Impact of computer-based treatment planning software on clinical judgment of dental students for planning prosthodontic rehabilitation

PubMed Central

Deshpande, Saee; Chahande, Jayashree

2014-01-01

Purpose Successful prosthodontic rehabilitation involves making many interrelated clinical decisions which have an impact on each other. Self-directed computer-based training has been shown to be a very useful tool to develop synthetic and analytical problem-solving skills among students. Thus, a computer-based case study and treatment planning (CSTP) software program was developed which would allow students to work through the process of comprehensive, multidisciplinary treatment planning for patients in a structured and logical manner. The present study was aimed at assessing the effect of this CSTP software on the clinical judgment of dental students while planning prosthodontic rehabilitation and to assess the students’ perceptions about using the program for its intended use. Methods A CSTP software program was developed and validated. The impact of this program on the clinical decision making skills of dental graduates was evaluated by real life patient encounters, using a modified and validated mini-CEX. Students’ perceptions about the program were obtained by a pre-validated feedback questionnaire. Results The faculty assessment scores of clinical judgment improved significantly after the use of this program. The majority of students felt it was an informative, useful, and innovative way of learning and they strongly felt that they had learnt the logical progression of planning, the insight into decision making, and the need for flexibility in treatment planning after using this program. Conclusion CSTP software was well received by the students. There was significant improvement in students’ clinical judgment after using this program. It should thus be envisaged fundamentally as an adjunct to conventional teaching techniques to improve students’ decision making skills and confidence. PMID:25170288

A practical guide to assessing clinical decision-making skills using the key features approach.

PubMed

Farmer, Elizabeth A; Page, Gordon

2005-12-01

This paper in the series on professional assessment provides a practical guide to writing key features problems (KFPs). Key features problems test clinical decision-making skills in written or computer-based formats. They are based on the concept of critical steps or 'key features' in decision making and represent an advance on the older, less reliable patient management problem (PMP) formats. The practical steps in writing these problems are discussed and illustrated by examples. Steps include assembling problem-writing groups, selecting a suitable clinical scenario or problem and defining its key features, writing the questions, selecting question response formats, preparing scoring keys, reviewing item quality and item banking. The KFP format provides educators with a flexible approach to testing clinical decision-making skills with demonstrated validity and reliability when constructed according to the guidelines provided.

Computer-assisted abdominal surgery: new technologies.

PubMed

Kenngott, H G; Wagner, M; Nickel, F; Wekerle, A L; Preukschas, A; Apitz, M; Schulte, T; Rempel, R; Mietkowski, P; Wagner, F; Termer, A; Müller-Stich, Beat P

2015-04-01

Computer-assisted surgery is a wide field of technologies with the potential to enable the surgeon to improve efficiency and efficacy of diagnosis, treatment, and clinical management. This review provides an overview of the most important new technologies and their applications. A MEDLINE database search was performed revealing a total of 1702 references. All references were considered for information on six main topics, namely image guidance and navigation, robot-assisted surgery, human-machine interface, surgical processes and clinical pathways, computer-assisted surgical training, and clinical decision support. Further references were obtained through cross-referencing the bibliography cited in each work. Based on their respective field of expertise, the authors chose 64 publications relevant for the purpose of this review. Computer-assisted systems are increasingly used not only in experimental studies but also in clinical studies. Although computer-assisted abdominal surgery is still in its infancy, the number of studies is constantly increasing, and clinical studies start showing the benefits of computers used not only as tools of documentation and accounting but also for directly assisting surgeons during diagnosis and treatment of patients. Further developments in the field of clinical decision support even have the potential of causing a paradigm shift in how patients are diagnosed and treated.

A medical informatics perspective on clinical decision support systems. Findings from the yearbook 2013 section on decision support.

PubMed

Bouaud, J; Lamy, J-B

2013-01-01

To summarize excellent research and to select best papers published in 2012 in the field of computer-based decision support in healthcare. A bibliographic search focused on clinical decision support systems (CDSSs) and computer provider order entry was performed, followed by a double-blind literature review. The review process yielded six papers, illustrating various aspects of clinical decision support. The first paper is a systematic review of CDSS intervention trials in real settings, and considers different types of possible outcomes. It emphasizes the heterogeneity of studies and confirms that CDSSs can improve process measures but that evidence lacks for other types of outcomes, especially clinical or economic. Four other papers tackle the safety of drug prescribing and show that CDSSs can be efficient in reducing prescription errors. The sixth paper exemplifies the growing role of ontological resources which can be used for several applications including decision support. CDSS research has to be continuously developed and assessed. The wide variety of systems and of interventions limits the understanding of factors of success of CDSS implementations. A standardization in the characterization of CDSSs and of intervention trial reporting will help to overcome this obstacle.

Assessing Clinical Reasoning (ASCLIRE): Instrument Development and Validation

ERIC Educational Resources Information Center

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

2015-01-01

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…

Using old technology to implement modern computer-aided decision support for primary diabetes care.

PubMed Central

Hunt, D. L.; Haynes, R. B.; Morgan, D.

2001-01-01

BACKGROUND: Implementation rates of interventions known to be beneficial for people with diabetes mellitus are often suboptimal. Computer-aided decision support systems (CDSSs) can improve these rates. The complexity of establishing a fully integrated electronic medical record that provides decision support, however, often prevents their use. OBJECTIVE: To develop a CDSS for diabetes care that can be easily introduced into primary care settings and diabetes clinics. THE SYSTEM: The CDSS uses fax-machine-based optical character recognition software for acquiring patient information. Simple, 1-page paper forms, completed by patients or health practitioners, are faxed to a central location. The information is interpreted and recorded in a database. This initiates a routine that matches the information against a knowledge base so that patient-specific recommendations can be generated. These are formatted and faxed back within 4-5 minutes. IMPLEMENTATION: The system is being introduced into 2 diabetes clinics. We are collecting information on frequency of use of the system, as well as satisfaction with the information provided. CONCLUSION: Computer-aided decision support can be provided in any setting with a fax machine, without the need for integrated electronic medical records or computerized data-collection devices. PMID:11825194

Using old technology to implement modern computer-aided decision support for primary diabetes care.

PubMed

Hunt, D L; Haynes, R B; Morgan, D

2001-01-01

Implementation rates of interventions known to be beneficial for people with diabetes mellitus are often suboptimal. Computer-aided decision support systems (CDSSs) can improve these rates. The complexity of establishing a fully integrated electronic medical record that provides decision support, however, often prevents their use. To develop a CDSS for diabetes care that can be easily introduced into primary care settings and diabetes clinics. THE SYSTEM: The CDSS uses fax-machine-based optical character recognition software for acquiring patient information. Simple, 1-page paper forms, completed by patients or health practitioners, are faxed to a central location. The information is interpreted and recorded in a database. This initiates a routine that matches the information against a knowledge base so that patient-specific recommendations can be generated. These are formatted and faxed back within 4-5 minutes. The system is being introduced into 2 diabetes clinics. We are collecting information on frequency of use of the system, as well as satisfaction with the information provided. Computer-aided decision support can be provided in any setting with a fax machine, without the need for integrated electronic medical records or computerized data-collection devices.

A computerized handheld decision-support system to improve pulmonary embolism diagnosis: a randomized trial.

PubMed

Roy, Pierre-Marie; Durieux, Pierre; Gillaizeau, Florence; Legall, Catherine; Armand-Perroux, Aurore; Martino, Ludovic; Hachelaf, Mohamed; Dubart, Alain-Eric; Schmidt, Jeannot; Cristiano, Mirko; Chretien, Jean-Marie; Perrier, Arnaud; Meyer, Guy

2009-11-17

Testing for pulmonary embolism often differs from that recommended by evidence-based guidelines. To assess the effectiveness of a handheld clinical decision-support system to improve the diagnostic work-up of suspected pulmonary embolism among patients in the emergency department. Cluster randomized trial. Assignment was by random-number table, providers were not blinded, and outcome assessment was automated. (ClinicalTrials.gov registration number: NCT00188032). 20 emergency departments in France. 1103 and 1768 consecutive outpatients with suspected pulmonary embolism. After a preintervention period involving 20 centers and 1103 patients, in which providers grew accustomed to inputting clinical data into handheld devices and investigators assessed baseline testing, emergency departments were randomly assigned to activation of a decision-support system on the devices (10 centers, 753 patients) or posters and pocket cards that showed validated diagnostic strategies (10 centers, 1015 patients). Appropriateness of diagnostic work-up, defined as any sequence of tests that yielded a posttest probability less than 5% or greater than 85% (primary outcome) or as strict adherence to guideline recommendations (secondary outcome); number of tests per patient (secondary outcome). The proportion of patients who received appropriate diagnostic work-ups was greater during the trial than in the preintervention period in both groups, but the increase was greater in the computer-based guidelines group (adjusted mean difference in increase, 19.3 percentage points favoring computer-based guidelines [95% CI, 2.9 to 35.6 percentage points]; P = 0.023). Among patients with appropriate work-ups, those in the computer-based guidelines group received slightly fewer tests than did patients in the paper guidelines group (mean tests per patient, 1.76 [SD, 0.98] vs. 2.25 [SD, 1.04]; P < 0.001). The study was not designed to show a difference in the clinical outcomes of patients during follow-up. A handheld decision-support system improved diagnostic decision making for patients with suspected pulmonary embolism in the emergency department.

Computer decision support system for the stomach cancer diagnosis

NASA Astrophysics Data System (ADS)

Polyakov, E. V.; Sukhova, O. G.; Korenevskaya, P. Y.; Ovcharova, V. S.; Kudryavtseva, I. O.; Vlasova, S. V.; Grebennikova, O. P.; Burov, D. A.; Yemelyanova, G. S.; Selchuk, V. Y.

2017-01-01

The paper considers the creation of the computer knowledge base containing the data of histological, cytologic, and clinical researches. The system is focused on improvement of diagnostics quality of stomach cancer - one of the most frequent death causes among oncologic patients.

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.

Creating Shareable Clinical Decision Support Rules for a Pharmacogenomics Clinical Guideline Using Structured Knowledge Representation.

PubMed

Linan, Margaret K; Sottara, Davide; Freimuth, Robert R

2015-01-01

Pharmacogenomics (PGx) guidelines contain drug-gene relationships, therapeutic and clinical recommendations from which clinical decision support (CDS) rules can be extracted, rendered and then delivered through clinical decision support systems (CDSS) to provide clinicians with just-in-time information at the point of care. Several tools exist that can be used to generate CDS rules that are based on computer interpretable guidelines (CIG), but none have been previously applied to the PGx domain. We utilized the Unified Modeling Language (UML), the Health Level 7 virtual medical record (HL7 vMR) model, and standard terminologies to represent the semantics and decision logic derived from a PGx guideline, which were then mapped to the Health eDecisions (HeD) schema. The modeling and extraction processes developed here demonstrate how structured knowledge representations can be used to support the creation of shareable CDS rules from PGx guidelines.

[Medical expert systems and clinical needs].

PubMed

Buscher, H P

1991-10-18

The rapid expansion of computer-based systems for problem solving or decision making in medicine, the so-called medical expert systems, emphasize the need for reappraisal of their indication and value. Where specialist knowledge is required, in particular where medical decisions are susceptible to error these systems will probably serve as a valuable support. In the near future computer-based systems should be able to aid the interpretation of findings of technical investigations and the control of treatment, especially where rapid reactions are necessary despite the need of complex analysis of investigated parameters. In the distant future complete support of diagnostic procedures from the history to final diagnosis is possible. It promises to be particularly attractive for the diagnosis of seldom diseases, for difficult differential diagnoses, and in the decision making in the case of expensive, risky or new diagnostic or therapeutic methods. The physician needs to be aware of certain dangers, ranging from misleading information up to abuse. Patient information depends often on subjective reports and error-prone observations. Although basing on problematic knowledge computer-born decisions may have an imperative effect on medical decision making. Also it must be born in mind that medical decisions should always combine the rational with a consideration of human motives.

A novel clinical decision support algorithm for constructing complete medication histories.

PubMed

Long, Ju; Yuan, Michael Juntao

2017-07-01

A patient's complete medication history is a crucial element for physicians to develop a full understanding of the patient's medical conditions and treatment options. However, due to the fragmented nature of medical data, this process can be very time-consuming and often impossible for physicians to construct a complete medication history for complex patients. In this paper, we describe an accurate, computationally efficient and scalable algorithm to construct a medication history timeline. The algorithm is developed and validated based on 1 million random prescription records from a large national prescription data aggregator. Our evaluation shows that the algorithm can be scaled horizontally on-demand, making it suitable for future delivery in a cloud-computing environment. We also propose that this cloud-based medication history computation algorithm could be integrated into Electronic Medical Records, enabling informed clinical decision-making at the point of care. Copyright © 2017 Elsevier B.V. All rights reserved.

Social, Organizational, and Contextual Characteristics of Clinical Decision Support Systems for Intensive Insulin Therapy: A Literature Review and Case Study

PubMed Central

Campion, Thomas R.; Waitman, Lemuel R.; May, Addison K.; Ozdas, Asli; Lorenzi, Nancy M.; Gadd, Cynthia S.

2009-01-01

Introduction: Evaluations of computerized clinical decision support systems (CDSS) typically focus on clinical performance changes and do not include social, organizational, and contextual characteristics explaining use and effectiveness. Studies of CDSS for intensive insulin therapy (IIT) are no exception, and the literature lacks an understanding of effective computer-based IIT implementation and operation. Results: This paper presents (1) a literature review of computer-based IIT evaluations through the lens of institutional theory, a discipline from sociology and organization studies, to demonstrate the inconsistent reporting of workflow and care process execution and (2) a single-site case study to illustrate how computer-based IIT requires substantial organizational change and creates additional complexity with unintended consequences including error. Discussion: Computer-based IIT requires organizational commitment and attention to site-specific technology, workflow, and care processes to achieve intensive insulin therapy goals. The complex interaction between clinicians, blood glucose testing devices, and CDSS may contribute to workflow inefficiency and error. Evaluations rarely focus on the perspective of nurses, the primary users of computer-based IIT whose knowledge can potentially lead to process and care improvements. Conclusion: This paper addresses a gap in the literature concerning the social, organizational, and contextual characteristics of CDSS in general and for intensive insulin therapy specifically. Additionally, this paper identifies areas for future research to define optimal computer-based IIT process execution: the frequency and effect of manual data entry error of blood glucose values, the frequency and effect of nurse overrides of CDSS insulin dosing recommendations, and comprehensive ethnographic study of CDSS for IIT. PMID:19815452

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).

Evidence of effectiveness of health care professionals using handheld computers: a scoping review of systematic reviews.

PubMed

Mickan, Sharon; Tilson, Julie K; Atherton, Helen; Roberts, Nia Wyn; Heneghan, Carl

2013-10-28

Handheld computers and mobile devices provide instant access to vast amounts and types of useful information for health care professionals. Their reduced size and increased processing speed has led to rapid adoption in health care. Thus, it is important to identify whether handheld computers are actually effective in clinical practice. A scoping review of systematic reviews was designed to provide a quick overview of the documented evidence of effectiveness for health care professionals using handheld computers in their clinical work. A detailed search, sensitive for systematic reviews was applied for Cochrane, Medline, EMBASE, PsycINFO, Allied and Complementary Medicine Database (AMED), Global Health, and Cumulative Index to Nursing and Allied Health Literature (CINAHL) databases. All outcomes that demonstrated effectiveness in clinical practice were included. Classroom learning and patient use of handheld computers were excluded. Quality was assessed using the Assessment of Multiple Systematic Reviews (AMSTAR) tool. A previously published conceptual framework was used as the basis for dual data extraction. Reported outcomes were summarized according to the primary function of the handheld computer. Five systematic reviews met the inclusion and quality criteria. Together, they reviewed 138 unique primary studies. Most reviewed descriptive intervention studies, where physicians, pharmacists, or medical students used personal digital assistants. Effectiveness was demonstrated across four distinct functions of handheld computers: patient documentation, patient care, information seeking, and professional work patterns. Within each of these functions, a range of positive outcomes were reported using both objective and self-report measures. The use of handheld computers improved patient documentation through more complete recording, fewer documentation errors, and increased efficiency. Handheld computers provided easy access to clinical decision support systems and patient management systems, which improved decision making for patient care. Handheld computers saved time and gave earlier access to new information. There were also reports that handheld computers enhanced work patterns and efficiency. This scoping review summarizes the secondary evidence for effectiveness of handheld computers and mhealth. It provides a snapshot of effective use by health care professionals across four key functions. We identified evidence to suggest that handheld computers provide easy and timely access to information and enable accurate and complete documentation. Further, they can give health care professionals instant access to evidence-based decision support and patient management systems to improve clinical decision making. Finally, there is evidence that handheld computers allow health professionals to be more efficient in their work practices. It is anticipated that this evidence will guide clinicians and managers in implementing handheld computers in clinical practice and in designing future research.

Social, organizational, and contextual characteristics of clinical decision support systems for intensive insulin therapy: a literature review and case study.

PubMed

Campion, Thomas R; Waitman, Lemuel R; May, Addison K; Ozdas, Asli; Lorenzi, Nancy M; Gadd, Cynthia S

2010-01-01

Evaluations of computerized clinical decision support systems (CDSS) typically focus on clinical performance changes and do not include social, organizational, and contextual characteristics explaining use and effectiveness. Studies of CDSS for intensive insulin therapy (IIT) are no exception, and the literature lacks an understanding of effective computer-based IIT implementation and operation. This paper presents (1) a literature review of computer-based IIT evaluations through the lens of institutional theory, a discipline from sociology and organization studies, to demonstrate the inconsistent reporting of workflow and care process execution and (2) a single-site case study to illustrate how computer-based IIT requires substantial organizational change and creates additional complexity with unintended consequences including error. Computer-based IIT requires organizational commitment and attention to site-specific technology, workflow, and care processes to achieve intensive insulin therapy goals. The complex interaction between clinicians, blood glucose testing devices, and CDSS may contribute to workflow inefficiency and error. Evaluations rarely focus on the perspective of nurses, the primary users of computer-based IIT whose knowledge can potentially lead to process and care improvements. This paper addresses a gap in the literature concerning the social, organizational, and contextual characteristics of CDSS in general and for intensive insulin therapy specifically. Additionally, this paper identifies areas for future research to define optimal computer-based IIT process execution: the frequency and effect of manual data entry error of blood glucose values, the frequency and effect of nurse overrides of CDSS insulin dosing recommendations, and comprehensive ethnographic study of CDSS for IIT. Copyright (c) 2009. Published by Elsevier Ireland Ltd.

Advance care planning for patients with amyotrophic lateral sclerosis.

PubMed

Levi, Benjamin H; Simmons, Zachary; Hanna, Courtney; Brothers, Allyson; Lehman, Erik; Farace, Elana; Bain, Megan; Stewart, Renee; Green, Michael J

2017-08-01

To determine whether an advance care planning (ACP) decision-aid could improve communication about end-of-life treatment wishes between patients with amyotrophic lateral sclerosis (ALS) and their clinicians. Forty-four patients with ALS (>21, English-speaking, without dementia) engaged in ACP using an interactive computer based decision-aid. Before participants completed the intervention, and again three months later, their clinicians reviewed three clinical vignettes, and made treatment decisions (n = 18) for patients. After patients indicated their agreement with the team's decisions, concordance was calculated. The mean concordance between patient wishes and the clinical team decisions was significantly higher post-intervention (post = 91.9%, 95% CI = 87.8, 96.1, vs. pre = 52.4%, 95% CI = 41.9, 62.9; p <0.001). Clinical team members reported greater confidence that their decisions accurately represented each patient's wishes post-intervention (mean = 6.5) compared to pre-intervention (mean = 3.3, 1 = low, 10 = high, p <0.001). Patients reported high satisfaction (mean = 26.4, SD = 3.2; 6 = low, 30 = high) and low decisional conflict (mean = 28.8, SD = 8.2; 20 = low, 80 = high) with decisions about end-of-life care, and high satisfaction with the decision-aid (mean = 52.7, SD = 5.7, 20 = low, 60 = high). Patient knowledge regarding ACP increased post-intervention (pre = 47.8% correct responses vs. post = 66.3%; p <0.001) without adversely affecting patient anxiety or self-determination. A computer based ACP decision-aid can significantly improve clinicians' understanding of ALS patients' wishes with regard to end-of-life medical care.

Nurses' Clinical Decision Making on Adopting a Wound Clinical Decision Support System.

PubMed

Khong, Peck Chui Betty; Hoi, Shu Yin; Holroyd, Eleanor; Wang, Wenru

2015-07-01

Healthcare information technology systems are considered the ideal tool to inculcate evidence-based nursing practices. The wound clinical decision support system was built locally to support nurses to manage pressure ulcer wounds in their daily practice. However, its adoption rate is not optimal. The study's objective was to discover the concepts that informed the RNs' decisions to adopt the wound clinical decision support system as an evidence-based technology in their nursing practice. This was an exploratory, descriptive, and qualitative design using face-to-face interviews, individual interviews, and active participatory observation. A purposive, theoretical sample of 14 RNs was recruited from one of the largest public tertiary hospitals in Singapore after obtaining ethics approval. After consenting, the nurses were interviewed and observed separately. Recruitment stopped when data saturation was reached. All transcribed interview data underwent a concurrent thematic analysis, whereas observational data were content analyzed independently and subsequently triangulated with the interview data. Eight emerging themes were identified, namely, use of the wound clinical decision support system, beliefs in the wound clinical decision support system, influences of the workplace culture, extent of the benefits, professional control over nursing practices, use of knowledge, gut feelings, and emotions (fear, doubt, and frustration). These themes represented the nurses' mental outlook as they made decisions on adopting the wound clinical decision support system in light of the complexities of their roles and workloads. This research has provided insight on the nurses' thoughts regarding their decision to interact with the computer environment in a Singapore context. It captured the nurses' complex thoughts when deciding whether to adopt or reject information technology as they practice in a clinical setting.

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.

Interactive Simulated Patient: Experiences with Collaborative E-Learning in Medicine

ERIC Educational Resources Information Center

Bergin, Rolf; Youngblood, Patricia; Ayers, Mary K.; Boberg, Jonas; Bolander, Klara; Courteille, Olivier; Dev, Parvati; Hindbeck, Hans; Edward, Leonard E., II; Stringer, Jennifer R.; Thalme, Anders; Fors, Uno G. H.

2003-01-01

Interactive Simulated Patient (ISP) is a computer-based simulation tool designed to provide medical students with the opportunity to practice their clinical problem solving skills. The ISP system allows students to perform most clinical decision-making procedures in a simulated environment, including history taking in natural language, many…

A Serious Game for Teaching Nursing Students Clinical Reasoning and Decision-Making Skills.

PubMed

Johnsen, Hege Mari; Fossum, Mariann; Vivekananda-Schmidt, Pirashanthie; Fruhling, Ann; Slettebø, Åshild

2016-01-01

The aim of this study was to design and pilot-test a serious game for teaching nursing students clinical reasoning and decision-making skills in caring for patients with chronic obstructive pulmonary disease. A video-based serious game prototype was developed. A purposeful sample of six participants tested and evaluated the prototype. Usability issues were identified regarding functionality and user-computer interface. However, overall the serious game was perceived to be useful, usable and likable to use.

Computer Decision Support to Improve Autism Screening and Care in Community Pediatric Clinics

ERIC Educational Resources Information Center

Bauer, Nerissa S.; Sturm, Lynne A.; Carroll, Aaron E.; Downs, Stephen M.

2013-01-01

An autism module was added to an existing computer decision support system (CDSS) to facilitate adherence to recommended guidelines for screening for autism spectrum disorders in primary care pediatric clinics. User satisfaction was assessed by survey and informal feedback at monthly meetings between clinical staff and the software team. To assess…

Comparison of Computer-based Clinical Decision Support Systems and Content for Diabetes Mellitus.

PubMed

Kantor, M; Wright, A; Burton, M; Fraser, G; Krall, M; Maviglia, S; Mohammed-Rajput, N; Simonaitis, L; Sonnenberg, F; Middleton, B

2011-01-01

Computer-based clinical decision support (CDS) systems have been shown to improve quality of care and workflow efficiency, and health care reform legislation relies on electronic health records and CDS systems to improve the cost and quality of health care in the United States; however, the heterogeneity of CDS content and infrastructure of CDS systems across sites is not well known. We aimed to determine the scope of CDS content in diabetes care at six sites, assess the capabilities of CDS in use at these sites, characterize the scope of CDS infrastructure at these sites, and determine how the sites use CDS beyond individual patient care in order to identify characteristics of CDS systems and content that have been successfully implemented in diabetes care. We compared CDS systems in six collaborating sites of the Clinical Decision Support Consortium. We gathered CDS content on care for patients with diabetes mellitus and surveyed institutions on characteristics of their site, the infrastructure of CDS at these sites, and the capabilities of CDS at these sites. The approach to CDS and the characteristics of CDS content varied among sites. Some commonalities included providing customizability by role or user, applying sophisticated exclusion criteria, and using CDS automatically at the time of decision-making. Many messages were actionable recommendations. Most sites had monitoring rules (e.g. assessing hemoglobin A1c), but few had rules to diagnose diabetes or suggest specific treatments. All sites had numerous prevention rules including reminders for providing eye examinations, influenza vaccines, lipid screenings, nephropathy screenings, and pneumococcal vaccines. Computer-based CDS systems vary widely across sites in content and scope, but both institution-created and purchased systems had many similar features and functionality, such as integration of alerts and reminders into the decision-making workflow of the provider and providing messages that are actionable recommendations.

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.

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.

Watson will see you now: a supercomputer to help clinicians make informed treatment decisions.

PubMed

Doyle-Lindrud, Susan

2015-02-01

IBM has collaborated with several cancer care providers to develop and train the IBM supercomputer Watson to help clinicians make informed treatment decisions. When a patient is seen in clinic, the oncologist can input all of the clinical information into the computer system. Watson will then review all of the data and recommend treatment options based on the latest evidence and guidelines. Once the oncologist makes the treatment decision, this information can be sent directly to the insurance company for approval. Watson has the ability to standardize care and accelerate the approval process, a benefit to the healthcare provider and the patient.

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.

Evidence of Effectiveness of Health Care Professionals Using Handheld Computers: A Scoping Review of Systematic Reviews

PubMed Central

2013-01-01

Background Handheld computers and mobile devices provide instant access to vast amounts and types of useful information for health care professionals. Their reduced size and increased processing speed has led to rapid adoption in health care. Thus, it is important to identify whether handheld computers are actually effective in clinical practice. Objective A scoping review of systematic reviews was designed to provide a quick overview of the documented evidence of effectiveness for health care professionals using handheld computers in their clinical work. Methods A detailed search, sensitive for systematic reviews was applied for Cochrane, Medline, EMBASE, PsycINFO, Allied and Complementary Medicine Database (AMED), Global Health, and Cumulative Index to Nursing and Allied Health Literature (CINAHL) databases. All outcomes that demonstrated effectiveness in clinical practice were included. Classroom learning and patient use of handheld computers were excluded. Quality was assessed using the Assessment of Multiple Systematic Reviews (AMSTAR) tool. A previously published conceptual framework was used as the basis for dual data extraction. Reported outcomes were summarized according to the primary function of the handheld computer. Results Five systematic reviews met the inclusion and quality criteria. Together, they reviewed 138 unique primary studies. Most reviewed descriptive intervention studies, where physicians, pharmacists, or medical students used personal digital assistants. Effectiveness was demonstrated across four distinct functions of handheld computers: patient documentation, patient care, information seeking, and professional work patterns. Within each of these functions, a range of positive outcomes were reported using both objective and self-report measures. The use of handheld computers improved patient documentation through more complete recording, fewer documentation errors, and increased efficiency. Handheld computers provided easy access to clinical decision support systems and patient management systems, which improved decision making for patient care. Handheld computers saved time and gave earlier access to new information. There were also reports that handheld computers enhanced work patterns and efficiency. Conclusions This scoping review summarizes the secondary evidence for effectiveness of handheld computers and mhealth. It provides a snapshot of effective use by health care professionals across four key functions. We identified evidence to suggest that handheld computers provide easy and timely access to information and enable accurate and complete documentation. Further, they can give health care professionals instant access to evidence-based decision support and patient management systems to improve clinical decision making. Finally, there is evidence that handheld computers allow health professionals to be more efficient in their work practices. It is anticipated that this evidence will guide clinicians and managers in implementing handheld computers in clinical practice and in designing future research. PMID:24165786

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.

Teaching clinical reasoning and decision-making skills to nursing students: Design, development, and usability evaluation of a serious game.

PubMed

Johnsen, Hege Mari; Fossum, Mariann; Vivekananda-Schmidt, Pirashanthie; Fruhling, Ann; Slettebø, Åshild

2016-10-01

Serious games (SGs) are a type of simulation technology that may provide nursing students with the opportunity to practice their clinical reasoning and decision-making skills in a safe and authentic environment. Despite the growing number of SGs developed for healthcare professionals, few SGs are video based or address the domain of home health care. This paper aims to describe the design, development, and usability evaluation of a video based SG for teaching clinical reasoning and decision-making skills to nursing students who care for patients with chronic obstructive pulmonary disease (COPD) in home healthcare settings. A prototype SG was developed. A unified framework of usability called TURF (Task, User, Representation, and Function) and SG theory were employed to ensure a user-centered design. The educational content was based on the clinical decision-making model, Bloom's taxonomy, and a Bachelor of Nursing curriculum. A purposeful sample of six participants evaluated the SG prototype in a usability laboratory. Cognitive walkthrough evaluations, a questionnaire, and individual interviews were used for the usability evaluation. The data were analyzed using qualitative deductive content analysis based on the TURF framework elements and related usability heuristics. The SG was perceived as being realistic, clinically relevant, and at an adequate level of complexity for the intended users. Usability issues regarding functionality and the user-computer interface design were identified. However, the SG was perceived as being easy to learn, and participants suggested that the SG could serve as a supplement to traditional training in laboratory and clinical settings. Using video based scenarios with an authentic COPD patient and a home healthcare registered nurse as actors contributed to increased realism. Using different theoretical approaches in the SG design was considered an advantage of the design process. The SG was perceived as being useful, usable, and satisfying. The achievement of the desired functionality and the minimization of user-computer interface issues emphasize the importance of conducting a usability evaluation during the SG development process. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Computer-aided diagnostic strategy selection.

PubMed

Greenes, R A

1986-03-01

Determination of the optimal diagnostic work-up strategy for the patient is becoming a major concern for the practicing physician. Overlap of the indications for various diagnostic procedures, differences in their invasiveness or risk, and high costs have made physicians aware of the need to consider the choice of procedure carefully, as well as its relation to management actions available. In this article, the author discusses research approaches that aim toward development of formal decision analytic methods to allow the physician to determine optimal strategy; clinical algorithms or rules as guides to physician decisions; improved measures for characterizing the performance of diagnostic tests; educational tools for increasing the familiarity of physicians with the concepts underlying these measures and analytic procedures; and computer-based aids for facilitating the employment of these resources in actual clinical practice.

A critical comparison of clinical decision instruments for computed tomographic scanning in mild closed traumatic brain injury in adolescents and adults.

PubMed

Stein, Sherman C; Fabbri, Andrea; Servadei, Franco; Glick, Henry A

2009-02-01

A number of clinical decision aids have been introduced to limit unnecessary computed tomographic scans in patients with mild traumatic brain injury. These aids differ in the risk factors they use to recommend a scan. We compare the instruments according to their sensitivity and specificity and recommend ones based on incremental benefit of correctly classifying patients as having surgical, nonsurgical, or no intracranial lesions. We performed a secondary analysis of prospectively collected database from 7,955 patients aged 10 years or older with mild traumatic brain injury to compare sensitivity and specificity of 6 common clinical decision strategies: the Canadian CT Head Rule, the Neurotraumatology Committee of the World Federation of Neurosurgical Societies, the New Orleans, the National Emergency X-Radiography Utilization Study II (NEXUS-II), the National Institute of Clinical Excellence guideline, and the Scandinavian Neurotrauma Committee guideline. Excluded from the database were patients for whom the history of trauma was unclear, the initial Glasgow Coma Scale score was less than 14, the injury was penetrating, vital signs were unstable, or who refused diagnostic tests. Patients revisiting the emergency department within 7 days were counted only once. The percentage of scans that would have been required by applying each of the 6 aids were Canadian CT head rule (high risk only) 53%, Canadian (medium & high risk) 56%, the Neurotraumatology Committee of the World Federation of Neurosurgical Societies 56%, New Orleans 69%, NEXUS-II 56%, National Institute of Clinical Excellence 71%, and the Scandinavian 50%. The 6 decision aids' sensitivities for surgical hematomas could not be distinguished statistically (P>.05). Sensitivity was 100% (95% confidence interval [CI] 96% to 100%) for NEXUS-II, 98.1% (95% CI 93% to 100%) for National Institute of Clinical Excellence, and 99.1% (95% CI 94% to 100%) for the other 4 clinical decision instruments. Sensitivity for any intracranial lesion ranged from 95.7% (95% CI 93% to 97%) (Scandinavian) to 100% (95% CI 98% to 100%) (National Institute of Clinical Excellence). In contrast, specificities varied between 30.9% (95% CI 30% to 32%) (National Institute of Clinical Excellence) and 52.9% (95% CI 52% to 54) (Scandinavian). NEXUS-II and the Scandinavian clinical decision aids displayed the best combination of sensitivity and specificity in this patient population. However, we cannot demonstrate that the higher sensitivity of NEXUS-II for surgical hematomas is statistically significant. Therefore, choosing which of the 2 clinical decision instruments to use must be based on decisionmakers' attitudes toward risk.

Architecture Design of Healthcare Software-as-a-Service Platform for Cloud-Based Clinical Decision Support Service.

PubMed

Oh, Sungyoung; Cha, Jieun; Ji, Myungkyu; Kang, Hyekyung; Kim, Seok; Heo, Eunyoung; Han, Jong Soo; Kang, Hyunggoo; Chae, Hoseok; Hwang, Hee; Yoo, Sooyoung

2015-04-01

To design a cloud computing-based Healthcare Software-as-a-Service (SaaS) Platform (HSP) for delivering healthcare information services with low cost, high clinical value, and high usability. We analyzed the architecture requirements of an HSP, including the interface, business services, cloud SaaS, quality attributes, privacy and security, and multi-lingual capacity. For cloud-based SaaS services, we focused on Clinical Decision Service (CDS) content services, basic functional services, and mobile services. Microsoft's Azure cloud computing for Infrastructure-as-a-Service (IaaS) and Platform-as-a-Service (PaaS) was used. The functional and software views of an HSP were designed in a layered architecture. External systems can be interfaced with the HSP using SOAP and REST/JSON. The multi-tenancy model of the HSP was designed as a shared database, with a separate schema for each tenant through a single application, although healthcare data can be physically located on a cloud or in a hospital, depending on regulations. The CDS services were categorized into rule-based services for medications, alert registration services, and knowledge services. We expect that cloud-based HSPs will allow small and mid-sized hospitals, in addition to large-sized hospitals, to adopt information infrastructures and health information technology with low system operation and maintenance costs.

Enhancing the informed consent process in psychiatric outpatients with a brief computer-based method.

PubMed

Morán-Sánchez, Inés; Luna, Aurelio; Pérez-Cárceles, Maria D

2016-11-30

Informed consent is a key element of ethical clinical research. Those with mental disorders may be at risk for impaired consent capacity. Problems with procedures may also contribute to patient's ´difficulties in understanding consent forms. The present investigation explores if a brief technologically based information presentation of the informed consent process may enhance psychiatric patients understanding and satisfaction. In this longitudinal, within-participants comparison study, patients who initially were judged to lack capacity to make research decisions (n=41) and a control group (n=47) were followed up. Decisional capacity, willingness to participate and cognitive and clinical scores were assessed at baseline and after receiving the computer-assisted enhanced consent. With sufficient cueing, patients with impaired research-related decision-making capacity at baseline were able to display enough understanding of the consent form. Patient satisfaction and willingness to participate also increased at follow up. Implications of these results for clinical practice and medical research involving people with mental disorders are discussed. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

A pilot study of distributed knowledge management and clinical decision support in the cloud.

PubMed

Dixon, Brian E; Simonaitis, Linas; Goldberg, Howard S; Paterno, Marilyn D; Schaeffer, Molly; Hongsermeier, Tonya; Wright, Adam; Middleton, Blackford

2013-09-01

Implement and perform pilot testing of web-based clinical decision support services using a novel framework for creating and managing clinical knowledge in a distributed fashion using the cloud. The pilot sought to (1) develop and test connectivity to an external clinical decision support (CDS) service, (2) assess the exchange of data to and knowledge from the external CDS service, and (3) capture lessons to guide expansion to more practice sites and users. The Clinical Decision Support Consortium created a repository of shared CDS knowledge for managing hypertension, diabetes, and coronary artery disease in a community cloud hosted by Partners HealthCare. A limited data set for primary care patients at a separate health system was securely transmitted to a CDS rules engine hosted in the cloud. Preventive care reminders triggered by the limited data set were returned for display to clinician end users for review and display. During a pilot study, we (1) monitored connectivity and system performance, (2) studied the exchange of data and decision support reminders between the two health systems, and (3) captured lessons. During the six month pilot study, there were 1339 patient encounters in which information was successfully exchanged. Preventive care reminders were displayed during 57% of patient visits, most often reminding physicians to monitor blood pressure for hypertensive patients (29%) and order eye exams for patients with diabetes (28%). Lessons learned were grouped into five themes: performance, governance, semantic interoperability, ongoing adjustments, and usability. Remote, asynchronous cloud-based decision support performed reasonably well, although issues concerning governance, semantic interoperability, and usability remain key challenges for successful adoption and use of cloud-based CDS that will require collaboration between biomedical informatics and computer science disciplines. Decision support in the cloud is feasible and may be a reasonable path toward achieving better support of clinical decision-making across the widest range of health care providers. Published by Elsevier B.V.

Determining flexor-tendon repair techniques via soft computing

NASA Technical Reports Server (NTRS)

Johnson, M.; Firoozbakhsh, K.; Moniem, M.; Jamshidi, M.

2001-01-01

An SC-based multi-objective decision-making method for determining the optimal flexor-tendon repair technique from experimental and clinical survey data, and with variable circumstances, was presented. Results were compared with those from the Taguchi method. Using the Taguchi method results in the need to perform ad-hoc decisions when the outcomes for individual objectives are contradictory to a particular preference or circumstance, whereas the SC-based multi-objective technique provides a rigorous straightforward computational process in which changing preferences and importance of differing objectives are easily accommodated. Also, adding more objectives is straightforward and easily accomplished. The use of fuzzy-set representations of information categories provides insight into their performance throughout the range of their universe of discourse. The ability of the technique to provide a "best" medical decision given a particular physician, hospital, patient, situation, and other criteria was also demonstrated.

Determining flexor-tendon repair techniques via soft computing.

PubMed

Johnson, M; Firoozbakhsh, K; Moniem, M; Jamshidi, M

2001-01-01

An SC-based multi-objective decision-making method for determining the optimal flexor-tendon repair technique from experimental and clinical survey data, and with variable circumstances, was presented. Results were compared with those from the Taguchi method. Using the Taguchi method results in the need to perform ad-hoc decisions when the outcomes for individual objectives are contradictory to a particular preference or circumstance, whereas the SC-based multi-objective technique provides a rigorous straightforward computational process in which changing preferences and importance of differing objectives are easily accommodated. Also, adding more objectives is straightforward and easily accomplished. The use of fuzzy-set representations of information categories provides insight into their performance throughout the range of their universe of discourse. The ability of the technique to provide a "best" medical decision given a particular physician, hospital, patient, situation, and other criteria was also demonstrated.

The fuzzy cube and causal efficacy: representation of concomitant mechanisms in stroke.

PubMed

Jobe, Thomas H.; Helgason, Cathy M.

1998-04-01

Twentieth century medical science has embraced nineteenth century Boolean probability theory based upon two-valued Aristotelian logic. With the later addition of bit-based, von Neumann structured computational architectures, an epistemology based on randomness has led to a bivalent epidemiological methodology that dominates medical decision making. In contrast, fuzzy logic, based on twentieth century multi-valued logic, and computational structures that are content addressed and adaptively modified, has advanced a new scientific paradigm for the twenty-first century. Diseases such as stroke involve multiple concomitant causal factors that are difficult to represent using conventional statistical methods. We tested which paradigm best represented this complex multi-causal clinical phenomenon-stroke. We show that the fuzzy logic paradigm better represented clinical complexity in cerebrovascular disease than current probability theory based methodology. We believe this finding is generalizable to all of clinical science since multiple concomitant causal factors are involved in nearly all known pathological processes.

Development of a clinical prediction model to calculate patient life expectancy: the measure of actuarial life expectancy (MALE).

PubMed

Clarke, M G; Kennedy, K P; MacDonagh, R P

2009-01-01

To develop a clinical prediction model enabling the calculation of an individual patient's life expectancy (LE) and survival probability based on age, sex, and comorbidity for use in the joint decision-making process regarding medical treatment. A computer software program was developed with a team of 3 clinicians, 2 professional actuaries, and 2 professional computer programmers. This incorporated statistical spreadsheet and database access design methods. Data sources included life insurance industry actuarial rating factor tables (public and private domain), Government Actuary Department UK life tables, professional actuarial sources, and evidence-based medical literature. The main outcome measures were numerical and graphical display of comorbidity-adjusted LE; 5-, 10-, and 15-year survival probability; in addition to generic UK population LE. Nineteen medical conditions, which impacted significantly on LE in actuarial terms and were commonly encountered in clinical practice, were incorporated in the final model. Numerical and graphical representations of statistical predictions of LE and survival probability were successfully generated for patients with either no comorbidity or a combination of the 19 medical conditions included. Validation and testing, including actuarial peer review, confirmed consistency with the data sources utilized. The evidence-based actuarial data utilized in this computer program design represent a valuable resource for use in the clinical decision-making process, where an accurate objective assessment of patient LE can so often make the difference between patients being offered or denied medical and surgical treatment. Ongoing development to incorporate additional comorbidities and enable Web-based access will enhance its use further.

An approach to quality and performance control in a computer-assisted clinical chemistry laboratory.

PubMed Central

Undrill, P E; Frazer, S C

1979-01-01

A locally developed, computer-based clinical chemistry laboratory system has been in operation since 1970. This utilises a Digital Equipment Co Ltd PDP 12 and an interconnected PDP 8/F computer. Details are presented of the performance and quality control techniques incorporated into the system. Laboratory performance is assessed through analysis of results from fixed-level control sera as well as from cumulative sum methods. At a simple level the presentation may be considered purely indicative, while at a more sophisticated level statistical concepts have been introduced to aid the laboratory controller in decision-making processes. PMID:438340

Objective Assessment of the Interfrontal Angle for Severity Grading and Operative Decision-Making in Metopic Synostosis.

PubMed

Anolik, Rachel A; Allori, Alexander C; Pourtaheri, Navid; Rogers, Gary F; Marcus, Jeffrey R

2016-05-01

The purpose of this study was to evaluate the utility of a previously validated interfrontal angle for classification of severity of metopic synostosis and as an aid to operative decision-making. An expert panel was asked to study 30 cases ranging from minor to severe metopic synostosis. Based on computed tomographic images of the skull and clinical photographs, they classified the severity of trigonocephaly (1 = normal, 2 = mild, 3 = moderate, and 4 = severe) and management (0 = nonoperative and 1 = operative). The severity scores and management reported by experts were then pooled and matched with the interfrontal angle computed from each respective computed tomographic scan. A threshold was identified at which most experts agree on operative management. Expert severity scores were higher for more acute interfrontal angles. There was a high concordance at the extremes of classifications, severe (4) and normal (1) (p < 0.0001); however, between interfrontal angles of 114.3 and 136.1 degrees, there exists a "gray zone," with severe discordance in expert rankings. An operative threshold of 118.2 degrees was identified, with the interfrontal angle able to predict the expert panel's decision to proceed with surgery 87.6 percent of the time. The interfrontal angle has been previously validated as a simple, accurate, and reproducible means for diagnosing trigonocephaly, but must be obtained from computed tomographic data. In this article, the authors demonstrate that the interfrontal angle can be used to further characterize the severity of trigonocephaly. It also correlated with expert decision-making for operative versus nonoperative management. This tool may be used as an adjunct to clinical decision-making when the decision to proceed with surgery may not be straightforward. Diagnostic, V.

Formal Representations of Eligibility Criteria: A Literature Review

PubMed Central

Weng, Chunhua; Tu, Samson W.; Sim, Ida; Richesson, Rachel

2010-01-01

Standards-based, computable knowledge representations for eligibility criteria are increasingly needed to provide computer-based decision support for automated research participant screening, clinical evidence application, and clinical research knowledge management. We surveyed the literature and identified five aspects of eligibility criteria knowledge representations that contribute to the various research and clinical applications: the intended use of computable eligibility criteria, the classification of eligibility criteria, the expression language for representing eligibility rules, the encoding of eligibility concepts, and the modeling of patient data. We consider three of them (expression language, codification of eligibility concepts, and patient data modeling), to be essential constructs of a formal knowledge representation for eligibility criteria. The requirements for each of the three knowledge constructs vary for different use cases, which therefore should inform the development and choice of the constructs toward cost-effective knowledge representation efforts. We discuss the implications of our findings for standardization efforts toward sharable knowledge representation of eligibility criteria. PMID:20034594

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

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

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.

Two decision aids for mode of delivery among women with previous caesarean section: randomised controlled trial.

PubMed

Montgomery, Alan A; Emmett, Clare L; Fahey, Tom; Jones, Claire; Ricketts, Ian; Patel, Roshni R; Peters, Tim J; Murphy, Deirdre J

2007-06-23

To determine the effects of two computer based decision aids on decisional conflict and mode of delivery among pregnant women with a previous caesarean section. Randomised trial, conducted from May 2004 to August 2006. Four maternity units in south west England, and Scotland. 742 pregnant women with one previous lower segment caesarean section and delivery expected at >or=37 weeks. Non-English speakers were excluded. Usual care: standard care given by obstetric and midwifery staff. Information programme: women navigated through descriptions and probabilities of clinical outcomes for mother and baby associated with planned vaginal birth, elective caesarean section, and emergency caesarean section. Decision analysis: mode of delivery was recommended based on utility assessments performed by the woman combined with probabilities of clinical outcomes within a concealed decision tree. Both interventions were delivered via a laptop computer after brief instructions from a researcher. Total score on decisional conflict scale, and mode of delivery. Women in the information programme (adjusted difference -6.2, 95% confidence interval -8.7 to -3.7) and the decision analysis (-4.0, -6.5 to -1.5) groups had reduced decisional conflict compared with women in the usual care group. The rate of vaginal birth was higher for women in the decision analysis group compared with the usual care group (37% v 30%, adjusted odds ratio 1.42, 0.94 to 2.14), but the rates were similar in the information programme and usual care groups. Decision aids can help women who have had a previous caesarean section to decide on mode of delivery in a subsequent pregnancy. The decision analysis approach might substantially affect national rates of caesarean section. Trial Registration Current Controlled Trials ISRCTN84367722.

Heart failure analysis dashboard for patient's remote monitoring combining multiple artificial intelligence technologies.

PubMed

Guidi, G; Pettenati, M C; Miniati, R; Iadanza, E

2012-01-01

In this paper we describe an Heart Failure analysis Dashboard that, combined with a handy device for the automatic acquisition of a set of patient's clinical parameters, allows to support telemonitoring functions. The Dashboard's intelligent core is a Computer Decision Support System designed to assist the clinical decision of non-specialist caring personnel, and it is based on three functional parts: Diagnosis, Prognosis, and Follow-up management. Four Artificial Intelligence-based techniques are compared for providing diagnosis function: a Neural Network, a Support Vector Machine, a Classification Tree and a Fuzzy Expert System whose rules are produced by a Genetic Algorithm. State of the art algorithms are used to support a score-based prognosis function. The patient's Follow-up is used to refine the diagnosis.

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.

Reinforcement learning in depression: A review of computational research.

PubMed

Chen, Chong; Takahashi, Taiki; Nakagawa, Shin; Inoue, Takeshi; Kusumi, Ichiro

2015-08-01

Despite being considered primarily a mood disorder, major depressive disorder (MDD) is characterized by cognitive and decision making deficits. Recent research has employed computational models of reinforcement learning (RL) to address these deficits. The computational approach has the advantage in making explicit predictions about learning and behavior, specifying the process parameters of RL, differentiating between model-free and model-based RL, and the computational model-based functional magnetic resonance imaging and electroencephalography. With these merits there has been an emerging field of computational psychiatry and here we review specific studies that focused on MDD. Considerable evidence suggests that MDD is associated with impaired brain signals of reward prediction error and expected value ('wanting'), decreased reward sensitivity ('liking') and/or learning (be it model-free or model-based), etc., although the causality remains unclear. These parameters may serve as valuable intermediate phenotypes of MDD, linking general clinical symptoms to underlying molecular dysfunctions. We believe future computational research at clinical, systems, and cellular/molecular/genetic levels will propel us toward a better understanding of the disease. Copyright © 2015 Elsevier Ltd. All rights reserved.

An in silico method to identify computer-based protocols worthy of clinical study: An insulin infusion protocol use case

PubMed Central

Wong, Anthony F; Pielmeier, Ulrike; Haug, Peter J; Andreassen, Steen

2016-01-01

Objective Develop an efficient non-clinical method for identifying promising computer-based protocols for clinical study. An in silico comparison can provide information that informs the decision to proceed to a clinical trial. The authors compared two existing computer-based insulin infusion protocols: eProtocol-insulin from Utah, USA, and Glucosafe from Denmark. Materials and Methods The authors used eProtocol-insulin to manage intensive care unit (ICU) hyperglycemia with intravenous (IV) insulin from 2004 to 2010. Recommendations accepted by the bedside clinicians directly link the subsequent blood glucose values to eProtocol-insulin recommendations and provide a unique clinical database. The authors retrospectively compared in silico 18 984 eProtocol-insulin continuous IV insulin infusion rate recommendations from 408 ICU patients with those of Glucosafe, the candidate computer-based protocol. The subsequent blood glucose measurement value (low, on target, high) was used to identify if the insulin recommendation was too high, on target, or too low. Results Glucosafe consistently provided more favorable continuous IV insulin infusion rate recommendations than eProtocol-insulin for on target (64% of comparisons), low (80% of comparisons), or high (70% of comparisons) blood glucose. Aggregated eProtocol-insulin and Glucosafe continuous IV insulin infusion rates were clinically similar though statistically significantly different (Wilcoxon signed rank test P = .01). In contrast, when stratified by low, on target, or high subsequent blood glucose measurement, insulin infusion rates from eProtocol-insulin and Glucosafe were statistically significantly different (Wilcoxon signed rank test, P < .001), and clinically different. Discussion This in silico comparison appears to be an efficient nonclinical method for identifying promising computer-based protocols. Conclusion Preclinical in silico comparison analytical framework allows rapid and inexpensive identification of computer-based protocol care strategies that justify expensive and burdensome clinical trials. PMID:26228765

Fracture risk assessment: improved evaluation of vertebral integrity among metastatic cancer patients to aid in surgical decision-making

NASA Astrophysics Data System (ADS)

Augustine, Kurt E.; Camp, Jon J.; Holmes, David R.; Huddleston, Paul M.; Lu, Lichun; Yaszemski, Michael J.; Robb, Richard A.

2012-03-01

Failure of the spine's structural integrity from metastatic disease can lead to both pain and neurologic deficit. Fractures that require treatment occur in over 30% of bony metastases. Our objective is to use computed tomography (CT) in conjunction with analytic techniques that have been previously developed to predict fracture risk in cancer patients with metastatic disease to the spine. Current clinical practice for cancer patients with spine metastasis often requires an empirical decision regarding spinal reconstructive surgery. Early image-based software systems used for CT analysis are time consuming and poorly suited for clinical application. The Biomedical Image Resource (BIR) at Mayo Clinic, Rochester has developed an image analysis computer program that calculates from CT scans, the residual load-bearing capacity in a vertebra with metastatic cancer. The Spine Cancer Assessment (SCA) program is built on a platform designed for clinical practice, with a workflow format that allows for rapid selection of patient CT exams, followed by guided image analysis tasks, resulting in a fracture risk report. The analysis features allow the surgeon to quickly isolate a single vertebra and obtain an immediate pre-surgical multiple parallel section composite beam fracture risk analysis based on algorithms developed at Mayo Clinic. The analysis software is undergoing clinical validation studies. We expect this approach will facilitate patient management and utilization of reliable guidelines for selecting among various treatment option based on fracture risk.

Creation of an Open Framework for Point-of-Care Computer-Assisted Reporting and Decision Support Tools for Radiologists.

PubMed

Alkasab, Tarik K; Bizzo, Bernardo C; Berland, Lincoln L; Nair, Sujith; Pandharipande, Pari V; Harvey, H Benjamin

2017-09-01

Decreasing unnecessary variation in radiology reporting and producing guideline-concordant reports is fundamental to radiology's success in value-based payment models and good for patient care. In this article, we present an open authoring system for point-of-care clinical decision support tools integrated into the radiologist reporting environment referred to as the computer-assisted reporting and decision support (CAR/DS) framework. The CAR/DS authoring system, described herein, includes: (1) a definition format for representing radiology clinical guidelines as structured, machine-readable Extensible Markup Language documents and (2) a user-friendly reference implementation to test the fidelity of the created definition files with the clinical guideline. The proposed definition format and reference implementation will enable content creators to develop CAR/DS tools that voice recognition software (VRS) vendors can use to extend the commercial tools currently in use. In making the definition format and reference implementation software freely available, we hope to empower individual radiologists, expert groups such as the ACR, and VRS vendors to develop a robust ecosystem of CAR/DS tools that can further improve the quality and efficiency of the patient care that our field provides. We hope that this initial effort can serve as the basis for a community-owned open standard for guideline definition that the imaging informatics and VRS vendor communities will embrace and strengthen. To this end, the ACR Assist™ initiative is intended to make the College's clinical content, including the Incidental Findings Committee White Papers, available for decision support tool creation based upon the herein described CAR/DS framework. Copyright © 2017 American College of Radiology. Published by Elsevier Inc. All rights reserved.

Perceived Barriers and Facilitators of Using a Web-Based Interactive Decision Aid for Colorectal Cancer Screening in Community Practice Settings: Findings From Focus Groups With Primary Care Clinicians and Medical Office Staff

PubMed Central

2013-01-01

Background Information is lacking about the capacity of those working in community practice settings to utilize health information technology for colorectal cancer screening. Objective To address this gap we asked those working in community practice settings to share their perspectives about how the implementation of a Web-based patient-led decision aid might affect patient-clinician conversations about colorectal cancer screening and the day-to-day clinical workflow. Methods Five focus groups in five community practice settings were conducted with 8 physicians, 1 physician assistant, and 18 clinic staff. Focus groups were organized using a semistructured discussion guide designed to identify factors that mediate and impede the use of a Web-based decision aid intended to clarify patient preferences for colorectal cancer screening and to trigger shared decision making during the clinical encounter. Results All physicians, the physician assistant, and 8 of the 18 clinic staff were active participants in the focus groups. Clinician and staff participants from each setting reported a belief that the Web-based patient-led decision aid could be an informative and educational tool; in all but one setting participants reported a readiness to recommend the tool to patients. The exception related to clinicians from one clinic who described a preference for patients having fewer screening choices, noting that a colonoscopy was the preferred screening modality for patients in their clinic. Perceived barriers to utilizing the Web-based decision aid included patients’ lack of Internet access or low computer literacy, and potential impediments to the clinics’ daily workflow. Expanding patients’ use of an online decision aid that is both easy to access and understand and that is utilized by patients outside of the office visit was described as a potentially efficient means for soliciting patients’ screening preferences. Participants described that a system to link the online decision aid to a computerized reminder system could promote a better understanding of patients’ screening preferences, though some expressed concern that such a system could be difficult to keep up and running. Conclusions Community practice clinicians and staff perceived the Web-based decision aid technology as promising but raised questions as to how the technology and resultant information would be integrated into their daily practice workflow. Additional research investigating how to best implement online decision aids should be conducted prior to the widespread adoption of such technology so as to maximize the benefits of the technology while minimizing workflow disruptions. PMID:24351420

Launching a virtual decision lab: development and field-testing of a web-based patient decision support research platform.

PubMed

Hoffman, Aubri S; Llewellyn-Thomas, Hilary A; Tosteson, Anna N A; O'Connor, Annette M; Volk, Robert J; Tomek, Ivan M; Andrews, Steven B; Bartels, Stephen J

2014-12-12

Over 100 trials show that patient decision aids effectively improve patients' information comprehension and values-based decision making. However, gaps remain in our understanding of several fundamental and applied questions, particularly related to the design of interactive, personalized decision aids. This paper describes an interdisciplinary development process for, and early field testing of, a web-based patient decision support research platform, or virtual decision lab, to address these questions. An interdisciplinary stakeholder panel designed the web-based research platform with three components: a) an introduction to shared decision making, b) a web-based patient decision aid, and c) interactive data collection items. Iterative focus groups provided feedback on paper drafts and online prototypes. A field test assessed a) feasibility for using the research platform, in terms of recruitment, usage, and acceptability; and b) feasibility of using the web-based decision aid component, compared to performance of a videobooklet decision aid in clinical care. This interdisciplinary, theory-based, patient-centered design approach produced a prototype for field-testing in six months. Participants (n = 126) reported that: the decision aid component was easy to use (98%), information was clear (90%), the length was appropriate (100%), it was appropriately detailed (90%), and it held their interest (97%). They spent a mean of 36 minutes using the decision aid and 100% preferred using their home/library computer. Participants scored a mean of 75% correct on the Decision Quality, Knowledge Subscale, and 74 out of 100 on the Preparation for Decision Making Scale. Completing the web-based decision aid reduced mean Decisional Conflict scores from 31.1 to 19.5 (p < 0.01). Combining decision science and health informatics approaches facilitated rapid development of a web-based patient decision support research platform that was feasible for use in research studies in terms of recruitment, acceptability, and usage. Within this platform, the web-based decision aid component performed comparably with the videobooklet decision aid used in clinical practice. Future studies may use this interactive research platform to study patients' decision making processes in real-time, explore interdisciplinary approaches to designing web-based decision aids, and test strategies for tailoring decision support to meet patients' needs and preferences.

The Morningside Initiative: Collaborative Development of a Knowledge Repository to Accelerate Adoption of Clinical Decision Support

DTIC Science & Technology

2010-01-01

Comparative Effectiveness Research, or other efforts to determine best practices and to develop guidelines based on meta-analysis and evidence - based medicine . An...authoritative reviews or other evidence - based medicine sources, but they have been made unambiguous and computable – a process which sounds...best practice recommendation created through an evidence - based medicine (EBM) development process. The lifecycle envisions four stages of refinement

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.

Architecture Design of Healthcare Software-as-a-Service Platform for Cloud-Based Clinical Decision Support Service

PubMed Central

Oh, Sungyoung; Cha, Jieun; Ji, Myungkyu; Kang, Hyekyung; Kim, Seok; Heo, Eunyoung; Han, Jong Soo; Kang, Hyunggoo; Chae, Hoseok; Hwang, Hee

2015-01-01

Objectives To design a cloud computing-based Healthcare Software-as-a-Service (SaaS) Platform (HSP) for delivering healthcare information services with low cost, high clinical value, and high usability. Methods We analyzed the architecture requirements of an HSP, including the interface, business services, cloud SaaS, quality attributes, privacy and security, and multi-lingual capacity. For cloud-based SaaS services, we focused on Clinical Decision Service (CDS) content services, basic functional services, and mobile services. Microsoft's Azure cloud computing for Infrastructure-as-a-Service (IaaS) and Platform-as-a-Service (PaaS) was used. Results The functional and software views of an HSP were designed in a layered architecture. External systems can be interfaced with the HSP using SOAP and REST/JSON. The multi-tenancy model of the HSP was designed as a shared database, with a separate schema for each tenant through a single application, although healthcare data can be physically located on a cloud or in a hospital, depending on regulations. The CDS services were categorized into rule-based services for medications, alert registration services, and knowledge services. Conclusions We expect that cloud-based HSPs will allow small and mid-sized hospitals, in addition to large-sized hospitals, to adopt information infrastructures and health information technology with low system operation and maintenance costs. PMID:25995962

Medication-related clinical decision support in computerized provider order entry systems: a review.

PubMed

Kuperman, Gilad J; Bobb, Anne; Payne, Thomas H; Avery, Anthony J; Gandhi, Tejal K; Burns, Gerard; Classen, David C; Bates, David W

2007-01-01

While medications can improve patients' health, the process of prescribing them is complex and error prone, and medication errors cause many preventable injuries. Computer provider order entry (CPOE) with clinical decision support (CDS), can improve patient safety and lower medication-related costs. To realize the medication-related benefits of CDS within CPOE, one must overcome significant challenges. Healthcare organizations implementing CPOE must understand what classes of CDS their CPOE systems can support, assure that clinical knowledge underlying their CDS systems is reasonable, and appropriately represent electronic patient data. These issues often influence to what extent an institution will succeed with its CPOE implementation and achieve its desired goals. Medication-related decision support is probably best introduced into healthcare organizations in two stages, basic and advanced. Basic decision support includes drug-allergy checking, basic dosing guidance, formulary decision support, duplicate therapy checking, and drug-drug interaction checking. Advanced decision support includes dosing support for renal insufficiency and geriatric patients, guidance for medication-related laboratory testing, drug-pregnancy checking, and drug-disease contraindication checking. In this paper, the authors outline some of the challenges associated with both basic and advanced decision support and discuss how those challenges might be addressed. The authors conclude with summary recommendations for delivering effective medication-related clinical decision support addressed to healthcare organizations, application and knowledge base vendors, policy makers, and researchers.

Features of Computer-Based Decision Aids: Systematic Review, Thematic Synthesis, and Meta-Analyses.

