each year, and around 40% of these cases occur in critical care units. Invasive fungal disease (IFD) in critically ill patients is associated with increased morbidity and mortality at a cost to both the individual and the National Health Service. In this paper, we report the results of a systematic review performed to identify and summarise the important risk factors derived from published multivariable analyses, risk prediction models and clinical decision rules for IFD in critically ill adult patients to inform the primary data collection for the Fungal Infection Risk Evaluation Study. Methods An internet search was performed to identify articles which investigated risk factors, risk prediction models or clinical decisions rules for IFD in critically ill adult patients. Eligible articles were identified in a staged process and were assessed by two investigators independently. The methodological quality of the reporting of the eligible articles was assessed using a set of questions addressing both general and statistical methodologies. Results Thirteen articles met the inclusion criteria, of which eight articles examined risk factors, four developed a risk prediction model or clinical decision rule and one evaluated a clinical decision rule. Studies varied in terms of objectives, risk factors, definitions and outcomes. The following risk factors were found in multiple studies to be significantly associated with IFD: surgery, total parenteral nutrition, fungal colonisation, renal replacement therapy, infection and/or sepsis, mechanical ventilation, diabetes, and Acute Physiology and Chronic Health Evaluation II (APACHE II) or APACHE III score. Several other risk factors were also found to be statistically significant in single studies only. Risk factor selection process and modelling strategy also varied across studies, and sample sizes were inadequate for obtaining reliable estimates. Conclusions This review shows a number of risk factors to be significantly associated with the development of IFD in critically ill adults. Methodological limitations were identified in the design and conduct of studies in this area, and caution should be used in their interpretation. PMID:22126425

The use of economic evaluation in CAM: an introductory framework

PubMed Central

2010-01-01

Background For CAM to feature prominently in health care decision-making there is a need to expand the evidence-base and to further incorporate economic evaluation into research priorities. In a world of scarce health care resources and an emphasis on efficiency and clinical efficacy, CAM, as indeed do all other treatments, requires rigorous evaluation to be considered in budget decision-making. Methods Economic evaluation provides the tools to measure the costs and health consequences of CAM interventions and thereby inform decision making. This article offers CAM researchers an introductory framework for understanding, undertaking and disseminating economic evaluation. The types of economic evaluation available for the study of CAM are discussed, and decision modelling is introduced as a method for economic evaluation with much potential for use in CAM. Two types of decision models are introduced, decision trees and Markov models, along with a worked example of how each method is used to examine costs and health consequences. This is followed by a discussion of how this information is used by decision makers. Conclusions Undoubtedly, economic evaluation methods form an important part of health care decision making. Without formal training it can seem a daunting task to consider economic evaluation, however, multidisciplinary teams provide an opportunity for health economists, CAM practitioners and other interested researchers, to work together to further develop the economic evaluation of CAM. PMID:21067622

The use of economic evaluation in CAM: an introductory framework.

PubMed

Ford, Emily; Solomon, Daniela; Adams, Jon; Graves, Nicholas

2010-11-11

For CAM to feature prominently in health care decision-making there is a need to expand the evidence-base and to further incorporate economic evaluation into research priorities.In a world of scarce health care resources and an emphasis on efficiency and clinical efficacy, CAM, as indeed do all other treatments, requires rigorous evaluation to be considered in budget decision-making. Economic evaluation provides the tools to measure the costs and health consequences of CAM interventions and thereby inform decision making. This article offers CAM researchers an introductory framework for understanding, undertaking and disseminating economic evaluation. The types of economic evaluation available for the study of CAM are discussed, and decision modelling is introduced as a method for economic evaluation with much potential for use in CAM. Two types of decision models are introduced, decision trees and Markov models, along with a worked example of how each method is used to examine costs and health consequences. This is followed by a discussion of how this information is used by decision makers. Undoubtedly, economic evaluation methods form an important part of health care decision making. Without formal training it can seem a daunting task to consider economic evaluation, however, multidisciplinary teams provide an opportunity for health economists, CAM practitioners and other interested researchers, to work together to further develop the economic evaluation of CAM.

Combined Clinical Parameters and Multiparametric Magnetic Resonance Imaging for Advanced Risk Modeling of Prostate Cancer-Patient-tailored Risk Stratification Can Reduce Unnecessary Biopsies.

PubMed

Radtke, Jan Philipp; Wiesenfarth, Manuel; Kesch, Claudia; Freitag, Martin T; Alt, Celine D; Celik, Kamil; Distler, Florian; Roth, Wilfried; Wieczorek, Kathrin; Stock, Christian; Duensing, Stefan; Roethke, Matthias C; Teber, Dogu; Schlemmer, Heinz-Peter; Hohenfellner, Markus; Bonekamp, David; Hadaschik, Boris A

2017-12-01

Multiparametric magnetic resonance imaging (mpMRI) is gaining widespread acceptance in prostate cancer (PC) diagnosis and improves significant PC (sPC; Gleason score≥3+4) detection. Decision making based on European Randomised Study of Screening for PC (ERSPC) risk-calculator (RC) parameters may overcome prostate-specific antigen (PSA) limitations. We added pre-biopsy mpMRI to ERSPC-RC parameters and developed risk models (RMs) to predict individual sPC risk for biopsy-naïve men and men after previous biopsy. We retrospectively analyzed clinical parameters of 1159 men who underwent mpMRI prior to MRI/transrectal ultrasound fusion biopsy between 2012 and 2015. Multivariate regression analyses were used to determine significant sPC predictors for RM development. The prediction performance was compared with ERSPC-RCs, RCs refitted on our cohort, Prostate Imaging Reporting and Data System (PI-RADS) v1.0, and ERSPC-RC plus PI-RADSv1.0 using receiver-operating characteristics (ROCs). Discrimination and calibration of the RM, as well as net decision and reduction curve analyses were evaluated based on resampling methods. PSA, prostate volume, digital-rectal examination, and PI-RADS were significant sPC predictors and included in the RMs together with age. The ROC area under the curve of the RM for biopsy-naïve men was comparable with ERSPC-RC3 plus PI-RADSv1.0 (0.83 vs 0.84) but larger compared with ERSPC-RC3 (0.81), refitted RC3 (0.80), and PI-RADS (0.76). For postbiopsy men, the novel RM's discrimination (0.81) was higher, compared with PI-RADS (0.78), ERSPC-RC4 (0.66), refitted RC4 (0.76), and ERSPC-RC4 plus PI-RADSv1.0 (0.78). Both RM benefits exceeded those of ERSPC-RCs and PI-RADS in the decision regarding which patient to receive biopsy and enabled the highest reduction rate of unnecessary biopsies. Limitations include a monocentric design and a lack of PI-RADSv2.0. The novel RMs, incorporating clinical parameters and PI-RADS, performed significantly better compared with RMs without PI-RADS and provided measurable benefit in making the decision to biopsy men at a suspicion of PC. For biopsy-naïve patients, both our RM and ERSPC-RC3 plus PI-RADSv1.0 exceeded the prediction performance compared with clinical parameters alone. Combined risk models including clinical and imaging parameters predict clinically relevant prostate cancer significantly better than clinical risk calculators and multiparametric magnetic resonance imaging alone. The risk models demonstrate a benefit in making a decision about which patient needs a biopsy and concurrently help avoid unnecessary biopsies. Copyright © 2017 European Association of Urology. Published by Elsevier B.V. All rights reserved.

Neural network modeling for surgical decisions on traumatic brain injury patients.

PubMed

Li, Y C; Liu, L; Chiu, W T; Jian, W S

2000-01-01

Computerized medical decision support systems have been a major research topic in recent years. Intelligent computer programs were implemented to aid physicians and other medical professionals in making difficult medical decisions. This report compares three different mathematical models for building a traumatic brain injury (TBI) medical decision support system (MDSS). These models were developed based on a large TBI patient database. This MDSS accepts a set of patient data such as the types of skull fracture, Glasgow Coma Scale (GCS), episode of convulsion and return the chance that a neurosurgeon would recommend an open-skull surgery for this patient. The three mathematical models described in this report including a logistic regression model, a multi-layer perceptron (MLP) neural network and a radial-basis-function (RBF) neural network. From the 12,640 patients selected from the database. A randomly drawn 9480 cases were used as the training group to develop/train our models. The other 3160 cases were in the validation group which we used to evaluate the performance of these models. We used sensitivity, specificity, areas under receiver-operating characteristics (ROC) curve and calibration curves as the indicator of how accurate these models are in predicting a neurosurgeon's decision on open-skull surgery. The results showed that, assuming equal importance of sensitivity and specificity, the logistic regression model had a (sensitivity, specificity) of (73%, 68%), compared to (80%, 80%) from the RBF model and (88%, 80%) from the MLP model. The resultant areas under ROC curve for logistic regression, RBF and MLP neural networks are 0.761, 0.880 and 0.897, respectively (P < 0.05). Among these models, the logistic regression has noticeably poorer calibration. This study demonstrated the feasibility of applying neural networks as the mechanism for TBI decision support systems based on clinical databases. The results also suggest that neural networks may be a better solution for complex, non-linear medical decision support systems than conventional statistical techniques such as logistic regression.

Revision Arthroscopic Repair Versus Latarjet Procedure in Patients With Recurrent Instability After Initial Repair Attempt: A Cost-Effectiveness Model.

PubMed

Makhni, Eric C; Lamba, Nayan; Swart, Eric; Steinhaus, Michael E; Ahmad, Christopher S; Romeo, Anthony A; Verma, Nikhil N

2016-09-01

To compare the cost-effectiveness of arthroscopic revision instability repair and Latarjet procedure in treating patients with recurrent instability after initial arthroscopic instability repair. An expected-value decision analysis of revision arthroscopic instability repair compared with Latarjet procedure for recurrent instability followed by failed repair attempt was modeled. Inputs regarding procedure cost, clinical outcomes, and health utilities were derived from the literature. Compared with revision arthroscopic repair, Latarjet was less expensive ($13,672 v $15,287) with improved clinical outcomes (43.78 v 36.76 quality-adjusted life-years). Both arthroscopic repair and Latarjet were cost-effective compared with nonoperative treatment (incremental cost-effectiveness ratios of 3,082 and 1,141, respectively). Results from sensitivity analyses indicate that under scenarios of high rates of stability postoperatively, along with improved clinical outcome scores, revision arthroscopic repair becomes increasingly cost-effective. Latarjet procedure for failed instability repair is a cost-effective treatment option, with lower costs and improved clinical outcomes compared with revision arthroscopic instability repair. However, surgeons must still incorporate clinical judgment into treatment algorithm formation. Level IV, expected value decision analysis. Copyright © 2016. Published by Elsevier Inc.

Decision-making theories and their usefulness to the midwifery profession both in terms of midwifery practice and the education of midwives.

PubMed

Jefford, Elaine; Fahy, Kathleen; Sundin, Deborah

2011-06-01

What are the strengths and limitations of existing Decision-Making Theories as a basis for guiding best practice clinical decision-making within a framework of midwifery philosophy? Each theory is compared in relation with how well they provide a teachable framework for midwifery clinical reasoning that is consistent with midwifery philosophy. Hypothetico-Deductive Theory, from which medical clinical reasoning is based; intuitive decision-making; Dual Processing Theory; The International Confederation of Midwives Clinical Decision-Making Framework; Australian Nursing and Midwifery Council Midwifery Practice Decisions Flowchart and Midwifery Practice. Best practice midwifery clinical Decision-Making Theory needs to give guidance about: (i) effective use of cognitive reasoning processes; (ii) how to include contextual and emotional factors; (iii) how to include the interests of the baby as an integral part of the woman; (iv) decision-making in partnership with woman; and (v) how to recognize/respond to clinical situations outside the midwife's legal/personal scope of practice. No existing Decision-Making Theory meets the needs of midwifery. Medical clinical reasoning has a good contribution to make in terms of cognitive reasoning processes. Two limitations of medical clinical reasoning are its reductionistic focus and privileging of reason to the exclusion of emotional and contextual factors. Hypothetico-deductive clinical reasoning is a necessary but insufficient condition for best practice clinical decision-making in midwifery. © 2011 Blackwell Publishing Asia Pty Ltd.

Distributed Cognition in Cancer Treatment Decision Making: An Application of the DECIDE Decision-Making Styles Typology.

PubMed

Krieger, Janice L; Krok-Schoen, Jessica L; Dailey, Phokeng M; Palmer-Wackerly, Angela L; Schoenberg, Nancy; Paskett, Electra D; Dignan, Mark

2017-07-01

Distributed cognition occurs when cognitive and affective schemas are shared between two or more people during interpersonal discussion. Although extant research focuses on distributed cognition in decision making between health care providers and patients, studies show that caregivers are also highly influential in the treatment decisions of patients. However, there are little empirical data describing how and when families exert influence. The current article addresses this gap by examining decisional support in the context of cancer randomized clinical trial (RCT) decision making. Data are drawn from in-depth interviews with rural, Appalachian cancer patients ( N = 46). Analysis of transcript data yielded empirical support for four distinct models of health decision making. The implications of these findings for developing interventions to improve the quality of treatment decision making and overall well-being are discussed.

Development of a clinical decision model for thyroid nodules.

PubMed

Stojadinovic, Alexander; Peoples, George E; Libutti, Steven K; Henry, Leonard R; Eberhardt, John; Howard, Robin S; Gur, David; Elster, Eric A; Nissan, Aviram

2009-08-10

Thyroid nodules represent a common problem brought to medical attention. Four to seven percent of the United States adult population (10-18 million people) has a palpable thyroid nodule, however the majority (>95%) of thyroid nodules are benign. While, fine needle aspiration remains the most cost effective and accurate diagnostic tool for thyroid nodules in current practice, over 20% of patients undergoing FNA of a thyroid nodule have indeterminate cytology (follicular neoplasm) with associated malignancy risk prevalence of 20-30%. These patients require thyroid lobectomy/isthmusectomy purely for the purpose of attaining a definitive diagnosis. Given that the majority (70-80%) of these patients have benign surgical pathology, thyroidectomy in these patients is conducted principally with diagnostic intent. Clinical models predictive of malignancy risk are needed to support treatment decisions in patients with thyroid nodules in order to reduce morbidity associated with unnecessary diagnostic surgery. Data were analyzed from a completed prospective cohort trial conducted over a 4-year period involving 216 patients with thyroid nodules undergoing ultrasound (US), electrical impedance scanning (EIS) and fine needle aspiration cytology (FNA) prior to thyroidectomy. A Bayesian model was designed to predict malignancy in thyroid nodules based on multivariate dependence relationships between independent covariates. Ten-fold cross-validation was performed to estimate classifier error wherein the data set was randomized into ten separate and unique train and test sets consisting of a training set (90% of records) and a test set (10% of records). A receiver-operating-characteristics (ROC) curve of these predictions and area under the curve (AUC) were calculated to determine model robustness for predicting malignancy in thyroid nodules. Thyroid nodule size, FNA cytology, US and EIS characteristics were highly predictive of malignancy. Cross validation of the model created with Bayesian Network Analysis effectively predicted malignancy [AUC = 0.88 (95%CI: 0.82-0.94)] in thyroid nodules. The positive and negative predictive values of the model are 83% (95%CI: 76%-91%) and 79% (95%CI: 72%-86%), respectively. An integrated predictive decision model using Bayesian inference incorporating readily obtainable thyroid nodule measures is clinically relevant, as it effectively predicts malignancy in thyroid nodules. This model warrants further validation testing in prospective clinical trials.

Bayesian averaging over Decision Tree models for trauma severity scoring.

PubMed

Schetinin, V; Jakaite, L; Krzanowski, W

2018-01-01

Health care practitioners analyse possible risks of misleading decisions and need to estimate and quantify uncertainty in predictions. We have examined the "gold" standard of screening a patient's conditions for predicting survival probability, based on logistic regression modelling, which is used in trauma care for clinical purposes and quality audit. This methodology is based on theoretical assumptions about data and uncertainties. Models induced within such an approach have exposed a number of problems, providing unexplained fluctuation of predicted survival and low accuracy of estimating uncertainty intervals within which predictions are made. Bayesian method, which in theory is capable of providing accurate predictions and uncertainty estimates, has been adopted in our study using Decision Tree models. Our approach has been tested on a large set of patients registered in the US National Trauma Data Bank and has outperformed the standard method in terms of prediction accuracy, thereby providing practitioners with accurate estimates of the predictive posterior densities of interest that are required for making risk-aware decisions. Copyright © 2017 Elsevier B.V. All rights reserved.

Determining if an older adult can make and execute decisions to live safely at home: a capacity assessment and intervention model

PubMed Central

Skelton, Felicia; Kunik, Mark E.; Regev, Tziona; Naik, Aanand D.

2009-01-01

Determining an older adult’s capacity to live safely and independently in the community presents a serious and complicated challenge to the health care system. Evaluating one’s ability to make and execute decisions regarding safe and independent living incorporates clinical assessments, bioethical considerations, and often legal declarations of capacity. Capacity assessments usually result in life changes for patients and their families, including a caregiver managing some everyday tasks, placement outside of the home, and even legal guardianship. The process of determining capacity and recommending intervention is often inefficient and highly variable in most cases. Physicians are rarely trained to conduct capacity assessments and assessment methods are heterogeneous. An interdisciplinary team of clinicians developed the capacity assessment and intervention (CAI) model at a community outpatient geriatrics clinic to address these critical gaps. This report follows one patient through the entire CAI model, describing processes for a typical case. It then examines two additional case reports that highlight common challenges in capacity assessment. The CAI model uses assessment methods common to geriatrics clinical practice and conducts assessments and interventions in a standardized fashion. Reliance on common, validated measures increases generalizability of the model across geriatrics practice settings and patient populations. PMID:19481271

The practice of evidence-based medicine involves the care of whole persons.

