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Sample records for decision support systems clinical

  1. Decision time for clinical decision support systems.

    PubMed

    O'Sullivan, Dympna; Fraccaro, Paolo; Carson, Ewart; Weller, Peter

    2014-08-01

    Clinical decision support systems are interactive software systems designed to help clinicians with decision-making tasks, such as determining a diagnosis or recommending a treatment for a patient. Clinical decision support systems are a widely researched topic in the computer science community, but their inner workings are less well understood by, and known to, clinicians. This article provides a brief explanation of clinical decision support systems and some examples of real-world systems. It also describes some of the challenges to implementing these systems in clinical environments and posits some reasons for the limited adoption of decision-support systems in practice. It aims to engage clinicians in the development of decision support systems that can meaningfully help with their decision-making tasks and to open a discussion about the future of automated clinical decision support as a part of healthcare delivery. © 2014 Royal College of Physicians.

  2. Clinical decision support foundations.

    PubMed

    Pradhan, Malcolm; Liaw, Siaw Teng

    2010-01-01

    This chapter gives an educational overview of: * The elements of a clinical decision; * The elements of decision making: prior probability, evidence (likelihood), posterior probability, actions, utility (value); * A framework for decision making, and support, encompassing validity, utility, importance and certainty; and * The required elements of a clinical decision support system. * The role of knowledge management in the construction and maintenance of clinical decision support.

  3. Creating clinical decision support systems for respiratory medicine.

    PubMed

    Tams, Carl G; Euliano, Neil R

    2015-01-01

    Clinical decision support systems are vital for advances in improving patient therapeutic care. We share lessons learned from creating two respiratory clinical decisions support systems for ventilating patients in a critical care setting.

  4. Considerations for a successful clinical decision support system.

    PubMed

    Castillo, Ranielle S; Kelemen, Arpad

    2013-07-01

    Clinical decision support systems have the potential to improve patient care in a multitude of ways. Clinical decision support systems can aid in the reduction of medical errors and reduction in adverse drug events, ensure comprehensive treatment of patient illnesses and conditions, encourage the adherence to guidelines, shorten patient length of stay, and decrease expenses over time. A clinical decision support system is one of the key components for reaching compliance for Meaningful Use. In this article, the advantages, potential drawbacks, and clinical decision support system adoption barriers are discussed, followed by an in-depth review of the characteristics that make a clinical decision support system successful. The legal and ethical issues that come with the implementation of a clinical decision support system within an organization and the future expectations of clinical decision support system are reviewed.

  5. Clinical Decision Support Systems and Prevention

    PubMed Central

    Njie, Gibril J.; Proia, Krista K.; Thota, Anilkrishna B.; Finnie, Ramona K.C.; Hopkins, David P.; Banks, Starr M.; Callahan, David B.; Pronk, Nicolaas P.; Rask, Kimberly J.; Lackland, Daniel T.; Kottke, Thomas E.

    2016-01-01

    Context Clinical decision support systems (CDSSs) can help clinicians assess cardiovascular disease (CVD) risk and manage CVD risk factors by providing tailored assessments and treatment recommendations based on individual patient data. The goal of this systematic review was to examine the effectiveness of CDSSs in improving screening for CVD risk factors, practices for CVD-related preventive care services such as clinical tests and prescribed treatments, and management of CVD risk factors. Evidence acquisition An existing systematic review (search period, January 1975–January 2011) of CDSSs for any condition was initially identified. Studies of CDSSs that focused on CVD prevention in that review were combined with studies identified through an updated search (January 2011–October 2012). Data analysis was conducted in 2013. Evidence synthesis A total of 45 studies qualified for inclusion in the review. Improvements were seen for recommended screening and other preventive care services completed by clinicians, recommended clinical tests completed by clinicians, and recommended treatments prescribed by clinicians (median increases of 3.8, 4.0, and 2.0 percentage points, respectively). Results were inconsistent for changes in CVD risk factors such as systolic and diastolic blood pressure, total and low-density lipoprotein cholesterol, and hemoglobin A1C levels. Conclusions CDSSs are effective in improving clinician practices related to screening and other preventive care services, clinical tests, and treatments. However, more evidence is needed from implementation of CDSSs within the broad context of comprehensive service delivery aimed at reducing CVD risk and CVD-related morbidity and mortality. PMID:26477805

  6. The reliability of an epilepsy treatment clinical decision support system.

    PubMed

    Standridge, Shannon; Faist, Robert; Pestian, John; Glauser, Tracy; Ittenbach, Richard

    2014-10-01

    We developed a content validated computerized epilepsy treatment clinical decision support system to assist clinicians with selecting the best antiepilepsy treatments. Before disseminating our computerized epilepsy treatment clinical decision support system, further rigorous validation testing was necessary. As reliability is a precondition of validity, we verified proof of reliability first. We evaluated the consistency of the epilepsy treatment clinical decision support system in three areas including the preferred antiepilepsy drug choice, the top three recommended choices, and the rank order of the three choices. We demonstrated 100% reliability on 15,000 executions involving a three-step process on five different common pediatric epilepsy syndromes. Evidence for the reliability of the epilepsy treatment clinical decision support system was essential for the long-term viability of the system, and served as a crucial component for the next phase of system validation.

  7. 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.

  8. Semantic Interoperability in Clinical Decision Support Systems: A Systematic Review.

    PubMed

    Marco-Ruiz, Luis; Bellika, Johan Gustav

    2015-01-01

    The interoperability of Clinical Decision Support (CDS) systems with other health information systems has become one of the main limitations to their broad adoption. Semantic interoperability must be granted in order to share CDS modules across different health information systems. Currently, numerous standards for different purposes are available to enable the interoperability of CDS systems. We performed a literature review to identify and provide an overview of the available standards that enable CDS interoperability in the areas of clinical information, decision logic, terminology, and web service interfaces.

  9. Improving the implementation of clinical decision support systems.

    PubMed

    Rüping, Stefan; Anguita, Alberto; Bucur, Anca; Cirstea, Traian Cristian; Jacobs, Björn; Torge, Antje

    2013-01-01

    Clinical decision support (CDS) systems promise to improve the quality of clinical care by helping physicians to make better, more informed decisions efficiently. However, the design and testing of CDS systems for practical medical use is cumbersome. It has been recognized that this may easily lead to a problematic mismatch between the developers' idea of the system and requirements from clinical practice. In this paper, we will present an approach to reduce the complexity of constructing a CDS system. The approach is based on an ontological annotation of data resources, which improves standardization and the semantic processing of data. This, in turn, allows to use data mining tools to automatically create hypotheses for CDS models, which reduces the manual workload in the creation of a new model. The approach is implemented in the context of EU research project p-medicine. A proof of concept implementation on data from an existing Leukemia study is presented.

  10. Clinical Decision Support Systems for Comorbidity: Architecture, Algorithms, and Applications

    PubMed Central

    Fan, Aihua; Tang, Yu

    2017-01-01

    In this paper, we present the design of a clinical decision support system (CDSS) for monitoring comorbid conditions. Specifically, we address the architecture of a CDSS by characterizing it from three layers and discuss the algorithms in each layer. Also we address the applications of CDSSs in a few real scenarios and analyze the accuracy of a CDSS in consideration of the potential conflicts when using multiple clinical practice guidelines concurrently. Finally, we compare the system performance in our design with that in the other design schemes. Our study shows that our proposed design can achieve a clinical decision in a shorter time than the other designs, while ensuring a high level of system accuracy. PMID:28373881

  11. Using nursing clinical decision support systems to achieve meaningful use.

    PubMed

    Harrison, Roberta L; Lyerla, Frank

    2012-07-01

    The Health Information Technology and Clinical Health Act (one component of the American Recovery and Reinvestment Act) is responsible for providing incentive payments to hospitals and eligible providers in an effort to support the adoption of electronic health records. Future penalties are planned for electronic health record noncompliance. In order to receive incentives and avoid penalties, hospitals and eligible providers must demonstrate "meaningful use" of their electronic health records. One of the meaningful-use objectives established by the Centers for Medicare & Medicaid Services involves the use of a clinical decision support rule that addresses a hospital-defined, high-priority condition. This article describes the Plan-Do-Study-Act process for creating and implementing a nursing clinical decision support system designed to improve guideline adherence for hypoglycemia management. This project identifies hypoglycemia management as the high-priority area. However, other facilities with different high-priority conditions may find the process presented in this article useful for implementing additional clinical decision support rules geared toward improving outcomes and meeting federal mandates.

  12. Clinical decision support systems: data quality management and governance.

    PubMed

    Liaw, Siaw-Teng

    2013-01-01

    This chapter examines data quality management (DQM) and information governance (IG) of electronic decision support (EDS) systems so that they are safe and fit for use by clinicians and patients and their carers. This is consistent with the ISO definition of data quality as being fit for purpose. The scope of DQM & IG should range from data creation and collection in clinical settings, through cleaning and, where obtained from multiple sources, linkage, storage, use by the EDS logic engine and algorithms, knowledge base and guidance provided, to curation and presentation. It must also include protocols and mechanisms to monitor the safety of EDS, which will feedback into DQM & IG activities. Ultimately, DQM & IG must be integrated across the data cycle to ensure that the EDS systems provide guidance that leads to safe and effective clinical decisions and care.

  13. Clinical decision support for perioperative information management systems.

    PubMed

    Wanderer, Jonathan P; Ehrenfeld, Jesse M

    2013-12-01

    Clinical decision support (CDS) systems are being used to optimize the increasingly complex care that our health care system delivers. These systems have become increasingly important in the delivery of perioperative care for patients undergoing cardiac, thoracic, and vascular procedures. The adoption of perioperative information management systems (PIMS) has allowed these technologies to enter the operating room and support the clinical work flow of anesthesiologists and operational processes. Constructing effective CDS systems necessitates an understanding of operative work flow and technical considerations as well as achieving integration with existing information systems. In this review, we describe published examples of CDS for PIMS, including support for cardiopulmonary bypass separation physiological alarms, β-blocker guideline adherence, enhanced revenue capture for arterial line placement, and detection of hemodynamic monitoring gaps. Although these and other areas are amenable to CDS systems, the challenges of latency and data reliability represent fundamental limitations on the potential application of these tools to specific types of clinical issues. Ultimately, we expect that CDS will remain an important tool in our efforts to optimize the quality of care delivered.

  14. A Clinical Decision Support System for Breast Cancer Patients

    NASA Astrophysics Data System (ADS)

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

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

  15. Features of computerized clinical decision support systems supportive of nursing practice: a literature review.

    PubMed

    Lee, Seonah

    2013-10-01

    This study aimed to organize the system features of decision support technologies targeted at nursing practice into assessment, problem identification, care plans, implementation, and outcome evaluation. It also aimed to identify the range of the five stage-related sequential decision supports that computerized clinical decision support systems provided. MEDLINE, CINAHL, and EMBASE were searched. A total of 27 studies were reviewed. The system features collected represented the characteristics of each category from patient assessment to outcome evaluation. Several features were common across the reviewed systems. For the sequential decision support, all of the reviewed systems provided decision support in sequence for patient assessment and care plans. Fewer than half of the systems included problem identification. There were only three systems operating in an implementation stage and four systems in outcome evaluation. Consequently, the key steps for sequential decision support functions were initial patient assessment, problem identification, care plan, and outcome evaluation. Providing decision support in such a full scope will effectively help nurses' clinical decision making. By organizing the system features, a comprehensive picture of nursing practice-oriented computerized decision support systems was obtained; however, the development of a guideline for better systems should go beyond the scope of a literature review.

  16. Design Recommendations for Pharmacogenomics Clinical Decision Support Systems

    PubMed Central

    Khelifi, Maher; Tarczy-Hornoch, Peter; Devine, Emily B.; Pratt, Wanda

    2017-01-01

    The use of pharmacogenomics (PGx) in clinical practice still faces challenges to fully adopt genetic information in targeting drug therapy. To incorporate genetics into clinical practice, many support the use of Pharmacogenomics Clinical Decision Support Systems (PGx-CDS) for medication prescriptions. This support was fueled by new guidelines to incorporate genetics for optimizing drug dosage and reducing adverse events. In addition, the complexity of PGx led to exploring CDS outside the paradigm of the basic CDS tools embedded in commercial electronic health records. Therefore, designing the right CDS is key to unleashing the full potential of pharmacogenomics and making it a part of clinicians’ daily workflow. In this work, we 1) identify challenges and barriers of the implementation of PGx-CDS in clinical settings, 2) develop a new design approach to CDS with functional characteristics that can improve the adoption of pharmacogenomics guidelines and thus patient safety, and 3) create design guidelines and recommendations for such PGx-CDS tools. PMID:28815136

  17. Decision Support for Diabetes in Scotland: Implementation and Evaluation of a Clinical Decision Support System.

    PubMed

    Conway, Nicholas; Adamson, Karen A; Cunningham, Scott G; Emslie Smith, Alistair; Nyberg, Peter; Smith, Blair H; Wales, Ann; Wake, Deborah J

    2017-09-01

    Automated clinical decision support systems (CDSS) are associated with improvements in health care delivery to those with long-term conditions, including diabetes. A CDSS was introduced to two Scottish regions (combined diabetes population ~30 000) via a national diabetes electronic health record. This study aims to describe users' reactions to the CDSS and to quantify impact on clinical processes and outcomes over two improvement cycles: December 2013 to February 2014 and August 2014 to November 2014. Feedback was sought via patient questionnaires, health care professional (HCP) focus groups, and questionnaires. Multivariable regression was used to analyze HCP SCI-Diabetes usage (with respect to CDSS message presence/absence) and case-control comparison of clinical processes/outcomes. Cases were patients whose HCP received a CDSS messages during the study period. Closely matched controls were selected from regions outside the study, following similar clinical practice (without CDSS). Clinical process measures were screening rates for diabetes-related complications. Clinical outcomes included HbA1c at 1 year. The CDSS had no adverse impact on consultations. HCPs were generally positive toward CDSS and used it within normal clinical workflow. CDSS messages were generated for 5692 cases, matched to 10 667 controls. Following clinic, the probability of patients being appropriately screened for complications more than doubled for most measures. Mean HbA1c improved in cases and controls but more so in cases (-2.3 mmol/mol [-0.2%] versus -1.1 [-0.1%], P = .003). The CDSS was well received; associated with improved efficiencies in working practices; and large improvements in guideline adherence. These evidence-based, early interventions can significantly reduce costly and devastating complications.

  18. System-Agnostic Clinical Decision Support Services: Benefits and Challenges for Scalable Decision Support

    PubMed Central

    Kawamoto, Kensaku; Del Fiol, Guilherme; Orton, Charles; Lobach, David F

    2010-01-01

    System-agnostic clinical decision support (CDS) services provide patient evaluation capabilities that are independent of specific CDS systems and system implementation contexts. While such system-agnostic CDS services hold great potential for facilitating the widespread implementation of CDS systems, little has been described regarding the benefits and challenges of their use. In this manuscript, the authors address this need by describing potential benefits and challenges of using a system-agnostic CDS service. This analysis is based on the authors’ formal assessments of, and practical experiences with, various approaches to developing, implementing, and maintaining CDS capabilities. In particular, the analysis draws on the authors’ experience developing and leveraging a system-agnostic CDS Web service known as SEBASTIAN. A primary potential benefit of using a system-agnostic CDS service is the relative ease and flexibility with which the service can be leveraged to implement CDS capabilities across applications and care settings. Other important potential benefits include facilitation of centralized knowledge management and knowledge sharing; the potential to support multiple underlying knowledge representations and knowledge resources through a common service interface; improved simplicity and componentization; easier testing and validation; and the enabling of distributed CDS system development. Conversely, important potential challenges include the increased effort required to develop knowledge resources capable of being used in many contexts and the critical need to standardize the service interface. Despite these challenges, our experiences to date indicate that the benefits of using a system-agnostic CDS service generally outweigh the challenges of using this approach to implementing and maintaining CDS systems. PMID:21603281

  19. System-agnostic clinical decision support services: benefits and challenges for scalable decision support.

    PubMed

    Kawamoto, Kensaku; Del Fiol, Guilherme; Orton, Charles; Lobach, David F

    2010-01-01

    System-agnostic clinical decision support (CDS) services provide patient evaluation capabilities that are independent of specific CDS systems and system implementation contexts. While such system-agnostic CDS services hold great potential for facilitating the widespread implementation of CDS systems, little has been described regarding the benefits and challenges of their use. In this manuscript, the authors address this need by describing potential benefits and challenges of using a system-agnostic CDS service. This analysis is based on the authors' formal assessments of, and practical experiences with, various approaches to developing, implementing, and maintaining CDS capabilities. In particular, the analysis draws on the authors' experience developing and leveraging a system-agnostic CDS Web service known as SEBASTIAN. A primary potential benefit of using a system-agnostic CDS service is the relative ease and flexibility with which the service can be leveraged to implement CDS capabilities across applications and care settings. Other important potential benefits include facilitation of centralized knowledge management and knowledge sharing; the potential to support multiple underlying knowledge representations and knowledge resources through a common service interface; improved simplicity and componentization; easier testing and validation; and the enabling of distributed CDS system development. Conversely, important potential challenges include the increased effort required to develop knowledge resources capable of being used in many contexts and the critical need to standardize the service interface. Despite these challenges, our experiences to date indicate that the benefits of using a system-agnostic CDS service generally outweigh the challenges of using this approach to implementing and maintaining CDS systems.

  20. 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

  1. Clinical decision support system for the diagnosis of adolescence health.

    PubMed

    Moutsouri, Irene; Nikou, Amalia; Pampalou, Machi; Lentza, Maria; Spyridakis, Paulos; Mathiopoulou, Natassa; Konsoulas, Dimitris; Lampou, Marianna; Alexiou, Athanasios

    2015-01-01

    It is common that children confront psychological problems when they reach puberty. These problems could easily be overcome, but in many cases they could be severe, leading to social estrangement or worse in madness or death. According to information collected we designed a questionnaire about the psychology of adolescents in order to help people in that age or their elders find out if they have health issues. We used already published researches and material concerning all the psychological problems a child can confront in order to make a reliable questionnaire and to develop the clinical decision support system. Our main objective is to publish and administrate a web-based free tool for sharing medical knowledge about any psychological disease a child can already have or develop during puberty.

  2. Clinical Decision Support Capabilities of Commercially-available Clinical Information Systems

    PubMed Central

    Wright, Adam; Sittig, Dean F.; Ash, Joan S.; Sharma, Sapna; Pang, Justine E.; Middleton, Blackford

    2009-01-01

    Background The most effective decision support systems are integrated with clinical information systems, such as inpatient and outpatient electronic health records (EHRs) and computerized provider order entry (CPOE) systems. Purpose The goal of this project was to describe and quantify the results of a study of decision support capabilities in Certification Commission for Health Information Technology (CCHIT) certified electronic health record systems. Methods The authors conducted a series of interviews with representatives of nine commercially available clinical information systems, evaluating their capabilities against 42 different clinical decision support features. Results Six of the nine reviewed systems offered all the applicable event-driven, action-oriented, real-time clinical decision support triggers required for initiating clinical decision support interventions. Five of the nine systems could access all the patient-specific data items identified as necessary. Six of the nine systems supported all the intervention types identified as necessary to allow clinical information systems to tailor their interventions based on the severity of the clinical situation and the user's workflow. Only one system supported all the offered choices identified as key to allowing physicians to take action directly from within the alert. Discussion The principal finding relates to system-by-system variability. The best system in our analysis had only a single missing feature (from 42 total) while the worst had eighteen.This dramatic variability in CDS capability among commercially available systems was unexpected and is a cause for concern. Conclusions These findings have implications for four distinct constituencies: purchasers of clinical information systems, developers of clinical decision support, vendors of clinical information systems and certification bodies. PMID:19567796

  3. Clinical Decision Support Systems (CDSS) in GRID Environments.

    PubMed

    Blanquer, Ignacio; Hernández, Vicente; Segrelles, Damià; Robles, Montserrat; García, Juan Miguel; Robledo, Javier Vicente

    2005-01-01

    This paper presents an architecture defined for searching and executing Clinical Decision Support Systems (CDSS) in a LCG2/GT2 Grid environment, using web-based protocols. A CDSS is a system that provides a classification of the patient illness according to the knowledge extracted from clinical practice and using the patient's information in a structured format. The CDSS classification engines can be installed in any site and can be used by different medical users from a Virtual Organization (VO). All users in a VO can consult and execute different classification engines that have been installed in the Grid independently of the platform, architecture or site where the engines are installed or the users are located. The present paper present a solution to requirements such as short-job execution, reducing the response delay on LCG2 environments and providing grid-enabled authenticated access through web portals. Resource discovering and job submission is performed through web services, which are also described in the article.

  4. [Knowledge management system for laboratory work and clinical decision support].

    PubMed

    Inada, Masanori; Sato, Mayumi; Yoneyama, Akiko

    2011-05-01

    This paper discusses a knowledge management system for clinical laboratories. In the clinical laboratory of Toranomon Hospital, we receive about 20 questions relevant to laboratory tests per day from medical doctors or co-medical staff. These questions mostly involve the essence to appropriately accomplish laboratory tests. We have to answer them carefully and suitably because an incorrect answer may cause a medical accident. Up to now, no method has been in place to achieve a rapid response and standardized answers. For this reason, the laboratory staff have responded to various questions based on their individual knowledge. We began to develop a knowledge management system to promote the knowledge of staff working for the laboratory. This system is a type of knowledge base for assisting the work, such as inquiry management, laboratory consultation, process management, and clinical support. It consists of several functions: guiding laboratory test information, managing inquiries from medical staff, reporting results of patient consultation, distributing laboratory staffs notes, and recording guidelines for laboratory medicine. The laboratory test information guide has 2,000 records of medical test information registered in the database with flexible retrieval. The inquiry management tool provides a methos to record all questions, answer easily, and retrieve cases. It helps staff to respond appropriately in a short period of time. The consulting report system treats patients' claims regarding medical tests. The laboratory staffs notes enter a file management system so they can be accessed to aid in clinical support. Knowledge sharing using this function can achieve the transition from individual to organizational learning. Storing guidelines for laboratory medicine will support EBM. Finally, it is expected that this system will support intellectual activity concerning laboratory work and contribute to the practice of knowledge management for clinical work support.

  5. Assessing the sensibility of two clinical decision support systems.

    PubMed

    Graham, Timothy A D; Bullard, Michael J; Kushniruk, Andre W; Holroyd, Brian R; Rowe, Brian H

    2008-10-01

    Clinicians in Emergency Medicine (EM) are increasingly exposed to guidelines and treatment recommendations. To help access and recall these recommendations, electronic Clinical Decision Support Systems (CDSS) have been developed. This study examined the use and sensibility of two CDSS designed for emergency physicians. CDDS for community acquired pneumonia (CAP) and neutropenic fever (NF) were developed by multidisciplinary teams and have been accessed via an intranet-based homepage (eCPG) for several years. Sensibility is a term coined by Feinstein that describes common sense aspects of a survey instrument. It was modified by emergency researchers to include four main headings: (1) Appropriateness; (2) Objectivity; (3) Content; and (4) Discriminative Power. Sensibility surveys were developed using an iterative approach for both the CAP and NF CDSS and distributed to all 25 emergency physicians at one Canadian site. The overall response rate was 88%. Respondents were 88% male and 83% were less than 40; all were attending EM physicians with specialty designations. A number reported never having used the CAP (21%) or NF (33%) CDSS; 54% (CAP) and 21% (NF) of respondents had used the respective CDSS less than 10 times. Overall, both CDSS were rated highly by users with a mean response of 4.95 (SD 0.56) for CAP and 5.62 (SD 0.62) for NF on a seven-point Likert scale. The majority or respondents (CAP 59%, NF 80%) felt that the NF CDSS was more likely than the CAP CDSS to decrease the chances of making a medical error in medication dose, antibiotic choice or patient disposition (4.61 vs. 5.81, p=0.008). Despite being in place for several years, CDSS for CAP and NF are not used by all EM clinicians. Users were generally satisfied with the CDSS and felt that the NF was more likely than the CAP CDSS to decrease medical errors. Additional research is required to determine the barriers to CDSS use.