PubMed

Syrowatka, Ania; Krömker, Dörthe; Meguerditchian, Ari N; Tamblyn, Robyn

2016-01-26

Patient information and education, such as decision aids, are gradually moving toward online, computer-based environments. Considerable research has been conducted to guide content and presentation of decision aids. However, given the relatively new shift to computer-based support, little attention has been given to how multimedia and interactivity can improve upon paper-based decision aids. The first objective of this review was to summarize published literature into a proposed classification of features that have been integrated into computer-based decision aids. Building on this classification, the second objective was to assess whether integration of specific features was associated with higher-quality decision making. Relevant studies were located by searching MEDLINE, Embase, CINAHL, and CENTRAL databases. The review identified studies that evaluated computer-based decision aids for adults faced with preference-sensitive medical decisions and reported quality of decision-making outcomes. A thematic synthesis was conducted to develop the classification of features. Subsequently, meta-analyses were conducted based on standardized mean differences (SMD) from randomized controlled trials (RCTs) that reported knowledge or decisional conflict. Further subgroup analyses compared pooled SMDs for decision aids that incorporated a specific feature to other computer-based decision aids that did not incorporate the feature, to assess whether specific features improved quality of decision making. Of 3541 unique publications, 58 studies met the target criteria and were included in the thematic synthesis. The synthesis identified six features: content control, tailoring, patient narratives, explicit values clarification, feedback, and social support. A subset of 26 RCTs from the thematic synthesis was used to conduct the meta-analyses. As expected, computer-based decision aids performed better than usual care or alternative aids; however, some features performed better than others. Integration of content control improved quality of decision making (SMD 0.59 vs 0.23 for knowledge; SMD 0.39 vs 0.29 for decisional conflict). In contrast, tailoring reduced quality of decision making (SMD 0.40 vs 0.71 for knowledge; SMD 0.25 vs 0.52 for decisional conflict). Similarly, patient narratives also reduced quality of decision making (SMD 0.43 vs 0.65 for knowledge; SMD 0.17 vs 0.46 for decisional conflict). Results were varied for different types of explicit values clarification, feedback, and social support. Integration of media rich or interactive features into computer-based decision aids can improve quality of preference-sensitive decision making. However, this is an emerging field with limited evidence to guide use. The systematic review and thematic synthesis identified features that have been integrated into available computer-based decision aids, in an effort to facilitate reporting of these features and to promote integration of such features into decision aids. The meta-analyses and associated subgroup analyses provide preliminary evidence to support integration of specific features into future decision aids. Further research can focus on clarifying independent contributions of specific features through experimental designs and refining the designs of features to improve effectiveness.

Computer-assisted adjuncts for aneurysmal morphologic assessment: toward more precise and accurate approaches

NASA Astrophysics Data System (ADS)

Rajabzadeh-Oghaz, Hamidreza; Varble, Nicole; Davies, Jason M.; Mowla, Ashkan; Shakir, Hakeem J.; Sonig, Ashish; Shallwani, Hussain; Snyder, Kenneth V.; Levy, Elad I.; Siddiqui, Adnan H.; Meng, Hui

2017-03-01

Neurosurgeons currently base most of their treatment decisions for intracranial aneurysms (IAs) on morphological measurements made manually from 2D angiographic images. These measurements tend to be inaccurate because 2D measurements cannot capture the complex geometry of IAs and because manual measurements are variable depending on the clinician's experience and opinion. Incorrect morphological measurements may lead to inappropriate treatment strategies. In order to improve the accuracy and consistency of morphological analysis of IAs, we have developed an image-based computational tool, AView. In this study, we quantified the accuracy of computer-assisted adjuncts of AView for aneurysmal morphologic assessment by performing measurement on spheres of known size and anatomical IA models. AView has an average morphological error of 0.56% in size and 2.1% in volume measurement. We also investigate the clinical utility of this tool on a retrospective clinical dataset and compare size and neck diameter measurement between 2D manual and 3D computer-assisted measurement. The average error was 22% and 30% in the manual measurement of size and aneurysm neck diameter, respectively. Inaccuracies due to manual measurements could therefore lead to wrong treatment decisions in 44% and inappropriate treatment strategies in 33% of the IAs. Furthermore, computer-assisted analysis of IAs improves the consistency in measurement among clinicians by 62% in size and 82% in neck diameter measurement. We conclude that AView dramatically improves accuracy for morphological analysis. These results illustrate the necessity of a computer-assisted approach for the morphological analysis of IAs.

Impaired Flexible Reward-Based Decision-Making in Binge Eating Disorder: Evidence from Computational Modeling and Functional Neuroimaging.

PubMed

Reiter, Andrea M F; Heinze, Hans-Jochen; Schlagenhauf, Florian; Deserno, Lorenz

2017-02-01

Despite its clinical relevance and the recent recognition as a diagnostic category in the DSM-5, binge eating disorder (BED) has rarely been investigated from a cognitive neuroscientific perspective targeting a more precise neurocognitive profiling of the disorder. BED patients suffer from a lack of behavioral control during recurrent binge eating episodes and thus fail to adapt their behavior in the face of negative consequences, eg, high risk for obesity. To examine impairments in flexible reward-based decision-making, we exposed BED patients (n=22) and matched healthy individuals (n=22) to a reward-guided decision-making task during functional resonance imaging (fMRI). Performing fMRI analysis informed via computational modeling of choice behavior, we were able to identify specific signatures of altered decision-making in BED. On the behavioral level, we observed impaired behavioral adaptation in BED, which was due to enhanced switching behavior, a putative deficit in striking a balance between exploration and exploitation appropriately. This was accompanied by diminished activation related to exploratory decisions in the anterior insula/ventro-lateral prefrontal cortex. Moreover, although so-called model-free reward prediction errors remained intact, representation of ventro-medial prefrontal learning signatures, incorporating inference on unchosen options, was reduced in BED, which was associated with successful decision-making in the task. On the basis of a computational psychiatry account, the presented findings contribute to defining a neurocognitive phenotype of BED.

Impaired Flexible Reward-Based Decision-Making in Binge Eating Disorder: Evidence from Computational Modeling and Functional Neuroimaging

PubMed Central

Reiter, Andrea M F; Heinze, Hans-Jochen; Schlagenhauf, Florian; Deserno, Lorenz

2017-01-01

Despite its clinical relevance and the recent recognition as a diagnostic category in the DSM-5, binge eating disorder (BED) has rarely been investigated from a cognitive neuroscientific perspective targeting a more precise neurocognitive profiling of the disorder. BED patients suffer from a lack of behavioral control during recurrent binge eating episodes and thus fail to adapt their behavior in the face of negative consequences, eg, high risk for obesity. To examine impairments in flexible reward-based decision-making, we exposed BED patients (n=22) and matched healthy individuals (n=22) to a reward-guided decision-making task during functional resonance imaging (fMRI). Performing fMRI analysis informed via computational modeling of choice behavior, we were able to identify specific signatures of altered decision-making in BED. On the behavioral level, we observed impaired behavioral adaptation in BED, which was due to enhanced switching behavior, a putative deficit in striking a balance between exploration and exploitation appropriately. This was accompanied by diminished activation related to exploratory decisions in the anterior insula/ventro-lateral prefrontal cortex. Moreover, although so-called model-free reward prediction errors remained intact, representation of ventro–medial prefrontal learning signatures, incorporating inference on unchosen options, was reduced in BED, which was associated with successful decision-making in the task. On the basis of a computational psychiatry account, the presented findings contribute to defining a neurocognitive phenotype of BED. PMID:27301429

SymptomCare@Home: Developing an Integrated Symptom Monitoring and Management System for Outpatients Receiving Chemotherapy.

PubMed

Beck, Susan L; Eaton, Linda H; Echeverria, Christina; Mooney, Kathi H

2017-10-01

SymptomCare@Home, an integrated symptom monitoring and management system, was designed as part of randomized clinical trials to help patients with cancer who receive chemotherapy in ambulatory clinics and often experience significant symptoms at home. An iterative design process was informed by chronic disease management theory and features of assessment and clinical decision support systems used in other diseases. Key stakeholders participated in the design process: nurse scientists, clinical experts, bioinformatics experts, and computer programmers. Especially important was input from end users, patients, and nurse practitioners participating in a series of studies testing the system. The system includes both a patient and clinician interface and fully integrates two electronic subsystems: a telephone computer-linked interactive voice response system and a Web-based Decision Support-Symptom Management System. Key features include (1) daily symptom monitoring, (2) self-management coaching, (3) alerting, and (4) nurse practitioner follow-up. The nurse practitioner is distinctively positioned to provide assessment, education, support, and pharmacologic and nonpharmacologic interventions to intensify management of poorly controlled symptoms at home. SymptomCare@Home is a model for providing telehealth. The system facilitates using evidence-based guidelines as part of a comprehensive symptom management approach. The design process and system features can be applied to other diseases and conditions.

The effectiveness of simulation activities on the cognitive abilities of undergraduate third-year nursing students: a randomised control trial.

PubMed

Secomb, Jacinta; McKenna, Lisa; Smith, Colleen

2012-12-01

To provide evidence on the effectiveness of simulation activities on the clinical decision-making abilities of undergraduate nursing students. Based on previous research, it was hypothesised that the higher the cognitive score, the greater the ability a nursing student would have to make informed valid decisions in their clinical practice. Globally, simulation is being espoused as an education method that increases the competence of health professionals. At present, there is very little evidence to support current investment in time and resources. Following ethical approval, fifty-eight third-year undergraduate nursing students were randomised in a pretest-post-test group-parallel controlled trial. The learning environment preferences (LEP) inventory was used to test cognitive abilities in order to refute the null hypothesis that activities in computer-based simulated learning environments have a negative effect on cognitive abilities when compared with activities in skills laboratory simulated learning environments. There was no significant difference in cognitive development following two cycles of simulation activities. Therefore, it is reasonable to assume that two simulation tasks, either computer-based or laboratory-based, have no effect on an undergraduate student's ability to make clinical decisions in practice. However, there was a significant finding for non-English first-language students, which requires further investigation. More longitudinal studies that quantify the education effects of simulation on the cognitive, affective and psychomotor attributes of health science students and professionals from both English-speaking and non-English-speaking backgrounds are urgently required. It is also recommended that to achieve increased participant numbers and prevent non-participation owing to absenteeism, further studies need to be imbedded directly into curricula. This investigation confirms the effect of simulation activities on real-life clinical practice, and the comparative learning benefits with traditional clinical practice and university education remain unknown. © 2012 Blackwell Publishing Ltd.

Personalizing Drug Selection Using Advanced Clinical Decision Support

PubMed Central

Pestian, John; Spencer, Malik; Matykiewicz, Pawel; Zhang, Kejian; Vinks, Alexander A.; Glauser, Tracy

2009-01-01

This article describes the process of developing an advanced pharmacogenetics clinical decision support at one of the United States’ leading pediatric academic medical centers. This system, called CHRISTINE, combines clinical and genetic data to identify the optimal drug therapy when treating patients with epilepsy or Attention Deficit Hyperactivity Disorder. In the discussion a description of clinical decision support systems is provided, along with an overview of neurocognitive computing and how it is applied in this setting. PMID:19898682

A qualitative analysis of how advanced practice nurses use clinical decision support systems.

PubMed

Weber, Scott

2007-12-01

The purpose of this study was to generate a grounded theory that will reflect the experiences of advanced practice nurses (APNs) working as critical care nurse practitioners (NPs) and clinical nurse specialists (CNS) with computer-based decision-making systems. A study design using grounded theory qualitative research methods and convenience sampling was employed in this study. Twenty-three APNs (13 CNS and 10 NPs) were recruited from 16 critical care units located in six large urban medical centers in the U.S. Midwest. Single-structured in-depth interviews with open-ended audio-taped questions were conducted with each APN. Through this process, APNs defined what they consider to be relevant themes and patterns of clinical decision system use in their critical care practices, and they identified the interrelatedness of the conceptual categories that emerged from the results. Data were analyzed using the constant comparative analysis method of qualitative research. APN participants were predominantly female, white/non-Hispanic, had a history of access to the clinical decision system used in their critical care settings for an average of 14 months, and had attended a formal training program to learn how to use clinical decision systems. "Forecasting decision outcomes," which was defined as the voluntary process employed to forecast the outcomes of patient care decisions in critical care prior to actual decision making, was the core variable describing system use that emerged from the responses. This variable consisted of four user constructs or components: (a) users' perceptions of their initial system learning experience, (b) users' sense of how well they understand how system technology works, (c) users' understanding of how system inferences are created or derived, and (d) users' relative trust of system-derived data. Each of these categories was further described through the grounded theory research process, and the relationships between the categories were identified. The findings of this study suggest that the main reason critical care APNs choose to integrate clinical decision systems into their practices is to provide an objective, scientifically derived, technology-based backup for human forecasting of the outcomes of patient care decisions prior to their actual decision making. Implications for nursing, health care, and technology research are presented.

Clinical Decision Support Tools for Selecting Interventions for Patients with Disabling Musculoskeletal Disorders: A Scoping Review.

PubMed

Gross, Douglas P; Armijo-Olivo, Susan; Shaw, William S; Williams-Whitt, Kelly; Shaw, Nicola T; Hartvigsen, Jan; Qin, Ziling; Ha, Christine; Woodhouse, Linda J; Steenstra, Ivan A

2016-09-01

Purpose We aimed to identify and inventory clinical decision support (CDS) tools for helping front-line staff select interventions for patients with musculoskeletal (MSK) disorders. Methods We used Arksey and O'Malley's scoping review framework which progresses through five stages: (1) identifying the research question; (2) identifying relevant studies; (3) selecting studies for analysis; (4) charting the data; and (5) collating, summarizing and reporting results. We considered computer-based, and other available tools, such as algorithms, care pathways, rules and models. Since this research crosses multiple disciplines, we searched health care, computing science and business databases. Results Our search resulted in 4605 manuscripts. Titles and abstracts were screened for relevance. The reliability of the screening process was high with an average percentage of agreement of 92.3 %. Of the located articles, 123 were considered relevant. Within this literature, there were 43 CDS tools located. These were classified into 3 main areas: computer-based tools/questionnaires (n = 8, 19 %), treatment algorithms/models (n = 14, 33 %), and clinical prediction rules/classification systems (n = 21, 49 %). Each of these areas and the associated evidence are described. The state of evidentiary support for CDS tools is still preliminary and lacks external validation, head-to-head comparisons, or evidence of generalizability across different populations and settings. Conclusions CDS tools, especially those employing rapidly advancing computer technologies, are under development and of potential interest to health care providers, case management organizations and funders of care. Based on the results of this scoping review, we conclude that these tools, models and systems should be subjected to further validation before they can be recommended for large-scale implementation for managing patients with MSK disorders.

Features of Computer-Based Decision Aids: Systematic Review, Thematic Synthesis, and Meta-Analyses

PubMed Central

Krömker, Dörthe; Meguerditchian, Ari N; Tamblyn, Robyn

2016-01-01

Background Patient information and education, such as decision aids, are gradually moving toward online, computer-based environments. Considerable research has been conducted to guide content and presentation of decision aids. However, given the relatively new shift to computer-based support, little attention has been given to how multimedia and interactivity can improve upon paper-based decision aids. Objective The first objective of this review was to summarize published literature into a proposed classification of features that have been integrated into computer-based decision aids. Building on this classification, the second objective was to assess whether integration of specific features was associated with higher-quality decision making. Methods Relevant studies were located by searching MEDLINE, Embase, CINAHL, and CENTRAL databases. The review identified studies that evaluated computer-based decision aids for adults faced with preference-sensitive medical decisions and reported quality of decision-making outcomes. A thematic synthesis was conducted to develop the classification of features. Subsequently, meta-analyses were conducted based on standardized mean differences (SMD) from randomized controlled trials (RCTs) that reported knowledge or decisional conflict. Further subgroup analyses compared pooled SMDs for decision aids that incorporated a specific feature to other computer-based decision aids that did not incorporate the feature, to assess whether specific features improved quality of decision making. Results Of 3541 unique publications, 58 studies met the target criteria and were included in the thematic synthesis. The synthesis identified six features: content control, tailoring, patient narratives, explicit values clarification, feedback, and social support. A subset of 26 RCTs from the thematic synthesis was used to conduct the meta-analyses. As expected, computer-based decision aids performed better than usual care or alternative aids; however, some features performed better than others. Integration of content control improved quality of decision making (SMD 0.59 vs 0.23 for knowledge; SMD 0.39 vs 0.29 for decisional conflict). In contrast, tailoring reduced quality of decision making (SMD 0.40 vs 0.71 for knowledge; SMD 0.25 vs 0.52 for decisional conflict). Similarly, patient narratives also reduced quality of decision making (SMD 0.43 vs 0.65 for knowledge; SMD 0.17 vs 0.46 for decisional conflict). Results were varied for different types of explicit values clarification, feedback, and social support. Conclusions Integration of media rich or interactive features into computer-based decision aids can improve quality of preference-sensitive decision making. However, this is an emerging field with limited evidence to guide use. The systematic review and thematic synthesis identified features that have been integrated into available computer-based decision aids, in an effort to facilitate reporting of these features and to promote integration of such features into decision aids. The meta-analyses and associated subgroup analyses provide preliminary evidence to support integration of specific features into future decision aids. Further research can focus on clarifying independent contributions of specific features through experimental designs and refining the designs of features to improve effectiveness. PMID:26813512

Decision Support Systems and the Conflict Model of Decision Making: A Stimulus for New Computer-Assisted Careers Guidance Systems.

ERIC Educational Resources Information Center

Ballantine, R. Malcolm

Decision Support Systems (DSSs) are computer-based decision aids to use when making decisions which are partially amenable to rational decision-making procedures but contain elements where intuitive judgment is an essential component. In such situations, DSSs are used to improve the quality of decision-making. The DSS approach is based on Simon's…

Facilitating adherence to the tobacco use treatment guideline with computer-mediated decision support systems: physician and clinic office manager perspectives.

PubMed

Marcy, Theodore W; Skelly, Joan; Shiffman, Richard N; Flynn, Brian S

2005-08-01

A majority of physicians do not adhere to all the elements of the evidence-based USPHS guideline on tobacco use and dependence treatment. Among physicians and clinic office managers in Vermont we assessed perceived barriers to guideline adherence. We then assessed attitudes towards a computer-mediated clinical decision support system (CDSS) to gauge whether this type of intervention could support performance of the guideline. A random sample of 600 Vermont primary care and subspecialty physicians were surveyed with a mailed survey instrument. A separate survey instrument was mailed to the census of 93 clinic office managers. The response rates of physicians and clinic office managers were 67% and 76%, respectively. Though most physicians were aware of the guideline and had positive attitudes towards it, there was a lack of familiarity with Vermont's smoking cessation resources as 35% would refer smokers to non-existent counseling resources and only 48% would refer patients to a toll-free quit line. Time constraints and the perception that smokers are unreceptive to counseling were the two most common barriers cited by both physicians and office managers. The vast majority of physicians (92%) have access to a computer in their outpatient clinics, and 68% have used computers during the course of a patient's visit. Four of the eight information management services that a CDSS could provide were highly valued by both physicians and clinic office managers. Interventions to improve adherence to the guideline should address the inaccurate perception that smokers are unreceptive to counseling, and physicians' lack of familiarity with resources. A CDSS may improve knowledge of these resources if the design addresses cost, space, and time limitations.

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.

Evidence-based medicine: what has happened in the past 50 years?

PubMed

Mellis, Craig

2015-01-01

Although the phrase 'evidence-based medicine' (EBM) was used for the first time in the medical literature less than 25 years ago, the history of EBM goes back for centuries. What is remarkable is how popular and how globally accepted the EBM movement has become in such a short time. Many famous, past clinicians have played major roles in the disciplines that preceded EBM, particularly 'clinical epidemiology'. It soon became clear to the early EBM champions that 'evidence' was only part of the clinical decision-making process. Consequently, both clinical expertise and the patient's values and preferences were rapidly incorporated into the concept we now know as 'EBM'. The current need for high-quality, easily accessible 'evidence-based summaries' for busy clinicians is now apparent, as traditional EBM requires both considerable time and skill. Consequently, there is a progressive move away from the primary literature (such as randomised controlled trials) to systematic reviews and other 'evidence-based summaries'. The future of EBM will almost certainly involve widespread utilisation of 'clinical (computer)-based decision support systems'. © 2014 The Author. Journal of Paediatrics and Child Health © 2014 Paediatrics and Child Health Division (Royal Australasian College of Physicians).

Predicting adverse hemodynamic events in critically ill patients.

PubMed

Yoon, Joo H; Pinsky, Michael R

2018-06-01

The art of predicting future hemodynamic instability in the critically ill has rapidly become a science with the advent of advanced analytical processed based on computer-driven machine learning techniques. How these methods have progressed beyond severity scoring systems to interface with decision-support is summarized. Data mining of large multidimensional clinical time-series databases using a variety of machine learning tools has led to our ability to identify alert artifact and filter it from bedside alarms, display real-time risk stratification at the bedside to aid in clinical decision-making and predict the subsequent development of cardiorespiratory insufficiency hours before these events occur. This fast evolving filed is primarily limited by linkage of high-quality granular to physiologic rationale across heterogeneous clinical care domains. Using advanced analytic tools to glean knowledge from clinical data streams is rapidly becoming a reality whose clinical impact potential is great.

Redesign of a computerized clinical reminder for colorectal cancer screening: a human-computer interaction evaluation

PubMed Central

2011-01-01

Background Based on barriers to the use of computerized clinical decision support (CDS) learned in an earlier field study, we prototyped design enhancements to the Veterans Health Administration's (VHA's) colorectal cancer (CRC) screening clinical reminder to compare against the VHA's current CRC reminder. Methods In a controlled simulation experiment, 12 primary care providers (PCPs) used prototypes of the current and redesigned CRC screening reminder in a within-subject comparison. Quantitative measurements were based on a usability survey, workload assessment instrument, and workflow integration survey. We also collected qualitative data on both designs. Results Design enhancements to the VHA's existing CRC screening clinical reminder positively impacted aspects of usability and workflow integration but not workload. The qualitative analysis revealed broad support across participants for the design enhancements with specific suggestions for improving the reminder further. Conclusions This study demonstrates the value of a human-computer interaction evaluation in informing the redesign of information tools to foster uptake, integration into workflow, and use in clinical practice. PMID:22126324

Optimal and Nonoptimal Computer-Based Test Designs for Making Pass-Fail Decisions

ERIC Educational Resources Information Center

Hambleton, Ronald K.; Xing, Dehui

2006-01-01

Now that many credentialing exams are being routinely administered by computer, new computer-based test designs, along with item response theory models, are being aggressively researched to identify specific designs that can increase the decision consistency and accuracy of pass-fail decisions. The purpose of this study was to investigate the…

Ubiquitous computing in shared-care environments.

PubMed

Koch, S

2006-07-01

In light of future challenges, such as growing numbers of elderly, increase in chronic diseases, insufficient health care budgets and problems with staff recruitment for the health-care sector, information and communication technology (ICT) becomes a possible means to meet these challenges. Organizational changes such as the decentralization of the health-care system lead to a shift from in-hospital to both advanced and basic home health care. Advanced medical technologies provide solutions for distant home care in form of specialist consultations and home monitoring. Furthermore, the shift towards home health care will increase mobile work and the establishment of shared care teams which require ICT-based solutions that support ubiquitous information access and cooperative work. Clinical documentation and decision support systems are the main ICT-based solutions of interest in the context of ubiquitous computing for shared care environments. This paper therefore describes the prerequisites for clinical documentation and decision support at the point of care, the impact of mobility on the documentation process, and how the introduction of ICT-based solutions will influence organizations and people. Furthermore, the role of dentistry in shared-care environments is discussed and illustrated in the form of a future scenario.

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.

Computer-based physician order entry: the state of the art.

PubMed Central

Sittig, D F; Stead, W W

1994-01-01

Direct computer-based physician order entry has been the subject of debate for over 20 years. Many sites have implemented systems successfully. Others have failed outright or flirted with disaster, incurring substantial delays, cost overruns, and threatened work actions. The rationale for physician order entry includes process improvement, support of cost-conscious decision making, clinical decision support, and optimization of physicians' time. Barriers to physician order entry result from the changes required in practice patterns, roles within the care team, teaching patterns, and institutional policies. Key ingredients for successful implementation include: the system must be fast and easy to use, the user interface must behave consistently in all situations, the institution must have broad and committed involvement and direction by clinicians prior to implementation, the top leadership of the organization must be committed to the project, and a group of problem solvers and users must meet regularly to work out procedural issues. This article reviews the peer-reviewed scientific literature to present the current state of the art of computer-based physician order entry. PMID:7719793

Exploration Clinical Decision Support System: Medical Data Architecture

NASA Technical Reports Server (NTRS)

Lindsey, Tony; Shetye, Sandeep; Shaw, Tianna (Editor)

2016-01-01

The Exploration Clinical Decision Support (ECDS) System project is intended to enhance the Exploration Medical Capability (ExMC) Element for extended duration, deep-space mission planning in HRP. A major development guideline is the Risk of "Adverse Health Outcomes & Decrements in Performance due to Limitations of In-flight Medical Conditions". ECDS attempts to mitigate that Risk by providing crew-specific health information, actionable insight, crew guidance and advice based on computational algorithmic analysis. The availability of inflight health diagnostic computational methods has been identified as an essential capability for human exploration missions. Inflight electronic health data sources are often heterogeneous, and thus may be isolated or not examined as an aggregate whole. The ECDS System objective provides both a data architecture that collects and manages disparate health data, and an active knowledge system that analyzes health evidence to deliver case-specific advice. A single, cohesive space-ready decision support capability that considers all exploration clinical measurements is not commercially available at present. Hence, this Task is a newly coordinated development effort by which ECDS and its supporting data infrastructure will demonstrate the feasibility of intelligent data mining and predictive modeling as a biomedical diagnostic support mechanism on manned exploration missions. The initial step towards ground and flight demonstrations has been the research and development of both image and clinical text-based computer-aided patient diagnosis. Human anatomical images displaying abnormal/pathological features have been annotated using controlled terminology templates, marked-up, and then stored in compliance with the AIM standard. These images have been filtered and disease characterized based on machine learning of semantic and quantitative feature vectors. The next phase will evaluate disease treatment response via quantitative linear dimension biomarkers that enable image content-based retrieval and criteria assessment. In addition, a data mining engine (DME) is applied to cross-sectional adult surveys for predicting occurrence of renal calculi, ranked by statistical significance of demographics and specific food ingestion. In addition to this precursor space flight algorithm training, the DME will utilize a feature-engineering capability for unstructured clinical text classification health discovery. The ECDS backbone is a proposed multi-tier modular architecture providing data messaging protocols, storage, management and real-time patient data access. Technology demonstrations and success metrics will be finalized in FY16.

IBM's Health Analytics and Clinical Decision Support.

PubMed

Kohn, M S; Sun, J; Knoop, S; Shabo, A; Carmeli, B; Sow, D; Syed-Mahmood, T; Rapp, W

2014-08-15

This survey explores the role of big data and health analytics developed by IBM in supporting the transformation of healthcare by augmenting evidence-based decision-making. Some problems in healthcare and strategies for change are described. It is argued that change requires better decisions, which, in turn, require better use of the many kinds of healthcare information. Analytic resources that address each of the information challenges are described. Examples of the role of each of the resources are given. There are powerful analytic tools that utilize the various kinds of big data in healthcare to help clinicians make more personalized, evidenced-based decisions. Such resources can extract relevant information and provide insights that clinicians can use to make evidence-supported decisions. There are early suggestions that these resources have clinical value. As with all analytic tools, they are limited by the amount and quality of data. Big data is an inevitable part of the future of healthcare. There is a compelling need to manage and use big data to make better decisions to support the transformation of healthcare to the personalized, evidence-supported model of the future. Cognitive computing resources are necessary to manage the challenges in employing big data in healthcare. Such tools have been and are being developed. The analytic resources, themselves, do not drive, but support healthcare transformation.

Design and implementation of the standards-based personal intelligent self-management system (PICS).

PubMed

von Bargen, Tobias; Gietzelt, Matthias; Britten, Matthias; Song, Bianying; Wolf, Klaus-Hendrik; Kohlmann, Martin; Marschollek, Michael; Haux, Reinhold

2013-01-01

Against the background of demographic change and a diminishing care workforce there is a growing need for personalized decision support. The aim of this paper is to describe the design and implementation of the standards-based personal intelligent care systems (PICS). PICS makes consistent use of internationally accepted standards such as the Health Level 7 (HL7) Arden syntax for the representation of the decision logic, HL7 Clinical Document Architecture for information representation and is based on a open-source service-oriented architecture framework and a business process management system. Its functionality is exemplified for the application scenario of a patient suffering from congestive heart failure. Several vital signs sensors provide data for the decision support system, and a number of flexible communication channels are available for interaction with patient or caregiver. PICS is a standards-based, open and flexible system enabling personalized decision support. Further development will include the implementation of components on small computers and sensor nodes.

Towards public health decision support: a systematic review of bidirectional communication approaches.

PubMed

Dixon, Brian E; Gamache, Roland E; Grannis, Shaun J

2013-05-01

To summarize the literature describing computer-based interventions aimed at improving bidirectional communication between clinical and public health. A systematic review of English articles using MEDLINE and Google Scholar. Search terms included public health, epidemiology, electronic health records, decision support, expert systems, and decision-making. Only articles that described the communication of information regarding emerging health threats from public health agencies to clinicians or provider organizations were included. Each article was independently reviewed by two authors. Ten peer-reviewed articles highlight a nascent but promising area of research and practice related to alerting clinicians about emerging threats. Current literature suggests that additional research and development in bidirectional communication infrastructure should focus on defining a coherent architecture, improving interoperability, establishing clear governance, and creating usable systems that will effectively deliver targeted, specific information to clinicians in support of patient and population decision-making. Increasingly available clinical information systems make it possible to deliver timely, relevant knowledge to frontline clinicians in support of population health. Future work should focus on developing a flexible, interoperable infrastructure for bidirectional communications capable of integrating public health knowledge into clinical systems and workflows.

A Knowledge-Based System for the Computer Assisted Diagnosis of Endoscopic Images

NASA Astrophysics Data System (ADS)

Kage, Andreas; Münzenmayer, Christian; Wittenberg, Thomas

Due to the actual demographic development the use of Computer-Assisted Diagnosis (CAD) systems becomes a more important part of clinical workflows and clinical decision making. Because changes on the mucosa of the esophagus can indicate the first stage of cancerous developments, there is a large interest to detect and correctly diagnose any such lesion. We present a knowledge-based system which is able to support a physician with the interpretation and diagnosis of endoscopic images of the esophagus. Our system is designed to support the physician directly during the examination of the patient, thus prodving diagnostic assistence at the point of care (POC). Based on an interactively marked region in an endoscopic image of interest, the system provides a diagnostic suggestion, based on an annotated reference image database. Furthermore, using relevant feedback mechanisms, the results can be enhanced interactively.

Electronic health records (EHRs): supporting ASCO's vision of cancer care.

PubMed

Yu, Peter; Artz, David; Warner, Jeremy

2014-01-01

ASCO's vision for cancer care in 2030 is built on the expanding importance of panomics and big data, and envisions enabling better health for patients with cancer by the rapid transformation of systems biology knowledge into cancer care advances. This vision will be heavily dependent on the use of health information technology for computational biology and clinical decision support systems (CDSS). Computational biology will allow us to construct models of cancer biology that encompass the complexity of cancer panomics data and provide us with better understanding of the mechanisms governing cancer behavior. The Agency for Healthcare Research and Quality promotes CDSS based on clinical practice guidelines, which are knowledge bases that grow too slowly to match the rate of panomic-derived knowledge. CDSS that are based on systems biology models will be more easily adaptable to rapid advancements and translational medicine. We describe the characteristics of health data representation, a model for representing molecular data that supports data extraction and use for panomic-based clinical research, and argue for CDSS that are based on systems biology and are algorithm-based.

Proposal for Development of EBM-CDSS (Evidence-Based Clinical Decision Support System) to Aid Prognostication in Terminally Ill Patients

DTIC Science & Technology

2011-10-01

inconsistency in the representation of the dataset. RST provides a mathematical tool for representing and reasoning about vagueness and inconsistency. Its...use of various mathematical , statistical and soft computing methodologies with the objective of identifying meaningful relationships between condition...Evidence-based Medicine and Health Outcomes Research, University of South Florida, Tampa, FL 2Department of Mathematics , Indiana University Northwest, Gary

Cone Beam Computed Tomographic imaging in orthodontics.

PubMed

Scarfe, W C; Azevedo, B; Toghyani, S; Farman, A G

2017-03-01

Over the last 15 years, cone beam computed tomographic (CBCT) imaging has emerged as an important supplemental radiographic technique for orthodontic diagnosis and treatment planning, especially in situations which require an understanding of the complex anatomic relationships and surrounding structures of the maxillofacial skeleton. CBCT imaging provides unique features and advantages to enhance orthodontic practice over conventional extraoral radiographic imaging. While it is the responsibility of each practitioner to make a decision, in tandem with the patient/family, consensus-derived, evidence-based clinical guidelines are available to assist the clinician in the decision-making process. Specific recommendations provide selection guidance based on variables such as phase of treatment, clinically-assessed treatment difficulty, the presence of dental and/or skeletal modifying conditions, and pathology. CBCT imaging in orthodontics should always be considered wisely as children have conservatively, on average, a three to five times greater radiation risk compared with adults for the same exposure. The purpose of this paper is to provide an understanding of the operation of CBCT equipment as it relates to image quality and dose, highlight the benefits of the technique in orthodontic practice, and provide guidance on appropriate clinical use with respect to radiation dose and relative risk, particularly for the paediatric patient. © 2017 Australian Dental Association.

Decision support systems for clinical radiological practice — towards the next generation

PubMed Central

Stivaros, S M; Gledson, A; Nenadic, G; Zeng, X-J; Keane, J; Jackson, A

2010-01-01

The huge amount of information that needs to be assimilated in order to keep pace with the continued advances in modern medical practice can form an insurmountable obstacle to the individual clinician. Within radiology, the recent development of quantitative imaging techniques, such as perfusion imaging, and the development of imaging-based biomarkers in modern therapeutic assessment has highlighted the need for computer systems to provide the radiological community with support for academic as well as clinical/translational applications. This article provides an overview of the underlying design and functionality of radiological decision support systems with examples tracing the development and evolution of such systems over the past 40 years. More importantly, we discuss the specific design, performance and usage characteristics that previous systems have highlighted as being necessary for clinical uptake and routine use. Additionally, we have identified particular failings in our current methodologies for data dissemination within the medical domain that must be overcome if the next generation of decision support systems is to be implemented successfully. PMID:20965900

A computerized clinical decision support system as a means of implementing depression guidelines.

PubMed

Trivedi, Madhukar H; Kern, Janet K; Grannemann, Bruce D; Altshuler, Kenneth Z; Sunderajan, Prabha

2004-08-01

The authors describe the history and current use of computerized systems for implementing treatment guidelines in general medicine as well as the development, testing, and early use of a computerized decision support system for depression treatment among "real-world" clinical settings in Texas. In 1999 health care experts from Europe and the United States met to confront the well-documented challenges of implementing treatment guidelines and to identify strategies for improvement. They suggested the integration of guidelines into computer systems that is incorporated into clinical workflow. Several studies have demonstrated improvements in physicians' adherence to guidelines when such guidelines are provided in a computerized format. Although computerized decision support systems are being used in many areas of medicine and have demonstrated improved patient outcomes, their use in psychiatric illness is limited. The authors designed and developed a computerized decision support system for the treatment of major depressive disorder by using evidence-based guidelines, transferring the knowledge gained from the Texas Medication Algorithm Project (TMAP). This computerized decision support system (CompTMAP) provides support in diagnosis, treatment, follow-up, and preventive care and can be incorporated into the clinical setting. CompTMAP has gone through extensive testing to ensure accuracy and reliability. Physician surveys have indicated a positive response to CompTMAP, although the sample was insufficient for statistical testing. CompTMAP is part of a new era of comprehensive computerized decision support systems that take advantage of advances in automation and provide more complete clinical support to physicians in clinical practice.

Application and Exploration of Big Data Mining in Clinical Medicine.

PubMed

Zhang, Yue; Guo, Shu-Li; Han, Li-Na; Li, Tie-Ling

2016-03-20

To review theories and technologies of big data mining and their application in clinical medicine. Literatures published in English or Chinese regarding theories and technologies of big data mining and the concrete applications of data mining technology in clinical medicine were obtained from PubMed and Chinese Hospital Knowledge Database from 1975 to 2015. Original articles regarding big data mining theory/technology and big data mining's application in the medical field were selected. This review characterized the basic theories and technologies of big data mining including fuzzy theory, rough set theory, cloud theory, Dempster-Shafer theory, artificial neural network, genetic algorithm, inductive learning theory, Bayesian network, decision tree, pattern recognition, high-performance computing, and statistical analysis. The application of big data mining in clinical medicine was analyzed in the fields of disease risk assessment, clinical decision support, prediction of disease development, guidance of rational use of drugs, medical management, and evidence-based medicine. Big data mining has the potential to play an important role in clinical medicine.

Evaluation of a Shared Decision-Making Intervention on the Utilization of Evidence-Based Psychotherapy in a VA Outpatient PTSD Clinic.

PubMed

Hessinger, Jonathan D; London, Melissa J; Baer, Sheila M

2017-03-13

The Veterans Health Administration (VHA) has continued to emphasize the availability, access, and utilization of high quality mental health care particularly in the treatment of posttraumatic stress disorder (PTSD). While dissemination and availability of evidence-based psychotherapies (EBPs) have only increased, treatment engagement and utilization have continued to be oft-noted challenges. Administrators, researchers, and individual clinicians have continued to develop and explore novel systemic and individualized interventions to address these issues. Pilot studies utilizing shared decision-making models to aid in veteran treatment selection have demonstrated the impact this approach may have on selection of and engagement in EBPs for PTSD. Based on these promising studies, a Department of Veterans Affairs (VA) outpatient PTSD clinic began to implement a shared-decision making intervention as part of a clinic redesign. In seeking to evaluate the impact of this intervention, archival clinical data from 1,056 veterans were reviewed by the authors for rates of treatment selection, EBP initiation, session attendance, and EBP completion. Time elapsed from consult until EBP initiation was also computed by the authors. These variables were then compared on the basis of whether the veteran received the shared-decision making intervention. Veterans who received the intervention were more likely to select and thus initiate an EBP for PTSD sooner than veterans who did not receive this intervention. Veterans, whether receiving the intervention or not, did not differ in therapy session attendance and completion. Implications of these findings and directions for future study are further discussed. (PsycINFO Database Record (c) 2017 APA, all rights reserved).

Revealing Neurocomputational Mechanisms of Reinforcement Learning and Decision-Making With the hBayesDM Package

PubMed Central

Ahn, Woo-Young; Haines, Nathaniel; Zhang, Lei

2017-01-01

Reinforcement learning and decision-making (RLDM) provide a quantitative framework and computational theories with which we can disentangle psychiatric conditions into the basic dimensions of neurocognitive functioning. RLDM offer a novel approach to assessing and potentially diagnosing psychiatric patients, and there is growing enthusiasm for both RLDM and computational psychiatry among clinical researchers. Such a framework can also provide insights into the brain substrates of particular RLDM processes, as exemplified by model-based analysis of data from functional magnetic resonance imaging (fMRI) or electroencephalography (EEG). However, researchers often find the approach too technical and have difficulty adopting it for their research. Thus, a critical need remains to develop a user-friendly tool for the wide dissemination of computational psychiatric methods. We introduce an R package called hBayesDM (hierarchical Bayesian modeling of Decision-Making tasks), which offers computational modeling of an array of RLDM tasks and social exchange games. The hBayesDM package offers state-of-the-art hierarchical Bayesian modeling, in which both individual and group parameters (i.e., posterior distributions) are estimated simultaneously in a mutually constraining fashion. At the same time, the package is extremely user-friendly: users can perform computational modeling, output visualization, and Bayesian model comparisons, each with a single line of coding. Users can also extract the trial-by-trial latent variables (e.g., prediction errors) required for model-based fMRI/EEG. With the hBayesDM package, we anticipate that anyone with minimal knowledge of programming can take advantage of cutting-edge computational-modeling approaches to investigate the underlying processes of and interactions between multiple decision-making (e.g., goal-directed, habitual, and Pavlovian) systems. In this way, we expect that the hBayesDM package will contribute to the dissemination of advanced modeling approaches and enable a wide range of researchers to easily perform computational psychiatric research within different populations. PMID:29601060

When should computed tomography angiography of pulmonary vessels be done in patients with low clinical probability of acute pulmonary embolism?

PubMed

Ródenas Quiñonero, I; Plasencia Martínez, J M; García Santos, J M

2018-02-24

When the probability of pulmonary embolism is low, the decision to do a computed tomography angiography (CTA) of the pulmonary vessels is based on the D-dimer concentration. However, excessive dependence on this parameter can result in unnecessary imaging studies, inappropriate treatment, or an inappropriate increase in the estimated probability of venous thromboembolism developing. The main objective of this study was to determine when CTA of pulmonary vessels could be avoided in patients with low clinical probability of pulmonary embolism through an efficient literature search of studies published about this question. Copyright © 2018 SERAM. Publicado por Elsevier España, S.L.U. All rights reserved.

EON: a component-based approach to automation of protocol-directed therapy.

PubMed Central

Musen, M A; Tu, S W; Das, A K; Shahar, Y

1996-01-01

Provision of automated support for planning protocol-directed therapy requires a computer program to take as input clinical data stored in an electronic patient-record system and to generate as output recommendations for therapeutic interventions and laboratory testing that are defined by applicable protocols. This paper presents a synthesis of research carried out at Stanford University to model the therapy-planning task and to demonstrate a component-based architecture for building protocol-based decision-support systems. We have constructed general-purpose software components that (1) interpret abstract protocol specifications to construct appropriate patient-specific treatment plans; (2) infer from time-stamped patient data higher-level, interval-based, abstract concepts; (3) perform time-oriented queries on a time-oriented patient database; and (4) allow acquisition and maintenance of protocol knowledge in a manner that facilitates efficient processing both by humans and by computers. We have implemented these components in a computer system known as EON. Each of the components has been developed, evaluated, and reported independently. We have evaluated the integration of the components as a composite architecture by implementing T-HELPER, a computer-based patient-record system that uses EON to offer advice regarding the management of patients who are following clinical trial protocols for AIDS or HIV infection. A test of the reuse of the software components in a different clinical domain demonstrated rapid development of a prototype application to support protocol-based care of patients who have breast cancer. PMID:8930854

Human Cognitive Limitations. Broad, Consistent, Clinical Application of Physiological Principles Will Require Decision Support.

PubMed

Morris, Alan H

2018-02-01

Our education system seems to fail to enable clinicians to broadly understand core physiological principles. The emphasis on reductionist science, including "omics" branches of research, has likely contributed to this decrease in understanding. Consequently, clinicians cannot be expected to consistently make clinical decisions linked to best physiological evidence. This is a large-scale problem with multiple determinants, within an even larger clinical decision problem: the failure of clinicians to consistently link their decisions to best evidence. Clinicians, like all human decision-makers, suffer from significant cognitive limitations. Detailed context-sensitive computer protocols can generate personalized medicine instructions that are well matched to individual patient needs over time and can partially resolve this problem.

IBM’s Health Analytics and Clinical Decision Support

PubMed Central

Sun, J.; Knoop, S.; Shabo, A.; Carmeli, B.; Sow, D.; Syed-Mahmood, T.; Rapp, W.

2014-01-01

Summary Objectives This survey explores the role of big data and health analytics developed by IBM in supporting the transformation of healthcare by augmenting evidence-based decision-making. Methods Some problems in healthcare and strategies for change are described. It is argued that change requires better decisions, which, in turn, require better use of the many kinds of healthcare information. Analytic resources that address each of the information challenges are described. Examples of the role of each of the resources are given. Results There are powerful analytic tools that utilize the various kinds of big data in healthcare to help clinicians make more personalized, evidenced-based decisions. Such resources can extract relevant information and provide insights that clinicians can use to make evidence-supported decisions. There are early suggestions that these resources have clinical value. As with all analytic tools, they are limited by the amount and quality of data. Conclusion Big data is an inevitable part of the future of healthcare. There is a compelling need to manage and use big data to make better decisions to support the transformation of healthcare to the personalized, evidence-supported model of the future. Cognitive computing resources are necessary to manage the challenges in employing big data in healthcare. Such tools have been and are being developed. The analytic resources, themselves, do not drive, but support healthcare transformation. PMID:25123736

Clinical evaluation of automated processing of electrocardiograms by the Veterans Administration program (AVA 3.4).

PubMed

Brohet, C R; Richman, H G

1979-06-01

Automated processing of electrocardiograms by the Veterans Administration program was evaluated for both agreement with physician interpretation and interpretative accuracy as assessed with nonelectrocardiographic criteria. One thousand unselected electrocardiograms were analyzed by two reviewer groups, one familiar and the other unfamiliar with the computer program. A significant number of measurement errors involving repolarization changes and left axis deviation occurred; however, interpretative disagreements related to statistical decision were largely language-related. Use of a printout with a more traditional format resulted in agreement with physician interpretation by both reviewer groups in more than 80 percent of cases. Overall sensitivity based on agreement with nonelectrocardiographic criteria was significantly greater with use of the computer program than with use of the conventional criteria utilized by the reviewers. This difference was particularly evident in the subgroup analysis of myocardial infarction and left ventricular hypertrophy. The degree of overdiagnosis of left ventricular hypertrophy and posteroinferior infarction was initially unacceptable, but this difficulty was corrected by adjustment of probabilities. Clinical acceptability of the Veterans Administration program appears to require greater physician education than that needed for other computer programs of electrocardiographic analysis; the flexibility of interpretation by statistical decision offers the potential for better diagnostic accuracy.

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

Entertainment education for breast cancer surgery decisions: a randomized trial among patients with low health literacy.

PubMed

Jibaja-Weiss, Maria L; Volk, Robert J; Granchi, Thomas S; Neff, Nancy E; Robinson, Emily K; Spann, Stephen J; Aoki, Noriaki; Friedman, Lois C; Beck, J Robert

2011-07-01

To evaluate an entertainment-based patient decision aid for early stage breast cancer surgery in low health literacy patients. Newly diagnosed female patients with early stage breast cancer from two public hospitals were randomized to receive an entertainment-based decision aid for breast cancer treatment along with usual care (intervention arm) or to receive usual care only (control arm). Pre-decision (baseline), pre-surgery, and 1-year follow-up assessments were conducted. Patients assigned to the intervention arm of the study were more likely than the controls to choose mastectomy rather than breast-conserving surgery; however, they appeared better informed and clearer about their surgical options than women assigned to the control group. No differences in satisfaction with the surgical decision or the decision-making process were observed between the patients who viewed the intervention and those assigned to the control group. Entertainment education may be a desirable strategy for informing lower health literate women about breast cancer surgery options. Incorporating patient decision aids, particularly computer-based decision aids, into standard clinical practice remains a challenge; however, patients may be directed to view programs at home or at public locations (e.g., libraries, community centers). Copyright © 2010 Elsevier Ireland Ltd. All rights reserved.

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.

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

Towards public health decision support: a systematic review of bidirectional communication approaches

PubMed Central

Dixon, Brian E; Gamache, Roland E; Grannis, Shaun J

2013-01-01

Objective To summarize the literature describing computer-based interventions aimed at improving bidirectional communication between clinical and public health. Materials and Methods A systematic review of English articles using MEDLINE and Google Scholar. Search terms included public health, epidemiology, electronic health records, decision support, expert systems, and decision-making. Only articles that described the communication of information regarding emerging health threats from public health agencies to clinicians or provider organizations were included. Each article was independently reviewed by two authors. Results Ten peer-reviewed articles highlight a nascent but promising area of research and practice related to alerting clinicians about emerging threats. Current literature suggests that additional research and development in bidirectional communication infrastructure should focus on defining a coherent architecture, improving interoperability, establishing clear governance, and creating usable systems that will effectively deliver targeted, specific information to clinicians in support of patient and population decision-making. Conclusions Increasingly available clinical information systems make it possible to deliver timely, relevant knowledge to frontline clinicians in support of population health. Future work should focus on developing a flexible, interoperable infrastructure for bidirectional communications capable of integrating public health knowledge into clinical systems and workflows. PMID:23467470

Effect of clinical decision rules, patient cost and malpractice information on clinician brain CT image ordering: a randomized controlled trial.

PubMed

Gimbel, Ronald W; Pirrallo, Ronald G; Lowe, Steven C; Wright, David W; Zhang, Lu; Woo, Min-Jae; Fontelo, Paul; Liu, Fang; Connor, Zachary

2018-03-12

The frequency of head computed tomography (CT) imaging for mild head trauma patients has raised safety and cost concerns. Validated clinical decision rules exist in the published literature and on-line sources to guide medical image ordering but are often not used by emergency department (ED) clinicians. Using simulation, we explored whether the presentation of a clinical decision rule (i.e. Canadian CT Head Rule - CCHR), findings from malpractice cases related to clinicians not ordering CT imaging in mild head trauma cases, and estimated patient out-of-pocket cost might influence clinician brain CT ordering. Understanding what type and how information may influence clinical decision making in the ordering advanced medical imaging is important in shaping the optimal design and implementation of related clinical decision support systems. Multi-center, double-blinded simulation-based randomized controlled trial. Following standardized clinical vignette presentation, clinicians made an initial imaging decision for the patient. This was followed by additional information on decision support rules, malpractice outcome review, and patient cost; each with opportunity to modify their initial order. The malpractice and cost information differed by assigned group to test the any temporal relationship. The simulation closed with a second vignette and an imaging decision. One hundred sixteen of the 167 participants (66.9%) initially ordered a brain CT scan. After CCHR presentation, the number of clinicians ordering a CT dropped to 76 (45.8%), representing a 21.1% reduction in CT ordering (P = 0.002). This reduction in CT ordering was maintained, in comparison to initial imaging orders, when presented with malpractice review information (p = 0.002) and patient cost information (p = 0.002). About 57% of clinicians changed their order during study, while 43% never modified their imaging order. This study suggests that ED clinician brain CT imaging decisions may be influenced by clinical decision support rules, patient out-of-pocket cost information and findings from malpractice case review. NCT03449862 , February 27, 2018, Retrospectively registered.

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.

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.

Application of Adaptive Decision Aiding Systems to Computer-Assisted Instruction. Final Report, January-December 1974.

ERIC Educational Resources Information Center

May, Donald M.; And Others

The minicomputer-based Computerized Diagnostic and Decision Training (CDDT) system described combines the principles of artificial intelligence, decision theory, and adaptive computer assisted instruction for training in electronic troubleshooting. The system incorporates an adaptive computer program which learns the student's diagnostic and…

Back to the Bedside: Developing a Bedside Aid for Concussion and Brain Injury Decisions in the Emergency Department

PubMed Central

Melnick, Edward R.; Lopez, Kevin; Hess, Erik P.; Abujarad, Fuad; Brandt, Cynthia A.; Shiffman, Richard N.; Post, Lori A.

2015-01-01

Context: Current information-rich electronic health record (EHR) interfaces require large, high-resolution screens running on desktop computers. This interface compromises the provider’s already limited time at the bedside by physically separating the patient from the doctor. The case study presented here describes a patient-centered clinical decision support (CDS) design process that aims to bring the physician back to the bedside by integrating a patient decision aid with CDS for shared use by the patient and provider on a touchscreen tablet computer for deciding whether or not to obtain a CT scan for minor head injury in the emergency department, a clinical scenario that could benefit from CDS but has failed previous implementation attempts. Case Description: This case study follows the user-centered design (UCD) approach to build a bedside aid that is useful and usable, and that promotes shared decision-making between patients and their providers using a tablet computer at the bedside. The patient-centered decision support design process focuses on the prototype build using agile software development, but also describes the following: (1) the requirement gathering phase including triangulated qualitative research (focus groups and cognitive task analysis) to understand current challenges, (2) features for patient education, the physician, and shared decision-making, (3) system architecture and technical requirements, and (4) future plans for formative usability testing and field testing. Lessons Learned: We share specific lessons learned and general recommendations from critical insights gained in the patient-centered decision support design process about early stakeholder engagement, EHR integration, external expert feedback, challenges to two users on a single device, project management, and accessibility. Conclusions: Successful implementation of this tool will require seamless integration into the provider’s workflow. This protocol can create an effective interface for shared decision-making and safe resource reduction at the bedside in the austere and dynamic clinical environment of the ED and is generalizable for these purposes in other clinical environments as well. PMID:26290885

Back to the Bedside: Developing a Bedside Aid for Concussion and Brain Injury Decisions in the Emergency Department.

PubMed

Melnick, Edward R; Lopez, Kevin; Hess, Erik P; Abujarad, Fuad; Brandt, Cynthia A; Shiffman, Richard N; Post, Lori A

2015-01-01

Current information-rich electronic health record (EHR) interfaces require large, high-resolution screens running on desktop computers. This interface compromises the provider's already limited time at the bedside by physically separating the patient from the doctor. The case study presented here describes a patient-centered clinical decision support (CDS) design process that aims to bring the physician back to the bedside by integrating a patient decision aid with CDS for shared use by the patient and provider on a touchscreen tablet computer for deciding whether or not to obtain a CT scan for minor head injury in the emergency department, a clinical scenario that could benefit from CDS but has failed previous implementation attempts. This case study follows the user-centered design (UCD) approach to build a bedside aid that is useful and usable, and that promotes shared decision-making between patients and their providers using a tablet computer at the bedside. The patient-centered decision support design process focuses on the prototype build using agile software development, but also describes the following: (1) the requirement gathering phase including triangulated qualitative research (focus groups and cognitive task analysis) to understand current challenges, (2) features for patient education, the physician, and shared decision-making, (3) system architecture and technical requirements, and (4) future plans for formative usability testing and field testing. We share specific lessons learned and general recommendations from critical insights gained in the patient-centered decision support design process about early stakeholder engagement, EHR integration, external expert feedback, challenges to two users on a single device, project management, and accessibility. Successful implementation of this tool will require seamless integration into the provider's workflow. This protocol can create an effective interface for shared decision-making and safe resource reduction at the bedside in the austere and dynamic clinical environment of the ED and is generalizable for these purposes in other clinical environments as well.

Accessibility, usability, and usefulness of a Web-based clinical decision support tool to enhance provider-patient communication around Self-management TO Prevent (STOP) Stroke.

PubMed

Anderson, Jane A; Godwin, Kyler M; Saleem, Jason J; Russell, Scott; Robinson, Joshua J; Kimmel, Barbara

2014-12-01

This article reports redesign strategies identified to create a Web-based user-interface for the Self-management TO Prevent (STOP) Stroke Tool. Members of a Stroke Quality Improvement Network (N = 12) viewed a visualization video of a proposed prototype and provided feedback on implementation barriers/facilitators. Stroke-care providers (N = 10) tested the Web-based prototype in think-aloud sessions of simulated clinic visits. Participants' dialogues were coded into themes. Access to comprehensive information and the automated features/systematized processes were the primary accessibility and usability facilitator themes. The need for training, time to complete the tool, and computer-centric care were identified as possible usability barriers. Patient accountability, reminders for best practice, goal-focused care, and communication/counseling themes indicate that the STOP Stroke Tool supports the paradigm of patient-centered care. The STOP Stroke Tool was found to prompt clinicians on secondary stroke-prevention clinical-practice guidelines, facilitate comprehensive documentation of evidence-based care, and support clinicians in providing patient-centered care through the shared decision-making process that occurred while using the action-planning/goal-setting feature of the tool. © The Author(s) 2013.

Teaching Advance Care Planning to Medical Students with a Computer-Based Decision Aid

PubMed Central

Levi, Benjamin H.

2013-01-01

Discussing end-of-life decisions with cancer patients is a crucial skill for physicians. This article reports findings from a pilot study evaluating the effectiveness of a computer-based decision aid for teaching medical students about advance care planning. Second-year medical students at a single medical school were randomized to use a standard advance directive or a computer-based decision aid to help patients with advance care planning. Students' knowledge, skills, and satisfaction were measured by self-report; their performance was rated by patients. 121/133 (91%) of students participated. The Decision-Aid Group (n=60) outperformed the Standard Group (n=61) in terms of students´ knowledge (p<0.01), confidence in helping patients with advance care planning (p<0.01), knowledge of what matters to patients (p=0.05), and satisfaction with their learning experience (p<0.01). Likewise, patients in the Decision Aid Group were more satisfied with the advance care planning method (p<0.01) and with several aspects of student performance. Use of a computer-based decision aid may be an effective way to teach medical students how to discuss advance care planning with cancer patients. PMID:20632222

Using Data Augmentation to Facilitate Conduct of Phase I–II Clinical Trials with Delayed Outcomes

PubMed Central

Jin, Ick Hoon; Liu, Suyu; Thall, Peter F.; Yuan, Ying

2014-01-01

A practical impediment in adaptive clinical trials is that outcomes must be observed soon enough to apply decision rules to choose treatments for new patients. For example, if outcomes take up to six weeks to evaluate and the accrual rate is one patient per week, on average three new patients will be accrued while waiting to evaluate the outcomes of the previous three patients. The question is how to treat the new patients. This logistical problem persists throughout the trial. Various ad hoc practical solutions are used, none entirely satisfactory. We focus on this problem in phase I–II clinical trials that use binary toxicity and efficacy, defined in terms of event times, to choose doses adaptively for successive cohorts. We propose a general approach to this problem that treats late-onset outcomes as missing data, uses data augmentation to impute missing outcomes from posterior predictive distributions computed from partial follow-up times and complete outcome data, and applies the design’s decision rules using the completed data. We illustrate the method with two cancer trials conducted using a phase I–II design based on efficacy-toxicity trade-offs, including a computer stimulation study. PMID:25382884

MDCT quantification is the dominant parameter in decision–making regarding chest tube drainage for stable patients with traumatic pneumothorax

PubMed Central

Cai, Wenli; Lee, June-Goo; Fikry, Karim; Yoshida, Hiroyuki; Novelline, Robert; de Moya, Marc

2013-01-01

It is commonly believed that the size of a pneumothorax is an important determinant of treatment decision, in particular regarding whether chest tube drainage (CTD) is required. However, the volumetric quantification of pneumothoraces has not routinely been performed in clinics. In this paper, we introduced an automated computer-aided volumetry (CAV) scheme for quantification of volume of pneumothoraces in chest multi-detect CT (MDCT) images. Moreover, we investigated the impact of accurate volume of pneumothoraces in the improvement of the performance in decision-making regarding CTD in the management of traumatic pneumothoraces. For this purpose, an occurrence frequency map was calculated for quantitative analysis of the importance of each clinical parameter in the decision-making regarding CTD by a computer simulation of decision-making using a genetic algorithm (GA) and a support vector machine (SVM). A total of 14 clinical parameters, including volume of pneumothorax calculated by our CAV scheme, was collected as parameters available for decision-making. The results showed that volume was the dominant parameter in decision-making regarding CTD, with an occurrence frequency value of 1.00. The results also indicated that the inclusion of volume provided the best performance that was statistically significant compared to the other tests in which volume was excluded from the clinical parameters. This study provides the scientific evidence for the application of CAV scheme in MDCT volumetric quantification of pneumothoraces in the management of clinically stable chest trauma patients with traumatic pneumothorax. PMID:22560899

MDCT quantification is the dominant parameter in decision-making regarding chest tube drainage for stable patients with traumatic pneumothorax.

PubMed

Cai, Wenli; Lee, June-Goo; Fikry, Karim; Yoshida, Hiroyuki; Novelline, Robert; de Moya, Marc

2012-07-01

It is commonly believed that the size of a pneumothorax is an important determinant of treatment decision, in particular regarding whether chest tube drainage (CTD) is required. However, the volumetric quantification of pneumothoraces has not routinely been performed in clinics. In this paper, we introduced an automated computer-aided volumetry (CAV) scheme for quantification of volume of pneumothoraces in chest multi-detect CT (MDCT) images. Moreover, we investigated the impact of accurate volume of pneumothoraces in the improvement of the performance in decision-making regarding CTD in the management of traumatic pneumothoraces. For this purpose, an occurrence frequency map was calculated for quantitative analysis of the importance of each clinical parameter in the decision-making regarding CTD by a computer simulation of decision-making using a genetic algorithm (GA) and a support vector machine (SVM). A total of 14 clinical parameters, including volume of pneumothorax calculated by our CAV scheme, was collected as parameters available for decision-making. The results showed that volume was the dominant parameter in decision-making regarding CTD, with an occurrence frequency value of 1.00. The results also indicated that the inclusion of volume provided the best performance that was statistically significant compared to the other tests in which volume was excluded from the clinical parameters. This study provides the scientific evidence for the application of CAV scheme in MDCT volumetric quantification of pneumothoraces in the management of clinically stable chest trauma patients with traumatic pneumothorax. Copyright © 2012 Elsevier Ltd. All rights reserved.

A decision-based perspective for the design of methods for systems design

NASA Technical Reports Server (NTRS)

Mistree, Farrokh; Muster, Douglas; Shupe, Jon A.; Allen, Janet K.

1989-01-01

Organization of material, a definition of decision based design, a hierarchy of decision based design, the decision support problem technique, a conceptual model design that can be manufactured and maintained, meta-design, computer-based design, action learning, and the characteristics of decisions are among the topics covered.

Hydra: A web-based system for cardiovascular analysis, diagnosis and treatment.