PubMed

Richardson, W Scott

2017-04-01

In this issue of the Journal, Dr. Fava posits that evidence-based medicine (EBM) was bound to fail. I share some of the concerns he expresses, yet I see more reasons for optimism. Having been on rounds with both Drs. Engel and Sackett, I reckon they would have agreed more than they disagreed. Their central teaching was the compassionate and well-informed care of sick persons. The model that emerged from these rounds was that patient care could be both person-centered and evidence-based, that clinical judgment was essential to both, and the decisions could and should be shared. Both clinicians and patients can bring knowledge from several sources into the shared decision making process in the clinical encounter, including evidence from clinical care research. I thank Dr. Fava for expressing legitimate doubts and providing useful criticism, yet I am cautiously optimistic that the model of EBM described here is robust enough to meet the challenges and is not doomed to fail. Copyright © 2017 Elsevier Inc. All rights reserved.

Creating and sharing clinical decision support content with Web 2.0: Issues and examples.

PubMed

Wright, Adam; Bates, David W; Middleton, Blackford; Hongsermeier, Tonya; Kashyap, Vipul; Thomas, Sean M; Sittig, Dean F

2009-04-01

Clinical decision support is a powerful tool for improving healthcare quality and patient safety. However, developing a comprehensive package of decision support interventions is costly and difficult. If used well, Web 2.0 methods may make it easier and less costly to develop decision support. Web 2.0 is characterized by online communities, open sharing, interactivity and collaboration. Although most previous attempts at sharing clinical decision support content have worked outside of the Web 2.0 framework, several initiatives are beginning to use Web 2.0 to share and collaborate on decision support content. We present case studies of three efforts: the Clinfowiki, a world-accessible wiki for developing decision support content; Partners Healthcare eRooms, web-based tools for developing decision support within a single organization; and Epic Systems Corporation's Community Library, a repository for sharing decision support content for customers of a single clinical system vendor. We evaluate the potential of Web 2.0 technologies to enable collaborative development and sharing of clinical decision support systems through the lens of three case studies; analyzing technical, legal and organizational issues for developers, consumers and organizers of clinical decision support content in Web 2.0. We believe the case for Web 2.0 as a tool for collaborating on clinical decision support content appears strong, particularly for collaborative content development within an organization.

Prediction of clinical behaviour and treatment for cancers.

PubMed

Futschik, Matthias E; Sullivan, Mike; Reeve, Anthony; Kasabov, Nikola

2003-01-01

Prediction of clinical behaviour and treatment for cancers is based on the integration of clinical and pathological parameters. Recent reports have demonstrated that gene expression profiling provides a powerful new approach for determining disease outcome. If clinical and microarray data each contain independent information then it should be possible to combine these datasets to gain more accurate prognostic information. Here, we have used existing clinical information and microarray data to generate a combined prognostic model for outcome prediction for diffuse large B-cell lymphoma (DLBCL). A prediction accuracy of 87.5% was achieved. This constitutes a significant improvement compared to the previously most accurate prognostic model with an accuracy of 77.6%. The model introduced here may be generally applicable to the combination of various types of molecular and clinical data for improving medical decision support systems and individualising patient care.

"Many miles to go …": a systematic review of the implementation of patient decision support interventions into routine clinical practice.

PubMed

Elwyn, Glyn; Scholl, Isabelle; Tietbohl, Caroline; Mann, Mala; Edwards, Adrian G K; Clay, Catharine; Légaré, France; van der Weijden, Trudy; Lewis, Carmen L; Wexler, Richard M; Frosch, Dominick L

2013-01-01

Two decades of research has established the positive effect of using patient-targeted decision support interventions: patients gain knowledge, greater understanding of probabilities and increased confidence in decisions. Yet, despite their efficacy, the effectiveness of these decision support interventions in routine practice has yet to be established; widespread adoption has not occurred. The aim of this review was to search for and analyze the findings of published peer-reviewed studies that investigated the success levels of strategies or methods where attempts were made to implement patient-targeted decision support interventions into routine clinical settings. An electronic search strategy was devised and adapted for the following databases: ASSIA, CINAHL, Embase, HMIC, Medline, Medline-in-process, OpenSIGLE, PsycINFO, Scopus, Social Services Abstracts, and the Web of Science. In addition, we used snowballing techniques. Studies were included after dual independent assessment. After assessment, 5322 abstracts yielded 51 articles for consideration. After examining full-texts, 17 studies were included and subjected to data extraction. The approach used in all studies was one where clinicians and their staff used a referral model, asking eligible patients to use decision support. The results point to significant challenges to the implementation of patient decision support using this model, including indifference on the part of health care professionals. This indifference stemmed from a reported lack of confidence in the content of decision support interventions and concern about disruption to established workflows, ultimately contributing to organizational inertia regarding their adoption. It seems too early to make firm recommendations about how best to implement patient decision support into routine practice because approaches that use a 'referral model' consistently report difficulties. We sense that the underlying issues that militate against the use of patient decision support and, more generally, limit the adoption of shared decision making, are under-investigated and under-specified. Future reports from implementation studies could be improved by following guidelines, for example the SQUIRE proposals, and by adopting methods that would be able to go beyond the 'barriers' and 'facilitators' approach to understand more about the nature of professional and organizational resistance to these tools. The lack of incentives that reward the use of these interventions needs to be considered as a significant impediment.

Applying Risk Prediction Models to Optimize Lung Cancer Screening: Current Knowledge, Challenges, and Future Directions.

PubMed

Sakoda, Lori C; Henderson, Louise M; Caverly, Tanner J; Wernli, Karen J; Katki, Hormuzd A

2017-12-01

Risk prediction models may be useful for facilitating effective and high-quality decision-making at critical steps in the lung cancer screening process. This review provides a current overview of published lung cancer risk prediction models and their applications to lung cancer screening and highlights both challenges and strategies for improving their predictive performance and use in clinical practice. Since the 2011 publication of the National Lung Screening Trial results, numerous prediction models have been proposed to estimate the probability of developing or dying from lung cancer or the probability that a pulmonary nodule is malignant. Respective models appear to exhibit high discriminatory accuracy in identifying individuals at highest risk of lung cancer or differentiating malignant from benign pulmonary nodules. However, validation and critical comparison of the performance of these models in independent populations are limited. Little is also known about the extent to which risk prediction models are being applied in clinical practice and influencing decision-making processes and outcomes related to lung cancer screening. Current evidence is insufficient to determine which lung cancer risk prediction models are most clinically useful and how to best implement their use to optimize screening effectiveness and quality. To address these knowledge gaps, future research should be directed toward validating and enhancing existing risk prediction models for lung cancer and evaluating the application of model-based risk calculators and its corresponding impact on screening processes and outcomes.

Calculated Parameters of Thyroid Homeostasis: Emerging Tools for Differential Diagnosis and Clinical Research

PubMed Central

Dietrich, Johannes W.; Landgrafe-Mende, Gabi; Wiora, Evelin; Chatzitomaris, Apostolos; Klein, Harald H.; Midgley, John E. M.; Hoermann, Rudolf

2016-01-01

Although technical problems of thyroid testing have largely been resolved by modern assay technology, biological variation remains a challenge. This applies to subclinical thyroid disease, non-thyroidal illness syndrome, and those 10% of hypothyroid patients, who report impaired quality of life, despite normal thyrotropin (TSH) concentrations under levothyroxine (L-T4) replacement. Among multiple explanations for this condition, inadequate treatment dosage and monotherapy with L-T4 in subjects with impaired deiodination have received major attention. Translation to clinical practice is difficult, however, since univariate reference ranges for TSH and thyroid hormones fail to deliver robust decision algorithms for therapeutic interventions in patients with more subtle thyroid dysfunctions. Advances in mathematical and simulative modeling of pituitary–thyroid feedback control have improved our understanding of physiological mechanisms governing the homeostatic behavior. From multiple cybernetic models developed since 1956, four examples have also been translated to applications in medical decision-making and clinical trials. Structure parameters representing fundamental properties of the processing structure include the calculated secretory capacity of the thyroid gland (SPINA-GT), sum activity of peripheral deiodinases (SPINA-GD) and Jostel’s TSH index for assessment of thyrotropic pituitary function, supplemented by a recently published algorithm for reconstructing the personal set point of thyroid homeostasis. In addition, a family of integrated models (University of California-Los Angeles platform) provides advanced methods for bioequivalence studies. This perspective article delivers an overview of current clinical research on the basis of mathematical thyroid models. In addition to a summary of large clinical trials, it provides previously unpublished results of validation studies based on simulation and clinical samples. PMID:27375554

Calculated Parameters of Thyroid Homeostasis: Emerging Tools for Differential Diagnosis and Clinical Research.

PubMed

Dietrich, Johannes W; Landgrafe-Mende, Gabi; Wiora, Evelin; Chatzitomaris, Apostolos; Klein, Harald H; Midgley, John E M; Hoermann, Rudolf

2016-01-01

Although technical problems of thyroid testing have largely been resolved by modern assay technology, biological variation remains a challenge. This applies to subclinical thyroid disease, non-thyroidal illness syndrome, and those 10% of hypothyroid patients, who report impaired quality of life, despite normal thyrotropin (TSH) concentrations under levothyroxine (L-T4) replacement. Among multiple explanations for this condition, inadequate treatment dosage and monotherapy with L-T4 in subjects with impaired deiodination have received major attention. Translation to clinical practice is difficult, however, since univariate reference ranges for TSH and thyroid hormones fail to deliver robust decision algorithms for therapeutic interventions in patients with more subtle thyroid dysfunctions. Advances in mathematical and simulative modeling of pituitary-thyroid feedback control have improved our understanding of physiological mechanisms governing the homeostatic behavior. From multiple cybernetic models developed since 1956, four examples have also been translated to applications in medical decision-making and clinical trials. Structure parameters representing fundamental properties of the processing structure include the calculated secretory capacity of the thyroid gland (SPINA-GT), sum activity of peripheral deiodinases (SPINA-GD) and Jostel's TSH index for assessment of thyrotropic pituitary function, supplemented by a recently published algorithm for reconstructing the personal set point of thyroid homeostasis. In addition, a family of integrated models (University of California-Los Angeles platform) provides advanced methods for bioequivalence studies. This perspective article delivers an overview of current clinical research on the basis of mathematical thyroid models. In addition to a summary of large clinical trials, it provides previously unpublished results of validation studies based on simulation and clinical samples.

Development of prognostic model for predicting survival after retrograde placement of ureteral stent in advanced gastrointestinal cancer patients and its evaluation by decision curve analysis.

PubMed

Kawano, Shingo; Komai, Yoshinobu; Ishioka, Junichiro; Sakai, Yasuyuki; Fuse, Nozomu; Ito, Masaaki; Kihara, Kazunori; Saito, Norio

2016-10-01

The aim of this study was to determine risk factors for survival after retrograde placement of ureteral stents and develop a prognostic model for advanced gastrointestinal tract (GIT: esophagus, stomach, colon and rectum) cancer patients. We examined the clinical records of 122 patients who underwent retrograde placement of a ureteral stent against malignant extrinsic ureteral obstruction. A prediction model for survival after stenting was developed. We compared its clinical usefulness with our previous model based on the results from nephrostomy cases by decision curve analysis. Median follow-up period was 201 days (8-1490) and 97 deaths occurred. The 1-year survival rate in this cohort was 29%. Based on multivariate analysis, primary site of colon origin, absence of retroperitoneal lymph node metastasis and serum albumin >3g/dL were significantly associated with a prolonged survival time. To develop a prognostic model, we divided the patients into 3 risk groups of favorable: 0-1 factors (N.=53), intermediate: 2 risk factors (N.=54), and poor: 3 risk factors (N.=15). There were significant differences in the survival profiles of these 3 risk groups (P<0.0001). Decision curve analyses revealed that the current model has a superior net benefit than our previous model for most of the examined probabilities. We have developed a novel prognostic model for GIT cancer patients who were treated with retrograde placement of a ureteral stent. The current model should help urologists and medical oncologists to predict survival in cases of malignant extrinsic ureteral obstruction.

Patentability aspects of computational cancer models

NASA Astrophysics Data System (ADS)

Lishchuk, Iryna

2017-07-01

Multiscale cancer models, implemented in silico, simulate tumor progression at various spatial and temporal scales. Having the innovative substance and possessing the potential of being applied as decision support tools in clinical practice, patenting and obtaining patent rights in cancer models seems prima facie possible. What legal hurdles the cancer models need to overcome for being patented we inquire from this paper.

[Factors influencing nurses' clinical decision making--focusing on critical thinking disposition].

PubMed

Park, Seungmi; Kwon, In Gak

2007-10-01

The purpose of this study was to investigate the factors influencing nurses' clinical decision making focusing on critical thinking disposition. The subjects of this study consisted of 505 nurses working at one of the general hospitals located in Seoul. Data was collected by a self-administered questionnaire between December 2006 and January 2007. Data was analyzed by one way ANOVA, Pearson correlation coefficients, and stepwise multiple regression using SPSS Win 14.0. The mean scores of critical thinking disposition and clinical decision making were 99.10 and 134.32 respectively. Clinical decision making scores were significantly higher in groups under continuing education, with a master or higher degree, with clinical experience more than 5 years, or with experts. Critical thinking disposition and its subscales have a significant correlation with clinical decision making. Intellectual eagerness/curiosity, prudence, clinical experience, intellectual honesty, self-confidence, and healthy skepticism were important factors influencing clinical decision making(adjusted R(2)=33%). Results of this study suggest that various strategies such as retaining experienced nurses, encouraging them to continue with education and enhancing critical thinking disposition are warranted for development of clinical decision making.

Decision-making in Swiss home-like childbirth: A grounded theory study.

PubMed

Meyer, Yvonne; Frank, Franziska; Schläppy Muntwyler, Franziska; Fleming, Valerie; Pehlke-Milde, Jessica

2017-12-01

Decision-making in midwifery, including a claim for shared decision-making between midwives and women, is of major significance for the health of mother and child. Midwives have little information about how to share decision-making responsibilities with women, especially when complications arise during birth. To increase understanding of decision-making in complex home-like birth settings by exploring midwives' and women's perspectives and to develop a dynamic model integrating participatory processes for making shared decisions. The study, based on grounded theory methodology, analysed 20 interviews of midwives and 20 women who had experienced complications in home-like births. The central phenomenon that arose from the data was "defining/redefining decision as a joint commitment to healthy childbirth". The sub-indicators that make up this phenomenon were safety, responsibility, mutual and personal commitments. These sub-indicators were also identified to influence temporal conditions of decision-making and to apply different strategies for shared decision-making. Women adopted strategies such as delegating a decision, making the midwife's decision her own, challenging a decision or taking a decision driven by the dynamics of childbirth. Midwives employed strategies such as remaining indecisive, approving a woman's decision, making an informed decision or taking the necessary decision. To respond to recommendations for shared responsibility for care, midwives need to strengthen their shared decision-making skills. The visual model of decision-making in childbirth derived from the data provides a framework for transferring clinical reasoning into practice. Copyright © 2017 Australian College of Midwives. Published by Elsevier Ltd. All rights reserved.

Gathering Real World Evidence with Cluster Analysis for Clinical Decision Support.

PubMed

Xia, Eryu; Liu, Haifeng; Li, Jing; Mei, Jing; Li, Xuejun; Xu, Enliang; Li, Xiang; Hu, Gang; Xie, Guotong; Xu, Meilin

2017-01-01

Clinical decision support systems are information technology systems that assist clinical decision-making tasks, which have been shown to enhance clinical performance. Cluster analysis, which groups similar patients together, aims to separate patient cases into phenotypically heterogenous groups and defining therapeutically homogeneous patient subclasses. Useful as it is, the application of cluster analysis in clinical decision support systems is less reported. Here, we describe the usage of cluster analysis in clinical decision support systems, by first dividing patient cases into similar groups and then providing diagnosis or treatment suggestions based on the group profiles. This integration provides data for clinical decisions and compiles a wide range of clinical practices to inform the performance of individual clinicians. We also include an example usage of the system under the scenario of blood lipid management in type 2 diabetes. These efforts represent a step toward promoting patient-centered care and enabling precision medicine.

Publishing web-based guidelines using interactive decision models.

PubMed

Sanders, G D; Nease, R F; Owens, D K

2001-05-01

Commonly used methods for guideline development and dissemination do not enable developers to tailor guidelines systematically to specific patient populations and update guidelines easily. We developed a web-based system, ALCHEMIST, that uses decision models and automatically creates evidence-based guidelines that can be disseminated, tailored and updated over the web. Our objective was to demonstrate the use of this system with clinical scenarios that provide challenges for guideline development. We used the ALCHEMIST system to develop guidelines for three clinical scenarios: (1) Chlamydia screening for adolescent women, (2) antiarrhythmic therapy for the prevention of sudden cardiac death; and (3) genetic testing for the BRCA breast-cancer mutation. ALCHEMIST uses information extracted directly from the decision model, combined with the additional information from the author of the decision model, to generate global guidelines. ALCHEMIST generated electronic web-based guidelines for each of the three scenarios. Using ALCHEMIST, we demonstrate that tailoring a guideline for a population at high-risk for Chlamydia changes the recommended policy for control of Chlamydia from contact tracing of reported cases to a population-based screening programme. We used ALCHEMIST to incorporate new evidence about the effectiveness of implantable cardioverter defibrillators (ICD) and demonstrate that the cost-effectiveness of use of ICDs improves from $74 400 per quality-adjusted life year (QALY) gained to $34 500 per QALY gained. Finally, we demonstrate how a clinician could use ALCHEMIST to incorporate a woman's utilities for relevant health states and thereby develop patient-specific recommendations for BRCA testing; the patient-specific recommendation improved quality-adjusted life expectancy by 37 days. The ALCHEMIST system enables guideline developers to publish both a guideline and an interactive decision model on the web. This web-based tool enables guideline developers to tailor guidelines systematically, to update guidelines easily, and to make the underlying evidence and analysis transparent for users.