  6. Clinical Decision Support Systems for the Practice of Evidence-based Medicine

    PubMed Central

    Sim, Ida; Gorman, Paul; Greenes, Robert A.; Haynes, R. Brian; Kaplan, Bonnie; Lehmann, Harold; Tang, Paul C.

    2001-01-01

    Background: The use of clinical decision support systems to facilitate the practice of evidence-based medicine promises to substantially improve health care quality. Objective: To describe, on the basis of the proceedings of the Evidence and Decision Support track at the 2000 AMIA Spring Symposium, the research and policy challenges for capturing research and practice-based evidence in machine-interpretable repositories, and to present recommendations for accelerating the development and adoption of clinical decision support systems for evidence-based medicine. Results: The recommendations fall into five broad areas—capture literature-based and practice-based evidence in machine-interpretable knowledge bases; develop maintainable technical and methodological foundations for computer-based decision support; evaluate the clinical effects and costs of clinical decision support systems and the ways clinical decision support systems affect and are affected by professional and organizational practices; identify and disseminate best practices for work flow–sensitive implementations of clinical decision support systems; and establish public policies that provide incentives for implementing clinical decision support systems to improve health care quality. Conclusions: Although the promise of clinical decision support system–facilitated evidence-based medicine is strong, substantial work remains to be done to realize the potential benefits. PMID:11687560

  7. A Scalable Architecture for Rule Engine Based Clinical Decision Support Systems.

    PubMed

    Chattopadhyay, Soumi; Banerjee, Ansuman; Banerjee, Nilanjan

    2015-01-01

    Clinical Decision Support systems (CDSS) have reached a fair level of sophistication and have emerged as the popular system of choice for their aid in clinical decision making. These decision support systems are based on rule engines navigate through a repertoire of clinical rules and multitudes of facts to assist a clinical expert to decide on the set of actuations in response to a medical situation. In this paper, we present the design of a scalable architecture for a rule engine based clinical decision system.

  8. Issues of trust and ethics in computerized clinical decision support systems.

    PubMed

    Alexander, Gregory L

    2006-01-01

    Clinical decision support systems are computer technologies that model and provide support for human decision-making processes. Decision support mechanisms facilitate and enhance a clinician's ability to make decisions at the point of care. Decisions are facilitated through technology by using automated mechanisms that provide alerts or messages to clinicians about a potential patient problem. A clinician's level of trust in these technologies to support decision making is affected by how knowledge is represented in these tools, their ability to make reasonable decisions, and how they are designed. Furthermore, ethical tensions occur if these systems do not promote standards, if clinicians do not understand how to use these systems, and when professional relationships are affected. Issues of trust and ethical concerns will be examined in this article, using a research study of midwestern nursing homes that implemented a clinical decision support system.

  9. 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.

  10. [International outcomes from attempts to implement a clinical decision support system in gastroenterology].

    PubMed

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

    2011-01-01

    This study aimed at describing the recent experience acquired with the implementation and use of clinical decision support system in gastroenterology in order to determine the level of development, tests used and advantages that such a system can offer to the medical practice. A search in the PubMed, LILACS and ISI Web of Knowledge databases for studies in decision-making support systems in gastroenterology including original papers produced from 2005 to 2010 was performed. A total of 104 scientific papers were retrieved initially. These were analyzed using inclusion and exclusion criteria, thus yielding nine studies for further analysis. The clinical decision support system analyzed in the present study showed a great variety of clinical problems regarding the investigation of a disease and the determination of a diagnosis. Eighty-nine per cent of the studies showed experimental models for clinical decision support system development. Seventy-eight per cent of the studies described the outcomes obtained with artificial intelligence technique. Two studies compared the clinical decision support system performance with that of a doctor, and only one research work described a controlled study evidencing improvements in the medical practice. The studies analyzed showed evidence of potential benefits that clinical decision support system can bring to the clinical practice. However, further controlled studies performed in medical day-to-day conditions and environment should be performed in order to provide more clear evidence of the usefulness of clinical decision support system in the medical practice.

  11. Modeling information flows in clinical decision support: key insights for enhancing system effectiveness.

    PubMed

    Medlock, Stephanie; Wyatt, Jeremy C; Patel, Vimla L; Shortliffe, Edward H; Abu-Hanna, Ameen

    2016-09-01

    A fundamental challenge in the field of clinical decision support is to determine what characteristics of systems make them effective in supporting particular types of clinical decisions. However, we lack such a theory of decision support itself and a model to describe clinical decisions and the systems to support them. This article outlines such a framework. We present a two-stream model of information flow within clinical decision-support systems (CDSSs): reasoning about the patient (the clinical stream), and reasoning about the user (the cognitive-behavioral stream). We propose that CDSS "effectiveness" be measured not only in terms of a system's impact on clinical care, but also in terms of how (and by whom) the system is used, its effect on work processes, and whether it facilitates appropriate decisions by clinicians and patients. Future research into which factors improve the effectiveness of decision support should not regard CDSSs as a single entity, but should instead differentiate systems based on their attributes, users, and the decision being supported. © The Author 2016. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

  12. Can computerized clinical decision support systems improve diabetes management? A systematic review and meta-analysis.

    PubMed

    Jeffery, R; Iserman, E; Haynes, R B

    2013-06-01

    To systematically review randomized trials that assessed the effects of computerized clinical decision support systems in ambulatory diabetes management compared with a non-computerized clinical decision support system control. We included all diabetes trials from a comprehensive computerized clinical decision support system overview completed in January 2010, and searched EMBASE, MEDLINE, INSPEC/COMPENDEX and Evidence-Based Medicine Reviews (EBMR) from January 2010 to April 2012. Reference lists of related reviews, included articles and Clinicaltrials.gov were also searched. Randomized controlled trials of patients with diabetes in ambulatory care settings comparing a computerized clinical decision support system intervention with a non-computerized clinical decision support system control, measuring either a process of care or a patient outcome, were included. Screening of studies, data extraction, risk of bias and quality of evidence assessments were carried out independently by two reviewers, and discrepancies were resolved through consensus or third-party arbitration. Authors were contacted for any missing data. Fifteen trials were included (13 from the previous review and two from the current search). Only one study was at low risk of bias, while the others were of moderate to high risk of bias because of methodological limitations. HbA1c (3 months' follow-up), quality of life and hospitalization (12 months' follow-up) were pooled and all favoured the computerized clinical decision support systems over the control, although none were statistically significant. Triglycerides and practitioner performance tended to favour computerized clinical decision support systems although results were too heterogeneous to pool. Computerized clinical decision support systems in diabetes management may marginally improve clinical outcomes, but confidence in the evidence is low because of risk of bias, inconsistency and imprecision. © 2012 The Authors. Diabetic Medicine

  13. Clinical decision modeling system

    PubMed Central

    Shi, Haiwen; Lyons-Weiler, James

    2007-01-01

    Background Decision analysis techniques can be applied in complex situations involving uncertainty and the consideration of multiple objectives. Classical decision modeling techniques require elicitation of too many parameter estimates and their conditional (joint) probabilities, and have not therefore been applied to the problem of identifying high-performance, cost-effective combinations of clinical options for diagnosis or treatments where many of the objectives are unknown or even unspecified. Methods We designed a Java-based software resource, the Clinical Decision Modeling System (CDMS), to implement Naïve Decision Modeling, and provide a use case based on published performance evaluation measures of various strategies for breast and lung cancer detection. Because cost estimates for many of the newer methods are not yet available, we assume equal cost. Our use case reveals numerous potentially high-performance combinations of clinical options for the detection of breast and lung cancer. Results Naïve Decision Modeling is a highly practical applied strategy which guides investigators through the process of establishing evidence-based integrative translational clinical research priorities. CDMS is not designed for clinical decision support. Inputs include performance evaluation measures and costs of various clinical options. The software finds trees with expected emergent performance characteristics and average cost per patient that meet stated filtering criteria. Key to the utility of the software is sophisticated graphical elements, including a tree browser, a receiver-operator characteristic surface plot, and a histogram of expected average cost per patient. The analysis pinpoints the potentially most relevant pairs of clinical options ('critical pairs') for which empirical estimates of conditional dependence may be critical. The assumption of independence can be tested with retrospective studies prior to the initiation of clinical trials designed to

  14. Medication-related Clinical Decision Support in Computerized Provider Order Entry Systems: A Review

    PubMed Central

    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. PMID:17068355

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

    ERIC Educational Resources Information Center

    Wolfenden, Andrew

    2012-01-01

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

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

    ERIC Educational Resources Information Center

    Wolfenden, Andrew

    2012-01-01

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

  17. The Effects of Computerized Clinical Decision Support Systems on Laboratory Test Ordering: A Systematic Review.

    PubMed

    Delvaux, Nicolas; Van Thienen, Katrien; Heselmans, Annemie; de Velde, Stijn Van; Ramaekers, Dirk; Aertgeerts, Bert

    2017-04-01

    - Inappropriate laboratory test ordering has been shown to be as high as 30%. This can have an important impact on quality of care and costs because of downstream consequences such as additional diagnostics, repeat testing, imaging, prescriptions, surgeries, or hospital stays. - To evaluate the effect of computerized clinical decision support systems on appropriateness of laboratory test ordering. - We used MEDLINE, Embase, CINAHL, MEDLINE In-Process and Other Non-Indexed Citations, Clinicaltrials.gov, Cochrane Library, and Inspec through December 2015. Investigators independently screened articles to identify randomized trials that assessed a computerized clinical decision support system aimed at improving laboratory test ordering by providing patient-specific information, delivered in the form of an on-screen management option, reminder, or suggestion through a computerized physician order entry using a rule-based or algorithm-based system relying on an evidence-based knowledge resource. Investigators extracted data from 30 papers about study design, various study characteristics, study setting, various intervention characteristics, involvement of the software developers in the evaluation of the computerized clinical decision support system, outcome types, and various outcome characteristics. - Because of heterogeneity of systems and settings, pooled estimates of effect could not be made. Data showed that computerized clinical decision support systems had little or no effect on clinical outcomes but some effect on compliance. Computerized clinical decision support systems targeted at laboratory test ordering for multiple conditions appear to be more effective than those targeted at a single condition.

  18. Improving clinical practice using clinical decision support systems: a systematic review of trials to identify features critical to success

    PubMed Central

    Kawamoto, Kensaku; Houlihan, Caitlin A; Balas, E Andrew; Lobach, David F

    2005-01-01

    Objective To identify features of clinical decision support systems critical for improving clinical practice. Design Systematic review of randomised controlled trials. Data sources Literature searches via Medline, CINAHL, and the Cochrane Controlled Trials Register up to 2003; and searches of reference lists of included studies and relevant reviews. Study selection Studies had to evaluate the ability of decision support systems to improve clinical practice. Data extraction Studies were assessed for statistically and clinically significant improvement in clinical practice and for the presence of 15 decision support system features whose importance had been repeatedly suggested in the literature. Results Seventy studies were included. Decision support systems significantly improved clinical practice in 68% of trials. Univariate analyses revealed that, for five of the system features, interventions possessing the feature were significantly more likely to improve clinical practice than interventions lacking the feature. Multiple logistic regression analysis identified four features as independent predictors of improved clinical practice: automatic provision of decision support as part of clinician workflow (P < 0.00001), provision of recommendations rather than just assessments (P = 0.0187), provision of decision support at the time and location of decision making (P = 0.0263), and computer based decision support (P = 0.0294). Of 32 systems possessing all four features, 30 (94%) significantly improved clinical practice. Furthermore, direct experimental justification was found for providing periodic performance feedback, sharing recommendations with patients, and requesting documentation of reasons for not following recommendations. Conclusions Several features were closely correlated with decision support systems' ability to improve patient care significantly. Clinicians and other stakeholders should implement clinical decision support systems that incorporate these

  19. Evaluating the use of a computerized clinical decision support system for asthma by pediatric pulmonologists.

    PubMed

    Lomotan, Edwin A; Hoeksema, Laura J; Edmonds, Diana E; Ramírez-Garnica, Gabriela; Shiffman, Richard N; Horwitz, Leora I

    2012-03-01

    To investigate use of a new guideline-based, computerized clinical decision support (CCDS) system for asthma in a pediatric pulmonology clinic of a large academic medical center. We conducted a qualitative evaluation including review of electronic data, direct observation, and interviews with all nine pediatric pulmonologists in the clinic. Outcome measures included patterns of computer use in relation to patient care, and themes surrounding the relationship between asthma care and computer use. The pediatric pulmonologists entered enough data to trigger the decision support system in 397/445 (89.2%) of all asthma visits from January 2009 to May 2009. However, interviews and direct observations revealed use of the decision support system was limited to documentation activities after clinic sessions ended. Reasons for delayed use reflected barriers common to general medical care and barriers specific to subspecialty care. Subspecialist-specific barriers included the perceived high complexity of patients, the impact of subject matter expertise on the types of decision support needed, and unique workflow concerns such as the need to create letters to referring physicians. Pediatric pulmonologists demonstrated low use of a computerized decision support system for asthma care because of a combination of general and subspecialist-specific factors. Subspecialist-specific factors should not be underestimated when designing guideline-based, computerized decision support systems for the subspecialty setting. Copyright © 2011 Elsevier Ireland Ltd. All rights reserved.

  20. Implementing an integrative multi-agent clinical decision support system with open source software.

    PubMed

    Sayyad Shirabad, Jelber; Wilk, Szymon; Michalowski, Wojtek; Farion, Ken

    2012-02-01

    Clinical decision making is a complex multi-stage process. Decision support can play an important role at each stage of this process. At present, the majority of clinical decision support systems have been focused on supporting only certain stages. In this paper we present the design and implementation of MET3-a prototype multi-agent system providing an integrative decision support that spans over the entire decision making process. The system helps physicians with data collection, diagnosis formulation, treatment planning and finding supporting evidence. MET3 integrates with external hospital information systems via HL7 messages and runs on various computing platforms available at the point of care (e.g., tablet computers, mobile phones). Building MET3 required sophisticated and reliable software technologies. In the past decade the open source software movement has produced mature, stable, industrial strength software systems with a large user base. Therefore, one of the decisions that should be considered before developing or acquiring a decision support system is whether or not one could use open source technologies instead of proprietary ones. We believe MET3 shows that the answer to this question is positive.

  1. A clinical recruiting management system for complex multi-site clinical trials using qualification decision support systems.

    PubMed

    Vahabzadeh, Massoud; Lin, Jia-Ling; Mezghanni, Mustapha; Contoreggi, Carlo; Leff, Michelle

    2007-10-11

    A clinical recruiting management system with qualification decision support systems was developed to increase the efficiency of screening and evaluation of participants during a recruiting process whereby recruiting for various protocols are conducted at multiple sites by different groups with process interdependencies. This system is seamlessly integrated into our enterprise-scale Human Research Information System (HuRIS), encompassing research participants' electronic health records (EHR), with real-time access to the clinical trial data.

  2. Integrating computerized clinical decision support systems into clinical work: A meta-synthesis of qualitative research.

    PubMed

    Miller, Anne; Moon, Brian; Anders, Shilo; Walden, Rachel; Brown, Steven; Montella, Diane

    2015-12-01

    Computerized clinical decision support systems (CDSS) are an emerging means for improving healthcare safety, quality and efficiency, but meta-analyses findings are mixed. This meta-synthesis aggregates qualitative research findings as possible explanations for variable quantitative research outcomes. Qualitative studies published between 2000 and 2013 in English, involving physicians, registered and advanced practice nurses' experience of CDSS use in clinical practice were included. PubMed and CINAHL databases were searched. Study titles and abstracts were screened against inclusion criteria. Retained studies were appraised against quality criteria. Findings were extracted iteratively from studies in the 4th quartile of quality scores. Two reviewers constructed themes inductively. A third reviewer applied the defined themes deductively achieving 92% agreement. 3798 unique records were returned; 56 met inclusion criteria and were reviewed against quality criteria. 9 studies were of sufficiently high quality for synthetic analysis. Five major themes (clinician-patient-system integration; user interface usability; the need for better 'algorithms'; system maturity; patient safety) were defined. Despite ongoing development, CDSS remains an emerging technology. Lack of understanding about and lack of consideration for the interaction between human decision makers and CDSS is a major reason for poor system adoption and use. Further high-quality qualitative research is needed to better understand human-system interaction issues. These issues may continue to confound quantitative study results if not addressed. Copyright © 2015. Published by Elsevier Ireland Ltd.

  3. Building a normative decision support system for clinical and operational risk management in hemodialysis.

    PubMed

    Cornalba, Chiara; Bellazzi, Roberto G; Bellazzi, Riccardo

    2008-09-01

    This paper describes the design and implementation of a decision support system for risk management in hemodialysis (HD) departments. The proposed system exploits a domain ontology to formalize the problem as a Bayesian network. It also relies on a software tool, able to automatically collect HD data, to learn the network conditional probabilities. By merging prior knowledge and the available data, the system allows to estimate risk profiles both for patients and HD departments. The risk management process is completed by an influence diagram that enables scenario analysis to choose the optimal decisions that mitigate a patient's risk. The methods and design of the decision support tool are described in detail, and the derived decision model is presented. Examples and case studies are also shown. The tool is one of the few examples of normative system explicitly conceived to manage operational and clinical risks in health care environments.

  4. The Use of Automated SNOMED CT Clinical Coding in Clinical Decision Support Systems for Preventive Care.

    PubMed

    Al-Hablani, Bader

    2017-01-01

    The objective of this study is to discuss and analyze the use of automated SNOMED CT clinical coding in clinical decision support systems (CDSSs) for preventive care. The central question that this study seeks to answer is whether the utilization of SNOMED CT in CDSSs can improve preventive care. PubMed, Google Scholar, and Cochrane Library were searched for articles published in English between 2001 and 2012 on SNOMED CT, CDSS, and preventive care. Outcome measures were the sensitivity or specificity of SNOMED CT coded data and the positive predictive value or negative predictive value of SNOMED CT coded data. Additionally, we documented the publication year, research question, study design, results, and conclusions of these studies. The reviewed studies suggested that SNOMED CT successfully represents clinical terms and negated clinical terms. The use of SNOMED CT in CDSS can be considered to provide an answer to the problem of medical errors as well as for preventive care in general. Enhancement of the modifiers and synonyms found in SNOMED CT will be necessary to improve the expected outcome of the integration of SNOMED CT with CDSS. Moreover, the application of the tree-augmented naïve (TAN) Bayesian network method can be considered the best technique to search SNOMED CT data and, consequently, to help improve preventive health services.

  5. The Use of Automated SNOMED CT Clinical Coding in Clinical Decision Support Systems for Preventive Care

    PubMed Central

    Al-Hablani, Bader

    2017-01-01

    Objective The objective of this study is to discuss and analyze the use of automated SNOMED CT clinical coding in clinical decision support systems (CDSSs) for preventive care. The central question that this study seeks to answer is whether the utilization of SNOMED CT in CDSSs can improve preventive care. Method PubMed, Google Scholar, and Cochrane Library were searched for articles published in English between 2001 and 2012 on SNOMED CT, CDSS, and preventive care. Outcome Measures Outcome measures were the sensitivity or specificity of SNOMED CT coded data and the positive predictive value or negative predictive value of SNOMED CT coded data. Additionally, we documented the publication year, research question, study design, results, and conclusions of these studies. Results The reviewed studies suggested that SNOMED CT successfully represents clinical terms and negated clinical terms. Conclusion The use of SNOMED CT in CDSS can be considered to provide an answer to the problem of medical errors as well as for preventive care in general. Enhancement of the modifiers and synonyms found in SNOMED CT will be necessary to improve the expected outcome of the integration of SNOMED CT with CDSS. Moreover, the application of the tree-augmented naïve (TAN) Bayesian network method can be considered the best technique to search SNOMED CT data and, consequently, to help improve preventive health services. PMID:28566995

  6. Tactical Decision Making and Decision Support Systems.

    ERIC Educational Resources Information Center

    Harmon, Joel I.

    1986-01-01

    The use of computerized decision support systems in higher education for making tactical institutional decisions is reviewed, with attention to the kind of administrative problems that lie somewhere between programmed to nonprogrammed decisions and require a combination of computer support and administrative judgment. (MSE)

  7. 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.

  8. Pharmacists' awareness of clinical decision support in pharmacy information systems: an exploratory evaluation.

    PubMed

    Hines, Lisa E; Saverno, Kim R; Warholak, Terri L; Taylor, Ann; Grizzle, Amy J; Murphy, John E; Malone, Daniel C

    2011-12-01

    Clinical decision support (CDS), such as drug-drug interaction (DDI) and drug-allergy checking, has been used in pharmacy information systems for several decades; however, there has been limited research on CDS use by practicing pharmacists. The purpose of this study was to document pharmacists' awareness of DDI and other medication-related CDS features available within pharmacy information systems. Researchers conducted on-site interviews with pharmacists throughout the state of Arizona from December 2008 to November 2009 regarding their pharmacy information systems features. Pharmacists were asked to provide information about DDI and other medication-related decision support features of the pharmacy software at their practice site. Descriptive statistics were used to summarize interview responses. Sixty-one pharmacists from a variety of practice settings completed the interview. All respondents indicated that their pharmacy system provided drug-allergy and DDI alerts. Approximately 60% of the pharmacists reported that their DDI decision support systems included recommendations for managing drug interactions. Two-thirds of respondents reported that their pharmacy's computer system permitted the addition of medications from other pharmacies and/or over-the-counter products to a patient's profile. Approximately 40% of the pharmacists reported that some drugs entered into the pharmacy computer system were not included in (or linked to) the electronic DDI checking. Most pharmacists indicated the presence of other medication-related decision support features, such as drug-disease (78%), drug-age precautions (67%), and inappropriate dosage alerts (79%). However, fewer pharmacists reported more advanced functionality, such as laboratory recommendations (34%) and pediatric dosing (39%). Overall, pharmacists' awareness regarding the many decision support functionalities of their systems was limited. Based on the study findings, it appears that there are a number of

  9. Mobile clinical decision support systems and applications: a literature and commercial review.

    PubMed

    Martínez-Pérez, Borja; de la Torre-Díez, Isabel; López-Coronado, Miguel; Sainz-de-Abajo, Beatriz; Robles, Montserrat; García-Gómez, Juan Miguel

    2014-01-01

    The latest advances in eHealth and mHealth have propitiated the rapidly creation and expansion of mobile applications for health care. One of these types of applications are the clinical decision support systems, which nowadays are being implemented in mobile apps to facilitate the access to health care professionals in their daily clinical decisions. The aim of this paper is twofold. Firstly, to make a review of the current systems available in the literature and in commercial stores. Secondly, to analyze a sample of applications in order to obtain some conclusions and recommendations. Two reviews have been done: a literature review on Scopus, IEEE Xplore, Web of Knowledge and PubMed and a commercial review on Google play and the App Store. Five applications from each review have been selected to develop an in-depth analysis and to obtain more information about the mobile clinical decision support systems. Ninety-two relevant papers and 192 commercial apps were found. Forty-four papers were focused only on mobile clinical decision support systems. One hundred seventy-one apps were available on Google play and 21 on the App Store. The apps are designed for general medicine and 37 different specialties, with some features common in all of them despite of the different medical fields objective. The number of mobile clinical decision support applications and their inclusion in clinical practices has risen in the last years. However, developers must be careful with their interface or the easiness of use, which can impoverish the experience of the users.