PubMed

Novo, J; Hermida, A; Ortega, M; Barreira, N; Penedo, M G; López, J E; Calvo, C

2017-02-01

Cardiovascular (CV) risk stratification is a highly complex process involving an extensive set of clinical trials to support the clinical decision-making process. There are many clinical conditions (e.g. diabetes, obesity, stress, etc.) that can lead to the early diagnosis or establishment of cardiovascular disease. In order to determine all these clinical conditions, a complete set of clinical patient analyses is typically performed, including a physical examination, blood analysis, electrocardiogram, blood pressure (BP) analysis, etc. This article presents a web-based system, called Hydra, which integrates a full and detailed set of services and functionalities for clinical decision support in order to help and improve the work of clinicians in cardiovascular patient diagnosis, risk assessment, treatment and monitoring over time. Hydra integrates a number of different services: a service for inputting all the information gathered by specialists (physical examination, habits, BP, blood analysis, electrocardiogram, etc.); a tool to automatically determine the CV risk stratification, including well-known standard risk stratification tables; and, finally, various tools to incorporate, analyze and graphically present the records of the ambulatory BP monitoring that provides BP analysis over a given period of time (24 or 48 hours). In addition, the platform presents a set of reports derived from all the information gathered from the patient in order to support physicians in their clinical decisions. Hydra was tested and validated in a real domain. In particular, internal medicine specialists at the Hypertension Unit of the Santiago de Compostela University Hospital (CHUS) validated the platform and used it in different clinical studies to demonstrate its utility. It was observed that the platform increased productivity and accuracy in the assessment of patient data yielding a cost reduction in clinical practice. This paper proposes a complete platform that includes different services for cardiovascular clinical decision support. It was also run as a web-based application to facilitate its use by clinicians, who can access the platform from any remote computer with Internet access. Hydra also includes different automated methods to facilitate the physicians' work and avoid potential errors in the analysis of patient data. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

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.

Biomedical Informatics for Computer-Aided Decision Support Systems: A Survey

PubMed Central

Belle, Ashwin; Kon, Mark A.; Najarian, Kayvan

2013-01-01

The volumes of current patient data as well as their complexity make clinical decision making more challenging than ever for physicians and other care givers. This situation calls for the use of biomedical informatics methods to process data and form recommendations and/or predictions to assist such decision makers. The design, implementation, and use of biomedical informatics systems in the form of computer-aided decision support have become essential and widely used over the last two decades. This paper provides a brief review of such systems, their application protocols and methodologies, and the future challenges and directions they suggest. PMID:23431259

Computer-aided detection systems to improve lung cancer early diagnosis: state-of-the-art and challenges

NASA Astrophysics Data System (ADS)

Traverso, A.; Lopez Torres, E.; Fantacci, M. E.; Cerello, P.

2017-05-01

Lung cancer is one of the most lethal types of cancer, because its early diagnosis is not good enough. In fact, the detection of pulmonary nodule, potential lung cancers, in Computed Tomography scans is a very challenging and time-consuming task for radiologists. To support radiologists, researchers have developed Computer-Aided Diagnosis (CAD) systems for the automated detection of pulmonary nodules in chest Computed Tomography scans. Despite the high level of technological developments and the proved benefits on the overall detection performance, the usage of Computer-Aided Diagnosis in clinical practice is far from being a common procedure. In this paper we investigate the causes underlying this discrepancy and present a solution to tackle it: the M5L WEB- and Cloud-based on-demand Computer-Aided Diagnosis. In addition, we prove how the combination of traditional imaging processing techniques with state-of-art advanced classification algorithms allows to build a system whose performance could be much larger than any Computer-Aided Diagnosis developed so far. This outcome opens the possibility to use the CAD as clinical decision support for radiologists.

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.

Effectiveness of a Case-Based Computer Program on Students' Ethical Decision Making.

PubMed

Park, Eun-Jun; Park, Mihyun

2015-11-01

The aim of this study was to test the effectiveness of a case-based computer program, using an integrative ethical decision-making model, on the ethical decision-making competency of nursing students in South Korea. This study used a pre- and posttest comparison design. Students in the intervention group used a computer program for case analysis assignments, whereas students in the standard group used a traditional paper assignment for case analysis. The findings showed that using the case-based computer program as a complementary tool for the ethics courses offered at the university enhanced students' ethical preparedness and satisfaction with the course. On the basis of the findings, it is recommended that nurse educators use a case-based computer program as a complementary self-study tool in ethics courses to supplement student learning without an increase in course hours, particularly in terms of analyzing ethics cases with dilemma scenarios and exercising ethical decision making. Copyright 2015, SLACK Incorporated.

Evaluation of Organizational Readiness in Clinical Settings for Social Supporting Evidence-Based Information Seeking Behavior after Introducing IT in a Developing Country.

PubMed

Kahouei, Mehdi; Alaei, Safollah; Panahi, Sohaila Sadat Ghazavi Shariat; Zadeh, Jamileh Mahdi

2015-01-01

The health sector of Iran has endeavored to encourage physicians and medical students to use research findings in their practice. Remarkable changes have occurred, including: holding computer skills courses, digital library workshops for physicians and students, and establishing websites in hospitals. The findings showed that a small number of the participants completely agreed that they were supported by supervisors and colleagues to use evidence-based information resources in their clinical decisions. Health care organizations in Iran need other organizational facilitators such as social influences, organizational support, leadership, strong organizational culture, and climate in order to implement evidence-based practice.

Adaptive Allocation of Decision Making Responsibility Between Human and Computer in Multi-Task Situations. Ph.D. Thesis

NASA Technical Reports Server (NTRS)

Chu, Y. Y.

1978-01-01

A unified formulation of computer-aided, multi-task, decision making is presented. Strategy for the allocation of decision making responsibility between human and computer is developed. The plans of a flight management systems are studied. A model based on the queueing theory was implemented.

Knowledge-based computer systems for radiotherapy planning.

PubMed

Kalet, I J; Paluszynski, W

1990-08-01

Radiation therapy is one of the first areas of clinical medicine to utilize computers in support of routine clinical decision making. The role of the computer has evolved from simple dose calculations to elaborate interactive graphic three-dimensional simulations. These simulations can combine external irradiation from megavoltage photons, electrons, and particle beams with interstitial and intracavitary sources. With the flexibility and power of modern radiotherapy equipment and the ability of computer programs that simulate anything the machinery can do, we now face a challenge to utilize this capability to design more effective radiation treatments. How can we manage the increased complexity of sophisticated treatment planning? A promising approach will be to use artificial intelligence techniques to systematize our present knowledge about design of treatment plans, and to provide a framework for developing new treatment strategies. Far from replacing the physician, physicist, or dosimetrist, artificial intelligence-based software tools can assist the treatment planning team in producing more powerful and effective treatment plans. Research in progress using knowledge-based (AI) programming in treatment planning already has indicated the usefulness of such concepts as rule-based reasoning, hierarchical organization of knowledge, and reasoning from prototypes. Problems to be solved include how to handle continuously varying parameters and how to evaluate plans in order to direct improvements.

Design and development of a mobile system for supporting emergency triage.

PubMed

Michalowski, W; Slowinski, R; Wilk, S; Farion, K J; Pike, J; Rubin, S

2005-01-01

Our objective was to design and develop a mobile clinical decision support system for emergency triage of different acute pain presentations. The system should interact with existing hospital information systems, run on mobile computing devices (handheld computers) and be suitable for operation in weak-connectivity conditions (with unstable connections between mobile clients and a server). The MET (Mobile Emergency Triage) system was designed following an extended client-server architecture. The client component, responsible for triage decision support, is built as a knowledge-based system, with domain ontology separated from generic problem solving methods and used for the automatic creation of a user interface. The MET system is well suited for operation in the Emergency Department of a hospital. The system's external interactions are managed by the server, while the MET clients, running on handheld computers are used by clinicians for collecting clinical data and supporting triage at the bedside. The functionality of the MET client is distributed into specialized modules, responsible for triaging specific types of acute pain presentations. The modules are stored on the server, and on request they can be transferred and executed on the mobile clients. The modular design provides for easy extension of the system's functionality. A clinical trial of the MET system validated the appropriateness of the system's design, and proved the usefulness and acceptance of the system in clinical practice. The MET system captures the necessary hospital data, allows for entry of patient information, and provides triage support. By operating on handheld computers, it fits into the regular emergency department workflow without introducing any hindrances or disruptions. It supports triage anytime and anywhere, directly at the point of care, and also can be used as an electronic patient chart, facilitating structured data collection.

Leveraging Electronic Tablets for General Pediatric Care

PubMed Central

McKee, S.; Dugan, T.M.; Downs, S.M.

2015-01-01

Summary Background We have previously shown that a scan-able paper based interface linked to a computerized clinical decision support system (CDSS) can effectively screen patients in pediatric waiting rooms and support the physician using evidence based care guidelines at the time of clinical encounter. However, the use of scan-able paper based interface has many inherent limitations including lacking real time communication with the CDSS and being prone to human and system errors. An electronic tablet based user interface can not only overcome these limitations, but may also support advanced functionality for clinical and research use. However, use of such devices for pediatric care is not well studied in clinical settings. Objective In this pilot study, we enhance our pediatric CDSS with an electronic tablet based user interface and evaluate it for usability as well as for changes in patient questionnaire completion rates. Methods Child Health Improvement through Computers Leveraging Electronic Tablets or CHICLET is an electronic tablet based user interface. It is developed to augment the existing scan-able paper interface to our CDSS. For the purposes of this study, we deployed CHICLET in one outpatient pediatric clinic. Usability factors for CHICLET were evaluated via caregiver and staff surveys. Results When compared to the scan-able paper based interface, we observed an 18% increase or 30% relative increase in question completion rates using CHICLET. This difference was statistically significant. Caregivers and staff survey results were positive for using CHICLET in clinical environment. Conclusions Electronic tablets are a viable interface for capturing patient self-report in pediatric waiting rooms. We further hypothesize that the use of electronic tablet based interfaces will drive advances in computerized clinical decision support and create opportunities for patient engagement. PMID:25848409

A "building block" approach to application development for education and decision support in radiology: implications for integrated clinical information systems environments.

PubMed

Greenes, R A

1991-11-01

Education and decision-support resources useful to radiologists are proliferating for the personal computer/workstation user or are potentially accessible via high-speed networks. These resources are typically made available through a set of application programs that tend to be developed in isolation and operate independently. Nonetheless, there is a growing need for an integrated environment for access to these resources in the context of professional work, during clinical problem-solving and decision-making activities, and for use in conjunction with other information resources. New application development environments are required to provide these capabilities. One such architecture for applications, which we have implemented in a prototype environment called DeSyGNER, is based on separately delineating the component information resources required for an application, termed entities, and the user interface and organizational paradigms, or composition methods, by which the entities are used to provide particular kinds of capability. Examples include composition methods to support query, book browsing, hyperlinking, tutorials, simulations, or question/answer testing. Future steps must address true integration of such applications with existing clinical information systems. We believe that the most viable approach for evolving this capability is based on the use of new software engineering methodologies, open systems, client-server communication, and delineation of standard message protocols.

Evidence-based practice of transfusion medicine: is it possible and what do the words mean?

PubMed

Vamvakas, Eleftherios C

2004-10-01

Evidence-based medicine (EBM) optimizes clinical decision making by dictating that clinical decisions be based on the best available research evidence and by integrating best research evidence with clinical expertise and patient values. Several rankings of the strength of the evidence generated from different types of clinical research designs have been presented, and, in addressing a particular problem, clinicians can base their decision making on the types of clinical reports that have been published, along with an assessment of the strengths and weaknesses of each study. At a policy level, the concept of EBM would dictate that policy decisions also be made based on the best available research evidence. In transfusion medicine, however, decisions are based on a broader range of inputs, and the criteria for evaluating the efficacy and/or cost-effectiveness of proposed interventions differ from those used in other areas. Reasons why policy decisions are often based on considerations other than the best research evidence include public expectations about transfusion safety and proposals for applying the precautionary principle to transfusion medicine. Using the debate over the appropriateness of introducing universal white-cell reduction as an example, this review describes 2 perspectives for assessing evidence and/or making clinical or policy decisions: the evidence-based approach and the precautionary-principle approach; and also considers whether decisions in transfusion medicine can be truly evidence based.

Predicting Motivation: Computational Models of PFC Can Explain Neural Coding of Motivation and Effort-based Decision-making in Health and Disease.

PubMed

Vassena, Eliana; Deraeve, James; Alexander, William H

2017-10-01

Human behavior is strongly driven by the pursuit of rewards. In daily life, however, benefits mostly come at a cost, often requiring that effort be exerted to obtain potential benefits. Medial PFC (MPFC) and dorsolateral PFC (DLPFC) are frequently implicated in the expectation of effortful control, showing increased activity as a function of predicted task difficulty. Such activity partially overlaps with expectation of reward and has been observed both during decision-making and during task preparation. Recently, novel computational frameworks have been developed to explain activity in these regions during cognitive control, based on the principle of prediction and prediction error (predicted response-outcome [PRO] model [Alexander, W. H., & Brown, J. W. Medial prefrontal cortex as an action-outcome predictor. Nature Neuroscience, 14, 1338-1344, 2011], hierarchical error representation [HER] model [Alexander, W. H., & Brown, J. W. Hierarchical error representation: A computational model of anterior cingulate and dorsolateral prefrontal cortex. Neural Computation, 27, 2354-2410, 2015]). Despite the broad explanatory power of these models, it is not clear whether they can also accommodate effects related to the expectation of effort observed in MPFC and DLPFC. Here, we propose a translation of these computational frameworks to the domain of effort-based behavior. First, we discuss how the PRO model, based on prediction error, can explain effort-related activity in MPFC, by reframing effort-based behavior in a predictive context. We propose that MPFC activity reflects monitoring of motivationally relevant variables (such as effort and reward), by coding expectations and discrepancies from such expectations. Moreover, we derive behavioral and neural model-based predictions for healthy controls and clinical populations with impairments of motivation. Second, we illustrate the possible translation to effort-based behavior of the HER model, an extended version of PRO model based on hierarchical error prediction, developed to explain MPFC-DLPFC interactions. We derive behavioral predictions that describe how effort and reward information is coded in PFC and how changing the configuration of such environmental information might affect decision-making and task performance involving motivation.

Structured representation for core elements of common clinical decision support interventions to facilitate knowledge sharing.

PubMed

Zhou, Li; Hongsermeier, Tonya; Boxwala, Aziz; Lewis, Janet; Kawamoto, Kensaku; Maviglia, Saverio; Gentile, Douglas; Teich, Jonathan M; Rocha, Roberto; Bell, Douglas; Middleton, Blackford

2013-01-01

At present, there are no widely accepted, standard approaches for representing computer-based clinical decision support (CDS) intervention types and their structural components. This study aimed to identify key requirements for the representation of five widely utilized CDS intervention types: alerts and reminders, order sets, infobuttons, documentation templates/forms, and relevant data presentation. An XML schema was proposed for representing these interventions and their core structural elements (e.g., general metadata, applicable clinical scenarios, CDS inputs, CDS outputs, and CDS logic) in a shareable manner. The schema was validated by building CDS artifacts for 22 different interventions, targeted toward guidelines and clinical conditions called for in the 2011 Meaningful Use criteria. Custom style sheets were developed to render the XML files in human-readable form. The CDS knowledge artifacts were shared via a public web portal. Our experience also identifies gaps in existing standards and informs future development of standards for CDS knowledge representation and sharing.

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

Application and Exploration of Big Data Mining in Clinical Medicine

PubMed Central

Zhang, Yue; Guo, Shu-Li; Han, Li-Na; Li, Tie-Ling

2016-01-01

Objective: To review theories and technologies of big data mining and their application in clinical medicine. Data Sources: Literatures published in English or Chinese regarding theories and technologies of big data mining and the concrete applications of data mining technology in clinical medicine were obtained from PubMed and Chinese Hospital Knowledge Database from 1975 to 2015. Study Selection: Original articles regarding big data mining theory/technology and big data mining's application in the medical field were selected. Results: This review characterized the basic theories and technologies of big data mining including fuzzy theory, rough set theory, cloud theory, Dempster–Shafer theory, artificial neural network, genetic algorithm, inductive learning theory, Bayesian network, decision tree, pattern recognition, high-performance computing, and statistical analysis. The application of big data mining in clinical medicine was analyzed in the fields of disease risk assessment, clinical decision support, prediction of disease development, guidance of rational use of drugs, medical management, and evidence-based medicine. Conclusion: Big data mining has the potential to play an important role in clinical medicine. PMID:26960378

Development of a diagnostic decision tree for obstructive pulmonary diseases based on real-life data

PubMed Central

in ’t Veen, Johannes C.C.M.; Dekhuijzen, P.N. Richard; van Heijst, Ellen; Kocks, Janwillem W.H.; Muilwijk-Kroes, Jacqueline B.; Chavannes, Niels H.; van der Molen, Thys

2016-01-01

The aim of this study was to develop and explore the diagnostic accuracy of a decision tree derived from a large real-life primary care population. Data from 9297 primary care patients (45% male, mean age 53±17 years) with suspicion of an obstructive pulmonary disease was derived from an asthma/chronic obstructive pulmonary disease (COPD) service where patients were assessed using spirometry, the Asthma Control Questionnaire, the Clinical COPD Questionnaire, history data and medication use. All patients were diagnosed through the Internet by a pulmonologist. The Chi-squared Automatic Interaction Detection method was used to build the decision tree. The tree was externally validated in another real-life primary care population (n=3215). Our tree correctly diagnosed 79% of the asthma patients, 85% of the COPD patients and 32% of the asthma–COPD overlap syndrome (ACOS) patients. External validation showed a comparable pattern (correct: asthma 78%, COPD 83%, ACOS 24%). Our decision tree is considered to be promising because it was based on real-life primary care patients with a specialist's diagnosis. In most patients the diagnosis could be correctly predicted. Predicting ACOS, however, remained a challenge. The total decision tree can be implemented in computer-assisted diagnostic systems for individual patients. A simplified version of this tree can be used in daily clinical practice as a desk tool. PMID:27730177

Development, Evaluation and Implementation of Chief Complaint Groupings to Activate Data Collection: A Multi-Center Study of Clinical Decision Support for Children with Head Trauma.

PubMed

Deakyne, S J; Bajaj, L; Hoffman, J; Alessandrini, E; Ballard, D W; Norris, R; Tzimenatos, L; Swietlik, M; Tham, E; Grundmeier, R W; Kuppermann, N; Dayan, P S

2015-01-01

Overuse of cranial computed tomography scans in children with blunt head trauma unnecessarily exposes them to radiation. The Pediatric Emergency Care Applied Research Network (PECARN) blunt head trauma prediction rules identify children who do not require a computed tomography scan. Electronic health record (EHR) based clinical decision support (CDS) may effectively implement these rules but must only be provided for appropriate patients in order to minimize excessive alerts. To develop, implement and evaluate site-specific groupings of chief complaints (CC) that accurately identify children with head trauma, in order to activate data collection in an EHR. As part of a 13 site clinical trial comparing cranial computed tomography use before and after implementation of CDS, four PECARN sites centrally developed and locally implemented CC groupings to trigger a clinical trial alert (CTA) to facilitate the completion of an emergency department head trauma data collection template. We tested and chose CC groupings to attain high sensitivity while maintaining at least moderate specificity. Due to variability in CCs available, identical groupings across sites were not possible. We noted substantial variability in the sensitivity and specificity of seemingly similar CC groupings between sites. The implemented CC groupings had sensitivities greater than 90% with specificities between 75-89%. During the trial, formal testing and provider feedback led to tailoring of the CC groupings at some sites. CC groupings can be successfully developed and implemented across multiple sites to accurately identify patients who should have a CTA triggered to facilitate EHR data collection. However, CC groupings will necessarily vary in order to attain high sensitivity and moderate-to-high specificity. In future trials, the balance between sensitivity and specificity should be considered based on the nature of the clinical condition, including prevalence and morbidity, in addition to the goals of the intervention being considered.

Personalized Clinical Diagnosis in Data Bases for Treatment Support in Phthisiology.

PubMed

Lugovkina, T K; Skornyakov, S N; Golubev, D N; Egorov, E A; Medvinsky, I D

2016-01-01

The decision-making is a key event in the clinical practice. The program products with clinical decision support models in electronic data-base as well as with fixed decision moments of the real clinical practice and treatment results are very actual instruments for improving phthisiological practice and may be useful in the severe cases caused by the resistant strains of Mycobacterium tuberculosis. The methodology for gathering and structuring of useful information (critical clinical signals for decisions) is described. Additional coding of clinical diagnosis characteristics was implemented for numeric reflection of the personal situations. The created methodology for systematization and coding Clinical Events allowed to improve the clinical decision models for better clinical results.

The multimedia computer for office-based patient education: a systematic review.

PubMed

Wofford, James L; Smith, Edward D; Miller, David P

2005-11-01

Use of the multimedia computer for education is widespread in schools and businesses, and yet computer-assisted patient education is rare. In order to explore the potential use of computer-assisted patient education in the office setting, we performed a systematic review of randomized controlled trials (search date April 2004 using MEDLINE and Cochrane databases). Of the 26 trials identified, outcome measures included clinical indicators (12/26, 46.1%), knowledge retention (12/26, 46.1%), health attitudes (15/26, 57.7%), level of shared decision-making (5/26, 19.2%), health services utilization (4/26, 17.6%), and costs (5/26, 19.2%), respectively. Four trials targeted patients with breast cancer, but the clinical issues were otherwise diverse. Reporting of the testing of randomization (76.9%) and appropriate analysis of main effect variables (70.6%) were more common than reporting of a reliable randomization process (35.3%), blinding of outcomes assessment (17.6%), or sample size definition (29.4%). We concluded that the potential for improving the efficiency of the office through computer-assisted patient education has been demonstrated, but better proof of the impact on clinical outcomes is warranted before this strategy is accepted in the office setting.

Cost-effectiveness analysis of unsafe abortion and alternative first-trimester pregnancy termination strategies in Nigeria and Ghana.

PubMed

Hu, Delphine; Grossman, Daniel; Levin, Carol; Blanchard, Kelly; Adanu, Richard; Goldie, Sue J

2010-06-01

To explore the policy implications of increasing access to safe abortion in Nigeria and Ghana, we developed a computer-based decision analytic model which simulates induced abortion and its potential complications in a cohort of women, and comparatively assessed the cost-effectiveness of unsafe abortion and three first-trimester abortion modalities: hospital-based dilatation and curettage, hospital- and clinic-based manual vacuum aspiration (MVA), and medical abortion using misoprostol (MA). Assuming all modalities are equally available, clinic-based MVA is the most cost-effective option in Nigeria. If clinic-based MVA is not available, MA is the next best strategy. Conversely, in Ghana, MA is the most cost-effective strategy, followed by clinic-based MVA if MA is not available. From a real world policy perspective, increasing access to safe abortion in favor over unsafe abortion is the single most important factor in saving lives and societal costs, and is more influential than the actual choice of safe abortion modality.

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.

A Knowledge-based System for Intelligent Support in Pharmacogenomics Evidence Assessment: Ontology-driven Evidence Representation and Retrieval.

PubMed

Lee, Chia-Ju; Devine, Beth; Tarczy-Hornoch, Peter

2017-01-01

Pharmacogenomics holds promise as a critical component of precision medicine. Yet, the use of pharmacogenomics in routine clinical care is minimal, partly due to the lack of efficient and effective use of existing evidence. This paper describes the design, development, implementation and evaluation of a knowledge-based system that fulfills three critical features: a) providing clinically relevant evidence, b) applying an evidence-based approach, and c) using semantically computable formalism, to facilitate efficient evidence assessment to support timely decisions on adoption of pharmacogenomics in clinical care. To illustrate functionality, the system was piloted in the context of clopidogrel and warfarin pharmacogenomics. In contrast to existing pharmacogenomics knowledge bases, the developed system is the first to exploit the expressivity and reasoning power of logic-based representation formalism to enable unambiguous expression and automatic retrieval of pharmacogenomics evidence to support systematic review with meta-analysis.

Cyborg practices: call-handlers and computerised decision support systems in urgent and emergency care.

PubMed

Pope, Catherine; Halford, Susan; Turnbull, Joanne; Prichard, Jane

2014-06-01

This article draws on data collected during a 2-year project examining the deployment of a computerised decision support system. This computerised decision support system was designed to be used by non-clinical staff for dealing with calls to emergency (999) and urgent care (out-of-hours) services. One of the promises of computerised decisions support technologies is that they can 'hold' vast amounts of sophisticated clinical knowledge and combine it with decision algorithms to enable standardised decision-making by non-clinical (clerical) staff. This article draws on our ethnographic study of this computerised decision support system in use, and we use our analysis to question the 'automated' vision of decision-making in healthcare call-handling. We show that embodied and experiential (human) expertise remains central and highly salient in this work, and we propose that the deployment of the computerised decision support system creates something new, that this conjunction of computer and human creates a cyborg practice.

Clinical decision-making and secondary findings in systems medicine.

PubMed

Fischer, T; Brothers, K B; Erdmann, P; Langanke, M

2016-05-21

Systems medicine is the name for an assemblage of scientific strategies and practices that include bioinformatics approaches to human biology (especially systems biology); "big data" statistical analysis; and medical informatics tools. Whereas personalized and precision medicine involve similar analytical methods applied to genomic and medical record data, systems medicine draws on these as well as other sources of data. Given this distinction, the clinical translation of systems medicine poses a number of important ethical and epistemological challenges for researchers working to generate systems medicine knowledge and clinicians working to apply it. This article focuses on three key challenges: First, we will discuss the conflicts in decision-making that can arise when healthcare providers committed to principles of experimental medicine or evidence-based medicine encounter individualized recommendations derived from computer algorithms. We will explore in particular whether controlled experiments, such as comparative effectiveness trials, should mediate the translation of systems medicine, or if instead individualized findings generated through "big data" approaches can be applied directly in clinical decision-making. Second, we will examine the case of the Riyadh Intensive Care Program Mortality Prediction Algorithm, pejoratively referred to as the "death computer," to demonstrate the ethical challenges that can arise when big-data-driven scoring systems are applied in clinical contexts. We argue that the uncritical use of predictive clinical algorithms, including those envisioned for systems medicine, challenge basic understandings of the doctor-patient relationship. Third, we will build on the recent discourse on secondary findings in genomics and imaging to draw attention to the important implications of secondary findings derived from the joint analysis of data from diverse sources, including data recorded by patients in an attempt to realize their "quantified self." This paper examines possible ethical challenges that are likely to be raised as systems medicine to be translated into clinical medicine. These include the epistemological challenges for clinical decision-making, the use of scoring systems optimized by big data techniques and the risk that incidental and secondary findings will significantly increase. While some ethical implications remain still hypothetical we should use the opportunity to prospectively identify challenges to avoid making foreseeable mistakes when systems medicine inevitably arrives in routine care.

Computer-Aided Diagnosis of Breast Cancer: A Multi-Center Demonstrator

DTIC Science & Technology

1998-10-01

Artificial Neural Network (ANN) approach to computer aided diagnosis of breast cancer from mammographic findings. An ANN has been developed to provide support for the clinical decision to perform breast biopsy. The system is designed to aid in the decision to biopsy those patients who have suspicious mammographic findings. The decision to biopsy can be viewed as a two stage process: 1)the mammographer views the mammogram and determines the presence or absence of image features such as calcifications and masses, 2) the presence and description of these features

Microelectronics and Computers in Medicine.

ERIC Educational Resources Information Center

Meindl, James D.

1982-01-01

The use of microelectronics and computers in medicine is reviewed, focusing on medical research; medical data collection, storage, retrieval, and manipulation; medical decision making; computed tomography; ultrasonic imaging; role in clinical laboratories; and use as adjuncts for diagnostic tests, monitors of critically-ill patients, and with the…

Knowledge translation of the American College of Emergency Physicians' clinical policy on syncope using computerized clinical decision support.

PubMed

Melnick, Edward R; Genes, Nicholas G; Chawla, Neal K; Akerman, Meredith; Baumlin, Kevin M; Jagoda, Andy

2010-06-01

To influence physician practice behavior after implementation of a computerized clinical decision support system (CDSS) based upon the recommendations from the 2007 ACEP Clinical Policy on Syncope. This was a pre-post intervention with a prospective cohort and retrospective controls. We conducted a medical chart review of consecutive adult patients with syncope. A computerized CDSS prompting physicians to explain their decision-making regarding imaging and admission in syncope patients based upon ACEP Clinical Policy recommendations was embedded into the emergency department information system (EDIS). The medical records of 410 consecutive adult patients presenting with syncope were reviewed prior to implementation, and 301 records were reviewed after implementation. Primary outcomes were physician practice behavior demonstrated by admission rate and rate of head computed tomography (CT) imaging before and after implementation. There was a significant difference in admission rate pre- and post-intervention (68.1% vs. 60.5% respectively, p = 0.036). There was no significant difference in the head CT imaging rate pre- and post-intervention (39.8% vs. 43.2%, p = 0.358). There were seven physicians who saw ten or more patients during the pre- and post-intervention. Subset analysis of these seven physicians' practice behavior revealed a slight significant difference in the admission rate pre- and post-intervention (74.3% vs. 63.9%, p = 0.0495) and no significant difference in the head CT scan rate pre- and post-intervention (42.9% vs. 45.4%, p = 0.660). The introduction of an evidence-based CDSS based upon ACEP Clinical Policy recommendations on syncope correlated with a change in physician practice behavior in an urban academic emergency department. This change suggests emergency medicine clinical practice guideline recommendations can be incorporated into the physician workflow of an EDIS to enhance the quality of practice.

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.

Automated high-grade prostate cancer detection and ranking on whole slide images

NASA Astrophysics Data System (ADS)

Huang, Chao-Hui; Racoceanu, Daniel

2017-03-01

Recently, digital pathology (DP) has been largely improved due to the development of computer vision and machine learning. Automated detection of high-grade prostate carcinoma (HG-PCa) is an impactful medical use-case showing the paradigm of collaboration between DP and computer science: given a field of view (FOV) from a whole slide image (WSI), the computer-aided system is able to determine the grade by classifying the FOV. Various approaches have been reported based on this approach. However, there are two reasons supporting us to conduct this work: first, there is still room for improvement in terms of detection accuracy of HG-PCa; second, a clinical practice is more complex than the operation of simple image classification. FOV ranking is also an essential step. E.g., in clinical practice, a pathologist usually evaluates a case based on a few FOVs from the given WSI. Then, makes decision based on the most severe FOV. This important ranking scenario is not yet being well discussed. In this work, we introduce an automated detection and ranking system for PCa based on Gleason pattern discrimination. Our experiments suggested that the proposed system is able to perform high-accuracy detection ( 95:57% +/- 2:1%) and excellent performance of ranking. Hence, the proposed system has a great potential to support the daily tasks in the medical routine of clinical pathology.

Grand Challenges in Clinical Decision Support v10

PubMed Central

Sittig, Dean F.; Wright, Adam; Osheroff, Jerome A.; Middleton, Blackford; Teich, Jonathan M.; Ash, Joan S.; Campbell, Emily; Bates, David W.

2008-01-01

There is a pressing need for high-quality, effective means of designing, developing, presenting, implementing, evaluating, and maintaining all types of clinical decision support capabilities for clinicians, patients and consumers. Using an iterative, consensus-building process we identified a rank-ordered list of the top 10 grand challenges in clinical decision support. This list was created to educate and inspire researchers, developers, funders, and policy-makers. The list of challenges in order of importance that they be solved if patients and organizations are to begin realizing the fullest benefits possible of these systems consists of: Improve the human-computer interface; Disseminate best practices in CDS design, development, and implementation; Summarize patient-level information; Prioritize and filter recommendations to the user; Create an architecture for sharing executable CDS modules and services; Combine recommendations for patients with co-morbidities; Prioritize CDS content development and implementation; Create internet-accessible clinical decision support repositories; Use freetext information to drive clinical decision support; Mine large clinical databases to create new CDS. Identification of solutions to these challenges is critical if clinical decision support is to achieve its potential and improve the quality, safety and efficiency of healthcare. PMID:18029232

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.

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.

Enabling drug discovery project decisions with integrated computational chemistry and informatics

NASA Astrophysics Data System (ADS)

Tsui, Vickie; Ortwine, Daniel F.; Blaney, Jeffrey M.

2017-03-01

Computational chemistry/informatics scientists and software engineers in Genentech Small Molecule Drug Discovery collaborate with experimental scientists in a therapeutic project-centric environment. Our mission is to enable and improve pre-clinical drug discovery design and decisions. Our goal is to deliver timely data, analysis, and modeling to our therapeutic project teams using best-in-class software tools. We describe our strategy, the organization of our group, and our approaches to reach this goal. We conclude with a summary of the interdisciplinary skills required for computational scientists and recommendations for their training.

Fractional flow reserve based on computed tomography: an overview.

PubMed

Secchi, Francesco; Alì, Marco; Faggiano, Elena; Cannaò, Paola Maria; Fedele, Marco; Tresoldi, Silvia; Di Leo, Giovanni; Auricchio, Ferdinando; Sardanelli, Francesco

2016-04-28

Computed tomography coronary angiography (CTCA) is a technique proved to provide high sensitivity and negative predictive value for the identification of anatomically significant coronary artery disease (CAD) when compared with invasive X-ray coronary angiography. While the CTCA limitation of a ionizing radiation dose delivered to patients is substantially overcome by recent technical innovations, a relevant limitation remains the only anatomical assessment of coronary stenoses in the absence of evaluation of their functional haemodynamic significance. This limitation is highly important for those stenosis graded as intermediate at the anatomical assessment. Recently, non-invasive methods based on computational fluid dynamics were developed to calculate vessel-specific fractional flow reserve (FFR) using data routinely acquired by CTCA [computed tomographic fractional flow reserve (CT-FFR)]. Here we summarize methods for CT-FFR and review the evidence available in the literature up to June 26, 2016, including 16 original articles and one meta-analysis. The perspective of CT-FFR may greatly impact on CAD diagnosis, prognostic evaluation, and treatment decision-making. The aim of this review is to describe technical characteristics and clinical applications of CT-FFR, also in comparison with catheter-based invasive FFR, in order to make a cost-benefit balance in terms of clinical management and patient's health.

A Pilot Study to Reduce Computed Tomography Utilization for Pediatric Mild Head Injury in the Emergency Department Using a Clinical Decision Support Tool and a Structured Parent Discussion Tool.

PubMed

Engineer, Rakesh S; Podolsky, Seth R; Fertel, Baruch S; Grover, Purva; Jimenez, Heather; Simon, Erin L; Smalley, Courtney M

2018-05-15

The American College of Emergency Physicians embarked on the "Choosing Wisely" campaign to avoid computed tomographic (CT) scans in patients with minor head injury who are at low risk based on validated decision rules. We hypothesized that a Pediatric Mild Head Injury Care Path could be developed and implemented to reduce inappropriate CT utilization with support of a clinical decision support tool (CDST) and a structured parent discussion tool. A quality improvement project was initiated for 9 weeks to reduce inappropriate CT utilization through 5 interventions: (1) engagement of leadership, (2) provider education, (3) incorporation of a parent discussion tool to guide discussion during the emergency department (ED) visit between the parent and the provider, (4) CDST embedded in the electronic medical record, and (5) importation of data into the note to drive compliance. Patients prospectively were enrolled when providers at a pediatric and a freestanding ED entered data into the CDST for decision making. Rate of care path utilization and head CT reduction was determined for all patients with minor head injury based on International Classification of Diseases, Ninth Revision codes. Targets for care path utilization and head CT reduction were established a priori. Results were compared with baseline data collected from 2013. The CDST was used in 176 (77.5%) of 227 eligible patients. Twelve patients were excluded based on a priori criteria. Adherence to recommendations occurred in 162 (99%) of 164 patients. Head CT utilization was reduced from 62.7% to 22% (odds ratio, 0.17; 95% confidence interval, 0.12-0.24) where CDST was used by the provider. There were no missed traumatic brain injuries in our study group. A Pediatric Mild Head Injury Care Path can be implemented in a pediatric and freestanding ED, resulting in reduced head CT utilization and high levels of adherence to CDST recommendations.

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.

"Sugar-Ray" School-Based Decision Groups.

ERIC Educational Resources Information Center

Hunt, John J.; And Others

1992-01-01

Investigates differences between high-achieving and low-achieving school-based decision groups in decision making. Decision groups (207 groups of 3 members each) used computer simulations to address problems facing principals concerning fourth grade academic achievement. Higher-achieving groups made more decisions and made a combination of related…

Computer Decision Support Software Safely Improves Glycemic Control in the Burn Intensive Care Unit: A Randomized Controlled Clinical Study

DTIC Science & Technology

2011-01-01

Program Jointly Managed by the USA MRMC, NIH, NASA, and the Juvenile Diabetes Research Foundation and Combat Casualty Care Division, United States Army...were performed in the CP group (p = 0.0003), and nursing staff compliance with CP recommendations was greater (p < 0.0001). Conclusions—Glycemic...enhanced consistency in practice, providing standardization among nursing staff. Keywords Glycemic control; hypoglycemia; computer decision support

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

Information-seeking behavior changes in community-based teaching practices.

PubMed

Byrnes, Jennifer A; Kulick, Tracy A; Schwartz, Diane G

2004-07-01

A National Library of Medicine information access grant allowed for a collaborative project to provide computer resources in fourteen clinical practice sites that enabled health care professionals to access medical information via PubMed and the Internet. Health care professionals were taught how to access quality, cost-effective information that was user friendly and would result in improved patient care. Selected sites were located in medically underserved areas and received a computer, a printer, and, during year one, a fax machine. Participants were provided dial-up Internet service or were connected to the affiliated hospital's network. Clinicians were trained in how to search PubMed as a tool for practicing evidence-based medicine and to support clinical decision making. Health care providers were also taught how to find patient-education materials and continuing education programs and how to network with other professionals. Prior to the training, participants completed a questionnaire to assess their computer skills and familiarity with searching the Internet, MEDLINE, and other health-related databases. Responses indicated favorable changes in information-seeking behavior, including an increased frequency in conducting MEDLINE searches and Internet searches for work-related information.

Impact of model-based risk analysis for liver surgery planning.

PubMed

Hansen, C; Zidowitz, S; Preim, B; Stavrou, G; Oldhafer, K J; Hahn, H K

2014-05-01

A model-based risk analysis for oncologic liver surgery was described in previous work (Preim et al. in Proceedings of international symposium on computer assisted radiology and surgery (CARS), Elsevier, Amsterdam, pp. 353–358, 2002; Hansen et al. Int I Comput Assist Radiol Surg 4(5):469–474, 2009). In this paper, we present an evaluation of this method. To prove whether and how the risk analysis facilitates the process of liver surgery planning, an explorative user study with 10 liver experts was conducted. The purpose was to compare and analyze their decision-making. The results of the study show that model-based risk analysis enhances the awareness of surgical risk in the planning stage. Participants preferred smaller resection volumes and agreed more on the safety margins’ width in case the risk analysis was available. In addition, time to complete the planning task and confidence of participants were not increased when using the risk analysis. This work shows that the applied model-based risk analysis may influence important planning decisions in liver surgery. It lays a basis for further clinical evaluations and points out important fields for future research.

Clinical Consultation Systems: Designing for the Physician as Computer User

PubMed Central

Shortliffe, Edward H.

1981-01-01

The barriers to successful implementation of consultation systems for physicians have been frequently discussed. Our research has been directed at the development of computer techniques that will heighten the acceptance of high performance decision making tools. We discuss two current projects at Stanford Medical School that address both practical and theoretical issues in the design and construction of clinically useful systems.

Artificial intelligence: the clinician of the future.

PubMed

Gallagher, S M

2001-09-01

Human beings have long been fascinated with the idea of artificial intelligence. This fascination is fueled by popular films such as Stanley Kubrick's 2001: A Space Odyssey and Stephen Spielberg's recent film, AI. However intriguing artificial intelligence may be, Hubert and Spencer Dreyfus contend that qualities exist that are uniquely human--the qualities thought to be inaccessible to the computer "mind." Patricia Benner further investigated the qualities that guide clinicians in making decisions and assessments that are not entirely evidence-based or grounded in scientific data. Perhaps it is the intuitive nature of the human being that separates us from the machine. The state of artificial intelligence is described herein, along with a discussion of computerized clinical decision-making and the role of the human being in these decisions.

E-pharmacovigilance: development and implementation of a computable knowledge base to identify adverse drug reactions.

PubMed

Neubert, Antje; Dormann, Harald; Prokosch, Hans-Ulrich; Bürkle, Thomas; Rascher, Wolfgang; Sojer, Reinhold; Brune, Kay; Criegee-Rieck, Manfred

2013-09-01

Computer-assisted signal generation is an important issue for the prevention of adverse drug reactions (ADRs). However, due to poor standardization of patients' medical data and a lack of computable medical drug knowledge the specificity of computerized decision support systems for early ADR detection is too low and thus those systems are not yet implemented in daily clinical practice. We report on a method to formalize knowledge about ADRs based on the Summary of Product Characteristics (SmPCs) and linking them with structured patient data to generate safety signals automatically and with high sensitivity and specificity. A computable ADR knowledge base (ADR-KB) that inherently contains standardized concepts for ADRs (WHO-ART), drugs (ATC) and laboratory test results (LOINC) was built. The system was evaluated in study populations of paediatric and internal medicine inpatients. A total of 262 different ADR concepts related to laboratory findings were linked to 212 LOINC terms. The ADR knowledge base was retrospectively applied to a study population of 970 admissions (474 internal and 496 paediatric patients), who underwent intensive ADR surveillance. The specificity increased from 7% without ADR-KB up to 73% in internal patients and from 19.6% up to 91% in paediatric inpatients, respectively. This study shows that contextual linkage of patients' medication data with laboratory test results is a useful and reasonable instrument for computer-assisted ADR detection and a valuable step towards a systematic drug safety process. The system enables automated detection of ADRs during clinical practice with a quality close to intensive chart review. © 2013 The Authors. British Journal of Clinical Pharmacology © 2013 The British Pharmacological Society.

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.

Feasibility and impact of a computer-guided consultation on guideline-based management of COPD in general practice.

PubMed

Angus, Robert M; Thompson, Elizabeth B; Davies, Lisa; Trusdale, Ann; Hodgson, Chris; McKnight, Eddie; Davies, Andrew; Pearson, Mike G

2012-12-01

Applying guidelines is a universal challenge that is often not met. Intelligent software systems that facilitate real-time management during a clinical interaction may offer a solution. To determine if the use of a computer-guided consultation that facilitates the National Institute for Health and Clinical Excellence-based chronic obstructive pulmonary disease (COPD) guidance and prompts clinical decision-making is feasible in primary care and to assess its impact on diagnosis and management in reviews of COPD patients. Practice nurses, one-third of whom had no specific respiratory training, undertook a computer-guided review in the usual consulting room setting using a laptop computer with the screen visible to them and to the patient. A total of 293 patients (mean (SD) age 69.7 (10.1) years, 163 (55.6%) male) with a diagnosis of COPD were randomly selected from GP databases in 16 practices and assessed. Of 236 patients who had spirometry, 45 (19%) did not have airflow obstruction and the guided clinical history changed the primary diagnosis from COPD in a further 24 patients. In the 191 patients with confirmed COPD, the consultations prompted management changes including 169 recommendations for altered prescribing of inhalers (addition or discontinuation, inhaler dose or device). In addition, 47% of the 55 current smokers were referred for smoking cessation support, 12 (6%) for oxygen assessment, and 47 (24%) for pulmonary rehabilitation. Computer-guided consultations are practicable in general practice. Primary care COPD databases were confirmed to contain a significant proportion of incorrectly assigned patients. They resulted in interventions and the rationalisation of prescribing in line with recommendations. Only in 22 (12%) of those fully assessed was no management change suggested. The introduction of a computer-guided consultation offers the prospect of comprehensive guideline quality management.

Cost-effectiveness of computer-assisted training in cognitive-behavioral therapy as an adjunct to standard care for addiction.

PubMed

Olmstead, Todd A; Ostrow, Cary D; Carroll, Kathleen M

2010-08-01

To determine the cost-effectiveness, from clinic and patient perspectives, of a computer-based version of cognitive-behavioral therapy (CBT4CBT) as an addition to regular clinical practice for substance dependence. PARTICIPANTS, DESIGN AND MEASUREMENTS: This cost-effectiveness study is based on a randomized clinical trial in which 77 individuals seeking treatment for substance dependence at an outpatient community setting were randomly assigned to treatment as usual (TAU) or TAU plus biweekly access to computer-based training in CBT (TAU plus CBT4CBT). The primary patient outcome measure was the total number of drug-free specimens provided during treatment. Incremental cost-effectiveness ratios (ICERs) and cost-effectiveness acceptability curves (CEACs) were used to determine the cost-effectiveness of TAU plus CBT4CBT relative to TAU alone. Results are presented from both the clinic and patient perspectives and are shown to be robust to (i) sensitivity analyses and (ii) a secondary objective patient outcome measure. The per patient cost of adding CBT4CBT to standard care was $39 ($27) from the clinic (patient) perspective. From the clinic (patient) perspective, TAU plus CBT4CBT is likely to be cost-effective when the threshold value to decision makers of an additional drug-free specimen is greater than approximately $21 ($15), and TAU alone is likely to be cost-effective when the threshold value is less than approximately $21 ($15). The ICERs for TAU plus CBT4CBT also compare favorably to ICERs reported elsewhere for other empirically validated therapies, including contingency management. TAU plus CBT4CBT appears to be a good value from both the clinic and patient perspectives. Copyright (c) 2010 Elsevier Ireland Ltd. All rights reserved.

Computer-Assisted Pregnancy Management

PubMed Central

Haug, Peter J.; Hebertson, Richard M.; Heywood, Reed E.; Larkin, Ronald; Swapp, Craig; Waterfall, Brian; Warner, Homer R.

1987-01-01

A computer system under development for the management of pregnancy is described. This system exploits expert systems tools in the HELP Hospital Information System to direct the collection of clinical data and to generate medical decisions aimed at enhancing and standardizing prenatal care.

Comparing the effect of a decision aid plus patient navigation with usual care on colorectal cancer screening completion in vulnerable populations: study protocol for a randomized controlled trial

PubMed Central

2014-01-01

Background Screening can reduce colorectal cancer (CRC) incidence and mortality. However, screening is underutilized in vulnerable patient populations, particularly among Latinos. Patient-directed decision aids can increase CRC screening knowledge, self-efficacy, and intent; however, their effect on actual screening test completion tends to be modest. This is probably because decision aids do not address some of the patient-specific barriers that prevent successful completion of CRC screening in these populations. These individual barriers might be addressed though patient navigation interventions. This study will test a combined decision aid and patient navigator intervention on screening completion in diverse populations of vulnerable primary care patients. Methods/Design We will conduct a multisite, randomized controlled trial with patient-level randomization. Planned enrollment is 300 patients aged 50 to 75 years at average CRC risk presenting for appointments at two primary clinics in North Carolina and New Mexico. Intervention participants will view a video decision aid immediately before the clinic visit. The 14 to 16 minute video presents information about fecal occult blood tests and colonoscopy and will be viewed on a portable computer tablet in English or Spanish. Clinic-based patient navigators are bilingual and bicultural and will provide both face-to-face and telephone-based navigation. Control participants will view an unrelated food safety video and receive usual care. The primary outcome is completion of a CRC screening test at six months. Planned subgroup analyses include examining intervention effectiveness in Latinos, who will be oversampled. Secondarily, the trial will evaluate the intervention effects on knowledge of CRC screening, self-efficacy, intent, and patient-provider communication. The study will also examine whether patient ethnicity, acculturation, language preference, or health insurance status moderate the intervention effect on CRC screening. Discussion This pragmatic randomized controlled trial will test a combined decision aid and patient navigator intervention targeting CRC screening completion. Findings from this trial may inform future interventions and implementation policies designed to promote CRC screening in vulnerable patient populations and to reduce screening disparities. Clinical trial registration ClinicalTrials.gov NCT02054598. PMID:25004983

Bayesian methodology for the design and interpretation of clinical trials in critical care medicine: a primer for clinicians.

PubMed

Kalil, Andre C; Sun, Junfeng

2014-10-01

To review Bayesian methodology and its utility to clinical decision making and research in the critical care field. Clinical, epidemiological, and biostatistical studies on Bayesian methods in PubMed and Embase from their inception to December 2013. Bayesian methods have been extensively used by a wide range of scientific fields, including astronomy, engineering, chemistry, genetics, physics, geology, paleontology, climatology, cryptography, linguistics, ecology, and computational sciences. The application of medical knowledge in clinical research is analogous to the application of medical knowledge in clinical practice. Bedside physicians have to make most diagnostic and treatment decisions on critically ill patients every day without clear-cut evidence-based medicine (more subjective than objective evidence). Similarly, clinical researchers have to make most decisions about trial design with limited available data. Bayesian methodology allows both subjective and objective aspects of knowledge to be formally measured and transparently incorporated into the design, execution, and interpretation of clinical trials. In addition, various degrees of knowledge and several hypotheses can be tested at the same time in a single clinical trial without the risk of multiplicity. Notably, the Bayesian technology is naturally suited for the interpretation of clinical trial findings for the individualized care of critically ill patients and for the optimization of public health policies. We propose that the application of the versatile Bayesian methodology in conjunction with the conventional statistical methods is not only ripe for actual use in critical care clinical research but it is also a necessary step to maximize the performance of clinical trials and its translation to the practice of critical care medicine.

Computer-Assisted Community Planning and Decision Making.

ERIC Educational Resources Information Center

College of the Atlantic, Bar Harbor, ME.

The College of the Atlantic (COA) developed a broad-based, interdisciplinary curriculum in ecological policy and community planning and decision-making that incorporates two primary computer-based tools: ARC/INFO Geographic Information System (GIS) and STELLA, a systems-dynamics modeling tool. Students learn how to use and apply these tools…

Decision Making about Computer Acquisition and Use in American Schools.

ERIC Educational Resources Information Center

Becker, Henry Jay

1993-01-01

Discusses the centralization and decentralization of decision making about computer use in elementary and secondary schools based on results of a 1989 national survey. Results unexpectedly indicate that more successful programs are the result of districtwide planning than individual teacher or school-level decision making. (LRW)

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.

Patient Perspectives on Low-Dose Computed Tomography for Lung Cancer Screening, New Mexico, 2014

PubMed Central

Sussman, Andrew L.; Murrietta, Ambroshia M.; Getrich, Christina M.; Rhyne, Robert; Crowell, Richard E.; Taylor, Kathryn L.; Reifler, Ellen J.; Wescott, Pamela H.; Saeed, Ali I.; Hoffman, Richard M.

2016-01-01

Introduction National guidelines call for annual lung cancer screening for high-risk smokers using low-dose computed tomography (LDCT). The objective of our study was to characterize patient knowledge and attitudes about lung cancer screening, smoking cessation, and shared decision making by patient and health care provider. Methods We conducted semistructured qualitative interviews with patients with histories of heavy smoking who received care at a Federally Qualified Health Center (FQHC Clinic) and at a comprehensive cancer center-affiliated chest clinic (Chest Clinic) in Albuquerque, New Mexico. The interviews, conducted from February through September 2014, focused on perceptions about health screening, knowledge and attitudes about LDCT screening, and preferences regarding decision aids. We used a systematic iterative analytic process to identify preliminary and emergent themes and to create a coding structure. Results We reached thematic saturation after 22 interviews (10 at the FQHC Clinic, 12 at the Chest Clinic). Most patients were unaware of LDCT screening for lung cancer but were receptive to the test. Some smokers said they would consider quitting smoking if their screening result were positive. Concerns regarding screening were cost, radiation exposure, and transportation issues. To support decision making, most patients said they preferred one-on-one discussions with a provider. They also valued decision support tools (print materials, videos), but raised concerns about readability and Internet access. Conclusion Implementing lung cancer screening in sociodemographically diverse populations poses significant challenges. The value of tobacco cessation counseling cannot be overemphasized. Effective interventions for shared decision making to undergo lung cancer screening will need the active engagement of health care providers and will require the use of accessible decision aids designed for people with low health literacy. PMID:27536900

The COPD Knowledge Base: enabling data analysis and computational simulation in translational COPD research.

PubMed

Cano, Isaac; Tényi, Ákos; Schueller, Christine; Wolff, Martin; Huertas Migueláñez, M Mercedes; Gomez-Cabrero, David; Antczak, Philipp; Roca, Josep; Cascante, Marta; Falciani, Francesco; Maier, Dieter

2014-11-28

Previously we generated a chronic obstructive pulmonary disease (COPD) specific knowledge base (http://www.copdknowledgebase.eu) from clinical and experimental data, text-mining results and public databases. This knowledge base allowed the retrieval of specific molecular networks together with integrated clinical and experimental data. The COPDKB has now been extended to integrate over 40 public data sources on functional interaction (e.g. signal transduction, transcriptional regulation, protein-protein interaction, gene-disease association). In addition we integrated COPD-specific expression and co-morbidity networks connecting over 6 000 genes/proteins with physiological parameters and disease states. Three mathematical models describing different aspects of systemic effects of COPD were connected to clinical and experimental data. We have completely redesigned the technical architecture of the user interface and now provide html and web browser-based access and form-based searches. A network search enables the use of interconnecting information and the generation of disease-specific sub-networks from general knowledge. Integration with the Synergy-COPD Simulation Environment enables multi-scale integrated simulation of individual computational models while integration with a Clinical Decision Support System allows delivery into clinical practice. The COPD Knowledge Base is the only publicly available knowledge resource dedicated to COPD and combining genetic information with molecular, physiological and clinical data as well as mathematical modelling. Its integrated analysis functions provide overviews about clinical trends and connections while its semantically mapped content enables complex analysis approaches. We plan to further extend the COPDKB by offering it as a repository to publish and semantically integrate data from relevant clinical trials. The COPDKB is freely available after registration at http://www.copdknowledgebase.eu.

Energy-Aware Computation Offloading of IoT Sensors in Cloudlet-Based Mobile Edge Computing.

PubMed

Ma, Xiao; Lin, Chuang; Zhang, Han; Liu, Jianwei

2018-06-15

Mobile edge computing is proposed as a promising computing paradigm to relieve the excessive burden of data centers and mobile networks, which is induced by the rapid growth of Internet of Things (IoT). This work introduces the cloud-assisted multi-cloudlet framework to provision scalable services in cloudlet-based mobile edge computing. Due to the constrained computation resources of cloudlets and limited communication resources of wireless access points (APs), IoT sensors with identical computation offloading decisions interact with each other. To optimize the processing delay and energy consumption of computation tasks, theoretic analysis of the computation offloading decision problem of IoT sensors is presented in this paper. In more detail, the computation offloading decision problem of IoT sensors is formulated as a computation offloading game and the condition of Nash equilibrium is derived by introducing the tool of a potential game. By exploiting the finite improvement property of the game, the Computation Offloading Decision (COD) algorithm is designed to provide decentralized computation offloading strategies for IoT sensors. Simulation results demonstrate that the COD algorithm can significantly reduce the system cost compared with the random-selection algorithm and the cloud-first algorithm. Furthermore, the COD algorithm can scale well with increasing IoT sensors.

Decision technologies and the independent professional: the future's challenge to learning and leadership

PubMed Central

Dowie, J.

2001-01-01

Most references to "leadership" and "learning" as sources of quality improvement in medical care reflect an implicit commitment to the decision technology of "clinical judgement". All attempts to sustain this waning decision technology by clinical guidelines, care pathways, "evidence based practice", problem based curricula, and other stratagems only increase the gap between what is expected of doctors in today's clinical situation and what is humanly possible, hence the morale, stress, and health problems they are increasingly experiencing. Clinical guidance programmes based on decision analysis represent the coming decision technology, and proactive adaptation will produce independent doctors who can deliver excellent evidence based and preference driven care while concentrating on the human aspects of the therapeutic relation, having been relieved of the unbearable burdens of knowledge and information processing currently laid on them. History is full of examples of the incumbents of dominant technologies preferring to die than to adapt, and medicine needs both learning and leadership if it is to avoid repeating this mistake. Key Words: decision technology; clinical guidance programmes; decision analysis PMID:11700381

A web-based tool to support shared decision making for people with a psychotic disorder: randomized controlled trial and process evaluation.

PubMed

van der Krieke, Lian; Emerencia, Ando C; Boonstra, Nynke; Wunderink, Lex; de Jonge, Peter; Sytema, Sjoerd

2013-10-07

Mental health policy makers encourage the development of electronic decision aids to increase patient participation in medical decision making. Evidence is needed to determine whether these decision aids are helpful in clinical practice and whether they lead to increased patient involvement and better outcomes. This study reports the outcome of a randomized controlled trial and process evaluation of a Web-based intervention to facilitate shared decision making for people with psychotic disorders. The study was carried out in a Dutch mental health institution. Patients were recruited from 2 outpatient teams for patients with psychosis (N=250). Patients in the intervention condition (n=124) were provided an account to access a Web-based information and decision tool aimed to support patients in acquiring an overview of their needs and appropriate treatment options provided by their mental health care organization. Patients were given the opportunity to use the Web-based tool either on their own (at their home computer or at a computer of the service) or with the support of an assistant. Patients in the control group received care as usual (n=126). Half of the patients in the sample were patients experiencing a first episode of psychosis; the other half were patients with a chronic psychosis. Primary outcome was patient-perceived involvement in medical decision making, measured with the Combined Outcome Measure for Risk Communication and Treatment Decision-making Effectiveness (COMRADE). Process evaluation consisted of questionnaire-based surveys, open interviews, and researcher observation. In all, 73 patients completed the follow-up measurement and were included in the final analysis (response rate 29.2%). More than one-third (48/124, 38.7%) of the patients who were provided access to the Web-based decision aid used it, and most used its full functionality. No differences were found between the intervention and control conditions on perceived involvement in medical decision making (COMRADE satisfaction with communication: F1,68=0.422, P=.52; COMRADE confidence in decision: F1,67=0.086, P=.77). In addition, results of the process evaluation suggest that the intervention did not optimally fit in with routine practice of the participating teams. The development of electronic decision aids to facilitate shared medical decision making is encouraged and many people with a psychotic disorder can work with them. This holds for both first-episode patients and long-term care patients, although the latter group might need more assistance. However, results of this paper could not support the assumption that the use of electronic decision aids increases patient involvement in medical decision making. This may be because of weak implementation of the study protocol and a low response rate.

Functional Assessment of Genetic Variants with Outcomes Adapted to Clinical Decision-Making

PubMed Central

Thouvenot, Pierre; Ben Yamin, Barbara; Fourrière, Lou; Lescure, Aurianne; Boudier, Thomas; Del Nery, Elaine; Chauchereau, Anne; Goldgar, David E.; Stoppa-Lyonnet, Dominique; Nicolas, Alain; Millot, Gaël A.

2016-01-01

Understanding the medical effect of an ever-growing number of human variants detected is a long term challenge in genetic counseling. Functional assays, based on in vitro or in vivo evaluations of the variant effects, provide essential information, but they require robust statistical validation, as well as adapted outputs, to be implemented in the clinical decision-making process. Here, we assessed 25 pathogenic and 15 neutral missense variants of the BRCA1 breast/ovarian cancer susceptibility gene in four BRCA1 functional assays. Next, we developed a novel approach that refines the variant ranking in these functional assays. Lastly, we developed a computational system that provides a probabilistic classification of variants, adapted to clinical interpretation. Using this system, the best functional assay exhibits a variant classification accuracy estimated at 93%. Additional theoretical simulations highlight the benefit of this ready-to-use system in the classification of variants after functional assessment, which should facilitate the consideration of functional evidences in the decision-making process after genetic testing. Finally, we demonstrate the versatility of the system with the classification of siRNAs tested for human cell growth inhibition in high throughput screening. PMID:27272900

Ag2S atomic switch-based `tug of war' for decision making

NASA Astrophysics Data System (ADS)

Lutz, C.; Hasegawa, T.; Chikyow, T.

2016-07-01

For a computing process such as making a decision, a software controlled chip of several transistors is necessary. Inspired by how a single cell amoeba decides its movements, the theoretical `tug of war' computing model was proposed but not yet implemented in an analogue device suitable for integrated circuits. Based on this model, we now developed a new electronic element for decision making processes, which will have no need for prior programming. The devices are based on the growth and shrinkage of Ag filaments in α-Ag2+δS gap-type atomic switches. Here we present the adapted device design and the new materials. We demonstrate the basic `tug of war' operation by IV-measurements and Scanning Electron Microscopy (SEM) observation. These devices could be the base for a CMOS-free new computer architecture.For a computing process such as making a decision, a software controlled chip of several transistors is necessary. Inspired by how a single cell amoeba decides its movements, the theoretical `tug of war' computing model was proposed but not yet implemented in an analogue device suitable for integrated circuits. Based on this model, we now developed a new electronic element for decision making processes, which will have no need for prior programming. The devices are based on the growth and shrinkage of Ag filaments in α-Ag2+δS gap-type atomic switches. Here we present the adapted device design and the new materials. We demonstrate the basic `tug of war' operation by IV-measurements and Scanning Electron Microscopy (SEM) observation. These devices could be the base for a CMOS-free new computer architecture. Electronic supplementary information (ESI) available. See DOI: 10.1039/c6nr00690f

[Giant splenic cyst in a teenager girl: Case report].

PubMed

Martínez Torres, Beatriz; Medina García, Manuel; Zafra Anta, Miguel Ángel; García Muñoz-Najar, Alejandro José; Tardío Dovao, Juan C

2017-06-01

Giant nonparasitic splenic epidermoid cysts are relatively uncommon. These lesions can lead abdominal pain, but most of then are asymptomatic, and they are discovered incidentally. We report a 13-y old female with a giant splenic epidermoid cystic, given the special interest of diagnostic and therapeutic decision-making of this rare entity. A 13-y old female with clinical history of abdominal pain since the last two months. On physical examination a firm, tender mass was palpable in left hypochondrium. Diagnosis of a large cystic splenic mass was made based on ultrasound and abdominal computed tomography scan. Splenectomy was performed, and histopathological-immunohistochemistry studies revealed findings suggestive of primary epithelial cyst. The post-operative clinical course was satisfactory and uneventful. Treatment of giant nonparasitic splenic cysts is surgical. Preserve splenic parenchyma must be the aim in an individualized decision-making. The different types of surgical modalities will be according to the diagnosis and clinical situation (cyst size, age, comorbidities).

Practical Applications of Digital Pathology.