Mapping clinical outcomes expectations to treatment decisions: an application to vestibular schwannoma management.

PubMed

Cheung, Steven W; Aranda, Derick; Driscoll, Colin L W; Parsa, Andrew T

2010-02-01

Complex medical decision making obligates tradeoff assessments among treatment outcomes expectations, but an accessible tool to perform the necessary analysis is conspicuously absent. We aimed to demonstrate methodology and feasibility of adapting conjoint analysis for mapping clinical outcomes expectations to treatment decisions in vestibular schwannoma (VS) management. Prospective. Tertiary medical center and US-based otologists/neurotologists. Treatment preference profiles among VS stakeholders-61 younger and 74 older prospective patients, 61 observation patients, and 60 surgeons-were assessed for the synthetic VS case scenario of a 10-mm tumor in association with useful hearing and normal facial function. Treatment attribute utility. Conjoint analysis attribute levels were set in accordance to the results of a meta-analysis. Forty-five case series were disaggregated to formulate microsurgery facial nerve and hearing preservation outcomes expectations models. Attribute utilities were computed and mapped to the realistic treatment choices of translabyrinthine craniotomy, middle fossa craniotomy, and gamma knife radiosurgery. Among the treatment attributes of likelihoods of causing deafness, temporary facial weakness for 2 months, and incurable cancer within 20 years, and recovery time, permanent deafness was less important to tumor surgeons, and temporary facial weakness was more important to tumor surgeons and observation patients (Wilcoxon rank-sum, p < 0.001). Inverse mapping of preference profiles to realistic treatment choices showed all study cohorts were inclined to choose gamma knife radiosurgery. Mapping clinical outcomes expectations to treatment decisions for a synthetic clinical scenario revealed inhomogeneous drivers of choice selection among study cohorts. Medical decision engines that analyze personal preferences of outcomes expectations for VS and many other diseases may be developed to promote shared decision making among health care stakeholders and transparency in the informed consent process.

Factors influencing a nurse's decision to question medication administration in a neonatal clinical care unit.

PubMed

Aydon, Laurene; Hauck, Yvonne; Zimmer, Margo; Murdoch, Jamee

2016-09-01

The aim of this study was to identify factors that influence nurse's decisions to question concerning aspects of medication administration within the context of a neonatal clinical care unit. Medication error in the neonatal setting can be high with this particularly vulnerable population. As the care giver responsible for medication administration, nurses are deemed accountable for most errors. However, they are recognised as the forefront of prevention. Minimal evidence is available around reasoning, decision making and questioning around medication administration. Therefore, this study focuses upon addressing the gap in knowledge around what nurses believe influences their decision to question. A critical incident design was employed where nurses were asked to describe clinical incidents around their decision to question a medication issue. Nurses were recruited from a neonatal clinical care unit and participated in an individual digitally recorded interview. One hundred and three nurses participated between December 2013-August 2014. Use of the constant comparative method revealed commonalities within transcripts. Thirty-six categories were grouped into three major themes: 'Working environment', 'Doing the right thing' and 'Knowledge about medications'. Findings highlight factors that influence nurses' decision to question issues around medication administration. Nurses feel it is their responsibility to do the right thing and speak up for their vulnerable patients to enhance patient safety. Negative dimensions within the themes will inform planning of educational strategies to improve patient safety, whereas positive dimensions must be reinforced within the multidisciplinary team. The working environment must support nurses to question and ultimately provide safe patient care. Clear and up to date policies, formal and informal education, role modelling by senior nurses, effective use of communication skills and a team approach can facilitate nurses to appropriately question aspects around medication administration. © 2016 John Wiley & Sons Ltd.

Deep Patient: An Unsupervised Representation to Predict the Future of Patients from the Electronic Health Records

PubMed Central

Miotto, Riccardo; Li, Li; Kidd, Brian A.; Dudley, Joel T.

2016-01-01

Secondary use of electronic health records (EHRs) promises to advance clinical research and better inform clinical decision making. Challenges in summarizing and representing patient data prevent widespread practice of predictive modeling using EHRs. Here we present a novel unsupervised deep feature learning method to derive a general-purpose patient representation from EHR data that facilitates clinical predictive modeling. In particular, a three-layer stack of denoising autoencoders was used to capture hierarchical regularities and dependencies in the aggregated EHRs of about 700,000 patients from the Mount Sinai data warehouse. The result is a representation we name “deep patient”. We evaluated this representation as broadly predictive of health states by assessing the probability of patients to develop various diseases. We performed evaluation using 76,214 test patients comprising 78 diseases from diverse clinical domains and temporal windows. Our results significantly outperformed those achieved using representations based on raw EHR data and alternative feature learning strategies. Prediction performance for severe diabetes, schizophrenia, and various cancers were among the top performing. These findings indicate that deep learning applied to EHRs can derive patient representations that offer improved clinical predictions, and could provide a machine learning framework for augmenting clinical decision systems. PMID:27185194

Deep Patient: An Unsupervised Representation to Predict the Future of Patients from the Electronic Health Records

NASA Astrophysics Data System (ADS)

Miotto, Riccardo; Li, Li; Kidd, Brian A.; Dudley, Joel T.

2016-05-01

Secondary use of electronic health records (EHRs) promises to advance clinical research and better inform clinical decision making. Challenges in summarizing and representing patient data prevent widespread practice of predictive modeling using EHRs. Here we present a novel unsupervised deep feature learning method to derive a general-purpose patient representation from EHR data that facilitates clinical predictive modeling. In particular, a three-layer stack of denoising autoencoders was used to capture hierarchical regularities and dependencies in the aggregated EHRs of about 700,000 patients from the Mount Sinai data warehouse. The result is a representation we name “deep patient”. We evaluated this representation as broadly predictive of health states by assessing the probability of patients to develop various diseases. We performed evaluation using 76,214 test patients comprising 78 diseases from diverse clinical domains and temporal windows. Our results significantly outperformed those achieved using representations based on raw EHR data and alternative feature learning strategies. Prediction performance for severe diabetes, schizophrenia, and various cancers were among the top performing. These findings indicate that deep learning applied to EHRs can derive patient representations that offer improved clinical predictions, and could provide a machine learning framework for augmenting clinical decision systems.

A novel personal health system with integrated decision support and guidance for the management of chronic liver disease.

PubMed

Kiefer, Stephan; Schäfer, Michael; Bransch, Marco; Brimmers, Peter; Bartolomé, Diego; Baños, Janie; Orr, James; Jones, Dave; Jara, Maximilian; Stockmann, Martin

2014-01-01

A personal health system platform for the management of patients with chronic liver disease that incorporates a novel approach to integrate decision support and guidance through care pathways for patients and their doctors is presented in this paper. The personal health system incorporates an integrated decision support engine that guides patients and doctors through the management of the disease by issuing tasks and providing recommendations to both the care team and the patient and by controlling the execution of a Care Flow Plan based on the results of tasks and the monitored health status of the patient. This Care Flow Plan represents a formal, business process based model of disease management designed off-line by domain experts on the basis of clinical guidelines, knowledge of care pathways and an organisational model for integrated, patient-centred care. In this way, remote monitoring and treatment are dynamically adapted to the patient's actual condition and clinical symptoms and allow flexible delivery of care with close integration of specialists, therapists and care-givers.

Mental workload as a key factor in clinical decision making.

PubMed

Byrne, Aidan

2013-08-01

The decision making process is central to the practice of a clinician and has traditionally been described in terms of the hypothetico-deductive model. More recently, models adapted from cognitive psychology, such as the dual process and script theories have proved useful in explaining patterns of practice not consistent with purely cognitive based practice. The purpose of this paper is to introduce the concept of mental workload as a key determinant of the type of cognitive processing used by clinicians. Published research appears to be consistent with 'schemata' based cognition as the principle mode of working for those engaged in complex tasks under time pressure. Although conscious processing of factual data is also used, it may be the primary mode of cognition only in situations where time pressure is not a factor. Further research on the decision making process should be based on outcomes which are not dependant on conscious recall of past actions or events and include a measure of mental workload. This further appears to support the concept of the patient, within the clinical environment, as the most effective learning resource.

Using the 360 degrees multisource feedback model to evaluate teaching and professionalism.

PubMed

Berk, Ronald A

2009-12-01

Student ratings have dominated as the primary and, frequently, only measure of teaching performance at colleges and universities for the past 50 years. Recently, there has been a trend toward augmenting those ratings with other data sources to broaden and deepen the evidence base. The 360 degrees multisource feedback (MSF) model used in management and industry for half a century and in clinical medicine for the last decade seemed like a best fit to evaluate teaching performance and professionalism. To adapt the 360 degrees MSF model to the assessment of teaching performance and professionalism of medical school faculty. The salient characteristics of the MSF models in industry and medicine were extracted from the literature. These characteristics along with 14 sources of evidence from eight possible raters, including students, self, peers, outside experts, mentors, alumni, employers, and administrators, based on the research in higher education were adapted to formative and summative decisions. Three 360 degrees MSF models were generated for three different decisions: (1) formative decisions and feedback about teaching improvement; (2) summative decisions and feedback for merit pay and contract renewal; and (3) formative decisions and feedback about professional behaviors in the academic setting. The characteristics of each model were listed. Finally, a top-10 list of the most persistent and, perhaps, intractable psychometric issues in executing these models was suggested to guide future research. The 360 degrees MSF model appears to be a useful framework for implementing a multisource evaluation of faculty teaching performance and professionalism in medical schools. This model can provide more accurate, reliable, fair, and equitable decisions than the one based on just a single source.

Improving the performance of surgery-based clinical pathways: a simulation-optimization approach.

PubMed

Ozcan, Yasar A; Tànfani, Elena; Testi, Angela

2017-03-01

This paper aims to improve the performance of clinical processes using clinical pathways (CPs). The specific goal of this research is to develop a decision support tool, based on a simulation-optimization approach, which identify the proper adjustment and alignment of resources to achieve better performance for both the patients and the health-care facility. When multiple perspectives are present in a decision problem, critical issues arise and often require the balancing of goals. In our approach, meeting patients' clinical needs in a timely manner, and to avoid worsening of clinical conditions, we assess the level of appropriate resources. The simulation-optimization model seeks and evaluates alternative resource configurations aimed at balancing the two main objectives-meeting patient needs and optimal utilization of beds and operating rooms.Using primary data collected at a Department of Surgery of a public hospital located in Genoa, Italy. The simulation-optimization modelling approach in this study has been applied to evaluate the thyroid surgical treatment together with the other surgery-based CPs. The low rate of bed utilization and the long elective waiting lists of the specialty under study indicates that the wards were oversized while the operating room capacity was the bottleneck of the system. The model enables hospital managers determine which objective has to be given priority, as well as the corresponding opportunity costs.

Perspective: Uses and misuses of thresholds in diagnostic decision making.

PubMed

Warner, Jeremy L; Najarian, Robert M; Tierney, Lawrence M

2010-03-01

The concept of thresholds plays a vital role in decisions involving the initiation, continuation, and completion of diagnostic testing. Much research has focused on the development of explicit thresholds, in the form of practice guidelines and decision analyses. However, these tools are used infrequently; most medical decisions are made at the bedside, using implicit thresholds. Study of these thresholds can lead to a deeper understanding of clinical decision making. The authors examine some factors constituting individual clinicians' implicit thresholds. They propose a model for static thresholds using the concept of situational gravity to explain why some thresholds are high, and some low. Next, they consider the hypothetical effects of incorrect placement of thresholds (miscalibration) and changes to thresholds during diagnosis (manipulation). They demonstrate these concepts using common clinical scenarios. Through analysis of miscalibration of thresholds, the authors demonstrate some common maladaptive clinical behaviors, which are nevertheless internally consistent. They then explain how manipulation of thresholds gives rise to common cognitive heuristics including premature closure and anchoring. They also discuss the case where no threshold has been exceeded despite exhaustive collection of data, which commonly leads to application of the availability or representativeness heuristics. Awareness of implicit thresholds allows for a more effective understanding of the processes of medical decision making and, possibly, to the avoidance of detrimental heuristics and their associated medical errors. Research toward accurately defining these thresholds for individual physicians and toward determining their dynamic properties during the diagnostic process may yield valuable insights.

Shared decision making and patient decision aids: knowledge, attitudes, and practices among Hawai'i physicians.

PubMed

Alden, Dana L; Friend, John; Chun, Maria B J

2013-11-01

As the health care field moves toward patient-centered care (PCC), increasing emphasis has been placed on the benefits of patient decision aids for promoting shared decision making (SDM). This study provides a baseline measure of knowledge, attitudes, and practices (KAP) among Hawai'i's physicians with respect to patient decision aids (DAs). Physicians throughout the State of Hawai'i were invited to complete a survey assessing their knowledge, attitudes, and practices with respect to the clinical use of DAs. One hundred and seventy four valid surveys were analyzed. Reported awareness and use of DAs were low, but recognition of the benefits of SDM and openness to the use of DAs were very high. The leading perceived barriers to the implementation of DAs were lack of awareness, lack of resources, and limited physician time to learn about DA technology. However, a significant majority of the respondents reported that DAs could empower patients by improving knowledge (88%), increasing satisfaction with the consultation process (81%), and increasing compliance (74%). Among physicians currently employing DAs, use of brochures or options matrix sheets was the most common aid tool. However, leading recommended DA formats were paper-based brochures for clinic use (75%) and interactive online website programs for outside clinic use (73.5%). Given growing emphasis on the PCC model and the recognized desire of many patients to participate in the medical decision making process, positive responses toward SDM and the use of DAs by Hawai'i physicians are promising.

[The Amsterdam wrist rules: the multicenter prospective derivation and external validation of a clinical decision rule for the use of radiography in acute wrist trauma].

PubMed

Walenkamp, Monique M J; Bentohami, Abdelali; Slaar, Annelie; Beerekamp, M S H Suzan; Maas, Mario; Jager, L C Cara; Sosef, Nico L; van Velde, Romuald; Ultee, Jan M; Steyerberg, Ewout W; Goslings, J C Carel; Schep, Niels W L

2016-01-01

Although only 39% of patients with wrist trauma have sustained a fracture, the majority of patients is routinely referred for radiography. The purpose of this study was to derive and externally validate a clinical decision rule that selects patients with acute wrist trauma in the Emergency Department (ED) for radiography. This multicenter prospective study consisted of three components: (1) derivation of a clinical prediction model for detecting wrist fractures in patients following wrist trauma; (2) external validation of this model; and (3) design of a clinical decision rule. The study was conducted in the EDs of five Dutch hospitals: one academic hospital (derivation cohort) and four regional hospitals (external validation cohort). We included all adult patients with acute wrist trauma. The main outcome was fracture of the wrist (distal radius, distal ulna or carpal bones) diagnosed on conventional X-rays. A total of 882 patients were analyzed; 487 in the derivation cohort and 395 in the validation cohort. We derived a clinical prediction model with eight variables: age; sex, swelling of the wrist; swelling of the anatomical snuffbox, visible deformation; distal radius tender to palpation; pain on radial deviation and painful axial compression of the thumb. The Area Under the Curve at external validation of this model was 0.81 (95% CI: 0.77-0.85). The sensitivity and specificity of the Amsterdam Wrist Rules (AWR) in the external validation cohort were 98% (95% CI: 95-99%) and 21% (95% CI: 15%-28). The negative predictive value was 90% (95% CI: 81-99%). The Amsterdam Wrist Rules is a clinical prediction rule with a high sensitivity and negative predictive value for fractures of the wrist. Although external validation showed low specificity and 100 % sensitivity could not be achieved, the Amsterdam Wrist Rules can provide physicians in the Emergency Department with a useful screening tool to select patients with acute wrist trauma for radiography. The upcoming implementation study will further reveal the impact of the Amsterdam Wrist Rules on the anticipated reduction of X-rays requested, missed fractures, Emergency Department waiting times and health care costs.

A three-talk model for shared decision making: multistage consultation process.

PubMed

Elwyn, Glyn; Durand, Marie Anne; Song, Julia; Aarts, Johanna; Barr, Paul J; Berger, Zackary; Cochran, Nan; Frosch, Dominick; Galasiński, Dariusz; Gulbrandsen, Pål; Han, Paul K J; Härter, Martin; Kinnersley, Paul; Lloyd, Amy; Mishra, Manish; Perestelo-Perez, Lilisbeth; Scholl, Isabelle; Tomori, Kounosuke; Trevena, Lyndal; Witteman, Holly O; Van der Weijden, Trudy

2017-11-06

Objectives  To revise an existing three-talk model for learning how to achieve shared decision making, and to consult with relevant stakeholders to update and obtain wider engagement. Design  Multistage consultation process. Setting  Key informant group, communities of interest, and survey of clinical specialties. Participants  19 key informants, 153 member responses from multiple communities of interest, and 316 responses to an online survey from medically qualified clinicians from six specialties. Results  After extended consultation over three iterations, we revised the three-talk model by making changes to one talk category, adding the need to elicit patient goals, providing a clear set of tasks for each talk category, and adding suggested scripts to illustrate each step. A new three-talk model of shared decision making is proposed, based on "team talk," "option talk," and "decision talk," to depict a process of collaboration and deliberation. Team talk places emphasis on the need to provide support to patients when they are made aware of choices, and to elicit their goals as a means of guiding decision making processes. Option talk refers to the task of comparing alternatives, using risk communication principles. Decision talk refers to the task of arriving at decisions that reflect the informed preferences of patients, guided by the experience and expertise of health professionals. Conclusions  The revised three-talk model of shared decision making depicts conversational steps, initiated by providing support when introducing options, followed by strategies to compare and discuss trade-offs, before deliberation based on informed preferences. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://group.bmj.com/group/rights-licensing/permissions.