  10. Design and application of a generic clinical decision support system for multiscale data.

    PubMed

    Mattila, Jussi; Koikkalainen, Juha; Virkki, Arho; van Gils, Mark; Lötjönen, Jyrki

    2012-01-01

    Medical research and clinical practice are currently being redefined by the constantly increasing amounts of multiscale patient data. New methods are needed to translate them into knowledge that is applicable in healthcare. Multiscale modeling has emerged as a way to describe systems that are the source of experimental data. Usually, a multiscale model is built by combining distinct models of several scales, integrating, e.g., genetic, molecular, structural, and neuropsychological models into a composite representation. We present a novel generic clinical decision support system, which models a patient's disease state statistically from heterogeneous multiscale data. Its goal is to aid in diagnostic work by analyzing all available patient data and highlighting the relevant information to the clinician. The system is evaluated by applying it to several medical datasets and demonstrated by implementing a novel clinical decision support tool for early prediction of Alzheimer's disease.

  11. Multi-site evaluation of a clinical decision support system for radiation therapy

    NASA Astrophysics Data System (ADS)

    Deshpande, Ruchi; DeMarco, John; Kessel, Kerstin; Liu, Brent J.

    2016-03-01

    We have developed an imaging informatics based decision support system that learns from retrospective treatment plans to provide recommendations for healthy tissue sparing to prospective incoming patients. This system incorporates a model of best practices from previous cases, specific to tumor anatomy. Ultimately, our hope is to improve clinical workflow efficiency, patient outcomes and to increase clinician confidence in decision-making. The success of such a system depends greatly on the training dataset, which in this case, is the knowledge base that the data-mining algorithm employs. The size and heterogeneity of the database is essential for good performance. Since most institutions employ standard protocols and practices for treatment planning, the diversity of this database can be greatly increased by including data from different institutions. This work presents the results of incorporating cross-country, multi-institutional data into our decision support system for evaluation and testing.

  12. A Web-Based Clinical Decision Support System for Depression Care Management

    PubMed Central

    Fortney, John C.; Pyne, Jeffrey M.; Steven, Christopher A.; Williams, J. Silas; Hedrick, Richard G.; Lunsford, Amanda K.; Raney, William N.; Ackerman, Betty A.; Ducker, Loretta O.; Bonner, Laura M.; Smith, Jeffrey L.

    2012-01-01

    Objective To inform the design of future informatics systems that support the chronic care model. Study Design We describe the development and functionality of a decision support system for the chronic care model of depression treatment, known as collaborative care. Dissemination of evidence-based collaborative care models has been slow, and fidelity to the evidence base has been poor during implementation initiatives. Implementation could be facilitated by a decision support system for depression care managers, the cornerstone of the collaborative care model. The Net Decision Support System (https://www.netdss.net/) is a free Web-based system that was developed to support depression care manager activities and to facilitate the dissemination of collaborative care models that maintain high fidelity to the evidence base. Methods The NetDSS was based on intervention materials used for a randomized trial of depression care management that improved clinical outcomes compared with usual care. The NetDSS was developed jointly by a cross-functional design team of psychiatrists, depression care managers, information technology specialists, technical writers, and researchers. Results The NetDSS has the following functional capabilities: patient registry, patient encounter scheduler, trial management, clinical decision support, progress note generator, and workload and outcomes report generator. The NetDSS guides the care manager through a self-documenting patient encounter using evidence-based scripts and self-scoring instruments. The NetDSS has been used to provide evidence-based depression care management to more than 1700 primary care patients. Conclusion Intervention protocols can be successfully converted to Web-based decision support systems that facilitate the implementation of evidence-based chronic care models into routine care with high fidelity. PMID:21348556

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

    PubMed

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

    2014-11-28

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

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

    PubMed Central

    2014-01-01

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

  15. Distributing knowledge maintenance for clinical decision-support systems: the "knowledge library" model.

    PubMed

    Geissbuhler, A; Miller, R A

    1999-01-01

    The maintenance of knowledge-rich clinical decision-support systems is challenging, in particular in the complex setting of a large academic medical center. Distributing the maintenance tasks to the source of expertise can address scalability, accuracy and currency issues. It also helps to foster a more global sense of ownership among the system users. The knowledge maintenance model must provide processes and tools to deal with a wide range of stakeholders (resident and attending physicians, consulting specialists, other care providers, case managers, ancillary departments), with knowledge embedded in legacy departmental systems, and with the continuous evolution of the content and form of the knowledge base. We describe and illustrate the "knowledge library" model in use at Vanderbilt University Medical Center for the distributed maintenance of the integrated knowledge base that drives the WizOrder clinical decision-support, physician order entry, and notes capture system.

  16. 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.

  17. 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

  18. Integrating complex business processes for knowledge-driven clinical decision support systems.

    PubMed

    Kamaleswaran, Rishikesan; McGregor, Carolyn

    2012-01-01

    This paper presents in detail the component of the Complex Business Process for Stream Processing framework that is responsible for integrating complex business processes to enable knowledge-driven Clinical Decision Support System (CDSS) recommendations. CDSSs aid the clinician in supporting the care of patients by providing accurate data analysis and evidence-based recommendations. However, the incorporation of a dynamic knowledge-management system that supports the definition and enactment of complex business processes and real-time data streams has not been researched. In this paper we discuss the process web service as an innovative method of providing contextual information to a real-time data stream processing CDSS.

  19. The perils of meta-regression to identify clinical decision support system success factors.

    PubMed

    Fillmore, Christopher L; Rommel, Casey A; Welch, Brandon M; Zhang, Mingyuan; Kawamoto, Kensaku

    2015-08-01

    Clinical decision support interventions are typically heterogeneous in nature, making it difficult to identify why some interventions succeed while others do not. One approach to identify factors important to the success of health information systems is the use of meta-regression techniques, in which potential explanatory factors are correlated with the outcome of interest. This approach, however, can result in misleading conclusions due to several issues. In this manuscript, we present a cautionary case study in the context of clinical decision support systems to illustrate the limitations of this type of analysis. We then discuss implications and recommendations for future work aimed at identifying success factors of medical informatics interventions. In particular, we identify the need for head-to-head trials in which the importance of system features is directly evaluated in a prospective manner.

  20. Development of a clinical decision support system for diabetes care: A pilot study.

    PubMed

    Sim, Livvi Li Wei; Ban, Kenneth Hon Kim; Tan, Tin Wee; Sethi, Sunil Kumar; Loh, Tze Ping

    2017-01-01

    Management of complex chronic diseases such as diabetes requires the assimilation and interpretation of multiple laboratory test results. Traditional electronic health records tend to display laboratory results in a piecemeal and segregated fashion. This makes the assembly and interpretation of results related to diabetes care challenging. We developed a diabetes-specific clinical decision support system (Diabetes Dashboard) interface for displaying glycemic, lipid and renal function results, in an integrated form with decision support capabilities, based on local clinical practice guidelines. The clinical decision support system included a dashboard feature that graphically summarized all relevant laboratory results and displayed them in a color-coded system that allowed quick interpretation of the metabolic control of the patients. An alert module informs the user of tests that are due for repeat testing. An interactive graph module was also developed for better visual appreciation of the trends of the laboratory results of the patient. In a pilot study involving case scenarios administered via an electronic questionnaire, the Diabetes Dashboard, compared to the existing laboratory reporting interface, significantly improved the identification of abnormal laboratory results, of the long-term trend of the laboratory tests and of tests due for repeat testing. However, the Diabetes Dashboard did not significantly improve the identification of patients requiring treatment adjustment or the amount of time spent on each case scenario. In conclusion, we have developed and shown that the use of the Diabetes Dashboard, which incorporates several decision support features, can improve the management of diabetes. It is anticipated that this dashboard will be most helpful when deployed in an outpatient setting, where physicians can quickly make clinical decisions based on summarized information and be alerted to pertinent areas of care that require additional attention.

  1. Development of a clinical decision support system for diabetes care: A pilot study

    PubMed Central

    Sim, Livvi Li Wei; Ban, Kenneth Hon Kim; Tan, Tin Wee; Sethi, Sunil Kumar; Loh, Tze Ping

    2017-01-01

    Management of complex chronic diseases such as diabetes requires the assimilation and interpretation of multiple laboratory test results. Traditional electronic health records tend to display laboratory results in a piecemeal and segregated fashion. This makes the assembly and interpretation of results related to diabetes care challenging. We developed a diabetes-specific clinical decision support system (Diabetes Dashboard) interface for displaying glycemic, lipid and renal function results, in an integrated form with decision support capabilities, based on local clinical practice guidelines. The clinical decision support system included a dashboard feature that graphically summarized all relevant laboratory results and displayed them in a color-coded system that allowed quick interpretation of the metabolic control of the patients. An alert module informs the user of tests that are due for repeat testing. An interactive graph module was also developed for better visual appreciation of the trends of the laboratory results of the patient. In a pilot study involving case scenarios administered via an electronic questionnaire, the Diabetes Dashboard, compared to the existing laboratory reporting interface, significantly improved the identification of abnormal laboratory results, of the long-term trend of the laboratory tests and of tests due for repeat testing. However, the Diabetes Dashboard did not significantly improve the identification of patients requiring treatment adjustment or the amount of time spent on each case scenario. In conclusion, we have developed and shown that the use of the Diabetes Dashboard, which incorporates several decision support features, can improve the management of diabetes. It is anticipated that this dashboard will be most helpful when deployed in an outpatient setting, where physicians can quickly make clinical decisions based on summarized information and be alerted to pertinent areas of care that require additional attention. PMID

  2. Formative evaluation of the accuracy of a clinical decision support system for cervical cancer screening.

    PubMed

    Wagholikar, Kavishwar Balwant; MacLaughlin, Kathy L; Kastner, Thomas M; Casey, Petra M; Henry, Michael; Greenes, Robert A; Liu, Hongfang; Chaudhry, Rajeev

    2013-01-01

    We previously developed and reported on a prototype clinical decision support system (CDSS) for cervical cancer screening. However, the system is complex as it is based on multiple guidelines and free-text processing. Therefore, the system is susceptible to failures. This report describes a formative evaluation of the system, which is a necessary step to ensure deployment readiness of the system. Care providers who are potential end-users of the CDSS were invited to provide their recommendations for a random set of patients that represented diverse decision scenarios. The recommendations of the care providers and those generated by the CDSS were compared. Mismatched recommendations were reviewed by two independent experts. A total of 25 users participated in this study and provided recommendations for 175 cases. The CDSS had an accuracy of 87% and 12 types of CDSS errors were identified, which were mainly due to deficiencies in the system's guideline rules. When the deficiencies were rectified, the CDSS generated optimal recommendations for all failure cases, except one with incomplete documentation. The crowd-sourcing approach for construction of the reference set, coupled with the expert review of mismatched recommendations, facilitated an effective evaluation and enhancement of the system, by identifying decision scenarios that were missed by the system's developers. The described methodology will be useful for other researchers who seek rapidly to evaluate and enhance the deployment readiness of complex decision support systems.

  3. Formative evaluation of the accuracy of a clinical decision support system for cervical cancer screening

    PubMed Central

    Wagholikar, Kavishwar Balwant; MacLaughlin, Kathy L; Kastner, Thomas M; Casey, Petra M; Henry, Michael; Greenes, Robert A; Liu, Hongfang; Chaudhry, Rajeev

    2013-01-01

    Objectives We previously developed and reported on a prototype clinical decision support system (CDSS) for cervical cancer screening. However, the system is complex as it is based on multiple guidelines and free-text processing. Therefore, the system is susceptible to failures. This report describes a formative evaluation of the system, which is a necessary step to ensure deployment readiness of the system. Materials and methods Care providers who are potential end-users of the CDSS were invited to provide their recommendations for a random set of patients that represented diverse decision scenarios. The recommendations of the care providers and those generated by the CDSS were compared. Mismatched recommendations were reviewed by two independent experts. Results A total of 25 users participated in this study and provided recommendations for 175 cases. The CDSS had an accuracy of 87% and 12 types of CDSS errors were identified, which were mainly due to deficiencies in the system's guideline rules. When the deficiencies were rectified, the CDSS generated optimal recommendations for all failure cases, except one with incomplete documentation. Discussion and conclusions The crowd-sourcing approach for construction of the reference set, coupled with the expert review of mismatched recommendations, facilitated an effective evaluation and enhancement of the system, by identifying decision scenarios that were missed by the system's developers. The described methodology will be useful for other researchers who seek rapidly to evaluate and enhance the deployment readiness of complex decision support systems. PMID:23564631

  4. Development of a Tobacco Cessation Clinical Decision Support System for Pediatric Emergency Nurses.

    PubMed

    Mahabee-Gittens, E Melinda; Dexheimer, Judith W; Gordon, Judith S

    2016-12-01

    Almost 50% of children who visit the pediatric emergency department are exposed to tobacco smoke. However, pediatric emergency nurses do not routinely address this issue. The incorporation of a clinical decision support system into the electronic health record may improve the rates of tobacco exposure screening and interventions. We used a mixed-methods design to develop, refine, and implement an evidence-based clinical decision support system to help nurses screen, educate, and assist caregivers to quit smoking. We included an advisory panel of emergency department experts and leaders and focus and user groups of nurses. The prompts include the following: (1) "Ask" about child smoke exposure and caregiver smoking; (2) "Advise" caregivers to reduce their child's smoke exposure by quitting smoking; (3) "Assess" interest; and (4) "Assist" caregivers to quit. The clinical decision support system was created to reflect nurses' suggestions and was implemented in five busy urgent care settings with 38 nurses. The nurses reported that the system was easy to use and helped them to address caregiver smoking. The use of this innovative tool may create a sustainable and disseminable model for prompting nurses to provide evidence-based tobacco cessation treatment.

  5. Intraoperative Clinical Decision Support for Anesthesia: A Narrative Review of Available Systems.

    PubMed

    Nair, Bala G; Gabel, Eilon; Hofer, Ira; Schwid, Howard A; Cannesson, Maxime

    2017-02-01

    With increasing adoption of anesthesia information management systems (AIMS), there is growing interest in utilizing AIMS data for intraoperative clinical decision support (CDS). CDS for anesthesia has the potential for improving quality of care, patient safety, billing, and compliance. Intraoperative CDS can range from passive and post hoc systems to active real-time systems that can detect ongoing clinical issues and deviations from best practice care. Real-time CDS holds the most promise because real-time alerts and guidance can drive provider behavior toward evidence-based standardized care during the ongoing case. In this review, we describe the different types of intraoperative CDS systems with specific emphasis on real-time systems. The technical considerations in developing and implementing real-time CDS are systematically covered. This includes the functional modules of a CDS system, development and execution of decision rules, and modalities to alert anesthesia providers concerning clinical issues. We also describe the regulatory aspects that affect development, implementation, and use of intraoperative CDS. Methods and measures to assess the effectiveness of intraoperative CDS are discussed. Last, we outline areas of future development of intraoperative CDS, particularly the possibility of providing predictive and prescriptive decision support.

  6. [A computerised clinical decision-support system for the management of depression in Primary Care].

    PubMed

    Aragonès, Enric; Comín, Eva; Cavero, Myriam; Pérez, Víctor; Molina, Cristina; Palao, Diego

    Despite its clinical relevance and its importance as a public health problem, there are major gaps in the management of depression. Evidence-based clinical guidelines are useful to improve processes and clinical outcomes. In order to make their implementation easier these guidelines have been transformed into computerised clinical decision support systems. In this article, a description is presented on the basics and characteristics of a new computerised clinical guideline for the management of major depression, developed in the public health system in Catalonia. This tool helps the clinician to establish reliable and accurate diagnoses of depression, to choose the best treatment a priori according to the disease and the patient characteristics. It also emphasises the importance of systematic monitoring to assess the clinical course, and to adjust therapeutic interventions to the patient's needs at all times. Copyright © 2016 Elsevier España, S.L.U. All rights reserved.

  7. The changing nature of clinical decision support systems: a focus on consumers, genomics, public health and decision safety.

    PubMed

    Coiera, E; Lau, A Y S; Tsafnat, G; Sintchenko, V; Magrabi, F

    2009-01-01

    To review the recent research literature in clinical decision support systems (CDSS). A review of recent literature was undertaken, focussing on CDSS evaluation, consumers and public health, the impact of translational bioinformatics on CDSS design, and CDSS safety. In recent years, researchers have concentrated much less on the development of decision technologies, and have focussed more on the impact of CDSS in the clinical world. Recent work highlights that traditional process measures of CDSS effectiveness, such as document relevance are poor proxy measures for decision outcomes. Measuring the dynamics of decision making, for example via decision velocity, may produce a more accurate picture of effectiveness. Another trend is the broadening of user base for CDSS beyond front line clinicians. Consumers are now a major focus for biomedical informatics, as are public health officials, tasked with detecting and managing disease outbreaks at a health system, rather than individual patient level. Bioinformatics is also changing the nature of CDSS. Apart from personalisation of therapy recommendations, translational bioinformatics is creating new challenges in the interpretation of the meaning of genetic data. Finally, there is much recent interest in the safety and effectiveness of computerised physician order entry (CPOE) systems, given that prescribing and administration errors are a significant cause of morbidity and mortality. Of note, there is still much controversy surrounding the contention that poorly designed, implemented or used CDSS may actually lead to harm. CDSS research remains an active and evolving area of research, as CDSS penetrate more widely beyond their traditional domain into consumer decision support, and as decisions become more complex, for example by involving sequence level genetic data.

  8. Implementation of a clinical decision support system using a service model: results of a feasibility study.

    PubMed

    Borbolla, Damian; Otero, Carlos; Lobach, David F; Kawamoto, Kensaku; Gomez Saldaño, Ana M; Staccia, Gustavo; Lopez, Gastón; Figar, Silvana; Luna, Daniel; Bernaldo de Quiros, Fernan Gonzalez

    2010-01-01

    Numerous studies have shown that the quality of health care is inadequate, and healthcare organizations are increasingly turning to clinical decision support systems (CDSS) to address this problem. In implementing CDSS, a highly promising architectural approach is the use of decision support services. However, there are few reported examples of successful implementations of operational CDSS using this approach. Here, we describe how Hospital Italiano de Buenos Aires evaluated the feasibility of using the SEBASTIAN clinical decision support Web service to implement a CDSS integrated with its electronic medical record system. The feasibility study consisted of three stages: first, end-user acceptability testing of the proposed CDSS through focus groups; second, the design and implementation of the system through integration of SEBASTIAN and the authoring of new rules; and finally, validation of system performance and accuracy. Through this study, we found that it is feasible to implement CDSS using a service-based approach. The CDSS is now under evaluation in a randomized controlled trial. The processes and lessons learned from this initiative are discussed.

  9. A shotgun wedding: business decision support meets clinical decision support.

    PubMed

    Oliveira, Jason

    2002-01-01

    By effectively closing the loop between the data, analytics, processes, and methods supporting business and clinical decision making, a healthcare organization closes the loop between its knowledge generation activities and its actions at the bedside: knowledge guiding actions, actions generating knowledge.

  10. Clinical decision support systems in child and adolescent psychiatry: a systematic review.

    PubMed

    Koposov, Roman; Fossum, Sturla; Frodl, Thomas; Nytrø, Øystein; Leventhal, Bennett; Sourander, Andre; Quaglini, Silvana; Molteni, Massimo; de la Iglesia Vayá, María; Prokosch, Hans-Ulrich; Barbarini, Nicola; Milham, Michael Peter; Castellanos, Francisco Xavier; Skokauskas, Norbert

    2017-04-28

    Psychiatric disorders are amongst the most prevalent and impairing conditions in childhood and adolescence. Unfortunately, it is well known that general practitioners (GPs) and other frontline health providers (i.e., child protection workers, public health nurses, and pediatricians) are not adequately trained to address these ubiquitous problems (Braddick et al. Child and Adolescent mental health in Europe: infrastructures, policy and programmes, European Communities, 2009; Levav et al. Eur Child Adolesc Psychiatry 13:395-401, 2004). Advances in technology may offer a solution to this problem with clinical decision support systems (CDSS) that are designed to help professionals make sound clinical decisions in real time. This paper offers a systematic review of currently available CDSS for child and adolescent mental health disorders prepared according to the PRISMA-Protocols (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Protocols). Applying strict eligibility criteria, the identified studies (n = 5048) were screened. Ten studies, describing eight original clinical decision support systems for child and adolescent psychiatric disorders, fulfilled inclusion criteria. Based on this systematic review, there appears to be a need for a new, readily available CDSS for child neuropsychiatric disorder which promotes evidence-based, best practices, while enabling consideration of national variation in practices by leveraging data-reuse to generate predictions regarding treatment outcome, addressing a broader cluster of clinical disorders, and targeting frontline practice environments.

  11. Clinical Decision Support and Palivizumab

    PubMed Central

    Hogan, A.; Michel, J.; Localio, A.R.; Karavite, D.; Song, L.; Ramos, M.J.; Fiks, A.G.; Lorch, S.; Grundmeier, R.W.

    2015-01-01

    Background and Objectives Palivizumab can reduce hospitalizations due to respiratory syncytial virus (RSV), but many eligible infants fail to receive the full 5-dose series. The efficacy of clinical decision support (CDS) in fostering palivizumab receipt has not been studied. We sought a comprehensive solution for identifying eligible patients and addressing barriers to palivizumab administration. Methods We developed workflow and CDS tools targeting patient identification and palivizumab administration. We randomized 10 practices to receive palivizumab-focused CDS and 10 to receive comprehensive CDS for premature infants in a 3-year longitudinal cluster-randomized trial with 2 baseline and 1 intervention RSV seasons. Results There were 356 children eligible to receive palivizumab, with 194 in the palivizumab-focused group and 162 in the comprehensive CDS group. The proportion of doses administered to children in the palivizumab-focused intervention group increased from 68.4% and 65.5% in the two baseline seasons to 84.7% in the intervention season. In the comprehensive intervention group, proportions of doses administered declined during the baseline seasons (from 71.9% to 62.4%) with partial recovery to 67.9% during the intervention season. The palivizumab-focused group improved by 19.2 percentage points in the intervention season compared to the prior baseline season (p < 0.001), while the comprehensive intervention group only improved 5.5 percentage points (p = 0.288). The difference in change between study groups was significant (p = 0.05). Conclusions Workflow and CDS tools integrated in an EHR may increase the administration of palivizumab. The support focused on palivizumab, rather than comprehensive intervention, was more effective at improving palivizumab administration. PMID:26767069

  12. The NIAID Division of AIDS enterprise information system: integrated decision support for global clinical research programs.

    PubMed

    Kagan, Jonathan M; Gupta, Nitin; Varghese, Suresh; Virkar, Hemant

    2011-12-01

    The National Institute of Allergy and Infectious Diseases (NIAID) Division of AIDS (DAIDS) Enterprise Information System (DAIDS-ES) is a web-based system that supports NIAID in the scientific, strategic, and tactical management of its global clinical research programs for HIV/AIDS vaccines, prevention, and therapeutics. Different from most commercial clinical trials information systems, which are typically protocol-driven, the DAIDS-ES was built to exchange information with those types of systems and integrate it in ways that help scientific program directors lead the research effort and keep pace with the complex and ever-changing global HIV/AIDS pandemic. Whereas commercially available clinical trials support systems are not usually disease-focused, DAIDS-ES was specifically designed to capture and incorporate unique scientific, demographic, and logistical aspects of HIV/AIDS treatment, prevention, and vaccine research in order to provide a rich source of information to guide informed decision-making. Sharing data across its internal components and with external systems, using defined vocabularies, open standards and flexible interfaces, the DAIDS-ES enables NIAID, its global collaborators and stakeholders, access to timely, quality information about NIAID-supported clinical trials which is utilized to: (1) analyze the research portfolio, assess capacity, identify opportunities, and avoid redundancies; (2) help support study safety, quality, ethics, and regulatory compliance; (3) conduct evidence-based policy analysis and business process re-engineering for improved efficiency. This report summarizes how the DAIDS-ES was conceptualized, how it differs from typical clinical trial support systems, the rationale for key design choices, and examples of how it is being used to advance the efficiency and effectiveness of NIAID's HIV/AIDS clinical research programs.