PubMed

Saeed-Vafa, Daryoush; Magliocco, Anthony M

2015-04-01

Virtual microscopy and advances in machine learning have paved the way for the ever-expanding field of digital pathology. Multiple image-based computing environments capable of performing automated quantitative and morphological analyses are the foundation on which digital pathology is built. The applications for digital pathology in the clinical setting are numerous and are explored along with the digital software environments themselves, as well as the different analytical modalities specific to digital pathology. Prospective studies, case-control analyses, meta-analyses, and detailed descriptions of software environments were explored that pertained to digital pathology and its use in the clinical setting. Many different software environments have advanced platforms capable of improving digital pathology and potentially influencing clinical decisions. The potential of digital pathology is vast, particularly with the introduction of numerous software environments available for use. With all the digital pathology tools available as well as those in development, the field will continue to advance, particularly in the era of personalized medicine, providing health care professionals with more precise prognostic information as well as helping them guide treatment decisions.

An Artificial Neural Network-Based Decision-Support System for Integrated Network Security

DTIC Science & Technology

2014-09-01

group that they need to know in order to make team-based decisions in real-time environments, (c) Employ secure cloud computing services to host mobile...THESIS Presented to the Faculty Department of Electrical and Computer Engineering Graduate School of Engineering and Management Air Force...out-of-the-loop syndrome and create complexity creep. As a result, full automation efforts can lead to inappropriate decision-making despite a

Selective increase of intention-based economic decisions by noninvasive brain stimulation to the dorsolateral prefrontal cortex.

PubMed

Nihonsugi, Tsuyoshi; Ihara, Aya; Haruno, Masahiko

2015-02-25

The intention behind another's action and the impact of the outcome are major determinants of human economic behavior. It is poorly understood, however, whether the two systems share a core neural computation. Here, we investigated whether the two systems are causally dissociable in the brain by integrating computational modeling, functional magnetic resonance imaging, and transcranial direct current stimulation experiments in a newly developed trust game task. We show not only that right dorsolateral prefrontal cortex (DLPFC) activity is correlated with intention-based economic decisions and that ventral striatum and amygdala activity are correlated with outcome-based decisions, but also that stimulation to the DLPFC selectively enhances intention-based decisions. These findings suggest that the right DLPFC is involved in the implementation of intention-based decisions in the processing of cooperative decisions. This causal dissociation of cortical and subcortical backgrounds may indicate evolutionary and developmental differences in the two decision systems. Copyright © 2015 the authors 0270-6474/15/53412-08$15.00/0.

Prognosis of the individual course of disease: the elements of time, heterogeneity and precision.

PubMed

Daumer, Martin; Neuhaus, Anneke; Herbert, Joseph; Ebers, George

2009-12-01

There is no gold standard in monitoring disease activity for clinical trials in multiple sclerosis. Various outcome measures, including relapses, disability and magnetic resonance imaging (MRI) measures have been used to demonstrate the efficacy of the different available therapies for multiple sclerosis. Recently, the potential limitations of these measures have received increasing attention, and these have stimulated research into more appropriate and sensitive outcome measures for clinical trials. For example, it has been shown that widely-used MRI measures add little, if any, independent information to that provided by more clinically relevant measures such as relapses and disability. Similarly, the Expanded Disability status Scale (EDSS), which is the most widely-used measure of disability related to multiple sclerosis, is insufficiently sensitive to detect robust changes in disability over the timeframes usually used in clinical trials. An alternative to the EDSS is the Multiple Sclerosis Severity Score (MSSS), a severity scale which relates clinical disability to disease duration. The MSSS was originally developed from a database of nearly ten thousand patients from eleven European countries and Australia and has since been reproduced in an independent dataset of 1134 patients from the placebo arms of randomised clinical trials. Based on the MSSS score, disease severity can be defined, which shows stability over time and may provide evidence-based decision support for patient management. Another alternative to measure disability is the objective quantification of physical activity. There is evidence that recent developments in pervasive computing using tiny accelerometers may have the potential to increase the reliability and precision of motor assessment, especially in the mid-range of the EDSS. The outcome measures discussed have potential use as online tools for evidence-based decision support which are increasingly being used in medical research and clinical decision-making. Copyright 2009 Elsevier Ltd. All rights reserved.

Entertainment education for prostate cancer screening: a randomized trial among primary care patients with low health literacy.

PubMed

Volk, Robert J; Jibaja-Weiss, Maria L; Hawley, Sarah T; Kneuper, Suzanne; Spann, Stephen J; Miles, Brian J; Hyman, David J

2008-12-01

To evaluate an entertainment-based patient decision aid for prostate cancer screening among patients with low or high health literacy. Male primary care patients from two clinical sites, one characterized as serving patients with low health literacy (n=149) and the second as serving patients with high health literacy (n=301), were randomized to receive an entertainment-based decision aid for prostate cancer screening or an audiobooklet-control aid with the same learner content but without the entertainment features. Postintervention and 2-week follow-up assessments were conducted. Patients at the low-literacy site were more engaged with the entertainment-based aid than patients at the high-literacy site. Overall, knowledge improved for all patients. Among patients at the low-literacy site, the entertainment-based aid was associated with lower decisional conflict and greater self-advocacy (i.e., mastering and obtaining information about screening) when compared to patients given the audiobooklet. No differences between the aids were observed for patients at the high-literacy site. Entertainment education may be an effective strategy for promoting informed decision making about prostate cancer screening among patients with lower health literacy. As barriers to implementing computer-based patient decision support programs decrease, alternative models for delivering these programs should be explored.

Computer-assisted categorizing of head computed tomography reports for clinical decision rule research.

PubMed

Wall, Stephen P; Mayorga, Oliver; Banfield, Christine E; Wall, Mark E; Aisic, Ilan; Auerbach, Carl; Gennis, Paul

2006-11-01

To develop software that categorizes electronic head computed tomography (CT) reports into groups useful for clinical decision rule research. Data were obtained from the Second National Emergency X-Radiography Utilization Study, a cohort of head injury patients having received head CT. CT reports were reviewed manually for presence or absence of clinically important subdural or epidural hematoma, defined as greater than 1.0 cm in width or causing mass effect. Manual categorization was done by 2 independent researchers blinded to each other's results. A third researcher adjudicated discrepancies. A random sample of 300 reports with radiologic abnormalities was selected for software development. After excluding reports categorized manually or by software as indeterminate (neither positive nor negative), we calculated sensitivity and specificity by using manual categorization as the standard. System efficiency was defined as the percentage of reports categorized as positive or negative, regardless of accuracy. Software was refined until analysis of the training data yielded sensitivity and specificity approximating 95% and efficiency exceeding 75%. To test the system, we calculated sensitivity, specificity, and efficiency, using the remaining 1,911 reports. Of the 1,911 reports, 160 had clinically important subdural or epidural hematoma. The software exhibited good agreement with manual categorization of all reports, including indeterminate ones (weighted kappa 0.62; 95% confidence interval [CI] 0.58 to 0.65). Sensitivity, specificity, and efficiency of the computerized system for identifying manual positives and negatives were 96% (95% CI 91% to 98%), 98% (95% CI 98% to 99%), and 79% (95% CI 77% to 80%), respectively. Categorizing head CT reports by computer for clinical decision rule research is feasible.

The Development of an Interactive Computer-Based Training Program for Timely and Humane On-Farm Pig Euthanasia.

PubMed

Mullins, Caitlyn R; Pairis-Garcia, Monique D; Campler, Magnus R; Anthony, Raymond; Johnson, Anna K; Coleman, Grahame J; Rault, Jean-Loup

2018-02-05

With extensive knowledge and training in the prevention, management, and treatment of disease conditions in animals, veterinarians play a critical role in ensuring good welfare on swine farms by training caretakers on the importance of timely euthanasia. To assist veterinarians and other industry professionals in training new and seasoned caretakers, an interactive computer-based training program was created. It consists of three modules, each containing five case studies, which cover three distinct production stages (breeding stock, piglets, and wean to grower-finisher pigs). Case study development was derived from five specific euthanasia criteria defined in the 2015 Common Swine Industry Audit, a nationally recognized auditing program used in the US. Case studies provide information regarding treatment history, clinical signs, and condition severity of the pig and prompt learners to make management decisions regarding pig treatment and care. Once a decision is made, feedback is provided so learners understand the appropriateness of their decision compared to current industry guidelines. In addition to training farm personnel, this program may also be a valuable resource if incorporated into veterinary, graduate, and continuing education curricula. This innovative tool represents the first interactive euthanasia-specific training program in the US swine industry and offers the potential to improve timely and humane on-farm pig euthanasia.

Louhi 2010: Special issue on Text and Data Mining of Health Documents

PubMed Central

2011-01-01

The papers presented in this supplement focus and reflect on computer use in every-day clinical work in hospitals and clinics such as electronic health record systems, pre-processing for computer aided summaries, clinical coding, computer decision systems, as well as related ethical concerns and security. Much of this work concerns itself by necessity with incorporation and development of language processing tools and methods, and as such this supplement aims at providing an arena for reporting on development in a diversity of languages. In the supplement we can read about some of the challenges identified above. PMID:21992545

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.

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

Towards Evidence-Based Practice in Language Intervention for Bilingual Children

ERIC Educational Resources Information Center

Thordardottir, Elin

2010-01-01

Evidence-based practice requires that clinical decisions be based on evidence from rigorously controlled research studies. At this time, very few studies have directly examined the efficacy of clinical intervention methods for bilingual children. Clinical decisions for this population cannot, therefore, be based on the strongest forms of research…

Primary care physicians' perspectives on computer-based health risk assessment tools for chronic diseases: a mixed methods study.

PubMed

Voruganti, Teja R; O'Brien, Mary Ann; Straus, Sharon E; McLaughlin, John R; Grunfeld, Eva

2015-09-24

Health risk assessment tools compute an individual's risk of developing a disease. Routine use of such tools by primary care physicians (PCPs) is potentially useful in chronic disease prevention. We sought physicians' awareness and perceptions of the usefulness, usability and feasibility of performing assessments with computer-based risk assessment tools in primary care settings. Focus groups and usability testing with a computer-based risk assessment tool were conducted with PCPs from both university-affiliated and community-based practices. Analysis was derived from grounded theory methodology. PCPs (n = 30) were aware of several risk assessment tools although only select tools were used routinely. The decision to use a tool depended on how use impacted practice workflow and whether the tool had credibility. Participants felt that embedding tools in the electronic medical records (EMRs) system might allow for health information from the medical record to auto-populate into the tool. User comprehension of risk could also be improved with computer-based interfaces that present risk in different formats. In this study, PCPs chose to use certain tools more regularly because of usability and credibility. Despite there being differences in the particular tools a clinical practice used, there was general appreciation for the usefulness of tools for different clinical situations. Participants characterised particular features of an ideal tool, feeling strongly that embedding risk assessment tools in the EMR would maximise accessibility and use of the tool for chronic disease management. However, appropriate practice workflow integration and features that facilitate patient understanding at point-of-care are also essential.

Use Of Clinical Decision Analysis In Predicting The Efficacy Of Newer Radiological Imaging Modalities: Radioscintigraphy Versus Single Photon Transverse Section Emission Computed Tomography

NASA Astrophysics Data System (ADS)

Prince, John R.

1982-12-01

Sensitivity, specificity, and predictive accuracy have been shown to be useful measures of the clinical efficacy of diagnostic tests and can be used to predict the potential improvement in diagnostic certitude resulting from the introduction of a competing technology. This communication demonstrates how the informal use of clinical decision analysis may guide health planners in the allocation of resources, purchasing decisions, and implementation of high technology. For didactic purposes the focus is on a comparison between conventional planar radioscintigraphy (RS) and single photon transverse section emission conputed tomography (SPECT). For example, positive predictive accuracy (PPA) for brain RS in a specialist hospital with a 50% disease prevalance is about 95%. SPECT should increase this predicted accuracy to 96%. In a primary care hospital with only a 15% disease prevalance the PPA is only 77% and SPECT may increase this accuracy to about 79%. Similar calculations based on published data show that marginal improvements are expected with SPECT in the liver. It is concluded that: a) The decision to purchase a high technology imaging modality such as SPECT for clinical purposes should be analyzed on an individual organ system and institutional basis. High technology may be justified in specialist hospitals but not necessarily in primary care hospitals. This is more dependent on disease prevalance than procedure volume; b) It is questionable whether SPECT imaging will be competitive with standard RS procedures. Research should concentrate on the development of different medical applications.

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.

Health decision making: lynchpin of evidence-based practice.

PubMed

Spring, Bonnie

2008-01-01

Health decision making is both the lynchpin and the least developed aspect of evidence-based practice. The evidence-based practice process requires integrating the evidence with consideration of practical resources and patient preferences and doing so via a process that is genuinely collaborative. Yet, the literature is largely silent about how to accomplish integrative, shared decision making. for evidence-based practice are discussed for 2 theories of clinician decision making (expected utility and fuzzy trace) and 2 theories of patient health decision making (transtheoretical model and reasoned action). Three suggestions are offered. First, it would be advantageous to have theory-based algorithms that weight and integrate the 3 data strands (evidence, resources, preferences) in different decisional contexts. Second, patients, not providers, make the decisions of greatest impact on public health, and those decisions are behavioral. Consequently, theory explicating how provider-patient collaboration can influence patient lifestyle decisions made miles from the provider's office is greatly needed. Third, although the preponderance of data on complex decisions supports a computational approach, such an approach to evidence-based practice is too impractical to be widely applied at present. More troublesomely, until patients come to trust decisions made computationally more than they trust their providers' intuitions, patient adherence will remain problematic. A good theory of integrative, collaborative health decision making remains needed.

Health Decision Making: Lynchpin of Evidence-Based Practice

PubMed Central

Spring, Bonnie

2008-01-01

Health decision making is both the lynchpin and the least developed aspect of evidence-based practice. The evidence-based practice process requires integrating the evidence with consideration of practical resources and patient preferences and doing so via a process that is genuinely collaborative. Yet, the literature is largely silent about how to accomplish integrative, shared decision making. Implications for evidence-based practice are discussed for 2 theories of clinician decision making (expected utility and fuzzy trace) and 2 theories of patient health decision making (transtheoretical model and reasoned action). Three suggestions are offered. First, it would be advantageous to have theory-based algorithms that weight and integrate the 3 data strands (evidence, resources, preferences) in different decisional contexts. Second, patients, not providers, make the decisions of greatest impact on public health, and those decisions are behavioral. Consequently, theory explicating how provider-patient collaboration can influence patient lifestyle decisions made miles from the provider's office is greatly needed. Third, although the preponderance of data on complex decisions supports a computational approach, such an approach to evidence-based practice is too impractical to be widely applied at present. More troublesomely, until patients come to trust decisions made computationally more than they trust their providers’ intuitions, patient adherence will remain problematic. A good theory of integrative, collaborative health decision making remains needed. PMID:19015288

Binary Decision Trees for Preoperative Periapical Cyst Screening Using Cone-beam Computed Tomography.

PubMed

Pitcher, Brandon; Alaqla, Ali; Noujeim, Marcel; Wealleans, James A; Kotsakis, Georgios; Chrepa, Vanessa

2017-03-01

Cone-beam computed tomographic (CBCT) analysis allows for 3-dimensional assessment of periradicular lesions and may facilitate preoperative periapical cyst screening. The purpose of this study was to develop and assess the predictive validity of a cyst screening method based on CBCT volumetric analysis alone or combined with designated radiologic criteria. Three independent examiners evaluated 118 presurgical CBCT scans from cases that underwent apicoectomies and had an accompanying gold standard histopathological diagnosis of either a cyst or granuloma. Lesion volume, density, and specific radiologic characteristics were assessed using specialized software. Logistic regression models with histopathological diagnosis as the dependent variable were constructed for cyst prediction, and receiver operating characteristic curves were used to assess the predictive validity of the models. A conditional inference binary decision tree based on a recursive partitioning algorithm was constructed to facilitate preoperative screening. Interobserver agreement was excellent for volume and density, but it varied from poor to good for the radiologic criteria. Volume and root displacement were strong predictors for cyst screening in all analyses. The binary decision tree classifier determined that if the volume of the lesion was >247 mm 3 , there was 80% probability of a cyst. If volume was <247 mm 3 and root displacement was present, cyst probability was 60% (78% accuracy). The good accuracy and high specificity of the decision tree classifier renders it a useful preoperative cyst screening tool that can aid in clinical decision making but not a substitute for definitive histopathological diagnosis after biopsy. Confirmatory studies are required to validate the present findings. Published by Elsevier Inc.

Models based on value and probability in health improve shared decision making.

PubMed

Ortendahl, Monica

2008-10-01

Diagnostic reasoning and treatment decisions are a key competence of doctors. A model based on values and probability provides a conceptual framework for clinical judgments and decisions, and also facilitates the integration of clinical and biomedical knowledge into a diagnostic decision. Both value and probability are usually estimated values in clinical decision making. Therefore, model assumptions and parameter estimates should be continually assessed against data, and models should be revised accordingly. Introducing parameter estimates for both value and probability, which usually pertain in clinical work, gives the model labelled subjective expected utility. Estimated values and probabilities are involved sequentially for every step in the decision-making process. Introducing decision-analytic modelling gives a more complete picture of variables that influence the decisions carried out by the doctor and the patient. A model revised for perceived values and probabilities by both the doctor and the patient could be used as a tool for engaging in a mutual and shared decision-making process in clinical work.

Automation of CT-based haemorrhagic stroke assessment for improved clinical outcomes: study protocol and design

PubMed Central

Chinda, Betty; Medvedev, George; Siu, William; Ester, Martin; Arab, Ali; Gu, Tao; Moreno, Sylvain; D’Arcy, Ryan C N; Song, Xiaowei

2018-01-01

Introduction Haemorrhagic stroke is of significant healthcare concern due to its association with high mortality and lasting impact on the survivors’ quality of life. Treatment decisions and clinical outcomes depend strongly on the size, spread and location of the haematoma. Non-contrast CT (NCCT) is the primary neuroimaging modality for haematoma assessment in haemorrhagic stroke diagnosis. Current procedures do not allow convenient NCCT-based haemorrhage volume calculation in clinical settings, while research-based approaches are yet to be tested for clinical utility; there is a demonstrated need for developing effective solutions. The project under review investigates the development of an automatic NCCT-based haematoma computation tool in support of accurate quantification of haematoma volumes. Methods and analysis Several existing research methods for haematoma volume estimation are studied. Selected methods are tested using NCCT images of patients diagnosed with acute haemorrhagic stroke. For inter-rater and intrarater reliability evaluation, different raters will analyse haemorrhage volumes independently. The efficiency with respect to time of haematoma volume assessments will be examined to compare with the results from routine clinical evaluations and planimetry assessment that are known to be more accurate. The project will target the development of an enhanced solution by adapting existing methods and integrating machine learning algorithms. NCCT-based information of brain haemorrhage (eg, size, volume, location) and other relevant information (eg, age, sex, risk factor, comorbidities) will be used in relation to clinical outcomes with future project development. Validity and reliability of the solution will be examined for potential clinical utility. Ethics and dissemination The project including procedures for deidentification of NCCT data has been ethically approved. The study involves secondary use of existing data and does not require new consent of participation. The team consists of clinical neuroimaging scientists, computing scientists and clinical professionals in neurology and neuroradiology and includes patient representatives. Research outputs will be disseminated following knowledge translation plans towards improving stroke patient care. Significant findings will be published in scientific journals. Anticipated deliverables include computer solutions for improved clinical assessment of haematoma using NCCT. PMID:29674371

An Internationally Consented Standard for Nursing Process-Clinical Decision Support Systems in Electronic Health Records.

PubMed

Müller-Staub, Maria; de Graaf-Waar, Helen; Paans, Wolter

2016-11-01

Nurses are accountable to apply the nursing process, which is key for patient care: It is a problem-solving process providing the structure for care plans and documentation. The state-of-the art nursing process is based on classifications that contain standardized concepts, and therefore, it is named Advanced Nursing Process. It contains valid assessments, nursing diagnoses, interventions, and nursing-sensitive patient outcomes. Electronic decision support systems can assist nurses to apply the Advanced Nursing Process. However, nursing decision support systems are missing, and no "gold standard" is available. The study aim is to develop a valid Nursing Process-Clinical Decision Support System Standard to guide future developments of clinical decision support systems. In a multistep approach, a Nursing Process-Clinical Decision Support System Standard with 28 criteria was developed. After pilot testing (N = 29 nurses), the criteria were reduced to 25. The Nursing Process-Clinical Decision Support System Standard was then presented to eight internationally known experts, who performed qualitative interviews according to Mayring. Fourteen categories demonstrate expert consensus on the Nursing Process-Clinical Decision Support System Standard and its content validity. All experts agreed the Advanced Nursing Process should be the centerpiece for the Nursing Process-Clinical Decision Support System and should suggest research-based, predefined nursing diagnoses and correct linkages between diagnoses, evidence-based interventions, and patient outcomes.

Combining tabular, rule-based, and procedural knowledge in computer-based guidelines for childhood immunization.

PubMed

Miller, P L; Frawley, S J; Sayward, F G; Yasnoff, W A; Duncan, L; Fleming, D W

1997-06-01

IMM/Serve is a computer program which implements the clinical guidelines for childhood immunization. IMM/Serve accepts as input a child's immunization history. It then indicates which vaccinations are due and which vaccinations should be scheduled next. The clinical guidelines for immunization are quite complex and are modified quite frequently. As a result, it is important that IMM/Serve's knowledge be represented in a format that facilitates the maintenance of that knowledge as the field evolves over time. To achieve this goal, IMM/Serve uses four representations for different parts of its knowledge base: (1) Immunization forecasting parameters that specify the minimum ages and wait-intervals for each dose are stored in tabular form. (2) The clinical logic that determines which set of forecasting parameters applies for a particular patient in each vaccine series is represented using if-then rules. (3) The temporal logic that combines dates, ages, and intervals to calculate recommended dates, is expressed procedurally. (4) The screening logic that checks each previous dose for validity is performed using a decision table that combines minimum ages and wait intervals with a small amount of clinical logic. A knowledge maintenance tool, IMM/Def, has been developed to help maintain the rule-based logic. The paper describes the design of IMM/Serve and the rationale and role of the different forms of knowledge used.

Visual saliency-based fast intracoding algorithm for high efficiency video coding

NASA Astrophysics Data System (ADS)

Zhou, Xin; Shi, Guangming; Zhou, Wei; Duan, Zhemin

2017-01-01

Intraprediction has been significantly improved in high efficiency video coding over H.264/AVC with quad-tree-based coding unit (CU) structure from size 64×64 to 8×8 and more prediction modes. However, these techniques cause a dramatic increase in computational complexity. An intracoding algorithm is proposed that consists of perceptual fast CU size decision algorithm and fast intraprediction mode decision algorithm. First, based on the visual saliency detection, an adaptive and fast CU size decision method is proposed to alleviate intraencoding complexity. Furthermore, a fast intraprediction mode decision algorithm with step halving rough mode decision method and early modes pruning algorithm is presented to selectively check the potential modes and effectively reduce the complexity of computation. Experimental results show that our proposed fast method reduces the computational complexity of the current HM to about 57% in encoding time with only 0.37% increases in BD rate. Meanwhile, the proposed fast algorithm has reasonable peak signal-to-noise ratio losses and nearly the same subjective perceptual quality.

Zero-block mode decision algorithm for H.264/AVC.

PubMed

Lee, Yu-Ming; Lin, Yinyi

2009-03-01

In the previous paper , we proposed a zero-block intermode decision algorithm for H.264 video coding based upon the number of zero-blocks of 4 x 4 DCT coefficients between the current macroblock and the co-located macroblock. The proposed algorithm can achieve significant improvement in computation, but the computation performance is limited for high bit-rate coding. To improve computation efficiency, in this paper, we suggest an enhanced zero-block decision algorithm, which uses an early zero-block detection method to compute the number of zero-blocks instead of direct DCT and quantization (DCT/Q) calculation and incorporates two adequate decision methods into semi-stationary and nonstationary regions of a video sequence. In addition, the zero-block decision algorithm is also applied to the intramode prediction in the P frame. The enhanced zero-block decision algorithm brings out a reduction of average 27% of total encoding time compared to the zero-block decision algorithm.

Computational prediction of multidisciplinary team decision-making for adjuvant breast cancer drug therapies: a machine learning approach.

PubMed

Lin, Frank P Y; Pokorny, Adrian; Teng, Christina; Dear, Rachel; Epstein, Richard J

2016-12-01

Multidisciplinary team (MDT) meetings are used to optimise expert decision-making about treatment options, but such expertise is not digitally transferable between centres. To help standardise medical decision-making, we developed a machine learning model designed to predict MDT decisions about adjuvant breast cancer treatments. We analysed MDT decisions regarding adjuvant systemic therapy for 1065 breast cancer cases over eight years. Machine learning classifiers with and without bootstrap aggregation were correlated with MDT decisions (recommended, not recommended, or discussable) regarding adjuvant cytotoxic, endocrine and biologic/targeted therapies, then tested for predictability using stratified ten-fold cross-validations. The predictions so derived were duly compared with those based on published (ESMO and NCCN) cancer guidelines. Machine learning more accurately predicted adjuvant chemotherapy MDT decisions than did simple application of guidelines. No differences were found between MDT- vs. ESMO/NCCN- based decisions to prescribe either adjuvant endocrine (97%, p = 0.44/0.74) or biologic/targeted therapies (98%, p = 0.82/0.59). In contrast, significant discrepancies were evident between MDT- and guideline-based decisions to prescribe chemotherapy (87%, p < 0.01, representing 43% and 53% variations from ESMO/NCCN guidelines, respectively). Using ten-fold cross-validation, the best classifiers achieved areas under the receiver operating characteristic curve (AUC) of 0.940 for chemotherapy (95% C.I., 0.922-0.958), 0.899 for the endocrine therapy (95% C.I., 0.880-0.918), and 0.977 for trastuzumab therapy (95% C.I., 0.955-0.999) respectively. Overall, bootstrap aggregated classifiers performed better among all evaluated machine learning models. A machine learning approach based on clinicopathologic characteristics can predict MDT decisions about adjuvant breast cancer drug therapies. The discrepancy between MDT- and guideline-based decisions regarding adjuvant chemotherapy implies that certain non-clincopathologic criteria, such as patient preference and resource availability, are factored into clinical decision-making by local experts but not captured by guidelines.

Education in the workplace for the physician: clinical management states as an organizing framework.

PubMed

Greenes, R A

2000-01-01

Medical educators are interested in approaches to making selected relevant knowledge available in the context of problem-based care. This is of value both during the process of care and as a means of organizing information for offline self-study. Four trends in health information technology are relevant to achieving the goal and can be expected to play a growing role in the future. First, health care enterprises are developing approaches for access to information resources related to the care of a patient, including clinical data and images but also communication tools, referral and other logistic tools, decision support, and educational materials. Second, information for patients and methods for patient-doctor interaction and decision making are becoming available. Third, computer-based methods for representation of practice guidelines are being developed to support applications that can incorporate their logic. Finally, considering patients as being in particular "clinical management states" (or CMSs) for specific problems, approaches are being developed to use guidelines as a kind of "predictive" framework to enable development of interfaces for problem-based clinical encounters. The guidelines for a CMS can be used to identify the kinds of resources specifically needed for clinical encounters of that type. As the above trends converge to produce problem-specific environments, professional specialty organizations and continuing medical education course designers will need to focus energies on organizing and updating medical knowledge to make it available in CMS-specific contexts.

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.

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.

Clinical care management and workflow by episodes.

PubMed Central

Claus, P. L.; Carpenter, P. C.; Chute, C. G.; Mohr, D. N.; Gibbons, P. S.

1997-01-01

This paper describes the implementation of clinically defined episodes of care and the introduction of an episode-based summary list of patient problems across Mayo Clinic Rochester in 1996 and 1997. Although Mayo's traditional paper-based system has always relied on a type of 'episode of care' (called the "registration") for patient and history management, a new, more clinically relevant definition of episode of care was put into practice in November 1996. This was done to improve care management and operational processes and to provide a basic construct for the electronic medical record. Also since November 1996, a computer-generated summary list of patient problems, the "Master Sheet Summary Report," organized by episode, has been placed in all patient histories. In the third quarter of 1997, the ability to view the episode-based problem summary online was made available to the 3000+ EMR-capable workstations deployed across the Mayo Rochester campus. In addition, the clinically oriented problem summarization process produces an improved basic "package" of clinical information expected to lead to improved analytic decision support, outcomes analysis and epidemiological research. PMID:9357595

Development of a computer-interpretable clinical guideline model for decision support in the differential diagnosis of hyponatremia.

PubMed

González-Ferrer, Arturo; Valcárcel, M Ángel; Cuesta, Martín; Cháfer, Joan; Runkle, Isabelle

2017-07-01

Hyponatremia is the most common type of electrolyte imbalance, occurring when serum sodium is below threshold levels, typically 135mmol/L. Electrolyte balance has been identified as one of the most challenging subjects for medical students, but also as one of the most relevant areas to learn about according to physicians and researchers. We present a computer-interpretable guideline (CIG) model that will be used for medical training to learn how to improve the diagnosis of hyponatremia applying an expert consensus document (ECDs). We used the PROForma set of tools to develop the model, using an iterative process involving two knowledge engineers (a computer science Ph.D. and a preventive medicine specialist) and two expert endocrinologists. We also carried out an initial validation of the model and a qualitative post-analysis from the results of a retrospective study (N=65 patients), comparing the consensus diagnosis of two experts with the output of the tool. The model includes over two-hundred "for", "against" and "neutral" arguments that are selectively triggered depending on the input value of more than forty patient-state variables. We share the methodology followed for the development process and the initial validation results, that achieved a high ratio of 61/65 agreements with the consensus diagnosis, having a kappa value of K=0.86 for overall agreement and K=0.80 for first-ranked agreement. Hospital care professionals involved in the project showed high expectations of using this tool for training, but the process to follow for a successful diagnosis and application is not trivial, as reported in this manuscript. Secondary benefits of using these tools are associated to improving research knowledge and existing clinical practice guidelines (CPGs) or ECDs. Beyond point-of-care clinical decision support, knowledge-based decision support systems are very attractive as a training tool, to help selected professionals to better understand difficult diseases that are underdiagnosed and/or incorrectly managed. Copyright © 2017 Elsevier B.V. All rights reserved.

Measuring outcomes in children's rehabilitation: a decision protocol.

PubMed

Law, M; King, G; Russell, D; MacKinnon, E; Hurley, P; Murphy, C

1999-06-01

To develop and test the feasibility and clinical utility of a computerized self-directed software program designed to enable service providers in children's rehabilitation to make decisions about the most appropriate outcome measures to use in client and program evaluation. A before-and-after design was used to test the feasibility and initial impact of the decision-making outcome software in improving knowledge and use of clinical outcome measures. A children's rehabilitation center in a city of 50,000. All service providers in the children's rehabilitation center. Disciplines represented included early childhood education, occupational therapy, physical therapy, speech and language pathology, audiology, social work, and psychology. Using a conceptual framework based on the International Classification of Impairment, Disability, and Handicap (ICIDH), an outcome measurement decision-making protocol was developed. The decision-making protocol was computerized in an educational software program with an attached database of critically appraised measures. Participants learned about outcome measures through the program and selected outcome measures that met their specifications. The computer software was tested for feasibility in the children's rehabilitation center for 6 months. Knowledge and use of clinical outcome measures were determined before and after the feasibility testing using a survey of all service providers currently at the centre and audits of 30 randomly selected rehabilitation records (at pretest, posttest, and follow-up). Service providers indicated that the outcomes software was easy to follow and believed that the use of the ICIDH framework helped them in making decisions about selecting outcome measures. Results of the survey indicated that there were significant changes in the service providers' level of comfort with selecting measures and knowing what measures were available. Use of outcome measures as identified through the audit did not change. The "All About Outcomes" software is clinically useful. Further research should evaluate whether using the software affects the use of outcome measures in clinical practice.

Deep learning in mammography and breast histology, an overview and future trends.

PubMed

Hamidinekoo, Azam; Denton, Erika; Rampun, Andrik; Honnor, Kate; Zwiggelaar, Reyer

2018-07-01

Recent improvements in biomedical image analysis using deep learning based neural networks could be exploited to enhance the performance of Computer Aided Diagnosis (CAD) systems. Considering the importance of breast cancer worldwide and the promising results reported by deep learning based methods in breast imaging, an overview of the recent state-of-the-art deep learning based CAD systems developed for mammography and breast histopathology images is presented. In this study, the relationship between mammography and histopathology phenotypes is described, which takes biological aspects into account. We propose a computer based breast cancer modelling approach: the Mammography-Histology-Phenotype-Linking-Model, which develops a mapping of features/phenotypes between mammographic abnormalities and their histopathological representation. Challenges are discussed along with the potential contribution of such a system to clinical decision making and treatment management. Crown Copyright © 2018. Published by Elsevier B.V. All rights reserved.

A rapid evidence-based service by librarians provided information to answer primary care clinical questions.

PubMed

McGowan, Jessie; Hogg, William; Rader, Tamara; Salzwedel, Doug; Worster, Danielle; Cogo, Elise; Rowan, Margo

2010-03-01

A librarian consultation service was offered to 88 primary care clinicians during office hours. This included a streamlined evidence-based process to answer questions in fewer than 20 min. This included a contact centre accessed through a Web-based platform and using hand-held devices and computers with Web access. Librarians were given technical training in evidence-based medicine, including how to summarise evidence. To describe the process and lessons learned from developing and operating a rapid response librarian consultation service for primary care clinicians. Evaluation included librarian interviews and a clinician exit satisfaction survey. Clinicians were positive about its impact on their clinical practice and decision making. The project revealed some important 'lessons learned' in the clinical use of hand-held devices, knowledge translation and training for clinicians and librarians. The Just-in-Time Librarian Consultation Service showed that it was possible to provide evidence-based answers to clinical questions in 15 min or less. The project overcame a number of barriers using innovative solutions. There are many opportunities to build on this experience for future joint projects of librarians and healthcare providers.

Neurostimulation options for failed back surgery syndrome: The need for rational and objective measurements. Proposal of an international clinical network using an integrated database and health economic analysis: the PROBACK network.

PubMed

Rigoard, P; Slavin, K

2015-03-01

In the context of failed back surgery syndrome (FBSS) treatment, the current practice in neurostimulation varies from center-to-center and most clinical decisions are based on an individual diagnosis. Neurostimulation evaluation tools and pain relief assessment are of major concern, as they now constitute one of the main biases of clinical trials. Moreover, the proliferation of technological devices, in a fertile and unsatisfied market, fosters and only furthers the confusion. There are three options available to apply scientific debates to our daily neurostimulation practice: intentional ignorance, standardized evidence-based practice or alternative data mining approach. In view of the impossibility of conducting multiple randomized clinical trials comparing various devices, one by one, the proposed concept would be to redefine the indications and the respective roles of the various spinal cord and peripheral nerve stimulation devices with large-scale computational modeling/data mining approach, by conducting a multicenter prospective database registry, supported by a clinician's global network called "PROBACK". We chose to specifically analyze 6 parameters: device coverage performance/coverage selectivity/persistence of the long-term electrical response (technical criteria) and comparative mapping of patient pain relief/persistence of the long-term clinical response/safety and complications occurrence (clinical criteria). Two types of analysis will be performed: immediate analysis (including cost analysis) and computational analysis, i.e. demonstration of the robustness of certain correlations of variables, in order to extract response predictors. By creating an international prospective database, the purpose of the PROBACK project was to set up a process of extraction and comparative analysis of data derived from the selection, implantation and follow-up of FBSS patients candidates for implanted neurostimulation. This evaluation strategy should help to change the opinions of each implanter and each health system towards a more rational decision-making approach subtended by mathematical reality. Copyright © 2014 Elsevier Masson SAS. All rights reserved.

Iterative evaluation in a mobile counseling and testing program to reach people of color at risk for HIV--new strategies improve program acceptability, effectiveness, and evaluation capabilities.

PubMed

Spielberg, Freya; Kurth, Ann; Reidy, William; McKnight, Teka; Dikobe, Wame; Wilson, Charles

2011-06-01

This article highlights findings from an evaluation that explored the impact of mobile versus clinic-based testing, rapid versus central-lab based testing, incentives for testing, and the use of a computer counseling program to guide counseling and automate evaluation in a mobile program reaching people of color at risk for HIV. The program's results show that an increased focus on mobile outreach using rapid testing, incentives and health information technology tools may improve program acceptability, quality, productivity and timeliness of reports. This article describes program design decisions based on continuous quality assessment efforts. It also examines the impact of the Computer Assessment and Risk Reduction Education computer tool on HIV testing rates, staff perception of counseling quality, program productivity, and on the timeliness of evaluation reports. The article concludes with a discussion of implications for programmatic responses to the Centers for Disease Control and Prevention's HIV testing recommendations.

ITERATIVE EVALUATION IN A MOBILE COUNSELING AND TESTING PROGRAM TO REACH PEOPLE OF COLOR AT RISK FOR HIV—NEW STRATEGIES IMPROVE PROGRAM ACCEPTABILITY, EFFECTIVENESS, AND EVALUATION CAPABILITIES

PubMed Central

Spielberg, Freya; Kurth, Ann; Reidy, William; McKnight, Teka; Dikobe, Wame; Wilson, Charles

2016-01-01

This article highlights findings from an evaluation that explored the impact of mobile versus clinic-based testing, rapid versus central-lab based testing, incentives for testing, and the use of a computer counseling program to guide counseling and automate evaluation in a mobile program reaching people of color at risk for HIV. The program’s results show that an increased focus on mobile outreach using rapid testing, incentives and health information technology tools may improve program acceptability, quality, productivity and timeliness of reports. This article describes program design decisions based on continuous quality assessment efforts. It also examines the impact of the Computer Assessment and Risk Reduction Education computer tool on HIV testing rates, staff perception of counseling quality, program productivity, and on the timeliness of evaluation reports. The article concludes with a discussion of implications for programmatic responses to the Centers for Disease Control and Prevention’s HIV testing recommendations. PMID:21689041

"Radio-oncomics" : The potential of radiomics in radiation oncology.

PubMed

Peeken, Jan Caspar; Nüsslin, Fridtjof; Combs, Stephanie E

2017-10-01

Radiomics, a recently introduced concept, describes quantitative computerized algorithm-based feature extraction from imaging data including computer tomography (CT), magnetic resonance imaging (MRT), or positron-emission tomography (PET) images. For radiation oncology it offers the potential to significantly influence clinical decision-making and thus therapy planning and follow-up workflow. After image acquisition, image preprocessing, and defining regions of interest by structure segmentation, algorithms are applied to calculate shape, intensity, texture, and multiscale filter features. By combining multiple features and correlating them with clinical outcome, prognostic models can be created. Retrospective studies have proposed radiomics classifiers predicting, e. g., overall survival, radiation treatment response, distant metastases, or radiation-related toxicity. Besides, radiomics features can be correlated with genomic information ("radiogenomics") and could be used for tumor characterization. Distinct patterns based on data-based as well as genomics-based features will influence radiation oncology in the future. Individualized treatments in terms of dose level adaption and target volume definition, as well as other outcome-related parameters will depend on radiomics and radiogenomics. By integration of various datasets, the prognostic power can be increased making radiomics a valuable part of future precision medicine approaches. This perspective demonstrates the evidence for the radiomics concept in radiation oncology. The necessity of further studies to integrate radiomics classifiers into clinical decision-making and the radiation therapy workflow is emphasized.

Cluster-randomized, controlled trial of computer-based decision support for selecting long-term anti-thrombotic therapy after acute ischaemic stroke.

PubMed

Weir, C J; Lees, K R; MacWalter, R S; Muir, K W; Wallesch, C-W; McLelland, E V; Hendry, A

2003-02-01

Identifying the appropriate long-term anti-thrombotic therapy following acute ischaemic stroke is a challenging area in which computer-based decision support may provide assistance. To evaluate the influence on prescribing practice of a computer-based decision support system (CDSS) that provided patient-specific estimates of the expected ischaemic and haemorrhagic vascular event rates under each potential anti-thrombotic therapy. Cluster-randomized controlled trial. We recruited patients who presented for a first investigation of ischaemic stroke or TIA symptoms, excluding those with a poor prognosis or major contraindication to anticoagulation. After observation of routine prescribing practice (6 months) in each hospital, centres were randomized for 6 months to either control (routine practice observed) or intervention (practice observed while the CDSS provided patient-specific information). We compared, between control and intervention centres, the risk reduction (estimated by the CDSS) in ischaemic and haemorrhagic vascular events achieved by long-term anti-thrombotic therapy, and the proportions of subjects prescribed the optimal therapy identified by the CDSS. Sixteen hospitals recruited 1952 subjects. When the CDSS provided information, the mean relative risk reduction attained by prescribing increased by 2.7 percentage units (95%CI -0.3 to 5.7) and the odds ratio for the optimal therapy being prescribed was 1.32 (0.83 to 1.80). Some 55% (5/9) of clinicians believed the CDSS had influenced their prescribing. Cluster-randomized trials provide excellent frameworks for evaluating novel clinical management methods. Our CDSS was feasible to implement and acceptable to clinicians, but did not substantially influence prescribing practice for anti-thrombotic drugs after acute ischaemic stroke.

Decision aids for patients.

PubMed

Lenz, Matthias; Buhse, Susanne; Kasper, Jürgen; Kupfer, Ramona; Richter, Tanja; Mühlhauser, Ingrid

2012-06-01

Patients want to be more involved in medical decision-making. To this end, some decision aids are now available. We present an overview of this subject, in which we explain the terms "shared decision-making", "decision aid", and "evidence-based patient information" and survey information on the available decision aids in German and other languages on the basis of a literature search in MEDLINE, EMBASE and PsycInfo and a current Cochrane Review. We also searched the Internet for providers of decision aids in Germany. Decision aids exist in the form of brochures, decision tables, videos, and computer programs; they address various topics in the prevention, diagnosis, and treatment of disease. They typically contain information on the advantages and disadvantages of the available options, as well as guidance for personal decision-making. They can be used alone or as a part of structured counseling or patient education. Minimal quality standards include an adequate evidence base, completeness, absence of bias, and intelligibility. Our search revealed 12 randomized controlled trials (RCTs) of decision aids in German and 106 RCTs of decision aids in other languages. These trials studied the outcome of the use of decision aids not just with respect to clinical developments, but also with respect to patient knowledge, adherence to treatment regimens, satisfaction, involvement in decision-making, autonomy preference, and decisional conflicts. Only a small fraction of the available decision aids were systematically developed and have been subjected to systematic evaluation. Patients are still not receiving the help in decision-making to which medical ethics entitles them. Structures need to be put in place for the sustainable development, evaluation and implementation of high-quality decision aids.

The impact of simulation sequencing on perceived clinical decision making.

PubMed

Woda, Aimee; Hansen, Jamie; Paquette, Mary; Topp, Robert

2017-09-01

An emerging nursing education trend is to utilize simulated learning experiences as a means to optimize competency and decision making skills. The purpose of this study was to examine differences in students' perception of clinical decision making and clinical decision making-related self-confidence and anxiety based on the sequence (order) in which they participated in a block of simulated versus hospital-based learning experiences. A quasi-experimental crossover design was used. Between and within group differences were found relative to self-confidence with the decision making process. When comparing groups, at baseline the simulation followed by hospital group had significantly higher self-confidence scores, however, at 14-weeks both groups were not significantly different. Significant within group differences were found in the simulation followed by hospital group only, demonstrating a significant decrease in clinical decision making related anxiety across the semester. Finally, there were no significant difference in; perceived clinical decision making within or between the groups at the two measurement points. Preliminary findings suggest that simulated learning experiences can be offered with alternating sequences without impacting the process, anxiety or confidence with clinical decision making. This study provides beginning evidence to guide curriculum development and allow flexibility based on student needs and available resources. Copyright © 2017. Published by Elsevier Ltd.

Comparing the effect of a decision aid plus patient navigation with usual care on colorectal cancer screening completion in vulnerable populations: study protocol for a randomized controlled trial.

PubMed

Brenner, Alison T; Getrich, Christina M; Pignone, Michael; Rhyne, Robert L; Hoffman, Richard M; McWilliams, Andrew; de Hernandez, Brisa Urquieta; Weaver, Mark A; Tapp, Hazel; Harbi, Khalil; Reuland, Daniel

2014-07-08

Screening can reduce colorectal cancer (CRC) incidence and mortality. However, screening is underutilized in vulnerable patient populations, particularly among Latinos. Patient-directed decision aids can increase CRC screening knowledge, self-efficacy, and intent; however, their effect on actual screening test completion tends to be modest. This is probably because decision aids do not address some of the patient-specific barriers that prevent successful completion of CRC screening in these populations. These individual barriers might be addressed though patient navigation interventions. This study will test a combined decision aid and patient navigator intervention on screening completion in diverse populations of vulnerable primary care patients. We will conduct a multisite, randomized controlled trial with patient-level randomization. Planned enrollment is 300 patients aged 50 to 75 years at average CRC risk presenting for appointments at two primary clinics in North Carolina and New Mexico. Intervention participants will view a video decision aid immediately before the clinic visit. The 14 to 16 minute video presents information about fecal occult blood tests and colonoscopy and will be viewed on a portable computer tablet in English or Spanish. Clinic-based patient navigators are bilingual and bicultural and will provide both face-to-face and telephone-based navigation. Control participants will view an unrelated food safety video and receive usual care. The primary outcome is completion of a CRC screening test at six months. Planned subgroup analyses include examining intervention effectiveness in Latinos, who will be oversampled. Secondarily, the trial will evaluate the intervention effects on knowledge of CRC screening, self-efficacy, intent, and patient-provider communication. The study will also examine whether patient ethnicity, acculturation, language preference, or health insurance status moderate the intervention effect on CRC screening. This pragmatic randomized controlled trial will test a combined decision aid and patient navigator intervention targeting CRC screening completion. Findings from this trial may inform future interventions and implementation policies designed to promote CRC screening in vulnerable patient populations and to reduce screening disparities. ClinicalTrials.gov NCT02054598.

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.

Coupling computer-interpretable guidelines with a drug-database through a web-based system – The PRESGUID project

PubMed Central

Dufour, Jean-Charles; Fieschi, Dominique; Fieschi, Marius

2004-01-01

Background Clinical Practice Guidelines (CPGs) available today are not extensively used due to lack of proper integration into clinical settings, knowledge-related information resources, and lack of decision support at the point of care in a particular clinical context. Objective The PRESGUID project (PREScription and GUIDelines) aims to improve the assistance provided by guidelines. The project proposes an online service enabling physicians to consult computerized CPGs linked to drug databases for easier integration into the healthcare process. Methods Computable CPGs are structured as decision trees and coded in XML format. Recommendations related to drug classes are tagged with ATC codes. We use a mapping module to enhance computerized guidelines coupling with a drug database, which contains detailed information about each usable specific medication. In this way, therapeutic recommendations are backed up with current and up-to-date information from the database. Results Two authoritative CPGs, originally diffused as static textual documents, have been implemented to validate the computerization process and to illustrate the usefulness of the resulting automated CPGs and their coupling with a drug database. We discuss the advantages of this approach for practitioners and the implications for both guideline developers and drug database providers. Other CPGs will be implemented and evaluated in real conditions by clinicians working in different health institutions. PMID:15053828

A Three-Phase Decision Model of Computer-Aided Coding for the Iranian Classification of Health Interventions (IRCHI).

PubMed

Azadmanjir, Zahra; Safdari, Reza; Ghazisaeedi, Marjan; Mokhtaran, Mehrshad; Kameli, Mohammad Esmail

2017-06-01

Accurate coded data in the healthcare are critical. Computer-Assisted Coding (CAC) is an effective tool to improve clinical coding in particular when a new classification will be developed and implemented. But determine the appropriate method for development need to consider the specifications of existing CAC systems, requirements for each type, our infrastructure and also, the classification scheme. The aim of the study was the development of a decision model for determining accurate code of each medical intervention in Iranian Classification of Health Interventions (IRCHI) that can be implemented as a suitable CAC system. first, a sample of existing CAC systems was reviewed. Then feasibility of each one of CAC types was examined with regard to their prerequisites for their implementation. The next step, proper model was proposed according to the structure of the classification scheme and was implemented as an interactive system. There is a significant relationship between the level of assistance of a CAC system and integration of it with electronic medical documents. Implementation of fully automated CAC systems is impossible due to immature development of electronic medical record and problems in using language for medical documenting. So, a model was proposed to develop semi-automated CAC system based on hierarchical relationships between entities in the classification scheme and also the logic of decision making to specify the characters of code step by step through a web-based interactive user interface for CAC. It was composed of three phases to select Target, Action and Means respectively for an intervention. The proposed model was suitable the current status of clinical documentation and coding in Iran and also, the structure of new classification scheme. Our results show it was practical. However, the model needs to be evaluated in the next stage of the research.

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.

Information-seeking behavior changes in community-based teaching practices*†

PubMed Central

Byrnes, Jennifer A.; Kulick, Tracy A.; Schwartz, Diane G.

2004-01-01

A National Library of Medicine information access grant allowed for a collaborative project to provide computer resources in fourteen clinical practice sites that enabled health care professionals to access medical information via PubMed and the Internet. Health care professionals were taught how to access quality, cost-effective information that was user friendly and would result in improved patient care. Selected sites were located in medically underserved areas and received a computer, a printer, and, during year one, a fax machine. Participants were provided dial-up Internet service or were connected to the affiliated hospital's network. Clinicians were trained in how to search PubMed as a tool for practicing evidence-based medicine and to support clinical decision making. Health care providers were also taught how to find patient-education materials and continuing education programs and how to network with other professionals. Prior to the training, participants completed a questionnaire to assess their computer skills and familiarity with searching the Internet, MEDLINE, and other health-related databases. Responses indicated favorable changes in information-seeking behavior, including an increased frequency in conducting MEDLINE searches and Internet searches for work-related information. PMID:15243639

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.

Designing a Clinical Framework to Guide Gross Motor Intervention Decisions for Infants and Young Children with Hypotonia

ERIC Educational Resources Information Center

Darrah, Johanna; O'Donnell, Maureen; Lam, Joyce; Story, Maureen; Wickenheiser, Diane; Xu, Kaishou; Jin, Xiaokun

2013-01-01

Clinical practice frameworks are a valuable component of clinical education, promoting informed clinical decision making based on the best available evidence and/or clinical experience. They encourage standardized intervention approaches and evaluation of practice. Based on an international project to support the development of an enhanced service…

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.

A Web-Based Tool to Support Shared Decision Making for People With a Psychotic Disorder: Randomized Controlled Trial and Process Evaluation

PubMed Central

Emerencia, Ando C; Boonstra, Nynke; Wunderink, Lex; de Jonge, Peter; Sytema, Sjoerd

2013-01-01

Background Mental health policy makers encourage the development of electronic decision aids to increase patient participation in medical decision making. Evidence is needed to determine whether these decision aids are helpful in clinical practice and whether they lead to increased patient involvement and better outcomes. Objective This study reports the outcome of a randomized controlled trial and process evaluation of a Web-based intervention to facilitate shared decision making for people with psychotic disorders. Methods The study was carried out in a Dutch mental health institution. Patients were recruited from 2 outpatient teams for patients with psychosis (N=250). Patients in the intervention condition (n=124) were provided an account to access a Web-based information and decision tool aimed to support patients in acquiring an overview of their needs and appropriate treatment options provided by their mental health care organization. Patients were given the opportunity to use the Web-based tool either on their own (at their home computer or at a computer of the service) or with the support of an assistant. Patients in the control group received care as usual (n=126). Half of the patients in the sample were patients experiencing a first episode of psychosis; the other half were patients with a chronic psychosis. Primary outcome was patient-perceived involvement in medical decision making, measured with the Combined Outcome Measure for Risk Communication and Treatment Decision-making Effectiveness (COMRADE). Process evaluation consisted of questionnaire-based surveys, open interviews, and researcher observation. Results In all, 73 patients completed the follow-up measurement and were included in the final analysis (response rate 29.2%). More than one-third (48/124, 38.7%) of the patients who were provided access to the Web-based decision aid used it, and most used its full functionality. No differences were found between the intervention and control conditions on perceived involvement in medical decision making (COMRADE satisfaction with communication: F1,68=0.422, P=.52; COMRADE confidence in decision: F1,67=0.086, P=.77). In addition, results of the process evaluation suggest that the intervention did not optimally fit in with routine practice of the participating teams. Conclusions The development of electronic decision aids to facilitate shared medical decision making is encouraged and many people with a psychotic disorder can work with them. This holds for both first-episode patients and long-term care patients, although the latter group might need more assistance. However, results of this paper could not support the assumption that the use of electronic decision aids increases patient involvement in medical decision making. This may be because of weak implementation of the study protocol and a low response rate. Trial Registration Dutch Trial Register (NTR) trial number: 10340; http://www.trialregister.nl/trialreg/admin/rctsearch.asp?Term=10340 (Archived by WebCite at http://www.webcitation.org/6Jj5umAeS). PMID:24100091

Goal-Directed Decision Making as Probabilistic Inference: A Computational Framework and Potential Neural Correlates

ERIC Educational Resources Information Center

Solway, Alec; Botvinick, Matthew M.

2012-01-01

Recent work has given rise to the view that reward-based decision making is governed by two key controllers: a habit system, which stores stimulus-response associations shaped by past reward, and a goal-oriented system that selects actions based on their anticipated outcomes. The current literature provides a rich body of computational theory…

Knowledge-based geographic information systems on the Macintosh computer: a component of the GypsES project

Treesearch

Gregory Elmes; Thomas Millette; Charles B. Yuill

1991-01-01

GypsES, a decision-support and expert system for the management of Gypsy Moth addresses five related research problems in a modular, computer-based project. The modules are hazard rating, monitoring, prediction, treatment decision and treatment implementation. One common component is a geographic information system designed to function intelligently. We refer to this...

MoCog1: A computer simulation of recognition-primed human decision making

NASA Technical Reports Server (NTRS)

Gevarter, William B.

1991-01-01

This report describes the successful results of the first stage of a research effort to develop a 'sophisticated' computer model of human cognitive behavior. Most human decision-making is of the experience-based, relatively straight-forward, largely automatic, type of response to internal goals and drives, utilizing cues and opportunities perceived from the current environment. This report describes the development of the architecture and computer program associated with such 'recognition-primed' decision-making. The resultant computer program was successfully utilized as a vehicle to simulate findings that relate how an individual's implicit theories orient them toward particular goals, with resultant cognitions, affects, and behavior in response to their environment. The present work is an expanded version and is based on research reported while the author was an employee of NASA ARC.

Data-mining to build a knowledge representation store for clinical decision support. Studies on curation and validation based on machine performance in multiple choice medical licensing examinations.

PubMed

Robson, Barry; Boray, Srinidhi

2016-06-01

Extracting medical knowledge by structured data mining of many medical records and from unstructured data mining of natural language source text on the Internet will become increasingly important for clinical decision support. Output from these sources can be transformed into large numbers of elements of knowledge in a Knowledge Representation Store (KRS), here using the notation and to some extent the algebraic principles of the Q-UEL Web-based universal exchange and inference language described previously, rooted in Dirac notation from quantum mechanics and linguistic theory. In a KRS, semantic structures or statements about the world of interest to medicine are analogous to natural language sentences seen as formed from noun phrases separated by verbs, prepositions and other descriptions of relationships. A convenient method of testing and better curating these elements of knowledge is by having the computer use them to take the test of a multiple choice medical licensing examination. It is a venture which perhaps tells us almost as much about the reasoning of students and examiners as it does about the requirements for Artificial Intelligence as employed in clinical decision making. It emphasizes the role of context and of contextual probabilities as opposed to the more familiar intrinsic probabilities, and of a preliminary form of logic that we call presyllogistic reasoning. Copyright © 2016 Elsevier Ltd. All rights reserved.

Access to augmentative and alternative communication: new technologies and clinical decision-making.

PubMed

Fager, Susan; Bardach, Lisa; Russell, Susanne; Higginbotham, Jeff

2012-01-01

Children with severe physical impairments require a variety of access options to augmentative and alternative communication (AAC) and computer technology. Access technologies have continued to develop, allowing children with severe motor control impairments greater independence and access to communication. This article will highlight new advances in access technology, including eye and head tracking, scanning, and access to mainstream technology, as well as discuss future advances. Considerations for clinical decision-making and implementation of these technologies will be presented along with case illustrations.

Introduction to Decision Support Systems for Risk Based Management of Contaminated Sites

EPA Science Inventory

A book on Decision Support Systems for Risk-based Management of contaminated sites is appealing for two reasons. First, it addresses the problem of contaminated sites, which has worldwide importance. Second, it presents Decision Support Systems (DSSs), which are powerful comput...

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.

Preoperative computer simulation for planning of vascular access surgery in hemodialysis patients.

PubMed

Zonnebeld, Niek; Huberts, Wouter; van Loon, Magda M; Delhaas, Tammo; Tordoir, Jan H M

2017-03-06

The arteriovenous fistula (AVF) is the preferred vascular access for hemodialysis patients. Unfortunately, 20-40% of all constructed AVFs fail to mature (FTM), and are therefore not usable for hemodialysis. AVF maturation importantly depends on postoperative blood volume flow. Predicting patient-specific immediate postoperative flow could therefore support surgical planning. A computational model predicting blood volume flow is available, but the effect of blood flow predictions on the clinical endpoint of maturation (at least 500 mL/min blood volume flow, diameter of the venous cannulation segment ≥4 mm) remains undetermined. A multicenter randomized clinical trial will be conducted in which 372 patients will be randomized (1:1 allocation ratio) between conventional healthcare and computational model-aided decision making. All patients are extensively examined using duplex ultrasonography (DUS) during preoperative assessment (12 venous and 11 arterial diameter measurements; 3 arterial volume flow measurements). The computational model will predict patient-specific immediate postoperative blood volume flows based on this DUS examination. Using these predictions, the preferred AVF configuration is recommended for the individual patient (radiocephalic, brachiocephalic, or brachiobasilic). The primary endpoint is FTM rate at six weeks in both groups, secondary endpoints include AVF functionality and patency rates at 6 and 12 months postoperatively. ClinicalTrials.gov (NCT02453412), and ToetsingOnline.nl (NL51610.068.14).

The impact of computer self-efficacy, computer anxiety, and perceived usability and acceptability on the efficacy of a decision support tool for colorectal cancer screening

PubMed Central

Lindblom, Katrina; Gregory, Tess; Flight, Ingrid H K; Zajac, Ian

2011-01-01

Objective This study investigated the efficacy of an internet-based personalized decision support (PDS) tool designed to aid in the decision to screen for colorectal cancer (CRC) using a fecal occult blood test. We tested whether the efficacy of the tool in influencing attitudes to screening was mediated by perceived usability and acceptability, and considered the role of computer self-efficacy and computer anxiety in these relationships. Methods Eighty-one participants aged 50–76 years worked through the on-line PDS tool and completed questionnaires on computer self-efficacy, computer anxiety, attitudes to and beliefs about CRC screening before and after exposure to the PDS, and perceived usability and acceptability of the tool. Results Repeated measures ANOVA found that PDS exposure led to a significant increase in knowledge about CRC and screening, and more positive attitudes to CRC screening as measured by factors from the Preventive Health Model. Perceived usability and acceptability of the PDS mediated changes in attitudes toward CRC screening (but not CRC knowledge), and computer self-efficacy and computer anxiety were significant predictors of individuals' perceptions of the tool. Conclusion Interventions designed to decrease computer anxiety, such as computer courses and internet training, may improve the acceptability of new health information technologies including internet-based decision support tools, increasing their impact on behavior change. PMID:21857024

Self-Organized Service Negotiation for Collaborative Decision Making

PubMed Central

Zhang, Bo; Zheng, Ziming

2014-01-01

This paper proposes a self-organized service negotiation method for CDM in intelligent and automatic manners. It mainly includes three phases: semantic-based capacity evaluation for the CDM sponsor, trust computation of the CDM organization, and negotiation selection of the decision-making service provider (DMSP). In the first phase, the CDM sponsor produces the formal semantic description of the complex decision task for DMSP and computes the capacity evaluation values according to participator instructions from different DMSPs. In the second phase, a novel trust computation approach is presented to compute the subjective belief value, the objective reputation value, and the recommended trust value. And in the third phase, based on the capacity evaluation and trust computation, a negotiation mechanism is given to efficiently implement the service selection. The simulation experiment results show that our self-organized service negotiation method is feasible and effective for CDM. PMID:25243228

Self-organized service negotiation for collaborative decision making.

PubMed

Zhang, Bo; Huang, Zhenhua; Zheng, Ziming

2014-01-01

This paper proposes a self-organized service negotiation method for CDM in intelligent and automatic manners. It mainly includes three phases: semantic-based capacity evaluation for the CDM sponsor, trust computation of the CDM organization, and negotiation selection of the decision-making service provider (DMSP). In the first phase, the CDM sponsor produces the formal semantic description of the complex decision task for DMSP and computes the capacity evaluation values according to participator instructions from different DMSPs. In the second phase, a novel trust computation approach is presented to compute the subjective belief value, the objective reputation value, and the recommended trust value. And in the third phase, based on the capacity evaluation and trust computation, a negotiation mechanism is given to efficiently implement the service selection. The simulation experiment results show that our self-organized service negotiation method is feasible and effective for CDM.

Entertainment Education for Prostate Cancer Screening: A Randomized Trial among Primary Care Patients with Low Health Literacy

PubMed Central

Volk, Robert J.; Jibaja-Weiss, Maria L.; Hawley, Sarah T.; Kneuper, Suzanne; Spann, Stephen J.; Miles, Brian J.; Hyman, David J.