Extended Axiomatic Conjoint Measurement: A Solution to a Methodological Problem in Studying Fertility-Related Behaviors.

ERIC Educational Resources Information Center

Nickerson, Carol A.; McClelland, Gary H.

1988-01-01

A methodology is developed based on axiomatic conjoint measurement to accompany a fertility decision-making model. The usefulness of the model is then demonstrated via an application to a study of contraceptive choice (N=100 male and female family-planning clinic clients). Finally, the validity of the model is evaluated. (TJH)

Evaluation of nursing practice: process and critique.

PubMed

Braunstein, M S

1998-01-01

This article describes the difficulties in conducting clinical trials to evaluate nursing practice models. Suggestions are offered for strengthening the process. A clinical trial of a nursing practice model based on a synthesis of Aristotelian theory with Rogers' science is described. The rationale for decisions regarding the research procedures used in presented. Methodological limitations of the study design and the specifications of the practice model are examined. It is concluded that clear specification of theoretical relationships within a practice model and clear identification of key intervening variables will enable researchers to better connect the treatment with the outcome.

Effective visualization of integrated knowledge and data to enable informed decisions in drug development and translational medicine

PubMed Central

2013-01-01

Integrative understanding of preclinical and clinical data is imperative to enable informed decisions and reduce the attrition rate during drug development. The volume and variety of data generated during drug development have increased tremendously. A new information model and visualization tool was developed to effectively utilize all available data and current knowledge. The Knowledge Plot integrates preclinical, clinical, efficacy and safety data by adding two concepts: knowledge from the different disciplines and protein binding. Internal and public available data were gathered and processed to allow flexible and interactive visualizations. The exposure was expressed as the unbound concentration of the compound and the treatment effect was normalized and scaled by including expert opinion on what a biologically meaningful treatment effect would be. The Knowledge Plot has been applied both retrospectively and prospectively in project teams in a number of different therapeutic areas, resulting in closer collaboration between multiple disciplines discussing both preclinical and clinical data. The Plot allows head to head comparisons of compounds and was used to support Candidate Drug selections and differentiation from comparators and competitors, back translation of clinical data, understanding the predictability of preclinical models and assays, reviewing drift in primary endpoints over the years, and evaluate or benchmark compounds in due diligence comparing multiple attributes. The Knowledge Plot concept allows flexible integration and visualization of relevant data for interpretation in order to enable scientific and informed decision-making in various stages of drug development. The concept can be used for communication, decision-making, knowledge management, and as a forward and back translational tool, that will result in an improved understanding of the competitive edge for a particular project or disease area portfolio. In addition, it also builds up a knowledge and translational continuum, which in turn will reduce the attrition rate and costs of clinical development by identifying poor candidates early. PMID:24098919

Clinic-Based Mobile Health Decision Support to Enhance Adult Epilepsy Self-Management: An Intervention Mapping Approach.

PubMed

Shegog, Ross; Begley, Charles E

2017-01-01

Epilepsy is a neurological disorder involving recurrent seizures. It affects approximately 5 million people in the U.S. To optimize their quality of life people with epilepsy are encouraged to engage in self-management (S-M) behaviors. These include managing their treatment (e.g., adhering to anti-seizure medication and clinical visit schedules), managing their seizures (e.g., responding to seizure episodes), managing their safety (e.g., monitoring and avoiding environmental seizure triggers), and managing their co-morbid conditions (e.g., anxiety, depression). The clinic-based Management Information Decision Support Epilepsy Tool (MINDSET) is a decision-support system founded on theory and empirical evidence. It is designed to increase awareness by adult patients (≥18 years) and their health-care provider regarding the patient's epilepsy S-M behaviors, facilitate communication during the clinic visit to prioritize S-M goals and strategies commensurate with the patient's needs, and increase the patient's self-efficacy to achieve those goals. The purpose of this paper is to describe the application of intervention mapping (IM) to develop, implement, and formatively evaluate the clinic-based MINDSET prototype and in developing implementation and evaluation plans. Deliverables comprised a logic model of the problem (IM Step 1); matrices of program objectives (IM Step 2); a program planning document comprising scope, sequence, theory-based methods, and practical strategies (IM Step 3); a functional MINDSET program prototype (IM Step 4); plans for implementation (IM Step 5); and evaluation (IM Step 6). IM provided a logical and systematic approach to developing and evaluating clinic-based decision support toward epilepsy S-M.

Eliciting expert opinion for economic models: an applied example.

PubMed

Leal, José; Wordsworth, Sarah; Legood, Rosa; Blair, Edward

2007-01-01

Expert opinion is considered as a legitimate source of information for decision-analytic modeling where required data are unavailable. Our objective was to develop a practical computer-based tool for eliciting expert opinion about the shape of the uncertainty distribution around individual model parameters. We first developed a prepilot survey with departmental colleagues to test a number of alternative approaches to eliciting opinions on the shape of the uncertainty distribution around individual parameters. This information was used to develop a survey instrument for an applied clinical example. This involved eliciting opinions from experts to inform a number of parameters involving Bernoulli processes in an economic model evaluating DNA testing for families with a genetic disease, hypertrophic cardiomyopathy. The experts were cardiologists, clinical geneticists, and laboratory scientists working with cardiomyopathy patient populations and DNA testing. Our initial prepilot work suggested that the more complex elicitation techniques advocated in the literature were difficult to use in practice. In contrast, our approach achieved a reasonable response rate (50%), provided logical answers, and was generally rated as easy to use by respondents. The computer software user interface permitted graphical feedback throughout the elicitation process. The distributions obtained were incorporated into the model, enabling the use of probabilistic sensitivity analysis. There is clearly a gap in the literature between theoretical elicitation techniques and tools that can be used in applied decision-analytic models. The results of this methodological study are potentially valuable for other decision analysts deriving expert opinion.

Digital technology and clinical decision making in depression treatment: Current findings and future opportunities.

PubMed

Hallgren, Kevin A; Bauer, Amy M; Atkins, David C

2017-06-01

Clinical decision making encompasses a broad set of processes that contribute to the effectiveness of depression treatments. There is emerging interest in using digital technologies to support effective and efficient clinical decision making. In this paper, we provide "snapshots" of research and current directions on ways that digital technologies can support clinical decision making in depression treatment. Practical facets of clinical decision making are reviewed, then research, design, and implementation opportunities where technology can potentially enhance clinical decision making are outlined. Discussions of these opportunities are organized around three established movements designed to enhance clinical decision making for depression treatment, including measurement-based care, integrated care, and personalized medicine. Research, design, and implementation efforts may support clinical decision making for depression by (1) improving tools to incorporate depression symptom data into existing electronic health record systems, (2) enhancing measurement of treatment fidelity and treatment processes, (3) harnessing smartphone and biosensor data to inform clinical decision making, (4) enhancing tools that support communication and care coordination between patients and providers and within provider teams, and (5) leveraging treatment and outcome data from electronic health record systems to support personalized depression treatment. The current climate of rapid changes in both healthcare and digital technologies facilitates an urgent need for research, design, and implementation of digital technologies that explicitly support clinical decision making. Ensuring that such tools are efficient, effective, and usable in frontline treatment settings will be essential for their success and will require engagement of stakeholders from multiple domains. © 2017 Wiley Periodicals, Inc.

A pattern-based analysis of clinical computer-interpretable guideline modeling languages.

PubMed

Mulyar, Nataliya; van der Aalst, Wil M P; Peleg, Mor

2007-01-01

Languages used to specify computer-interpretable guidelines (CIGs) differ in their approaches to addressing particular modeling challenges. The main goals of this article are: (1) to examine the expressive power of CIG modeling languages, and (2) to define the differences, from the control-flow perspective, between process languages in workflow management systems and modeling languages used to design clinical guidelines. The pattern-based analysis was applied to guideline modeling languages Asbru, EON, GLIF, and PROforma. We focused on control-flow and left other perspectives out of consideration. We evaluated the selected CIG modeling languages and identified their degree of support of 43 control-flow patterns. We used a set of explicitly defined evaluation criteria to determine whether each pattern is supported directly, indirectly, or not at all. PROforma offers direct support for 22 of 43 patterns, Asbru 20, GLIF 17, and EON 11. All four directly support basic control-flow patterns, cancellation patterns, and some advance branching and synchronization patterns. None support multiple instances patterns. They offer varying levels of support for synchronizing merge patterns and state-based patterns. Some support a few scenarios not covered by the 43 control-flow patterns. CIG modeling languages are remarkably close to traditional workflow languages from the control-flow perspective, but cover many fewer workflow patterns. CIG languages offer some flexibility that supports modeling of complex decisions and provide ways for modeling some decisions not covered by workflow management systems. Workflow management systems may be suitable for clinical guideline applications.

Diagnostic classification scheme in Iranian breast cancer patients using a decision tree.

PubMed

Malehi, Amal Saki

2014-01-01

The objective of this study was to determine a diagnostic classification scheme using a decision tree based model. The study was conducted as a retrospective case-control study in Imam Khomeini hospital in Tehran during 2001 to 2009. Data, including demographic and clinical-pathological characteristics, were uniformly collected from 624 females, 312 of them were referred with positive diagnosis of breast cancer (cases) and 312 healthy women (controls). The decision tree was implemented to develop a diagnostic classification scheme using CART 6.0 Software. The AUC (area under curve), was measured as the overall performance of diagnostic classification of the decision tree. Five variables as main risk factors of breast cancer and six subgroups as high risk were identified. The results indicated that increasing age, low age at menarche, single and divorced statues, irregular menarche pattern and family history of breast cancer are the important diagnostic factors in Iranian breast cancer patients. The sensitivity and specificity of the analysis were 66% and 86.9% respectively. The high AUC (0.82) also showed an excellent classification and diagnostic performance of the model. Decision tree based model appears to be suitable for identifying risk factors and high or low risk subgroups. It can also assists clinicians in making a decision, since it can identify underlying prognostic relationships and understanding the model is very explicit.

Clinicians’ Use of Personality Disorder Models within a Particular Treatment Setting: A Longitudinal Comparison of Temporal Consistency and Clinical Utility

PubMed Central

Samuel, Douglas B.; Widiger, Thomas A.

2012-01-01

An active line of current investigation is how the five-factor model (FFM) of personality disorder might be applied by clinicians and particularly, how clinically useful this model is in comparison to the existing nomenclature. The current study is the first to investigate the temporal consistency of clinicians’ application of the FFM and the DSM-IV-TR to their own patients. Results indicated that FFM ratings were relatively stable over six-months of treatment, supporting their use by clinicians, but also indexed potentially important clinical changes. Additionally, ratings of utility provided by the clinicians suggested that the FFM was more useful for clinical decision making than was the DSM-IV-TR model. We understand the clinical utility findings within the context of previous research indicating that the FFM is most useful among patients who are not prototypic for a personality disorder. PMID:24288580

HOSPITAL MANAGERS' NEED FOR INFORMATION ON HEALTH TECHNOLOGY INVESTMENTS.

PubMed

Ølholm, Anne Mette; Kidholm, Kristian; Birk-Olsen, Mette; Christensen, Janne Buck

2015-01-01

There is growing interest in implementing hospital-based health technology assessment (HB-HTA) as a tool to facilitate decision making based on a systematic and multidisciplinary assessment of evidence. However, the decision-making process, including the informational needs of hospital decision makers, is not well described. The objective was to review empirical studies analysing the information that hospital decision makers need when deciding about health technology (HT) investments. A systematic review of empirical studies published in English or Danish from 2000 to 2012 was carried out. The literature was assessed by two reviewers working independently. The identified informational needs were assessed with regard to their agreement with the nine domains of EUnetHTA's Core Model. A total of 2,689 articles were identified and assessed. The review process resulted in 14 relevant studies containing 74 types of information that hospital decision makers found relevant. In addition to information covered by the Core Model, other types of information dealing with political and strategic aspects were identified. The most frequently mentioned types of information in the literature related to clinical, economic and political/strategic aspects. Legal, social, and ethical aspects were seldom considered most important. Hospital decision makers are able to describe their information needs when deciding on HT investments. The different types of information were not of equal importance to hospital decision makers, however, and full agreement between EUnetHTA's Core Model and the hospital decision-makers' informational needs was not observed. They also need information on political and strategic aspects not covered by the Core Model.

Application of a Judgment Model toward Measurement of Clinical Judgment in Senior Nursing Students

ERIC Educational Resources Information Center

Pongmarutai, Tiwaporn

2010-01-01

Clinical judgment, defined as "the application of the nurse's knowledge and experience in making decisions about client care" (The National Council of State Boards of Nursing, 2005, p. 2), has been recognized as a vital and essential skill for healthcare providers when caring for clients. Undisputedly, nurses represent the largest…

School-Based Clinics: A Guide for Advocates. Developing Policy Statements, Educating Decision Makers, Enlisting Local Support.

ERIC Educational Resources Information Center

Center for Population Options, Washington, DC.

School-based clinics (SBCs) are comprehensive primary health care facilities located within or on the grounds of middle, junior, or senior high schools. Varying in size and organizational structure, SBCs have emerged as an effective model for advancing adolescent health. They have gained attention because of their potential for treating problems…

Knowledge of Fecal Calprotectin and Infliximab Trough Levels Alters Clinical Decision-making for IBD Outpatients on Maintenance Infliximab Therapy.

PubMed

Huang, Vivian W; Prosser, Connie; Kroeker, Karen I; Wang, Haili; Shalapay, Carol; Dhami, Neil; Fedorak, Darryl K; Halloran, Brendan; Dieleman, Levinus A; Goodman, Karen J; Fedorak, Richard N

2015-06-01

Infliximab is an effective therapy for inflammatory bowel disease (IBD). However, more than 50% of patients lose response. Empiric dose intensification is not effective for all patients because not all patients have objective disease activity or subtherapeutic drug level. The aim was to determine how an objective marker of disease activity or therapeutic drug monitoring affects clinical decisions regarding maintenance infliximab therapy in outpatients with IBD. Consecutive patients with IBD on maintenance infliximab therapy were invited to participate by providing preinfusion stool and blood samples. Fecal calprotectin (FCP) and infliximab trough levels (ITLs) were measured by enzyme linked immunosorbent assay. Three decisions were compared: (1) actual clinical decision, (2) algorithmic FCP or ITL decisions, and (3) expert panel decision based on (a) clinical data, (b) clinical data plus FCP, and (c) clinical data plus FCP plus ITL. In secondary analysis, Receiver-operating curves were used to assess the ability of FCP and ITL in predicting clinical disease activity or remission. A total of 36 sets of blood and stool were available for analysis; median FCP 191.5 μg/g, median ITLs 7.3 μg/mL. The actual clinical decision differed from the hypothetical decision in 47.2% (FCP algorithm); 69.4% (ITL algorithm); 25.0% (expert panel clinical decision); 44.4% (expert panel clinical plus FCP); 58.3% (expert panel clinical plus FCP plus ITL) cases. FCP predicted clinical relapse (area under the curve [AUC] = 0.417; 95% confidence interval [CI], 0.197-0.641) and subtherapeutic ITL (AUC = 0.774; 95% CI, 0.536-1.000). ITL predicted clinical remission (AUC = 0.498; 95% CI, 0.254-0.742) and objective remission (AUC = 0.773; 95% CI, 0.622-0.924). Using FCP and ITLs in addition to clinical data results in an increased number of decisions to optimize management in outpatients with IBD on stable maintenance infliximab therapy.

Shared Decision-making in the Emergency Department: Respecting Patient Autonomy When Seconds Count.

PubMed

Hess, Erik P; Grudzen, Corita R; Thomson, Richard; Raja, Ali S; Carpenter, Christopher R

2015-07-01

Shared decision-making (SDM), a collaborative process in which patients and providers make health care decisions together, taking into account the best scientific evidence available, as well as the patient's values and preferences, is being increasingly advocated as the optimal approach to decision-making for many health care decisions. The rapidly paced and often chaotic environment of the emergency department (ED), however, is a unique clinical setting that offers many practical and contextual challenges. Despite these challenges, in a recent survey emergency physicians reported there to be more than one reasonable management option for over 50% of their patients and that they take an SDM approach in 58% of such patients. SDM has also been selected as the topic on which to develop a future research agenda at the 2016 Academic Emergency Medicine consensus conference, "Shared Decision-making in the Emergency Department: Development of a Policy-relevant Patient-centered Research Agenda" (http://www.saem.org/annual-meeting/education/2016-aem-consensus-conference). In this paper the authors describe the conceptual model of SDM as originally conceived by Charles and Gafni and highlight aspects of the model relevant to the practice of emergency medicine. In addition, through the use of vignettes from the authors' clinical practices, the applicability of SDM to contemporary EM practice is illustrated and the ethical and pragmatic implications of taking an SDM approach are explored. It is hoped that this document will be read in advance of the 2016 Academic Emergency Medicine consensus conference, to facilitate group discussions at the conference. © 2015 by the Society for Academic Emergency Medicine.