  13. 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

  14. Test Case Selection in Pre-Deployment Testing of Complex Clinical Decision Support Systems

    PubMed Central

    Tso, Geoffrey J.; Yuen, Kaeli; Martins, Susana; Tu, Samson W.; Ashcraft, Michael; Heidenreich, Paul; Hoffman, Brian B.; Goldstein, Mary K.

    2016-01-01

    Clinical decision support (CDS) systems with complex logic are being developed. Ensuring the quality of CDS is imperative, but there is no consensus on testing standards. We tested ATHENA-HTN CDS after encoding updated hypertension guidelines into the system. A logic flow and a complexity analysis of the encoding were performed to guide testing. 100 test cases were selected to test the major pathways in the CDS logic flow, and the effectiveness of the testing was analyzed. The encoding contained 26 decision points and 3120 possible output combinations. The 100 cases selected tested all of the major pathways in the logic, but only 1% of the possible output combinations. Test case selection is one of the most challenging aspects in CDS testing and has a major impact on testing coverage. A test selection strategy should take into account the complexity of the system, identification of major logic pathways, and available resources. PMID:27570678

  15. Physicians' Compliance with a Clinical Decision Support System Alerting during the Prescribing Process.

    PubMed

    Baypinar, Fatih; Kingma, Hylke Jan; van der Hoeven, Ruud T M; Becker, Matthijs L

    2017-06-01

    Clinical decision support systems have been shown to improve practitioner performance. Most systems designed to prevent medication errors generate lists with patients who fulfill the criteria of the algorithm. These lists are reviewed by a pharmacist and physicians are contacted by telephone. Presenting pop-up alerts as part of the workflow with a clear recommendation is a feature critical to success. Therefore we implemented three algorithms in a clinical decision support system alerting during the medication ordering process. We analyzed whether the recommendations in these alerts were followed. We evaluated 1. whether folic or folinic acid was co-prescribed more frequently within 48 h after ordering methotrexate, 2. whether vitamin D or analogues were co-prescribed more frequently within 48 h after ordering bisphophonates and 3. whether sodium lowering drugs were stopped more frequently within one hour in patients with hyponatremia. We analyzed the difference in the 48 days before implementation and the 43 days after implementation, using Pearson's Chi(2) test. Co-prescription of folic or folinic acid increased from 54 to 91% (p = 0.014), co-prescription of vitamin D or analogues increased from 11 to 40% (p = 0.001) and the number of stopped orders for sodium lowering drugs increased from 3 to 14% (p = 0.002). This clinical decision support system that alerts physicians for preventable medication errors during the medication ordering process is an effective approach to improve prescribing behavior.

  16. Decision Support Systems in Libraries.

    ERIC Educational Resources Information Center

    Heindel, Allan; Napier, H. Albert

    Following a review of the contributions of computers and managerial science/operations research to the management of libraries, this paper introduces the concept of decision support systems. DSS, a blending of these techniques, can lead to more effective decisions by library managers. A case study of the utilization of a DSS in the budgeting…

  17. An extended SQL for temporal data management in clinical decision-support systems.

    PubMed

    Das, A K; Tu, S W; Purcell, G P; Musen, M A

    1992-01-01

    We are developing a database implementation to support temporal data management for the T-HELPER physician workstation, an advice system for protocol-based care of patients who have HIV disease. To understand the requirements for the temporal database, we have analyzed the types of temporal predicates found in clinical-trial protocols. We extend the standard relational data model in three ways to support these querying requirements. First, we incorporate timestamps into the two-dimensional relational table to store the temporal dimension of both instant- and interval-based data. Second, we develop a set of operations on timepoints and intervals to manipulate timestamped data. Third, we modify the relational query language SQL so that its underlying algebra supports the specified operations on timestamps in relational tables. We show that our temporal extension to SQL meets the temporal data-management needs of protocol-directed decision support.

  18. An extended SQL for temporal data management in clinical decision-support systems.

    PubMed Central

    Das, A. K.; Tu, S. W.; Purcell, G. P.; Musen, M. A.

    1992-01-01

    We are developing a database implementation to support temporal data management for the T-HELPER physician workstation, an advice system for protocol-based care of patients who have HIV disease. To understand the requirements for the temporal database, we have analyzed the types of temporal predicates found in clinical-trial protocols. We extend the standard relational data model in three ways to support these querying requirements. First, we incorporate timestamps into the two-dimensional relational table to store the temporal dimension of both instant- and interval-based data. Second, we develop a set of operations on timepoints and intervals to manipulate timestamped data. Third, we modify the relational query language SQL so that its underlying algebra supports the specified operations on timestamps in relational tables. We show that our temporal extension to SQL meets the temporal data-management needs of protocol-directed decision support. PMID:1482853

  19. Computerized Clinical Decision Support: Contributions from 2014.

    PubMed

    Bouaud, J; Koutkias, V

    2015-08-13

    To summarize recent research and propose a selection of best papers published in 2014 in the field of computerized clinical decision support for the Decision Support section of the IMIA yearbook. A literature review was performed by searching two bibliographic databases for papers related to clinical decision support systems (CDSSs) and computerized provider order entry systems in order to select a list of candidate best papers to be then peer-reviewed by external reviewers. A consensus meeting between the two section editors and the editorial team was finally organized to conclude on the selection of best papers. Among the 1,254 returned papers published in 2014, the full review process selected four best papers. The first one is an experimental contribution to a better understanding of unintended uses of CDSSs. The second paper describes the effective use of previously collected data to tailor and adapt a CDSS. The third paper presents an innovative application that uses pharmacogenomic information to support personalized medicine. The fourth paper reports on the long-term effect of the routine use of a CDSS for antibiotic therapy. As health information technologies spread more and more meaningfully, CDSSs are improving to answer users' needs more accurately. The exploitation of previously collected data and the use of genomic data for decision support has started to materialize. However, more work is still needed to address issues related to the correct usage of such technologies, and to assess their effective impact in the long term.

  20. Computerized Clinical Decision Support: Contributions from 2014

    PubMed Central

    Koutkias, V.

    2015-01-01

    Summary Objective To summarize recent research and propose a selection of best papers published in 2014 in the field of computerized clinical decision support for the Decision Support section of the IMIA yearbook. Method A literature review was performed by searching two bibliographic databases for papers related to clinical decision support systems (CDSSs) and computerized provider order entry systems in order to select a list of candidate best papers to be then peer-reviewed by external reviewers. A consensus meeting between the two section editors and the editorial team was finally organized to conclude on the selection of best papers. Results Among the 1,254 returned papers published in 2014, the full review process selected four best papers. The first one is an experimental contribution to a better understanding of unintended uses of CDSSs. The second paper describes the effective use of previously collected data to tailor and adapt a CDSS. The third paper presents an innovative application that uses pharmacogenomic information to support personalized medicine. The fourth paper reports on the long-term effect of the routine use of a CDSS for antibiotic therapy. Conclusions As health information technologies spread more and more meaningfully, CDSSs are improving to answer users’ needs more accurately. The exploitation of previously collected data and the use of genomic data for decision support has started to materialize. However, more work is still needed to address issues related to the correct usage of such technologies, and to assess their effective impact in the long term. PMID:26293858

  1. [Human body meridian spatial decision support system for clinical treatment and teaching of acupuncture and moxibustion].

    PubMed

    Wu, Dehua

    2016-01-01

    The spatial position and distribution of human body meridian are expressed limitedly in the decision support system (DSS) of acupuncture and moxibustion at present, which leads to the failure to give the effective quantitative analysis on the spatial range and the difficulty for the decision-maker to provide a realistic spatial decision environment. Focusing on the limit spatial expression in DSS of acupuncture and moxibustion, it was proposed that on the basis of the geographic information system, in association of DSS technology, the design idea was developed on the human body meridian spatial DSS. With the 4-layer service-oriented architecture adopted, the data center integrated development platform was taken as the system development environment. The hierarchical organization was done for the spatial data of human body meridian via the directory tree. The structured query language (SQL) server was used to achieve the unified management of spatial data and attribute data. The technologies of architecture, configuration and plug-in development model were integrated to achieve the data inquiry, buffer analysis and program evaluation of the human body meridian spatial DSS. The research results show that the human body meridian spatial DSS could reflect realistically the spatial characteristics of the spatial position and distribution of human body meridian and met the constantly changeable demand of users. It has the powerful spatial analysis function and assists with the scientific decision in clinical treatment and teaching of acupuncture and moxibustion. It is the new attempt to the informatization research of human body meridian.

  2. Computerized Clinical Decision Support: Contributions from 2015.

    PubMed

    Koutkias, V; Bouaud, J

    2016-11-10

    To summarize recent research and select the best papers published in 2015 in the field of computerized clinical decision support for the Decision Support section of the IMIA yearbook. A literature review was performed by searching two bibliographic databases for papers related to clinical decision support systems (CDSSs) and computerized provider order entry (CPOE) systems. The aim was to identify a list of candidate best papers from the retrieved papers that were then peer-reviewed by external reviewers. A consensus meeting between the two section editors and the IMIA editorial team was finally conducted to conclude in the best paper selection. Among the 974 retrieved papers, the entire review process resulted in the selection of four best papers. One paper reports on a CDSS routinely applied in pediatrics for more than 10 years, relying on adaptations of the Arden Syntax. Another paper assessed the acceptability and feasibility of an important CPOE evaluation tool in hospitals outside the US where it was developed. The third paper is a systematic, qualitative review, concerning usability flaws of medication-related alerting functions, providing an important evidence-based, methodological contribution in the domain of CDSS design and development in general. Lastly, the fourth paper describes a study quantifying the effect of a complex, continuous-care, guideline-based CDSS on the correctness and completeness of clinicians' decisions. While there are notable examples of routinely used decision support systems, this 2015 review on CDSSs and CPOE systems still shows that, despite methodological contributions, theoretical frameworks, and prototype developments, these technologies are not yet widely spread (at least with their full functionalities) in routine clinical practice. Further research, testing, evaluation, and training are still needed for these tools to be adopted in clinical practice and, ultimately, illustrate the benefits that they promise.

  3. Mobile Clinical Decision Support System for Acid-base Balance Diagnosis and Treatment Recommendation.

    PubMed

    Mandzuka, Mensur; Begic, Edin; Boskovic, Dusanka; Begic, Zijo; Masic, Izet

    2017-06-01

    This paper presents mobile application implementing a decision support system for acid-base disorder diagnosis and treatment recommendation. The application was developed using the official integrated development environment for the Android platform (to maximize availability and minimize investments in specialized hardware) called Android Studio. The application identifies disorder, based on the blood gas analysis, evaluates whether the disorder has been compensated, and based on additional input related to electrolyte imbalance, provides recommendations for treatment. The application is a tool in the hands of the user, which provides assistance during acid-base disorders treatment. The application will assist the physician in clinical practice and is focused on the treatment in intensive care.

  4. A Critical Review of the Theoretical Frameworks and the Conceptual Factors in the Adoption of Clinical Decision Support Systems.

    PubMed

    Khong, Peck Chui Betty; Holroyd, Eleanor; Wang, Wenru

    2015-12-01

    The clinical decision support system is utilized to translate knowledge into evidence-based practice in clinical settings. Many studies have been conducted to understand users' adoption of the clinical decision support system. A critical review was conducted to understand the theoretical or conceptual frameworks used to inform the studies on the adoption of the clinical decision support system. The review identified 15 theoretical and conceptual frameworks using multiple hybrids of theories and concepts. The Technology Acceptance Model was the most frequently used baseline framework combined with frameworks such as the diffusion of innovation, social theory, longitudinal theory, and so on. The results from these articles yielded multiple concepts influencing the adoption of the clinical decision support system. These concepts can be recategorized into nine major concepts, namely, the information system, person (user or patient), social, organization, perceived benefits, emotions, trustability, relevance (fitness), and professionalism. None of the studies found all the nine concepts. That said, most of them have identified the information system, organization, and person concepts as three of its concepts affecting the use of the clinical decision support system. Within each of the concepts, its subconcepts were noted to be very varied. Yet each of these subconcepts has significantly contributed toward the different facets of the concepts. A pluralistic framework was built using the concepts and subconcepts to provide an overall framework construct for future study on the adoption of the clinical decision support system.

  5. Analysis of Nursing Clinical Decision Support Requests and Strategic Plan in a Large Academic Health System.

    PubMed

    Whalen, Kimberly; Bavuso, Karen; Bouyer-Ferullo, Sharon; Goldsmith, Denise; Fairbanks, Amanda; Gesner, Emily; Lagor, Charles; Collins, Sarah

    2016-01-01

    To understand requests for nursing Clinical Decision Support (CDS) interventions at a large integrated health system undergoing vendor-based EHR implementation. In addition, to establish a process to guide both short-term implementation and long-term strategic goals to meet nursing CDS needs. We conducted an environmental scan to understand current state of nursing CDS over three months. The environmental scan consisted of a literature review and an analysis of CDS requests received from across our health system. We identified existing high priority CDS and paper-based tools used in nursing practice at our health system that guide decision-making. A total of 46 nursing CDS requests were received. Fifty-six percent (n=26) were specific to a clinical specialty; 22 percent (n=10) were focused on facilitating clinical consults in the inpatient setting. "Risk Assessments/Risk Reduction/Promotion of Healthy Habits" (n=23) was the most requested High Priority Category received for nursing CDS. A continuum of types of nursing CDS needs emerged using the Data-Information-Knowledge-Wisdom Conceptual Framework: 1) facilitating data capture, 2) meeting information needs, 3) guiding knowledge-based decision making, and 4) exposing analytics for wisdom-based clinical interpretation by the nurse. Identifying and prioritizing paper-based tools that can be modified into electronic CDS is a challenge. CDS strategy is an evolving process that relies on close collaboration and engagement with clinical sites for short-term implementation and should be incorporated into a long-term strategic plan that can be optimized and achieved overtime. The Data-Information-Knowledge-Wisdom Conceptual Framework in conjunction with the High Priority Categories established may be a useful tool to guide a strategic approach for meeting short-term nursing CDS needs and aligning with the organizational strategic plan.

  6. Stroke navigator--a clinical decision support system for acute stroke.

    PubMed

    van Zon, Kees; Lord, William P; Lagor, Charles; Theiss, Stephan; Brosig, Torge; Siebler, Mario

    2008-11-06

    The Stroke Navigator is a clinical decision support system aimed at improving the diagnosis and treatment of acute stroke. It combines an audit trail, a differential diagnosis window, an interactive stroke protocol map, and a list of recommendations for hospital staff. It provides a patient-specific overview of the workflow status and of the available clinical findings, with the goal of improving the continuity of care. For this purpose, it uses a workflow engine that was specifically designed to meet the demands of clinical practice. The Stroke Navigator furthermore calculates and displays the probabilities of various stroke differential diagnoses. The demonstration will introduce these and other features by means of a hypothetical patient case. It will also summarize the status of alpha-testing the first prototype.

  7. Knowledge discovery in clinical decision support systems for pain management: a systematic review.

    PubMed

    Pombo, Nuno; Araújo, Pedro; Viana, Joaquim

    2014-01-01

    The occurrence of pain accounts for billions of dollars in annual medical expenditures; loss of quality of life and decreased worker productivity contribute to indirect costs. As pain is highly subjective, clinical decision support systems (CDSSs) can be critical for improving the accuracy of pain assessment and offering better support for clinical decision-making. This review is focused on computer technologies for pain management that allow CDSSs to obtain knowledge from the clinical data produced by either patients or health care professionals. A comprehensive literature search was conducted in several electronic databases to identify relevant articles focused on computerised systems that constituted CDSSs and include data or results related to pain symptoms from patients with acute or chronic pain, published between 1992 and 2011 in the English language. In total, thirty-nine studies were analysed; thirty-two were selected from 1245 citations, and seven were obtained from reference tracking. The results highlighted the following clusters of computer technologies: rule-based algorithms, artificial neural networks, nonstandard set theory, and statistical learning algorithms. In addition, several methodologies were found for content processing such as terminologies, questionnaires, and scores. The median accuracy ranged from 53% to 87.5%. Computer technologies that have been applied in CDSSs are important but not determinant in improving the systems' accuracy and the clinical practice, as evidenced by the moderate correlation among the studies. However, these systems play an important role in the design of computerised systems oriented to a patient's symptoms as is required for pain management. Several limitations related to CDSSs were observed: the lack of integration with mobile devices, the reduced use of web-based interfaces, and scarce capabilities for data to be inserted by patients. Copyright © 2013 Elsevier B.V. All rights reserved.

  8. A clinical decision support system with an integrated EMR for diagnosis of peripheral neuropathy.

    PubMed

    Kunhimangalam, Reeda; Ovallath, Sujith; Joseph, Paul K

    2014-04-01

    The prevalence of peripheral neuropathy in general population is ever increasing. The diagnosis and classification of peripheral neuropathies is often difficult as it involves careful clinical and electro-diagnostic examination by an expert neurologist. In developing countries a large percentage of the disease remains undiagnosed due to lack of adequate number of experts. In this study a novel clinical decision support system has been developed using a fuzzy expert system. The study was done to provide a solution to the demand of systems that can improve health care by accurate diagnosis in limited time, in the absence of specialists. It employs a graphical user interface and a fuzzy logic controller with rule viewer for identification of the type of peripheral neuropathy. An integrated medical records database is also developed for the storage and retrieval of the data. The system consists of 24 input fields, which includes the clinical values of the diagnostic test and the clinical symptoms. The output field is the disease diagnosis, whether it is Motor (Demyelinating/Axonopathy) neuropathy, sensory (Demyelinating/Axonopathy) neuropathy, mixed type or a normal case. The results obtained were compared with the expert's opinion and the system showed 93.27 % accuracy. The study aims at showing that Fuzzy Expert Systems may prove useful in providing diagnostic and predictive medical opinions. It enables the clinicians to arrive at a better diagnosis as it keeps the expert knowledge in an intelligent system to be used efficiently and effectively.

  9. Clinical decision support systems at the Vienna General Hospital using Arden Syntax: Design, implementation, and integration.

    PubMed

    Schuh, Christian; de Bruin, Jeroen S; Seeling, Walter

    2015-12-01

    The Allgemeines Krankenhaus Informations Management (AKIM) project was started at the Vienna General Hospital (VGH) several years ago. This led to the introduction of a new hospital information system (HIS), and the installation of the expert system platform (EXP) for the integration of Arden-Syntax-based clinical decision support systems (CDSSs). In this report we take a look at the milestones achieved and the challenges faced in the creation and modification of CDSSs, and their integration into the HIS over the last three years. We introduce a three-stage development method, which is followed in nearly all CDSS projects at the Medical University of Vienna and the VGH. Stage one comprises requirements engineering and system conception. Stage two focuses on the implementation and testing of the system. Finally, stage three describes the deployment and integration of the system in the VGH HIS. The HIS provides a clinical work environment for healthcare specialists using customizable graphical interfaces known as parametric medical documents. Multiple Arden Syntax servers are employed to host and execute the CDSS knowledge bases: two embedded in the EXP for production and development, and a further three in clinical routine for production, development, and quality assurance. Three systems are discussed; the systems serve different purposes in different clinical areas, but are all implemented with Arden Syntax. MONI-ICU is an automated surveillance system for monitoring healthcare-associated infections in the intensive care setting. TSM-CDS is a CDSS used for risk prediction in the formation of cutaneous melanoma metastases. Finally, TacroDS is a CDSS for the manipulation of dosages for tacrolimus, an immunosuppressive agent used after kidney transplantation. Problems in development and integration were related to data quality or availability, although organizational difficulties also caused delays in development and integration. Since the inception of the AKIM

  10. Design of a nursing clinical decision support system applying nursing diagnosis and nursing evaluation model based data mining.

    PubMed

    Kim, Hyungyung; Kim, Insook; Chae, Yougmoon

    2006-01-01

    This study a methodological study; to acquire knowledge on the nursing process by steps of knowledge definition, collection, and representation; then, to design a data warehouse and nursing process clinical decision support system.

  11. Enabling cross-platform clinical decision support through Web-based decision support in commercial electronic health record systems: proposal and evaluation of initial prototype implementations.

    PubMed

    Zhang, Mingyuan; Velasco, Ferdinand T; Musser, R Clayton; Kawamoto, Kensaku

    2013-01-01

    Enabling clinical decision support (CDS) across multiple electronic health record (EHR) systems has been a desired but largely unattained aim of clinical informatics, especially in commercial EHR systems. A potential opportunity for enabling such scalable CDS is to leverage vendor-supported, Web-based CDS development platforms along with vendor-supported application programming interfaces (APIs). Here, we propose a potential staged approach for enabling such scalable CDS, starting with the use of custom EHR APIs and moving towards standardized EHR APIs to facilitate interoperability. We analyzed three commercial EHR systems for their capabilities to support the proposed approach, and we implemented prototypes in all three systems. Based on these analyses and prototype implementations, we conclude that the approach proposed is feasible, already supported by several major commercial EHR vendors, and potentially capable of enabling cross-platform CDS at scale.

  12. Enabling Cross-Platform Clinical Decision Support through Web-Based Decision Support in Commercial Electronic Health Record Systems: Proposal and Evaluation of Initial Prototype Implementations

    PubMed Central

    Zhang, Mingyuan; Velasco, Ferdinand T.; Musser, R. Clayton; Kawamoto, Kensaku

    2013-01-01

    Enabling clinical decision support (CDS) across multiple electronic health record (EHR) systems has been a desired but largely unattained aim of clinical informatics, especially in commercial EHR systems. A potential opportunity for enabling such scalable CDS is to leverage vendor-supported, Web-based CDS development platforms along with vendor-supported application programming interfaces (APIs). Here, we propose a potential staged approach for enabling such scalable CDS, starting with the use of custom EHR APIs and moving towards standardized EHR APIs to facilitate interoperability. We analyzed three commercial EHR systems for their capabilities to support the proposed approach, and we implemented prototypes in all three systems. Based on these analyses and prototype implementations, we conclude that the approach proposed is feasible, already supported by several major commercial EHR vendors, and potentially capable of enabling cross-platform CDS at scale. PMID:24551426

  13. Computerized clinical decision support systems for chronic disease management: a decision-maker-researcher partnership systematic review.

    PubMed

    Roshanov, Pavel S; Misra, Shikha; Gerstein, Hertzel C; Garg, Amit X; Sebaldt, Rolf J; Mackay, Jean A; Weise-Kelly, Lorraine; Navarro, Tamara; Wilczynski, Nancy L; Haynes, R Brian

    2011-08-03

    The use of computerized clinical decision support systems (CCDSSs) may improve chronic disease management, which requires recurrent visits to multiple health professionals, ongoing disease and treatment monitoring, and patient behavior modification. The objective of this review was to determine if CCDSSs improve the processes of chronic care (such as diagnosis, treatment, and monitoring of disease) and associated patient outcomes (such as effects on biomarkers and clinical exacerbations). We conducted a decision-maker-researcher partnership systematic review. We searched MEDLINE, EMBASE, Ovid's EBM Reviews database, Inspec, and reference lists for potentially eligible articles published up to January 2010. We included randomized controlled trials that compared the use of CCDSSs to usual practice or non-CCDSS controls. Trials were eligible if at least one component of the CCDSS was designed to support chronic disease management. We considered studies 'positive' if they showed a statistically significant improvement in at least 50% of relevant outcomes. Of 55 included trials, 87% (n = 48) measured system impact on the process of care and 52% (n = 25) of those demonstrated statistically significant improvements. Sixty-five percent (36/55) of trials measured impact on, typically, non-major (surrogate) patient outcomes, and 31% (n = 11) of those demonstrated benefits. Factors of interest to decision makers, such as cost, user satisfaction, system interface and feature sets, unique design and deployment characteristics, and effects on user workflow were rarely investigated or reported. A small majority (just over half) of CCDSSs improved care processes in chronic disease management and some improved patient health. Policy makers, healthcare administrators, and practitioners should be aware that the evidence of CCDSS effectiveness is limited, especially with respect to the small number and size of studies measuring patient outcomes.