2008-01-01

Objective To evaluate an entertainment-based patient decision aid for prostate cancer screening among patients with low or high health literacy. Methods Male primary care patients from two clinical sites, one characterized as serving patients with low health literacy (n=149) and the second as serving patients with high health literacy (n=301), were randomized to receive an entertainment-based decision aid for prostate cancer screening or an audiobooklet-control aid with the same learner content but without the entertainment features. Postintervention and 2-week follow-up assessments were conducted. Results Patients at the low-literacy site were more engaged with the entertainment-based aid than patients at the high-literacy site. Overall, knowledge improved for all patients. Among patients at the low-literacy site, the entertainment-based aid was associated with lower decisional conflict and greater self-advocacy (i.e., mastering and obtaining information about screening) when compared to patients given the audiobooklet. No differences between the aids were observed for patients at the high-literacy site. Conclusions Entertainment education may be an effective strategy for promoting informed decision making about prostate cancer screening among patients with lower health literacy. Practice Implications As barriers to implementing computer-based patient decision support programs decrease, alternative models for delivering these programs should be explored. PMID:18760888

Soldier Decision-Making for Allocation of Intelligence, Surveillance, and Reconnaissance Assets

DTIC Science & Technology

2014-06-01

Judgments; also called Algoritmic or Statistical Judgements Computer Science , Psychology, and Statistics Actuarial or algorithmic...Jan. 2011. [17] R. M. Dawes, D. Faust, and P. E. Meehl, “Clinical versus Actuarial Judgment,” Science , vol. 243, no. 4899, pp. 1668–1674, 1989. [18...School of Computer Science

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.

Towards meaningful medication-related clinical decision support: recommendations for an initial implementation.

PubMed

Phansalkar, S; Wright, A; Kuperman, G J; Vaida, A J; Bobb, A M; Jenders, R A; Payne, T H; Halamka, J; Bloomrosen, M; Bates, D W

2011-01-01

Clinical decision support (CDS) can improve safety, quality, and cost-effectiveness of patient care, especially when implemented in computerized provider order entry (CPOE) applications. Medication-related decision support logic forms a large component of the CDS logic in any CPOE system. However, organizations wishing to implement CDS must either purchase the computable clinical content or develop it themselves. Content provided by vendors does not always meet local expectations. Most organizations lack the resources to customize the clinical content and the expertise to implement it effectively. In this paper, we describe the recommendations of a national expert panel on two basic medication-related CDS areas, specifically, drug-drug interaction (DDI) checking and duplicate therapy checking. The goals of this study were to define a starter set of medication-related alerts that healthcare organizations can implement in their clinical information systems. We also draw on the experiences of diverse institutions to highlight the realities of implementing medication decision support. These findings represent the experiences of institutions with a long history in the domain of medication decision support, and the hope is that this guidance may improve the feasibility and efficiency CDS adoption across healthcare settings.

Library service delivery via hand-held computers--the right information at the point of care.

PubMed

Peterson, Mary

2004-03-01

Today's health and medical librarians are well aware of the move towards evidence-based clinical practice which has emerged during the past decade. Hand-in-hand with this trend is the need for health practitioners to have access to the best possible evidence to help them in their clinical decision making. Libraries have a key role in the provision of information to their clients, and this means keeping abreast, not only of the various information sources available, but also the means by which those sources may be used. This paper will examine the effects that the hand-held computer is having on the work practices of our clients-library users. It is hoped that the paper will give an insight into the various types of library material which are suitable for use with hand-held devices, and an understanding of their advantages and limitations.

OCIS: 15 years' experience with patient-centered computing.

PubMed

Enterline, J P; Lenhard, R E; Blum, B I; Majidi, F M; Stuart, G J

1994-01-01

In the mid-1970s, the medical and administrative staff of the Oncology Center at Johns Hopkins Hospital recognized a need for a computer-based clinical decision-support system that organized patients' information according to the care continuum, rather than as a series of event-specific data. This is especially important in cancer patients, because of the long periods in which they receive complex medical treatment and the enormous amounts of data generated by extremely ill patients with multiple interrelated diseases. During development of the Oncology Clinical Information System (OCIS), it became apparent that administrative services, research systems, ancillary functions (such as drug and blood product ordering), and financial processes should be integrated with the basic patient-oriented database. With the structured approach used in applications development, new modules were added as the need for additional functions arose. The system has since been moved to a modern network environment with the capacity for client-server processing.

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.

Joint kinematic calculation based on clinical direct kinematic versus inverse kinematic gait models.

PubMed

Kainz, H; Modenese, L; Lloyd, D G; Maine, S; Walsh, H P J; Carty, C P

2016-06-14

Most clinical gait laboratories use the conventional gait analysis model. This model uses a computational method called Direct Kinematics (DK) to calculate joint kinematics. In contrast, musculoskeletal modelling approaches use Inverse Kinematics (IK) to obtain joint angles. IK allows additional analysis (e.g. muscle-tendon length estimates), which may provide valuable information for clinical decision-making in people with movement disorders. The twofold aims of the current study were: (1) to compare joint kinematics obtained by a clinical DK model (Vicon Plug-in-Gait) with those produced by a widely used IK model (available with the OpenSim distribution), and (2) to evaluate the difference in joint kinematics that can be solely attributed to the different computational methods (DK versus IK), anatomical models and marker sets by using MRI based models. Eight children with cerebral palsy were recruited and presented for gait and MRI data collection sessions. Differences in joint kinematics up to 13° were found between the Plug-in-Gait and the gait 2392 OpenSim model. The majority of these differences (94.4%) were attributed to differences in the anatomical models, which included different anatomical segment frames and joint constraints. Different computational methods (DK versus IK) were responsible for only 2.7% of the differences. We recommend using the same anatomical model for kinematic and musculoskeletal analysis to ensure consistency between the obtained joint angles and musculoskeletal estimates. Copyright © 2016 Elsevier Ltd. All rights reserved.

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.

Extensions to regret-based decision curve analysis: an application to hospice referral for terminal patients.

PubMed

Tsalatsanis, Athanasios; Barnes, Laura E; Hozo, Iztok; Djulbegovic, Benjamin

2011-12-23

Despite the well documented advantages of hospice care, most terminally ill patients do not reap the maximum benefit from hospice services, with the majority of them receiving hospice care either prematurely or delayed. Decision systems to improve the hospice referral process are sorely needed. We present a novel theoretical framework that is based on well-established methodologies of prognostication and decision analysis to assist with the hospice referral process for terminally ill patients. We linked the SUPPORT statistical model, widely regarded as one of the most accurate models for prognostication of terminally ill patients, with the recently developed regret based decision curve analysis (regret DCA). We extend the regret DCA methodology to consider harms associated with the prognostication test as well as harms and effects of the management strategies. In order to enable patients and physicians in making these complex decisions in real-time, we developed an easily accessible web-based decision support system available at the point of care. The web-based decision support system facilitates the hospice referral process in three steps. First, the patient or surrogate is interviewed to elicit his/her personal preferences regarding the continuation of life-sustaining treatment vs. palliative care. Then, regret DCA is employed to identify the best strategy for the particular patient in terms of threshold probability at which he/she is indifferent between continuation of treatment and of hospice referral. Finally, if necessary, the probabilities of survival and death for the particular patient are computed based on the SUPPORT prognostication model and contrasted with the patient's threshold probability. The web-based design of the CDSS enables patients, physicians, and family members to participate in the decision process from anywhere internet access is available. We present a theoretical framework to facilitate the hospice referral process. Further rigorous clinical evaluation including testing in a prospective randomized controlled trial is required and planned.

Extensions to Regret-based Decision Curve Analysis: An application to hospice referral for terminal patients

PubMed Central

2011-01-01

Background Despite the well documented advantages of hospice care, most terminally ill patients do not reap the maximum benefit from hospice services, with the majority of them receiving hospice care either prematurely or delayed. Decision systems to improve the hospice referral process are sorely needed. Methods We present a novel theoretical framework that is based on well-established methodologies of prognostication and decision analysis to assist with the hospice referral process for terminally ill patients. We linked the SUPPORT statistical model, widely regarded as one of the most accurate models for prognostication of terminally ill patients, with the recently developed regret based decision curve analysis (regret DCA). We extend the regret DCA methodology to consider harms associated with the prognostication test as well as harms and effects of the management strategies. In order to enable patients and physicians in making these complex decisions in real-time, we developed an easily accessible web-based decision support system available at the point of care. Results The web-based decision support system facilitates the hospice referral process in three steps. First, the patient or surrogate is interviewed to elicit his/her personal preferences regarding the continuation of life-sustaining treatment vs. palliative care. Then, regret DCA is employed to identify the best strategy for the particular patient in terms of threshold probability at which he/she is indifferent between continuation of treatment and of hospice referral. Finally, if necessary, the probabilities of survival and death for the particular patient are computed based on the SUPPORT prognostication model and contrasted with the patient's threshold probability. The web-based design of the CDSS enables patients, physicians, and family members to participate in the decision process from anywhere internet access is available. Conclusions We present a theoretical framework to facilitate the hospice referral process. Further rigorous clinical evaluation including testing in a prospective randomized controlled trial is required and planned. PMID:22196308

Added Value of Contrast-Enhanced Ultrasound on Biopsies of Focal Hepatic Lesions Invisible on Fusion Imaging Guidance.

PubMed

Kang, Tae Wook; Lee, Min Woo; Song, Kyoung Doo; Kim, Mimi; Kim, Seung Soo; Kim, Seong Hyun; Ha, Sang Yun

2017-01-01

To assess whether contrast-enhanced ultrasonography (CEUS) with Sonazoid can improve the lesion conspicuity and feasibility of percutaneous biopsies for focal hepatic lesions invisible on fusion imaging of real-time ultrasonography (US) with computed tomography/magnetic resonance images, and evaluate its impact on clinical decision making. The Institutional Review Board approved this retrospective study. Between June 2013 and January 2015, 711 US-guided percutaneous biopsies were performed for focal hepatic lesions. Biopsies were performed using CEUS for guidance if lesions were invisible on fusion imaging. We retrospectively evaluated the number of target lesions initially invisible on fusion imaging that became visible after applying CEUS, using a 4-point scale. Technical success rates of biopsies were evaluated based on histopathological results. In addition, the occurrence of changes in clinical decision making was assessed. Among 711 patients, 16 patients (2.3%) were included in the study. The median size of target lesions was 1.1 cm (range, 0.5-1.9 cm) in pre-procedural imaging. After CEUS, 15 of 16 (93.8%) focal hepatic lesions were visualized. The conspicuity score was significantly increased after adding CEUS, as compared to that on fusion imaging (p < 0.001). The technical success rate of biopsy was 87.6% (14/16). After biopsy, there were changes in clinical decision making for 11 of 16 patients (68.8%). The addition of CEUS could improve the conspicuity of focal hepatic lesions invisible on fusion imaging. This dual guidance using CEUS and fusion imaging may affect patient management via changes in clinical decision-making.

Cost effectiveness of molecular profiling for adjuvant decision making in patients with node-negative breast cancer.

PubMed

Bonastre, Julia; Marguet, Sophie; Lueza, Beranger; Michiels, Stefan; Delaloge, Suzette; Saghatchian, Mahasti

2014-11-01

To conduct an economic evaluation of the 70-gene signature used to guide adjuvant chemotherapy decision making both in patients with node-negative breast cancer (NNBC) and in the subgroup of estrogen receptor (ER) -positive patients. We used a mixed approach combining patient-level data from a multicenter validation study of the 70-gene signature (untreated patients) and secondary sources for chemotherapy efficacy, unit costs, and utility values. Three strategies on which to base the decision to administer adjuvant chemotherapy were compared: the 70-gene signature, Adjuvant! Online, and chemotherapy in all patients. In the base-case analysis, costs from the French National Insurance Scheme, life-years (LYs), and quality-adjusted life-years (QALYs) were computed for the three strategies over a 10-year period. Cost-effectiveness acceptability curves using the net monetary benefit were computed, combining bootstrap and probabilistic sensitivity analyses. The mean differences in LYs and QALYs were similar between the three strategies. The 70-gene signature strategy was associated with a higher cost, with a mean difference of €2,037 (range, €1,472 to €2,515) compared with Adjuvant! Online and of €657 (95% CI, -€642 to €3,130) compared with systematic chemotherapy. For a €50,000 per QALY willingness-to-pay threshold, the probability of being the most cost-effective strategy was 92% (76% in ER-positive patients) for the Adjuvant! Online strategy, 6% (4% in ER-positive patients) for the systematic chemotherapy strategy, and 2% (20% in ER-positive patients) for the 70-gene strategy. Optimizing adjuvant chemotherapy decision making based on the 70-gene signature is unlikely to be cost effective in patients with NNBC. © 2014 by American Society of Clinical Oncology.

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.

Diagnostic classification of cancer using DNA microarrays and artificial intelligence.

PubMed

Greer, Braden T; Khan, Javed

2004-05-01

The application of artificial intelligence (AI) to microarray data has been receiving much attention in recent years because of the possibility of automated diagnosis in the near future. Studies have been published predicting tumor type, estrogen receptor status, and prognosis using a variety of AI algorithms. The performance of intelligent computing decisions based on gene expression signatures is in some cases comparable to or better than the current clinical decision schemas. The goal of these tools is not to make clinicians obsolete, but rather to give clinicians one more tool in their armamentarium to accurately diagnose and hence better treat cancer patients. Several such applications are summarized in this chapter, and some of the common pitfalls are noted.

Attention as an effect not a cause

PubMed Central

Krauzlis, Richard J.; Bollimunta, Anil; Arcizet, Fabrice; Wang, Lupeng

2014-01-01

Attention is commonly thought to be important for managing the limited resources available in sensory areas of neocortex. Here we present an alternative view that attention arises as a byproduct of circuits centered on the basal ganglia involved in value-based decision-making. The central idea is that decision-making depends on properly estimating the current state of the animal and its environment, and that the weighted inputs to the currently prevailing estimate give rise to the filter-like properties of attention. After outlining this new framework, we describe findings from physiology, anatomy, computational and clinical work that support this point of view. We conclude that the brain mechanisms responsible for attention employ a conserved circuit motif that predates the emergence of the neocortex. PMID:24953964

CAD-RADS - a new clinical decision support tool for coronary computed tomography angiography.

PubMed

Foldyna, Borek; Szilveszter, Bálint; Scholtz, Jan-Erik; Banerji, Dahlia; Maurovich-Horvat, Pál; Hoffmann, Udo

2018-04-01

Coronary computed tomography angiography (CTA) has been established as an accurate method to non-invasively assess coronary artery disease (CAD). The proposed 'Coronary Artery Disease Reporting and Data System' (CAD-RADS) may enable standardised reporting of the broad spectrum of coronary CTA findings related to the presence, extent and composition of coronary atherosclerosis. The CAD-RADS classification is a comprehensive tool for summarising findings on a per-patient-basis dependent on the highest-grade coronary artery lesion, ranging from CAD-RADS 0 (absence of CAD) to CAD-RADS 5 (total occlusion of a coronary artery). In addition, it provides suggestions for clinical management for each classification, including further testing and therapeutic options. Despite some limitations, CAD-RADS may facilitate improved communication between imagers and patient caregivers. As such, CAD-RADS may enable a more efficient use of coronary CTA leading to more accurate utilisation of invasive coronary angiograms. Furthermore, widespread use of CAD-RADS may facilitate registry-based research of diagnostic and prognostic aspects of CTA. • CAD-RADS is a tool for standardising coronary CTA reports. • CAD-RADS includes clinical treatment recommendations based on CTA findings. • CAD-RADS has the potential to reduce variability of CTA reports.

Clinical Information Systems - From Yesterday to Tomorrow.

PubMed

Gardner, R M

2016-06-30

To review the history of clinical information systems over the past twenty-five years and project anticipated changes to those systems over the next twenty-five years. Over 250 Medline references about clinical information systems, quality of patient care, and patient safety were reviewed. Books, Web resources, and the author's personal experience with developing the HELP system were also used. There have been dramatic improvements in the use and acceptance of clinical computing systems and Electronic Health Records (EHRs), especially in the United States. Although there are still challenges with the implementation of such systems, the rate of progress has been remarkable. Over the next twenty-five years, there will remain many important opportunities and challenges. These opportunities include understanding complex clinical computing issues that must be studied, understood and optimized. Dramatic improvements in quality of care and patient safety must be anticipated as a result of the use of clinical information systems. These improvements will result from a closer involvement of clinical informaticians in the optimization of patient care processes. Clinical information systems and computerized clinical decision support have made contributions to medicine in the past. Therefore, by using better medical knowledge, optimized clinical information systems, and computerized clinical decision, we will enable dramatic improvements in both the quality and safety of patient care in the next twenty-five years.

Big-Data Based Decision-Support Systems to Improve Clinicians' Cognition.

PubMed

Roosan, Don; Samore, Matthew; Jones, Makoto; Livnat, Yarden; Clutter, Justin

2016-01-01

Complex clinical decision-making could be facilitated by using population health data to inform clinicians. In two previous studies, we interviewed 16 infectious disease experts to understand complex clinical reasoning. For this study, we focused on answers from the experts on how clinical reasoning can be supported by population-based Big-Data. We found cognitive strategies such as trajectory tracking, perspective taking, and metacognition has the potential to improve clinicians' cognition to deal with complex problems. These cognitive strategies could be supported by population health data, and all have important implications for the design of Big-Data based decision-support tools that could be embedded in electronic health records. Our findings provide directions for task allocation and design of decision-support applications for health care industry development of Big data based decision-support systems.

Big-Data Based Decision-Support Systems to Improve Clinicians’ Cognition

PubMed Central

Roosan, Don; Samore, Matthew; Jones, Makoto; Livnat, Yarden; Clutter, Justin

2016-01-01

Complex clinical decision-making could be facilitated by using population health data to inform clinicians. In two previous studies, we interviewed 16 infectious disease experts to understand complex clinical reasoning. For this study, we focused on answers from the experts on how clinical reasoning can be supported by population-based Big-Data. We found cognitive strategies such as trajectory tracking, perspective taking, and metacognition has the potential to improve clinicians’ cognition to deal with complex problems. These cognitive strategies could be supported by population health data, and all have important implications for the design of Big-Data based decision-support tools that could be embedded in electronic health records. Our findings provide directions for task allocation and design of decision-support applications for health care industry development of Big data based decision-support systems. PMID:27990498

Basic research and 12 years of clinical experience in computer-assisted navigation technology: a review.

PubMed

Ewers, R; Schicho, K; Undt, G; Wanschitz, F; Truppe, M; Seemann, R; Wagner, A

2005-01-01

Computer-aided surgical navigation technology is commonly used in craniomaxillofacial surgery. It offers substantial improvement regarding esthetic and functional aspects in a range of surgical procedures. Based on augmented reality principles, where the real operative site is merged with computer generated graphic information, computer-aided navigation systems were employed, among other procedures, in dental implantology, arthroscopy of the temporomandibular joint, osteotomies, distraction osteogenesis, image guided biopsies and removals of foreign bodies. The decision to perform a procedure with or without computer-aided intraoperative navigation depends on the expected benefit to the procedure as well as on the technical expenditure necessary to achieve that goal. This paper comprises the experience gained in 12 years of research, development and routine clinical application. One hundred and fifty-eight operations with successful application of surgical navigation technology--divided into five groups--are evaluated regarding the criteria "medical benefit" and "technical expenditure" necessary to perform these procedures. Our results indicate that the medical benefit is likely to outweight the expenditure of technology with few exceptions (calvaria transplant, resection of the temporal bone, reconstruction of the orbital floor). Especially in dental implantology, specialized software reduces time and additional costs necessary to plan and perform procedures with computer-aided surgical navigation.

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.

Artificial intelligence within the chemical laboratory.

PubMed

Winkel, P

1994-01-01

Various techniques within the area of artificial intelligence such as expert systems and neural networks may play a role during the problem-solving processes within the clinical biochemical laboratory. Neural network analysis provides a non-algorithmic approach to information processing, which results in the ability of the computer to form associations and to recognize patterns or classes among data. It belongs to the machine learning techniques which also include probabilistic techniques such as discriminant function analysis and logistic regression and information theoretical techniques. These techniques may be used to extract knowledge from example patients to optimize decision limits and identify clinically important laboratory quantities. An expert system may be defined as a computer program that can give advice in a well-defined area of expertise and is able to explain its reasoning. Declarative knowledge consists of statements about logical or empirical relationships between things. Expert systems typically separate declarative knowledge residing in a knowledge base from the inference engine: an algorithm that dynamically directs and controls the system when it searches its knowledge base. A tool is an expert system without a knowledge base. The developer of an expert system uses a tool by entering knowledge into the system. Many, if not the majority of problems encountered at the laboratory level are procedural. A problem is procedural if it is possible to write up a step-by-step description of the expert's work or if it can be represented by a decision tree. To solve problems of this type only small expert system tools and/or conventional programming are required.(ABSTRACT TRUNCATED AT 250 WORDS)

Distinct roles of dopamine and subthalamic nucleus in learning and probabilistic decision making.

PubMed

Coulthard, Elizabeth J; Bogacz, Rafal; Javed, Shazia; Mooney, Lucy K; Murphy, Gillian; Keeley, Sophie; Whone, Alan L

2012-12-01

Even simple behaviour requires us to make decisions based on combining multiple pieces of learned and new information. Making such decisions requires both learning the optimal response to each given stimulus as well as combining probabilistic information from multiple stimuli before selecting a response. Computational theories of decision making predict that learning individual stimulus-response associations and rapid combination of information from multiple stimuli are dependent on different components of basal ganglia circuitry. In particular, learning and retention of memory, required for optimal response choice, are significantly reliant on dopamine, whereas integrating information probabilistically is critically dependent upon functioning of the glutamatergic subthalamic nucleus (computing the 'normalization term' in Bayes' theorem). Here, we test these theories by investigating 22 patients with Parkinson's disease either treated with deep brain stimulation to the subthalamic nucleus and dopaminergic therapy or managed with dopaminergic therapy alone. We use computerized tasks that probe three cognitive functions-information acquisition (learning), memory over a delay and information integration when multiple pieces of sequentially presented information have to be combined. Patients performed the tasks ON or OFF deep brain stimulation and/or ON or OFF dopaminergic therapy. Consistent with the computational theories, we show that stopping dopaminergic therapy impairs memory for probabilistic information over a delay, whereas deep brain stimulation to the region of the subthalamic nucleus disrupts decision making when multiple pieces of acquired information must be combined. Furthermore, we found that when participants needed to update their decision on the basis of the last piece of information presented in the decision-making task, patients with deep brain stimulation of the subthalamic nucleus region did not slow down appropriately to revise their plan, a pattern of behaviour that mirrors the impulsivity described clinically in some patients with subthalamic nucleus deep brain stimulation. Thus, we demonstrate distinct mechanisms for two important facets of human decision making: first, a role for dopamine in memory consolidation, and second, the critical importance of the subthalamic nucleus in successful decision making when multiple pieces of information must be combined.

Personal digital assistant use by nurse practitioners: a descriptive study.

PubMed

Stroud, Sally D; Smith, Carol A; Erkel, Elizabeth A

2009-01-01

We sought to describe the prevalence and patterns of use of personal digital assistants (PDAs) among active nurse practitioners (NPs). A descriptive correlational survey was conducted among NPs in the United States (N = 126). Participants were randomly selected from members of the American Academy of Nurse Practitioners who had listed a practice site on their application. Sixty-four percent of participants used PDAs. A drug reference was reported to be the most useful and frequently installed application. A large majority of PDA users believed that PDA use supported clinical decision making (91%), promoted patient safety (89%), and increased productivity (75%). Sixty-two percent predicted that PDA use would change their practice within the next 5 years. As innovative PDA applications with potential to improve patient outcomes become increasingly available, handheld computer skills will be a fundamental practice competency. To prevent errors in clinical decision making with quick access to PDA reference materials, NPs must critically evaluate the legitimacy and worth of PDA software programs. There is a critical need to evaluate the effectiveness of PDA use in clinical settings and develop an evidence base to guide use of the PDA in solving clinical problems.

A regret theory approach to decision curve analysis: a novel method for eliciting decision makers' preferences and decision-making.

PubMed

Tsalatsanis, Athanasios; Hozo, Iztok; Vickers, Andrew; Djulbegovic, Benjamin

2010-09-16

Decision curve analysis (DCA) has been proposed as an alternative method for evaluation of diagnostic tests, prediction models, and molecular markers. However, DCA is based on expected utility theory, which has been routinely violated by decision makers. Decision-making is governed by intuition (system 1), and analytical, deliberative process (system 2), thus, rational decision-making should reflect both formal principles of rationality and intuition about good decisions. We use the cognitive emotion of regret to serve as a link between systems 1 and 2 and to reformulate DCA. First, we analysed a classic decision tree describing three decision alternatives: treat, do not treat, and treat or no treat based on a predictive model. We then computed the expected regret for each of these alternatives as the difference between the utility of the action taken and the utility of the action that, in retrospect, should have been taken. For any pair of strategies, we measure the difference in net expected regret. Finally, we employ the concept of acceptable regret to identify the circumstances under which a potentially wrong strategy is tolerable to a decision-maker. We developed a novel dual visual analog scale to describe the relationship between regret associated with "omissions" (e.g. failure to treat) vs. "commissions" (e.g. treating unnecessary) and decision maker's preferences as expressed in terms of threshold probability. We then proved that the Net Expected Regret Difference, first presented in this paper, is equivalent to net benefits as described in the original DCA. Based on the concept of acceptable regret we identified the circumstances under which a decision maker tolerates a potentially wrong decision and expressed it in terms of probability of disease. We present a novel method for eliciting decision maker's preferences and an alternative derivation of DCA based on regret theory. Our approach may be intuitively more appealing to a decision-maker, particularly in those clinical situations when the best management option is the one associated with the least amount of regret (e.g. diagnosis and treatment of advanced cancer, etc).

A regret theory approach to decision curve analysis: A novel method for eliciting decision makers' preferences and decision-making

PubMed Central

2010-01-01

Background Decision curve analysis (DCA) has been proposed as an alternative method for evaluation of diagnostic tests, prediction models, and molecular markers. However, DCA is based on expected utility theory, which has been routinely violated by decision makers. Decision-making is governed by intuition (system 1), and analytical, deliberative process (system 2), thus, rational decision-making should reflect both formal principles of rationality and intuition about good decisions. We use the cognitive emotion of regret to serve as a link between systems 1 and 2 and to reformulate DCA. Methods First, we analysed a classic decision tree describing three decision alternatives: treat, do not treat, and treat or no treat based on a predictive model. We then computed the expected regret for each of these alternatives as the difference between the utility of the action taken and the utility of the action that, in retrospect, should have been taken. For any pair of strategies, we measure the difference in net expected regret. Finally, we employ the concept of acceptable regret to identify the circumstances under which a potentially wrong strategy is tolerable to a decision-maker. Results We developed a novel dual visual analog scale to describe the relationship between regret associated with "omissions" (e.g. failure to treat) vs. "commissions" (e.g. treating unnecessary) and decision maker's preferences as expressed in terms of threshold probability. We then proved that the Net Expected Regret Difference, first presented in this paper, is equivalent to net benefits as described in the original DCA. Based on the concept of acceptable regret we identified the circumstances under which a decision maker tolerates a potentially wrong decision and expressed it in terms of probability of disease. Conclusions We present a novel method for eliciting decision maker's preferences and an alternative derivation of DCA based on regret theory. Our approach may be intuitively more appealing to a decision-maker, particularly in those clinical situations when the best management option is the one associated with the least amount of regret (e.g. diagnosis and treatment of advanced cancer, etc). PMID:20846413

Type-2 fuzzy set extension of DEMATEL method combined with perceptual computing for decision making

NASA Astrophysics Data System (ADS)

Hosseini, Mitra Bokaei; Tarokh, Mohammad Jafar

2013-05-01

Most decision making methods used to evaluate a system or demonstrate the weak and strength points are based on fuzzy sets and evaluate the criteria with words that are modeled with fuzzy sets. The ambiguity and vagueness of the words and different perceptions of a word are not considered in these methods. For this reason, the decision making methods that consider the perceptions of decision makers are desirable. Perceptual computing is a subjective judgment method that considers that words mean different things to different people. This method models words with interval type-2 fuzzy sets that consider the uncertainty of the words. Also, there are interrelations and dependency between the decision making criteria in the real world; therefore, using decision making methods that cannot consider these relations is not feasible in some situations. The Decision-Making Trail and Evaluation Laboratory (DEMATEL) method considers the interrelations between decision making criteria. The current study used the combination of DEMATEL and perceptual computing in order to improve the decision making methods. For this reason, the fuzzy DEMATEL method was extended into type-2 fuzzy sets in order to obtain the weights of dependent criteria based on the words. The application of the proposed method is presented for knowledge management evaluation criteria.

Competency in health care management: a training model in epidemiologic methods for assessing and improving the quality of clinical practice through evidence-based decision making.

PubMed

Hudak, R P; Jacoby, I; Meyer, G S; Potter, A L; Hooper, T I; Krakauer, H

1997-01-01

This article describes a training model that focuses on health care management by applying epidemiologic methods to assess and improve the quality of clinical practice. The model's uniqueness is its focus on integrating clinical evidence-based decision making with fundamental principles of resource management to achieve attainable, cost-effective, high-quality health outcomes. The target students are current and prospective clinical and administrative executives who must optimize decision making at the clinical and managerial levels of health care organizations.

ICADx: interpretable computer aided diagnosis of breast masses

NASA Astrophysics Data System (ADS)

Kim, Seong Tae; Lee, Hakmin; Kim, Hak Gu; Ro, Yong Man

2018-02-01

In this study, a novel computer aided diagnosis (CADx) framework is devised to investigate interpretability for classifying breast masses. Recently, a deep learning technology has been successfully applied to medical image analysis including CADx. Existing deep learning based CADx approaches, however, have a limitation in explaining the diagnostic decision. In real clinical practice, clinical decisions could be made with reasonable explanation. So current deep learning approaches in CADx are limited in real world deployment. In this paper, we investigate interpretability in CADx with the proposed interpretable CADx (ICADx) framework. The proposed framework is devised with a generative adversarial network, which consists of interpretable diagnosis network and synthetic lesion generative network to learn the relationship between malignancy and a standardized description (BI-RADS). The lesion generative network and the interpretable diagnosis network compete in an adversarial learning so that the two networks are improved. The effectiveness of the proposed method was validated on public mammogram database. Experimental results showed that the proposed ICADx framework could provide the interpretability of mass as well as mass classification. It was mainly attributed to the fact that the proposed method was effectively trained to find the relationship between malignancy and interpretations via the adversarial learning. These results imply that the proposed ICADx framework could be a promising approach to develop the CADx system.

A cost-utility analysis of the use of preoperative computed tomographic angiography in abdomen-based perforator flap breast reconstruction.

PubMed

Offodile, Anaeze C; Chatterjee, Abhishek; Vallejo, Sergio; Fisher, Carla S; Tchou, Julia C; Guo, Lifei

2015-04-01

Computed tomographic angiography is a diagnostic tool increasingly used for preoperative vascular mapping in abdomen-based perforator flap breast reconstruction. This study compared the use of computed tomographic angiography and the conventional practice of Doppler ultrasonography only in postmastectomy reconstruction using a cost-utility model. Following a comprehensive literature review, a decision analytic model was created using the three most clinically relevant health outcomes in free autologous breast reconstruction with computed tomographic angiography versus Doppler ultrasonography only. Cost and utility estimates for each health outcome were used to derive the quality-adjusted life-years and incremental cost-utility ratio. One-way sensitivity analysis was performed to scrutinize the robustness of the authors' results. Six studies and 782 patients were identified. Cost-utility analysis revealed a baseline cost savings of $3179, a gain in quality-adjusted life-years of 0.25. This yielded an incremental cost-utility ratio of -$12,716, implying a dominant choice favoring preoperative computed tomographic angiography. Sensitivity analysis revealed that computed tomographic angiography was costlier when the operative time difference between the two techniques was less than 21.3 minutes. However, the clinical advantage of computed tomographic angiography over Doppler ultrasonography only showed that computed tomographic angiography would still remain the cost-effective option even if it offered no additional operating time advantage. The authors' results show that computed tomographic angiography is a cost-effective technology for identifying lower abdominal perforators for autologous breast reconstruction. Although the perfect study would be a randomized controlled trial of the two approaches with true cost accrual, the authors' results represent the best available evidence.

Technical challenges, past and future, in implementing THERESA: a one million patient, one billion item computer-based patient record and decision support system

NASA Astrophysics Data System (ADS)

Camp, Henry N.

1996-02-01

Challenges in implementing a computer-based patient record (CPR)--such as absolute data integrity, high availability, permanent on-line storage of very large complex records, rapid search times, ease of use, commercial viability, and portability to other hospitals and doctor's offices--are given along with their significance, the solutions, and their successes. The THERESA CPR has been used sine 1983 in direct patient care by a public hospital that is the primary care provider to 350,000 people. It has 1000 beds with 45,000 admissions and 750,000 outpatient visits annually. The system supports direct provider entry, including by physicians, of complete medical `documents'. Its demonstration site currently contains 1.1 billion data items on 1 million patients. It is also a clinical decision-aiding tool used for quality assurance and cost containment, for teaching as faculty and students can easily find and `thumb through' all cases similar to a particular study, and for research with over a billion medical items that can be searched and analyzed on-line within context and with continuity. The same software can also run in a desktop microcomputer managing a private practice physician's office.

Artificial intelligence techniques applied to the development of a decision-support system for diagnosing celiac disease.

PubMed

Tenório, Josceli Maria; Hummel, Anderson Diniz; Cohrs, Frederico Molina; Sdepanian, Vera Lucia; Pisa, Ivan Torres; de Fátima Marin, Heimar

2011-11-01

Celiac disease (CD) is a difficult-to-diagnose condition because of its multiple clinical presentations and symptoms shared with other diseases. Gold-standard diagnostic confirmation of suspected CD is achieved by biopsying the small intestine. To develop a clinical decision-support system (CDSS) integrated with an automated classifier to recognize CD cases, by selecting from experimental models developed using intelligence artificial techniques. A web-based system was designed for constructing a retrospective database that included 178 clinical cases for training. Tests were run on 270 automated classifiers available in Weka 3.6.1 using five artificial intelligence techniques, namely decision trees, Bayesian inference, k-nearest neighbor algorithm, support vector machines and artificial neural networks. The parameters evaluated were accuracy, sensitivity, specificity and area under the ROC curve (AUC). AUC was used as a criterion for selecting the CDSS algorithm. A testing database was constructed including 38 clinical CD cases for CDSS evaluation. The diagnoses suggested by CDSS were compared with those made by physicians during patient consultations. The most accurate method during the training phase was the averaged one-dependence estimator (AODE) algorithm (a Bayesian classifier), which showed accuracy 80.0%, sensitivity 0.78, specificity 0.80 and AUC 0.84. This classifier was integrated into the web-based decision-support system. The gold-standard validation of CDSS achieved accuracy of 84.2% and k=0.68 (p<0.0001) with good agreement. The same accuracy was achieved in the comparison between the physician's diagnostic impression and the gold standard k=0. 64 (p<0.0001). There was moderate agreement between the physician's diagnostic impression and CDSS k=0.46 (p=0.0008). The study results suggest that CDSS could be used to help in diagnosing CD, since the algorithm tested achieved excellent accuracy in differentiating possible positive from negative CD diagnoses. This study may contribute towards developing of a computer-assisted environment to support CD diagnosis. Copyright © 2011 Elsevier Ireland Ltd. All rights reserved.

Measurement of smaller colon polyp in CT colonography images using morphological image processing.

PubMed

Manjunath, K N; Siddalingaswamy, P C; Prabhu, G K

2017-11-01

Automated measurement of the size and shape of colon polyps is one of the challenges in Computed tomography colonography (CTC). The objective of this retrospective study was to improve the sensitivity and specificity of smaller polyp measurement in CTC using image processing techniques. A domain knowledge-based method has been implemented with hybrid method of colon segmentation, morphological image processing operators for detecting the colonic structures, and the decision-making system for delineating the smaller polyp-based on a priori knowledge. The method was applied on 45 CTC dataset. The key finding was that the smaller polyps were accurately measured. In addition to 6-9 mm range, polyps of even <5 mm were also detected. The results were validated qualitatively and quantitatively using both 2D MPR and 3D view. Implementation was done on a high-performance computer with parallel processing. It takes [Formula: see text] min for measuring the smaller polyp in a dataset of 500 CTC images. With this method, [Formula: see text] and [Formula: see text] were achieved. The domain-based approach with morphological image processing has given good results. The smaller polyps were measured accurately which helps in making right clinical decisions. Qualitatively and quantitatively the results were acceptable when compared to the ground truth at [Formula: see text].

Quantum decision-maker theory and simulation

NASA Astrophysics Data System (ADS)

Zak, Michail; Meyers, Ronald E.; Deacon, Keith S.

2000-07-01

A quantum device simulating the human decision making process is introduced. It consists of quantum recurrent nets generating stochastic processes which represent the motor dynamics, and of classical neural nets describing the evolution of probabilities of these processes which represent the mental dynamics. The autonomy of the decision making process is achieved by a feedback from the mental to motor dynamics which changes the stochastic matrix based upon the probability distribution. This feedback replaces unavailable external information by an internal knowledge- base stored in the mental model in the form of probability distributions. As a result, the coupled motor-mental dynamics is described by a nonlinear version of Markov chains which can decrease entropy without an external source of information. Applications to common sense based decisions as well as to evolutionary games are discussed. An example exhibiting self-organization is computed using quantum computer simulation. Force on force and mutual aircraft engagements using the quantum decision maker dynamics are considered.

@neurIST - chronic disease management through integration of heterogeneous data and computer-interpretable guideline services.

PubMed

Dunlop, R; Arbona, A; Rajasekaran, H; Lo Iacono, L; Fingberg, J; Summers, P; Benkner, S; Engelbrecht, G; Chiarini, A; Friedrich, C M; Moore, B; Bijlenga, P; Iavindrasana, J; Hose, R D; Frangi, A F

2008-01-01

This paper presents an overview of computerised decision support for clinical practice. The concept of computer-interpretable guidelines is introduced in the context of the @neurIST project, which aims at supporting the research and treatment of asymptomatic unruptured cerebral aneurysms by bringing together heterogeneous data, computing and complex processing services. The architecture is generic enough to adapt it to the treatment of other diseases beyond cerebral aneurysms. The paper reviews the generic requirements of the @neurIST system and presents the innovative work in distributing executable clinical guidelines.

Evidence-based medicine, clinical practice guidelines, and common sense in the management of osteoporosis.

PubMed

Lewiecki, E Michael; Binkley, Neil

2009-01-01

To evaluate the benefits and limitations of randomized controlled trials (RCTs), clinical practice guidelines (CPGs), and clinical judgment in the management of osteoporosis. A review was conducted of the English-language literature on the origins and applications of RCTs, CPGs, evidence-based medicine, and clinical judgment in the management of osteoporosis. Evidence-based medicine is use of the currently available best evidence in making clinical decisions for individual patients. CPGs are recommendations for making clinical decisions based on research evidence, sometimes with consideration of expert opinion, health care policy, and costs of care. The highest levels of medical evidence are usually thought to be RCTs and meta-analyses of high-quality RCTs. Although it is desirable and appropriate for clinicians to consider research evidence from RCTs and recommendations presented in CPGs in making clinical decisions, other factors-such as patient preference, comorbidities, affordability, and availability of care-are important for the actual implementation of evidence-based medicine. Decisions about who to treat, which drug to use, how best to monitor, and how long to treat require clinical skills in addition to knowledge of medical research. The necessity of integrating common sense and clinical judgment is highlighted by the fact that many patients treated for osteoporosis in clinical practice would not qualify for participation in the pivotal clinical trials that demonstrated efficacy and safety of the drugs used to treat them.

Computational biomedicine: a challenge for the twenty-first century.

PubMed

Coveney, Peter V; Shublaq, Nour W

2012-01-01

With the relentless increase of computer power and the widespread availability of digital patient-specific medical data, we are now entering an era when it is becoming possible to develop predictive models of human disease and pathology, which can be used to support and enhance clinical decision-making. The approach amounts to a grand challenge to computational science insofar as we need to be able to provide seamless yet secure access to large scale heterogeneous personal healthcare data in a facile way, typically integrated into complex workflows-some parts of which may need to be run on high performance computers-in a facile way that is integrated into clinical decision support software. In this paper, we review the state of the art in terms of case studies drawn from neurovascular pathologies and HIV/AIDS. These studies are representative of a large number of projects currently being performed within the Virtual Physiological Human initiative. They make demands of information technology at many scales, from the desktop to national and international infrastructures for data storage and processing, linked by high performance networks.

InformedTogether: Usability Evaluation of a Web-Based Decision Aid to Facilitate Shared Advance Care Planning for Severe Chronic Obstructive Pulmonary Disease

PubMed Central

Uhler, Lauren M; Pérez Figueroa, Rafael E; Dickson, Mark; McCullagh, Lauren; Kushniruk, Andre; Monkman, Helen; Witteman, Holly O

2015-01-01

Background Advance care planning may help patients receive treatments that better align with their goals for care. We developed a Web-based decision aid called InformedTogether to facilitate shared advance care planning between chronic obstructive pulmonary disease (COPD) patients and their doctors. Objective Our objective was to assess the usability of the InformedTogether decision aid, including whether users could interact with the decision aid to engage in tasks required for shared decision making, whether users found the decision aid acceptable, and implications for redesign. Methods We conducted an observational study with 15 patients and 8 doctors at two ethnically and socioeconomically diverse outpatient clinics. Data included quantitative and qualitative observations of patients and doctors using the decision aid on tablet or laptop computers and data from semistructured interviews. Patients were shown the decision aid by a researcher acting as the doctor. Pulmonary doctors were observed using the decision aid independently and asked to think aloud (ie, verbalize their thoughts). A thematic analysis was implemented to explore key issues related to decision aid usability. Results Although patients and doctors found InformedTogether acceptable and would recommend that doctors use the decision aid with COPD patients, many patients had difficulty understanding the icon arrays that were used to communicate estimated prognoses and could not articulate the definitions of the two treatment choices—Full Code and Do Not Resuscitate (DNR). Minor usability problems regarding content, links, layout, and consistency were also identified and corresponding recommendations were outlined. In particular, participants suggested including more information about potential changes in quality of life resulting from the alternative advance directives. Some doctor participants thought the decision aid was too long and some thought it may cause nervousness among patients due to the topic area. Conclusions A decision aid for shared advance care planning for severe COPD was found acceptable to most COPD patients and their doctors. However, many patient participants did not demonstrate understanding of the treatment options or prognostic estimates. Many participants endorsed the use of the decision aid between doctors and their patients with COPD, although they desired more information about quality of life. The design must optimize comprehensibility, including revising the presentation of statistical information in the icon array, and feasibility of integration into clinical workflow, including shortening the decision aid. PMID:27025896

Analysis of underlying causes of inter-expert disagreement in retinopathy of prematurity diagnosis. Application of machine learning principles.

PubMed

Ataer-Cansizoglu, E; Kalpathy-Cramer, J; You, S; Keck, K; Erdogmus, D; Chiang, M F

2015-01-01

Inter-expert variability in image-based clinical diagnosis has been demonstrated in many diseases including retinopathy of prematurity (ROP), which is a disease affecting low birth weight infants and is a major cause of childhood blindness. In order to better understand the underlying causes of variability among experts, we propose a method to quantify the variability of expert decisions and analyze the relationship between expert diagnoses and features computed from the images. Identification of these features is relevant for development of computer-based decision support systems and educational systems in ROP, and these methods may be applicable to other diseases where inter-expert variability is observed. The experiments were carried out on a dataset of 34 retinal images, each with diagnoses provided independently by 22 experts. Analysis was performed using concepts of Mutual Information (MI) and Kernel Density Estimation. A large set of structural features (a total of 66) were extracted from retinal images. Feature selection was utilized to identify the most important features that correlated to actual clinical decisions by the 22 study experts. The best three features for each observer were selected by an exhaustive search on all possible feature subsets and considering joint MI as a relevance criterion. We also compared our results with the results of Cohen's Kappa [36] as an inter-rater reliability measure. The results demonstrate that a group of observers (17 among 22) decide consistently with each other. Mean and second central moment of arteriolar tortuosity is among the reasons of disagreement between this group and the rest of the observers, meaning that the group of experts consider amount of tortuosity as well as the variation of tortuosity in the image. Given a set of image-based features, the proposed analysis method can identify critical image-based features that lead to expert agreement and disagreement in diagnosis of ROP. Although tree-based features and various statistics such as central moment are not popular in the literature, our results suggest that they are important for diagnosis.

Computers in medical education 1: evaluation of a problem-orientated learning package.

PubMed

Devitt, P; Palmer, E

1998-04-01

A computer-based learning package has been developed, aimed at expanding students' knowledge base, as well as improving data-handling abilities and clinical problem-solving skills. The program was evaluated by monitoring its use by students, canvassing users' opinions and measuring its effectiveness as a learning tool compared to tutorials on the same material. Evaluation was undertaken using three methods: initially, by a questionnaire on computers as a learning tool and the applicability of the content: second, through monitoring by the computer of student use, decisions and performance; finally, through pre- and post-test assessment of fifth-year students who either used a computer package or attended a tutorial on equivalent material. Most students provided positive comments on the learning material and expressed a willingness to see computer-aided learning (CAL) introduced into the curriculum. Over a 3-month period, 26 modules in the program were used on 1246 occasions. Objective measurement showed a significant gain in knowledge, data handling and problem-solving skills. Computer-aided learning is a valuable learning resource that deserves better attention in medical education. When used appropriately, the computer can be an effective learning resource, not only for the delivery of knowledge. but also to help students develop their problem-solving skills.

[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.

Capturing structured, pulmonary disease-specific data elements in electronic health records.

PubMed

Gronkiewicz, Cynthia; Diamond, Edward J; French, Kim D; Christodouleas, John; Gabriel, Peter E

2015-04-01

Electronic health records (EHRs) have the potential to improve health-care quality by allowing providers to make better decisions at the point of care based on electronically aggregated data and by facilitating clinical research. These goals are easier to achieve when key, disease-specific clinical information is documented as structured data elements (SDEs) that computers can understand and process, rather than as free-text/natural-language narrative. This article reviews the benefits of capturing disease-specific SDEs. It highlights several design and implementation considerations, including the impact on efficiency and expressivity of clinical documentation and the importance of adhering to data standards when available. Pulmonary disease-specific examples of collection instruments are provided from two commonly used commercial EHRs. Future developments that can leverage SDEs to improve clinical quality and research are discussed.

Using a Group Decision Support System for Creativity.

ERIC Educational Resources Information Center

Aiken, Milam; Riggs, Mary

1993-01-01

A computer-based group decision support system (GDSS) to increase collaborative group productivity and creativity is explained. Various roles for the computer are identified, and implementation of GDSS systems at the University of Mississippi and International Business Machines are described. The GDSS is seen as fostering productivity through…

A Comparative Study of Different EEG Reference Choices for Diagnosing Unipolar Depression.

PubMed

Mumtaz, Wajid; Malik, Aamir Saeed

2018-06-02

The choice of an electroencephalogram (EEG) reference has fundamental importance and could be critical during clinical decision-making because an impure EEG reference could falsify the clinical measurements and subsequent inferences. In this research, the suitability of three EEG references was compared while classifying depressed and healthy brains using a machine-learning (ML)-based validation method. In this research, the EEG data of 30 unipolar depressed subjects and 30 age-matched healthy controls were recorded. The EEG data were analyzed in three different EEG references, the link-ear reference (LE), average reference (AR), and reference electrode standardization technique (REST). The EEG-based functional connectivity (FC) was computed. Also, the graph-based measures, such as the distances between nodes, minimum spanning tree, and maximum flow between the nodes for each channel pair, were calculated. An ML scheme provided a mechanism to compare the performances of the extracted features that involved a general framework such as the feature extraction (graph-based theoretic measures), feature selection, classification, and validation. For comparison purposes, the performance metrics such as the classification accuracies, sensitivities, specificities, and F scores were computed. When comparing the three references, the diagnostic accuracy showed better performances during the REST, while the LE and AR showed less discrimination between the two groups. Based on the results, it can be concluded that the choice of appropriate reference is critical during the clinical scenario. The REST reference is recommended for future applications of EEG-based diagnosis of mental illnesses.

Automation of CT-based haemorrhagic stroke assessment for improved clinical outcomes: study protocol and design.

PubMed

Chinda, Betty; Medvedev, George; Siu, William; Ester, Martin; Arab, Ali; Gu, Tao; Moreno, Sylvain; D'Arcy, Ryan C N; Song, Xiaowei

2018-04-19

Haemorrhagic stroke is of significant healthcare concern due to its association with high mortality and lasting impact on the survivors' quality of life. Treatment decisions and clinical outcomes depend strongly on the size, spread and location of the haematoma. Non-contrast CT (NCCT) is the primary neuroimaging modality for haematoma assessment in haemorrhagic stroke diagnosis. Current procedures do not allow convenient NCCT-based haemorrhage volume calculation in clinical settings, while research-based approaches are yet to be tested for clinical utility; there is a demonstrated need for developing effective solutions. The project under review investigates the development of an automatic NCCT-based haematoma computation tool in support of accurate quantification of haematoma volumes. Several existing research methods for haematoma volume estimation are studied. Selected methods are tested using NCCT images of patients diagnosed with acute haemorrhagic stroke. For inter-rater and intrarater reliability evaluation, different raters will analyse haemorrhage volumes independently. The efficiency with respect to time of haematoma volume assessments will be examined to compare with the results from routine clinical evaluations and planimetry assessment that are known to be more accurate. The project will target the development of an enhanced solution by adapting existing methods and integrating machine learning algorithms. NCCT-based information of brain haemorrhage (eg, size, volume, location) and other relevant information (eg, age, sex, risk factor, comorbidities) will be used in relation to clinical outcomes with future project development. Validity and reliability of the solution will be examined for potential clinical utility. The project including procedures for deidentification of NCCT data has been ethically approved. The study involves secondary use of existing data and does not require new consent of participation. The team consists of clinical neuroimaging scientists, computing scientists and clinical professionals in neurology and neuroradiology and includes patient representatives. Research outputs will be disseminated following knowledge translation plans towards improving stroke patient care. Significant findings will be published in scientific journals. Anticipated deliverables include computer solutions for improved clinical assessment of haematoma using NCCT. © Article author(s) (or their employer(s) unless otherwise stated in the text of the article) 2018. All rights reserved. No commercial use is permitted unless otherwise expressly granted.

TBIdoc: 3D content-based CT image retrieval system for traumatic brain injury

NASA Astrophysics Data System (ADS)

Li, Shimiao; Gong, Tianxia; Wang, Jie; Liu, Ruizhe; Tan, Chew Lim; Leong, Tze Yun; Pang, Boon Chuan; Lim, C. C. Tchoyoson; Lee, Cheng Kiang; Tian, Qi; Zhang, Zhuo

2010-03-01

Traumatic brain injury (TBI) is a major cause of death and disability. Computed Tomography (CT) scan is widely used in the diagnosis of TBI. Nowadays, large amount of TBI CT data is stacked in the hospital radiology department. Such data and the associated patient information contain valuable information for clinical diagnosis and outcome prediction. However, current hospital database system does not provide an efficient and intuitive tool for doctors to search out cases relevant to the current study case. In this paper, we present the TBIdoc system: a content-based image retrieval (CBIR) system which works on the TBI CT images. In this web-based system, user can query by uploading CT image slices from one study, retrieval result is a list of TBI cases ranked according to their 3D visual similarity to the query case. Specifically, cases of TBI CT images often present diffuse or focal lesions. In TBIdoc system, these pathological image features are represented as bin-based binary feature vectors. We use the Jaccard-Needham measure as the similarity measurement. Based on these, we propose a 3D similarity measure for computing the similarity score between two series of CT slices. nDCG is used to evaluate the system performance, which shows the system produces satisfactory retrieval results. The system is expected to improve the current hospital data management in TBI and to give better support for the clinical decision-making process. It may also contribute to the computer-aided education in TBI.

SANDS: a service-oriented architecture for clinical decision support in a National Health Information Network.

PubMed

Wright, Adam; Sittig, Dean F

2008-12-01

In this paper, we describe and evaluate a new distributed architecture for clinical decision support called SANDS (Service-oriented Architecture for NHIN Decision Support), which leverages current health information exchange efforts and is based on the principles of a service-oriented architecture. The architecture allows disparate clinical information systems and clinical decision support systems to be seamlessly integrated over a network according to a set of interfaces and protocols described in this paper. The architecture described is fully defined and developed, and six use cases have been developed and tested using a prototype electronic health record which links to one of the existing prototype National Health Information Networks (NHIN): drug interaction checking, syndromic surveillance, diagnostic decision support, inappropriate prescribing in older adults, information at the point of care and a simple personal health record. Some of these use cases utilize existing decision support systems, which are either commercially or freely available at present, and developed outside of the SANDS project, while other use cases are based on decision support systems developed specifically for the project. Open source code for many of these components is available, and an open source reference parser is also available for comparison and testing of other clinical information systems and clinical decision support systems that wish to implement the SANDS architecture. The SANDS architecture for decision support has several significant advantages over other architectures for clinical decision support. The most salient of these are:

A new CAD approach for improving efficacy of cancer screening

NASA Astrophysics Data System (ADS)

Zheng, Bin; Qian, Wei; Li, Lihua; Pu, Jiantao; Kang, Yan; Lure, Fleming; Tan, Maxine; Qiu, Yuchen

2015-03-01

Since performance and clinical utility of current computer-aided detection (CAD) schemes of detecting and classifying soft tissue lesions (e.g., breast masses and lung nodules) is not satisfactory, many researchers in CAD field call for new CAD research ideas and approaches. The purpose of presenting this opinion paper is to share our vision and stimulate more discussions of how to overcome or compensate the limitation of current lesion-detection based CAD schemes in the CAD research community. Since based on our observation that analyzing global image information plays an important role in radiologists' decision making, we hypothesized that using the targeted quantitative image features computed from global images could also provide highly discriminatory power, which are supplementary to the lesion-based information. To test our hypothesis, we recently performed a number of independent studies. Based on our published preliminary study results, we demonstrated that global mammographic image features and background parenchymal enhancement of breast MR images carried useful information to (1) predict near-term breast cancer risk based on negative screening mammograms, (2) distinguish between true- and false-positive recalls in mammography screening examinations, and (3) classify between malignant and benign breast MR examinations. The global case-based CAD scheme only warns a risk level of the cases without cueing a large number of false-positive lesions. It can also be applied to guide lesion-based CAD cueing to reduce false-positives but enhance clinically relevant true-positive cueing. However, before such a new CAD approach is clinically acceptable, more work is needed to optimize not only the scheme performance but also how to integrate with lesion-based CAD schemes in the clinical practice.

Evaluating a computer aid for assessing stomach symptoms (ECASS): study protocol for a randomised controlled trial.

PubMed

Moore, Helen J; Nixon, Catherine; Tariq, Anisah; Emery, Jon; Hamilton, Willie; Hoare, Zoë; Kershenbaum, Anne; Neal, Richard D; Ukoumunne, Obioha C; Usher-Smith, Juliet; Walter, Fiona M; Whyte, Sophie; Rubin, Greg

2016-04-04

For most cancers, only a minority of patients have symptoms meeting the National Institute for Health and Clinical Excellence guidance for urgent referral. For gastro-oesophageal cancers, the 'alarm' symptoms of dysphagia and weight loss are reported by only 32 and 8 % of patients, respectively, and their presence correlates with advanced-stage disease. Electronic clinical decision-support tools that integrate with clinical computer systems have been developed for general practice, although uncertainty remains concerning their effectiveness. The objectives of this trial are to optimise the intervention and establish the acceptability of both the intervention and randomisation, confirm the suitability and selection of outcome measures, finalise the design for the phase III definitive trial, and obtain preliminary estimates of the intervention effect. This is a two-arm, multi-centre, cluster-randomised, controlled phase II trial design, which will extend over a 16-month period, across 60 general practices within the North East and North Cumbria and the Eastern Local Clinical Research Network areas. Practices will be randomised to receive either the intervention (the electronic clinical decision-support tool) or to act as a control (usual care). From these practices, we will recruit 3000 adults who meet the trial eligibility criteria and present to their GP with symptoms suggestive of gastro-oesophageal cancer. The main measures are the process data, which include the practitioner outcomes, service outcomes, diagnostic intervals, health economic outcomes, and patient outcomes. One-on-one interviews in a sub-sample of 30 patient-GP dyads will be undertaken to understand the impact of the use or non-use of the electronic clinical decision-support tool in the consultation. A further 10-15 GPs will be interviewed to identify and gain an understanding of the facilitators and constraints influencing implementation of the electronic clinical decision-support tool in practice. We aim to generate new knowledge on the process measures regarding the use of electronic clinical decision-support tools in primary care in general and to inform a subsequent definitive phase III trial. Preliminary data on the impact of the support tool on resource utilisation and health care costs will also be collected. ISRCTN Registry, ISRCTN12595588 .

An Ontology-Based, Mobile-Optimized System for Pharmacogenomic Decision Support at the Point-of-Care

PubMed Central

Miñarro-Giménez, Jose Antonio; Blagec, Kathrin; Boyce, Richard D.; Adlassnig, Klaus-Peter; Samwald, Matthias

2014-01-01

Background The development of genotyping and genetic sequencing techniques and their evolution towards low costs and quick turnaround have encouraged a wide range of applications. One of the most promising applications is pharmacogenomics, where genetic profiles are used to predict the most suitable drugs and drug dosages for the individual patient. This approach aims to ensure appropriate medical treatment and avoid, or properly manage, undesired side effects. Results We developed the Medicine Safety Code (MSC) service, a novel pharmacogenomics decision support system, to provide physicians and patients with the ability to represent pharmacogenomic data in computable form and to provide pharmacogenomic guidance at the point-of-care. Pharmacogenomic data of individual patients are encoded as Quick Response (QR) codes and can be decoded and interpreted with common mobile devices without requiring a centralized repository for storing genetic patient data. In this paper, we present the first fully functional release of this system and describe its architecture, which utilizes Web Ontology Language 2 (OWL 2) ontologies to formalize pharmacogenomic knowledge and to provide clinical decision support functionalities. Conclusions The MSC system provides a novel approach for enabling the implementation of personalized medicine in clinical routine. PMID:24787444

An ontology-based, mobile-optimized system for pharmacogenomic decision support at the point-of-care.

PubMed

Miñarro-Giménez, Jose Antonio; Blagec, Kathrin; Boyce, Richard D; Adlassnig, Klaus-Peter; Samwald, Matthias

2014-01-01

The development of genotyping and genetic sequencing techniques and their evolution towards low costs and quick turnaround have encouraged a wide range of applications. One of the most promising applications is pharmacogenomics, where genetic profiles are used to predict the most suitable drugs and drug dosages for the individual patient. This approach aims to ensure appropriate medical treatment and avoid, or properly manage, undesired side effects. We developed the Medicine Safety Code (MSC) service, a novel pharmacogenomics decision support system, to provide physicians and patients with the ability to represent pharmacogenomic data in computable form and to provide pharmacogenomic guidance at the point-of-care. Pharmacogenomic data of individual patients are encoded as Quick Response (QR) codes and can be decoded and interpreted with common mobile devices without requiring a centralized repository for storing genetic patient data. In this paper, we present the first fully functional release of this system and describe its architecture, which utilizes Web Ontology Language 2 (OWL 2) ontologies to formalize pharmacogenomic knowledge and to provide clinical decision support functionalities. The MSC system provides a novel approach for enabling the implementation of personalized medicine in clinical routine.

Dimensional psychiatry: mental disorders as dysfunctions of basic learning mechanisms.

PubMed

Heinz, Andreas; Schlagenhauf, Florian; Beck, Anne; Wackerhagen, Carolin

2016-08-01

It has been questioned that the more than 300 mental disorders currently listed in international disease classification systems all have a distinct neurobiological correlate. Here, we support the idea that basic dimensions of mental dysfunctions, such as alterations in reinforcement learning, can be identified, which interact with individual vulnerability and psychosocial stress factors and, thus, contribute to syndromes of distress across traditional nosological boundaries. We further suggest that computational modeling of learning behavior can help to identify specific alterations in reinforcement-based decision-making and their associated neurobiological correlates. For example, attribution of salience to drug-related cues associated with dopamine dysfunction in addiction can increase habitual decision-making via promotion of Pavlovian-to-instrumental transfer as indicated by computational modeling of the effect of Pavlovian-conditioned stimuli (here affectively positive or alcohol-related cues) on instrumental approach and avoidance behavior. In schizophrenia, reward prediction errors can be modeled computationally and associated with functional brain activation, thus revealing reduced encoding of such learning signals in the ventral striatum and compensatory activation in the frontal cortex. With respect to negative mood states, it has been shown that both reduced functional activation of the ventral striatum elicited by reward-predicting stimuli and stress-associated activation of the hypothalamic-pituitary-adrenal axis in interaction with reduced serotonin transporter availability and increased amygdala activation by aversive cues contribute to clinical depression; altogether these observations support the notion that basic learning mechanisms, such as Pavlovian and instrumental conditioning and Pavlovian-to-instrumental transfer, represent a basic dimension of mental disorders that can be mechanistically characterized using computational modeling and associated with specific clinical syndromes across established nosological boundaries. Instead of pursuing a narrow focus on single disorders defined by clinical tradition, we suggest that neurobiological research should focus on such basic dimensions, which can be studied in and compared among several mental disorders.

A review and a framework of handheld computer adoption in healthcare.

PubMed

Lu, Yen-Chiao; Xiao, Yan; Sears, Andrew; Jacko, Julie A

2005-06-01

Wide adoption of mobile computing technology can potentially improve information access, enhance workflow, and promote evidence-based practice to make informed and effective decisions at the point of care. Handheld computers or personal digital assistants (PDAs) offer portable and unobtrusive access to clinical data and relevant information at the point of care. This article reviews the literature on issues related to adoption of PDAs in health care and barriers to PDA adoption. Studies showed that PDAs were used widely in health care providers' practice, and the level of use is expected to rise rapidly. Most care providers found PDAs to be functional and useful in areas of documentation, medical reference, and access to patient data. Major barriers to adoption were identified as usability, security concerns, and lack of technical and organizational support. PDAs offer health care practitioners advantages to enhance their clinical practice. However, better designed PDA hardware and software applications, more institutional support, seamless integration of PDA technology with hospital information systems, and satisfactory security measures are necessary to increase acceptance and wide use of PDAs in healthcare.

A theory-based decision aid for patients with cancer: results of feasibility and acceptability testing of DecisionKEYS for cancer.