Implementing shared decision making in federally qualified health centers, a quasi-experimental design study: the Office-Guidelines Applied to Practice (Office-GAP) program.

PubMed

Olomu, Adesuwa; Hart-Davidson, William; Luo, Zhehui; Kelly-Blake, Karen; Holmes-Rovner, Margaret

2016-08-02

Use of Shared Decision-Making (SDM) and Decision Aids (DAs) has been encouraged but is not regularly implemented in primary care. The Office-Guidelines Applied to Practice (Office-GAP) intervention is an application of a previous model revised to address guidelines based care for low-income populations with diabetes and coronary heart disease (CHD). To evaluate Office-GAP Program feasibility and preliminary efficacy on medication use, patient satisfaction with physician communication and confidence in decision in low-income population with diabetes and coronary heart disease (CHD) in a Federally Qualified Healthcare Center (FQHC). Ninety-five patients participated in an Office-GAP program. A quasi-experimental design study, over 6 months with 12-month follow-up. Office-GAP program integrates health literacy, communication skills education for patients and physicians, patient/physician decision support tools and SDM into routine care. 1) Implementation rates of planned program elements 2) Patient satisfaction with communication and confidence in decision, and 3) Medication prescription rates. We used the GEE method for hierarchical logistic models, controlling for confounding. Feasibility of the Office-GAP program in the FQHC setting was established. We found significant increase in use of Aspirin/Plavix, statin and beta-blocker during follow-up compared to baseline: Aspirin OR 1.5 (95 % CI: 1.1, 2.2) at 3-months, 1.9 (1.3, 2.9) at 6-months, and 1.8 (1.2, 2.8) at 12-months. Statin OR 1.1 (1.0, 1.3) at 3-months and 1.5 (1.1, 2.2) at 12-months; beta-blocker 1.8 (1.1, 2.9) at 6-months and 12-months. Program elements were consistently used (≥ 98 % clinic attendance at training and tool used). Patient satisfaction with communication and confidence in decision increased. The use of Office-GAP program to teach SDM and use of DAs in real time was demonstrated to be feasible in FQHCs. It has the potential to improve satisfaction with physician communication and confidence in decisions and to improve medication use. The Office-GAP program is a brief, efficient platform for delivering patient and provider education in SDM and could serve as a model for implementing guideline based care for all chronic diseases in outpatient clinical settings. Further evaluation is needed to establish feasibility outside clinical study, reach, effectiveness and cost-effectiveness of this approach.

Enhancing clinical decision making: development of a contiguous definition and conceptual framework.

PubMed

Tiffen, Jennifer; Corbridge, Susan J; Slimmer, Lynda

2014-01-01

Clinical decision making is a term frequently used to describe the fundamental role of the nurse practitioner; however, other terms have been used interchangeably. The purpose of this article is to begin the process of developing a definition and framework of clinical decision making. The developed definition was "Clinical decision making is a contextual, continuous, and evolving process, where data are gathered, interpreted, and evaluated in order to select an evidence-based choice of action." A contiguous framework for clinical decision making specific for nurse practitioners is also proposed. Having a clear and unique understanding of clinical decision making will allow for consistent use of the term, which is relevant given the changing educational requirements for nurse practitioners and broadening scope of practice. Copyright © 2014 Elsevier Inc. All rights reserved.

Development and Validation of a Primary Care-Based Family Health History and Decision Support Program (MeTree)

PubMed Central

Orlando, Lori A.; Buchanan, Adam H.; Hahn, Susan E.; Christianson, Carol A.; Powell, Karen P.; Skinner, Celette Sugg; Chesnut, Blair; Blach, Colette; Due, Barbara; Ginsburg, Geoffrey S.; Henrich, Vincent C.

2016-01-01

INTRODUCTION Family health history is a strong predictor of disease risk. To reduce the morbidity and mortality of many chronic diseases, risk-stratified evidence-based guidelines strongly encourage the collection and synthesis of family health history to guide selection of primary prevention strategies. However, the collection and synthesis of such information is not well integrated into clinical practice. To address barriers to collection and use of family health histories, the Genomedical Connection developed and validated MeTree, a Web-based, patient-facing family health history collection and clinical decision support tool. MeTree is designed for integration into primary care practices as part of the genomic medicine model for primary care. METHODS We describe the guiding principles, operational characteristics, algorithm development, and coding used to develop MeTree. Validation was performed through stakeholder cognitive interviewing, a genetic counseling pilot program, and clinical practice pilot programs in 2 community-based primary care clinics. RESULTS Stakeholder feedback resulted in changes to MeTree’s interface and changes to the phrasing of clinical decision support documents. The pilot studies resulted in the identification and correction of coding errors and the reformatting of clinical decision support documents. MeTree’s strengths in comparison with other tools are its seamless integration into clinical practice and its provision of action-oriented recommendations guided by providers’ needs. LIMITATIONS The tool was validated in a small cohort. CONCLUSION MeTree can be integrated into primary care practices to help providers collect and synthesize family health history information from patients with the goal of improving adherence to risk-stratified evidence-based guidelines. PMID:24044145

Economic comparison of common treatment protocols and J5 vaccination for clinical mastitis in dairy herds using optimized culling decisions.

PubMed

Kessels, J A; Cha, E; Johnson, S K; Welcome, F L; Kristensen, A R; Gröhn, Y T

2016-05-01

This study used an existing dynamic optimization model to compare costs of common treatment protocols and J5 vaccination for clinical mastitis in US dairy herds. Clinical mastitis is an infection of the mammary gland causing major economic losses in dairy herds due to reduced milk production, reduced conception, and increased risk of mortality and culling for infected cows. Treatment protocols were developed to reflect common practices in dairy herds. These included targeted therapy following pathogen identification, and therapy without pathogen identification using a broad-spectrum antimicrobial or treating with the cheapest treatment option. The cost-benefit of J5 vaccination was also estimated. Effects of treatment were accounted for as changes in treatment costs, milk loss due to mastitis, milk discarded due to treatment, and mortality. Following ineffective treatments, secondary decisions included extending the current treatment, alternative treatment, discontinuing treatment, and pathogen identification followed by recommended treatment. Average net returns for treatment protocols and vaccination were generated using an existing dynamic programming model. This model incorporates cow and pathogen characteristics to optimize management decisions to treat, inseminate, or cull cows. Of the treatment protocols where 100% of cows received recommended treatment, pathogen-specific identification followed by recommended therapy yielded the highest average net returns per cow per year. Out of all treatment scenarios, the highest net returns were achieved with selecting the cheapest treatment option and discontinuing treatment, or alternate treatment with a similar spectrum therapy; however, this may not account for the full consequences of giving nonrecommended therapies to cows with clinical mastitis. Vaccination increased average net returns in all scenarios. Copyright © 2016 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.

Use of antipsychotic blood levels in clinician decision making: A cross-over study using clinical vignettes of patients with schizophrenia.

PubMed

Savitz, Adam; Melkote, Rama; Riley, Ralph; Pobre, Maria A; McQuarrie, Kelly; Williamson, David; Banderas, Benjamin

2018-05-19

The cause of treatment failure of antipsychotic medications is often difficult to determine in patients with schizophrenia. Evaluation of antipsychotic blood levels (ABLs) may aid clinicians in determining the cause of antipsychotic failure. The Clinical Assessment of the Schizophrenia Patient (CASP) was developed to evaluate clinical decision making during outpatient visits. The CASP assesses changes in medications, psychosocial treatments, and acute interventions along with factors influencing clinical decision making. Nine vignettes representative of clinical situations in patients with schizophrenia were created in two versions (one with ABLs, one without ABLs). The CASP was used to evaluate clinical decisions using the vignettes. Thirty-four clinicians participated in the study. In 8 out of 9 vignettes, most clinicians (at least 89.7%) made a different clinical decision with ABLs compared to without ABLs. In assessing the usefulness of ABLs, a majority (60.7%-85.7%, depending on the vignette) of clinicians responded that ABLs changed their clinical decision for 8 vignettes. Most clinicians (79%-93%) responded that they were more confident in their decisions with ABL information. This study demonstrated that ABLs have the potential to influence clinical decision making in the treatment of patients with schizophrenia. Copyright © 2018. Published by Elsevier B.V.

Clinical decision regret among critical care nurses: a qualitative analysis.

PubMed

Arslanian-Engoren, Cynthia; Scott, Linda D

2014-01-01

Decision regret is a negative cognitive emotion associated with experiences of guilt and situations of interpersonal harm. These negative affective responses may contribute to emotional exhaustion in critical care nurses (CCNs), increased staff turnover rates and high medication error rates. Yet, little is known about clinical decision regret among CCNs or the conditions or situations (e.g., feeling sleepy) that may precipitate its occurrence. To examine decision regret among CCNs, with an emphasis on clinical decisions made when nurses were most sleepy. A content analytic approach was used to examine the narrative descriptions of clinical decisions by CCNs when sleepy. Six decision regret themes emerged that represented deviations in practice or performance behaviors that were attributed to fatigued CCNs. While 157 CCNs disclosed a clinical decision they made at work while sleepy, the prevalence may be underestimated and warrants further investigation. Copyright © 2014 Elsevier Inc. All rights reserved.

Electronic decision support for diagnostic imaging in a primary care setting

PubMed Central

Reed, Martin H

2011-01-01

Methods Clinical guideline adherence for diagnostic imaging (DI) and acceptance of electronic decision support in a rural community family practice clinic was assessed over 36 weeks. Physicians wrote 904 DI orders, 58% of which were addressed by the Canadian Association of Radiologists guidelines. Results Of those orders with guidelines, 76% were ordered correctly; 24% were inappropriate or unnecessary resulting in a prompt from clinical decision support. Physicians followed suggestions from decision support to improve their DI order on 25% of the initially inappropriate orders. The use of decision support was not mandatory, and there were significant variations in use rate. Initially, 40% reported decision support disruptive in their work flow, which dropped to 16% as physicians gained experience with the software. Conclusions Physicians supported the concept of clinical decision support but were reluctant to change clinical habits to incorporate decision support into routine work flow. PMID:21486884

THE GAP BETWEEN ECONOMIC EVALUATIONS AND CLINICAL PRACTICE: A SYSTEMATIC REVIEW OF ECONOMIC EVALUATIONS ON DABIGATRAN FOR ATRIAL FIBRILLATION.

PubMed

Rolden, Herbert J A; van der Wilt, Gert Jan; Maas, Angela H E M; Grutters, Janneke P C

2018-06-18

As model-based economic evaluations (MBEEs) are widely used to make decisions in the context of policy, it is imperative that they represent clinical practice. Here, we assess the relevance of MBEEs on dabigatran for the prevention of stroke in patients with atrial fibrillation (AF). We performed a systematic review on the basis of a developed questionnaire, tailored to oral anticoagulation in patients with AF. Included studies had a full body text in English, compared dabigatran with a vitamin K antagonist, were not dedicated to one or more subgroup(s), and yielded an incremental cost-effectiveness ratio. The relevance of all MBEEs was assessed on the basis of ten context-independent factors, which encompassed clinical outcomes and treatment duration. The MBEEs performed for the United States were assessed on the basis of seventeen context-dependent factors, which were related to the country's target population and clinical environment. The search yielded twenty-nine MBEEs, of which six were performed for the United States. On average, 54 percent of the context-independent factors were included per study, and 37 percent of the seventeen context-dependent factors in the U.S. The share of relevant factors per study did not increase over time. MBEEs on dabigatran leave out several relevant factors, limiting their usefulness to decision makers. We strongly urge health economic researchers to improve the relevance of their MBEEs by including context-independent relevance factors, and modeling context-dependent factors befitting the decision context concerned.

An Environment for Guideline-based Decision Support Systems for Outpatients Monitoring.

PubMed

Zini, Elisa M; Lanzola, Giordano; Bossi, Paolo; Quaglini, Silvana

2017-08-11

We propose an architecture for monitoring outpatients that relies on mobile technologies for acquiring data. The goal is to better control the onset of possible side effects between the scheduled visits at the clinic. We analyze the architectural components required to ensure a high level of abstraction from data. Clinical practice guidelines were formalized with Alium, an authoring tool based on the PROforma language, using SNOMED-CT as a terminology standard. The Alium engine is accessible through a set of APIs that may be leveraged for implementing an application based on standard web technologies to be used by doctors at the clinic. Data sent by patients using mobile devices need to be complemented with those already available in the Electronic Health Record to generate personalized recommendations. Thus a middleware pursuing data abstraction is required. To comply with current standards, we adopted the HL7 Virtual Medical Record for Clinical Decision Support Logical Model, Release 2. The developed architecture for monitoring outpatients includes: (1) a guideline-based Decision Support System accessible through a web application that helps the doctors with prevention, diagnosis and treatment of therapy side effects; (2) an application for mobile devices, which allows patients to regularly send data to the clinic. In order to tailor the monitoring procedures to the specific patient, the Decision Support System also helps physicians with the configuration of the mobile application, suggesting the data to be collected and the associated collection frequency that may change over time, according to the individual patient's conditions. A proof of concept has been developed with a system for monitoring the side effects of chemo-radiotherapy in head and neck cancer patients. Our environment introduces two main innovation elements with respect to similar works available in the literature. First, in order to meet the specific patients' needs, in our work the Decision Support System also helps the physicians in properly configuring the mobile application. Then the Decision Support System is also continuously fed by patient-reported outcomes.

Multimorbidity, clinical decision making and health care delivery in New Zealand Primary care: a qualitative study.

PubMed

Stokes, Tim; Tumilty, Emma; Doolan-Noble, Fiona; Gauld, Robin

2017-04-05

Multimorbidity is a major issue for primary care. We aimed to explore primary care professionals' accounts of managing multimorbidity and its impact on clinical decision making and regional health care delivery. Qualitative interviews with 12 General Practitioners and 4 Primary Care Nurses in New Zealand's Otago region. Thematic analysis was conducted using the constant comparative method. Primary care professionals encountered challenges in providing care to patients with multimorbidity with respect to both clinical decision making and health care delivery. Clinical decision making occurred in time-limited consultations where the challenges of complexity and inadequacy of single disease guidelines were managed through the use of "satisficing" (care deemed satisfactory and sufficient for a given patient) and sequential consultations utilising relational continuity of care. The New Zealand primary care co-payment funding model was seen as a barrier to the delivery of care as it discourages sequential consultations, a problem only partially addressed through the use of the additional capitation based funding stream of Care Plus. Fragmentation of care also occurred within general practice and across the primary/secondary care interface. These findings highlight specific New Zealand barriers to the delivery of primary care to patients living with multimorbidity. There is a need to develop, implement and nationally evaluate a revised version of Care Plus that takes account of these barriers.

Identifying design considerations for a shared decision aid for use at the point of outpatient clinical care: An ethnographic study at an inner city clinic.

PubMed

Hajizadeh, Negin; Perez Figueroa, Rafael E; Uhler, Lauren M; Chiou, Erin; Perchonok, Jennifer E; Montague, Enid

2013-03-06

Computerized decision aids could facilitate shared decision-making at the point of outpatient clinical care. The objective of this study was to investigate whether a computerized shared decision aid would be feasible to implement in an inner-city clinic by evaluating the current practices in shared decision-making, clinicians' use of computers, patient and clinicians' attitudes and beliefs toward computerized decision aids, and the influence of time on shared decision-making. Qualitative data analysis of observations and semi-structured interviews with patients and clinicians at an inner-city outpatient clinic. The findings provided an exploratory look at the prevalence of shared decision-making and attitudes about health information technology and decision aids. A prominent barrier to clinicians engaging in shared decision-making was a lack of perceived patient understanding of medical information. Some patients preferred their clinicians make recommendations for them rather than engage in formal shared decision-making. Health information technology was an integral part of the clinic visit and welcomed by most clinicians and patients. Some patients expressed the desire to engage with health information technology such as viewing their medical information on the computer screen with their clinicians. All participants were receptive to the idea of a decision aid integrated within the clinic visit although some clinicians were concerned about the accuracy of prognostic estimates for complex medical problems. We identified several important considerations for the design and implementation of a computerized decision aid including opportunities to: bridge clinician-patient communication about medical information while taking into account individual patients' decision-making preferences, complement expert clinician judgment with prognostic estimates, take advantage of patient waiting times, and make tasks involved during the clinic visit more efficient. These findings should be incorporated into the design and implementation of a computerized shared decision aid at an inner-city hospital.

onlineDeCISion.org: a web-based decision aid for DCIS treatment.

PubMed

Ozanne, Elissa M; Schneider, Katharine H; Soeteman, Djøra; Stout, Natasha; Schrag, Deborah; Fordis, Michael; Punglia, Rinaa S

2015-11-01

Women diagnosed with DCIS face complex treatment decisions and often do so with inaccurate and incomplete understanding of the risks and benefits involved. Our objective was to create a tool to guide these decisions for both providers and patients. We developed a web-based decision aid designed to provide clinicians with tailored information about a patient’s recurrence risks and survival outcomes following different treatment strategies for DCIS. A theoretical framework, microsimulation model (Soeteman et al., J Natl Cancer 105:774–781, 2013) and best practices for web-based decision tools guided the development of the decision aid. The development process used semi-structured interviews and usability testing with key stakeholders, including a diverse group of multidisciplinary clinicians and a patient advocate. We developed onlineDeCISion.​org to include the following features that were rated as important by the stakeholders: (1) descriptions of each of the standard treatment options available; (2) visual projections of the likelihood of time-specific (10-year and lifetime) breast-preservation, recurrence, and survival outcomes; and (3) side-by-side comparisons of down-stream effects of each treatment choice. All clinicians reviewing the decision aid in usability testing were interested in using it in their clinical practice. The decision aid is available in a web-based format and is planned to be publicly available. To improve treatment decision making in patients with DCIS, we have developed a web-based decision aid onlineDeCISion.​org that conforms to best practices and that clinicians are interested in using in their clinics with patients to better inform treatment decisions.