  14. Computerized clinical decision support systems for chronic disease management: A decision-maker-researcher partnership systematic review

    PubMed Central

    2011-01-01

    Background The use of computerized clinical decision support systems (CCDSSs) may improve chronic disease management, which requires recurrent visits to multiple health professionals, ongoing disease and treatment monitoring, and patient behavior modification. The objective of this review was to determine if CCDSSs improve the processes of chronic care (such as diagnosis, treatment, and monitoring of disease) and associated patient outcomes (such as effects on biomarkers and clinical exacerbations). Methods We conducted a decision-maker-researcher partnership systematic review. We searched MEDLINE, EMBASE, Ovid's EBM Reviews database, Inspec, and reference lists for potentially eligible articles published up to January 2010. We included randomized controlled trials that compared the use of CCDSSs to usual practice or non-CCDSS controls. Trials were eligible if at least one component of the CCDSS was designed to support chronic disease management. We considered studies 'positive' if they showed a statistically significant improvement in at least 50% of relevant outcomes. Results Of 55 included trials, 87% (n = 48) measured system impact on the process of care and 52% (n = 25) of those demonstrated statistically significant improvements. Sixty-five percent (36/55) of trials measured impact on, typically, non-major (surrogate) patient outcomes, and 31% (n = 11) of those demonstrated benefits. Factors of interest to decision makers, such as cost, user satisfaction, system interface and feature sets, unique design and deployment characteristics, and effects on user workflow were rarely investigated or reported. Conclusions A small majority (just over half) of CCDSSs improved care processes in chronic disease management and some improved patient health. Policy makers, healthcare administrators, and practitioners should be aware that the evidence of CCDSS effectiveness is limited, especially with respect to the small number and size of studies measuring patient outcomes

  15. The Development of a Clinical Decision Support System for the Management of Pediatric Food Allergy.

    PubMed

    Otto, Alana K; Dyer, Ashley A; Warren, Christopher M; Walkner, Madeline; Smith, Bridget M; Gupta, Ruchi S

    2017-06-01

    Pediatricians are often first-line providers for children with food allergy. Food allergy management guidelines have been developed but are cumbersome and confusing, and significant variation exists in pediatricians' management practices. We therefore consolidated the guidelines into 5 key steps for pediatricians caring for patients with food allergy and used rapid-cycle improvement methods to create a clinical decision support system to facilitate the management of food allergy in the primary care setting. This report details the development of the Food Allergy Support Tool (FAST), its pilot testing in 4 primary care pediatric practices, and our ongoing efforts to improve its utility and ease of use. Key themes identified during these processes include the importance of both initial and ongoing provider education as well as the limitations of a tool that must be actively initiated by providers.

  16. Development and impact of computerised decision support systems for clinical management of depression: A systematic review.

    PubMed

    Triñanes, Yolanda; Atienza, Gerardo; Louro-González, Arturo; de-las-Heras-Liñero, Elena; Alvarez-Ariza, María; Palao, Diego J

    2015-01-01

    One of the proposals for improving clinical practice is to introduce computerised decision support systems (CDSS) and integrate these with electronic medical records. Accordingly, this study sought to systematically review evidence on the effectiveness of CDSS in the management of depression. A search was performed in Medline, EMBASE and PsycInfo, in order to do this. The quality of quantitative studies was assessed using the SIGN method, and qualitative studies using the CASPe checklist. Seven studies were identified (3 randomised clinical trials, 3 non-randomised trials, and one qualitative study). The CDSS assessed incorporated content drawn from guidelines and other evidence-based products. In general, the CDSS had a positive impact on different aspects, such as the screening and diagnosis, treatment, improvement in depressive symptoms and quality of life, and referral of patients. The use of CDSS could thus serve to optimise care of depression in various scenarios by providing recommendations based on the best evidence available and facilitating decision-making in clinical practice. Copyright © 2014 SEP y SEPB. Published by Elsevier España. All rights reserved.

  17. Partnering With Your Health System to Select and Implement Clinical Decision Support for Imaging.

    PubMed

    Jensen, Jeff D; Durand, Daniel J

    2017-02-01

    Recent legislation mandates the documentation of appropriateness criteria consultation when ordering advanced imaging for Medicare patients to remain eligible for reimbursement. Implementation of imaging clinical decision support (CDS) is a solution adopted by many systems to automate compliance with the new requirements. This article is intended to help radiologists who are employed by, contracted with, or otherwise affiliated with systems planning to implement CDS in the near future and ensure that they are able to understand and contribute to the process wherever possible. It includes an in-depth discussion of the legislation, evidence for and against the efficacy of imaging CDS, considerations for selecting a CDS vendor, tips for configuring CDS in a fashion consistent with departmental goals, and pointers for implementation and change management. Copyright © 2016 American College of Radiology. Published by Elsevier Inc. All rights reserved.

  18. Clinical decision support systems for improving diagnostic accuracy and achieving precision medicine.

    PubMed

    Castaneda, Christian; Nalley, Kip; Mannion, Ciaran; Bhattacharyya, Pritish; Blake, Patrick; Pecora, Andrew; Goy, Andre; Suh, K Stephen

    2015-01-01

    , and logistical concerns. Ensuring data security and protection of patient rights while simultaneously facilitating standardization is paramount to maintaining public support. The capabilities of supercomputing need to be applied strategically. A standardized, methodological implementation must be applied to developed artificial intelligence systems with the ability to integrate data and information into clinically relevant knowledge. Ultimately, the integration of bioinformatics and clinical data in a clinical decision support system promises precision medicine and cost effective and personalized patient care.

  19. A decision-supported outpatient practice system.

    PubMed Central

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

    1996-01-01

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

  20. Evaluating acceptance and user experience of a guideline-based clinical decision support system execution platform.

    PubMed

    Buenestado, David; Elorz, Javier; Pérez-Yarza, Eduardo G; Iruetaguena, Ander; Segundo, Unai; Barrena, Raúl; Pikatza, Juan M

    2013-04-01

    This study aims to determine what the initial disposition of physicians towards the use of Clinical Decision Support Systems (CDSS) based on Computerised Clinical Guidelines and Protocols (CCGP) is; and whether their prolonged utilisation has a positive effect on their intention to adopt them in the future. For a period of 3 months, 8 volunteer paediatricians monitored each up to 10 asthmatic patients using two CCGPs deployed in the-GuidesMed CDSS. A Technology Acceptance Model (TAM) questionnaire was supplied to them before and after using the system. Results from both questionnaires are analysed searching for significant improvements in opinion between them. An additional survey was performed to analyse the usability of the system. It was found that initial disposition of physicians towards e-GuidesMed is good. Improvement between the pre and post iterations of the TAM questionnaire has been found to be statistically significant. Nonetheless, slightly lower values in the Compatibility and Habit variables show that participants perceive possible difficulties to integrate e-GuidesMed into their daily routine. The variable Facilitators shows the highest correlation with the Intention to Use. Usability of the system has also been rated very high and, in this regard, no fundamental flaw has been detected. Initial views towards e-GuidesMed are positive, and become reinforced after continued utilisation of the system. In order to achieve an effective implementation, it becomes essential to facilitate conditions to integrate the system into the physician's daily routine.

  1. The Utilization of a Clinical Decision Support System to Manage Adult Type 2 Diabetes: A Correlational Study

    ERIC Educational Resources Information Center

    Faught, I. Charie

    2012-01-01

    While the Institute of Medicine (2001) has promoted health information technology to improve the process of care such as compliance with clinical practice guidelines and quicker access to clinical information, diagnostic tests, and treatment results, very little was known about how a clinical decision support system can contribute to diabetes…

  2. The Utilization of a Clinical Decision Support System to Manage Adult Type 2 Diabetes: A Correlational Study

    ERIC Educational Resources Information Center

    Faught, I. Charie

    2012-01-01

    While the Institute of Medicine (2001) has promoted health information technology to improve the process of care such as compliance with clinical practice guidelines and quicker access to clinical information, diagnostic tests, and treatment results, very little was known about how a clinical decision support system can contribute to diabetes…

  3. Clinical decision support systems for brain tumor characterization using advanced magnetic resonance imaging techniques.

    PubMed

    Tsolaki, Evangelia; Kousi, Evanthia; Svolos, Patricia; Kapsalaki, Efthychia; Theodorou, Kyriaki; Kappas, Constastine; Tsougos, Ioannis

    2014-04-28

    In recent years, advanced magnetic resonance imaging (MRI) techniques, such as magnetic resonance spectroscopy, diffusion weighted imaging, diffusion tensor imaging and perfusion weighted imaging have been used in order to resolve demanding diagnostic problems such as brain tumor characterization and grading, as these techniques offer a more detailed and non-invasive evaluation of the area under study. In the last decade a great effort has been made to import and utilize intelligent systems in the so-called clinical decision support systems (CDSS) for automatic processing, classification, evaluation and representation of MRI data in order for advanced MRI techniques to become a part of the clinical routine, since the amount of data from the aforementioned techniques has gradually increased. Hence, the purpose of the current review article is two-fold. The first is to review and evaluate the progress that has been made towards the utilization of CDSS based on data from advanced MRI techniques. The second is to analyze and propose the future work that has to be done, based on the existing problems and challenges, especially taking into account the new imaging techniques and parameters that can be introduced into intelligent systems to significantly improve their diagnostic specificity and clinical application.

  4. Designing an automated clinical decision support system to match clinical practice guidelines for opioid therapy for chronic pain

    PubMed Central

    2010-01-01

    Background Opioid prescribing for chronic pain is common and controversial, but recommended clinical practices are followed inconsistently in many clinical settings. Strategies for increasing adherence to clinical practice guideline recommendations are needed to increase effectiveness and reduce negative consequences of opioid prescribing in chronic pain patients. Methods Here we describe the process and outcomes of a project to operationalize the 2003 VA/DOD Clinical Practice Guideline for Opioid Therapy for Chronic Non-Cancer Pain into a computerized decision support system (DSS) to encourage good opioid prescribing practices during primary care visits. We based the DSS on the existing ATHENA-DSS. We used an iterative process of design, testing, and revision of the DSS by a diverse team including guideline authors, medical informatics experts, clinical content experts, and end-users to convert the written clinical practice guideline into a computable algorithm to generate patient-specific recommendations for care based upon existing information in the electronic medical record (EMR), and a set of clinical tools. Results The iterative revision process identified numerous and varied problems with the initially designed system despite diverse expert participation in the design process. The process of operationalizing the guideline identified areas in which the guideline was vague, left decisions to clinical judgment, or required clarification of detail to insure safe clinical implementation. The revisions led to workable solutions to problems, defined the limits of the DSS and its utility in clinical practice, improved integration into clinical workflow, and improved the clarity and accuracy of system recommendations and tools. Conclusions Use of this iterative process led to development of a multifunctional DSS that met the approval of the clinical practice guideline authors, content experts, and clinicians involved in testing. The process and experiences described

  5. Mobile Clinical Decision Support System for Acid-base Balance Diagnosis and Treatment Recommendation

    PubMed Central

    Mandzuka, Mensur; Begic, Edin; Boskovic, Dusanka; Begic, Zijo; Masic, Izet

    2017-01-01

    Introduction: This paper presents mobile application implementing a decision support system for acid-base disorder diagnosis and treatment recommendation. Material and methods: The application was developed using the official integrated development environment for the Android platform (to maximize availability and minimize investments in specialized hardware) called Android Studio. Results: The application identifies disorder, based on the blood gas analysis, evaluates whether the disorder has been compensated, and based on additional input related to electrolyte imbalance, provides recommendations for treatment. Conclusion: The application is a tool in the hands of the user, which provides assistance during acid-base disorders treatment. The application will assist the physician in clinical practice and is focused on the treatment in intensive care. PMID:28883678

  6. Characterizing the Access of Clinical Decision Support Offered by Immunization Information System in Minnesota

    PubMed Central

    Rajamani, Sripriya; Bieringer, Aaron; Muscoplat, Miriam

    2015-01-01

    Background: Healthy People 2020 aims to improve population health by increasing immunization rates to decrease vaccine-preventable infectious diseases. Amongst the many strategies, role of immunization information systems (IIS) are recognized by studies and taskforce reports. IIS are unique in their offering of clinical decision support for immunizations (CDSi) which are utilized by healthcare providers. Federal initiatives such as Meaningful Use (MU) and Affordable Care Act (ACA) aim to improve immunization rates through use of technology and expanding access to immunization services respectively. MU, the Electronic Health Record (EHR) incentive program includes use of IIS CDSi functionality as part of Stage 3. It is essential to understand access and use patterns of IIS CDSi, so as to utilize it better to improve immunization services. Objectives: To understand the utilization of clinical decision support for immunizations (CDSi) offered by immunization information system in Minnesota and to analyze the variability of its use across providers and EHR implementations. Methods: IIS in Minnesota (Minnesota Immunization Information Connection: MIIC) offers CDSi that is accessed through EHRs and branded as Alternate Access (AA). Data from MIIC and technical documents were reviewed to create details on organizations which implemented AA functionality. Data on EHR adoption in clinics and local health departments was obtained from Minnesota eHealth assessment reports. Data on access were tracked from January 2015 through mid-October 2015 through weekly specialized reports to track the queries by organization, volume and day of the week. Data were analyzed, findings were synthesized and reviewed with subject matter experts. Results: Currently 25 healthcare systems/organizations which represent 599 individual provider sites have implemented the AA functionality. Analysis of their EHR platform pointed to two EHRs (Epic and PH-Doc) as dominant products in Minnesota for

  7. Thinking Processes in Social Workers' Use of a Clinical Decision Support System: A Qualitative Study

    ERIC Educational Resources Information Center

    Monnickendam, Menachem; Savaya, Riki; Waysman, Mark

    2005-01-01

    The authors examined the thinking processes in the use of a decision support system (DSS) by social workers in a human services agency to determine whether they used the system to improve their case reasoning. Information was obtained from in-depth interviews with eight social workers who used a DSS in their work and from content analysis of…

  8. Efficacy of a clinical decision-support system in an HIV practice: a randomized trial.

    PubMed

    Robbins, Gregory K; Lester, William; Johnson, Kristin L; Chang, Yuchiao; Estey, Gregory; Surrao, Dominic; Zachary, Kimon; Lammert, Sara M; Chueh, Henry C; Meigs, James B; Freedberg, Kenneth A

    2012-12-04

    Data to support improved patient outcomes from clinical decision-support systems (CDSSs) are lacking in HIV care. To test the efficacy of a CDSS in improving HIV outcomes in an outpatient clinic. Randomized, controlled trial. (ClinicalTrials.gov registration number: NCT00678600) Massachusetts General Hospital HIV Clinic. HIV care providers and their patients. Computer alerts were generated for virologic failure (HIV RNA level >400 copies/mL after a previous HIV RNA level ≤400 copies/mL), evidence of suboptimal follow-up, and 11 abnormal laboratory test results. Providers received interactive computer alerts, facilitating appointment rescheduling and repeated laboratory testing, for half of their patients and static alerts for the other half. The primary end point was change in CD4 cell count. Other end points included time to clinical event, 6-month suboptimal follow-up, and severe laboratory toxicity. Thirty-three HIV care providers followed 1011 patients with HIV. In the intervention group, the mean increase in CD4 cell count was greater (0.0053 vs. 0.0032 × 109 cells/L per month; difference, 0.0021 × 109 cells/L per month [95% CI, 0.0001 to 0.004]; P = 0.040) and the rate of 6-month suboptimal follow-up was lower (20.6 vs. 30.1 events per 100 patient-years; P = 0.022) than those in the control group. Median time to next scheduled appointment was shorter in the intervention group than in the control group after a suboptimal follow-up alert (1.71 vs. 3.48 months; P < 0.001) and after a toxicity alert (2.79 vs. >6 months; P = 0.072). More than 90% of providers supported adopting the CDSS as part of standard care. This was a 1-year informatics study conducted at a single hospital subspecialty clinic. A CDSS using interactive provider alerts improved CD4 cell counts and clinic follow-up for patients with HIV. Wider implementation of such systems can provide important clinical benefits. National Institute of Allergy and Infectious Diseases.

  9. Clinical Decision Support Systems and Prevention: A Community Guide Cardiovascular Disease Systematic Review.

    PubMed

    Njie, Gibril J; Proia, Krista K; Thota, Anilkrishna B; Finnie, Ramona K C; Hopkins, David P; Banks, Starr M; Callahan, David B; Pronk, Nicolaas P; Rask, Kimberly J; Lackland, Daniel T; Kottke, Thomas E

    2015-11-01

    Clinical decision support systems (CDSSs) can help clinicians assess cardiovascular disease (CVD) risk and manage CVD risk factors by providing tailored assessments and treatment recommendations based on individual patient data. The goal of this systematic review was to examine the effectiveness of CDSSs in improving screening for CVD risk factors, practices for CVD-related preventive care services such as clinical tests and prescribed treatments, and management of CVD risk factors. An existing systematic review (search period, January 1975-January 2011) of CDSSs for any condition was initially identified. Studies of CDSSs that focused on CVD prevention in that review were combined with studies identified through an updated search (January 2011-October 2012). Data analysis was conducted in 2013. A total of 45 studies qualified for inclusion in the review. Improvements were seen for recommended screening and other preventive care services completed by clinicians, recommended clinical tests completed by clinicians, and recommended treatments prescribed by clinicians (median increases of 3.8, 4.0, and 2.0 percentage points, respectively). Results were inconsistent for changes in CVD risk factors such as systolic and diastolic blood pressure, total and low-density lipoprotein cholesterol, and hemoglobin A1C levels. CDSSs are effective in improving clinician practices related to screening and other preventive care services, clinical tests, and treatments. However, more evidence is needed from implementation of CDSSs within the broad context of comprehensive service delivery aimed at reducing CVD risk and CVD-related morbidity and mortality. Published by Elsevier Inc.

  10. [From library to clinical decision support systems: access of general practitioner to quality information].

    PubMed

    Fauquert, B

    2012-09-01

    Since 2003, the following tools have been implemented in Belgium for improving the access of general practioners to the EBM literature: the Digital Library for Health and the evidence-linker of the CEBAM, the portal EBMPracticeNet.be and the multidimensional electronic clinical decision support EBMeDS. The aim of this article is to show the progress achieved in the information dissemination toward the belgian general practioners, particularly the access from the electronic health record. From the literature published these last years, the opportunities cited by the users are for using EBM and the strong willingness for using these literature access in the future; the limits are the medical data coding, the irrelevance of the search results, the alerts fatigue induced by EBMeDS. The achievements done and planned for the new EBMPracticeNet guidelines portal and the EBMeDS system are explained in the aim of informing belgian healthcare professionals. These projects are claiming for lauching a participatory process in the production and dissemination of EBM information. The discussion is focused on the belgian healthcare system advantages, the solutions for a reasonable implementation of these projects and for increasing the place of an evidence-based information in the healthcare decision process. Finally the input of these projects to the continuing medical education and to the healthcare quality are discussed, in a context of multifactorial interaction healthcare design (complexity design).

  11. Clinical decision support system for early detection of prostate cancer from benign hyperplasia of prostate.

    PubMed

    Ghaderzadeh, Mustafa

    2013-01-01

    There has been a growing research interest in the use of intelligent methods in medical informatics studies. Intelligent computer programs were implemented to aid physicians and other medical professionals in making difficult medical decisions. Prostate Neoplasia problems including benign hyperplasia and cancer of prostate are very common and cause significant delay in recovery and often require costly investigations before coming to its diagnosis. The conventional approach to build medical diagnostic system requires the formulation of rules by which the input data can be analyzed. But the formulation of such rules is very difficult with large sets of input data. Realizing the difficulty, a number of quantitative mathematical and statistical models including pattern classification technique such as Artificial neural networks (ANN), rolled based system, discriminate analysis and regression analysis has been applied as an alternative to conventional clinical and medical diagnostic. Among the mathematical and statistical modeling techniques used in medical decision support, Artificial neural networks attract many attentions in recent studies and in the last decade, the use of neural networks has become widely accepted in medical applications. This is manifested by an increasing number of medical devices currently available on the market with embedded AI algorithms, together with an accelerating pace of publication in medical journals, with over 500 academic publications year featuring Artificial Neural Networks (ANNs).

  12. Computerized clinical decision support systems for drug prescribing and management: a decision-maker-researcher partnership systematic review.

    PubMed

    Hemens, Brian J; Holbrook, Anne; Tonkin, Marita; Mackay, Jean A; Weise-Kelly, Lorraine; Navarro, Tamara; Wilczynski, Nancy L; Haynes, R Brian

    2011-08-03

    Computerized clinical decision support systems (CCDSSs) for drug therapy management are designed to promote safe and effective medication use. Evidence documenting the effectiveness of CCDSSs for improving drug therapy is necessary for informed adoption decisions. The objective of this review was to systematically review randomized controlled trials assessing the effects of CCDSSs for drug therapy management on process of care and patient outcomes. We also sought to identify system and study characteristics that predicted benefit. We conducted a decision-maker-researcher partnership systematic review. We updated our earlier reviews (1998, 2005) by searching MEDLINE, EMBASE, EBM Reviews, Inspec, and other databases, and consulting reference lists through January 2010. Authors of 82% of included studies confirmed or supplemented extracted data. We included only randomized controlled trials that evaluated the effect on process of care or patient outcomes of a CCDSS for drug therapy management compared to care provided without a CCDSS. A study was considered to have a positive effect (i.e., CCDSS showed improvement) if at least 50% of the relevant study outcomes were statistically significantly positive. Sixty-five studies met our inclusion criteria, including 41 new studies since our previous review. Methodological quality was generally high and unchanged with time. CCDSSs improved process of care performance in 37 of the 59 studies assessing this type of outcome (64%, 57% of all studies). Twenty-nine trials assessed patient outcomes, of which six trials (21%, 9% of all trials) reported improvements. CCDSSs inconsistently improved process of care measures and seldomly improved patient outcomes. Lack of clear patient benefit and lack of data on harms and costs preclude a recommendation to adopt CCDSSs for drug therapy management.