PubMed

Hollen, Patricia J; Gralla, Richard J; Jones, Randy A; Thomas, Christopher Y; Brenin, David R; Weiss, Geoffrey R; Schroen, Anneke T; Petroni, Gina R

2013-03-01

Appropriate utilization of treatment is a goal for all patients undergoing cancer treatment. Proper treatment maximizes benefit and limits exposure to unnecessary measures. This report describes findings of the feasibility and acceptability of implementing a short, clinic-based decision aid and presents an in-depth clinical profile of the participants. This descriptive study used a prospective, quantitative approach to obtain the feasibility and acceptability of a decision aid (DecisionKEYS for Balancing Choices) for use in clinical settings. It combined results of trials of patients with three different common malignancies. All groups used the same decision aid series. Participants included 80 patients with solid tumors (22 with newly diagnosed breast cancer, 19 with advanced prostate cancer, and 39 with advanced lung cancer) and their 80 supporters as well as their physicians and nurses, for a total of 160 participants and 10 health professionals. The decision aid was highly acceptable to patient and supporter participants in all diagnostic groups. It was feasible for use in clinic settings; the overall value was rated highly. Of six physicians, all found the interactive format with the help of the nurse as feasible and acceptable. Nurses also rated the decision aid favorably. This intervention provides the opportunity to enhance decision making about cancer treatment and warrants further study including larger and more diverse groups. Strengths of the study included a theoretical grounding, feasibility testing of a practical clinic-based intervention, and summative evaluation of acceptability of the intervention by patient and supporter pairs. Further research also is needed to test the effectiveness of the decision aid in diverse clinical settings and to determine if this intervention can decrease overall costs.

The interaction of patient race, provider bias, and clinical ambiguity on pain management decisions

PubMed Central

Hirsh, Adam T.; Hollingshead, Nicole A.; Ashburn-Nardo, Leslie; Kroenke, Kurt

2015-01-01

Although racial disparities in pain care are widely reported, much remains to be known about the role of provider and contextual factors. We used computer-simulated patients to examine the influence of patient race, provider racial bias, and clinical ambiguity on pain decisions. One hundred twenty nine medical residents/fellows made assessment (pain intensity) and treatment (opioid and non-opioid analgesics) decisions for 12 virtual patients with acute pain. Race (Black/White) and clinical ambiguity (high/low) were manipulated across vignettes. Participants completed the Implicit Association Test and feeling thermometers, which assess implicit and explicit racial biases, respectively. Individual- and group-level analyses indicated that race and ambiguity had an interactive effect on providers’ decisions, such that decisions varied as a function of ambiguity for White but not Black patients. Individual differences across providers were observed for the effect of race and ambiguity on decisions; however providers’ implicit and explicit biases did not account for this variability. These data highlight the complexity of racial disparities and suggest that differences in care between White and Black patients are, in part, attributable to the nature (i.e., ambiguity) of the clinical scenario. The current study suggests that interventions to reduce disparities should differentially target patient, provider, and contextual factors. PMID:25828370

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.

A prospective observational study to assess the diagnostic accuracy of clinical decision rules for children presenting to emergency departments after head injuries (protocol): the Australasian Paediatric Head Injury Rules Study (APHIRST).

PubMed

Babl, Franz E; Lyttle, Mark D; Bressan, Silvia; Borland, Meredith; Phillips, Natalie; Kochar, Amit; Dalziel, Stuart R; Dalton, Sarah; Cheek, John A; Furyk, Jeremy; Gilhotra, Yuri; Neutze, Jocelyn; Ward, Brenton; Donath, Susan; Jachno, Kim; Crowe, Louise; Williams, Amanda; Oakley, Ed

2014-06-13

Head injuries in children are responsible for a large number of emergency department visits. Failure to identify a clinically significant intracranial injury in a timely fashion may result in long term neurodisability and death. Whilst cranial computed tomography (CT) provides rapid and definitive identification of intracranial injuries, it is resource intensive and associated with radiation induced cancer. Evidence based head injury clinical decision rules have been derived to aid physicians in identifying patients at risk of having a clinically significant intracranial injury. Three rules have been identified as being of high quality and accuracy: the Canadian Assessment of Tomography for Childhood Head Injury (CATCH) from Canada, the Children's Head Injury Algorithm for the Prediction of Important Clinical Events (CHALICE) from the UK, and the prediction rule for the identification of children at very low risk of clinically important traumatic brain injury developed by the Pediatric Emergency Care Applied Research Network (PECARN) from the USA. This study aims to prospectively validate and compare the performance accuracy of these three clinical decision rules when applied outside the derivation setting. This study is a prospective observational study of children aged 0 to less than 18 years presenting to 10 emergency departments within the Paediatric Research in Emergency Departments International Collaborative (PREDICT) research network in Australia and New Zealand after head injuries of any severity. Predictor variables identified in CATCH, CHALICE and PECARN clinical decision rules will be collected. Patients will be managed as per the treating clinicians at the participating hospitals. All patients not undergoing cranial CT will receive a follow up call 14 to 90 days after the injury. Outcome data collected will include results of cranial CTs (if performed) and details of admission, intubation, neurosurgery and death. The performance accuracy of each of the rules will be assessed using rule specific outcomes and inclusion and exclusion criteria. This study will allow the simultaneous comparative application and validation of three major paediatric head injury clinical decision rules outside their derivation setting. The study is registered with the Australian New Zealand Clinical Trials Registry (ANZCTR)- ACTRN12614000463673 (registered 2 May 2014).

[The effects of case-based learning using video on clinical decision making and learning motivation in undergraduate nursing students].

PubMed

Yoo, Moon-Sook; Park, Jin-Hee; Lee, Si-Ra

2010-12-01

The purpose of this study was to examine the effects of case-base learning (CBL) using video on clinical decision-making and learning motivation. This research was conducted between June 2009 and April 2010 as a nonequivalent control group non-synchronized design. The study population was 44 third year nursing students who enrolled in a college of nursing, A University in Korea. The nursing students were divided into the CBL and the control group. The intervention was the CBL with three cases using video. The controls attended a traditional live lecture on the same topics. With questionnaires objective clinical decision-making, subjective clinical decision-making, and learning motivation were measured before the intervention, and 10 weeks after the intervention. Significant group differences were observed in clinical decision-making and learning motivation. The post-test scores of clinical decision-making in the CBL group were statistically higher than the control group. Learning motivation was also significantly higher in the CBL group than in the control group. These results indicate that CBL using video is effective in enhancing clinical decision-making and motivating students to learn by encouraging self-directed learning and creating more interest and curiosity in learning.

Bayesian inference and decision theory - A framework for decision making in natural resource management

USGS Publications Warehouse

Dorazio, R.M.; Johnson, F.A.

2003-01-01

Bayesian inference and decision theory may be used in the solution of relatively complex problems of natural resource management, owing to recent advances in statistical theory and computing. In particular, Markov chain Monte Carlo algorithms provide a computational framework for fitting models of adequate complexity and for evaluating the expected consequences of alternative management actions. We illustrate these features using an example based on management of waterfowl habitat.

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.

Radiomics-based features for pattern recognition of lung cancer histopathology and metastases.

PubMed

Ferreira Junior, José Raniery; Koenigkam-Santos, Marcel; Cipriano, Federico Enrique Garcia; Fabro, Alexandre Todorovic; Azevedo-Marques, Paulo Mazzoncini de

2018-06-01

lung cancer is the leading cause of cancer-related deaths in the world, and its poor prognosis varies markedly according to tumor staging. Computed tomography (CT) is the imaging modality of choice for lung cancer evaluation, being used for diagnosis and clinical staging. Besides tumor stage, other features, like histopathological subtype, can also add prognostic information. In this work, radiomics-based CT features were used to predict lung cancer histopathology and metastases using machine learning models. local image datasets of confirmed primary malignant pulmonary tumors were retrospectively evaluated for testing and validation. CT images acquired with same protocol were semiautomatically segmented. Tumors were characterized by clinical features and computer attributes of intensity, histogram, texture, shape, and volume. Three machine learning classifiers used up to 100 selected features to perform the analysis. radiomics-based features yielded areas under the receiver operating characteristic curve of 0.89, 0.97, and 0.92 at testing and 0.75, 0.71, and 0.81 at validation for lymph nodal metastasis, distant metastasis, and histopathology pattern recognition, respectively. the radiomics characterization approach presented great potential to be used in a computational model to aid lung cancer histopathological subtype diagnosis as a "virtual biopsy" and metastatic prediction for therapy decision support without the necessity of a whole-body imaging scanning. Copyright © 2018 Elsevier B.V. All rights reserved.

User needs elicitation via analytic hierarchy process (AHP). A case study on a Computed Tomography (CT) scanner.

PubMed

Pecchia, Leandro; Martin, Jennifer L; Ragozzino, Angela; Vanzanella, Carmela; Scognamiglio, Arturo; Mirarchi, Luciano; Morgan, Stephen P

2013-01-05

The rigorous elicitation of user needs is a crucial step for both medical device design and purchasing. However, user needs elicitation is often based on qualitative methods whose findings can be difficult to integrate into medical decision-making. This paper describes the application of AHP to elicit user needs for a new CT scanner for use in a public hospital. AHP was used to design a hierarchy of 12 needs for a new CT scanner, grouped into 4 homogenous categories, and to prepare a paper questionnaire to investigate the relative priorities of these. The questionnaire was completed by 5 senior clinicians working in a variety of clinical specialisations and departments in the same Italian public hospital. Although safety and performance were considered the most important issues, user needs changed according to clinical scenario. For elective surgery, the five most important needs were: spatial resolution, processing software, radiation dose, patient monitoring, and contrast medium. For emergency, the top five most important needs were: patient monitoring, radiation dose, contrast medium control, speed run, spatial resolution. AHP effectively supported user need elicitation, helping to develop an analytic and intelligible framework of decision-making. User needs varied according to working scenario (elective versus emergency medicine) more than clinical specialization. This method should be considered by practitioners involved in decisions about new medical technology, whether that be during device design or before deciding whether to allocate budgets for new medical devices according to clinical functions or according to hospital department.

Knowledge Translation of the PERC Rule for Suspected Pulmonary Embolism: A Blueprint for Reducing the Number of CT Pulmonary Angiograms.

PubMed

Drescher, Michael J; Fried, Jeremy; Brass, Ryan; Medoro, Amanda; Murphy, Timothy; Delgado, João

2017-10-01

Computerized decision support decreases the number of computed tomography pulmonary angiograms (CTPA) for pulmonary embolism (PE) ordered in emergency departments, but it is not always well accepted by emergency physicians. We studied a department-endorsed, evidence-based clinical protocol that included the PE rule-out criteria (PERC) rule, multi-modal education using principles of knowledge translation (KT), and clinical decision support embedded in our order entry system, to decrease the number of unnecessary CTPA ordered. We performed a historically controlled observational before-after study for one year pre- and post-implementation of a departmentally-endorsed protocol. We included patients > 18 in whom providers suspected PE and who did not have a contraindication to CTPA. Providers entered clinical information into a diagnostic pathway via computerized order entry. Prior to protocol implementation, we provided education to ordering providers. The primary outcome measure was the number of CTPA ordered per 1,000 visits one year before vs. after implementation. CTPA declined from 1,033 scans for 98,028 annual visits (10.53 per 1,000 patient visits (95% CI [9.9-11.2]) to 892 scans for 101,172 annual visits (8.81 per 1,000 patient visits (95% CI [8.3-9.4]) p<0.001. The absolute reduction in PACT ordered was 1.72 per 1,000 visits (a 16% reduction). Patient characteristics were similar for both periods. Knowledge translation clinical decision support using the PERC rule significantly reduced the number of CTPA ordered.

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.

A Comparison of Computational Cognitive Models: Agent-Based Systems Versus Rule-Based Architectures

DTIC Science & Technology

2003-03-01

Java™ How To Program , Prentice Hall, 1999. Friedman-Hill, E., Jess, The Expert System Shell for the Java Platform, Sandia National Laboratories, 2001...transition from the descriptive NDM theory to a computational model raises several questions: Who is an experienced decision maker? How do you model the...progression from being a novice to an experienced decision maker? How does the model account for previous experiences? Are there situations where

On the Development of a Computing Infrastructure that Facilitates IPPD from a Decision-Based Design Perspective

NASA Technical Reports Server (NTRS)

Hale, Mark A.; Craig, James I.; Mistree, Farrokh; Schrage, Daniel P.

1995-01-01

Integrated Product and Process Development (IPPD) embodies the simultaneous application of both system and quality engineering methods throughout an iterative design process. The use of IPPD results in the time-conscious, cost-saving development of engineering systems. Georgia Tech has proposed the development of an Integrated Design Engineering Simulator that will merge Integrated Product and Process Development with interdisciplinary analysis techniques and state-of-the-art computational technologies. To implement IPPD, a Decision-Based Design perspective is encapsulated in an approach that focuses on the role of the human designer in product development. The approach has two parts and is outlined in this paper. First, an architecture, called DREAMS, is being developed that facilitates design from a decision-based perspective. Second, a supporting computing infrastructure, called IMAGE, is being designed. The current status of development is given and future directions are outlined.

Computers in medicine: liability issues for physicians.

PubMed

Hafner, A W; Filipowicz, A B; Whitely, W P

1989-07-01

Physicians routinely use computers to store, access, and retrieve medical information. As computer use becomes even more widespread in medicine, failure to utilize information systems may be seen as a violation of professional custom and lead to findings of professional liability. Even when a technology is not widespread, failure to incorporate it into medical practice may give rise to liability if the technology is accessible to the physician and reduces risk to the patient. Improvement in the availability of medical information sources imposes a greater burden on the physician to keep current and to obtain informed consent from patients. To routinely perform computer-assisted literature searches for informed consent and diagnosis is 'good medicine'. Clinical and diagnostic applications of computer technology now include computer-assisted decision making with the aid of sophisticated databases. Although such systems will expand the knowledge base and competence of physicians, malfunctioning software raises a major liability question. Also, complex computer-driven technology is used in direct patient care. Defective or improperly used hardware or software can lead to patient injury, thus raising additional complicated questions of professional liability and product liability.

Barriers and decisions when answering clinical questions at the point of care: a grounded theory study.

PubMed

Cook, David A; Sorensen, Kristi J; Wilkinson, John M; Berger, Richard A

2013-11-25

Answering clinical questions affects patient-care decisions and is important to continuous professional development. The process of point-of-care learning is incompletely understood. To understand what barriers and enabling factors influence physician point-of-care learning and what decisions physicians face during this process. Focus groups with grounded theory analysis. Focus group discussions were transcribed and then analyzed using a constant comparative approach to identify barriers, enabling factors, and key decisions related to physician information-seeking activities. Academic medical center and outlying community sites. Purposive sample of 50 primary care and subspecialist internal medicine and family medicine physicians, interviewed in 11 focus groups. Insufficient time was the main barrier to point-of-care learning. Other barriers included the patient comorbidities and contexts, the volume of available information, not knowing which resource to search, doubt that the search would yield an answer, difficulty remembering questions for later study, and inconvenient access to computers. Key decisions were whether to search (reasons to search included infrequently seen conditions, practice updates, complex questions, and patient education), when to search (before, during, or after the clinical encounter), where to search (with the patient present or in a separate room), what type of resource to use (colleague or computer), what specific resource to use (influenced first by efficiency and second by credibility), and when to stop. Participants noted that key features of efficiency (completeness, brevity, and searchability) are often in conflict. Physicians perceive that insufficient time is the greatest barrier to point-of-care learning, and efficiency is the most important determinant in selecting an information source. Designing knowledge resources and systems to target key decisions may improve learning and patient care.

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.

Bayesian design of decision rules for failure detection

NASA Technical Reports Server (NTRS)

Chow, E. Y.; Willsky, A. S.

1984-01-01

The formulation of the decision making process of a failure detection algorithm as a Bayes sequential decision problem provides a simple conceptualization of the decision rule design problem. As the optimal Bayes rule is not computable, a methodology that is based on the Bayesian approach and aimed at a reduced computational requirement is developed for designing suboptimal rules. A numerical algorithm is constructed to facilitate the design and performance evaluation of these suboptimal rules. The result of applying this design methodology to an example shows that this approach is potentially a useful one.

Using electronic data to predict the probability of true bacteremia from positive blood cultures.

PubMed

Wang, S J; Kuperman, G J; Ohno-Machado, L; Onderdonk, A; Sandige, H; Bates, D W

2000-01-01

As part of a project to help physicians make more appropriate treatment decisions, we implemented a clinical prediction rule that computes the probability of true bacteremia for positive blood cultures and displays this information when culture results are viewed online. Prior to implementing the rule, we performed a revalidation study to verify the accuracy of the previously published logistic regression model. We randomly selected 114 cases of positive blood cultures from a recent one-year period and performed a paper chart review with the help of infectious disease experts to determine whether the cultures were true positives or contaminants. Based on the results of this revalidation study, we updated the probabilities reported by the model and made additional enhancements to improve the accuracy of the rule. Next, we implemented the rule into our hospital's laboratory computer system so that the probability information was displayed with all positive blood culture results. We displayed the prediction rule information on approximately half of the 2184 positive blood cultures at our hospital that were randomly selected during a 6-month period. During the study, we surveyed 54 housestaff to obtain their opinions about the usefulness of this intervention. Fifty percent (27/54) indicated that the information had influenced their belief of the probability of bacteremia in their patients, and in 28% (15/54) of cases it changed their treatment decision. Almost all (98% (53/54)) indicated that they wanted to continue receiving this information. We conclude that the probability information provided by this clinical prediction rule is considered useful to physicians when making treatment decisions.

Clinical overview: a framework for analysis.

PubMed

Bossen, Claus; Jensen, Lotte G

2013-01-01

In this presentation, we investigate concepts and theories for analysing how healthcare professionals achieve overview of patient cases. By 'overview' we mean the situation in which a healthcare professional with sufficient certainty and in concrete situations knows how to proceed based on available information upon a patient. Achieving overview is central for the efficient and safe use of healthcare IT systems, and for the realization of the potential improvements of healthcare that are behind investments in such systems. We focus on the theories of decision-making, sensemaking, narratives, ethnomethodology and distributed cognition. Whereas decision-making theory tend to be sequential and normative, we find the concept of 'functional deployment' in sensemaking theory, 'emplotment' in narrative theory, the focus on 'members' methods' in ethnomethodology and the inclusion of 'computational artifacts' in distributed cognition helpful.

Diagnostic Machine Learning Models for Acute Abdominal Pain: Towards an e-Learning Tool for Medical Students.

PubMed

Khumrin, Piyapong; Ryan, Anna; Judd, Terry; Verspoor, Karin

2017-01-01

Computer-aided learning systems (e-learning systems) can help medical students gain more experience with diagnostic reasoning and decision making. Within this context, providing feedback that matches students' needs (i.e. personalised feedback) is both critical and challenging. In this paper, we describe the development of a machine learning model to support medical students' diagnostic decisions. Machine learning models were trained on 208 clinical cases presenting with abdominal pain, to predict five diagnoses. We assessed which of these models are likely to be most effective for use in an e-learning tool that allows students to interact with a virtual patient. The broader goal is to utilise these models to generate personalised feedback based on the specific patient information requested by students and their active diagnostic hypotheses.

False-Positive Cases of Fluorodeoxyglucose-Positron Emission Tomography/Computed Tomographic Scans in Metastasis of Esophageal Cancer

PubMed Central

Yamatsuji, Tomoki; Ishida, Naomasa; Takaoka, Munenori; Hayashi, Jiro; Yoshida, Kazuhiro; Shigemitsu, Kaori; Urakami, Atsushi; Haisa, Minoru; Naomoto, Yoshio

2017-01-01

Of 129 esophagectomies at our institute from June 2010 to March 2015, we experienced three preoperative positron emission tomography-computed tomographic (PET/CT) false positives. Bone metastasis was originally suspected in 2 cases, but they were later found to be bone metastasis negative after a preoperative bone biopsy and clinical course observation. The other cases suspected of mediastinal lymph node metastasis were diagnosed as inflammatory lymphadenopathy by a pathological examination of the removed lymph nodes. Conducting a PET/CT is useful when diagnosing esophageal cancer metastasis, but we need to be aware of the possibility of false positives. Therapeutic decisions should be made based on appropriate and accurate diagnoses, with pathological diagnosis actively introduced if necessary. PMID:28469502

Added Value of Contrast-Enhanced Ultrasound on Biopsies of Focal Hepatic Lesions Invisible on Fusion Imaging Guidance

PubMed Central

Kang, Tae Wook; Song, Kyoung Doo; Kim, Mimi; Kim, Seung Soo; Kim, Seong Hyun; Ha, Sang Yun

2017-01-01

Objective To assess whether contrast-enhanced ultrasonography (CEUS) with Sonazoid can improve the lesion conspicuity and feasibility of percutaneous biopsies for focal hepatic lesions invisible on fusion imaging of real-time ultrasonography (US) with computed tomography/magnetic resonance images, and evaluate its impact on clinical decision making. Materials and Methods The Institutional Review Board approved this retrospective study. Between June 2013 and January 2015, 711 US-guided percutaneous biopsies were performed for focal hepatic lesions. Biopsies were performed using CEUS for guidance if lesions were invisible on fusion imaging. We retrospectively evaluated the number of target lesions initially invisible on fusion imaging that became visible after applying CEUS, using a 4-point scale. Technical success rates of biopsies were evaluated based on histopathological results. In addition, the occurrence of changes in clinical decision making was assessed. Results Among 711 patients, 16 patients (2.3%) were included in the study. The median size of target lesions was 1.1 cm (range, 0.5–1.9 cm) in pre-procedural imaging. After CEUS, 15 of 16 (93.8%) focal hepatic lesions were visualized. The conspicuity score was significantly increased after adding CEUS, as compared to that on fusion imaging (p < 0.001). The technical success rate of biopsy was 87.6% (14/16). After biopsy, there were changes in clinical decision making for 11 of 16 patients (68.8%). Conclusion The addition of CEUS could improve the conspicuity of focal hepatic lesions invisible on fusion imaging. This dual guidance using CEUS and fusion imaging may affect patient management via changes in clinical decision-making. PMID:28096725

The effect of computerized provider order entry systems on clinical care and work processes in emergency departments: a systematic review of the quantitative literature.

PubMed

Georgiou, Andrew; Prgomet, Mirela; Paoloni, Richard; Creswick, Nerida; Hordern, Antonia; Walter, Scott; Westbrook, Johanna

2013-06-01

We undertake a systematic review of the quantitative literature related to the effect of computerized provider order entry systems in the emergency department (ED). We searched MEDLINE, EMBASE, Inspec, CINAHL, and CPOE.org for English-language studies published between January 1990 and May 2011. We identified 1,063 articles, of which 22 met our inclusion criteria. Sixteen used a pre/post design; 2 were randomized controlled trials. Twelve studies reported outcomes related to patient flow/clinical work, 7 examined decision support systems, and 6 reported effects on patient safety. There were no studies that measured decision support systems and its effect on patient flow/clinical work. Computerized provider order entry was associated with an increase in time spent on computers (up to 16.2% for nurses and 11.3% for physicians), with no significant change in time spent on patient care. Computerized provider order entry with decision support systems was related to significant decreases in prescribing errors (ranging from 17 to 201 errors per 100 orders), potential adverse drug events (0.9 per 100 orders), and prescribing of excessive dosages (31% decrease for a targeted set of renal disease medications). There are tangible benefits associated with computerized provider order entry/decision support systems in the ED environment. Nevertheless, when considered as part of a framework of technical, clinical, and organizational components of the ED, the evidence base is neither consistent nor comprehensive. Multimethod research approaches (including qualitative research) can contribute to understanding of the multiple dimensions of ED care delivery, not as separate entities but as essential components of a highly integrated system of care. Copyright © 2013 American College of Emergency Physicians. Published by Mosby, Inc. All rights reserved.

Clinical Information Systems – From Yesterday to Tomorrow

PubMed Central

2016-01-01

Summary Objectives To review the history of clinical information systems over the past twenty-five years and project anticipated changes to those systems over the next twenty-five years. Methods Over 250 Medline references about clinical information systems, quality of patient care, and patient safety were reviewed. Books, Web resources, and the author’s personal experience with developing the HELP system were also used. Results There have been dramatic improvements in the use and acceptance of clinical computing systems and Electronic Health Records (EHRs), especially in the United States. Although there are still challenges with the implementation of such systems, the rate of progress has been remarkable. Over the next twenty-five years, there will remain many important opportunities and challenges. These opportunities include understanding complex clinical computing issues that must be studied, understood and optimized. Dramatic improvements in quality of care and patient safety must be anticipated as a result of the use of clinical information systems. These improvements will result from a closer involvement of clinical informaticians in the optimization of patient care processes. Conclusions Clinical information systems and computerized clinical decision support have made contributions to medicine in the past. Therefore, by using better medical knowledge, optimized clinical information systems, and computerized clinical decision, we will enable dramatic improvements in both the quality and safety of patient care in the next twenty-five years. PMID:27362589

Towards ethical decision support and knowledge management in neonatal intensive care.

PubMed

Yang, L; Frize, M; Eng, P; Walker, R; Catley, C

2004-01-01

Recent studies in neonatal medicine, clinical nursing, and cognitive psychology have indicated the need to augment current decision-making practice in neonatal intensive care units with computerized, intelligent decision support systems. Rapid progress in artificial intelligence and knowledge management facilitates the design of collaborative ethical decision-support tools that allow clinicians to provide better support for parents facing inherently difficult choices, such as when to withdraw aggressive treatment. The appropriateness of using computers to support ethical decision-making is critically analyzed through research and literature review. In ethical dilemmas, multiple diverse participants need to communicate and function as a team to select the best treatment plan. In order to do this, physicians require reliable estimations of prognosis, while parents need a highly useable tool to help them assimilate complex medical issues and address their own value system. Our goal is to improve and structuralize the ethical decision-making that has become an inevitable part of modern neonatal care units. The paper contributes to clinical decision support by outlining the needs and basis for ethical decision support and justifying the proposed development efforts.

Privacy-Preserving Patient-Centric Clinical Decision Support System on Naïve Bayesian Classification.

PubMed

Liu, Ximeng; Lu, Rongxing; Ma, Jianfeng; Chen, Le; Qin, Baodong

2016-03-01

Clinical decision support system, which uses advanced data mining techniques to help clinician make proper decisions, has received considerable attention recently. The advantages of clinical decision support system include not only improving diagnosis accuracy but also reducing diagnosis time. Specifically, with large amounts of clinical data generated everyday, naïve Bayesian classification can be utilized to excavate valuable information to improve a clinical decision support system. Although the clinical decision support system is quite promising, the flourish of the system still faces many challenges including information security and privacy concerns. In this paper, we propose a new privacy-preserving patient-centric clinical decision support system, which helps clinician complementary to diagnose the risk of patients' disease in a privacy-preserving way. In the proposed system, the past patients' historical data are stored in cloud and can be used to train the naïve Bayesian classifier without leaking any individual patient medical data, and then the trained classifier can be applied to compute the disease risk for new coming patients and also allow these patients to retrieve the top- k disease names according to their own preferences. Specifically, to protect the privacy of past patients' historical data, a new cryptographic tool called additive homomorphic proxy aggregation scheme is designed. Moreover, to leverage the leakage of naïve Bayesian classifier, we introduce a privacy-preserving top- k disease names retrieval protocol in our system. Detailed privacy analysis ensures that patient's information is private and will not be leaked out during the disease diagnosis phase. In addition, performance evaluation via extensive simulations also demonstrates that our system can efficiently calculate patient's disease risk with high accuracy in a privacy-preserving way.

MoCog1: A computer simulation of recognition-primed human decision making, considering emotions

NASA Technical Reports Server (NTRS)

Gevarter, William B.

1992-01-01

The successful results of the first stage of a research effort to develop a versatile computer model of motivated human cognitive behavior are reported. Most human decision making appears to be an experience-based, relatively straightforward, largely automatic response to situations, utilizing cues and opportunities perceived from the current environment. The development, considering emotions, of the architecture and computer program associated with such 'recognition-primed' decision-making is described. The resultant computer program (MoCog1) was successfully utilized as a vehicle to simulate earlier findings that relate how an individual's implicit theories orient the individual toward particular goals, with resultant cognitions, affects, and behavior in response to their environment.

MoCog1: A computer simulation of recognition-primed human decision making

NASA Technical Reports Server (NTRS)

Gevarter, William B.

1991-01-01

The results of the first stage of a research effort to develop a 'sophisticated' computer model of human cognitive behavior are described. Most human decision making is an experience-based, relatively straight-forward, largely automatic response to internal goals and drives, utilizing cues and opportunities perceived from the current environment. The development of the architecture and computer program (MoCog1) associated with such 'recognition-primed' decision making is discussed. The resultant computer program was successfully utilized as a vehicle to simulate earlier findings that relate how an individual's implicit theories orient the individual toward particular goals, with resultant cognitions, affects, and behavior in response to their environment.

Decision Making and Reward in Frontal Cortex

PubMed Central

Kennerley, Steven W.; Walton, Mark E.

2011-01-01

Patients with damage to the prefrontal cortex (PFC)—especially the ventral and medial parts of PFC—often show a marked inability to make choices that meet their needs and goals. These decision-making impairments often reflect both a deficit in learning concerning the consequences of a choice, as well as deficits in the ability to adapt future choices based on experienced value of the current choice. Thus, areas of PFC must support some value computations that are necessary for optimal choice. However, recent frameworks of decision making have highlighted that optimal and adaptive decision making does not simply rest on a single computation, but a number of different value computations may be necessary. Using this framework as a guide, we summarize evidence from both lesion studies and single-neuron physiology for the representation of different value computations across PFC areas. PMID:21534649

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.

Assessing Professional Decision-Making Abilities.

ERIC Educational Resources Information Center

McNergney, Robert; Hinson, Stephanie

1985-01-01

Describes Teacher Development Decision Exercises, a computer-based method of diagnosing abilities of elementary and secondary school supervisors (principals, staff developers, curriculum coordinators) to make professional preactive or planning decisions. This approval simulates assessment of supervisors' abilities to use professional knowledge to…

Deep learning aided decision support for pulmonary nodules diagnosing: a review.

PubMed

Yang, Yixin; Feng, Xiaoyi; Chi, Wenhao; Li, Zhengyang; Duan, Wenzhe; Liu, Haiping; Liang, Wenhua; Wang, Wei; Chen, Ping; He, Jianxing; Liu, Bo

2018-04-01

Deep learning techniques have recently emerged as promising decision supporting approaches to automatically analyze medical images for different clinical diagnosing purposes. Diagnosing of pulmonary nodules by using computer-assisted diagnosing has received considerable theoretical, computational, and empirical research work, and considerable methods have been developed for detection and classification of pulmonary nodules on different formats of images including chest radiographs, computed tomography (CT), and positron emission tomography in the past five decades. The recent remarkable and significant progress in deep learning for pulmonary nodules achieved in both academia and the industry has demonstrated that deep learning techniques seem to be promising alternative decision support schemes to effectively tackle the central issues in pulmonary nodules diagnosing, including feature extraction, nodule detection, false-positive reduction, and benign-malignant classification for the huge volume of chest scan data. The main goal of this investigation is to provide a comprehensive state-of-the-art review of the deep learning aided decision support for pulmonary nodules diagnosing. As far as the authors know, this is the first time that a review is devoted exclusively to deep learning techniques for pulmonary nodules diagnosing.

Estimating the Reliability of the CITAR Computer Courseware Evaluation System.

ERIC Educational Resources Information Center

Micceri, Theodore

In today's complex computer-based teaching (CBT)/computer-assisted instruction market, flashy presentations frequently prove the most important purchasing element, while instructional design and content are secondary to form. Courseware purchasers must base decisions upon either a vendor's presentation or some published evaluator rating.…

Withdrawal of anticancer therapy in advanced disease: a systematic literature review.

PubMed

Clarke, G; Johnston, S; Corrie, P; Kuhn, I; Barclay, S

2015-11-11

Current guidelines set out when to start anticancer treatments, but not when to stop as the end of life approaches. Conventional cytotoxic agents are administered intravenously and have major life-threatening toxicities. Newer drugs include molecular targeted agents (MTAs), in particular, small molecule kinase-inhibitors (KIs), which are administered orally. These have fewer life-threatening toxicities, and are increasingly used to palliate advanced cancer, generally offering additional months of survival benefit. MTAs are substantially more expensive, between £2-8 K per month, and perceived as easier to start than stop. A systematic review of decision-making concerning the withdrawal of anticancer drugs towards the end of life within clinical practice, with a particular focus on MTAs. Nine electronic databases searched. PRISMA guidelines followed. Forty-two studies included. How are decisions made? Decision-making was shared and ongoing, including stopping, starting and trying different treatments. Oncologists often experienced 'professional role dissonance' between their self-perception as 'treaters', and talking about end of life care. Why are decisions made? Clinical factors: disease progression, worsening functional status, treatment side-effects. Non-clinical factors: physicians' personal experience, values, emotions. Some patients continued treatment to maintain 'hope', often reflecting limited understanding of palliative goals. When are decisions made? Limited evidence reveals patients' decisions based upon quality of life benefits. Clinicians found timing withdrawal particularly challenging. Who makes the decisions? Decisions were based within physician-patient interaction. Oncologists report that decisions around stopping chemotherapy treatment are challenging, with limited evidence-based guidance outside of clinical trial protocols. The increasing availability of oral MTAs is transforming the management of incurable cancer; blurring boundaries between active treatment and palliative care. No studies specifically addressing decision-making around stopping MTAs in clinical practice were identified. There is a need to develop an evidence base to support physicians and patients with decision-making around the withdrawal of these high cost treatments.

Implementation of a scalable, web-based, automated clinical decision support risk-prediction tool for chronic kidney disease using C-CDA and application programming interfaces.

PubMed

Samal, Lipika; D'Amore, John D; Bates, David W; Wright, Adam

2017-11-01

Clinical decision support tools for risk prediction are readily available, but typically require workflow interruptions and manual data entry so are rarely used. Due to new data interoperability standards for electronic health records (EHRs), other options are available. As a clinical case study, we sought to build a scalable, web-based system that would automate calculation of kidney failure risk and display clinical decision support to users in primary care practices. We developed a single-page application, web server, database, and application programming interface to calculate and display kidney failure risk. Data were extracted from the EHR using the Consolidated Clinical Document Architecture interoperability standard for Continuity of Care Documents (CCDs). EHR users were presented with a noninterruptive alert on the patient's summary screen and a hyperlink to details and recommendations provided through a web application. Clinic schedules and CCDs were retrieved using existing application programming interfaces to the EHR, and we provided a clinical decision support hyperlink to the EHR as a service. We debugged a series of terminology and technical issues. The application was validated with data from 255 patients and subsequently deployed to 10 primary care clinics where, over the course of 1 year, 569 533 CCD documents were processed. We validated the use of interoperable documents and open-source components to develop a low-cost tool for automated clinical decision support. Since Consolidated Clinical Document Architecture-based data extraction extends to any certified EHR, this demonstrates a successful modular approach to clinical decision support. © The Author 2017. Published by Oxford University Press on behalf of the American Medical Informatics Association.

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.

[Cognitive traps and clinical decisions].

PubMed

Motterlini, Matteo

2017-12-01

We are fallible, we have limited computational capabilities, limited access to information, little memory. Moreover, in everyday life, we feel joy, fear, anger, and other emotions that influence our decisions in a little, "calculated" way. Not everyone, however, is also aware that the mistakes we make are often systematic and therefore, in particular circumstances, are foreseeable. Doctors and patients are constantly called upon to make decisions. They need to identify relevant information (for example, the symptoms or outcome of an examination), formulate a judgment (for example a diagnosis), choose an action course among the various possible ones based on one's own preferences (e.g. medication or surgery), so act. The exact size of the medical error is unknown, but probably huge. In fact, the more we investigate and the more we find. Often these mistakes depend on the cognitive process. Any (rational) decision requires, in particular, an assessment of the possible effects of the action it implements; for example how much pleasure or pain it will cause us. In the medical field, too, the principle of informed consent provides that the patient's preferences and values are to guide clinical choices. Yet, not always the preferences that people express before making an experience match with their preferences after living that experience. Some ingenious experiments suggest (in a seemingly paradoxical way) that before a direct experience, people prefer less pain; after that experience they prefer more, but with a better memory.

Exploiting Temporal Constraints of Clinical Guidelines by Applying OpenEHR Archetypes.

PubMed

Cintho, Lilian Mie Mukai; Garcia, Diego; da Silva Santos, Bruno Henrique; Sacchi, Lucia; Quaglini, Silvana; Moro, Claudia Maria Cabral

2017-01-01

Studies describing Computer-Interpretable Clinical Guidelines (CIG) with temporal constrains (TC) generally have not addressed issues related to their integration into Electronic Health Record (EHR) systems. This study aimed to represent TCs contained in clinical guidelines by applying archetypes and Guideline Definition Language (GDL) to incorporate decision support into EHRs. An example of each TC class in the clinical guideline for management of Atrial Fibrillation was represented using archetypes and GDL.

Clinical decision support provided within physician order entry systems: a systematic review of features effective for changing clinician behavior.

PubMed

Kawamoto, Kensaku; Lobach, David F

2003-01-01

Computerized physician order entry (CPOE) systems represent an important tool for providing clinical decision support. In undertaking this systematic review, our objective was to identify the features of CPOE-based clinical decision support systems (CDSSs) most effective at modifying clinician behavior. For this review, two independent reviewers systematically identified randomized controlled trials that evaluated the effectiveness of CPOE-based CDSSs in changing clinician behavior. Furthermore, each included study was assessed for the presence of 14 CDSS features. We screened 10,023 citations and included 11 studies. Of the 10 studies comparing a CPOE-based CDSS intervention against a non-CDSS control group, 7 reported a significant desired change in professional practice. Moreover, meta-regression analysis revealed that automatic provision of the decision support was strongly associated with improved professional practice (adjusted odds ratio, 23.72; 95% confidence interval, 1.75-infiniti). Thus, we conclude that automatic provision of decision support is a critical feature of successful CPOE-based CDSS interventions.

Designing Computerized Decision Support That Works for Clinicians and Families

PubMed Central

Fiks, Alexander G.

2011-01-01

Evidence-based decision-making is central to the practice of pediatrics. Clinical trials and other biomedical research provide a foundation for this process, and practice guidelines, drawing from their results, inform the optimal management of an increasing number of childhood health problems. However, many clinicians fail to adhere to guidelines. Clinical decision support delivered using health information technology, often in the form of electronic health records, provides a tool to deliver evidence-based information to the point of care and has the potential to overcome barriers to evidence-based practice. An increasing literature now informs how these systems should be designed and implemented to most effectively improve outcomes in pediatrics. Through the examples of computerized physician order entry, as well as the impact of alerts at the point of care on immunization rates, the delivery of evidence-based asthma care, and the follow-up of children with attention deficit hyperactivity disorder, the following review addresses strategies for success in using these tools. The following review argues that, as decision support evolves, the clinician should no longer be the sole target of information and alerts. Through the Internet and other technologies, families are increasingly seeking health information and gathering input to guide health decisions. By enlisting clinical decision support systems to deliver evidence-based information to both clinicians and families, help families express their preferences and goals, and connect families to the medical home, clinical decision support may ultimately be most effective in improving outcomes. PMID:21315295

[Treatment regulations and treatment limits: factors influencing clinical decision-making].

PubMed

Baberg, H T; Kielstein, R; de Zeeuw, J; Sass, H-M

2002-08-02

Providing or withholding of treatment is based on a variety of factors. We sought for criteria in clinical decision making and reviewed attitudes towards clinical intuition and the patient's will. 503 physicians (25.6 % females; mean age 36.3) in 49 departments at nine hospitals of the universities Bochum and Magdeburg filled in a validated questionnaire. The most important factors in the decision to carry out a therapy were "international standards" and "own experience". The decision to omit a therapy was mainly influenced by the "patient's wish". Physicians with a higher status judged their own experience higher than young physicians, who considered the experience of colleagues more important. "Severe accompanying illnesses" and "multimorbidity" were the most frequently named reasons to withdraw a therapy. Intuitive decision-making was rare, especially in young physicians, although these decisions were seldom risky and often successful. A patient's will plays a prominent role in clinical decision making, especially in decisions to withdraw or to withhold treatment. Cost containment and research interest have been called less important, a remarkable response from research-based university hospitals. Also remarkable is the recognition and importance of clinical intuition in situations of complex or missing information. This important aspect is rarely discussed in the literature or in medical education. The widely voiced concern that priorities in clinical care are guided by scientific interest, financial or technical possibilities could not be confirmed.

SANDS: A Service-Oriented Architecture for Clinical Decision Support in a National Health Information Network

PubMed Central

Wright, Adam; Sittig, Dean F.

2008-01-01

In this paper we describe and evaluate a new distributed architecture for clinical decision support called SANDS (Service-oriented Architecture for NHIN Decision Support), which leverages current health information exchange efforts and is based on the principles of a service-oriented architecture. The architecture allows disparate clinical information systems and clinical decision support systems to be seamlessly integrated over a network according to a set of interfaces and protocols described in this paper. The architecture described is fully defined and developed, and six use cases have been developed and tested using a prototype electronic health record which links to one of the existing prototype National Health Information Networks (NHIN): drug interaction checking, syndromic surveillance, diagnostic decision support, inappropriate prescribing in older adults, information at the point of care and a simple personal health record. Some of these use cases utilize existing decision support systems, which are either commercially or freely available at present, and developed outside of the SANDS project, while other use cases are based on decision support systems developed specifically for the project. Open source code for many of these components is available, and an open source reference parser is also available for comparison and testing of other clinical information systems and clinical decision support systems that wish to implement the SANDS architecture. PMID:18434256

Human-computer interface for the study of information fusion concepts in situation analysis and command decision support systems

NASA Astrophysics Data System (ADS)

Roy, Jean; Breton, Richard; Paradis, Stephane

2001-08-01

Situation Awareness (SAW) is essential for commanders to conduct decision-making (DM) activities. Situation Analysis (SA) is defined as a process, the examination of a situation, its elements, and their relations, to provide and maintain a product, i.e., a state of SAW for the decision maker. Operational trends in warfare put the situation analysis process under pressure. This emphasizes the need for a real-time computer-based Situation analysis Support System (SASS) to aid commanders in achieving the appropriate situation awareness, thereby supporting their response to actual or anticipated threats. Data fusion is clearly a key enabler for SA and a SASS. Since data fusion is used for SA in support of dynamic human decision-making, the exploration of the SA concepts and the design of data fusion techniques must take into account human factor aspects in order to ensure a cognitive fit of the fusion system with the decision-maker. Indeed, the tight human factor aspects in order to ensure a cognitive fit of the fusion system with the decision-maker. Indeed, the tight integration of the human element with the SA technology is essential. Regarding these issues, this paper provides a description of CODSI (Command Decision Support Interface), and operational- like human machine interface prototype for investigations in computer-based SA and command decision support. With CODSI, one objective was to apply recent developments in SA theory and information display technology to the problem of enhancing SAW quality. It thus provides a capability to adequately convey tactical information to command decision makers. It also supports the study of human-computer interactions for SA, and methodologies for SAW measurement.

Making the Right Decisions: Leadership in 1-to-1 Computing in Education

ERIC Educational Resources Information Center

Towndrow, Phillip A.; Vallance, Michael

2013-01-01

Purpose: The purpose of this paper is to detail the necessity for more informed decision making and leadership in the implementation of 1-to-1 computing in education. Design/methodology/approach: The contexts of high-tech countries of Singapore and Japan are used as case studies to contextualize and support four evidence-based recommendations for…

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.

The New York City eClinician Project: using Personal Digital Assistants and wireless internet access to support emergency preparedness and enhance clinical care in community health centers.

PubMed

Adusumilli, Sri Raj; Tobin, Jonathan N; Younge, Richard G; Kendall, Mat; Kukafka, Rita; Khan, Sharib; Chang, Otto; Mahabir, Kasandra

2006-01-01

The New York City Department of Health and Mental Hygiene, The Community Health Care Association of New York State and Clinical Directors Network are collaborating on the "eClinician Project," which has distributed seven hundred public health-friendly, wireless (WiFi) enabled Personal Digital Assistants (PDAs) to primary care clinicians working in New York City, federally funded, Community Health Centers (CHC) which serve minority underserved communities that suffer a disproportionate burden of chronic disease and lack access to health promotion disease prevention services. Each participating health center also received a wireless router to create an onsite internet hot spot to enable clinicians to have internet access. The goals of the eClinician Project are to: 1) To encourage adoption of information technology among providers in Community Health Centers in New York City by providing PDAs as a first line strategy towards achieving this goal, 2) enhance access to information on emergency preparedness, 3) improve patient outcomes by providing PDA-based clinical decision-support tools that support evidence-based care, 4) encourage chronic care management and health promotion/disease prevention activities, and 5) increase productivity and efficiency. CHC clinicians have received a hands-on, on-site orientation to PDAs. Ongoing training has continued via online CME-accredited webcasts (see www.CDNetwork.org). Clinical decision-support tools are available for download via the eClinician project web portal (see www.eClinician.org ). Public health alerts can be delivered to the PDAs or to the clinicians' desktop computers. Pre and post training surveys, in addition to a case study, have been used to evaluate the population demographics, PDA adoption by the clinicians, clinician attitudes towards using PDAs, PDA influence on clinical-decision making and barriers to adoption of PDAs and information technology in general.

An information management system for patients with tuberculosis: usability assessment with end-users.

PubMed

Darby, Jonathan; Black, Jim; Morrison, David; Buising, Kirsty

2012-01-01

Information systems with clinical decision support (CDS) offer great potential to assist the co-ordination of patients with chronic diseases and to improve patient care. Despite this, few have entered routine clinical use. Tuberculosis (TB) is an infection of public health importance. It has complex interactions with many comorbid conditions, requires close supervised care and prolonged treatment for effective cure. These features make it suitable for use with an information management system with CDS features. In close consultation with key stakeholders, a clinical application was developed for the management of TB patients in Victoria. A formal usability assessment using semi-structured case-scenario based exercises was performed. Subjects were 12 individuals closely involved in the care of TB patients, including Infectious Diseases and Respiratory Physicians, and Public Health Nurses. Two researchers conducted the sessions, independently analysed responses and discrepancies compared to the voice record for validity. Despite varied computer experience, responses were positive regarding user interface and content. Data location was not always intuitive, however this improved with familiarity of the program. Decision support was considered valuable, with useful suggestions for expansion of these features. Automated reporting for correspondence and notification to the Health Department were felt worth the initial investment in data entry. An important workflow-based issue regarding dismissal of alerts and several errors were detected. Usability assessment validated many design elements of the system, provided a unique insight into workflow issues faced by users and hopefully will impact on its ultimate clinical utility.

Requirements for the formal representation of pathophysiology mechanisms by clinicians

PubMed Central

Helvensteijn, M.; Kokash, N.; Martorelli, I.; Sarwar, D.; Islam, S.; Grenon, P.; Hunter, P.

2016-01-01

Knowledge of multiscale mechanisms in pathophysiology is the bedrock of clinical practice. If quantitative methods, predicting patient-specific behaviour of these pathophysiology mechanisms, are to be brought to bear on clinical decision-making, the Human Physiome community and Clinical community must share a common computational blueprint for pathophysiology mechanisms. A number of obstacles stand in the way of this sharing—not least the technical and operational challenges that must be overcome to ensure that (i) the explicit biological meanings of the Physiome's quantitative methods to represent mechanisms are open to articulation, verification and study by clinicians, and that (ii) clinicians are given the tools and training to explicitly express disease manifestations in direct contribution to modelling. To this end, the Physiome and Clinical communities must co-develop a common computational toolkit, based on this blueprint, to bridge the representation of knowledge of pathophysiology mechanisms (a) that is implicitly depicted in electronic health records and the literature, with (b) that found in mathematical models explicitly describing mechanisms. In particular, this paper makes use of a step-wise description of a specific disease mechanism as a means to elicit the requirements of representing pathophysiological meaning explicitly. The computational blueprint developed from these requirements addresses the Clinical community goals to (i) organize and manage healthcare resources in terms of relevant disease-related knowledge of mechanisms and (ii) train the next generation of physicians in the application of quantitative methods relevant to their research and practice. PMID:27051514

CardioClassifier: disease- and gene-specific computational decision support for clinical genome interpretation.

PubMed

Whiffin, Nicola; Walsh, Roddy; Govind, Risha; Edwards, Matthew; Ahmad, Mian; Zhang, Xiaolei; Tayal, Upasana; Buchan, Rachel; Midwinter, William; Wilk, Alicja E; Najgebauer, Hanna; Francis, Catherine; Wilkinson, Sam; Monk, Thomas; Brett, Laura; O'Regan, Declan P; Prasad, Sanjay K; Morris-Rosendahl, Deborah J; Barton, Paul J R; Edwards, Elizabeth; Ware, James S; Cook, Stuart A

2018-01-25

PurposeInternationally adopted variant interpretation guidelines from the American College of Medical Genetics and Genomics (ACMG) are generic and require disease-specific refinement. Here we developed CardioClassifier (http://www.cardioclassifier.org), a semiautomated decision-support tool for inherited cardiac conditions (ICCs).MethodsCardioClassifier integrates data retrieved from multiple sources with user-input case-specific information, through an interactive interface, to support variant interpretation. Combining disease- and gene-specific knowledge with variant observations in large cohorts of cases and controls, we refined 14 computational ACMG criteria and created three ICC-specific rules.ResultsWe benchmarked CardioClassifier on 57 expertly curated variants and show full retrieval of all computational data, concordantly activating 87.3% of rules. A generic annotation tool identified fewer than half as many clinically actionable variants (64/219 vs. 156/219, Fisher's P = 1.1  ×  10 -18 ), with important false positives, illustrating the critical importance of disease and gene-specific annotations. CardioClassifier identified putatively disease-causing variants in 33.7% of 327 cardiomyopathy cases, comparable with leading ICC laboratories. Through addition of manually curated data, variants found in over 40% of cardiomyopathy cases are fully annotated, without requiring additional user-input data.ConclusionCardioClassifier is an ICC-specific decision-support tool that integrates expertly curated computational annotations with case-specific data to generate fast, reproducible, and interactive variant pathogenicity reports, according to best practice guidelines.GENETICS in MEDICINE advance online publication, 25 January 2018; doi:10.1038/gim.2017.258.

Translational Radiomics: Defining the Strategy Pipeline and Considerations for Application-Part 2: From Clinical Implementation to Enterprise.

PubMed

Shaikh, Faiq; Franc, Benjamin; Allen, Erastus; Sala, Evis; Awan, Omer; Hendrata, Kenneth; Halabi, Safwan; Mohiuddin, Sohaib; Malik, Sana; Hadley, Dexter; Shrestha, Rasu

2018-03-01

Enterprise imaging has channeled various technological innovations to the field of clinical radiology, ranging from advanced imaging equipment and postacquisition iterative reconstruction tools to image analysis and computer-aided detection tools. More recently, the advancement in the field of quantitative image analysis coupled with machine learning-based data analytics, classification, and integration has ushered in the era of radiomics, a paradigm shift that holds tremendous potential in clinical decision support as well as drug discovery. However, there are important issues to consider to incorporate radiomics into a clinically applicable system and a commercially viable solution. In this two-part series, we offer insights into the development of the translational pipeline for radiomics from methodology to clinical implementation (Part 1) and from that point to enterprise development (Part 2). In Part 2 of this two-part series, we study the components of the strategy pipeline, from clinical implementation to building enterprise solutions. Copyright © 2017 American College of Radiology. Published by Elsevier Inc. All rights reserved.

The interaction of patient race, provider bias, and clinical ambiguity on pain management decisions.

PubMed

Hirsh, Adam T; Hollingshead, Nicole A; Ashburn-Nardo, Leslie; Kroenke, Kurt

2015-06-01

Although racial disparities in pain care are widely reported, much remains to be known about the role of provider and contextual factors. We used computer-simulated patients to examine the influence of patient race, provider racial bias, and clinical ambiguity on pain decisions. One hundred twenty-nine medical residents/fellows made assessment (pain intensity) and treatment (opioid and nonopioid analgesics) decisions for 12 virtual patients with acute pain. Race (black/white) and clinical ambiguity (high/low) were manipulated across vignettes. Participants completed the Implicit Association Test and feeling thermometers, which assess implicit and explicit racial biases, respectively. Individual- and group-level analyses indicated that race and ambiguity had an interactive effect on providers' decisions, such that decisions varied as a function of ambiguity for white but not for black patients. Individual differences across providers were observed for the effect of race and ambiguity on decisions; however, providers' implicit and explicit biases did not account for this variability. These data highlight the complexity of racial disparities and suggest that differences in care between white and black patients are, in part, attributable to the nature (ie, ambiguity) of the clinical scenario. The current study suggests that interventions to reduce disparities should differentially target patient, provider, and contextual factors. This study examined the unique and collective influence of patient race, provider racial bias, and clinical ambiguity on providers' pain management decisions. These results could inform the development of interventions aimed at reducing disparities and improving pain care. Copyright © 2015 American Pain Society. Published by Elsevier Inc. All rights reserved.

MIT Laboratory for Computer Science Progress Report 27

DTIC Science & Technology

1990-06-01

because of the natural, yet unexploited, concurrence that characterizes contemporary and prospective applications from business to sensory computing...432. 14 Advanced Network Architecture Academic Staff D. Clark, Group Leader D. Tennenhouse J. Saltzer Research Staff J. Davin K. Sollins Graduate...Murray Hill, NJ, July 1989. 23 24 Clinical Decision Making Academic Staff R. Patil P. Szolovits, Group Leader G. Rennels Collaborating Investigators M

[Use of the computer as a tool for the implementation of the nursing process--the experience of the Sâo Paulo/UNIFESP].

PubMed

de Barros, Alba Lúcia; Fakih, Flávio Trevisani; Michel, Jeanne Liliane

2002-01-01

This article reports the pathway used to build a prototype of a computer nurse's clinical decision making support system, using NANDA, NIC and NOC classifications, as an auxiliary tool in the insertion of nursing data in the computerized patient record of Hospital São Paulo/UNIFESP.

Decisions, Decisions, Decisions: Recreation Site Choice with Expected Congestion and Social Interaction

ERIC Educational Resources Information Center

Snipes, Katherine H.

2009-01-01

A set of computer-based recreation choice experiments were run to examine the effect of expected congestion and social interactions on the decision making process. MouseTrace is a process-tracing program that recorded individual subject's information acquisitions and provided the necessary information to determine if subjects used attribute-based…

Distinct Roles of Dopamine and Subthalamic Nucleus in Learning and Probabilistic Decision Making

ERIC Educational Resources Information Center

Coulthard, Elizabeth J.; Bogacz, Rafal; Javed, Shazia; Mooney, Lucy K.; Murphy, Gillian; Keeley, Sophie; Whone, Alan L.

2012-01-01

Even simple behaviour requires us to make decisions based on combining multiple pieces of learned and new information. Making such decisions requires both learning the optimal response to each given stimulus as well as combining probabilistic information from multiple stimuli before selecting a response. Computational theories of decision making…

Emotion-affected decision making in human simulation.

PubMed

Zhao, Y; Kang, J; Wright, D K

2006-01-01

Human modelling is an interdisciplinary research field. The topic, emotion-affected decision making, was originally a cognitive psychology issue, but is now recognized as an important research direction for both computer science and biomedical modelling. The main aim of this paper is to attempt to bridge the gap between psychology and bioengineering in emotion-affected decision making. The work is based on Ortony's theory of emotions and bounded rationality theory, and attempts to connect the emotion process with decision making. A computational emotion model is proposed, and the initial framework of this model in virtual human simulation within the platform of Virtools is presented.

A Three-Phase Decision Model of Computer-Aided Coding for the Iranian Classification of Health Interventions (IRCHI)

PubMed Central

Azadmanjir, Zahra; Safdari, Reza; Ghazisaeedi, Marjan; Mokhtaran, Mehrshad; Kameli, Mohammad Esmail

2017-01-01

Introduction: Accurate coded data in the healthcare are critical. Computer-Assisted Coding (CAC) is an effective tool to improve clinical coding in particular when a new classification will be developed and implemented. But determine the appropriate method for development need to consider the specifications of existing CAC systems, requirements for each type, our infrastructure and also, the classification scheme. Aim: The aim of the study was the development of a decision model for determining accurate code of each medical intervention in Iranian Classification of Health Interventions (IRCHI) that can be implemented as a suitable CAC system. Methods: first, a sample of existing CAC systems was reviewed. Then feasibility of each one of CAC types was examined with regard to their prerequisites for their implementation. The next step, proper model was proposed according to the structure of the classification scheme and was implemented as an interactive system. Results: There is a significant relationship between the level of assistance of a CAC system and integration of it with electronic medical documents. Implementation of fully automated CAC systems is impossible due to immature development of electronic medical record and problems in using language for medical documenting. So, a model was proposed to develop semi-automated CAC system based on hierarchical relationships between entities in the classification scheme and also the logic of decision making to specify the characters of code step by step through a web-based interactive user interface for CAC. It was composed of three phases to select Target, Action and Means respectively for an intervention. Conclusion: The proposed model was suitable the current status of clinical documentation and coding in Iran and also, the structure of new classification scheme. Our results show it was practical. However, the model needs to be evaluated in the next stage of the research. PMID:28883671

Monitoring of Intracranial Pressure in Meningitis.

PubMed

Depreitere, Bart; Bruyninckx, Dominike; Güiza, Fabian

2016-01-01

The literature on intracranial pressure (ICP) monitoring in meningitis is limited to case reports and a handful of descriptive series. The aim of this study is to investigate relationships among ICP, cerebral perfusion pressure (CPP), and outcome in meningitis and to identify whether ICP affected clinical decisions. Between 1999 and 2011, a total of 17 patients with meningitis underwent ICP monitoring at the University Hospitals Leuven. Charts were reviewed for clinical history, ICP/CPP data, imaging findings, and Glasgow Outcome Scale score. Univariate correlations were computed for outcome and ICP/CPP variables, computed tomography characteristics, and Corticosteroid Randomization After Significant Head Injury outcome model variables. Treatment decisions were assessed regarding whether or not they were based on ICP. At drain placement, Glasgow Coma Scale scores showed a median of 8 (range 3-12). Six of 17 patients had either one or two nonreactive pupils. Significant correlations with outcome were found for the highest documented ICP value (r = -0.70), the number of episodes when CPP <50 mmHg (r =-0.50), the lowest documented CPP value (r = 0.61), and pupil reactivity (r = 0.57). Treatment was influenced by ICP in all patients. The results support the notion that in meningitis high ICP and low CPP represent secondary insults. The poor condition of the patients illustrates that the level of suspicion for increased ICP in meningitis may not be high enough.

Impact of four training conditions on physician use of a web-based clinical decision support system.

PubMed

Kealey, Edith; Leckman-Westin, Emily; Finnerty, Molly T

2013-09-01

Training has been identified as an important barrier to implementation of clinical decision support systems (CDSSs), but little is known about the effectiveness of different training approaches. Using an observational retrospective cohort design, we examined the impact of four training conditions on physician use of a CDSS: (1) computer lab training with individualized follow-up (CL-FU) (n=40), (2) computer lab training without follow-up (CL) (n=177), (3) lecture demonstration (LD) (n=16), or (4) no training (NT) (n=134). Odds ratios of any use and ongoing use under training conditions were compared to no training over a 2-year follow-up period. CL-FU was associated with the highest percent of active users and odds for any use (90.0%, odds ratio (OR)=10.2, 95% confidence interval (CI): 3.2-32.9) and ongoing use (60.0%, OR=6.1 95% CI: 2.6-13.7), followed by CL (any use=81.4%, OR=5.3, CI: 2.9-9.6; ongoing use=28.8%, OR=1.7, 95% CI: 1.0-3.0). LD was not superior to no training (any use=47%, ongoing use=22.4%). Training format may have differential effects on initial and long-term follow-up of CDSSs use by physicians. Copyright © 2013 Elsevier B.V. All rights reserved.

The use of decision analysis to examine ethical decision making by critical care nurses.

PubMed

Hughes, K K; Dvorak, E M

1997-01-01

To examine the extent to which critical care staff nurses make ethical decisions that coincide with those recommended by a decision analytic model. Nonexperimental, ex post facto. Midwestern university-affiliated 500 bed tertiary care medical center. One hundred critical care staff nurses randomly selected from seven critical care units. Complete responses were obtained from 82 nurses (for a final response rate of 82%). The dependent variable--consistent decision making--was measured as staff nurses' abilities to make ethical decisions that coincided with those prescribed by the decision model. Subjects completed two instruments, the Ethical Decision Analytic Model, a computer-administered instrument designed to measure staff nurses' abilities to make consistent decisions about a chemically-impaired colleague; and a Background Inventory. The results indicate marked consensus among nurses when informal methods were used. However, there was little consistency between the nurses' informal decisions and those recommended by the decision analytic model. Although 50% (n = 41) of all nurses chose a course of action that coincided with the model's least optimal alternative, few nurses agreed with the model as to the most optimal course of action. The findings also suggest that consistency was unrelated (p > 0.05) to the nurses' educational background or years of clinical experience; that most subjects reported receiving little or no education in decision making during their basic nursing education programs; but that exposure to decision-making strategies was related to years of nursing experience (p < 0.05). The findings differ from related studies that have found a moderate degree of consistency between nurses and decision analytic models for strictly clinical decision tasks, especially when those tasks were less complex. However, the findings partially coincide with other findings that decision analysis may not be particularly well-suited to the critical care environment. Additional research is needed to determine whether critical care nurses use the same decision-making methods as do other nurses; and to clarify the effects of decision task (clinical versus ethical) on nurses' decision making. It should not be assumed that methods used to study nurses' clinical decision making are applicable for all nurses or all types of decisions, including ethical decisions.

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

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.

Decision theory, reinforcement learning, and the brain.