Gating the holes in the Swiss cheese (part I): Expanding professor Reason's model for patient safety.

PubMed

Seshia, Shashi S; Bryan Young, G; Makhinson, Michael; Smith, Preston A; Stobart, Kent; Croskerry, Pat

2018-02-01

Although patient safety has improved steadily, harm remains a substantial global challenge. Additionally, safety needs to be ensured not only in hospitals but also across the continuum of care. Better understanding of the complex cognitive factors influencing health care-related decisions and organizational cultures could lead to more rational approaches, and thereby to further improvement. A model integrating the concepts underlying Reason's Swiss cheese theory and the cognitive-affective biases plus cascade could advance the understanding of cognitive-affective processes that underlie decisions and organizational cultures across the continuum of care. Thematic analysis, qualitative information from several sources being used to support argumentation. Complex covert cognitive phenomena underlie decisions influencing health care. In the integrated model, the Swiss cheese slices represent dynamic cognitive-affective (mental) gates: Reason's successive layers of defence. Like firewalls and antivirus programs, cognitive-affective gates normally allow the passage of rational decisions but block or counter unsounds ones. Gates can be breached (ie, holes created) at one or more levels of organizations, teams, and individuals, by (1) any element of cognitive-affective biases plus (conflicts of interest and cognitive biases being the best studied) and (2) other potential error-provoking factors. Conversely, flawed decisions can be blocked and consequences minimized; for example, by addressing cognitive biases plus and error-provoking factors, and being constantly mindful. Informed shared decision making is a neglected but critical layer of defence (cognitive-affective gate). The integrated model can be custom tailored to specific situations, and the underlying principles applied to all methods for improving safety. The model may also provide a framework for developing and evaluating strategies to optimize organizational cultures and decisions. The concept is abstract, the model is virtual, and the best supportive evidence is qualitative and indirect. The proposed model may help enhance rational decision making across the continuum of care, thereby improving patient safety globally. © 2017 The Authors. Journal of Evaluation in Clinical Practice published by John Wiley & Sons, Ltd.

Examining the Influence of Context and Professional Culture on Clinical Reasoning Through Rhetorical-Narrative Analysis.

PubMed

Peters, Amanda; Vanstone, Meredith; Monteiro, Sandra; Norman, Geoff; Sherbino, Jonathan; Sibbald, Matthew

2017-05-01

According to the dual process model of reasoning, physicians make diagnostic decisions using two mental systems: System 1, which is rapid, unconscious, and intuitive, and System 2, which is slow, rational, and analytical. Currently, little is known about physicians' use of System 1 or intuitive reasoning in practice. In a qualitative study of clinical reasoning, physicians were asked to tell stories about times when they used intuitive reasoning while working up an acutely unwell patient, and we combine socio-narratology and rhetorical theory to analyze physicians' stories. Our analysis reveals that in describing their work, physicians draw on two competing narrative structures: one that is aligned with an evidence-based medicine approach valuing System 2 and one that is aligned with cooperative decision making involving others in the clinical environment valuing System 1. Our findings support an understanding of clinical reasoning as distributed, contextual, and influenced by professional culture.

The Enterprise Data Trust at Mayo Clinic: a semantically integrated warehouse of biomedical data

PubMed Central

Beck, Scott A; Fisk, Thomas B; Mohr, David N

2010-01-01

Mayo Clinic's Enterprise Data Trust is a collection of data from patient care, education, research, and administrative transactional systems, organized to support information retrieval, business intelligence, and high-level decision making. Structurally it is a top-down, subject-oriented, integrated, time-variant, and non-volatile collection of data in support of Mayo Clinic's analytic and decision-making processes. It is an interconnected piece of Mayo Clinic's Enterprise Information Management initiative, which also includes Data Governance, Enterprise Data Modeling, the Enterprise Vocabulary System, and Metadata Management. These resources enable unprecedented organization of enterprise information about patient, genomic, and research data. While facile access for cohort definition or aggregate retrieval is supported, a high level of security, retrieval audit, and user authentication ensures privacy, confidentiality, and respect for the trust imparted by our patients for the respectful use of information about their conditions. PMID:20190054

The Enterprise Data Trust at Mayo Clinic: a semantically integrated warehouse of biomedical data.

PubMed

Chute, Christopher G; Beck, Scott A; Fisk, Thomas B; Mohr, David N

2010-01-01

Mayo Clinic's Enterprise Data Trust is a collection of data from patient care, education, research, and administrative transactional systems, organized to support information retrieval, business intelligence, and high-level decision making. Structurally it is a top-down, subject-oriented, integrated, time-variant, and non-volatile collection of data in support of Mayo Clinic's analytic and decision-making processes. It is an interconnected piece of Mayo Clinic's Enterprise Information Management initiative, which also includes Data Governance, Enterprise Data Modeling, the Enterprise Vocabulary System, and Metadata Management. These resources enable unprecedented organization of enterprise information about patient, genomic, and research data. While facile access for cohort definition or aggregate retrieval is supported, a high level of security, retrieval audit, and user authentication ensures privacy, confidentiality, and respect for the trust imparted by our patients for the respectful use of information about their conditions.

Using Best Practices to Extract, Organize, and Reuse Embedded Decision Support Content Knowledge Rules from Mature Clinical Systems.

PubMed

DesAutels, Spencer J; Fox, Zachary E; Giuse, Dario A; Williams, Annette M; Kou, Qing-Hua; Weitkamp, Asli; Neal R, Patel; Bettinsoli Giuse, Nunzia

2016-01-01

Clinical decision support (CDS) knowledge, embedded over time in mature medical systems, presents an interesting and complex opportunity for information organization, maintenance, and reuse. To have a holistic view of all decision support requires an in-depth understanding of each clinical system as well as expert knowledge of the latest evidence. This approach to clinical decision support presents an opportunity to unify and externalize the knowledge within rules-based decision support. Driven by an institutional need to prioritize decision support content for migration to new clinical systems, the Center for Knowledge Management and Health Information Technology teams applied their unique expertise to extract content from individual systems, organize it through a single extensible schema, and present it for discovery and reuse through a newly created Clinical Support Knowledge Acquisition and Archival Tool (CS-KAAT). CS-KAAT can build and maintain the underlying knowledge infrastructure needed by clinical systems.

The use of emotional intelligence capabilities in clinical reasoning and decision-making: A qualitative, exploratory study.

PubMed

Hutchinson, Marie; Hurley, John; Kozlowski, Desirée; Whitehair, Leeann

2018-02-01

To explore clinical nurses' experiences of using emotional intelligence capabilities during clinical reasoning and decision-making. There has been little research exploring whether, or how, nurses employ emotional intelligence (EI) in clinical reasoning and decision-making. Qualitative phase of a larger mixed-methods study. Semistructured qualitative interviews with a purposive sample of registered nurses (n = 12) following EI training and coaching. Constructivist thematic analysis was employed to analyse the narrative transcripts. Three themes emerged: the sensibility to engage EI capabilities in clinical contexts, motivation to actively engage with emotions in clinical decision-making and incorporating emotional and technical perspectives in decision-making. Continuing to separate cognition and emotion in research, theorising and scholarship on clinical reasoning is counterproductive. Understanding more about nurses' use of EI has the potential to improve the calibre of decisions, and the safety and quality of care delivered. © 2017 John Wiley & Sons Ltd.

Non-clinical influences on clinical decision-making: a major challenge to evidence-based practice.

PubMed

Hajjaj, F M; Salek, M S; Basra, M K A; Finlay, A Y

2010-05-01

This article reviews an aspect of daily clinical practice which is of critical importance in virtually every clinical consultation, but which is seldom formally considered. Non-clinical influences on clinical decision-making profoundly affect medical decisions. These influences include patient-related factors such as socioeconomic status, quality of life and patient's expectations and wishes, physician-related factors such as personal characteristics and interaction with their professional community, and features of clinical practice such as private versus public practice as well as local management policies. This review brings together the different strands of knowledge concerning non-clinical influences on clinical decision-making. This aspect of decision-making may be the biggest obstacle to the reality of practising evidence-based medicine. It needs to be understood in order to develop clinical strategies that will facilitate the practice of evidence-based medicine.

Prediction of risk of recurrence of venous thromboembolism following treatment for a first unprovoked venous thromboembolism: systematic review, prognostic model and clinical decision rule, and economic evaluation.

PubMed

Ensor, Joie; Riley, Richard D; Jowett, Sue; Monahan, Mark; Snell, Kym Ie; Bayliss, Susan; Moore, David; Fitzmaurice, David

2016-02-01

Unprovoked first venous thromboembolism (VTE) is defined as VTE in the absence of a temporary provoking factor such as surgery, immobility and other temporary factors. Recurrent VTE in unprovoked patients is highly prevalent, but easily preventable with oral anticoagulant (OAC) therapy. The unprovoked population is highly heterogeneous in terms of risk of recurrent VTE. The first aim of the project is to review existing prognostic models which stratify individuals by their recurrence risk, therefore potentially allowing tailored treatment strategies. The second aim is to enhance the existing research in this field, by developing and externally validating a new prognostic model for individual risk prediction, using a pooled database containing individual patient data (IPD) from several studies. The final aim is to assess the economic cost-effectiveness of the proposed prognostic model if it is used as a decision rule for resuming OAC therapy, compared with current standard treatment strategies. Standard systematic review methodology was used to identify relevant prognostic model development, validation and cost-effectiveness studies. Bibliographic databases (including MEDLINE, EMBASE and The Cochrane Library) were searched using terms relating to the clinical area and prognosis. Reviewing was undertaken by two reviewers independently using pre-defined criteria. Included full-text articles were data extracted and quality assessed. Critical appraisal of included full texts was undertaken and comparisons made of model performance. A prognostic model was developed using IPD from the pooled database of seven trials. A novel internal-external cross-validation (IECV) approach was used to develop and validate a prognostic model, with external validation undertaken in each of the trials iteratively. Given good performance in the IECV approach, a final model was developed using all trials data. A Markov patient-level simulation was used to consider the economic cost-effectiveness of using a decision rule (based on the prognostic model) to decide on resumption of OAC therapy (or not). Three full-text articles were identified by the systematic review. Critical appraisal identified methodological and applicability issues; in particular, all three existing models did not have external validation. To address this, new prognostic models were sought with external validation. Two potential models were considered: one for use at cessation of therapy (pre D-dimer), and one for use after cessation of therapy (post D-dimer). Model performance measured in the external validation trials showed strong calibration performance for both models. The post D-dimer model performed substantially better in terms of discrimination (c = 0.69), better separating high- and low-risk patients. The economic evaluation identified that a decision rule based on the final post D-dimer model may be cost-effective for patients with predicted risk of recurrence of over 8% annually; this suggests continued therapy for patients with predicted risks ≥ 8% and cessation of therapy otherwise. The post D-dimer model performed strongly and could be useful to predict individuals' risk of recurrence at any time up to 2-3 years, thereby aiding patient counselling and treatment decisions. A decision rule using this model may be cost-effective for informing clinical judgement and patient opinion in treatment decisions. Further research may investigate new predictors to enhance model performance and aim to further externally validate to confirm performance in new, non-trial populations. Finally, it is essential that further research is conducted to develop a model predicting bleeding risk on therapy, to manage the balance between the risks of recurrence and bleeding. This study is registered as PROSPERO CRD42013003494. The National Institute for Health Research Health Technology Assessment programme.

When to trust our learners? Clinical teachers' perceptions of decision variables in the entrustment process.

PubMed

Duijn, Chantal C M A; Welink, Lisanne S; Bok, Harold G J; Ten Cate, Olle T J

2018-06-01

Clinical training programs increasingly use entrustable professional activities (EPAs) as focus of assessment. However, questions remain about which information should ground decisions to trust learners. This qualitative study aimed to identify decision variables in the workplace that clinical teachers find relevant in the elaboration of the entrustment decision processes. The findings can substantiate entrustment decision-making in the clinical workplace. Focus groups were conducted with medical and veterinary clinical teachers, using the structured consensus method of the Nominal Group Technique to generate decision variables. A ranking was made based on a relevance score assigned by the clinical teachers to the different decision variables. Field notes, audio recordings and flip chart lists were analyzed and subsequently translated and, as a form of axial coding, merged into one list, combining the decision variables that were similar in their meaning. A list of 11 and 17 decision variables were acknowledged as relevant by the medical and veterinary teacher groups, respectively. The focus groups yielded 21 unique decision variables that were considered relevant to inform readiness to perform a clinical task on a designated level of supervision. The decision variables consisted of skills, generic qualities, characteristics, previous performance or other information. We were able to group the decision variables into five categories: ability, humility, integrity, reliability and adequate exposure. To entrust a learner to perform a task at a specific level of supervision, a supervisor needs information to support such a judgement. This trust cannot be credited on a single case at a single moment of assessment, but requires different variables and multiple sources of information. This study provides an overview of decision variables giving evidence to justify the multifactorial process of making an entrustment decision.

A UML approach to process modelling of clinical practice guidelines for enactment.

PubMed

Knape, T; Hederman, L; Wade, V P; Gargan, M; Harris, C; Rahman, Y

2003-01-01

Although clinical practice guidelines (CPGs) have been suggested as a means of encapsulating best practice in evidence-based medical treatment, their usage in clinical environments has been disappointing. Criticisms of guideline representations have been that they are predominantly narrative and are difficult to incorporate into clinical information systems. This paper analyses the use of UML process modelling techniques for guideline representation and proposes the automated generation of executable guidelines using XMI. This hybrid UML-XMI approach provides flexible authoring of guideline decision and control structures whilst integrating appropriate data flow. It also uses an open XMI standard interface to allow the use of authoring tools and process control systems from multiple vendors. The paper first surveys CPG modelling formalisms followed by a brief introduction to process modelling in UMI. Furthermore, the modelling of CPGs in UML is presented leading to a case study of encoding a diabetes mellitus CPG using UML.

Monte Carlo decision curve analysis using aggregate data.

PubMed

Hozo, Iztok; Tsalatsanis, Athanasios; Djulbegovic, Benjamin

2017-02-01

Decision curve analysis (DCA) is an increasingly used method for evaluating diagnostic tests and predictive models, but its application requires individual patient data. The Monte Carlo (MC) method can be used to simulate probabilities and outcomes of individual patients and offers an attractive option for application of DCA. We constructed a MC decision model to simulate individual probabilities of outcomes of interest. These probabilities were contrasted against the threshold probability at which a decision-maker is indifferent between key management strategies: treat all, treat none or use predictive model to guide treatment. We compared the results of DCA with MC simulated data against the results of DCA based on actual individual patient data for three decision models published in the literature: (i) statins for primary prevention of cardiovascular disease, (ii) hospice referral for terminally ill patients and (iii) prostate cancer surgery. The results of MC DCA and patient data DCA were identical. To the extent that patient data DCA were used to inform decisions about statin use, referral to hospice or prostate surgery, the results indicate that MC DCA could have also been used. As long as the aggregate parameters on distribution of the probability of outcomes and treatment effects are accurately described in the published reports, the MC DCA will generate indistinguishable results from individual patient data DCA. We provide a simple, easy-to-use model, which can facilitate wider use of DCA and better evaluation of diagnostic tests and predictive models that rely only on aggregate data reported in the literature. © 2017 Stichting European Society for Clinical Investigation Journal Foundation.

Accuracy and Calibration of Computational Approaches for Inpatient Mortality Predictive Modeling.

PubMed

Nakas, Christos T; Schütz, Narayan; Werners, Marcus; Leichtle, Alexander B

2016-01-01

Electronic Health Record (EHR) data can be a key resource for decision-making support in clinical practice in the "big data" era. The complete database from early 2012 to late 2015 involving hospital admissions to Inselspital Bern, the largest Swiss University Hospital, was used in this study, involving over 100,000 admissions. Age, sex, and initial laboratory test results were the features/variables of interest for each admission, the outcome being inpatient mortality. Computational decision support systems were utilized for the calculation of the risk of inpatient mortality. We assessed the recently proposed Acute Laboratory Risk of Mortality Score (ALaRMS) model, and further built generalized linear models, generalized estimating equations, artificial neural networks, and decision tree systems for the predictive modeling of the risk of inpatient mortality. The Area Under the ROC Curve (AUC) for ALaRMS marginally corresponded to the anticipated accuracy (AUC = 0.858). Penalized logistic regression methodology provided a better result (AUC = 0.872). Decision tree and neural network-based methodology provided even higher predictive performance (up to AUC = 0.912 and 0.906, respectively). Additionally, decision tree-based methods can efficiently handle Electronic Health Record (EHR) data that have a significant amount of missing records (in up to >50% of the studied features) eliminating the need for imputation in order to have complete data. In conclusion, we show that statistical learning methodology can provide superior predictive performance in comparison to existing methods and can also be production ready. Statistical modeling procedures provided unbiased, well-calibrated models that can be efficient decision support tools for predicting inpatient mortality and assigning preventive measures.

Evaluating clinical librarian services: a systematic review.