  13. Personalised management of women with cervical abnormalities using a clinical decision support scoring system.

    PubMed

    Kyrgiou, Maria; Pouliakis, Abraham; Panayiotides, John G; Margari, Niki; Bountris, Panagiotis; Valasoulis, George; Paraskevaidi, Maria; Bilirakis, Evripidis; Nasioutziki, Maria; Loufopoulos, Aristotelis; Haritou, Maria; Koutsouris, Dimitrios D; Karakitsos, Petros; Paraskevaidis, Evangelos

    2016-04-01

    To develop a clinical decision support scoring system (DSSS) based on artificial neural networks (ANN) for personalised management of women with cervical abnormalities. We recruited women with cervical abnormalities and healthy controls that attended for opportunistic screening between 2006 and 2014 in 3 University Hospitals. We prospectively collected detailed patient characteristics, the colposcopic impression and performed a series of biomarkers using a liquid-based cytology sample. These included HPV DNA typing, E6&E7 mRNA by NASBA or flow cytometry and p16INK4a immunostaining. We used ANNs to combine the cytology and biomarker results and develop a clinical DSSS with the aim to improve the diagnostic accuracy of tests and quantify the individual's risk for different histological diagnoses. We used histology as the gold standard. We analysed data from 2267 women that had complete or partial dataset of clinical and molecular data during their initial or followup visits (N=3565). Accuracy parameters (sensitivity, specificity, positive and negative predictive values) were assessed for the cytological result and/or HPV status and for the DSSS. The ANN predicted with higher accuracy the chances of high-grade (CIN2+), low grade (HPV/CIN1) and normal histology than cytology with or without HPV test. The sensitivity for prediction of CIN2 or worse was 93.0%, specificity 99.2% with high positive (93.3%) and negative (99.2%) predictive values. The DSSS based on an ANN of multilayer perceptron (MLP) type, can predict with the highest accuracy the histological diagnosis in women with abnormalities at cytology when compared with the use of tests alone. A user-friendly software based on this technology could be used to guide clinician decision making towards a more personalised care. Copyright © 2016 Elsevier Inc. All rights reserved.

  14. icuARM-An ICU Clinical Decision Support System Using Association Rule Mining

    PubMed Central

    Chanani, Nikhil; Venugopalan, Janani; Maher, Kevin; Wang, May Dongmei

    2013-01-01

    The rapid development of biomedical monitoring technologies has enabled modern intensive care units (ICUs) to gather vast amounts of multimodal measurement data about their patients. However, processing large volumes of complex data in real-time has become a big challenge. Together with ICU physicians, we have designed and developed an ICU clinical decision support system icuARM based on associate rule mining (ARM), and a publicly available research database MIMIC-II (Multi-parameter Intelligent Monitoring in Intensive Care II) that contains more than 40,000 ICU records for 30,000+patients. icuARM is constructed with multiple association rules and an easy-to-use graphical user interface (GUI) for care providers to perform real-time data and information mining in the ICU setting. To validate icuARM, we have investigated the associations between patients' conditions such as comorbidities, demographics, and medications and their ICU outcomes such as ICU length of stay. Coagulopathy surfaced as the most dangerous co-morbidity that leads to the highest possibility (54.1%) of prolonged ICU stay. In addition, women who are older than 50 years have the highest possibility (38.8%) of prolonged ICU stay. For clinical conditions treatable with multiple drugs, icuARM suggests that medication choice can be optimized based on patient-specific characteristics. Overall, icuARM can provide valuable insights for ICU physicians to tailor a patient's treatment based on his or her clinical status in real time. PMID:27170860

  15. What We Can Learn from Amazon for Clinical Decision Support Systems.

    PubMed

    Abid, Sidra; Keshavjee, Karim; Karim, Arsalan; Guergachi, Aziz

    2017-01-01

    Health care continue to lag behind other industries, such as retail and financial services, in the use of decision-support-like tools. Amazon is particularly prolific in the use of advanced predictive and prescriptive analytics to assist its customers to purchase more, while increasing satisfaction, retention, repeat-purchases and loyalty. How can we do the same in health care? In this paper, we explore various elements of the Amazon website and Amazon's data science and big data practices to gather inspiration for re-designing clinical decision support in the health care sector. For each Amazon element we identified, we present one or more clinical applications to help us better understand where Amazon's.

  16. Transforming User Needs into Functional Requirements for an Antibiotic Clinical Decision Support System

    PubMed Central

    Bright, T.J.

    2013-01-01

    Summary Background Many informatics studies use content analysis to generate functional requirements for system development. Explication of this translational process from qualitative data to functional requirements can strengthen the understanding and scientific rigor when applying content analysis in informatics studies. Objective To describe a user-centered approach transforming emergent themes derived from focus group data into functional requirements for informatics solutions and to illustrate these methods to the development of an antibiotic clinical decision support system (CDS). Methods The approach consisted of five steps: 1) identify unmet therapeutic planning information needs via Focus Group Study-I, 2) develop a coding framework of therapeutic planning themes to refine the domain scope to antibiotic therapeutic planning, 3) identify functional requirements of an antibiotic CDS system via Focus Group Study-II, 4) discover informatics solutions and functional requirements from coded data, and 5) determine the types of information needed to support the antibiotic CDS system and link with the identified informatics solutions and functional requirements. Results The coding framework for Focus Group Study-I revealed unmet therapeutic planning needs. Twelve subthemes emerged and were clustered into four themes; analysis indicated a need for an antibiotic CDS intervention. Focus Group Study-II included five types of information needs. Comments from the Barrier/Challenge to information access and Function/Feature themes produced three informatics solutions and 13 functional requirements of an antibiotic CDS system. Comments from the Patient, Institution, and Domain themes generated required data elements for each informatics solution. Conclusion This study presents one example explicating content analysis of focus group data and the analysis process to functional requirements from narrative data. Illustration of this 5-step method was used to develop an

  17. Cost-Effectiveness of an Electronic Medical Record Based Clinical Decision Support System

    PubMed Central

    Gilmer, Todd P; O'Connor, Patrick J; Sperl-Hillen, JoAnn M; Rush, William A; Johnson, Paul E; Amundson, Gerald H; Asche, Stephen E; Ekstrom, Heidi L

    2012-01-01

    Background and Objective Medical groups have invested billions of dollars in electronic medical records (EMRs), but few studies have examined the cost-effectiveness of EMR-based clinical decision support (CDS). This study examined the cost-effectiveness of EMR-based CDS for adults with diabetes from the perspective of the health care system. Data Sources/Setting Clinical outcome and cost data from a randomized clinical trial of EMR-based CDS were used as inputs into a diabetes simulation model. The simulation cohort included 1,092 patients with diabetes with A1c above goal at baseline. Study Design The United Kingdom Prospective Diabetes Study Outcomes Model, a validated simulation model of diabetes, was used to evaluate remaining life years, quality-adjusted life years (QALYs), and health care costs over patient lifetimes (40-year time horizon) from the health system perspective. Principal Findings Patients in the intervention group had significantly lowered A1c (0.26 percent, p = .014) relative to patients in the control arm. Intervention costs were $120 (SE = 45) per patient in the first year and $76 (SE = 45) per patient in the following years. In the base case analysis, EMR-based CDS increased lifetime QALYs by 0.04 (SE = 0.01) and increased lifetime costs by $112 (SE = 660), resulting in an incremental cost-effectiveness ratio of $3,017 per QALY. The cost-effectiveness of EMR-based CDS persisted in one-way, two-way, and probabilistic sensitivity analyses. Conclusions Widespread adoption of sophisticated EMR-based CDS has the potential to modestly improve the quality of care for patients with chronic conditions without substantially increasing costs to the health care system. PMID:22578085

  18. 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.

  19. Clinical Decision Support System to Enhance Quality Control of Spirometry Using Information and Communication Technologies

    PubMed Central

    2014-01-01

    Background We recently demonstrated that quality of spirometry in primary care could markedly improve with remote offline support from specialized professionals. It is hypothesized that implementation of automatic online assessment of quality of spirometry using information and communication technologies may significantly enhance the potential for extensive deployment of a high quality spirometry program in integrated care settings. Objective The objective of the study was to elaborate and validate a Clinical Decision Support System (CDSS) for automatic online quality assessment of spirometry. Methods The CDSS was done through a three step process including: (1) identification of optimal sampling frequency; (2) iterations to build-up an initial version using the 24 standard spirometry curves recommended by the American Thoracic Society; and (3) iterations to refine the CDSS using 270 curves from 90 patients. In each of these steps the results were checked against one expert. Finally, 778 spirometry curves from 291 patients were analyzed for validation purposes. Results The CDSS generated appropriate online classification and certification in 685/778 (88.1%) of spirometry testing, with 96% sensitivity and 95% specificity. Conclusions Consequently, only 93/778 (11.9%) of spirometry testing required offline remote classification by an expert, indicating a potential positive role of the CDSS in the deployment of a high quality spirometry program in an integrated care setting. PMID:25600957

  20. Usability Evaluation of a Clinical Decision Support System for Geriatric ED Pain Treatment.

    PubMed

    Genes, Nicholas; Kim, Min Soon; Thum, Frederick L; Rivera, Laura; Beato, Rosemary; Song, Carolyn; Soriano, Jared; Kannry, Joseph; Baumlin, Kevin; Hwang, Ula

    2016-01-01

    Older adults are at risk for inadequate emergency department (ED) pain care. Unrelieved acute pain is associated with poor outcomes. Clinical decision support systems (CDSS) hold promise to improve patient care, but CDSS quality varies widely, particularly when usability evaluation is not employed. To conduct an iterative usability and redesign process of a novel geriatric abdominal pain care CDSS. We hypothesized this process would result in the creation of more usable and favorable pain care interventions. Thirteen emergency physicians familiar with the Electronic Health Record (EHR) in use at the study site were recruited. Over a 10-week period, 17 1-hour usability test sessions were conducted across 3 rounds of testing. Participants were given 3 patient scenarios and provided simulated clinical care using the EHR, while interacting with the CDSS interventions. Quantitative System Usability Scores (SUS), favorability scores and qualitative narrative feedback were collected for each session. Using a multi-step review process by an interdisciplinary team, positive and negative usability issues in effectiveness, efficiency, and satisfaction were considered, prioritized and incorporated in the iterative redesign process of the CDSS. Video analysis was used to determine the appropriateness of the CDS appearances during simulated clinical care. Over the 3 rounds of usability evaluations and subsequent redesign processes, mean SUS progressively improved from 74.8 to 81.2 to 88.9; mean favorability scores improved from 3.23 to 4.29 (1 worst, 5 best). Video analysis revealed that, in the course of the iterative redesign processes, rates of physicians' acknowledgment of CDS interventions increased, however most rates of desired actions by physicians (such as more frequent pain score updates) decreased. The iterative usability redesign process was instrumental in improving the usability of the CDSS; if implemented in practice, it could improve geriatric pain care. The

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

    PubMed

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

    2013-08-01

    Clinical decision-support systems (CDSSs) comprise systems as diverse as sophisticated platforms to store and manage clinical data, tools to alert clinicians of problematic situations, or decision-making tools to assist clinicians. Irrespective of the kind of decision-support task CDSSs should be smoothly integrated within the clinical information system, interacting with other components, in particular with the electronic health record (EHR). However, despite decades of developments, most CDSSs lack interoperability features. We deal with the interoperability problem of CDSSs and EHRs by exploiting the dual-model methodology. This methodology distinguishes a reference model and archetypes. A reference model is represented by a stable and small object-oriented model that describes the generic properties of health record information. For their part, archetypes are reusable and domain-specific definitions of clinical concepts in the form of structured and constrained combinations of the entities of the reference model. We rely on archetypes to make the CDSS compatible with EHRs from different institutions. Concretely, we use archetypes for modelling the clinical concepts that the CDSS requires, in conjunction with a series of knowledge-intensive mappings relating the archetypes to the data sources (EHR and/or other archetypes) they depend on. We introduce a comprehensive approach, including a set of tools as well as methodological guidelines, to deal with the interoperability of CDSSs and EHRs based on archetypes. Archetypes are used to build a conceptual layer of the kind of a virtual health record (VHR) over the EHR whose contents need to be integrated and used in the CDSS, associating them with structural and terminology-based semantics. Subsequently, the archetypes are mapped to the EHR by means of an expressive mapping language and specific-purpose tools. We also describe a case study where the tools and methodology have been employed in a CDSS to support

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

    PubMed Central

    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. PMID:28269880

  3. 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.

  4. Reducing Risk with Clinical Decision Support

    PubMed Central

    Maloney, F.L.; Feblowitz, J.; Samal, L.; Sato, L.; Wright, A.

    2014-01-01

    Summary Objective Identify clinical opportunities to intervene to prevent a malpractice event and determine the proportion of malpractice claims potentially preventable by clinical decision support (CDS). Materials and Methods Cross-sectional review of closed malpractice claims over seven years from one malpractice insurance company and seven hospitals in the Boston area. For each event, clinical opportunities to intervene to avert the malpractice event and the presence or absence of CDS that might have a role in preventing the event, were assigned by a panel of expert raters. Compensation paid out to resolve a claim (indemnity), was associated with each CDS type. Results Of the 477 closed malpractice cases, 359 (75.3%) were categorized as substantiated and 195 (54%) had at least one opportunity to intervene. Common opportunities to intervene related to performance of procedure, diagnosis, and fall prevention. We identified at least one CDS type for 63% of substantiated claims. The 41 CDS types identified included clinically significant test result alerting, diagnostic decision support and electronic tracking of instruments. Cases with at least one associated intervention accounted for $40.3 million (58.9%) of indemnity. Discussion CDS systems and other forms of health information technology (HIT) are expected to improve quality of care, but their potential to mitigate risk had not previously been quantified. Our results suggest that, in addition to their known benefits for quality and safety, CDS systems within HIT have a potential role in decreasing malpractice payments. Conclusion More than half of malpractice events and over $40 million of indemnity were potentially preventable with CDS. PMID:25298814

  5. Evaluation of clinical rules in a standalone pharmacy based clinical decision support system for hospitalized and nursing home patients.

    PubMed

    de Wit, Hugo A J M; Mestres Gonzalvo, Carlota; Cardenas, Jenny; Derijks, Hieronymus J; Janknegt, Rob; van der Kuy, Paul-Hugo M; Winkens, Bjorn; Schols, Jos M G A

    2015-06-01

    To improve the current standalone pharmacy clinical decision support system (CDSS) by identifying and quantifying the benefits and limitations of the system. Alerts and handling of the executed clinical rules were extracted from the CDSS from the period September 2011 to December 2011. The number of executed clinical rule alerts, number of actions on alerts, and the reason why alerts were classified as not relevant were analyzed. The alerts where considered clinically relevant when the pharmacist needed to contact the physician. The 4065 alerts have been separated into: 1137 (28.0%) new alerts, 2797 (68.8%) repeat alerts and 131 (3.2%) double alerts. When the alerts were analyzed, only 3.6% were considered clinically relevant. Reasons why alerts were considered as not to be relevant were: (a) the dosage was correct or already adjusted, (b) the drug was (temporarily) stopped and (c) the monitored laboratory value or drug dosage had already reverted to be within the reference limits. The reasons for no action were linked to three categorical limitations of the used system: 'algorithm alert criteria', 'CDSS optimization', and 'data delivery'. This study highlighted a number of ways in which the CDSS could be improved. These different aspects have been identified as important for developing an efficient CDSS. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

  6. Four principles for user interface design of computerised clinical decision support systems.

    PubMed

    Kanstrup, Anne Marie; Christiansen, Marion Berg; Nøhr, Christian

    2011-01-01

    The paper presents results from a design research project of a user interface (UI) for a Computerised Clinical Decision Support System (CDSS). The ambition has been to design Human-Computer Interaction (HCI) that can minimise medication errors. Through an iterative design process a digital prototype for prescription of medicine has been developed. This paper presents results from the formative evaluation of the prototype conducted in a simulation laboratory with ten participating physicians. Data from the simulation is analysed by use of theory on how users perceive information. The conclusion is a model, which sum up four principles of interaction for design of CDSS. The four principles for design of user interfaces for CDSS are summarised as four A's: All in one, At a glance, At hand and Attention. The model emphasises integration of all four interaction principles in the design of user interfaces for CDSS, i.e. the model is an integrated model which we suggest as a guide for interaction design when working with preventing medication errors.

  7. 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.

  8. Solar energy decision support system

    NASA Astrophysics Data System (ADS)

    Ramachandra, T. V.; Rajeev Kumar, J.; Vamsee Krishna, S.; Shruthi, B. V.

    2006-03-01

    Energy plays a prominent role in human society. As a result of technological and industrial developments, the demand for energy is rapidly increasing. Existing power sources that are mainly fossil fuel based are leaving an unacceptable legacy of waste and pollution apart from diminishing stock of fuels. Hence, the focus is now shifted to large-scale propagation of renewable energy. Renewable energy technologies are clean sources of energy that have a much lower environmental impact than conventional energy technologies. Solar energy is one such renewable energy. Most renewable energy comes either directly or indirectly from the sun. Estimation of solar energy potential of a region requires detailed solar radiation climatology, and it is necessary to collect extensive radiation data of high accuracy covering all climatic zones of the region. In this regard, a decision support system (DSS) would help to estimate solar energy potential considering the regions’ energy requirement. This article explains the design and implementation of DSS for assessment of solar energy. The DSS with executive information systems and reporting tools helps to tap vast data resources and deliver information. The main hypothesis is that this tool can be used to form a core of practical methodology that is resilient and can be used by decision-making bodies to assess various scenarios. It also offers means of entering, accessing and interpreting the information for the purpose of sound decision-making.

  9. Solar energy decision support system

    NASA Astrophysics Data System (ADS)

    Ramachandra, T. V.; Jha, Rajeev Kumar; Vamsee Krishna, S.; Shruthi, B. V.

    2005-12-01

    Energy plays a prominent role in human society. As a result of technological and industrial development, the demand for energy is rapidly increasing. Existing power sources that are mainly fossil fuel based are leaving an unacceptable legacy of waste and pollution apart from diminishing stock of fuels. Hence, the focus is now shifted to large-scale propagation of renewable energy. Renewable energy technologies are clean sources of energy that have a much lower environmental impact than conventional energy technologies. Solar energy is one such renewable energy. Most renewable energy comes either directly or indirectly from the sun. Estimation of solar energy potential of a region requires detailed solar radiation climatology, and it is necessary to collect extensive radiation data of high accuracy covering all climatic zones of the region. In this regard, a decision support system (DSS) would help in estimating solar energy potential considering the region's energy requirement. This article explains the design and implementation of DSS for assessment of solar energy. The DSS with executive information systems and reporting tools helps to tap vast data resources and deliver information. The main hypothesis is that this tool can be used to form a core of practical methodology that will result in more resilient in time and can be used by decision-making bodies to assess various scenarios. It also offers means of entering, accessing, and interpreting the information for the purpose of sound decision making.

  10. Query Reformulation for Clinical Decision Support Search

    DTIC Science & Technology

    2014-11-01

    Query Reformulation for Clinical Decision Support Search Luca Soldaini, Arman Cohan, Andrew Yates, Nazli Goharian, Ophir Frieder Information...work, we present a query reformulation approach that addresses the unique formulation of case reports, making them suitable to be used on a general... reformulation approach does not directly take into account the generic question type (diagnosis, test, treatment) provided with each approach. To ameliorate

  11. Development of EBM-CDSS (Evidence-Based Clinical Decision Support System) to AIG Prognostication in Terminally Ill Patients

    DTIC Science & Technology

    Goal of the project is to develop an Evidence-based Clinical Decision Support (CDSS- EBM ) system and make it available at the point of care to improve...prognostication of the life expectancy of terminally ill patients to improve referral of patients to hospice. In addition, the EBM -CDSS will be

  12. An Automated System for Generating Situation-Specific Decision Support in Clinical Order Entry from Local Empirical Data

    ERIC Educational Resources Information Center

    Klann, Jeffrey G.

    2011-01-01

    Clinical Decision Support is one of the only aspects of health information technology that has demonstrated decreased costs and increased quality in healthcare delivery, yet it is extremely expensive and time-consuming to create, maintain, and localize. Consequently, a majority of health care systems do not utilize it, and even when it is…

  13. An Automated System for Generating Situation-Specific Decision Support in Clinical Order Entry from Local Empirical Data

    ERIC Educational Resources Information Center

    Klann, Jeffrey G.

    2011-01-01

    Clinical Decision Support is one of the only aspects of health information technology that has demonstrated decreased costs and increased quality in healthcare delivery, yet it is extremely expensive and time-consuming to create, maintain, and localize. Consequently, a majority of health care systems do not utilize it, and even when it is…

  14. A feasibility study for a clinical decision support system prompting HIV testing.

    PubMed

    Chadwick, D R; Hall, C; Rae, C; Rayment, Ml; Branch, M; Littlewood, J; Sullivan, A

    2017-07-01

    Levels of undiagnosed HIV infection and late presentation remain high globally despite attempts to increase testing. The objective of this study was to evaluate a risk-based prototype application to prompt HIV testing when patients undergo routine blood tests. Two computer physician order entry (CPOE) systems were modified using the application to prompt health care workers (HCWs) to add an HIV test when other tests selected suggested that the patient was at higher risk of HIV infection. The application was applied for a 3-month period in two areas, in a large London hospital and in general practices in Teesside/North Yorkshire. At the end of the evaluation period, HCWs were interviewed to assess the usability and acceptability of the prompt. Numbers of HIV tests ordered in the general practice areas were also compared before and after the prompt's introduction. The system was found to be both useable and generally acceptable to hospital doctors, general practitioners and nurse practitioners, with little evidence of prompt/alert fatigue. The issue of the prompt appearing late in the patient consultation did lead to some difficulties, particularly around discussion of the test and consent. In the general practices, around 1 in 10 prompts were accepted and there was a 6% increase in testing rates over the 3-month study period (P = 0.169). Using a CPOE-based clinical decision support application to prompt HIV testing appears both feasible and acceptable to HCWs. Refining the application to provide more accurate risk stratification is likely to make it more effective. © 2016 British HIV Association.

  15. Usability of clinical decision support system as a facilitator for learning the assistive technology adaptation process.

    PubMed

    Danial-Saad, Alexandra; Kuflik, Tsvi; Weiss, Patrice L Tamar; Schreuer, Naomi

    2016-01-01

    The aim of this study was to evaluate the usability of Ontology Supported Computerized Assistive Technology Recommender (OSCAR), a Clinical Decision Support System (CDSS) for the assistive technology adaptation process, its impact on learning the matching process, and to determine the relationship between its usability and learnability. Two groups of expert and novice clinicians (total, n = 26) took part in this study. Each group filled out system usability scale (SUS) to evaluate OSCAR's usability. The novice group completed a learning questionnaire to assess OSCAR's effect on their ability to learn the matching process. Both groups rated OSCAR's usability as "very good", (M [SUS] = 80.7, SD = 11.6, median = 83.7) by the novices, and (M [SUS] = 81.2, SD = 6.8, median = 81.2) by the experts. The Mann-Whitney results indicated that no significant differences were found between the expert and novice groups in terms of OSCAR's usability. A significant positive correlation existed between the usability of OSCAR and the ability to learn the adaptation process (rs = 0.46, p = 0.04). Usability is an important factor in the acceptance of a system. The successful application of user-centered design principles during the development of OSCAR may serve as a case study that models the significant elements to be considered, theoretically and practically in developing other systems. Implications for Rehabilitation Creating a CDSS with a focus on its usability is an important factor for its acceptance by its users. Successful usability outcomes can impact the learning process of the subject matter in general, and the AT prescription process in particular. The successful application of User-Centered Design principles during the development of OSCAR may serve as a case study that models the significant elements to be considered, theoretically and practically. The study emphasizes the importance of close collaboration between the developers and

  16. 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.

  17. Personalizing Drug Selection Using Advanced Clinical Decision Support.

    PubMed

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

    2009-06-23

    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.

  18. 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

  19. The Effect of Level of Patient Acuity, Critical Care Experience, and ACLS Certification on Clinical Decision Making: Implications for Computer Decision Support Systems

    PubMed Central

    Henry, Suzanne Bakken

    1990-01-01

    This study examined the effect of patient acuity, critical care experience, and ACLS certification on clinical decision making. Each subject (N=68) completed two computerized clinical simulations. Ventricular tachycardia (VT) represented the high acuity situation and atrial flutter (AF) the lower acuity situation. Clinical decision making was measured by proficiency score, patient outcome (cure/die), and amount of data collected. In the AF simulation, proficiency scores were higher (p=.000), more dysrhythmias were cured (p<.005), and more data were collected (p=.040) than in the VT simulation. Experienced and inexperienced nurses did not differ on proficiency score, however, inexperienced nurses collected more data (p=.048) and cured fewer atrial flutter simulations (p=.04). ACLS certified nurses had higher proficiency scores (p=.033) and collected less data (p=.048). Clinical decision making on two simulations was affected by patient acuity, critical care experience, and ACLS certification. These findings have implications for the design and implementation of clinical decision support systems.