PubMed

Dayan, Peter; Daw, Nathaniel D

2008-12-01

Decision making is a core competence for animals and humans acting and surviving in environments they only partially comprehend, gaining rewards and punishments for their troubles. Decision-theoretic concepts permeate experiments and computational models in ethology, psychology, and neuroscience. Here, we review a well-known, coherent Bayesian approach to decision making, showing how it unifies issues in Markovian decision problems, signal detection psychophysics, sequential sampling, and optimal exploration and discuss paradigmatic psychological and neural examples of each problem. We discuss computational issues concerning what subjects know about their task and how ambitious they are in seeking optimal solutions; we address algorithmic topics concerning model-based and model-free methods for making choices; and we highlight key aspects of the neural implementation of decision making.

Virtue vs utility: Alternative foundations for computer ethics

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

Artz, J.M.

1994-12-31

Ethical decisions within the field of computers and information systems are made at two levels by two distinctly different groups of people. At the level of general principles, ethical issues are debated by academics and industry representatives in an attempt to decide what is proper behavior on issues such as hacking, privacy, and copying software. At another level, that of particular situations, individuals make ethical decisions regarding what is good and proper for them in their particular situation. They may use the general rules provided by the experts or they may decide that these rules do not apply in theirmore » particular situation. Currently, the literature on computer ethics provides some opinions regarding the general rules, and some guidance for developing further general rules. What is missing is guidance for individuals making ethical decisions in particular situations. For the past two hundred years, ethics has been dominated by conduct based ethical theories such as utilitarianism which attempt to describe how people must be behave in order to be moral individuals. Recently, weaknesses in conduct based approaches such as utilitarianism have led moral philosophers to reexamine character based ethical theories such as virtue ethics which dates back to the Greek philosophers Plato and Aristotle. This paper will compare utilitarianism and virtue ethics with respect to the foundations they provide for computer ethics. It will be argued that the very nature of computer ethics and the need to provide guidance to individuals making particular moral decisions points to the ethics of virtue as a superior philosophical foundation for computer ethics. The paper will conclude with the implications of this position for researchers, teachers and writers within the field of computer ethics.« less

A problem solving and decision making toolbox for approaching clinical problems and decisions.

PubMed

Margolis, C; Jotkowitz, A; Sitter, H

2004-08-01

In this paper, we begin by presenting three real patients and then review all the practical conceptual tools that have been suggested for systematically analyzing clinical problems. Each of these conceptual tools (e.g. Evidence-Based Medicine, Clinical Practice Guidelines, Decision Analysis) deals mainly with a different type or aspect of clinical problems. We suggest that all of these conceptual tools can be thought of as belonging in the clinician's toolbox for solving clinical problems and making clinical decisions. A heuristic for guiding the clinician in using the tools is proposed. The heuristic is then used to analyze management of the three patients presented at the outset. Copyright 2004 Birkhäuser Verlag, Basel

Efficacy of an evidence-based clinical decision support in primary care practices: a randomized clinical trial.

PubMed

McGinn, Thomas G; McCullagh, Lauren; Kannry, Joseph; Knaus, Megan; Sofianou, Anastasia; Wisnivesky, Juan P; Mann, Devin M

2013-09-23

There is consensus that incorporating clinical decision support into electronic health records will improve quality of care, contain costs, and reduce overtreatment, but this potential has yet to be demonstrated in clinical trials. To assess the influence of a customized evidence-based clinical decision support tool on the management of respiratory tract infections and on the effectiveness of integrating evidence at the point of care. In a randomized clinical trial, we implemented 2 well-validated integrated clinical prediction rules, namely, the Walsh rule for streptococcal pharyngitis and the Heckerling rule for pneumonia. INTERVENTIONS AND MAIN OUTCOMES AND MEASURES: The intervention group had access to the integrated clinical prediction rule tool and chose whether to complete risk score calculators, order medications, and generate progress notes to assist with complex decision making at the point of care. The intervention group completed the integrated clinical prediction rule tool in 57.5% of visits. Providers in the intervention group were significantly less likely to order antibiotics than the control group (age-adjusted relative risk, 0.74; 95% CI, 0.60-0.92). The absolute risk of the intervention was 9.2%, and the number needed to treat was 10.8. The intervention group was significantly less likely to order rapid streptococcal tests compared with the control group (relative risk, 0.75; 95% CI, 0.58-0.97; P= .03). The integrated clinical prediction rule process for integrating complex evidence-based clinical decision report tools is of relevant importance for national initiatives, such as Meaningful Use. TRIAL REGISTRATION clinicaltrials.gov Identifier: NCT01386047.

Cross-sectional study to examine evidence-based practice skills and behaviors of physical therapy graduates: is there a knowledge-to-practice gap?

PubMed

Manns, Patricia J; Norton, Amy V; Darrah, Johanna

2015-04-01

Curricula changes in physical therapist education programs in Canada emphasize evidence-based practice skills, including literature retrieval and evaluation. Do graduates use these skills in practice? The aim of this study was to evaluate the use of research information in the clinical decision making of therapists with different years of experience and evidence-based practice preparation. Perceptions about evidence-based practice were explored qualitatively. A cross-sectional study with 4 graduating cohorts was conducted. Eighty physical therapists representing 4 different graduating cohorts participated in interviews focused on 2 clinical scenarios. Participants had varying years of clinical experience (range=1-15 years) and academic knowledge of evidence-based practice skills. Therapists discussed the effectiveness of interventions related to the scenarios and identified the sources of information used to reach decisions. Participants also answered general questions related to evidence-based practice knowledge. Recent graduates demonstrated better knowledge of evidence-based practice skills compared with therapists with 6 to 15 years of clinical experience. However, all groups used clinical experience most frequently as their source of information for clinical decisions. Research evidence was infrequently included in decision making. This study used a convenience sample of therapists who agreed to volunteer for the study. The results suggest a knowledge-to-practice gap; graduates are not using the new skills to inform their practice. Tailoring academic evidence-based activities more to the time constraints of clinical practice may help students to be more successful in applying evidence in practice. Academic programs need to do more to create and nurture environments in both academic and clinical settings to ensure students practice using evidence-based practice skills across settings. © 2015 American Physical Therapy Association.

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

Use of agents to implement an integrated computing environment

NASA Technical Reports Server (NTRS)

Hale, Mark A.; Craig, James I.

1995-01-01

Integrated Product and Process Development (IPPD) embodies the simultaneous application to both system and quality engineering methods throughout an iterative design process. The use of IPPD results in the time-conscious, cost-saving development of engineering systems. To implement IPPD, a Decision-Based Design perspective is encapsulated in an approach that focuses on the role of the human designer in product development. The approach has two parts and is outlined in this paper. First, an architecture, called DREAMS, is being developed that facilitates design from a decision-based perspective. Second, a supporting computing infrastructure, called IMAGE, is being designed. Agents are used to implement the overall infrastructure on the computer. Successful agent utilization requires that they be made of three components: the resource, the model, and the wrap. Current work is focused on the development of generalized agent schemes and associated demonstration projects. When in place, the technology independent computing infrastructure will aid the designer in systematically generating knowledge used to facilitate decision-making.

A Rural Community's Involvement in the Design and Usability Testing of a Computer-Based Informed Consent Process for the Personalized Medicine Research Project

PubMed Central

Mahnke, Andrea N; Plasek, Joseph M; Hoffman, David G; Partridge, Nathan S; Foth, Wendy S; Waudby, Carol J; Rasmussen, Luke V; McManus, Valerie D; McCarty, Catherine A

2014-01-01

Many informed consent studies demonstrate that research subjects poorly retain and understand information in written consent documents. Previous research in multimedia consent is mixed in terms of success for improving participants’ understanding, satisfaction, and retention. This failure may be due to a lack of a community-centered design approach to building the interventions. The goal of this study was to gather information from the community to determine the best way to undertake the consent process. Community perceptions regarding different computer-based consenting approaches were evaluated, and a computer-based consent was developed and tested. A second goal was to evaluate whether participants make truly informed decisions to participate in research. Simulations of an informed consent process were videotaped to document the process. Focus groups were conducted to determine community attitudes towards a computer-based informed consent process. Hybrid focus groups were conducted to determine the most acceptable hardware device. Usability testing was conducted on a computer-based consent prototype using a touch-screen kiosk. Based on feedback, a computer-based consent was developed. Representative study participants were able to easily complete the consent, and all were able to correctly answer the comprehension check questions. Community involvement in developing a computer-based consent proved valuable for a population-based genetic study. These findings may translate to other types of informed consents, such as genetic clinical trials consents. A computer-based consent may serve to better communicate consistent, clear, accurate, and complete information regarding the risks and benefits of study participation. Additional analysis is necessary to measure the level of comprehension of the check-question answers by larger numbers of participants. The next step will involve contacting participants to measure whether understanding of what they consented to is retained over time. PMID:24273095

Factors Affecting Utilization of Information Output of Computer-Based Modeling Procedures in Local Government Organizations.

ERIC Educational Resources Information Center

Komsky, Susan

Fiscal Impact Budgeting Systems (FIBS) are sophisticated computer based modeling procedures used in local government organizations, whose results, however, are often overlooked or ignored by decision makers. A study attempted to discover the reasons for this situation by focusing on four factors: potential usefulness, faith in computers,…

Implementing clinical guidelines for chronic obstructive pulmonary disease: barriers and solutions

PubMed Central

Overington, Jeff D.; Huang, Yao C.; Abramson, Michael J.; Brown, Juliet L.; Goddard, John R.; Bowman, Rayleen V.; Fong, Kwun M.

2014-01-01

Chronic obstructive pulmonary disease (COPD) is a complex chronic lung disease characterised by progressive fixed airflow limitation and acute exacerbations that frequently require hospitalisation. Evidence-based clinical guidelines for the diagnosis and management of COPD are now widely available. However, the uptake of these COPD guidelines in clinical practice is highly variable, as is the case for many other chronic disease guidelines. Studies have identified many barriers to implementation of COPD and other guidelines, including factors such as lack of familiarity with guidelines amongst clinicians and inadequate implementation programs. Several methods for enhancing adherence to clinical practice guidelines have been evaluated, including distribution methods, professional education sessions, electronic health records (EHR), point of care reminders and computer decision support systems (CDSS). Results of these studies are mixed to date, and the most effective ways to implement clinical practice guidelines remain unclear. Given the significant resources dedicated to evidence-based medicine, effective dissemination and implementation of best practice at the patient level is an important final step in the process of guideline development. Future efforts should focus on identifying optimal methods for translating the evidence into everyday clinical practice to ensure that patients receive the best care. PMID:25478199

Direct migration motion estimation and mode decision to decoder for a low-complexity decoder Wyner-Ziv video coding

NASA Astrophysics Data System (ADS)

Lei, Ted Chih-Wei; Tseng, Fan-Shuo

2017-07-01

This paper addresses the problem of high-computational complexity decoding in traditional Wyner-Ziv video coding (WZVC). The key focus is the migration of two traditionally high-computationally complex encoder algorithms, namely motion estimation and mode decision. In order to reduce the computational burden in this process, the proposed architecture adopts the partial boundary matching algorithm and four flexible types of block mode decision at the decoder. This approach does away with the need for motion estimation and mode decision at the encoder. The experimental results show that the proposed padding block-based WZVC not only decreases decoder complexity to approximately one hundredth that of the state-of-the-art DISCOVER decoding but also outperforms DISCOVER codec by up to 3 to 4 dB.

Randomized Trial of a Web-Based Intervention to Address Barriers to Clinical Trials.

PubMed

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

2016-02-10

Lack of knowledge and negative attitudes have been identified as barriers to participation in clinical trials by patients with cancer. We developed Preparatory Education About Clinical Trials (PRE-ACT), a theory-guided, Web-based, interactive computer program, to deliver tailored video educational content to patients in an effort to overcome barriers to considering clinical trials as a treatment option. A prospective, randomized clinical trial compared PRE-ACT with a control condition that provided general clinical trials information produced by the National Cancer Institute (NCI) in text format. One thousand two hundred fifty-five patients with cancer were randomly allocated before their initial visit with an oncologist to PRE-ACT (n = 623) or control (n = 632). PRE-ACT had three main components: assessment of clinical trials knowledge and attitudinal barriers, values assessment with clarification back to patients, and provision of a video library tailored to address each patient's barriers. Outcomes included knowledge and attitudes and preparation for decision making about clinical trials. Both PRE-ACT and control interventions improved knowledge and attitudes (all P < .001) compared with baseline. Patients randomly allocated to PRE-ACT showed a significantly greater increase in knowledge (P < .001) and a significantly greater decrease in attitudinal barriers (P < .001) than did their control (text-only) counterparts. Participants in both arms significantly increased their preparedness to consider clinical trials (P < .001), and there was a trend favoring the PRE-ACT group (P < .09). PRE-ACT was also associated with greater patient satisfaction than was NCI text alone. These data show that patient education before the first oncologist visit improves knowledge, attitudes, and preparation for decision making about clinical trials. Both text and tailored video were effective. The PRE-ACT interactive video program was more effective than NCI text in improving knowledge and reducing attitudinal barriers. © 2015 by American Society of Clinical Oncology.

Clinician Use of a Palmtop Drug Reference Guide

PubMed Central

Rothschild, Jeffrey M.; Lee, Thomas H.; Bae, Taran; Bates, David W.

2002-01-01

Objective: Problems involving drug knowledge are one of the most common causes of serious medication errors. Although the information that clinicians need is often available somewhere, retrieving it expeditiously has been problematic. At the same time, clinicians are faced with an ever-expanding pharmacology knowledge base. Recently, point-of-care technology has become more widely available and more practical with the advent of handheld, or palmtop, computing. Therefore, the authors evaluated the clinical contribution of a drug database developed for the handheld computer. ePocrates Rx (formerly known as qRx; ePocrates, San Carlos, California) is a comprehensive drug information guide that is downloadable free from the Internet and designed for the Palm OS platform align="right". Design: A seven-day online survey of 3,000 randomly selected ePocrates Rx users was conducted during March 2000. Measurements: User technology experience, product evaluation and usage patterns, and the effects of the drug reference database on information-seeking behavior, practice efficiency, decision making, and patient care. Results: The survey response rate was 32 percent (n=946). Physicians reported that ePocrates Rx saves time during information retrieval, is easily incorporated into their usual workflow, and improves drug-related decision making. They also felt that it reduced the rate of preventable adverse drug events. Conclusions: Self-reported perceptions by responding clinicians endorse improved access to drug information and improved practice efficiency associated with the use of handheld devices. The clinical and practical value of using these devices in clinical settings will clearly grow further as wireless communication becomes more ubiquitous and as more applications become available. PMID:11971883

Myocardial perfusion 320-row multidetector computed tomography-guided treatment strategy for the clinical management of patients with recent acute-onset chest pain: Design of the CArdiac cT in the treatment of acute CHest pain (CATCH)-2 randomized controlled trial.

PubMed

Sørgaard, Mathias; Linde, Jesper J; Hove, Jens D; Petersen, Jan R; Jørgensen, Tem B S; Abdulla, Jawdat; Heitmann, Merete; Kragelund, Charlotte; Hansen, Thomas Fritz; Udholm, Patricia M; Pihl, Christian; Kühl, J Tobias; Engstrøm, Thomas; Jensen, Jan Skov; Høfsten, Dan E; Kelbæk, Henning; Kofoed, Klaus F

2016-09-01

Patients admitted with chest pain are a diagnostic challenge because the majority does not have coronary artery disease (CAD). Assessment of CAD with coronary computed tomography angiography (CCTA) is safe, cost-effective, and accurate, albeit with a modest specificity. Stress myocardial computed tomography perfusion (CTP) has been shown to increase the specificity when added to CCTA, without lowering the sensitivity. This article describes the design of a randomized controlled trial, CATCH-2, comparing a clinical diagnostic management strategy of CCTA alone against CCTA in combination with CTP. Patients with acute-onset chest pain older than 50 years and with at least one cardiovascular risk factor for CAD are being prospectively enrolled to this study from 6 different clinical sites since October 2013. A total of 600 patients will be included. Patients are randomized 1:1 to clinical management based on CCTA or on CCTA in combination with CTP, determining the need for further testing with invasive coronary angiography including measurement of the fractional flow reserve in vessels with coronary artery lesions. Patients are scanned with a 320-row multidetector computed tomography scanner. Decisions to revascularize the patients are taken by the invasive cardiologist independently of the study allocation. The primary end point is the frequency of revascularization. Secondary end points of clinical outcome are also recorded. The CATCH-2 will determine whether CCTA in combination with CTP is diagnostically superior to CCTA alone in the management of patients with acute-onset chest pain. Copyright © 2015 Elsevier Inc. All rights reserved.

Spinning the wheels: a CAPS survey of ethical issues in pediatric surgery.

PubMed

Bagwell, C E; Goodwin, S R

1992-11-01

To evaluate decision-making factors of pediatric surgeons when faced with ethical dilemmas in a clinical setting, questionnaires were mailed to members of the Canadian Association of Paediatric Surgeons. The surgeons were asked to respond to scenarios regarding ethical dilemmas in the treatment of children. Fifty-one responses (57%) were computer analyzed based on chosen responses to the clinical dilemma and demographic factors such as age, sex, marital status, country of citizenship, religion, and "religiousness," a determination of religious conviction as viewed by the respondent. In addition, ethical convictions were sought regarding abortion, fetal research, AIDS, HIV testing, denial of medical care due to religious beliefs (Jehovah's Witness), and limitations in health care access for indigents. In general, respondents found it difficult to separate ethical guidelines for determining aggressive treatment--absolute value of life; best interests of the child; parental authority; and ability of the child to engage in social, intellectual, or emotional attachments (quality of life)--in the face of actual patient care issues. In fact, results of this survey indicate that the operating surgeon applies his/her medical knowledge and surgical "experience" to each individual case, incorporating his or her own ethical beliefs (in a respondent's words: "In the operating room, the surgeon must satisfy his own conscience in making decisions") while cognizant of legal guidelines for "standard care" ("Decisions would be based on personal experience, and what the local society believes to be right").(ABSTRACT TRUNCATED AT 250 WORDS)

Towards an Intelligent Textbook of Neurology

PubMed Central

Reggia, James A.; Pula, Thaddeus P.; Price, Thomas R.; Perricone, Barry T.

1980-01-01

We define an intelligent textbook of medicine to be a computer system that: (1) provides for storage and selective retrieval of synthesized clinical knowledge for reference purposes; and (2) supports the application by computer of its knowledge to patient information to assist physicians with decision making. This paper describes an experimental system called KMS (a Knowledge Management System) for creating and using intelligent medical textbooks. KMS is domain-independent, supports multiple inference methods and representation languages, and is designed for direct use by physicians during the knowledge acquisition process. It is presented here in the context of the development of an Intelligent Textbook of Neurology. We suggest that KMS has the potential to overcome some of the problems that have inhibited the use of knowledge-based systems by physicians in the past.

Epilepsy analytic system with cloud computing.

PubMed

Shen, Chia-Ping; Zhou, Weizhi; Lin, Feng-Seng; Sung, Hsiao-Ya; Lam, Yan-Yu; Chen, Wei; Lin, Jeng-Wei; Pan, Ming-Kai; Chiu, Ming-Jang; Lai, Feipei

2013-01-01

Biomedical data analytic system has played an important role in doing the clinical diagnosis for several decades. Today, it is an emerging research area of analyzing these big data to make decision support for physicians. This paper presents a parallelized web-based tool with cloud computing service architecture to analyze the epilepsy. There are many modern analytic functions which are wavelet transform, genetic algorithm (GA), and support vector machine (SVM) cascaded in the system. To demonstrate the effectiveness of the system, it has been verified by two kinds of electroencephalography (EEG) data, which are short term EEG and long term EEG. The results reveal that our approach achieves the total classification accuracy higher than 90%. In addition, the entire training time accelerate about 4.66 times and prediction time is also meet requirements in real time.

Patients' Values in Clinical Decision-Making.

PubMed

Faggion, Clovis Mariano; Pachur, Thorsten; Giannakopoulos, Nikolaos Nikitas

2017-09-01

Shared decision-making involves the participation of patient and dental practitioner. Well-informed decision-making requires that both parties understand important concepts that may influence the decision. This fourth article in a series of 4 aims to discuss the importance of patients' values when a clinical decision is made. We report on how to incorporate important concepts for well-informed, shared decision-making. Here, we present patient values as an important issue, in addition to previously established topics such as the risk of bias of a study, cost-effectiveness of treatment approaches, and a comparison of therapeutic benefit with potential side effects. We provide 2 clinical examples and suggestions for a decision tree, based on the available evidence. The information reported in this article may improve the relationship between patient and dental practitioner, resulting in more well-informed clinical decisions. Copyright © 2017 Elsevier Inc. All rights reserved.

[Value-based cancer care. From traditional evidence-based decision making to balanced decision making within frameworks of shared values].

PubMed

Palazzo, Salvatore; Filice, Aldo; Mastroianni, Candida; Biamonte, Rosalbino; Conforti, Serafino; Liguori, Virginia; Turano, Salvatore; De Simone, Rosanna; Rovito, Antonio; Manfredi, Caterina; Minardi, Stefano; Vilardo, Emmanuelle; Loizzo, Monica; Oriolo, Carmela

2016-04-01

Clinical decision making in oncology is based so far on the evidence of efficacy from high-quality clinical research. Data collection and analysis from experimental studies provide valuable insight into response rates and progression-free or overall survival. Data processing generates valuable information for medical professionals involved in cancer patient care, enabling them to make objective and unbiased choices. The increased attention of many scientific associations toward a more rational resource consumption in clinical decision making is mirrored in the Choosing Wisely campaign against the overuse or misuse of exams and procedures of little or no benefit for the patient. This cultural movement has been actively promoting care solutions based on the concept of "value". As a result, the value-based decision-making process for cancer care should not be dissociated from economic sustainability and from ethics of the affordability, also given the growing average cost of the most recent cancer drugs. In support of this orientation, the National Comprehensive Cancer Network (NCCN) has developed innovative and "complex" guidelines based on values, defined as "evidence blocks", with the aim of assisting the medical community in making overall sustainable choices.

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.

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.

Call for action: how to improve use of patient-reported outcomes to guide clinical decision making in rheumatoid arthritis.

PubMed

Fautrel, Bruno; Alten, Rieke; Kirkham, Bruce; de la Torre, Inmaculada; Durand, Frederick; Barry, Jane; Holzkaemper, Thorsten; Fakhouri, Walid; Taylor, Peter C

2018-06-01

Current guidelines for the management of rheumatoid arthritis (RA) recommend early treatment and a treat-to-target goal of remission or low disease activity. Over the past decade, this approach has been extremely successful in reducing disease activity and joint damage in patients with RA. At the same time, however, overall patient perception of well-being appears to have decreased with respect to outcome measures considered important by patients themselves, such as pain, fatigue, physical function and quality of life. The timely and effective use of patient-reported outcomes (PROs) could encourage physicians to focus more on the impact of RA on patients and how patients are feeling. This in turn would facilitate shared decision making between patients and physicians, ultimately leading to a more patient-centered approach and improved patient care. Indeed, PROs provide information about individual patients that complements information provided by physical assessment and composite scores, and can also be used to guide patient care, such as determining whether a clinic visit is needed or whether treatment modifications are necessary. This is particularly important for patients who do not achieve the aspirational target of remission or low disease activity with pharmacological treatment. A number of validated PRO questionnaires are available, but how and which PROs should be incorporated into rheumatology clinical practice as part of the decision-making process is still controversial. Combining PROs with technology, such as computer adaptive tests, electronic PRO systems, web-based platforms and patient dashboards, could further aid PRO integration into daily rheumatology clinical practice.

Assessment of cognition in mild cognitive impairment: A comparative study

PubMed Central

Snyder, Peter J.; Jackson, Colleen E.; Petersen, Ronald C.; Khachaturian, Ara S.; Kaye, Jeffrey; Albert, Marilyn S.; Weintraub, Sandra

2014-01-01

The demand for rapidly administered, sensitive, and reliable cognitive assessments that are specifically designed for identifying individuals in the earliest stages of cognitive decline (and to measure subtle change over time) has escalated as the emphasis in Alzheimer’s disease clinical research has shifted from clinical diagnosis and treatment toward the goal of developing presymptomatic neuroprotective therapies. To meet these changing clinical requirements, cognitive measures or tailored batteries of tests must be validated and determined to be fit-for-use for the discrimination between cognitively healthy individuals and persons who are experiencing very subtle cognitive changes that likely signal the emergence of early mild cognitive impairment. We sought to collect and review data systematically from a wide variety of (mostly computer-administered) cognitive measures, all of which are currently marketed or distributed with the claims that these instruments are sensitive and reliable for the early identification of disease or, if untested for this purpose, are promising tools based on other variables. The survey responses for 16 measures/batteries are presented in brief in this review; full survey responses and summary tables are archived and publicly available on the Campaign to Prevent Alzheimer’s Disease by 2020 Web site (http://pad2020.org). A decision tree diagram highlighting critical decision points for selecting measures to meet varying clinical trials requirements has also been provided. Ultimately, the survey questionnaire, framework, and decision guidelines provided in this review should remain as useful aids for the evaluation of any new or updated sets of instruments in the years to come. PMID:21575877

Selecting an Architecture for a Safety-Critical Distributed Computer System with Power, Weight and Cost Considerations

NASA Technical Reports Server (NTRS)

Torres-Pomales, Wilfredo

2014-01-01

This report presents an example of the application of multi-criteria decision analysis to the selection of an architecture for a safety-critical distributed computer system. The design problem includes constraints on minimum system availability and integrity, and the decision is based on the optimal balance of power, weight and cost. The analysis process includes the generation of alternative architectures, evaluation of individual decision criteria, and the selection of an alternative based on overall value. In this example presented here, iterative application of the quantitative evaluation process made it possible to deliberately generate an alternative architecture that is superior to all others regardless of the relative importance of cost.

Using Best Practices to Extract, Organize, and Reuse Embedded Decision Support Content Knowledge Rules from Mature Clinical Systems

PubMed Central

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. PMID:28269846

Dynamic Decision Making under Uncertainty and Partial Information

DTIC Science & Technology

2017-01-30

order to address these problems, we investigated efficient computational methodologies for dynamic decision making under uncertainty and partial...information. In the course of this research, we developed and studied efficient simulation-based methodologies for dynamic decision making under...uncertainty and partial information; (ii) studied the application of these decision making models and methodologies to practical problems, such as those

Use of Decision Models in the Development of Evidence-Based Clinical Preventive Services Recommendations: Methods of the U.S. Preventive Services Task Force.

PubMed

Owens, Douglas K; Whitlock, Evelyn P; Henderson, Jillian; Pignone, Michael P; Krist, Alex H; Bibbins-Domingo, Kirsten; Curry, Susan J; Davidson, Karina W; Ebell, Mark; Gillman, Matthew W; Grossman, David C; Kemper, Alex R; Kurth, Ann E; Maciosek, Michael; Siu, Albert L; LeFevre, Michael L

2016-10-04

The U.S. Preventive Services Task Force (USPSTF) develops evidence-based recommendations about preventive care based on comprehensive systematic reviews of the best available evidence. Decision models provide a complementary, quantitative approach to support the USPSTF as it deliberates about the evidence and develops recommendations for clinical and policy use. This article describes the rationale for using modeling, an approach to selecting topics for modeling, and how modeling may inform recommendations about clinical preventive services. Decision modeling is useful when clinical questions remain about how to target an empirically established clinical preventive service at the individual or program level or when complex determinations of magnitude of net benefit, overall or among important subpopulations, are required. Before deciding whether to use decision modeling, the USPSTF assesses whether the benefits and harms of the preventive service have been established empirically, assesses whether there are key issues about applicability or implementation that modeling could address, and then defines the decision problem and key questions to address through modeling. Decision analyses conducted for the USPSTF are expected to follow best practices for modeling. For chosen topics, the USPSTF assesses the strengths and limitations of the systematically reviewed evidence and the modeling analyses and integrates the results of each to make preventive service recommendations.

Decision-making based on emotional images.

PubMed

Katahira, Kentaro; Fujimura, Tomomi; Okanoya, Kazuo; Okada, Masato

2011-01-01

The emotional outcome of a choice affects subsequent decision making. While the relationship between decision making and emotion has attracted attention, studies on emotion and decision making have been independently developed. In this study, we investigated how the emotional valence of pictures, which was stochastically contingent on participants' choices, influenced subsequent decision making. In contrast to traditional value-based decision-making studies that used money or food as a reward, the "reward value" of the decision outcome, which guided the update of value for each choice, is unknown beforehand. To estimate the reward value of emotional pictures from participants' choice data, we used reinforcement learning models that have successfully been used in previous studies for modeling value-based decision making. Consequently, we found that the estimated reward value was asymmetric between positive and negative pictures. The negative reward value of negative pictures (relative to neutral pictures) was larger in magnitude than the positive reward value of positive pictures. This asymmetry was not observed in valence for an individual picture, which was rated by the participants regarding the emotion experienced upon viewing it. These results suggest that there may be a difference between experienced emotion and the effect of the experienced emotion on subsequent behavior. Our experimental and computational paradigm provides a novel way for quantifying how and what aspects of emotional events affect human behavior. The present study is a first step toward relating a large amount of knowledge in emotion science and in taking computational approaches to value-based decision making.

Multicriteria meta-heuristics for AGV dispatching control based on computational intelligence.

PubMed

Naso, David; Turchiano, Biagio

2005-04-01

In many manufacturing environments, automated guided vehicles are used to move the processed materials between various pickup and delivery points. The assignment of vehicles to unit loads is a complex problem that is often solved in real-time with simple dispatching rules. This paper proposes an automated guided vehicles dispatching approach based on computational intelligence. We adopt a fuzzy multicriteria decision strategy to simultaneously take into account multiple aspects in every dispatching decision. Since the typical short-term view of dispatching rules is one of the main limitations of such real-time assignment heuristics, we also incorporate in the multicriteria algorithm a specific heuristic rule that takes into account the empty-vehicle travel on a longer time-horizon. Moreover, we also adopt a genetic algorithm to tune the weights associated to each decision criteria in the global decision algorithm. The proposed approach is validated by means of a comparison with other dispatching rules, and with other recently proposed multicriteria dispatching strategies also based on computational Intelligence. The analysis of the results obtained by the proposed dispatching approach in both nominal and perturbed operating conditions (congestions, faults) confirms its effectiveness.

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.

Applicability of randomized trials in radiation oncology to standard clinical practice.

PubMed

Apisarnthanarax, Smith; Swisher-McClure, Samuel; Chiu, Wing K; Kimple, Randall J; Harris, Stephen L; Morris, David E; Tepper, Joel E

2013-08-15

Randomized controlled trials (RCTs) are commonly used to inform clinical practice; however, it is unclear how generalizable RCT data are to patients in routine clinical practice. The authors of this report assessed the availability and applicability of randomized evidence guiding medical decisions in a cohort of patients who were evaluated for consideration of definitive management in a radiation oncology clinic. The medical records of consecutive, new patient consultations between January and March 2007 were reviewed. Patient medical decisions were classified as those with (Group 1) or without (Group 2) available, relevant level I evidence (phase 3 RCT) supporting recommended treatments. Group 1 medical decisions were further divided into 3 groups based on the extent of fulfilling eligibility criteria for each RCT: Group 1A included decisions that fulfilled all eligibility criteria; Group 1B, decisions that did not fulfill at least 1 minor eligibility criteria; or Group 1C, decisions that did not fulfill at least 1 major eligibility criteria. Patient and clinical characteristics were tested for correlations with the availability of evidence. Of the 393 evaluable patients, malignancies of the breast (30%), head and neck (18%), and genitourinary system (14%) were the most common presenting primary disease sites. Forty-seven percent of all medical decisions (n = 451) were made without available (36%) or applicable (11%) randomized evidence to inform clinical decision making. Primary tumor diagnosis was significantly associated with the availability of evidence (P < .0001). A significant proportion of medical decisions in an academic radiation oncology clinic were made without available or applicable level I evidence, underscoring the limitations of relying solely on RCTs for the development of evidence-based health care. Copyright © 2013 American Cancer Society.

Data-driven decision support for radiologists: re-using the National Lung Screening Trial dataset for pulmonary nodule management.

PubMed

Morrison, James J; Hostetter, Jason; Wang, Kenneth; Siegel, Eliot L

2015-02-01

Real-time mining of large research trial datasets enables development of case-based clinical decision support tools. Several applicable research datasets exist including the National Lung Screening Trial (NLST), a dataset unparalleled in size and scope for studying population-based lung cancer screening. Using these data, a clinical decision support tool was developed which matches patient demographics and lung nodule characteristics to a cohort of similar patients. The NLST dataset was converted into Structured Query Language (SQL) tables hosted on a web server, and a web-based JavaScript application was developed which performs real-time queries. JavaScript is used for both the server-side and client-side language, allowing for rapid development of a robust client interface and server-side data layer. Real-time data mining of user-specified patient cohorts achieved a rapid return of cohort cancer statistics and lung nodule distribution information. This system demonstrates the potential of individualized real-time data mining using large high-quality clinical trial datasets to drive evidence-based clinical decision-making.

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.

Operating Policies and Procedures of Computer Data-Base Systems.

ERIC Educational Resources Information Center

Anderson, David O.

Speaking on the operating policies and procedures of computer data bases containing information on students, the author divides his remarks into three parts: content decisions, data base security, and user access. He offers nine recommended practices that should increase the data base's usefulness to the user community: (1) the cost of developing…

Science and intuition: do both have a place in clinical decision making?

PubMed

Pearson, Helen

Intuition is widely used in clinical decision making yet its use is underestimated compared to scientific decision-making methods. Information processing is used within scientific decision making and is methodical and analytical, whereas intuition relies more on a practitioner's perception. Intuition is an unconscious process and may be referred to as a 'sixth sense', 'hunch' or 'gut feeling'. It is not underpinned by valid and reliable measures. Expert health professionals use a rapid, automatic process to recognise familiar problems instantly. Intuition could therefore involve pattern recognition, where experts draw on experiences, so could be perceived as a cognitive skill rather than a perception or knowing without knowing how. The NHS places great importance on evidence-based practice but intuition is seemingly becoming an acceptable way of thinking and knowing in clinical decision making. Recognising nursing as an art allows intuition to be used and the environment or situation to be interpreted to help inform decision making. Intuition can be used in conjunction with evidence-based practice and to achieve good outcomes and deserves to be acknowledged within clinical practice.

Informatics in dental education: a horizon of opportunity.

PubMed

Abbey, L M

1989-11-01

Computers have presented society with the largest array of opportunities since the printing press. More specifically in dental education they represent the path to freedom from the memory-based curriculum. Computers allow us to be constantly in touch with the entire scope of knowledge necessary for decision making in every aspect of the process of preparing young men and women to practice dentistry. No longer is it necessary to spend the energy or time previously used to memorize facts, test for retention of facts or be concerned with remembering facts when dealing with our patients. Modern information management systems can assume that task allowing dentists to concentrate on understanding, skill, judgement and wisdom while helping patients deal with their problems within a health care system that is simultaneously baffling in its complexity and overflowing with options. This paper presents a summary of the choices facing dental educators as computers continue to afford us the freedom to look differently at teaching, research and practice. The discussion will elaborate some of the ways dental educators must think differently about the educational process in order to utilize fully the power of computers in curriculum development and tracking, integration of basic and clinical teaching, problem solving, patient management, record keeping and research. Some alternative strategies will be discussed that may facilitate the transition from the memory-based to the computer-based curriculum and practice.

Patient-specific computer-based decision support in primary healthcare--a randomized trial.

PubMed

Kortteisto, Tiina; Raitanen, Jani; Komulainen, Jorma; Kunnamo, Ilkka; Mäkelä, Marjukka; Rissanen, Pekka; Kaila, Minna

2014-01-20

Computer-based decision support systems are a promising method for incorporating research evidence into clinical practice. However, evidence is still scant on how such information technology solutions work in primary healthcare when support is provided across many health problems. In Finland, we designed a trial where a set of evidence-based, patient-specific reminders was introduced into the local Electronic Patient Record (EPR) system. The aim was to measure the effects of such reminders on patient care. The hypothesis was that the total number of triggered reminders would decrease in the intervention group compared with the control group, indicating an improvement in patient care. From July 2009 to October 2010 all the patients of one health center were randomized to an intervention or a control group. The intervention consisted of patient-specific reminders concerning 59 different health conditions triggered when the healthcare professional (HCP) opened and used the EPR. In the intervention group, the triggered reminders were shown to the HCP; in the control group, the triggered reminders were not shown. The primary outcome measure was the change in the number of reminders triggered over 12 months. We developed a unique data gathering method, the Repeated Study Virtual Health Check (RSVHC), and used Generalized Estimation Equations (GEE) for analysing the incidence rate ratio, which is a measure of the relative difference in percentage change in the numbers of reminders triggered in the intervention group and the control group. In total, 13,588 participants were randomized and included. Contrary to our expectation, the total number of reminders triggered increased in both the intervention and the control groups. The primary outcome measure did not show a significant difference between the groups. However, with the inclusion of patients followed up over only six months, the total number of reminders increased significantly less in the intervention group than in the control group when the confounding factors (age, gender, number of diagnoses and medications) were controlled for. Computerized, tailored reminders in primary care did not decrease during the 12 months of follow-up time after the introduction of a patient-specific decision support system. ClinicalTrial.gov NCT00915304.

Computer network for improving quality and efficiency of children's primary health care

NASA Astrophysics Data System (ADS)

Deutsch, Larry

1995-10-01

Health care is at its best when both the practitioner and patient are well-informed. In many central urban and remote rural areas, however, health care is characterized by a lack of continuity and coordination among providers. In these areas, a local information infrastructure and a patient-centered system of primary care are missing. Decision-making and ability to follow through is hampered, with limited involvement of patients in planning care and insufficient aggregate data for cost analysis, outcome research, community health planning, and other purposes. A Children's Health Network has been designed to extend current information technology to these underserved areas. Our approach to improving quality of individual care and controlling costs emphasizes use of computerized clinical information networks for better decision making and continuity, and secondarily through data aggregation for financial, research, and public health functions. This is in distinction to information systems centered on billing and administrative needs and to cost-control efforts which rely on fiscal and managerial ('gatekeeper') mechanisms. A uniform data base among sites serving the same population will answer several clinical and public health needs.

How updating textual clinical practice guidelines impacts clinical decision support systems: a case study with bladder cancer management.

PubMed

Bouaud, Jacques; Séroussi, Brigitte; Brizon, Ambre; Culty, Thibault; Mentré, France; Ravery, Vincent

2007-01-01

Guideline-based clinical decision support systems (CDSSs) can be effective in increasing physician compliance with recommendations. However, the ever growing pace at which medical knowledge is produced requires that clinical practice guidelines (CPGs) be updated regularly. It is therefore mandatory that CDSSs be revised accordingly. The French Association for Urology publishes CPGs on bladder cancer management every 2 years. We studied the impact of the 2004 revision of these guidelines, with respect to the 2002 version with a CDSS, UroDoc. We proposed a typology of knowledge base modifications resulting from the update of CPGs making the difference between practice, clinical conditions and recommendations refinement as opposed to new practice and new recommendations. The number of formalized recommendations increased from 577 in 2002 to 1,081 in 2004. We evaluated the two versions of UroDoc on a randomized sample of patient records. A single new practice that modifies a decision taken in 49% of all recorded decisions leads to a fall from 67% to 46% of the compliance rate of decisions.

Evaluating Computer-Based Assessment in a Risk-Based Model

ERIC Educational Resources Information Center

Zakrzewski, Stan; Steven, Christine; Ricketts, Chris

2009-01-01

There are three purposes for evaluation: evaluation for action to aid the decision making process, evaluation for understanding to further enhance enlightenment and evaluation for control to ensure compliance to standards. This article argues that the primary function of evaluation in the "Catherine Wheel" computer-based assessment (CBA)…

The prediction of breast cancer biopsy outcomes using two CAD approaches that both emphasize an intelligible decision process

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

Elter, M.; Schulz-Wendtland, R.; Wittenberg, T.

2007-11-15

Mammography is the most effective method for breast cancer screening available today. However, the low positive predictive value of breast biopsy resulting from mammogram interpretation leads to approximately 70% unnecessary biopsies with benign outcomes. To reduce the high number of unnecessary breast biopsies, several computer-aided diagnosis (CAD) systems have been proposed in the last several years. These systems help physicians in their decision to perform a breast biopsy on a suspicious lesion seen in a mammogram or to perform a short term follow-up examination instead. We present two novel CAD approaches that both emphasize an intelligible decision process to predictmore » breast biopsy outcomes from BI-RADS findings. An intelligible reasoning process is an important requirement for the acceptance of CAD systems by physicians. The first approach induces a global model based on decison-tree learning. The second approach is based on case-based reasoning and applies an entropic similarity measure. We have evaluated the performance of both CAD approaches on two large publicly available mammography reference databases using receiver operating characteristic (ROC) analysis, bootstrap sampling, and the ANOVA statistical significance test. Both approaches outperform the diagnosis decisions of the physicians. Hence, both systems have the potential to reduce the number of unnecessary breast biopsies in clinical practice. A comparison of the performance of the proposed decision tree and CBR approaches with a state of the art approach based on artificial neural networks (ANN) shows that the CBR approach performs slightly better than the ANN approach, which in turn results in slightly better performance than the decision-tree approach. The differences are statistically significant (p value <0.001). On 2100 masses extracted from the DDSM database, the CRB approach for example resulted in an area under the ROC curve of A(z)=0.89{+-}0.01, the decision-tree approach in A(z)=0.87{+-}0.01, and the ANN approach in A(z)=0.88{+-}0.01.« less

The normalization heuristic: an untested hypothesis that may misguide medical decisions.

PubMed

Aberegg, Scott K; O'Brien, James M

2009-06-01

Medical practice is increasingly informed by the evidence from randomized controlled trials. When such evidence is not available, clinical hypotheses based on pathophysiological reasoning and common sense guide clinical decision making. One commonly utilized general clinical hypothesis is the assumption that normalizing abnormal laboratory values and physiological parameters will lead to improved patient outcomes. We refer to the general use of this clinical hypothesis to guide medical therapeutics as the "normalization heuristic". In this paper, we operationally define this heuristic and discuss its limitations as a rule of thumb for clinical decision making. We review historical and contemporaneous examples of normalization practices as empirical evidence for the normalization heuristic and to highlight its frailty as a guide for clinical decision making.

Deep learning aided decision support for pulmonary nodules diagnosing: a review

PubMed Central

Yang, Yixin; Feng, Xiaoyi; Chi, Wenhao; Li, Zhengyang; Duan, Wenzhe; Liu, Haiping; Liang, Wenhua; Wang, Wei; Chen, Ping

2018-01-01

Deep learning techniques have recently emerged as promising decision supporting approaches to automatically analyze medical images for different clinical diagnosing purposes. Diagnosing of pulmonary nodules by using computer-assisted diagnosing has received considerable theoretical, computational, and empirical research work, and considerable methods have been developed for detection and classification of pulmonary nodules on different formats of images including chest radiographs, computed tomography (CT), and positron emission tomography in the past five decades. The recent remarkable and significant progress in deep learning for pulmonary nodules achieved in both academia and the industry has demonstrated that deep learning techniques seem to be promising alternative decision support schemes to effectively tackle the central issues in pulmonary nodules diagnosing, including feature extraction, nodule detection, false-positive reduction, and benign-malignant classification for the huge volume of chest scan data. The main goal of this investigation is to provide a comprehensive state-of-the-art review of the deep learning aided decision support for pulmonary nodules diagnosing. As far as the authors know, this is the first time that a review is devoted exclusively to deep learning techniques for pulmonary nodules diagnosing. PMID:29780633

Intention to adopt clinical decision support systems in a developing country: effect of Physician’s perceived professional autonomy, involvement and belief: a cross-sectional study

PubMed Central

2012-01-01

Background Computer-based clinical decision support systems (CDSS) are regarded as a key element to enhance decision-making in a healthcare environment to improve the quality of medical care delivery. The concern of having new CDSS unused is still one of the biggest issues in developing countries for the developers and implementers of clinical IT systems. The main objectives of this study are to determine whether (1) the physician’s perceived professional autonomy, (2) involvement in the decision to implement CDSS and (3) the belief that CDSS will improve job performance increase the intention to adopt CDSS. Four hypotheses were formulated and tested. Methods A questionnaire-based survey conducted between July 2010 and December 2010. The study was conducted in seven public and five private hospitals in Kuala Lumpur, Malaysia. Before contacting the hospitals, necessary permission was obtained from the Ministry of Health, Malaysia and the questionnaire was vetted by the ethics committee of the ministry. Physicians working in 12 hospitals from 10 different specialties participated in the study. The sampling method used was stratified random sampling and the physicians were stratified based on the specialty. A total of 450 physicians were selected using a random number generator. Each of these physicians was given a questionnaire and out of 450 questionnaires, 335 (response rate – 74%) were returned and 309 (69%) were deemed usable. Results The hypotheses were tested using Structural Equation Modeling (SEM). Salient results are: (1) Physicians’ perceived threat to professional autonomy lowers the intention to use CDSS (p < 0.01); (2) Physicians involvement in the planning, design and implementation increases their intention to use CDSS (p < 0.01); (3) Physicians belief that the new CDSS will improve his/her job performance increases their intention to use CDSS (p < 0.01). Conclusion The proposed model with the three main constructs (physician’s professional characteristic, involvement and belief) explains 47% of the variance in the intention to use CDSS. This is significantly higher than the models addressed so far. The results will have a major impact in implementing CDSS in developing countries. PMID:23216866

Intention to adopt clinical decision support systems in a developing country: effect of physician's perceived professional autonomy, involvement and belief: a cross-sectional study.

PubMed

Sambasivan, Murali; Esmaeilzadeh, Pouyan; Kumar, Naresh; Nezakati, Hossein

2012-12-05

Computer-based clinical decision support systems (CDSS) are regarded as a key element to enhance decision-making in a healthcare environment to improve the quality of medical care delivery. The concern of having new CDSS unused is still one of the biggest issues in developing countries for the developers and implementers of clinical IT systems. The main objectives of this study are to determine whether (1) the physician's perceived professional autonomy, (2) involvement in the decision to implement CDSS and (3) the belief that CDSS will improve job performance increase the intention to adopt CDSS. Four hypotheses were formulated and tested. A questionnaire-based survey conducted between July 2010 and December 2010. The study was conducted in seven public and five private hospitals in Kuala Lumpur, Malaysia. Before contacting the hospitals, necessary permission was obtained from the Ministry of Health, Malaysia and the questionnaire was vetted by the ethics committee of the ministry. Physicians working in 12 hospitals from 10 different specialties participated in the study. The sampling method used was stratified random sampling and the physicians were stratified based on the specialty. A total of 450 physicians were selected using a random number generator. Each of these physicians was given a questionnaire and out of 450 questionnaires, 335 (response rate--74%) were returned and 309 (69%) were deemed usable. The hypotheses were tested using Structural Equation Modeling (SEM). Salient results are: (1) Physicians' perceived threat to professional autonomy lowers the intention to use CDSS (p < 0.01); (2) Physicians involvement in the planning, design and implementation increases their intention to use CDSS (p < 0.01); (3) Physicians belief that the new CDSS will improve his/her job performance increases their intention to use CDSS (p < 0.01). The proposed model with the three main constructs (physician's professional characteristic, involvement and belief) explains 47% of the variance in the intention to use CDSS. This is significantly higher than the models addressed so far. The results will have a major impact in implementing CDSS in developing countries.

Formal ontologies in biomedical knowledge representation.

PubMed

Schulz, S; Jansen, L

2013-01-01

Medical decision support and other intelligent applications in the life sciences depend on increasing amounts of digital information. Knowledge bases as well as formal ontologies are being used to organize biomedical knowledge and data. However, these two kinds of artefacts are not always clearly distinguished. Whereas the popular RDF(S) standard provides an intuitive triple-based representation, it is semantically weak. Description logics based ontology languages like OWL-DL carry a clear-cut semantics, but they are computationally expensive, and they are often misinterpreted to encode all kinds of statements, including those which are not ontological. We distinguish four kinds of statements needed to comprehensively represent domain knowledge: universal statements, terminological statements, statements about particulars and contingent statements. We argue that the task of formal ontologies is solely to represent universal statements, while the non-ontological kinds of statements can nevertheless be connected with ontological representations. To illustrate these four types of representations, we use a running example from parasitology. We finally formulate recommendations for semantically adequate ontologies that can efficiently be used as a stable framework for more context-dependent biomedical knowledge representation and reasoning applications like clinical decision support systems.

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

Decision making in recurrent neuronal circuits.

PubMed

Wang, Xiao-Jing

2008-10-23

Decision making has recently emerged as a central theme in neurophysiological studies of cognition, and experimental and computational work has led to the proposal of a cortical circuit mechanism of elemental decision computations. This mechanism depends on slow recurrent synaptic excitation balanced by fast feedback inhibition, which not only instantiates attractor states for forming categorical choices but also long transients for gradually accumulating evidence in favor of or against alternative options. Such a circuit endowed with reward-dependent synaptic plasticity is able to produce adaptive choice behavior. While decision threshold is a core concept for reaction time tasks, it can be dissociated from a general decision rule. Moreover, perceptual decisions and value-based economic choices are described within a unified framework in which probabilistic choices result from irregular neuronal activity as well as iterative interactions of a decision maker with an uncertain environment or other unpredictable decision makers in a social group.

Clinical intuition in the nursing process and decision-making-A mixed-studies review.

PubMed

Melin-Johansson, Christina; Palmqvist, Rebecca; Rönnberg, Linda

2017-12-01

To review what is characteristic of registered nurses' intuition in clinical settings, in relationships and in the nursing process. Intuition is a controversial concept and nurses believe that there are difficulties in how they should explain their nursing actions or decisions based on intuition. Much of the evidence from the body of research indicates that nurses value their intuition in a variety of clinical settings. More information on how nurses integrate intuition as a core element in daily clinical work would contribute to an improved understanding on how they go about this. Intuition deserves a place in evidence-based activities, where intuition is an important component associated with the nursing process. An integrative review strengthened with a mixed-studies review. Literature searches were conducted in the databases CINAHL, PubMed and PsycINFO, and literature published 1985-2016 were included. The findings in the studies were analysed with content analysis, and the synthesis process entailed a reasoning between the authors. After a quality assessment, 16 studies were included. The analysis and synthesis resulted in three categories. The characteristics of intuition in the nurse's daily clinical activities include application, assertiveness and experiences; in the relationships with patients' intuition include unique connections, mental and bodily responses, and personal qualities; and in the nursing process include support and guidance, component and clues in decision-making, and validating decisions. Intuition is more than simply a "gut feeling," and it is a process based on knowledge and care experience and has a place beside research-based evidence. Nurses integrate both analysis and synthesis of intuition alongside objective data when making decisions. They should rely on their intuition and use this knowledge in clinical practice as a support in decision-making, which increases the quality and safety of patient care. We find that intuition plays a key role in more or less all of the steps in the nursing process as a base for decision-making that supports safe patient care, and is a validated component of nursing clinical care expertise. © 2017 John Wiley & Sons Ltd.

Bayesian randomized clinical trials: From fixed to adaptive design.

PubMed

Yin, Guosheng; Lam, Chi Kin; Shi, Haolun

2017-08-01

Randomized controlled studies are the gold standard for phase III clinical trials. Using α-spending functions to control the overall type I error rate, group sequential methods are well established and have been dominating phase III studies. Bayesian randomized design, on the other hand, can be viewed as a complement instead of competitive approach to the frequentist methods. For the fixed Bayesian design, the hypothesis testing can be cast in the posterior probability or Bayes factor framework, which has a direct link to the frequentist type I error rate. Bayesian group sequential design relies upon Bayesian decision-theoretic approaches based on backward induction, which is often computationally intensive. Compared with the frequentist approaches, Bayesian methods have several advantages. The posterior predictive probability serves as a useful and convenient tool for trial monitoring, and can be updated at any time as the data accrue during the trial. The Bayesian decision-theoretic framework possesses a direct link to the decision making in the practical setting, and can be modeled more realistically to reflect the actual cost-benefit analysis during the drug development process. Other merits include the possibility of hierarchical modeling and the use of informative priors, which would lead to a more comprehensive utilization of information from both historical and longitudinal data. From fixed to adaptive design, we focus on Bayesian randomized controlled clinical trials and make extensive comparisons with frequentist counterparts through numerical studies. Copyright © 2017 Elsevier Inc. All rights reserved.

Bridging the guideline implementation gap: a systematic, document-centered approach to guideline implementation.

PubMed

Shiffman, Richard N; Michel, George; Essaihi, Abdelwaheb; Thornquist, Elizabeth

2004-01-01

A gap exists between the information contained in published clinical practice guidelines and the knowledge and information that are necessary to implement them. This work describes a process to systematize and make explicit the translation of document-based knowledge into workflow-integrated clinical decision support systems. This approach uses the Guideline Elements Model (GEM) to represent the guideline knowledge. Implementation requires a number of steps to translate the knowledge contained in guideline text into a computable format and to integrate the information into clinical workflow. The steps include: (1) selection of a guideline and specific recommendations for implementation, (2) markup of the guideline text, (3) atomization, (4) deabstraction and (5) disambiguation of recommendation concepts, (6) verification of rule set completeness, (7) addition of explanations, (8) building executable statements, (9) specification of origins of decision variables and insertions of recommended actions, (10) definition of action types and selection of associated beneficial services, (11) choice of interface components, and (12) creation of requirement specification. The authors illustrate these component processes using examples drawn from recent experience translating recommendations from the National Heart, Lung, and Blood Institute's guideline on management of chronic asthma into a workflow-integrated decision support system that operates within the Logician electronic health record system. Using the guideline document as a knowledge source promotes authentic translation of domain knowledge and reduces the overall complexity of the implementation task. From this framework, we believe that a better understanding of activities involved in guideline implementation will emerge.

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.

Coronary Computed Tomographic Angiography-Derived Fractional Flow Reserve for Therapeutic Decision Making.

PubMed

Tesche, Christian; Vliegenthart, Rozemarijn; Duguay, Taylor M; De Cecco, Carlo N; Albrecht, Moritz H; De Santis, Domenico; Langenbach, Marcel C; Varga-Szemes, Akos; Jacobs, Brian E; Jochheim, David; Baquet, Moritz; Bayer, Richard R; Litwin, Sheldon E; Hoffmann, Ellen; Steinberg, Daniel H; Schoepf, U Joseph

2017-12-15

This study investigated the performance of coronary computed tomography angiography (cCTA) with cCTA-derived fractional flow reserve (CT-FFR) compared with invasive coronary angiography (ICA) with fractional flow reserve (FFR) for therapeutic decision making in patients with suspected coronary artery disease (CAD). Seventy-four patients (62 ± 11 years, 62% men) with at least 1 coronary stenosis of ≥50% on clinically indicated dual-source cCTA, who had subsequently undergone ICA with FFR measurement, were retrospectively evaluated. CT-FFR values were computed using an on-site machine-learning algorithm to assess the functional significance of CAD. The therapeutic strategy (optimal medical therapy alone vs revascularization) and the appropriate revascularization procedure (percutaneous coronary intervention vs coronary artery bypass grafting) were selected using cCTA-CT-FFR. Thirty-six patients (49%) had a functionally significant CAD based on ICA-FFR. cCTA-CT-FFR correctly identified a functionally significant CAD and the need of revascularization in 35 of 36 patients (97%). When revascularization was deemed indicated, the same revascularization procedure (32 percutaneous coronary interventions and 3 coronary artery bypass grafting) was chosen in 35 of 35 patients (100%). Overall, identical management strategies were selected in 73 of the 74 patients (99%). cCTA-CT-FFR shows excellent performance to identify patients with and without the need for revascularization and to select the appropriate revascularization strategy. cCTA-CT-FFR as a noninvasive "one-stop shop" has the potential to change diagnostic workflows and to directly inform therapeutic decision making in patients with suspected CAD. Copyright © 2017 Elsevier Inc. All rights reserved.

Ethically-based clinical decision-making in physical therapy: process and issues.

PubMed

Finch, Elspeth; Geddes, E Lynne; Larin, Hélène

2005-01-01

The identification and consideration of relevant ethical issues in clinical decision-making, and the education of health care professionals (HCPs) in these skills are key factors in providing quality health care. This qualitative study explores the way in which physical therapists (PTs) integrate ethical issues into clinical practice decisions and identifies ethical themes used by PTs. A purposive sample of eight PTs was asked to describe a recent ethically-based clinical decision. Transcribed interviews were coded and themes identified related to the following categories: 1) the integration of ethical issues in the clinical decision-making process, 2) patient welfare, 3) professional ethos of the PT, and 4) health care economics and business practices. Participants readily described clinical situations involving ethical issues but rarely identified specific conflicting ethical issues in their description. Ethical dilemmas were more frequently resolved when there were fewer emotional sequelae associated with the dilemma, and the PT had a clear understanding of professional ethos, valued patient autonomy, and explored a variety of alternative actions before implementing one. HCP students need to develop a clear professional ethos and an increased understanding of the economic factors that will present ethical issues in practice.

Information and Communication Technologies for the Dissemination of Clinical Practice Guidelines to Health Professionals: A Systematic Review.

PubMed

De Angelis, Gino; Davies, Barbara; King, Judy; McEwan, Jessica; Cavallo, Sabrina; Loew, Laurianne; Wells, George A; Brosseau, Lucie

2016-11-30

The transfer of research knowledge into clinical practice can be a continuous challenge for researchers. Information and communication technologies, such as websites and email, have emerged as popular tools for the dissemination of evidence to health professionals. The objective of this systematic review was to identify research on health professionals' perceived usability and practice behavior change of information and communication technologies for the dissemination of clinical practice guidelines. We used a systematic approach to retrieve and extract data about relevant studies. We identified 2248 citations, of which 21 studies met criteria for inclusion; 20 studies were randomized controlled trials, and 1 was a controlled clinical trial. The following information and communication technologies were evaluated: websites (5 studies), computer software (3 studies), Web-based workshops (2 studies), computerized decision support systems (2 studies), electronic educational game (1 study), email (2 studies), and multifaceted interventions that consisted of at least one information and communication technology component (6 studies). Website studies demonstrated significant improvements in perceived usefulness and perceived ease of use, but not for knowledge, reducing barriers, and intention to use clinical practice guidelines. Computer software studies demonstrated significant improvements in perceived usefulness, but not for knowledge and skills. Web-based workshop and email studies demonstrated significant improvements in knowledge, perceived usefulness, and skills. An electronic educational game intervention demonstrated a significant improvement from baseline in knowledge after 12 and 24 weeks. Computerized decision support system studies demonstrated variable findings for improvement in skills. Multifaceted interventions demonstrated significant improvements in beliefs about capabilities, perceived usefulness, and intention to use clinical practice guidelines, but variable findings for improvements in skills. Most multifaceted studies demonstrated significant improvements in knowledge. The findings suggest that health professionals' perceived usability and practice behavior change vary by type of information and communication technology. Heterogeneity and the paucity of properly conducted studies did not allow for a clear comparison between studies and a conclusion on the effectiveness of information and communication technologies as a knowledge translation strategy for the dissemination of clinical practice guidelines. ©Gino De Angelis, Barbara Davies, Judy King, Jessica McEwan, Sabrina Cavallo, Laurianne Loew, George A Wells, Lucie Brosseau. Originally published in JMIR Medical Education (http://mededu.jmir.org), 30.11.2016.

Characteristics of knowledge content in a curated online evidence library.

PubMed

Varada, Sowmya; Lacson, Ronilda; Raja, Ali S; Ip, Ivan K; Schneider, Louise; Osterbur, David; Bain, Paul; Vetrano, Nicole; Cellini, Jacqueline; Mita, Carol; Coletti, Margaret; Whelan, Julia; Khorasani, Ramin

2018-05-01

To describe types of recommendations represented in a curated online evidence library, report on the quality of evidence-based recommendations pertaining to diagnostic imaging exams, and assess underlying knowledge representation. The evidence library is populated with clinical decision rules, professional society guidelines, and locally developed best practice guidelines. Individual recommendations were graded based on a standard methodology and compared using chi-square test. Strength of evidence ranged from grade 1 (systematic review) through grade 5 (recommendations based on expert opinion). Finally, variations in the underlying representation of these recommendations were identified. The library contains 546 individual imaging-related recommendations. Only 15% (16/106) of recommendations from clinical decision rules were grade 5 vs 83% (526/636) from professional society practice guidelines and local best practice guidelines that cited grade 5 studies (P < .0001). Minor head trauma, pulmonary embolism, and appendicitis were topic areas supported by the highest quality of evidence. Three main variations in underlying representations of recommendations were "single-decision," "branching," and "score-based." Most recommendations were grade 5, largely because studies to test and validate many recommendations were absent. Recommendation types vary in amount and complexity and, accordingly, the structure and syntax of statements they generate. However, they can be represented in single-decision, branching, and score-based representations. In a curated evidence library with graded imaging-based recommendations, evidence quality varied widely, with decision rules providing the highest-quality recommendations. The library may be helpful in highlighting evidence gaps, comparing recommendations from varied sources on similar clinical topics, and prioritizing imaging recommendations to inform clinical decision support implementation.

Integration of an OWL-DL knowledge base with an EHR prototype and providing customized information.

PubMed

Jing, Xia; Kay, Stephen; Marley, Tom; Hardiker, Nicholas R

2014-09-01

When clinicians use electronic health record (EHR) systems, their ability to obtain general knowledge is often an important contribution to their ability to make more informed decisions. In this paper we describe a method by which an external, formal representation of clinical and molecular genetic knowledge can be integrated into an EHR such that customized knowledge can be delivered to clinicians in a context-appropriate manner.Web Ontology Language-Description Logic (OWL-DL) is a formal knowledge representation language that is widely used for creating, organizing and managing biomedical knowledge through the use of explicit definitions, consistent structure and a computer-processable format, particularly in biomedical fields. In this paper we describe: 1) integration of an OWL-DL knowledge base with a standards-based EHR prototype, 2) presentation of customized information from the knowledge base via the EHR interface, and 3) lessons learned via the process. The integration was achieved through a combination of manual and automatic methods. Our method has advantages for scaling up to and maintaining knowledge bases of any size, with the goal of assisting clinicians and other EHR users in making better informed health care decisions.