PubMed

Brettle, Alison; Maden-Jenkins, Michelle; Anderson, Lucy; McNally, Rosalind; Pratchett, Tracey; Tancock, Jenny; Thornton, Debra; Webb, Anne

2011-03-01

  Previous systematic reviews have indicated limited evidence and poor quality evaluations of clinical librarian (CL) services. Rigorous evaluations should demonstrate the value of CL services, but guidance is needed before this can be achieved.   To undertake a systematic review which examines models of CL services, quality, methods and perspectives of clinical librarian service evaluations.   Systematic review methodology and synthesis of evidence, undertaken collaboratively by a group of 8 librarians to develop research and critical appraisal skills.   There are four clear models of clinical library service provision. Clinical librarians are effective in saving health professionals time, providing relevant, useful information and high quality services. Clinical librarians have a positive effect on clinical decision making by contributing to better informed decisions, diagnosis and choice of drug or therapy. The quality of CL studies is improving, but more work is needed on reducing bias and providing evidence of specific impacts on patient care. The Critical Incident Technique as part of a mixed method approach appears to offer a useful approach to demonstrating impact.   This systematic review provides practical guidance regarding the evaluation of CL services. It also provides updated evidence regarding the effectiveness and impact of CL services. The approach used was successful in developing research and critical appraisal skills in a group of librarians. © 2010 The authors. Health Information and Libraries Journal © 2010 Health Libraries Group.

Cost-effectiveness of Skin Cancer Referral and Consultation Using Teledermoscopy in Australia.

PubMed

Snoswell, Centaine L; Caffery, Liam J; Whitty, Jennifer A; Soyer, H Peter; Gordon, Louisa G

2018-06-01

International literature has shown that teledermoscopy referral may be a viable method for skin cancer referral; however, no economic investigations have occurred in Australia. To assess the cost-effectiveness of teledermoscopy as a referral mechanism for skin cancer diagnosis and management in Australia. Cost-effectiveness analysis using a decision-analytic model of Australian primary care, informed by publicly available data. We compared the costs of teledermoscopy referral (electronic referral containing digital dermoscopic images) vs usual care (a written referral letter) for specialist dermatologist review of a suspected skin cancer. Cost and time in days to clinical resolution, where clinical resolution was defined as diagnosis by a dermatologist or excision by a general practitioner. Probabilistic sensitivity analysis was performed to examine the uncertainty of the main results. Findings from the decision-analytic model showed that the mean time to clinical resolution was 9 days (range, 1-50 days) with teledermoscopy referral compared with 35 days (range, 0-138 days) with usual care alone (difference, 26 days; 95% credible interval [CrI], 13-38 days). The estimated mean cost difference between teledermoscopy referral (A$318.39) vs usual care (A$263.75) was A$54.64 (95% CrI, A$22.69-A$97.35) per person. The incremental cost per day saved to clinical resolution was A$2.10 (95% CrI, A$0.87-A$5.29). Using teledermoscopy for skin cancer referral and triage in Australia would cost A$54.64 extra per case on average but would result in clinical resolution 26 days sooner than usual care. Implementation recommendations depend on the preferences of the Australian health system decision makers for either lower cost or expedited clinical resolution. Further research around the clinical significance of expedited clinical resolution and its importance for patients could inform implementation recommendations for the Australian setting.

Skill sharing and delegation practice in two Queensland regional allied health cancer care services: a comparison of tasks.

PubMed

Passfield, Juanine; Nielsen, Ilsa; Brebner, Neil; Johnstone, Cara

2017-07-24

Objective Delegation and skill sharing are emerging service strategies for allied health (AH) professionals working in Queensland regional cancer care services. The aim of the present study was to describe the consistency between two services for the types and frequency of tasks provided and the agreement between teams in the decision to delegate or skill share clinical tasks, thereby determining the potential applicability to other services. Methods Datasets provided by two similar services were collated. Descriptive statistical analyses were used to assess the extent of agreement. Results In all, 214 tasks were identified as being undertaken by the services (92% agreement). Across the services, 70 tasks were identified as high frequency (equal to or more frequently than weekly) and 29 as not high frequency (46% agreement). Of the 68 tasks that were risk assessed, agreement was 66% for delegation and 60% for skill sharing, with high-frequency and intervention tasks more likely to be delegated. Conclusions Strong consistency was apparent for the clinical tasks undertaken by the two cancer care AH teams, with moderate agreement for the frequency of tasks performed. The proportion of tasks considered appropriate for skill sharing and/or delegation was similar, although variation at the task level was apparent. Further research is warranted to examine the range of factors that affect the decision to skill share or delegate. What is known about the topic? There is limited research evidence regarding the use of skill sharing and delegation service models for AH in cancer care services. In particular, the extent to which decisions about task safety and appropriateness for delegation or skill sharing can be generalised across services has not been investigated. What does this paper add? This study investigated the level of clinical task consistency between two similar AH cancer care teams in regional centres. It also examined the level of agreement with regard to delegation and skill sharing to provide an indication of the level of local service influence on workforce and service model decisions. What are the implications for practitioners? Local factors have a modest influence on delegation and skill sharing decisions of AH teams. Practitioners need to be actively engaged in decision making at the local level to ensure the clinical service model meets local needs. However, teams should also capitalise on commonalities between settings to limit duplication of training and resource development through collaborative networks.

Periodic limb movements of sleep: empirical and theoretical evidence supporting objective at-home monitoring

PubMed Central

Moro, Marilyn; Goparaju, Balaji; Castillo, Jelina; Alameddine, Yvonne; Bianchi, Matt T

2016-01-01

Introduction Periodic limb movements of sleep (PLMS) may increase cardiovascular and cerebrovascular morbidity. However, most people with PLMS are either asymptomatic or have nonspecific symptoms. Therefore, predicting elevated PLMS in the absence of restless legs syndrome remains an important clinical challenge. Methods We undertook a retrospective analysis of demographic data, subjective symptoms, and objective polysomnography (PSG) findings in a clinical cohort with or without obstructive sleep apnea (OSA) from our laboratory (n=443 with OSA, n=209 without OSA). Correlation analysis and regression modeling were performed to determine predictors of periodic limb movement index (PLMI). Markov decision analysis with TreeAge software compared strategies to detect PLMS: in-laboratory PSG, at-home testing, and a clinical prediction tool based on the regression analysis. Results Elevated PLMI values (>15 per hour) were observed in >25% of patients. PLMI values in No-OSA patients correlated with age, sex, self-reported nocturnal leg jerks, restless legs syndrome symptoms, and hypertension. In OSA patients, PLMI correlated only with age and self-reported psychiatric medications. Regression models indicated only a modest predictive value of demographics, symptoms, and clinical history. Decision modeling suggests that at-home testing is favored as the pretest probability of PLMS increases, given plausible assumptions regarding PLMS morbidity, costs, and assumed benefits of pharmacological therapy. Conclusion Although elevated PLMI values were commonly observed, routinely acquired clinical information had only weak predictive utility. As the clinical importance of elevated PLMI continues to evolve, it is likely that objective measures such as PSG or at-home PLMS monitors will prove increasingly important for clinical and research endeavors. PMID:27540316

A two-stage clinical decision support system for early recognition and stratification of patients with sepsis: an observational cohort study.

PubMed

Amland, Robert C; Lyons, Jason J; Greene, Tracy L; Haley, James M

2015-10-01

To examine the diagnostic accuracy of a two-stage clinical decision support system for early recognition and stratification of patients with sepsis. Observational cohort study employing a two-stage sepsis clinical decision support to recognise and stratify patients with sepsis. The stage one component was comprised of a cloud-based clinical decision support with 24/7 surveillance to detect patients at risk of sepsis. The cloud-based clinical decision support delivered notifications to the patients' designated nurse, who then electronically contacted a provider. The second stage component comprised a sepsis screening and stratification form integrated into the patient electronic health record, essentially an evidence-based decision aid, used by providers to assess patients at bedside. Urban, 284 acute bed community hospital in the USA; 16,000 hospitalisations annually. Data on 2620 adult patients were collected retrospectively in 2014 after the clinical decision support was implemented. 'Suspected infection' was the established gold standard to assess clinical decision support clinimetric performance. A sepsis alert activated on 417 (16%) of 2620 adult patients hospitalised. Applying 'suspected infection' as standard, the patient population characteristics showed 72% sensitivity and 73% positive predictive value. A postalert screening conducted by providers at bedside of 417 patients achieved 81% sensitivity and 94% positive predictive value. Providers documented against 89% patients with an alert activated by clinical decision support and completed 75% of bedside screening and stratification of patients with sepsis within one hour from notification. A clinical decision support binary alarm system with cross-checking functionality improves early recognition and facilitates stratification of patients with sepsis.

Implementation of a cystic fibrosis lung transplant referral patient decision aid in routine clinical practice: an observational study.

PubMed

Stacey, Dawn; Vandemheen, Katherine L; Hennessey, Rosamund; Gooyers, Tracy; Gaudet, Ena; Mallick, Ranjeeta; Salgado, Josette; Freitag, Andreas; Berthiaume, Yves; Brown, Neil; Aaron, Shawn D

2015-02-07

The decision to have lung transplantation as treatment for end-stage lung disease from cystic fibrosis (CF) has benefits and serious risks. Although patient decision aids are effective interventions for helping patients reach a quality decision, little is known about implementing them in clinical practice. Our study evaluated a sustainable approach for implementing a patient decision aid for adults with CF considering referral for lung transplantation. A prospective pragmatic observational study was guided by the Knowledge-to-Action Framework. Healthcare professionals in all 23 Canadian CF clinics were eligible. We surveyed participants regarding perceived barriers and facilitators to patient decision aid use. Interventions tailored to address modifiable identified barriers included training, access to decision aids, and conference calls. The primary outcome was >80% use of the decision aid in year 2. Of 23 adult CF clinics, 18 participated (78.2%) and 13 had healthcare professionals attend training. Baseline barriers were healthcare professionals' inadequate knowledge for supporting patients making decisions (55%), clarifying patients' values for outcomes of options (58%), and helping patients handle conflicting views of others (71%). Other barriers were lack of time (52%) and needing to change how transplantation is discussed (42%). Baseline facilitators were healthcare professionals feeling comfortable discussing bad transplantation outcomes (74%), agreeing the decision aid would be easy to experiment with (71%) and use in the CF clinic (87%), and agreeing that using the decision aid would not require reorganization of the CF clinic (90%). After implementing the decision aid with interventions tailored to the barriers, decision aid use increased from 29% at baseline to 85% during year 1 and 92% in year 2 (p < 0.001). Compared to baseline, more healthcare professionals at the end of the study were confident in supporting decision-making (p = 0.03) but continued to feel inadequate ability with supporting patients to handle conflicting views (p = 0.01). Most Canadian CF clinics agreed to participate in the study. Interventions were used to target identified modifiable barriers to using the patient decision aid in routine CF clinical practice. CF clinics reported using it with almost all patients in the second year.

Available clinical markers of treatment outcome integrated in mathematical models to guide therapy in HIV infection.

PubMed

Vergu, Elisabeta; Mallet, Alain; Golmard, Jean-Louis

2004-02-01

Because treatment failure in many HIV-infected persons may be due to multiple causes, including resistance to antiretroviral agents, it is important to better tailor drug therapy to individual patients. This improvement requires the prediction of treatment outcome from baseline immunological or virological factors, and from results of resistance tests. Here, we review briefly the available clinical factors that have an impact on therapy outcome, and discuss the role of a predictive modelling approach integrating these factors proposed in a previous work. Mathematical and statistical models could become essential tools to address questions that are difficult to study clinically and experimentally, thereby guiding decisions in the choice of individualized drug regimens.

Case-based explanation of non-case-based learning methods.

PubMed Central

Caruana, R.; Kangarloo, H.; Dionisio, J. D.; Sinha, U.; Johnson, D.

1999-01-01

We show how to generate case-based explanations for non-case-based learning methods such as artificial neural nets or decision trees. The method uses the trained model (e.g., the neural net or the decision tree) as a distance metric to determine which cases in the training set are most similar to the case that needs to be explained. This approach is well suited to medical domains, where it is important to understand predictions made by complex machine learning models, and where training and clinical practice makes users adept at case interpretation. PMID:10566351

Pain Assessment and Treatment Disparities: A Virtual Human Technology Investigation

PubMed Central

Hirsh, Adam T.; George, Steven Z.; Robinson, Michael E.

2009-01-01

Pain assessment and treatment is influenced by patient demographic characteristics and nonverbal expressions. Methodological challenges have limited the empirical investigation of these issues. The current analogue study employed an innovative research design and novel virtual human (VH) technology to investigate disparities in pain-related clinical decision making. Fifty-four nurses viewed vignettes consisting of a video clip of the VH patient and clinical summary information describing a post-surgical context. Participants made assessment (pain intensity and unpleasantness) and treatment (non-opioid and opioid medications) decisions on computerized visual analogue scales. VH demographic cues of sex, race, and age, as well as facial expression of pain, were systematically manipulated and hypothesized to influence decision ratings. Idiographic and nomothetic statistical analyses were conducted to test these hypotheses. Idiographic results indicated that sex, race, age, and pain expression cues accounted for significant, unique variance in decision policies among many nurses. Pain expression was the most salient cue in this context. Nomothetic results indicated differences within VH cues of interest; the size and consistency of these differences varied across policy domains. This study demonstrates the application of VH technology and lens model methodology to the study of disparities in pain-related decision making. Assessment and treatment of acute post-surgical pain often varies based on VH demographic and facial expression cues. These data contribute to the existing literature on disparities in pain practice and highlight the potential of a novel approach that may serve as a model for future investigation of these critical issues. PMID:19269742

Pain assessment and treatment disparities: a virtual human technology investigation.

PubMed

Hirsh, Adam T; George, Steven Z; Robinson, Michael E

2009-05-01

Pain assessment and treatment is influenced by patient demographic characteristics and nonverbal expressions. Methodological challenges have limited the empirical investigation of these issues. The current analogue study employed an innovative research design and novel virtual human (VH) technology to investigate disparities in pain-related clinical decision-making. Fifty-four nurses viewed vignettes consisting of a video clip of the VH patient and clinical summary information describing a post-surgical context. Participants made assessment (pain intensity and unpleasantness) and treatment (non-opioid and opioid medications) decisions on computerized visual analogue scales. VH demographic cues of sex, race, and age, as well as facial expression of pain, were systematically manipulated and hypothesized to influence decision ratings. Idiographic and nomothetic statistical analyses were conducted to test these hypotheses. Idiographic results indicated that sex, race, age, and pain expression cues accounted for significant, unique variance in decision policies among many nurses. Pain expression was the most salient cue in this context. Nomothetic results indicated differences within VH cues of interest; the size and consistency of these differences varied across policy domains. This study demonstrates the application of VH technology and lens model methodology to the study of disparities in pain-related decision-making. Assessment and treatment of acute post-surgical pain often varies based on VH demographic and facial expression cues. These data contribute to the existing literature on disparities in pain practice and highlight the potential of a novel approach that may serve as a model for future investigation of these critical issues.

Development of an online, publicly accessible naive Bayesian decision support tool for mammographic mass lesions based on the American College of Radiology (ACR) BI-RADS lexicon.

PubMed

Benndorf, Matthias; Kotter, Elmar; Langer, Mathias; Herda, Christoph; Wu, Yirong; Burnside, Elizabeth S

2015-06-01

To develop and validate a decision support tool for mammographic mass lesions based on a standardized descriptor terminology (BI-RADS lexicon) to reduce variability of practice. We used separate training data (1,276 lesions, 138 malignant) and validation data (1,177 lesions, 175 malignant). We created naïve Bayes (NB) classifiers from the training data with tenfold cross-validation. Our "inclusive model" comprised BI-RADS categories, BI-RADS descriptors, and age as predictive variables; our "descriptor model" comprised BI-RADS descriptors and age. The resulting NB classifiers were applied to the validation data. We evaluated and compared classifier performance with ROC-analysis. In the training data, the inclusive model yields an AUC of 0.959; the descriptor model yields an AUC of 0.910 (P < 0.001). The inclusive model is superior to the clinical performance (BI-RADS categories alone, P < 0.001); the descriptor model performs similarly. When applied to the validation data, the inclusive model yields an AUC of 0.935; the descriptor model yields an AUC of 0.876 (P < 0.001). Again, the inclusive model is superior to the clinical performance (P < 0.001); the descriptor model performs similarly. We consider our classifier a step towards a more uniform interpretation of combinations of BI-RADS descriptors. We provide our classifier at www.ebm-radiology.com/nbmm/index.html . • We provide a decision support tool for mammographic masses at www.ebm-radiology.com/nbmm/index.html . • Our tool may reduce variability of practice in BI-RADS category assignment. • A formal analysis of BI-RADS descriptors may enhance radiologists' diagnostic performance.

"And I think that we can fix it": mental models used in high-risk surgical decision making.

PubMed

Kruser, Jacqueline M; Pecanac, Kristen E; Brasel, Karen J; Cooper, Zara; Steffens, Nicole M; McKneally, Martin F; Schwarze, Margaret L

2015-04-01

To examine how surgeons use the "fix-it" model to communicate with patients before high-risk operations. The "fix-it" model characterizes disease as an isolated abnormality that can be restored to normal form and function through medical intervention. This mental model is familiar to patients and physicians, but it is ineffective for chronic conditions and treatments that cannot achieve normalcy. Overuse may lead to permissive decision making favoring intervention. Efforts to improve surgical decision making will need to consider how mental models function in clinical practice, including "fix-it." We observed surgeons who routinely perform high-risk surgery during preoperative discussions with patients. We used qualitative content analysis to explore the use of "fix-it" in 48 audio-recorded conversations. Surgeons used the "fix-it" model for 2 separate purposes during preoperative conversations: (1) as an explanatory tool to facilitate patient understanding of disease and surgery, and (2) as a deliberation framework to assist in decision making. Although surgeons commonly used "fix-it" as an explanatory model, surgeons explicitly discussed limitations of the "fix-it" model as an independent rationale for operating as they deliberated about the value of surgery. Although the use of "fix-it" is familiar for explaining medical information to patients, surgeons recognize that the model can be problematic for determining the value of an operation. Whether patients can transition between understanding how their disease is fixed with surgery to a subsequent deliberation about whether they should have surgery is unclear and may have broader implications for surgical decision making.