  20. The expenditure of computer-related worktime using clinical decision support systems in chronic pain therapy.

    PubMed

    Hecht, Timm; Bundscherer, Anika C; Lassen, Christoph L; Lindenberg, Nicole; Graf, Bernhard M; Ittner, Karl-Peter; Wiese, Christoph H R

    2015-08-01

    Estimate the expenditure of computer-related worktime resulting from the use of clinical decision support systems (CDSS) to prevent adverse drug reactions (ADR) among patients undergoing chronic pain therapy and compare the employed check systems with respect to performance and practicability. Data were collected retrospectively from 113 medical records of patients under chronic pain therapy during 2012/2013. Patient-specific medications were checked for potential drug-drug interactions (DDI) using two publicly available CDSS, Apotheken Umschau (AU) and Medscape (MS), and a commercially available CDSS AiDKlinik® (AID). The time needed to analyze patient pharmacotherapy for DDIs was taken with a stopwatch. Measurements included the time needed for running the analysis and printing the results. CDSS were compared with respect to the expenditure of time and usability. Only patient pharmacotherapies with at least two prescribed drugs and fitting the criteria of the corresponding CDSS were analyzed. Additionally, a qualitative evaluation of the used check systems was performed, employing a questionnaire asking five pain physicians to compare and rate the performance and practicability of the three CDSSs. The AU tool took a total of 3:55:45 h with an average of 0:02:32 h for 93 analyzed patient regimens and led to the discovery of 261 DDIs. Using the Medscape interaction checker required a total of 1:28:35 h for 38 patients with an average of 0:01:58 h and a yield of 178 interactions. The CDSS AID required a total of 3:12:27 h for 97 patients with an average time of analysis of 0:01:59 h and the discovery of 170 DDIs. According to the pain physicians the CDSS AID was chosen as the preferred tool. Applying a CDSS to examine a patients drug regimen for potential DDIs causes an average extra expenditure of work time of 2:09 min, which extends patient treatment time by 25 % on average. Nevertheless, the authors believe that the extra expenditure of time employing a CDSS is

  1. The Effects of Clinical Decision Support Systems on Medication Safety: An Overview

    PubMed Central

    Jia, Pengli; Zhang, Longhao; Chen, Jingjing; Zhao, Pujing; Zhang, Mingming

    2016-01-01

    Background The clinical decision support system(CDSS) has potential to improving medication safety. However, the effects of the intervention were conflicting and uncertain. Meanwhile, the reporting and methodological quality of this field were unknown. Objective The aim of this overview is to evaluate the effects of CDSS on medication safety and to examine the methodological and reporting quality. Methods PubMed, Embase and Cochrane Library were searched to August 2015. Systematic reviews (SRs) investigating the effects of CDSS on medication safety were included. Outcomes were determined in advance and assessed separately for process of care and patient outcomes. The methodological quality was assessed by Assessment of Multiple Systematic Reviews (AMSTAR) and the reporting quality was examined by Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). Results Twenty systematic reviews, consisting of 237 unique randomized controlled trials(RCTs) and 176 non-RCTs were included. Evidence that CDSS significantly impacted process of care was found in 108 out of 143 unique studies of the 16 SRs examining this effect (75%). Only 18 out of 90 unique studies of the 13 SRs reported significantly evidence that CDSS positively impacted patient outcomes (20%). Ratings for the overall scores of AMSTAR resulted in a mean score of 8.3 with a range of scores from 7.5 to 10.5. The reporting quality was varied. Some contents were particularly strong. However, some contents were poor. Conclusions CDSS reduces medication error by obviously improving process of care and inconsistently improving patient outcomes. Larger samples and longer-term studies are required to ensure more reliable evidence base on the effects of CDSS on patient outcomes. The methodological and reporting quality were varied and some realms need to be improved. PMID:27977697

  2. Real-time clinical decision support system with data stream mining.

    PubMed

    Zhang, Yang; Fong, Simon; Fiaidhi, Jinan; Mohammed, Sabah

    2012-01-01

    This research aims to describe a new design of data stream mining system that can analyze medical data stream and make real-time prediction. The motivation of the research is due to a growing concern of combining software technology and medical functions for the development of software application that can be used in medical field of chronic disease prognosis and diagnosis, children healthcare, diabetes diagnosis, and so forth. Most of the existing software technologies are case-based data mining systems. They only can analyze finite and structured data set and can only work well in their early years and can hardly meet today's medical requirement. In this paper, we describe a clinical-support-system based data stream mining technology; the design has taken into account all the shortcomings of the existing clinical support systems.

  3. User Centered Clinical Decision Support Tools

    PubMed Central

    Sofianou, A.; Kannry, J.; Mann, D.M.; McGinn, T.G.

    2014-01-01

    Summary Background Dissemination and adoption of clinical decision support (CDS) tools is a major initiative of the Affordable Care Act’s Meaningful Use program. Adoption of CDS tools is multipronged with personal, organizational, and clinical settings factoring into the successful utilization rates. Specifically, the diffusion of innovation theory implies that ‘early adopters’ are more inclined to use CDS tools and younger physicians tend to be ranked in this category. Objective This study examined the differences in adoption of CDS tools across providers’ training level. Participants From November 2010 to 2011, 168 residents and attendings from an academic medical institution were enrolled into a randomized controlled trial. Intervention The intervention arm had access to the CDS tool through the electronic health record (EHR) system during strep and pneumonia patient visits. Main Measures The EHR system recorded details on how intervention arm interacted with the CDS tool including acceptance of the initial CDS alert, completion of risk-score calculators and the signing of medication order sets. Using the EHR data, the study performed bivariate tests and general estimating equation (GEE) modeling to examine the differences in adoption of the CDS tool across residents and attendings. Key Results The completion rates of the CDS calculator and medication order sets were higher amongst first year residents compared to all other training levels. Attendings were the less likely to accept the initial step of the CDS tool (29.3%) or complete the medication order sets (22.4%) that guided their prescription decisions, resulting in attendings ordering more antibiotics (37.1%) during an CDS encounter compared to residents. Conclusion There is variation in adoption of CDS tools across training levels. Attendings tended to accept the tool less but ordered more medications. CDS tools should be tailored to clinicians’ training levels. PMID:25589914

  4. Strategic Decision Making and Group Decision Support Systems.

    ERIC Educational Resources Information Center

    McGrath, Michael Robert

    1986-01-01

    Institutional strategic decisions require the participation of every individual with a significant stake in the solution, and group decision support systems are being developed to respond to the political and consensual problems of collective decision-making. (MSE)

  5. Prototype to practice: Developing and testing a clinical decision support system for secondary stroke prevention in a veterans healthcare facility.

    PubMed

    Anderson, Jane A; Willson, Pamela; Peterson, Nancy J; Murphy, Chris; Kent, Thomas A

    2010-01-01

    A clinical decision support system that guides nurse practitioners and other healthcare providers in secondary stroke prevention was developed by a multidisciplinary team with funding received from the Veterans Health Administration Office of Nursing Services. This article presents alpha-testing results obtained while using an integrated model for clinical decision support system development that emphasizes end-user perspectives throughout the development process. Before-after and descriptive methods were utilized to evaluate functionality and usability of the prototype among a sample of multidisciplinary clinicians. The predominant functionality feature of the tool is automated prompting and documentation of secondary stroke prevention guidelines in the electronic medical record. Documentation of guidelines was compared among multidisciplinary providers (N = 15) using test case scenarios and two documentation systems, standard versus the prototype. Usability was evaluated with an investigator-developed questionnaire and one open-ended question. The prototype prompted a significant increase (P < .05) in provider documentation for six of 11 guidelines as compared with baseline documentation while using the standard system. Of a possible 56 points, usability was scored high (mean, 48.9 [SD, 6.8]). These results support that guideline prompting has been successfully engineered to produce a usable and useful clinical decision support system for secondary stroke prevention.

  6. Publication, discovery and interoperability of Clinical Decision Support Systems: A Linked Data approach.

    PubMed

    Marco-Ruiz, Luis; Pedrinaci, Carlos; Maldonado, J A; Panziera, Luca; Chen, Rong; Bellika, J Gustav

    2016-08-01

    The high costs involved in the development of Clinical Decision Support Systems (CDSS) make it necessary to share their functionality across different systems and organizations. Service Oriented Architectures (SOA) have been proposed to allow reusing CDSS by encapsulating them in a Web service. However, strong barriers in sharing CDS functionality are still present as a consequence of lack of expressiveness of services' interfaces. Linked Services are the evolution of the Semantic Web Services paradigm to process Linked Data. They aim to provide semantic descriptions over SOA implementations to overcome the limitations derived from the syntactic nature of Web services technologies. To facilitate the publication, discovery and interoperability of CDS services by evolving them into Linked Services that expose their interfaces as Linked Data. We developed methods and models to enhance CDS SOA as Linked Services that define a rich semantic layer based on machine interpretable ontologies that powers their interoperability and reuse. These ontologies provided unambiguous descriptions of CDS services properties to expose them to the Web of Data. We developed models compliant with Linked Data principles to create a semantic representation of the components that compose CDS services. To evaluate our approach we implemented a set of CDS Linked Services using a Web service definition ontology. The definitions of Web services were linked to the models developed in order to attach unambiguous semantics to the service components. All models were bound to SNOMED-CT and public ontologies (e.g. Dublin Core) in order to count on a lingua franca to explore them. Discovery and analysis of CDS services based on machine interpretable models was performed reasoning over the ontologies built. Linked Services can be used effectively to expose CDS services to the Web of Data by building on current CDS standards. This allows building shared Linked Knowledge Bases to provide machine

  7. ClinicalAccess: a clinical decision support tool.

    PubMed

    Crowell, Karen; Vardell, Emily

    2015-01-01

    ClinicalAccess is a new clinical decision support tool that uses a question-and-answer format to mirror clinical decision-making strategies. The unique format of ClinicalAccess delivers concise, authoritative answers to more than 120,000 clinical questions. This column presents a review of the product, a sample search, and a comparison with other point-of-care search engines.

  8. Medical Question Answering for Clinical Decision Support.

    PubMed

    Goodwin, Travis R; Harabagiu, Sanda M

    2016-10-01

    The goal of modern Clinical Decision Support (CDS) systems is to provide physicians with information relevant to their management of patient care. When faced with a medical case, a physician asks questions about the diagnosis, the tests, or treatments that should be administered. Recently, the TREC-CDS track has addressed this challenge by evaluating results of retrieving relevant scientific articles where the answers of medical questions in support of CDS can be found. Although retrieving relevant medical articles instead of identifying the answers was believed to be an easier task, state-of-the-art results are not yet sufficiently promising. In this paper, we present a novel framework for answering medical questions in the spirit of TREC-CDS by first discovering the answer and then selecting and ranking scientific articles that contain the answer. Answer discovery is the result of probabilistic inference which operates on a probabilistic knowledge graph, automatically generated by processing the medical language of large collections of electronic medical records (EMRs). The probabilistic inference of answers combines knowledge from medical practice (EMRs) with knowledge from medical research (scientific articles). It also takes into account the medical knowledge automatically discerned from the medical case description. We show that this novel form of medical question answering (Q/A) produces very promising results in (a) identifying accurately the answers and (b) it improves medical article ranking by 40%.

  9. Medical Question Answering for Clinical Decision Support

    PubMed Central

    Goodwin, Travis R.; Harabagiu, Sanda M.

    2017-01-01

    The goal of modern Clinical Decision Support (CDS) systems is to provide physicians with information relevant to their management of patient care. When faced with a medical case, a physician asks questions about the diagnosis, the tests, or treatments that should be administered. Recently, the TREC-CDS track has addressed this challenge by evaluating results of retrieving relevant scientific articles where the answers of medical questions in support of CDS can be found. Although retrieving relevant medical articles instead of identifying the answers was believed to be an easier task, state-of-the-art results are not yet sufficiently promising. In this paper, we present a novel framework for answering medical questions in the spirit of TREC-CDS by first discovering the answer and then selecting and ranking scientific articles that contain the answer. Answer discovery is the result of probabilistic inference which operates on a probabilistic knowledge graph, automatically generated by processing the medical language of large collections of electronic medical records (EMRs). The probabilistic inference of answers combines knowledge from medical practice (EMRs) with knowledge from medical research (scientific articles). It also takes into account the medical knowledge automatically discerned from the medical case description. We show that this novel form of medical question answering (Q/A) produces very promising results in (a) identifying accurately the answers and (b) it improves medical article ranking by 40%. PMID:28758046

  10. A Framework and Model for Evaluating Clinical Decision Support Architectures

    PubMed Central

    Wright, Adam; Sittig, Dean F.

    2008-01-01

    In this paper, we develop a four-phase model for evaluating architectures for clinical decision support that focuses on: defining a set of desirable features for a decision support architecture; building a proof-of-concept prototype; demonstrating that the architecture is useful by showing that it can be integrated with existing decision support systems and comparing its coverage to that of other architectures. We apply this framework to several well-known decision support architectures, including Arden Syntax, GLIF, SEBASTIAN and SAGE PMID:18462999

  11. Direct and Electronic Health Record Access to the Clinical Decision Support for Immunizations in the Minnesota Immunization Information System

    PubMed Central

    Rajamani, Sripriya; Bieringer, Aaron; Wallerius, Stephanie; Jensen, Daniel; Winden, Tamara; Muscoplat, Miriam Halstead

    2016-01-01

    Immunization information systems (IIS) are population-based and confidential computerized systems maintained by public health agencies containing individual data on immunizations from participating health care providers. IIS hold comprehensive vaccination histories given across providers and over time. An important aspect to IIS is the clinical decision support for immunizations (CDSi), consisting of vaccine forecasting algorithms to determine needed immunizations. The study objective was to analyze the CDSi presentation by IIS in Minnesota (Minnesota Immunization Information Connection [MIIC]) through direct access by IIS interface and by access through electronic health records (EHRs) to outline similarities and differences. The immunization data presented were similar across the three systems examined, but with varying ability to integrate data across MIIC and EHR, which impacts immunization data reconciliation. Study findings will lead to better understanding of immunization data display, clinical decision support, and user functionalities with the ultimate goal of promoting IIS CDSi to improve vaccination rates. PMID:28050128

  12. Evaluation of User Interface and Workflow Design of a Bedside Nursing Clinical Decision Support System

    PubMed Central

    Yuan, Michael Juntao; Finley, George Mike; Mills, Christy; Johnson, Ron Kim

    2013-01-01

    Background Clinical decision support systems (CDSS) are important tools to improve health care outcomes and reduce preventable medical adverse events. However, the effectiveness and success of CDSS depend on their implementation context and usability in complex health care settings. As a result, usability design and validation, especially in real world clinical settings, are crucial aspects of successful CDSS implementations. Objective Our objective was to develop a novel CDSS to help frontline nurses better manage critical symptom changes in hospitalized patients, hence reducing preventable failure to rescue cases. A robust user interface and implementation strategy that fit into existing workflows was key for the success of the CDSS. Methods Guided by a formal usability evaluation framework, UFuRT (user, function, representation, and task analysis), we developed a high-level specification of the product that captures key usability requirements and is flexible to implement. We interviewed users of the proposed CDSS to identify requirements, listed functions, and operations the system must perform. We then designed visual and workflow representations of the product to perform the operations. The user interface and workflow design were evaluated via heuristic and end user performance evaluation. The heuristic evaluation was done after the first prototype, and its results were incorporated into the product before the end user evaluation was conducted. First, we recruited 4 evaluators with strong domain expertise to study the initial prototype. Heuristic violations were coded and rated for severity. Second, after development of the system, we assembled a panel of nurses, consisting of 3 licensed vocational nurses and 7 registered nurses, to evaluate the user interface and workflow via simulated use cases. We recorded whether each session was successfully completed and its completion time. Each nurse was asked to use the National Aeronautics and Space Administration

  13. Evaluation of user interface and workflow design of a bedside nursing clinical decision support system.

    PubMed

    Yuan, Michael Juntao; Finley, George Mike; Long, Ju; Mills, Christy; Johnson, Ron Kim

    2013-01-31

    Clinical decision support systems (CDSS) are important tools to improve health care outcomes and reduce preventable medical adverse events. However, the effectiveness and success of CDSS depend on their implementation context and usability in complex health care settings. As a result, usability design and validation, especially in real world clinical settings, are crucial aspects of successful CDSS implementations. Our objective was to develop a novel CDSS to help frontline nurses better manage critical symptom changes in hospitalized patients, hence reducing preventable failure to rescue cases. A robust user interface and implementation strategy that fit into existing workflows was key for the success of the CDSS. Guided by a formal usability evaluation framework, UFuRT (user, function, representation, and task analysis), we developed a high-level specification of the product that captures key usability requirements and is flexible to implement. We interviewed users of the proposed CDSS to identify requirements, listed functions, and operations the system must perform. We then designed visual and workflow representations of the product to perform the operations. The user interface and workflow design were evaluated via heuristic and end user performance evaluation. The heuristic evaluation was done after the first prototype, and its results were incorporated into the product before the end user evaluation was conducted. First, we recruited 4 evaluators with strong domain expertise to study the initial prototype. Heuristic violations were coded and rated for severity. Second, after development of the system, we assembled a panel of nurses, consisting of 3 licensed vocational nurses and 7 registered nurses, to evaluate the user interface and workflow via simulated use cases. We recorded whether each session was successfully completed and its completion time. Each nurse was asked to use the National Aeronautics and Space Administration (NASA) Task Load Index to self

  14. Decision Support Systems: The Need, The Challenge.

    ERIC Educational Resources Information Center

    Roberts, Michael M.

    1982-01-01

    The evolution of decision support systems (DSS) has enabled computer and information technology to assist the management process of decision making. Decision support systems are designed to look forward in time, to forecast outcomes of uncertain events. A 70-item bibliography is included. (MLW)

  15. An Ontology-Based Clinical Decision Support System for the Management of Patients with Multiple Chronic Disorders.

    PubMed

    Galopin, Alexandre; Bouaud, Jacques; Pereira, Suzanne; Seroussi, Brigitte

    2015-01-01

    Decision support systems, as means of disseminating clinical practice guidelines, are powerful software that may lead to an improvement of medical practices. However, they are not always efficient and may suffer from limitations among which are lack of flexibility and weaknesses in the integration of several clinical practice guidelines (CPGs) for the management of patients with multiple chronic disorders. We propose a framework based on an ontological modeling of CPG contents as rules. The ontology provides the required flexibility to adapt patient data and enable the provision of appropriate recommendations expressed at various levels of abstraction. To solve decisional conflicts that occur when combining multiple sources of recommendations, we proposed a method based on the subsumption graph of the patient profiles corresponding to the rules. A prototype CDSS implementing this approach has been developed. Results are given on a clinical case to illustrate the assets of ontological reasoning in increasing the number of issued recommendations and thereby the reliability of decision support.

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

    PubMed

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

    2016-05-01

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

  17. Clinical Information System Services and Capabilities Desired for Scalable, Standards-Based, Service-oriented Decision Support: Consensus Assessment of the Health Level 7 Clinical Decision Support Work Group

    PubMed Central

    Kawamoto, Kensaku; Jacobs, Jason; Welch, Brandon M.; Huser, Vojtech; Paterno, Marilyn D.; Del Fiol, Guilherme; Shields, David; Strasberg, Howard R.; Haug, Peter J.; Liu, Zhijing; Jenders, Robert A.; Rowed, David W.; Chertcoff, Daryl; Fehre, Karsten; Adlassnig, Klaus-Peter; Curtis, A. Clayton

    2012-01-01

    A standards-based, service-oriented architecture for clinical decision support (CDS) has the potential to significantly enhance CDS scalability and robustness. To enable such a CDS architecture, the Health Level 7 CDS Work Group reviewed the literature, hosted multi-stakeholder discussions, and consulted domain experts to identify and prioritize the services and capabilities required from clinical information systems (CISs) to enable service-oriented CDS. In addition, relevant available standards were identified. Through this process, ten CIS services and eight CIS capabilities were identified as being important for enabling scalable, service-oriented CDS. In particular, through a survey of 46 domain experts, five services and capabilities were identified as being especially critical: 1) the use of standard information models and terminologies; 2) the ability to leverage a Decision Support Service (DSS); 3) support for a clinical data query service; 4) support for an event subscription and notification service; and 5) support for a user communication service. PMID:23304315

  18. The process of development of a prioritization tool for a clinical decision support build within a computerized provider order entry system: Experiences from St Luke's Health System.

    PubMed

    Wolf, Matthew; Miller, Suzanne; DeJong, Doug; House, John A; Dirks, Carl; Beasley, Brent

    2016-09-01

    To establish a process for the development of a prioritization tool for a clinical decision support build within a computerized provider order entry system and concurrently to prioritize alerts for Saint Luke's Health System. The process of prioritizing clinical decision support alerts included (a) consensus sessions to establish a prioritization process and identify clinical decision support alerts through a modified Delphi process and (b) a clinical decision support survey to validate the results. All members of our health system's physician quality organization, Saint Luke's Care as well as clinicians, administrators, and pharmacy staff throughout Saint Luke's Health System, were invited to participate in this confidential survey. The consensus sessions yielded a prioritization process through alert contextualization and associated Likert-type scales. Utilizing this process, the clinical decision support survey polled the opinions of 850 clinicians with a 64.7 percent response rate. Three of the top rated alerts were approved for the pre-implementation build at Saint Luke's Health System: Acute Myocardial Infarction Core Measure Sets, Deep Vein Thrombosis Prophylaxis within 4 h, and Criteria for Sepsis. This study establishes a process for developing a prioritization tool for a clinical decision support build within a computerized provider order entry system that may be applicable to similar institutions. © The Author(s) 2015.

  19. Anatomy of a Decision Support System.

    ERIC Educational Resources Information Center

    Chachra, Vinod; Heterick, Robert C.

    1982-01-01

    The decision support system (DSS) environment, the functional requirements of a DSS, and the architectural requirements of the computer systems and communications network necessary to support a DSS are discussed. Changes in the computing environment that are necessary to implement decision support systems are suggested. (Author/MLW)

  20. Joint Command Decision Support System

    DTIC Science & Technology

    2011-06-01

    Greenley et al. 2006) resulted in the identification of a set of overarching principles for the implementation of Joint Command Decision Support (Hales...and adjustment of resources, and longer term feasibility planning. As highlighted in the Joint Staff Front End Analysis report ( Greenley et al. 2006...Townsend (2006). The Federal Response to Hurricane Katrina Lessons Learned, Washington, D.C. February 2006. Greenley , A., Baker, K. & Cochran, L. (2006

  1. Best Practices in Clinical Decision Support

    PubMed Central

    Wright, Adam; Phansalkar, Shobha; Bloomrosen, Meryl; Jenders, Robert A.; Bobb, Anne M.; Halamka, John D.; Kuperman, Gilad; Payne, Thomas H.; Teasdale, S.; Vaida, A. J.; Bates, D. W.