The Clinical Intuition Exploration Guide: A Decision-Making Tool for Counselors and Supervisors

ERIC Educational Resources Information Center

Jeffrey, Aaron

2012-01-01

Clinical intuition is a common experience among counselors, yet many do not know what to do with intuition when it occurs. This article reviews the role intuition plays in clinical work and presents the research-based Clinical Intuition Exploration Guide to help counselors navigate the decision-making process. The guide consists of self-reflection…

A predictive approach to selecting the size of a clinical trial, based on subjective clinical opinion.

PubMed

Spiegelhalter, D J; Freedman, L S

1986-01-01

The 'textbook' approach to determining sample size in a clinical trial has some fundamental weaknesses which we discuss. We describe a new predictive method which takes account of prior clinical opinion about the treatment difference. The method adopts the point of clinical equivalence (determined by interviewing the clinical participants) as the null hypothesis. Decision rules at the end of the study are based on whether the interval estimate of the treatment difference (classical or Bayesian) includes the null hypothesis. The prior distribution is used to predict the probabilities of making the decisions to use one or other treatment or to reserve final judgement. It is recommended that sample size be chosen to control the predicted probability of the last of these decisions. An example is given from a multi-centre trial of superficial bladder cancer.

Building Bridges between Perceptual and Economic Decision-Making: Neural and Computational Mechanisms.

PubMed

Summerfield, Christopher; Tsetsos, Konstantinos

2012-01-01

Investigation into the neural and computational bases of decision-making has proceeded in two parallel but distinct streams. Perceptual decision-making (PDM) is concerned with how observers detect, discriminate, and categorize noisy sensory information. Economic decision-making (EDM) explores how options are selected on the basis of their reinforcement history. Traditionally, the sub-fields of PDM and EDM have employed different paradigms, proposed different mechanistic models, explored different brain regions, disagreed about whether decisions approach optimality. Nevertheless, we argue that there is a common framework for understanding decisions made in both tasks, under which an agent has to combine sensory information (what is the stimulus) with value information (what is it worth). We review computational models of the decision process typically used in PDM, based around the idea that decisions involve a serial integration of evidence, and assess their applicability to decisions between good and gambles. Subsequently, we consider the contribution of three key brain regions - the parietal cortex, the basal ganglia, and the orbitofrontal cortex (OFC) - to perceptual and EDM, with a focus on the mechanisms by which sensory and reward information are integrated during choice. We find that although the parietal cortex is often implicated in the integration of sensory evidence, there is evidence for its role in encoding the expected value of a decision. Similarly, although much research has emphasized the role of the striatum and OFC in value-guided choices, they may play an important role in categorization of perceptual information. In conclusion, we consider how findings from the two fields might be brought together, in order to move toward a general framework for understanding decision-making in humans and other primates.

Building Bridges between Perceptual and Economic Decision-Making: Neural and Computational Mechanisms

PubMed Central

Summerfield, Christopher; Tsetsos, Konstantinos

2012-01-01

Investigation into the neural and computational bases of decision-making has proceeded in two parallel but distinct streams. Perceptual decision-making (PDM) is concerned with how observers detect, discriminate, and categorize noisy sensory information. Economic decision-making (EDM) explores how options are selected on the basis of their reinforcement history. Traditionally, the sub-fields of PDM and EDM have employed different paradigms, proposed different mechanistic models, explored different brain regions, disagreed about whether decisions approach optimality. Nevertheless, we argue that there is a common framework for understanding decisions made in both tasks, under which an agent has to combine sensory information (what is the stimulus) with value information (what is it worth). We review computational models of the decision process typically used in PDM, based around the idea that decisions involve a serial integration of evidence, and assess their applicability to decisions between good and gambles. Subsequently, we consider the contribution of three key brain regions – the parietal cortex, the basal ganglia, and the orbitofrontal cortex (OFC) – to perceptual and EDM, with a focus on the mechanisms by which sensory and reward information are integrated during choice. We find that although the parietal cortex is often implicated in the integration of sensory evidence, there is evidence for its role in encoding the expected value of a decision. Similarly, although much research has emphasized the role of the striatum and OFC in value-guided choices, they may play an important role in categorization of perceptual information. In conclusion, we consider how findings from the two fields might be brought together, in order to move toward a general framework for understanding decision-making in humans and other primates. PMID:22654730

An imaging informatics-based ePR (electronic patient record) system for providing decision support in evaluating dose optimization in stroke rehabilitation

NASA Astrophysics Data System (ADS)

Liu, Brent J.; Winstein, Carolee; Wang, Ximing; Konersman, Matt; Martinez, Clarisa; Schweighofer, Nicolas

2012-02-01

Stroke is one of the major causes of death and disability in America. After stroke, about 65% of survivors still suffer from severe paresis, while rehabilitation treatment strategy after stroke plays an essential role in recovery. Currently, there is a clinical trial (NIH award #HD065438) to determine the optimal dose of rehabilitation for persistent recovery of arm and hand paresis. For DOSE (Dose Optimization Stroke Evaluation), laboratory-based measurements, such as the Wolf Motor Function test, behavioral questionnaires (e.g. Motor Activity Log-MAL), and MR, DTI, and Transcranial Magnetic Stimulation (TMS) imaging studies are planned. Current data collection processes are tedious and reside in various standalone systems including hardcopy forms. In order to improve the efficiency of this clinical trial and facilitate decision support, a web-based imaging informatics system has been implemented together with utilizing mobile devices (eg, iPAD, tablet PC's, laptops) for collecting input data and integrating all multi-media data into a single system. The system aims to provide clinical imaging informatics management and a platform to develop tools to predict the treatment effect based on the imaging studies and the treatment dosage with mathematical models. Since there is a large amount of information to be recorded within the DOSE project, the system provides clinical data entry through mobile device applications thus allowing users to collect data at the point of patient interaction without typing into a desktop computer, which is inconvenient. Imaging analysis tools will also be developed for structural MRI, DTI, and TMS imaging studies that will be integrated within the system and correlated with the clinical and behavioral data. This system provides a research platform for future development of mathematical models to evaluate the differences between prediction and reality and thus improve and refine the models rapidly and efficiently.

Shared decision-making – transferring research into practice: the Analytic Hierarchy Process (AHP)

PubMed Central

Dolan, James G.

2008-01-01

Objective To illustrate how the Analytic Hierarchy Process (AHP) can be used to promote shared decision-making and enhance clinician-patient communication. Methods Tutorial review. Results The AHP promotes shared decision making by creating a framework that is used to define the decision, summarize the information available, prioritize information needs, elicit preferences and values, and foster meaningful communication among decision stakeholders. Conclusions The AHP and related multi-criteria methods have the potential for improving the quality of clinical decisions and overcoming current barriers to implementing shared decision making in busy clinical settings. Further research is needed to determine the best way to implement these tools and to determine their effectiveness. Practice Implications Many clinical decisions involve preference-based trade-offs between competing risks and benefits. The AHP is a well-developed method that provides a practical approach for improving patient-provider communication, clinical decision-making, and the quality of patient care in these situations. PMID:18760559

The Effect of Multimedia Replacing Text in Resident Clinical Decision-Making Assessment

ERIC Educational Resources Information Center

Chang, Todd P.; Schrager, Sheree M.; Rake, Alyssa J.; Chan, Michael W.; Pham, Phung K.; Christman, Grant

2017-01-01

Multimedia in assessing clinical decision-making skills (CDMS) has been poorly studied, particularly in comparison to traditional text-based assessments. The literature suggests multimedia is more difficult for trainees. We hypothesize that pediatric residents score lower in diagnostic skill when clinical vignettes use multimedia rather than text…

Tools to Promote Shared Decision Making in Serious Illness: A Systematic Review.

PubMed

Austin, C Adrian; Mohottige, Dinushika; Sudore, Rebecca L; Smith, Alexander K; Hanson, Laura C

2015-07-01

Serious illness impairs function and threatens survival. Patients facing serious illness value shared decision making, yet few decision aids address the needs of this population. To perform a systematic review of evidence about decision aids and other exportable tools that promote shared decision making in serious illness, thereby (1) identifying tools relevant to the treatment decisions of seriously ill patients and their caregivers, (2) evaluating the quality of evidence for these tools, and (3) summarizing their effect on outcomes and accessibility for clinicians. We searched PubMed, CINAHL, and PsychInfo from January 1, 1995, through October 31, 2014, and identified additional studies from reference lists and other systematic reviews. Clinical trials with random or nonrandom controls were included if they tested print, video, or web-based tools for advance care planning (ACP) or decision aids for serious illness. We extracted data on the study population, design, results, and risk for bias using the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) criteria. Each tool was evaluated for its effect on patient outcomes and accessibility. Seventeen randomized clinical trials tested decision tools in serious illness. Nearly all the trials were of moderate or high quality and showed that decision tools improve patient knowledge and awareness of treatment choices. The available tools address ACP, palliative care and goals of care communication, feeding options in dementia, lung transplant in cystic fibrosis, and truth telling in terminal cancer. Five randomized clinical trials provided further evidence that decision tools improve ACP documentation, clinical decisions, and treatment received. Clinicians can access and use evidence-based tools to engage seriously ill patients in shared decision making. This field of research is in an early stage; future research is needed to develop novel decision aids for other serious diagnoses and key decisions. Health care delivery organizations should prioritize the use of currently available tools that are evidence based and effective.

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.

Utility of bleb imaging with anterior segment optical coherence tomography in clinical decision-making after trabeculectomy.

PubMed

Singh, Mandeep; Aung, Tin; Aquino, Maria C; Chew, Paul T K

2009-08-01

To determine if imaging of blebs with anterior segment optical coherence tomography (ASOCT) affects clinical decision-making with regard to laser suture lysis (LSL) after trabeculectomy. In this prospective observational case series, we included patients with poorly controlled intraocular pressure (IOP) after standardized trabeculectomy from May to November 2006. One observer assessed IOP, anterior chamber depth and bleb formation, and recorded a decision of whether or not to undertake LSL based on clinical grounds. A second observer masked to clinical data recorded a decision of whether or not to perform LSL based on ASOCT assessment of scleral flap position, presence of a sub-flap space, patency of the internal ostium, and bleb wall thickening. We compared the 2 observers' decisions to determine how ASOCT influenced decision-making. Seven eyes of 7 patients were included. On the basis of clinical examination, LSL was recommended in all 7 (100.0%) cases due to presence of elevated IOP, deep anterior chambers and poorly formed blebs. Using ASOCT, LSL was recommended in 5/7 (71.4%) cases with apposed scleral flaps, absent sub-flap spaces, and absent bleb wall thickening. In 2/7 (28.7%) cases, LSL was not recommended based on ASOCT findings of an elevated scleral flap, a patent sub-flap space, and bleb wall thickening. All 7 patients had good IOP control and formed blebs at a mean of 8.4+/-2.6 months after trabeculectomy, with a mean IOP of 14.3+/-3.2 mm Hg with no medications. This small study suggests that ASOCT imaging may affect decision-making with regard to LSL by providing information not apparent on clinical examination.

A shared computer-based problem-oriented patient record for the primary care team.

PubMed

Linnarsson, R; Nordgren, K

1995-01-01

1. INTRODUCTION. A computer-based patient record (CPR) system, Swedestar, has been developed for use in primary health care. The principal aim of the system is to support continuous quality improvement through improved information handling, improved decision-making, and improved procedures for quality assurance. The Swedestar system has evolved during a ten-year period beginning in 1984. 2. SYSTEM DESIGN. The design philosophy is based on the following key factors: a shared, problem-oriented patient record; structured data entry based on an extensive controlled vocabulary; advanced search and query functions, where the query language has the most important role; integrated decision support for drug prescribing and care protocols and guidelines; integrated procedures for quality assurance. 3. A SHARED PROBLEM-ORIENTED PATIENT RECORD. The core of the CPR system is the problem-oriented patient record. All problems of one patient, recorded by different members of the care team, are displayed on the problem list. Starting from this list, a problem follow-up can be made, one problem at a time or for several problems simultaneously. Thus, it is possible to get an integrated view, across provider categories, of those problems of one patient that belong together. This shared problem-oriented patient record provides an important basis for the primary care team work. 4. INTEGRATED DECISION SUPPORT. The decision support of the system includes a drug prescribing module and a care protocol module. The drug prescribing module is integrated with the patient records and includes an on-line check of the patient's medication list for potential interactions and data-driven reminders concerning major drug problems. Care protocols have been developed for the most common chronic diseases, such as asthma, diabetes, and hypertension. The patient records can be automatically checked according to the care protocols. 5. PRACTICAL EXPERIENCE. The Swedestar system has been implemented in a primary care area with 30,000 inhabitants. It is being used by all the primary care team members: 15 general practitioners, 25 district nurses, and 10 physiotherapists. Several years of practical experience of the CPR system shows that it has a positive impact on quality of care on four levels: 1) improved clinical follow-up of individual patients; 2) facilitated follow-up of aggregated data such as practice activity analysis, annual reports, and clinical indicators; 3) automated medical audit; and 4) concurrent audit. Within that primary care area, quality of care has improved substantially in several aspects due to the use of the CPR system [1].

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

Decision support in psychiatry – a comparison between the diagnostic outcomes using a computerized decision support system versus manual diagnosis

PubMed Central

Bergman, Lars G; Fors, Uno GH

2008-01-01

Background Correct diagnosis in psychiatry may be improved by novel diagnostic procedures. Computerized Decision Support Systems (CDSS) are suggested to be able to improve diagnostic procedures, but some studies indicate possible problems. Therefore, it could be important to investigate CDSS systems with regard to their feasibility to improve diagnostic procedures as well as to save time. Methods This study was undertaken to compare the traditional 'paper and pencil' diagnostic method SCID1 with the computer-aided diagnostic system CB-SCID1 to ascertain processing time and accuracy of diagnoses suggested. 63 clinicians volunteered to participate in the study and to solve two paper-based cases using either a CDSS or manually. Results No major difference between paper and pencil and computer-supported diagnosis was found. Where a difference was found it was in favour of paper and pencil. For example, a significantly shorter time was found for paper and pencil for the difficult case, as compared to computer support. A significantly higher number of correct diagnoses were found in the diffilt case for the diagnosis 'Depression' using the paper and pencil method. Although a majority of the clinicians found the computer method supportive and easy to use, it took a longer time and yielded fewer correct diagnoses than with paper and pencil. Conclusion This study could not detect any major difference in diagnostic outcome between traditional paper and pencil methods and computer support for psychiatric diagnosis. Where there were significant differences, traditional paper and pencil methods were better than the tested CDSS and thus we conclude that CDSS for diagnostic procedures may interfere with diagnosis accuracy. A limitation was that most clinicians had not previously used the CDSS system under study. The results of this study, however, confirm that CDSS development for diagnostic purposes in psychiatry has much to deal with before it can be used for routine clinical purposes. PMID:18261222

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.

Certainty, leaps of faith, and tradition: rethinking clinical interventions.

PubMed

Dzurec, L C

1998-12-01

Clinical decision making requires that clinicians think quickly and in ways that will foster optimal, safe client care. Tradition influences clinical decision making, enhancing efficiency of resulting nursing action; however, since many decisions must be based on data that are either uncertain, incomplete, or indirect, clinicians are readily ensnared in processes involving potentially faulty logic associated with tradition. The author addresses the tenacity of tradition and then focuses on three processes--consensus formation, the grounding of certainty in inductive reasoning, and affirming the consequent--that have affected clinical decision making. For some recipients of care, tradition has had a substantial and invalid influence on their ability to access care.

Pattern recognition for passive polarimetric data using nonparametric classifiers

NASA Astrophysics Data System (ADS)

Thilak, Vimal; Saini, Jatinder; Voelz, David G.; Creusere, Charles D.

2005-08-01

Passive polarization based imaging is a useful tool in computer vision and pattern recognition. A passive polarization imaging system forms a polarimetric image from the reflection of ambient light that contains useful information for computer vision tasks such as object detection (classification) and recognition. Applications of polarization based pattern recognition include material classification and automatic shape recognition. In this paper, we present two target detection algorithms for images captured by a passive polarimetric imaging system. The proposed detection algorithms are based on Bayesian decision theory. In these approaches, an object can belong to one of any given number classes and classification involves making decisions that minimize the average probability of making incorrect decisions. This minimum is achieved by assigning an object to the class that maximizes the a posteriori probability. Computing a posteriori probabilities requires estimates of class conditional probability density functions (likelihoods) and prior probabilities. A Probabilistic neural network (PNN), which is a nonparametric method that can compute Bayes optimal boundaries, and a -nearest neighbor (KNN) classifier, is used for density estimation and classification. The proposed algorithms are applied to polarimetric image data gathered in the laboratory with a liquid crystal-based system. The experimental results validate the effectiveness of the above algorithms for target detection from polarimetric data.

Designing Real-time Decision Support for Trauma Resuscitations

PubMed Central

Yadav, Kabir; Chamberlain, James M.; Lewis, Vicki R.; Abts, Natalie; Chawla, Shawn; Hernandez, Angie; Johnson, Justin; Tuveson, Genevieve; Burd, Randall S.

2016-01-01

Background Use of electronic clinical decision support (eCDS) has been recommended to improve implementation of clinical decision rules. Many eCDS tools, however, are designed and implemented without taking into account the context in which clinical work is performed. Implementation of the pediatric traumatic brain injury (TBI) clinical decision rule at one Level I pediatric emergency department includes an electronic questionnaire triggered when ordering a head computed tomography using computerized physician order entry (CPOE). Providers use this CPOE tool in less than 20% of trauma resuscitation cases. A human factors engineering approach could identify the implementation barriers that are limiting the use of this tool. Objectives The objective was to design a pediatric TBI eCDS tool for trauma resuscitation using a human factors approach. The hypothesis was that clinical experts will rate a usability-enhanced eCDS tool better than the existing CPOE tool for user interface design and suitability for clinical use. Methods This mixed-methods study followed usability evaluation principles. Pediatric emergency physicians were surveyed to identify barriers to using the existing eCDS tool. Using standard trauma resuscitation protocols, a hierarchical task analysis of pediatric TBI evaluation was developed. Five clinical experts, all board-certified pediatric emergency medicine faculty members, then iteratively modified the hierarchical task analysis until reaching consensus. The software team developed a prototype eCDS display using the hierarchical task analysis. Three human factors engineers provided feedback on the prototype through a heuristic evaluation, and the software team refined the eCDS tool using a rapid prototyping process. The eCDS tool then underwent iterative usability evaluations by the five clinical experts using video review of 50 trauma resuscitation cases. A final eCDS tool was created based on their feedback, with content analysis of the evaluations performed to ensure all concerns were identified and addressed. Results Among 26 EPs (76% response rate), the main barriers to using the existing tool were that the information displayed is redundant and does not fit clinical workflow. After the prototype eCDS tool was developed based on the trauma resuscitation hierarchical task analysis, the human factors engineers rated it to be better than the CPOE tool for nine of 10 standard user interface design heuristics on a three-point scale. The eCDS tool was also rated better for clinical use on the same scale, in 84% of 50 expert–video pairs, and was rated equivalent in the remainder. Clinical experts also rated barriers to use of the eCDS tool as being low. Conclusions An eCDS tool for diagnostic imaging designed using human factors engineering methods has improved perceived usability among pediatric emergency physicians. PMID:26300010

A computer decision aid for medical prevention: a pilot qualitative study of the Personalized Estimate of Risks (EsPeR) system

PubMed Central

Colombet, Isabelle; Dart, Thierry; Leneveut, Laurence; Zunino, Sylvain; Ménard, Joël; Chatellier, Gilles

2003-01-01

Background Many preventable diseases such as ischemic heart diseases and breast cancer prevail at a large scale in the general population. Computerized decision support systems are one of the solutions for improving the quality of prevention strategies. Methods The system called EsPeR (Personalised Estimate of Risks) combines calculation of several risks with computerisation of guidelines (cardiovascular prevention, screening for breast cancer, colorectal cancer, uterine cervix cancer, and prostate cancer, diagnosis of depression and suicide risk). We present a qualitative evaluation of its ergonomics, as well as it's understanding and acceptance by a group of general practitioners. We organised four focus groups each including 6–11 general practitioners. Physicians worked on several structured clinical scenari os with the help of EsPeR, and three senior investigators leaded structured discussion sessions. Results The initial sessions identified several ergonomic flaws of the system that were easily corrected. Both clinical scenarios and discussion sessions identified several problems related to the insufficient comprehension (expression of risks, definition of familial history of disease), and difficulty for the physicians to accept some of the recommendations. Conclusion Educational, socio-professional and organisational components (i.e. time constraints for training and use of the EsPeR system during consultation) as well as acceptance of evidence-based decision-making should be taken into account before launching computerised decision support systems, or their application in randomised trials. PMID:14641924

Clinical decision-making by midwives: managing case complexity.

PubMed

Cioffi, J; Markham, R

1997-02-01

In making clinical judgements, it is argued that midwives use 'shortcuts' or heuristics based on estimated probabilities to simplify the decision-making task. Midwives (n = 30) were given simulated patient assessment situations of high and low complexity and were required to think aloud. Analysis of verbal protocols showed that subjective probability judgements (heuristics) were used more frequently in the high than low complexity case and predominated in the last quarter of the assessment period for the high complexity case. 'Representativeness' was identified more frequently in the high than in the low case, but was the dominant heuristic in both. Reports completed after each simulation suggest that heuristics based on memory for particular conditions affect decisions. It is concluded that midwives use heuristics, derived mainly from their clinical experiences, in an attempt to save cognitive effort and to facilitate reasonably accurate decisions in the decision-making process.

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.…

Evidence-based clinical practice, [corrected] evidence-based medicine and the Cochrane collaboration.

PubMed

Gambrill, E

1999-03-01

Encouraging professionals in training and later to consider practice-related research findings when making important clinical decisions is an on-going concern. Evidenced-Based Medicine (EBM) and the Cochrane Collaboration (CC) provide a source of tools and ideas for doing so, as well as a roster of colleagues who share this interest. Evidenced-based medicine involves integrating clinical expertise with the best available external evidence from systematic research as well as considering the values and expectations of patients/clients. Advantage can be taken of educational formats developed in EBM, such as problem-based learning and critical-appraisal workshops in which participants learn how to ask key answerable questions related to important clinical practice questions (e.g., regarding effectiveness, accuracy of assessment measures, prediction, prevention, and quality of clinical practice guidelines) and to access and critically appraise related research. The Cochrane Collaboration is a world-wide network of centers that prepare, maintain, and disseminate high-quality systematic reviews on the efficacy of healthcare. These databases allow access to evidence related to clinical practice decisions. Forging reciprocal working relationships with those involved in EBM reciprocal and the CC should contribute to the pursuit of shared goals such as basing clinical decisions on the best-available evidence and involving clients as informed consumers.

Clinical Decision Support for a Multicenter Trial of Pediatric Head Trauma

PubMed Central

Swietlik, Marguerite; Deakyne, Sara; Hoffman, Jeffrey M.; Grundmeier, Robert W.; Paterno, Marilyn D.; Rocha, Beatriz H.; Schaeffer, Molly H; Pabbathi, Deepika; Alessandrini, Evaline; Ballard, Dustin; Goldberg, Howard S.; Kuppermann, Nathan; Dayan, Peter S.

2016-01-01

Summary Introduction For children who present to emergency departments (EDs) due to blunt head trauma, ED clinicians must decide who requires computed tomography (CT) scanning to evaluate for traumatic brain injury (TBI). The Pediatric Emergency Care Applied Research Network (PECARN) derived and validated two age-based prediction rules to identify children at very low risk of clinically-important traumatic brain injuries (ciTBIs) who do not typically require CT scans. In this case report, we describe the strategy used to implement the PECARN TBI prediction rules via electronic health record (EHR) clinical decision support (CDS) as the intervention in a multicenter clinical trial. Methods Thirteen EDs participated in this trial. The 10 sites receiving the CDS intervention used the Epic® EHR. All sites implementing EHR-based CDS built the rules by using the vendor’s CDS engine. Based on a sociotechnical analysis, we designed the CDS so that recommendations could be displayed immediately after any provider entered prediction rule data. One central site developed and tested the intervention package to be exported to other sites. The intervention package included a clinical trial alert, an electronic data collection form, the CDS rules and the format for recommendations. Results The original PECARN head trauma prediction rules were derived from physician documentation while this pragmatic trial led each site to customize their workflows and allow multiple different providers to complete the head trauma assessments. These differences in workflows led to varying completion rates across sites as well as differences in the types of providers completing the electronic data form. Site variation in internal change management processes made it challenging to maintain the same rigor across all sites. This led to downstream effects when data reports were developed. Conclusions The process of a centralized build and export of a CDS system in one commercial EHR system successfully supported a multicenter clinical trial. PMID:27437059

Disciplined Decision Making in an Interdisciplinary Environment: Some Implications for Clinical Applications of Statistical Process Control.

ERIC Educational Resources Information Center

Hantula, Donald A.

1995-01-01

Clinical applications of statistical process control (SPC) in human service organizations are considered. SPC is seen as providing a standard set of criteria that serves as a common interface for data-based decision making, which may bring decision making under the control of established contingencies rather than the immediate contingencies of…

Developing genomic knowledge bases and databases to support clinical management: current perspectives.

PubMed

Huser, Vojtech; Sincan, Murat; Cimino, James J

2014-01-01

Personalized medicine, the ability to tailor diagnostic and treatment decisions for individual patients, is seen as the evolution of modern medicine. We characterize here the informatics resources available today or envisioned in the near future that can support clinical interpretation of genomic test results. We assume a clinical sequencing scenario (germline whole-exome sequencing) in which a clinical specialist, such as an endocrinologist, needs to tailor patient management decisions within his or her specialty (targeted findings) but relies on a genetic counselor to interpret off-target incidental findings. We characterize the genomic input data and list various types of knowledge bases that provide genomic knowledge for generating clinical decision support. We highlight the need for patient-level databases with detailed lifelong phenotype content in addition to genotype data and provide a list of recommendations for personalized medicine knowledge bases and databases. We conclude that no single knowledge base can currently support all aspects of personalized recommendations and that consolidation of several current resources into larger, more dynamic and collaborative knowledge bases may offer a future path forward.

Developing genomic knowledge bases and databases to support clinical management: current perspectives

PubMed Central

Huser, Vojtech; Sincan, Murat; Cimino, James J

2014-01-01

Personalized medicine, the ability to tailor diagnostic and treatment decisions for individual patients, is seen as the evolution of modern medicine. We characterize here the informatics resources available today or envisioned in the near future that can support clinical interpretation of genomic test results. We assume a clinical sequencing scenario (germline whole-exome sequencing) in which a clinical specialist, such as an endocrinologist, needs to tailor patient management decisions within his or her specialty (targeted findings) but relies on a genetic counselor to interpret off-target incidental findings. We characterize the genomic input data and list various types of knowledge bases that provide genomic knowledge for generating clinical decision support. We highlight the need for patient-level databases with detailed lifelong phenotype content in addition to genotype data and provide a list of recommendations for personalized medicine knowledge bases and databases. We conclude that no single knowledge base can currently support all aspects of personalized recommendations and that consolidation of several current resources into larger, more dynamic and collaborative knowledge bases may offer a future path forward. PMID:25276091

Mixture-based gatekeeping procedures in adaptive clinical trials.

PubMed

Kordzakhia, George; Dmitrienko, Alex; Ishida, Eiji

2018-01-01

Clinical trials with data-driven decision rules often pursue multiple clinical objectives such as the evaluation of several endpoints or several doses of an experimental treatment. These complex analysis strategies give rise to "multivariate" multiplicity problems with several components or sources of multiplicity. A general framework for defining gatekeeping procedures in clinical trials with adaptive multistage designs is proposed in this paper. The mixture method is applied to build a gatekeeping procedure at each stage and inferences at each decision point (interim or final analysis) are performed using the combination function approach. An advantage of utilizing the mixture method is that it enables powerful gatekeeping procedures applicable to a broad class of settings with complex logical relationships among the hypotheses of interest. Further, the combination function approach supports flexible data-driven decisions such as a decision to increase the sample size or remove a treatment arm. The paper concludes with a clinical trial example that illustrates the methodology by applying it to develop an adaptive two-stage design with a mixture-based gatekeeping procedure.

Health professionals' decision-making in wound management: a grounded theory.

PubMed

Gillespie, Brigid M; Chaboyer, Wendy; St John, Winsome; Morley, Nicola; Nieuwenhoven, Paul

2015-06-01

To develop a conceptual understanding of the decision-making processes used by healthcare professionals in wound care practice. With the global move towards using an evidence-base in standardizing wound care practices and the need to reduce hospital wound care costs, it is important to understand health professionals' decision-making in this important yet under-researched area. A grounded theory approach was used to explore clinical decision-making of healthcare professionals in wound care practice. Interviews were conducted with 20 multi-disciplinary participants from nursing, surgery, infection control and wound care who worked at a metropolitan hospital in Australia. Data were collected during 2012-2013. Constant comparative analysis underpinned by Strauss and Corbin's framework was used to identify clinical decision-making processes. The core category was 'balancing practice-based knowledge with evidence-based knowledge'. Participants' clinical practice and actions embedded the following processes: 'utilizing the best available information', 'using a consistent approach in wound assessment' and 'using a multidisciplinary approach'. The substantive theory explains how practice and evidence knowledge was balanced and the variation in use of intuitive practice-based knowledge versus evidence-based knowledge. Participants considered patients' needs and preferences, costs, outcomes, technologies, others' expertise and established practices. Participants' decision-making tended to be more heavily weighted towards intuitive practice-based processes. These findings offer a better understanding of the processes used by health professionals' in their decision-making in wound care. Such an understanding may inform the development of evidence-based interventions that lead to better patient outcomes. © 2014 John Wiley & Sons Ltd.

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.

Option Grids to facilitate shared decision making for patients with Osteoarthritis of the knee: protocol for a single site, efficacy trial.

PubMed

Marrin, Katy; Wood, Fiona; Firth, Jill; Kinsey, Katharine; Edwards, Adrian; Brain, Kate E; Newcombe, Robert G; Nye, Alan; Pickles, Timothy; Hawthorne, Kamila; Elwyn, Glyn

2014-04-07

Despite policy interest, an ethical imperative, and evidence of the benefits of patient decision support tools, the adoption of shared decision making (SDM) in day-to-day clinical practice remains slow and is inhibited by barriers that include culture and attitudes; resources and time pressures. Patient decision support tools often require high levels of health and computer literacy. Option Grids are one-page evidence-based summaries of the available condition-specific treatment options, listing patients' frequently asked questions. They are designed to be sufficiently brief and accessible enough to support a better dialogue between patients and clinicians during routine consultations. This paper describes a study to assess whether an Option Grid for osteoarthritis of the knee (OA of the knee) facilitates SDM, and explores the use of Option Grids by patients disadvantaged by language or poor health literacy. This will be a stepped wedge exploratory trial involving 72 patients with OA of the knee referred from primary medical care to a specialist musculoskeletal service in Oldham. Six physiotherapists will sequentially join the trial and consult with six patients using usual care procedures. After a period of brief training in using the Option Grid, the same six physiotherapists will consult with six further patients using an Option Grid in the consultation. The primary outcome will be efficacy of the Option Grid in facilitating SDM as measured by observational scores using the OPTION scale. Comparisons will be made between patients who have received the Option Grid and those who received usual care. A Decision Quality Measure (DQM) will assess quality of decision making. The health literacy of patients will be measured using the REALM-R instrument. Consultations will be observed and audio-recorded. Interviews will be conducted with the physiotherapists, patients and any interpreters present to explore their views of using the Option Grid. Option Grids offer a potential solution to the barriers to implementing traditional decision aids into routine clinical practice. The study will assess whether Option Grids can facilitate SDM in day-to-day clinical practice and explore their use with patients disadvantaged by language or poor health literacy. Current Controlled Trials ISRCTN94871417.

Neighborhood graph and learning discriminative distance functions for clinical decision support.

PubMed

Tsymbal, Alexey; Zhou, Shaohua Kevin; Huber, Martin

2009-01-01

There are two essential reasons for the slow progress in the acceptance of clinical case retrieval and similarity search-based decision support systems; the especial complexity of clinical data making it difficult to define a meaningful and effective distance function on them and the lack of transparency and explanation ability in many existing clinical case retrieval decision support systems. In this paper, we try to address these two problems by introducing a novel technique for visualizing inter-patient similarity based on a node-link representation with neighborhood graphs and by considering two techniques for learning discriminative distance function that help to combine the power of strong "black box" learners with the transparency of case retrieval and nearest neighbor classification.

Optimal Sequential Rules for Computer-Based Instruction.

ERIC Educational Resources Information Center

Vos, Hans J.

1998-01-01

Formulates sequential rules for adapting the appropriate amount of instruction to learning needs in the context of computer-based instruction. Topics include Bayesian decision theory, threshold and linear-utility structure, psychometric model, optimal sequential number of test questions, and an empirical example of sequential instructional…

Have Your Computer Call My Computer.

ERIC Educational Resources Information Center

Carabi, Peter

1992-01-01

As more school systems adopt site-based management, local decision makers need greater access to all kinds of information. Microcomputer-based networks can help with classroom management, scheduling, student program design, counselor recommendations, and financial reporting operations. Administrators are provided with planning tips and a sample…

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 Application of Web-based Computer-assisted Instruction Courseware within Health Assessment

NASA Astrophysics Data System (ADS)

Xiuyan, Guo

Health assessment is a clinical nursing course and places emphasis on clinical skills. The application of computer-assisted instruction in the field of nursing teaching solved the problems in the traditional lecture class. This article stated teaching experience of web-based computer-assisted instruction, based upon a two-year study of computer-assisted instruction courseware use within the course health assessment. The computer-assisted instruction courseware could develop teaching structure, simulate clinical situations, create teaching situations and facilitate students study.

Image-based diagnostic aid for interstitial lung disease with secondary data integration

NASA Astrophysics Data System (ADS)

Depeursinge, Adrien; Müller, Henning; Hidki, Asmâa; Poletti, Pierre-Alexandre; Platon, Alexandra; Geissbuhler, Antoine

2007-03-01

Interstitial lung diseases (ILDs) are a relatively heterogeneous group of around 150 illnesses with often very unspecific symptoms. The most complete imaging method for the characterisation of ILDs is the high-resolution computed tomography (HRCT) of the chest but a correct interpretation of these images is difficult even for specialists as many diseases are rare and thus little experience exists. Moreover, interpreting HRCT images requires knowledge of the context defined by clinical data of the studied case. A computerised diagnostic aid tool based on HRCT images with associated medical data to retrieve similar cases of ILDs from a dedicated database can bring quick and precious information for example for emergency radiologists. The experience from a pilot project highlighted the need for detailed database containing high-quality annotations in addition to clinical data. The state of the art is studied to identify requirements for image-based diagnostic aid for interstitial lung disease with secondary data integration. The data acquisition steps are detailed. The selection of the most relevant clinical parameters is done in collaboration with lung specialists from current literature, along with knowledge bases of computer-based diagnostic decision support systems. In order to perform high-quality annotations of the interstitial lung tissue in the HRCT images an annotation software and its own file format is implemented for DICOM images. A multimedia database is implemented to store ILD cases with clinical data and annotated image series. Cases from the University & University Hospitals of Geneva (HUG) are retrospectively and prospectively collected to populate the database. Currently, 59 cases with certified diagnosis and their clinical parameters are stored in the database as well as 254 image series of which 26 have their regions of interest annotated. The available data was used to test primary visual features for the classification of lung tissue patterns. These features show good discriminative properties for the separation of five classes of visual observations.

Use of RSS feeds for the implementation of clinical reminder.

PubMed

Chi, Wen-Chou; Wen, Chia-Hsien; Lin, Chih-Yu; Poon, Sek-Kwong; Huang, Shih-Che

2012-06-01

A computer-based reminder system can help physicians get right information and make right decisions in daily clinical work in time. This study presents a RSS-based Clinical Reminding System (RCRS) designed for reminding clinicians to deal with their varied unfinished clinical works. The RCRS was implemented in a hospital to automatically generate messages for every clinician on the basis of clinical information gathered from the hospital information system (HIS) and send them by RSS feed. In order to allow all physicians to participate in the project, the RCRS was integrated with the Computerized Physician Order Entry (COPE) system to provide messages whenever a clinician logs in the HIS; the connection on screen lets the clinician easily make some response. The system can help clinicians focus on patient care without keeping track of the schedule of clinical chores stored in various systems. Two physicians, also directors from Clinical Informatics Research & Development Center (CIRD) who were appointed as project leaders of the RCRS project who went through the entire development process were chosen as interviewees to obtain a preliminary evaluation of the system. The results show the "Information Content" of this system was suggested to be modified, and "Information Accuracy", "Formats", "Ease of use" and "Timeliness" of the system were appropriate to meet the system design purposes.

Quantitative imaging biomarkers: the application of advanced image processing and analysis to clinical and preclinical decision making.

PubMed

Prescott, Jeffrey William

2013-02-01

The importance of medical imaging for clinical decision making has been steadily increasing over the last four decades. Recently, there has also been an emphasis on medical imaging for preclinical decision making, i.e., for use in pharamaceutical and medical device development. There is also a drive towards quantification of imaging findings by using quantitative imaging biomarkers, which can improve sensitivity, specificity, accuracy and reproducibility of imaged characteristics used for diagnostic and therapeutic decisions. An important component of the discovery, characterization, validation and application of quantitative imaging biomarkers is the extraction of information and meaning from images through image processing and subsequent analysis. However, many advanced image processing and analysis methods are not applied directly to questions of clinical interest, i.e., for diagnostic and therapeutic decision making, which is a consideration that should be closely linked to the development of such algorithms. This article is meant to address these concerns. First, quantitative imaging biomarkers are introduced by providing definitions and concepts. Then, potential applications of advanced image processing and analysis to areas of quantitative imaging biomarker research are described; specifically, research into osteoarthritis (OA), Alzheimer's disease (AD) and cancer is presented. Then, challenges in quantitative imaging biomarker research are discussed. Finally, a conceptual framework for integrating clinical and preclinical considerations into the development of quantitative imaging biomarkers and their computer-assisted methods of extraction is presented.

SIGI: A Computer-Based System of Interactive Guidance and Information.

ERIC Educational Resources Information Center

Educational Testing Service, Princeton, NJ.

This pamphlet describes SIGI, a computer-based System of Interactive Guidance and Information designed to help students in community and junior colleges make career decisions. SIGI is based on a humanistic philosophy, a theory of guidance that emphasizes individual values, a vast store of occupational data, and a strategy for processing…

Designing for Interaction: Six Steps to Designing Computer-Supported Group-Based Learning

ERIC Educational Resources Information Center

Strijbos, J. W.; Martens, R. L.; Jochems, W. M. G.

2004-01-01

At present, the design of computer-supported group-based learning (CSGBL) is often based on subjective decisions regarding tasks, pedagogy and technology, or concepts such as "cooperative learning" and "collaborative learning." Critical review reveals these concepts as insufficiently substantial to serve as a basis for CSGBL design. Furthermore,…

MIT Laboratory for Computer Science Progress Report 26

DTIC Science & Technology

1989-06-01

conteinporary and prospective applications from business to sensory computing. In Sqst.-ns., Languagcs, and Nr/o4orks, our objective is to provide the...numbers 363 through 400. 1,i Advanced Network Architecture Academic Staff D. Clark, Group Leader D. Tennenhouse Restarch Staff J. Davin K. Sollins Graduate...Zurich, Switzerland, May 1989. 23 24 Clinical Decision Making Academic Staff R. Patil P. Szolovits, Group Leader Collaborating Investigators M

Cancer patient decision making related to clinical trial participation: an integrative review with implications for patients' relational autonomy.

PubMed

Bell, Jennifer A H; Balneaves, Lynda G

2015-04-01

Oncology clinical trials are necessary for the improvement of patient care as they have the ability to confirm the efficacy and safety of novel cancer treatments and in so doing, contribute to a solid evidence base on which practitioners and patients can make informed treatment decisions. However, only 3-5 % of adult cancer patients enroll in clinical trials. Lack of participation compromises the success of clinical trials and squanders an opportunity for improving patient outcomes. This literature review summarizes the factors and contexts that influence cancer patient decision making related to clinical trial participation. An integrative review was undertaken within PubMed, CINAHL, and EMBASE databases for articles written between 1995 and 2012 and archived under relevant keywords. Articles selected were data-based, written in English, and limited to adult cancer patients. In the 51 articles reviewed, three main types of factors were identified that influence cancer patients' decision making about participation in clinical trials: personal, social, and system factors. Subthemes included patients' trust in their physician and the research process, undue influence within the patient-physician relationship, and systemic social inequalities. How these factors interact and influence patients' decision-making process and relational autonomy, however, is insufficiently understood. Future research is needed to further elucidate the sociopolitical barriers and facilitators of clinical trial participation and to enhance ethical practice within clinical trial enrolment. This research will inform targeted education and support interventions to foster patients' relational autonomy in the decision-making process and potentially improve clinical trial participation rates.

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.

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.

The Ultimate Sampling Dilemma in Experience-Based Decision Making

ERIC Educational Resources Information Center

Fiedler, Klaus

2008-01-01

Computer simulations and 2 experiments demonstrate the ultimate sampling dilemma, which constitutes a serious obstacle to inductive inferences in a probabilistic world. Participants were asked to take the role of a manager who is to make purchasing decisions based on positive versus negative feedback about 3 providers in 2 different product…

Computer-Based Aids for Learning, Job Performance, and Decision Making in Military Applications: Emergent Technology and Challenges

DTIC Science & Technology

2003-10-01

paper, which addresses the following questions: Is it worth it? What do we know about the value of technology applications in learning ( education and......fax) fletcher@ida.org SUMMARY Technology -based systems for education , training, and performance aiding (including decision aiding) may pose the

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

Wu, Qishi; Zhu, Mengxia; Rao, Nageswara S

We propose an intelligent decision support system based on sensor and computer networks that incorporates various component techniques for sensor deployment, data routing, distributed computing, and information fusion. The integrated system is deployed in a distributed environment composed of both wireless sensor networks for data collection and wired computer networks for data processing in support of homeland security defense. We present the system framework and formulate the analytical problems and develop approximate or exact solutions for the subtasks: (i) sensor deployment strategy based on a two-dimensional genetic algorithm to achieve maximum coverage with cost constraints; (ii) data routing scheme tomore » achieve maximum signal strength with minimum path loss, high energy efficiency, and effective fault tolerance; (iii) network mapping method to assign computing modules to network nodes for high-performance distributed data processing; and (iv) binary decision fusion rule that derive threshold bounds to improve system hit rate and false alarm rate. These component solutions are implemented and evaluated through either experiments or simulations in various application scenarios. The extensive results demonstrate that these component solutions imbue the integrated system with the desirable and useful quality of intelligence in decision making.« less

Decision Support Systems: Applications in Statistics and Hypothesis Testing.

ERIC Educational Resources Information Center

Olsen, Christopher R.; Bozeman, William C.

1988-01-01

Discussion of the selection of appropriate statistical procedures by educators highlights a study conducted to investigate the effectiveness of decision aids in facilitating the use of appropriate statistics. Experimental groups and a control group using a printed flow chart, a computer-based decision aid, and a standard text are described. (11…

How Students' Values Are Intertwined with Decisions in a Socio-Scientific Issue

ERIC Educational Resources Information Center

Paraskeva-Hadjichambi, Demetra; Hadjichambis, Andreas Ch.; Korfiatis, Konstantinos

2015-01-01

The present study incorporated a scaffolding decision making procedure on an authentic environmental socio-scientific issue and investigated how students' decisions are intertwined with their values. Computer-based activities provided necessary information and allowed for the consideration of multiple aspects of the issue, the study of the effects…

Mass Conflagration: An Analysis and Adaptation of the Shipboard Damage Control Organization

DTIC Science & Technology

1991-03-01

the span of control narrows, as each supervisor is able to better monitor the actions and environment of his subordinates. (6) Communciation and... computed decision is reached by the decision makers, often based on a prior formal doctrine or methodology. [Ref. 4:p. 364] While no decision process

Pharmacogenomic knowledge representation, reasoning and genome-based clinical decision support based on OWL 2 DL ontologies.

PubMed

Samwald, Matthias; Miñarro Giménez, Jose Antonio; Boyce, Richard D; Freimuth, Robert R; Adlassnig, Klaus-Peter; Dumontier, Michel

2015-02-22

Every year, hundreds of thousands of patients experience treatment failure or adverse drug reactions (ADRs), many of which could be prevented by pharmacogenomic testing. However, the primary knowledge needed for clinical pharmacogenomics is currently dispersed over disparate data structures and captured in unstructured or semi-structured formalizations. This is a source of potential ambiguity and complexity, making it difficult to create reliable information technology systems for enabling clinical pharmacogenomics. We developed Web Ontology Language (OWL) ontologies and automated reasoning methodologies to meet the following goals: 1) provide a simple and concise formalism for representing pharmacogenomic knowledge, 2) finde errors and insufficient definitions in pharmacogenomic knowledge bases, 3) automatically assign alleles and phenotypes to patients, 4) match patients to clinically appropriate pharmacogenomic guidelines and clinical decision support messages and 5) facilitate the detection of inconsistencies and overlaps between pharmacogenomic treatment guidelines from different sources. We evaluated different reasoning systems and test our approach with a large collection of publicly available genetic profiles. Our methodology proved to be a novel and useful choice for representing, analyzing and using pharmacogenomic data. The Genomic Clinical Decision Support (Genomic CDS) ontology represents 336 SNPs with 707 variants; 665 haplotypes related to 43 genes; 22 rules related to drug-response phenotypes; and 308 clinical decision support rules. OWL reasoning identified CDS rules with overlapping target populations but differing treatment recommendations. Only a modest number of clinical decision support rules were triggered for a collection of 943 public genetic profiles. We found significant performance differences across available OWL reasoners. The ontology-based framework we developed can be used to represent, organize and reason over the growing wealth of pharmacogenomic knowledge, as well as to identify errors, inconsistencies and insufficient definitions in source data sets or individual patient data. Our study highlights both advantages and potential practical issues with such an ontology-based approach.

[An expert system of aiding decision making in breast pathology connected to a clinical data base].

PubMed

Brunet, M; Durrleman, S; Ferber, J; Ganascia, J G; Hacene, K; Hirt, F; Jouniaux, F; Meeus, L

1987-01-01

The René Huguenin Cancer Center holds a medical file for each patient which is intended to store and process medical data. Since 1970, we introduced computerization: a development plan was elaborated and simultaneously a statistical software (Clotilde--GSI/CFRO) was selected. Thus, we now have access to a large database, structured according to medical rationale, and utilizable with methods of artificial intelligence towards three objectives: improved data acquisition, decision making and exploitation. The first application was to breast pathology, which represents one of the Center's primary activities. The structure of the data concerning patients is by all criteria part of the medical knowledge. This information needs to be presented as well as processed with a suitable language. To this end, we chose a language-oriented object, Mering II, usable with Apple and IBM 4 micro-computers. This project has already allowed to work out an operational model.

Evidence-based dentistry: fundamentals for the dentist.

PubMed

Bauer, Janet; Chiappelli, Francesco; Spackman, Sue; Prolo, Paolo; Stevenson, Richard

2006-06-01

This article explains the fundamentals of evidence-based dentistry for the dentist. Evidence-based dentistry is a discipline whose primary participant is the translational researcher. Recent developments have emphasized the importance of this discipline (clinical and translational research) for improving health care. The process of evidence-based dentistry is the reciprocation of new and existing evidence between dentists and quantitative and qualitative researchers, facilitated by the translational researcher. The product of this reciprocation is the clinical practice guideline, or best evidence, that provides the patient options in choosing treatments or services. These options are quantified and qualified by decision, utility, and cost data. Using shared decision-making, the dentist and patient arrive at a mutual understanding of which option best meets an acceptable and preferred treatment course that is cost effective. This option becomes the clinical decision.

Developing points-based risk-scoring systems in the presence of competing risks.

PubMed

Austin, Peter C; Lee, Douglas S; D'Agostino, Ralph B; Fine, Jason P

2016-09-30

Predicting the occurrence of an adverse event over time is an important issue in clinical medicine. Clinical prediction models and associated points-based risk-scoring systems are popular statistical methods for summarizing the relationship between a multivariable set of patient risk factors and the risk of the occurrence of an adverse event. Points-based risk-scoring systems are popular amongst physicians as they permit a rapid assessment of patient risk without the use of computers or other electronic devices. The use of such points-based risk-scoring systems facilitates evidence-based clinical decision making. There is a growing interest in cause-specific mortality and in non-fatal outcomes. However, when considering these types of outcomes, one must account for competing risks whose occurrence precludes the occurrence of the event of interest. We describe how points-based risk-scoring systems can be developed in the presence of competing events. We illustrate the application of these methods by developing risk-scoring systems for predicting cardiovascular mortality in patients hospitalized with acute myocardial infarction. Code in the R statistical programming language is provided for the implementation of the described methods. © 2016 The Authors. Statistics in Medicine published by John Wiley & Sons Ltd. © 2016 The Authors. Statistics in Medicine published by John Wiley & Sons Ltd.

Towards ontology-based decision support systems for complex ultrasound diagnosis in obstetrics and gynecology.

PubMed

Maurice, P; Dhombres, F; Blondiaux, E; Friszer, S; Guilbaud, L; Lelong, N; Khoshnood, B; Charlet, J; Perrot, N; Jauniaux, E; Jurkovic, D; Jouannic, J-M

2017-05-01

We have developed a new knowledge base intelligent system for obstetrics and gynecology ultrasound imaging, based on an ontology and a reference image collection. This study evaluates the new system to support accurate annotations of ultrasound images. We have used the early ultrasound diagnosis of ectopic pregnancies as a model clinical issue. The ectopic pregnancy ontology was derived from medical texts (4260 ultrasound reports of ectopic pregnancy from a specialist center in the UK and 2795 Pubmed abstracts indexed with the MeSH term "Pregnancy, Ectopic") and the reference image collection was built on a selection from 106 publications. We conducted a retrospective analysis of the signs in 35 scans of ectopic pregnancy by six observers using the new system. The resulting ectopic pregnancy ontology consisted of 1395 terms, and 80 images were collected for the reference collection. The observers used the knowledge base intelligent system to provide a total of 1486 sign annotations. The precision, recall and F-measure for the annotations were 0.83, 0.62 and 0.71, respectively. The global proportion of agreement was 40.35% 95% CI [38.64-42.05]. The ontology-based intelligent system provides accurate annotations of ultrasound images and suggests that it may benefit non-expert operators. The precision rate is appropriate for accurate input of a computer-based clinical decision support and could be used to support medical imaging diagnosis of complex conditions in obstetrics and gynecology. Copyright © 2017. Published by Elsevier Masson SAS.

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).

Group Augmentation in Realistic Visual-Search Decisions via a Hybrid Brain-Computer Interface.

PubMed

Valeriani, Davide; Cinel, Caterina; Poli, Riccardo

2017-08-10

Groups have increased sensing and cognition capabilities that typically allow them to make better decisions. However, factors such as communication biases and time constraints can lead to less-than-optimal group decisions. In this study, we use a hybrid Brain-Computer Interface (hBCI) to improve the performance of groups undertaking a realistic visual-search task. Our hBCI extracts neural information from EEG signals and combines it with response times to build an estimate of the decision confidence. This is used to weigh individual responses, resulting in improved group decisions. We compare the performance of hBCI-assisted groups with the performance of non-BCI groups using standard majority voting, and non-BCI groups using weighted voting based on reported decision confidence. We also investigate the impact on group performance of a computer-mediated form of communication between members. Results across three experiments suggest that the hBCI provides significant advantages over non-BCI decision methods in all cases. We also found that our form of communication increases individual error rates by almost 50% compared to non-communicating observers, which also results in worse group performance. Communication also makes reported confidence uncorrelated with the decision correctness, thereby nullifying its value in weighing votes. In summary, best decisions are achieved by hBCI-assisted, non-communicating groups.

Are We Ready for Positron Emission Tomography/Computed Tomography-based Target Volume Definition in Lymphoma Radiation Therapy?

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

Yeoh, Kheng-Wei; Mikhaeel, N. George, E-mail: George.Mikhaeel@gstt.nhs.uk

2013-01-01

Fluorine-18 fluorodeoxyglucose (FDG)-positron emission tomography (PET)/computed tomography (CT) has become indispensable for the clinical management of lymphomas. With consistent evidence that it is more accurate than anatomic imaging in the staging and response assessment of many lymphoma subtypes, its utility continues to increase. There have therefore been efforts to incorporate PET/CT data into radiation therapy decision making and in the planning process. Further, there have also been studies investigating target volume definition for radiation therapy using PET/CT data. This article will critically review the literature and ongoing studies on the above topics, examining the value and methods of adding PET/CTmore » data to the radiation therapy treatment algorithm. We will also discuss the various challenges and the areas where more evidence is required.« less

An analysis of nursing students' decision-making in teams during simulations of acute patient deterioration.

PubMed

Bucknall, Tracey K; Forbes, Helen; Phillips, Nicole M; Hewitt, Nicky A; Cooper, Simon; Bogossian, Fiona

2016-10-01

The aim of this study was to examine the decision-making of nursing students during team based simulations on patient deterioration to determine the sources of information, the types of decisions made and the influences underpinning their decisions. Missed, misinterpreted or mismanaged physiological signs of deterioration in hospitalized patients lead to costly serious adverse events. Not surprisingly, an increased focus on clinical education and graduate nurse work readiness has resulted. A descriptive exploratory design. Clinical simulation laboratories in three Australian universities were used to run team based simulations with a patient actor. A convenience sample of 97 final-year nursing students completed simulations, with three students forming a team. Four teams from each university were randomly selected for detailed analysis. Cued recall during video review of team based simulation exercises to elicit descriptions of individual and team based decision-making and reflections on performance were audio-recorded post simulation (2012) and transcribed. Students recalled 11 types of decisions, including: information seeking; patient assessment; diagnostic; intervention/treatment; evaluation; escalation; prediction; planning; collaboration; communication and reflective. Patient distress, uncertainty and a lack of knowledge were frequently recalled influences on decisions. Incomplete information, premature diagnosis and a failure to consider alternatives when caring for patients is likely to lead to poor quality decisions. All health professionals have a responsibility in recognizing and responding to clinical deterioration within their scope of practice. A typology of nursing students' decision-making in teams, in this context, highlights the importance of individual knowledge, leadership and communication. © 2016 John Wiley & Sons Ltd.

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.

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.

Development and preliminary user testing of the DCIDA (Dynamic computer interactive decision application) for 'nudging' patients towards high quality decisions.

PubMed

Bansback, Nick; Li, Linda C; Lynd, Larry; Bryan, Stirling

2014-08-01

Patient decision aids (PtDA) are developed to facilitate informed, value-based decisions about health. Research suggests that even when informed with necessary evidence and information, cognitive errors can prevent patients from choosing the option that is most congruent with their own values. We sought to utilize principles of behavioural economics to develop a computer application that presents information from conventional decision aids in a way that reduces these errors, subsequently promoting higher quality decisions. The Dynamic Computer Interactive Decision Application (DCIDA) was developed to target four common errors that can impede quality decision making with PtDAs: unstable values, order effects, overweighting of rare events, and information overload. Healthy volunteers were recruited to an interview to use three PtDAs converted to the DCIDA on a computer equipped with an eye tracker. Participants were first used a conventional PtDA, and then subsequently used the DCIDA version. User testing was assessed based on whether respondents found the software both usable: evaluated using a) eye-tracking, b) the system usability scale, and c) user verbal responses from a 'think aloud' protocol; and useful: evaluated using a) eye-tracking, b) whether preferences for options were changed, and c) and the decisional conflict scale. Of the 20 participants recruited to the study, 11 were male (55%), the mean age was 35, 18 had at least a high school education (90%), and 8 (40%) had a college or university degree. Eye-tracking results, alongside a mean system usability scale score of 73 (range 68-85), indicated a reasonable degree of usability for the DCIDA. The think aloud study suggested areas for further improvement. The DCIDA also appeared to be useful to participants wherein subjects focused more on the features of the decision that were most important to them (21% increase in time spent focusing on the most important feature). Seven subjects (25%) changed their preferred option when using DCIDA. Preliminary results suggest that DCIDA has potential to improve the quality of patient decision-making. Next steps include larger studies to test individual components of DCIDA and feasibility testing with patients making real decisions.

American Cancer Society Lung Cancer Screening Guidelines

PubMed Central

Wender, Richard; Fontham, Elizabeth T. H.; Barrera, Ermilo; Colditz, Graham A.; Church, Timothy R.; Ettinger, David S.; Etzioni, Ruth; Flowers, Christopher R.; Gazelle, G. Scott; Kelsey, Douglas K.; LaMonte, Samuel J.; Michaelson, James S.; Oeffinger, Kevin C.; Shih, Ya-Chen Tina; Sullivan, Daniel C.; Travis, William; Walter, Louise; Wolf, Andrew M. D.; Brawley, Otis W.; Smith, Robert A.

2013-01-01

Findings from the National Cancer Institute’s National Lung Screening Trial established that lung cancer mortality in specific high-risk groups can be reduced by annual screening with low-dose computed tomography. These findings indicate that the adoption of lung cancer screening could save many lives. Based on the results of the National Lung Screening Trial, the American Cancer Society is issuing an initial guideline for lung cancer screening. This guideline recommends that clinicians with access to high-volume, high-quality lung cancer screening and treatment centers should initiate a discussion about screening with apparently healthy patients aged 55 years to 74 years who have at least a 30-pack-year smoking history and who currently smoke or have quit within the past 15 years. A process of informed and shared decision-making with a clinician related to the potential benefits, limitations, and harms associated with screening for lung cancer with low-dose computed tomography should occur before any decision is made to initiate lung cancer screening. Smoking cessation counseling remains a high priority for clinical attention in discussions with current smokers, who should be informed of their continuing risk of lung cancer. Screening should not be viewed as an alternative to smoking cessation. PMID:23315954

Use of an interactive tool to assess patients' willingness-to-pay.

PubMed

Matthews, D; Rocchi, A; Wang, E C; Gafni, A

2001-10-01

Assessment of willingness to pay (WTP) has become an important issue in health care technology assessment and in providing insight into the risks and benefits of treatment options. We have accordingly explored the use of an interactive method for assessment of WTP. To illustrate our methodology, we describe the development and testing of an interactive tool to administer a WTP survey in a dental setting. The tool was developed to measure patient preference and strength of preference for three dental anesthetic options in a research setting. It delivered written and verbal formats simultaneously, including information about the risks and benefits of treatment options, insurance, and user-based WTP scenarios and questions on previous dental experience. Clinical information was presented using a modified decision aid. Subjects could request additional clinical information and review this information throughout the survey. Information and question algorithms were individualized, depending on the subject's reported clinical status and previous responses. Initial pretesting resulted in substantial modifications to the initial tool: shortening the clinical information (by making more of it optional reading) and personalizing the text to more fully engage the user. In terms of results 196 general population subjects were recruited using random-digit dialing in southwestern Ontario, Canada. Comprehension was tested to ensure the instrument clearly conveyed the clinical information; the average score was 97%. Subjects rated the instrument as easy/very easy to use (99%), interesting/very interesting (91%), and neither long nor short (72.4%). Most subjects were comfortable/very comfortable with a computer (84%). Indirect evaluation revealed most subjects completed the survey in the expected time (30 min). Additional information was requested by 50% of subjects, an average of 2.9 times each. Most subjects wanted this type of information available in the provider's office for use in clinical decision making (92%). Despite extensive pretesting, three "bugs" remained undiscovered until live use. We have demonstrated that the detailed information, complex algorithms, and cognitively challenging questions involved in a WTP survey can be successfully administered using a tailor-made, patient-based, interactive computer tool. Key lessons regarding the use of such tools include allowing the user to set the pace of information flow and tailor the content, engaging the user by personalizing the textual information, inclusion of tests of comprehension and offering opportunities for correction, and pretesting by fully mimicking the live environment.

A Multimodal Search Engine for Medical Imaging Studies.

PubMed

Pinho, Eduardo; Godinho, Tiago; Valente, Frederico; Costa, Carlos

2017-02-01

The use of digital medical imaging systems in healthcare institutions has increased significantly, and the large amounts of data in these systems have led to the conception of powerful support tools: recent studies on content-based image retrieval (CBIR) and multimodal information retrieval in the field hold great potential in decision support, as well as for addressing multiple challenges in healthcare systems, such as computer-aided diagnosis (CAD). However, the subject is still under heavy research, and very few solutions have become part of Picture Archiving and Communication Systems (PACS) in hospitals and clinics. This paper proposes an extensible platform for multimodal medical image retrieval, integrated in an open-source PACS software with profile-based CBIR capabilities. In this article, we detail a technical approach to the problem by describing its main architecture and each sub-component, as well as the available web interfaces and the multimodal query techniques applied. Finally, we assess our implementation of the engine with computational performance benchmarks.

Hydrodynamics Analysis and CFD Simulation of Portal Venous System by TIPS and LS.

PubMed

Wang, Meng; Zhou, Hongyu; Huang, Yaozhen; Gong, Piyun; Peng, Bing; Zhou, Shichun

2015-06-01

In cirrhotic patients, portal hypertension is often associated with a hyperdynamic changes. Transjugular Intrahepatic Portosystemic Shunt (TIPS) and Laparoscopic splenectomy are both treatments for liver cirrhosis due to portal hypertension. While, the two different interventions have different effects on hemodynamics after operation and the possibilities of triggering PVT are different. How hemodynamics of portal vein system evolving with two different operations remain unknown. Based on ultrasound and established numerical methods, CFD technique is applied to analyze hemodynamic changes after TIPS and Laparoscopic splenectomy. In this paper, we applied two 3-D flow models to the hemodynamic analysis for two patients who received a TIPS and a laparoscopic splenectomy, both therapies for treating portal hypertension induced diseases. The current computer simulations give a quantitative analysis of the interplay between hemodynamics and TIPS or splenectomy. In conclusion, the presented computational model can be used for the theoretical analysis of TIPS and laparoscopic splenectomy, clinical decisions could be made based on the simulation results with personal properly treatment.