Model-based decision making in early clinical development: minimizing the impact of a blood pressure adverse event.

PubMed

Stroh, Mark; Addy, Carol; Wu, Yunhui; Stoch, S Aubrey; Pourkavoos, Nazaneen; Groff, Michelle; Xu, Yang; Wagner, John; Gottesdiener, Keith; Shadle, Craig; Wang, Hong; Manser, Kimberly; Winchell, Gregory A; Stone, Julie A

2009-03-01

We describe how modeling and simulation guided program decisions following a randomized placebo-controlled single-rising oral dose first-in-man trial of compound A where an undesired transient blood pressure (BP) elevation occurred in fasted healthy young adult males. We proposed a lumped-parameter pharmacokinetic-pharmacodynamic (PK/PD) model that captured important aspects of the BP homeostasis mechanism. Four conceptual units characterized the feedback PD model: a sinusoidal BP set point, an effect compartment, a linear effect model, and a system response. To explore approaches for minimizing the BP increase, we coupled the PD model to a modified PK model to guide oral controlled-release (CR) development. The proposed PK/PD model captured the central tendency of the observed data. The simulated BP response obtained with theoretical release rate profiles suggested some amelioration of the peak BP response with CR. This triggered subsequent CR formulation development; we used actual dissolution data from these candidate CR formulations in the PK/PD model to confirm a potential benefit in the peak BP response. Though this paradigm has yet to be tested in the clinic, our model-based approach provided a common rational framework to more fully utilize the limited available information for advancing the program.

Clinical decision-making among new graduate nurses attending residency programs in Saudi Arabia.

PubMed

Al-Dossary, Reem Nassar; Kitsantas, Panagiota; Maddox, P J

2016-02-01

This study examined the impact of residency programs on clinical decision-making of new Saudi graduate nurses who completed a residency program compared to new Saudi graduate nurses who did not participate in residency programs. This descriptive study employed a convenience sample (N=98) of new graduate nurses from three hospitals in Saudi Arabia. A self-administered questionnaire was used to collect data. Clinical decision-making skills were measured using the Clinical Decision Making in Nursing Scale. Descriptive statistics, independent t-tests, and multiple linear regression analysis were utilized to examine the effect of residency programs on new graduate nurses' clinical decision-making skills. On average, resident nurses had significantly higher levels of clinical decision-making skills than non-residents (t=23.25, p=0.000). Enrollment in a residency program explained 86.9% of the variance in total clinical decision making controlling for age and overall grade point average. The findings of this study support evidence in the nursing literature conducted primarily in the US and Europe that residency programs have a positive influence on new graduate nurses' clinical decision-making skills. This is the first study to examine the impact of residency programs on clinical decision-making among new Saudi graduate nurses who completed a residency program. The findings of this study underscore the need for the development and implementation of residency programs for all new nurses. Copyright © 2015 Elsevier Inc. All rights reserved.

Development of a tiered and binned genetic counseling model for informed consent in the era of multiplex testing for cancer susceptibility.

PubMed

Bradbury, Angela R; Patrick-Miller, Linda; Long, Jessica; Powers, Jacquelyn; Stopfer, Jill; Forman, Andrea; Rybak, Christina; Mattie, Kristin; Brandt, Amanda; Chambers, Rachelle; Chung, Wendy K; Churpek, Jane; Daly, Mary B; Digiovanni, Laura; Farengo-Clark, Dana; Fetzer, Dominique; Ganschow, Pamela; Grana, Generosa; Gulden, Cassandra; Hall, Michael; Kohler, Lynne; Maxwell, Kara; Merrill, Shana; Montgomery, Susan; Mueller, Rebecca; Nielsen, Sarah; Olopade, Olufunmilayo; Rainey, Kimberly; Seelaus, Christina; Nathanson, Katherine L; Domchek, Susan M

2015-06-01

Multiplex genetic testing, including both moderate- and high-penetrance genes for cancer susceptibility, is associated with greater uncertainty than traditional testing, presenting challenges to informed consent and genetic counseling. We sought to develop a new model for informed consent and genetic counseling for four ongoing studies. Drawing from professional guidelines, literature, conceptual frameworks, and clinical experience, a multidisciplinary group developed a tiered-binned genetic counseling approach proposed to facilitate informed consent and improve outcomes of cancer susceptibility multiplex testing. In this model, tier 1 "indispensable" information is presented to all patients. More specific tier 2 information is provided to support variable informational needs among diverse patient populations. Clinically relevant information is "binned" into groups to minimize information overload, support informed decision making, and facilitate adaptive responses to testing. Seven essential elements of informed consent are provided to address the unique limitations, risks, and uncertainties of multiplex testing. A tiered-binned model for informed consent and genetic counseling has the potential to address the challenges of multiplex testing for cancer susceptibility and to support informed decision making and adaptive responses to testing. Future prospective studies including patient-reported outcomes are needed to inform how to best incorporate multiplex testing for cancer susceptibility into clinical practice.Genet Med 17 6, 485-492.

Mild cognitive impairment is associated with poorer decision-making in community-based older persons.

PubMed

Han, S Duke; Boyle, Patricia A; James, Bryan D; Yu, Lei; Bennett, David A

2015-04-01

To test the hypothesis that mild cognitive impairment (MCI) is associated with poorer financial and healthcare decision-making. Community-based epidemiological cohort study. Communities throughout northeastern Illinois. Older persons without dementia from the Rush Memory and Aging Project (N = 730). All participants underwent a detailed clinical evaluation and decision-making assessment using a measure that closely approximates materials used in real-world financial and healthcare settings. This allowed for measurement of total decision-making and financial and healthcare decision-making. Regression models were used to examine whether MCI was associated with a lower level of decision-making. In subsequent analyses, the relationship between specific cognitive systems (episodic memory, semantic memory, working memory, perceptual speed, visuospatial ability) and decision-making was explored in participants with MCI. MCI was associated with lower total, financial, and healthcare decision-making scores after accounting for the effects of age, education, and sex. The effect of MCI on total decision-making was equivalent to the effect of more than 10 additional years of age. Additional models showed that, when considering multiple cognitive systems, perceptual speed accounted for the most variance in decision-making in participants with MCI. Persons with MCI may have poorer financial and healthcare decision-making in real-world situations, and perceptual speed may be an important contributor to poorer decision-making in persons with MCI. © 2015, Copyright the Authors Journal compilation © 2015, The American Geriatrics Society.

A Comparison of Reinforcement Learning Models for the Iowa Gambling Task Using Parameter Space Partitioning

ERIC Educational Resources Information Center

Steingroever, Helen; Wetzels, Ruud; Wagenmakers, Eric-Jan

2013-01-01

The Iowa gambling task (IGT) is one of the most popular tasks used to study decision-making deficits in clinical populations. In order to decompose performance on the IGT in its constituent psychological processes, several cognitive models have been proposed (e.g., the Expectancy Valence (EV) and Prospect Valence Learning (PVL) models). Here we…

Computer modeling of prostate cancer treatment. A paradigm for oncologic management?

PubMed

Miles, B J; Kattan, M W

1995-04-01

This article discusses the relevance of computer modeling to the management of prostate cancer. Several computer modeling techniques are reviewed and the advantages and disadvantages of each are discussed. An example that uses a computer model to compare alternative strategies for clinically localized prostate cancer is examined in detail. The quality of the data used in computer models is critical, and these models play an important role in medical decision making.

mobilityRERC state of the science conference: Considerations for developing an evidence base for wheeled mobility and seating service delivery.

PubMed

Cohen, Laura; Greer, Nancy; Berliner, Elise; Sprigle, Stephen

2013-11-01

This article, developed as background content for discussion during the Mobility Rehabilitation Engineering Research Center State of the Science Conference, reviews research surrounding wheeled mobility and seating (WMS) service delivery, discusses the challenges of improving clinical decision-making, and discusses research approaches used to study and improve health services in other practice areas that might be leveraged to develop the evidence base for WMS. Narrative literature review. An overview of existing research found general agreement across models of WMS service delivery but little high quality evidence to support the recommended approaches and few studies of the relationship between service delivery steps and individual patient outcomes. The definition of successful clinical decision-making is different for different stakeholders. Clinical decision-making should incorporate the best available evidence along with patient values, preferences, circumstances, and clinical expertise. To advance the evidence base for WMS service delivery, alternatives to randomized controlled trials should be considered and reliable and valid outcome measures developed. Technological advances offer tremendous opportunities for individuals with complex rehabilitation technology needs. However, with ongoing scrutiny of WMS service delivery there is an increased need for evidence to support the clinical decision-making process and to support evidence-based coverage policies for WMS services and technologies. An evidence base for wheeled mobility and seating services is an important component of the clinical decision-making process. At present, there is little evidence regarding essential components of the wheeled mobility and seating evaluation or the relationship between the evaluation process and patient outcomes. Many factors can confound this relationship and present challenges to research in this area. All stakeholders (i.e. clinicians, rehabilitation technology suppliers, manufacturers, researchers, payers, policy makers, and wheelchair users) need to work together to develop and support an evidence base for wheeled mobility and seating service delivery.

A task-based support architecture for developing point-of-care clinical decision support systems for the emergency department.

PubMed

Wilk, S; Michalowski, W; O'Sullivan, D; Farion, K; Sayyad-Shirabad, J; Kuziemsky, C; Kukawka, B

2013-01-01

The purpose of this study was to create a task-based support architecture for developing clinical decision support systems (CDSSs) that assist physicians in making decisions at the point-of-care in the emergency department (ED). The backbone of the proposed architecture was established by a task-based emergency workflow model for a patient-physician encounter. The architecture was designed according to an agent-oriented paradigm. Specifically, we used the O-MaSE (Organization-based Multi-agent System Engineering) method that allows for iterative translation of functional requirements into architectural components (e.g., agents). The agent-oriented paradigm was extended with ontology-driven design to implement ontological models representing knowledge required by specific agents to operate. The task-based architecture allows for the creation of a CDSS that is aligned with the task-based emergency workflow model. It facilitates decoupling of executable components (agents) from embedded domain knowledge (ontological models), thus supporting their interoperability, sharing, and reuse. The generic architecture was implemented as a pilot system, MET3-AE--a CDSS to help with the management of pediatric asthma exacerbation in the ED. The system was evaluated in a hospital ED. The architecture allows for the creation of a CDSS that integrates support for all tasks from the task-based emergency workflow model, and interacts with hospital information systems. Proposed architecture also allows for reusing and sharing system components and knowledge across disease-specific CDSSs.

Effects of Clinical Decision Topic on Patients' Involvement in and Satisfaction With Decisions and Their Subsequent Implementation.

PubMed

Freidl, Marion; Pesola, Francesca; Konrad, Jana; Puschner, Bernd; Kovacs, Attila Istvan; De Rosa, Corrado; Fiorillo, Andrea; Krogsgaard Bording, Malene; Kawohl, Wolfram; Rössler, Wulf; Nagy, Marietta; Munk-Jørgensen, Povl; Slade, Mike

2016-06-01

Clinical decision making is an important aspect of mental health care. Predictors of how patients experience decision making and whether decisions are implemented are underresearched. This study investigated the relationship between decision topic and involvement in the decision, satisfaction with it, and its subsequent implementation from both staff and patient perspectives. As part of the Clinical Decision Making and Outcome in Routine Care for People With Severe Mental Illness study, patients (N=588) and their providers (N=213) were recruited from community-based mental health services in six European countries. Both completed bimonthly assessments for one year using the Clinical Decision Making in Routine Care Scale to assess the decision topic and implementation; both also completed the Clinical Decision Making Involvement and Satisfaction Scale. Three categories of decision topics were determined: treatment (most frequently cited), social, and financial. The topic identified as most important remained stable over the follow-up. Patients were more likely to rate their involvement as active rather than passive for social decisions (odds ratio [OR]=5.7, p<.001) and financial decisions (OR=9.5, p<.001). They were more likely to report higher levels of satisfaction rather than lower levels for social decisions (OR=1.5, p=.01) and financial decisions (OR=1.7, p=.01). Social decisions were more likely to be partly implemented (OR=3.0, p<.001) or fully implemented (OR=1.7, p=.03) than not implemented. Patients reported poorer involvement, satisfaction, and implementation in regard to treatment-related decisions, compared with social and financial decisions. Clinicians may need to employ different interactional styles for different types of decisions to maximize satisfaction and decision implementation.

PSYCHE: An Object-Oriented Approach to Simulating Medical Education

PubMed Central

Mullen, Jamie A.

1990-01-01

Traditional approaches to computer-assisted instruction (CAI) do not provide realistic simulations of medical education, in part because they do not utilize heterogeneous knowledge bases for their source of domain knowledge. PSYCHE, a CAI program designed to teach hypothetico-deductive psychiatric decision-making to medical students, uses an object-oriented implementation of an intelligent tutoring system (ITS) to model the student, domain expert, and tutor. It models the transactions between the participants in complex transaction chains, and uses heterogeneous knowledge bases to represent both domain and procedural knowledge in clinical medicine. This object-oriented approach is a flexible and dynamic approach to modeling, and represents a potentially valuable tool for the investigation of medical education and decision-making.

Using patient self-reports to study heterogeneity of treatment effects in major depressive disorder

PubMed Central

Kessler, R.C.; van Loo, H.M.; Wardenaar, K.J.; Bossarte, R.M.; Brenner, L.A.; Ebert, D.D; de Jonge, P.; Nierenberg, A.A.; Rosellini, A.J.; Sampson, N.A.; Schoevers, R.A.; Wilcox, M.A.; Zaslavsky, A.M.

2016-01-01

Aims Clinicians need guidance to address the heterogeneity of treatment responses of patients with major depressive disorder (MDD). While prediction schemes based on symptom clustering and biomarkers have so far not yielded results of sufficient strength to inform clinical decision-making, prediction schemes based on big data predictive analytic models might be more practically useful. Methods We review evidence suggesting that prediction equations based on symptoms and other easily-assessed clinical features found in previous research to predict MDD treatment outcomes might provide a foundation for developing predictive analytic clinical decision support models that could help clinicians select optimal (personalized) MDD treatments. These methods could also be useful in targeting patient subsamples for more expensive biomarker assessments. Results Approximately two dozen baseline variables obtained from medical records or patient reports have been found repeatedly in MDD treatment trials to predict overall treatment outcomes (i.e., intervention versus control) or differential treatment outcomes (i.e., intervention A versus intervention B). Similar evidence has been found in observational studies of MDD persistence-severity. However, no treatment studies have yet attempted to develop treatment outcome equations using the full set of these predictors. Promising preliminary empirical results coupled with recent developments in statistical methodology suggest that models could be developed to provide useful clinical decision support in personalized treatment selection. These tools could also provide a strong foundation to increase statistical power in focused studies of biomarkers and MDD heterogeneity of treatment response in subsequent controlled trials. Conclusions Coordinated efforts are needed to develop a protocol for systematically collecting information about established predictors of heterogeneity of MDD treatment response in large observational treatment studies, applying and refining these models in subsequent pragmatic trials, carrying out pooled secondary analyses to extract the maximum amount of information from these coordinated studies, and using this information to focus future discovery efforts in the segment of the patient population in which continued uncertainty about treatment response exists. PMID:26810628

Why not try harder? Computational approach to motivation deficits in neuro-psychiatric diseases.

PubMed

Pessiglione, Mathias; Vinckier, Fabien; Bouret, Sébastien; Daunizeau, Jean; Le Bouc, Raphaël

2017-11-29

Motivation deficits, such as apathy, are pervasive in both neurological and psychiatric diseases. Even when they are not the core symptom, they reduce quality of life, compromise functional outcome and increase the burden for caregivers. They are currently assessed with clinical scales that do not give any mechanistic insight susceptible to guide therapeutic intervention. Here, we present another approach that consists of phenotyping the behaviour of patients in motivation tests, using computational models. These formal models impose a precise and operational definition of motivation that is embedded in decision theory. Motivation can be defined as the function that orients and activates the behaviour according to two attributes: a content (the goal) and a quantity (the goal value). Decision theory offers a way to quantify motivation, as the cost that patients would accept to endure in order to get the benefit of achieving their goal. We then review basic and clinical studies that have investigated the trade-off between the expected cost entailed by potential actions and the expected benefit associated with potential rewards. These studies have shown that the trade-off between effort and reward involves specific cortical, subcortical and neuromodulatory systems, such that it may be shifted in particular clinical conditions, and reinstated by appropriate treatments. Finally, we emphasize the promises of computational phenotyping for clinical purposes. Ideally, there would be a one-to-one mapping between specific neural components and distinct computational variables and processes of the decision model. Thus, fitting computational models to patients' behaviour would allow inferring of the dysfunctional mechanism in both cognitive terms (e.g. hyposensitivity to reward) and neural terms (e.g. lack of dopamine). This computational approach may therefore not only give insight into the motivation deficit but also help personalize treatment. © The Author (2017). Published by Oxford University Press on behalf of the Guarantors of Brain. All rights reserved. For Permissions, please email: journals.permissions@oup.com.