    2010-01-01

    Background Evidence demonstrates that clinical decision support (CDS) is a powerful tool for improving healthcare quality and ensuring patient safety. However, implementing and maintaining effective decision support interventions presents multiple technical and organizational challenges. Purpose To identify best practices for CDS, using the domain of preventive care reminders as an example. Methods We assembled a panel of experts in CDS and held a series of facilitated online and inperson discussions. We analyzed the results of these discussions using a grounded theory method to elicit themes and best practices. Results Eight best practice themes were identified as important: deliver CDS in the most appropriate ways, develop effective governance structures, consider use of incentives, be aware of workflow, keep content current, monitor and evaluate impact, maintain high quality data, and consider sharing content. Keys themes within each of these areas were also described. Conclusion Successful implementation of CDS requires consideration of both technical and socio-technical factors. The themes identified in this study provide guidance on crucial factors that need consideration when CDS is implemented across healthcare settings. These best practice themes may be useful for developers, implementers, and users of decision support. PMID:21991299

  2. PathOS: a decision support system for reporting high throughput sequencing of cancers in clinical diagnostic laboratories.

    PubMed

    Doig, Kenneth D; Fellowes, Andrew; Bell, Anthony H; Seleznev, Andrei; Ma, David; Ellul, Jason; Li, Jason; Doyle, Maria A; Thompson, Ella R; Kumar, Amit; Lara, Luis; Vedururu, Ravikiran; Reid, Gareth; Conway, Thomas; Papenfuss, Anthony T; Fox, Stephen B

    2017-04-24

    The increasing affordability of DNA sequencing has allowed it to be widely deployed in pathology laboratories. However, this has exposed many issues with the analysis and reporting of variants for clinical diagnostic use. Implementing a high-throughput sequencing (NGS) clinical reporting system requires a diverse combination of capabilities, statistical methods to identify variants, global variant databases, a validated bioinformatics pipeline, an auditable laboratory workflow, reproducible clinical assays and quality control monitoring throughout. These capabilities must be packaged in software that integrates the disparate components into a useable system. To meet these needs, we developed a web-based application, PathOS, which takes variant data from a patient sample through to a clinical report. PathOS has been used operationally in the Peter MacCallum Cancer Centre for two years for the analysis, curation and reporting of genetic tests for cancer patients, as well as the curation of large-scale research studies. PathOS has also been deployed in cloud environments allowing multiple institutions to use separate, secure and customisable instances of the system. Increasingly, the bottleneck of variant curation is limiting the adoption of clinical sequencing for molecular diagnostics. PathOS is focused on providing clinical variant curators and pathology laboratories with a decision support system needed for personalised medicine. While the genesis of PathOS has been within cancer molecular diagnostics, the system is applicable to NGS clinical reporting generally. The widespread availability of genomic sequencers has highlighted the limited availability of software to support clinical decision-making in molecular pathology. PathOS is a system that has been developed and refined in a hospital laboratory context to meet the needs of clinical diagnostics. The software is available as a set of Docker images and source code at https://github.com/PapenfussLab/PathOS .

  3. Comparative outcome studies of clinical decision support software: limitations to the practice of evidence-based system acquisition.

    PubMed

    Dhiman, Gaurav Jay; Amber, Kyle T; Goodman, Kenneth W

    2015-04-01

    Clinical decision support systems (CDSSs) assist clinicians with patient diagnosis and treatment. However, inadequate attention has been paid to the process of selecting and buying systems. The diversity of CDSSs, coupled with research obstacles, marketplace limitations, and legal impediments, has thwarted comparative outcome studies and reduced the availability of reliable information and advice for purchasers. We review these limitations and recommend several comparative studies, which were conducted in phases; studies conducted in phases and focused on limited outcomes of safety, efficacy, and implementation in varied clinical settings. Additionally, we recommend the increased availability of guidance tools to assist purchasers with evidence-based purchases. Transparency is necessary in purchasers' reporting of system defects and vendors' disclosure of marketing conflicts of interest to support methodologically sound studies. Taken together, these measures can foster the evolution of evidence-based tools that, in turn, will enable and empower system purchasers to make wise choices and improve the care of patients.

  4. Precision diagnosis: a view of the clinical decision support systems (CDSS) landscape through the lens of critical care.

    PubMed

    Belard, Arnaud; Buchman, Timothy; Forsberg, Jonathan; Potter, Benjamin K; Dente, Christopher J; Kirk, Allan; Elster, Eric

    2017-04-01

    Improving diagnosis and treatment depends on clinical monitoring and computing. Clinical decision support systems (CDSS) have been in existence for over 50 years. While the literature points to positive impacts on quality and patient safety, outcomes, and the avoidance of medical errors, technical and regulatory challenges continue to retard their rate of integration into clinical care processes and thus delay the refinement of diagnoses towards personalized care. We conducted a systematic review of pertinent articles in the MEDLINE, US Department of Health and Human Services, Agency for Health Research and Quality, and US Food and Drug Administration databases, using a Boolean approach to combine terms germane to the discussion (clinical decision support, tools, systems, critical care, trauma, outcome, cost savings, NSQIP, APACHE, SOFA, ICU, and diagnostics). References were selected on the basis of both temporal and thematic relevance, and subsequently aggregated around four distinct themes: the uses of CDSS in the critical and surgical care settings, clinical insertion challenges, utilization leading to cost-savings, and regulatory concerns. Precision diagnosis is the accurate and timely explanation of each patient's health problem and further requires communication of that explanation to patients and surrogate decision-makers. Both accuracy and timeliness are essential to critical care, yet computed decision support systems (CDSS) are scarce. The limitation arises from the technical complexity associated with integrating and filtering large data sets from diverse sources. Provider mistrust and resistance coupled with the absence of clear guidance from regulatory bodies further retard acceptance of CDSS. While challenges to develop and deploy CDSS are substantial, the clinical, quality, and economic impacts warrant the effort, especially in disciplines requiring complex decision-making, such as critical and surgical care. Improving diagnosis in health care

  5. Development and Feasibility of a Real-Time Clinical Decision Support System for Traumatic Brain Injury Anesthesia Care.

    PubMed

    Kiatchai, Taniga; Colletti, Ashley A; Lyons, Vivian H; Grant, Rosemary M; Vavilala, Monica S; Nair, Bala G

    2017-01-25

    Real-time clinical decision support (CDS) integrated with anesthesia information management systems (AIMS) can generate point of care reminders to improve quality of care. To develop, implement and evaluate a real-time clinical decision support system for anesthetic management of pediatric traumatic brain injury (TBI) patients undergoing urgent neurosurgery. We iteratively developed a CDS system for pediatric TBI patients undergoing urgent neurosurgery. The system automatically detects eligible cases and evidence-based key performance indicators (KPIs). Unwanted clinical events trigger and display real-time messages on the AIMS computer screen. Main outcomes were feasibility of detecting eligible cases and KPIs, and user acceptance. The CDS system was triggered in 22 out of 28 (79%) patients. The sensitivity of detecting continuously sampled KPIs reached 93.8%. For intermittently sampled KPIs, sensitivity and specificity reached 90.9% and 100%, respectively. 88% of providers reported that CDS helped with TBI anesthesia care. CDS implementation is feasible and acceptable with a high rate of case capture and appropriate generation of alert and guidance messages for TBI anesthesia care.

  6. Using SNOMED CT Expression Constraints to Bridge the Gap Between Clinical Decision-Support Systems and Electronic Health Records.

    PubMed

    Martínez-Salvador, Begoña; Marcos, Mar; Mañas, Alejandro; Maldonado, José Alberto; Robles, Monserrat

    2016-01-01

    Clinical decision-support systems (CDSSs) should be able to interact with the electronic health record (EHR) to obtain the patient data they require. A recent solution for the interoperability of CDSSs and EHR systems consists in the use of a mediated schema which provides a unified view of their two schemas. The use of such a mediated schema requires the definition of a mapping between this schema and the EHR one. In this paper we investigate the use of the SNOMED CT Expression Constraint Language to characterize these mappings.

  7. Transforming user needs into functional requirements for an antibiotic clinical decision support system: explicating content analysis for system design.

    PubMed

    Bright, T J

    2013-01-01

    Many informatics studies use content analysis to generate functional requirements for system development. Explication of this translational process from qualitative data to functional requirements can strengthen the understanding and scientific rigor when applying content analysis in informatics studies. To describe a user-centered approach transforming emergent themes derived from focus group data into functional requirements for informatics solutions and to illustrate these methods to the development of an antibiotic clinical decision support system (CDS). THE APPROACH CONSISTED OF FIVE STEPS: 1) identify unmet therapeutic planning information needs via Focus Group Study-I, 2) develop a coding framework of therapeutic planning themes to refine the domain scope to antibiotic therapeutic planning, 3) identify functional requirements of an antibiotic CDS system via Focus Group Study-II, 4) discover informatics solutions and functional requirements from coded data, and 5) determine the types of information needed to support the antibiotic CDS system and link with the identified informatics solutions and functional requirements. The coding framework for Focus Group Study-I revealed unmet therapeutic planning needs. Twelve subthemes emerged and were clustered into four themes; analysis indicated a need for an antibiotic CDS intervention. Focus Group Study-II included five types of information needs. Comments from the Barrier/Challenge to information access and Function/Feature themes produced three informatics solutions and 13 functional requirements of an antibiotic CDS system. Comments from the Patient, Institution, and Domain themes generated required data elements for each informatics solution. This study presents one example explicating content analysis of focus group data and the analysis process to functional requirements from narrative data. Illustration of this 5-step method was used to develop an antibiotic CDS system, resolving unmet antibiotic prescribing

  8. Clinical decision support: the power behind the electronic health record.

    PubMed

    Glaser, John

    2008-07-01

    There are six strategic objectives for promoting adoption of clinical decision support: Use a standardized format for representing clinical data and CDS interventions. Ensure appropriate access to clinical data and CDS interventions. Provide support and incentives for providers to use CDS. Disseminate information about best practices for system design, CDS delivery, and CDS implementation. Continue national demonstrations and evaluation of CDS use. Leverage data interchange between EHRs.

  9. Decision Support Systems for Academic Administration.

    ERIC Educational Resources Information Center

    Moore, Laurence J.; Greenwood, Allen G.

    1984-01-01

    The history and features of Decision Support Systems (DSS) and use of the approach by academic administrators are discussed. The objective of DSS is to involve the manager/decision maker in the decision-analysis process while simultaneously relieving that person of the burden of developing and performing detailed analysis. DSS represents a…

  10. Decision Support Systems for Academic Administration.

    ERIC Educational Resources Information Center

    Moore, Laurence J.; Greenwood, Allen G.

    1984-01-01

    The history and features of Decision Support Systems (DSS) and use of the approach by academic administrators are discussed. The objective of DSS is to involve the manager/decision maker in the decision-analysis process while simultaneously relieving that person of the burden of developing and performing detailed analysis. DSS represents a…

  11. Implementation of Clinical Pharmacogenomics within a Large Health System: From Electronic Health Record Decision Support to Consultation Services.

    PubMed

    Hicks, J Kevin; Stowe, David; Willner, Marc A; Wai, Maya; Daly, Thomas; Gordon, Steven M; Lashner, Bret A; Parikh, Sumit; White, Robert; Teng, Kathryn; Moss, Timothy; Erwin, Angelika; Chalmers, Jeffrey; Eng, Charis; Knoer, Scott

    2016-08-01

    The number of clinically relevant gene-based guidelines and recommendations pertaining to drug prescribing continues to grow. Incorporating gene-drug interaction information into the drug-prescribing process can help optimize pharmacotherapy outcomes and improve patient safety. However, pharmacogenomic implementation barriers exist such as integration of pharmacogenomic results into electronic health records (EHRs), development and deployment of pharmacogenomic decision support tools to EHRs, and feasible models for establishing ambulatory pharmacogenomic clinics. We describe the development of pharmacist-managed pharmacogenomic services within a large health system. The Clinical Pharmacogenetics Implementation Consortium guidelines for HLA-B*57:01-abacavir, HLA-B*15:02-carbamazepine, and TPMT-thiopurines (i.e., azathioprine, mercaptopurine, and thioguanine) were systematically integrated into patient care. Sixty-three custom rules and alerts (20 for TPMT-thiopurines, 8 for HLA-B*57:01-abacavir, and 35 for HLA-B*15:02-anticonvulsants) were developed and deployed to the EHR for the purpose of providing point-of-care pharmacogenomic decision support. In addition, a pharmacist and physician-geneticist collaboration established a pharmacogenomics ambulatory clinic. This clinic provides genetic testing when warranted, result interpretation along with pharmacotherapy recommendations, and patient education. Our processes for developing these pharmacogenomic services and solutions for addressing implementation barriers are presented. © 2016 Pharmacotherapy Publications, Inc.

  12. A Clinical Decision Support System for Integrating Tuberculosis and HIV Care in Kenya: A Human-Centered Design Approach

    PubMed Central

    Catalani, Caricia; Green, Eric; Owiti, Philip; Keny, Aggrey; Diero, Lameck; Yeung, Ada; Israelski, Dennis; Biondich, Paul

    2014-01-01

    With the aim of integrating HIV and tuberculosis care in rural Kenya, a team of researchers, clinicians, and technologists used the human-centered design approach to facilitate design, development, and deployment processes of new patient-specific TB clinical decision support system for medical providers. In Kenya, approximately 1.6 million people are living with HIV and have a 20-times higher risk of dying of tuberculosis. Although tuberculosis prevention and treatment medication is widely available, proven to save lives, and prioritized by the World Health Organization, ensuring that it reaches the most vulnerable communities remains challenging. Human-centered design, used in the fields of industrial design and information technology for decades, is an approach to improving the effectiveness and impact of innovations that has been scarcely used in the health field. Using this approach, our team followed a 3-step process, involving mixed methods assessment to (1) understand the situation through the collection and analysis of site observation sessions and key informant interviews; (2) develop a new clinical decision support system through iterative prototyping, end-user engagement, and usability testing; and, (3) implement and evaluate the system across 24 clinics in rural West Kenya. Through the application of this approach, we found that human-centered design facilitated the process of digital innovation in a complex and resource-constrained context. PMID:25170939

  13. A clinical decision support system for integrating tuberculosis and HIV care in Kenya: a human-centered design approach.

    PubMed

    Catalani, Caricia; Green, Eric; Owiti, Philip; Keny, Aggrey; Diero, Lameck; Yeung, Ada; Israelski, Dennis; Biondich, Paul

    2014-01-01

    With the aim of integrating HIV and tuberculosis care in rural Kenya, a team of researchers, clinicians, and technologists used the human-centered design approach to facilitate design, development, and deployment processes of new patient-specific TB clinical decision support system for medical providers. In Kenya, approximately 1.6 million people are living with HIV and have a 20-times higher risk of dying of tuberculosis. Although tuberculosis prevention and treatment medication is widely available, proven to save lives, and prioritized by the World Health Organization, ensuring that it reaches the most vulnerable communities remains challenging. Human-centered design, used in the fields of industrial design and information technology for decades, is an approach to improving the effectiveness and impact of innovations that has been scarcely used in the health field. Using this approach, our team followed a 3-step process, involving mixed methods assessment to (1) understand the situation through the collection and analysis of site observation sessions and key informant interviews; (2) develop a new clinical decision support system through iterative prototyping, end-user engagement, and usability testing; and, (3) implement and evaluate the system across 24 clinics in rural West Kenya. Through the application of this approach, we found that human-centered design facilitated the process of digital innovation in a complex and resource-constrained context.

  14. Virtual medical record implementation for enhancing clinical decision support.

    PubMed

    Gomoi, Valentin-Sergiu; Dragu, Daniel; Stoicu-Tivadar, Vasile

    2012-01-01

    Development of clinical decision support systems (CDS) is a process which highly depends on the local databases, this resulting in low interoperability. To increase the interoperability of CDS a standard representation of clinical information is needed. The paper suggests a CDS architecture which integrates several HL7 standards and the new vMR (virtual Medical Record). The clinical information for the CDS systems (the vMR) is represented with Topic Maps technology. Beside the implementation of the vMR, the architecture integrates: a Data Manager, an interface, a decision making system (based on Egadss), a retrieving data module. Conclusions are issued.

  15. A Hyperknowledge Framework of Decision Support Systems.

    ERIC Educational Resources Information Center

    Chang, Ai-Mei; And Others

    1994-01-01

    Presents a hyperknowledge framework of decision support systems (DSS). This framework formalizes specifics about system functionality, representation of knowledge, navigation of the knowledge system, and user-interface traits as elements of a DSS environment that conforms closely to human cognitive processes in decision making. (Contains 52…

  16. Development and Implementation of the Clinical Decision Support System for Patients With Cancer and Nurses' Experiences Regarding the System.

    PubMed

    Yılmaz, Arzu Akman; Ozdemir, Leyla

    2017-01-01

    The purpose of this study was to develop and implement the clinical decision support system (CDSS) for oncology nurses in the care of patients with cancer and to explore the nurses' experiences about the system. The study was conducted using a mixed-methods research design with 14 nurses working at a gynecological oncology clinic at a university hospital in Turkey. The nurses stated that they did not experience any problems during the implementation of the CDSS, and its usage facilitated the assessment of patients' needs and care management. The results indicated that the CDSS supported the nurses' decision-making process about patients' needs and preparation of individual care plans. The CDSS should be developed and implemented by the nurses working with patients with cancer. AMAÇ: Amaç kanser hastalarının bakımına yönelik klinik karar destek sistemi oluşturmak, uygulamak (KKDS) ve sistemi kullanan hemşirelerin deneyimlerini incelemektir. YÖNTEM: Çalışma kalitatif ve kantitatif araştırma yöntemleri kullanılarak Türkiyede'ki bir üniversite hastanesinin jinekolojik onkoloji servisinde çalışan 14 hemşire ile yürütülmüştür. Hemşireler KKDS'ni kullanırken herhangi bir sorun yaşamadıklarını ve sistemin hasta gereksinimlerini değerlendirmeyi ve bakım yönetimini kolaylaştırdığını belirtmişlerdir. SONUÇ: Bulgular hastanın gereksinimlerine karar verme sürecinde ve bireysel bakım planları hazırlamada KKDS'nin hemşireleri desteklediğini göstermektedir. HEMŞIRELIK UYGULAMALARI IÇIN ÖNERILER: Kanserli hastaların bakımına yönelik KKDS geliştirilebilir ve hemşireler tarafından klinikte kullanılabilir. © 2015 NANDA International, Inc.

  17. Decision support systems in health economics.

    PubMed

    Quaglini, S; Dazzi, L; Stefanelli, M; Barosi, G; Marchetti, M

    1999-08-01

    This article describes a system addressed to different health care professionals for building, using, and sharing decision support systems for resource allocation. The system deals with selected areas, namely the choice of diagnostic tests, the therapy planning, and the instrumentation purchase. Decision support is based on decision-analytic models, incorporating an explicit knowledge representation of both the medical domain knowledge and the economic evaluation theory. Application models are built on top of meta-models, that are used as guidelines for making explicit both the cost and effectiveness components. This approach improves the transparency and soundness of the collaborative decision-making process and facilitates the result interpretation.

  18. A Framework for Classifying Decision Support Systems

    PubMed Central

    Sim, Ida; Berlin, Amy

    2003-01-01

    Background Computer-based clinical decision support systems (CDSSs) vary greatly in design and function. A taxonomy for classifying CDSS structure and function would help efforts to describe and understand the variety of CDSSs in the literature, and to explore predictors of CDSS effectiveness and generalizability. Objective To define and test a taxonomy for characterizing the contextual, technical, and workflow features of CDSSs. Methods We retrieved and analyzed 150 English language articles published between 1975 and 2002 that described computer systems designed to assist physicians and/or patients with clinical decision making. We identified aspects of CDSS structure or function and iterated our taxonomy until additional article reviews did not result in any new descriptors or taxonomic modifications. Results Our taxonomy comprises 95 descriptors along 24 descriptive axes. These axes are in 5 categories: Context, Knowledge and Data Source, Decision Support, Information Delivery, and Workflow. The axes had an average of 3.96 coded choices each. 75% of the descriptors had an inter-rater agreement kappa of greater than 0.6. Conclusions We have defined and tested a comprehensive, multi-faceted taxonomy of CDSSs that shows promising reliability for classifying CDSSs reported in the literature. PMID:14728243

  19. Evolutionary and Neural Computing Based Decision Support System for Disease Diagnosis from Clinical Data Sets in Medical Practice.

    PubMed

    Sudha, M

    2017-09-27

    As a recent trend, various computational intelligence and machine learning approaches have been used for mining inferences hidden in the large clinical databases to assist the clinician in strategic decision making. In any target data the irrelevant information may be detrimental, causing confusion for the mining algorithm and degrades the prediction outcome. To address this issue, this study attempts to identify an intelligent approach to assist disease diagnostic procedure using an optimal set of attributes instead of all attributes present in the clinical data set. In this proposed Application Specific Intelligent Computing (ASIC) decision support system, a rough set based genetic algorithm is employed in pre-processing phase and a back propagation neural network is applied in training and testing phase. ASIC has two phases, the first phase handles outliers, noisy data, and missing values to obtain a qualitative target data to generate appropriate attribute reduct sets from the input data using rough computing based genetic algorithm centred on a relative fitness function measure. The succeeding phase of this system involves both training and testing of back propagation neural network classifier on the selected reducts. The model performance is evaluated with widely adopted existing classifiers. The proposed ASIC system for clinical decision support has been tested with breast cancer, fertility diagnosis and heart disease data set from the University of California at Irvine (UCI) machine learning repository. The proposed system outperformed the existing approaches attaining the accuracy rate of 95.33%, 97.61%, and 93.04% for breast cancer, fertility issue and heart disease diagnosis.

  20. Using Visualization in Cockpit Decision Support Systems

    SciTech Connect

    Aragon, Cecilia R.

    2005-07-01

    In order to safely operate their aircraft, pilots must makerapid decisions based on integrating and processing large amounts ofheterogeneous information. Visual displays are often the most efficientmethod of presenting safety-critical data to pilots in real time.However, care must be taken to ensure the pilot is provided with theappropriate amount of information to make effective decisions and notbecome cognitively overloaded. The results of two usability studies of aprototype airflow hazard visualization cockpit decision support systemare summarized. The studies demonstrate that such a system significantlyimproves the performance of helicopter pilots landing under turbulentconditions. Based on these results, design principles and implicationsfor cockpit decision support systems using visualization arepresented.

  1. Decision Support Systems and Public Policy Analysis.

    ERIC Educational Resources Information Center

    Hall, Owen P., Jr.

    1986-01-01

    This article outlines an approach for developing and applying computerized decision support systems to the formulation and evaluation of public policy. To meet the challenge of financial resource limitations, new management systems must be developed to improve both governmental efficiency and decision-making effectiveness. (Author/BS)

  2. Construction of a clinical decision support system for undergoing surgery based on domain ontology and rules reasoning.

    PubMed

    Bau, Cho-Tsan; Chen, Rung-Ching; Huang, Chung-Yi

    2014-05-01

    To construct a clinical decision support system (CDSS) for undergoing surgery based on domain ontology and rules reasoning in the setting of hospitalized diabetic patients. The ontology was created with a modified ontology development method, including specification and conceptualization, formalization, implementation, and evaluation and maintenance. The Protégé-Web Ontology Language editor was used to implement the ontology. Embedded clinical knowledge was elicited to complement the domain ontology with formal concept analysis. The decision rules were translated into JENA format, which JENA can use to infer recommendations based on patient clinical situations. The ontology includes 31 classes and 13 properties, plus 38 JENA rules that were built to generate recommendations. The evaluation studies confirmed the correctness of the ontology, acceptance of recommendations, satisfaction with the system, and usefulness of the ontology for glycemic management of diabetic patients undergoing surgery, especially for domain experts. The contribution of this research is to set up an evidence-based hybrid ontology and an evaluation method for CDSS. The system can help clinicians to achieve inpatient glycemic control in diabetic patients undergoing surgery while avoiding hypoglycemia.

  3. Construction of a Clinical Decision Support System for Undergoing Surgery Based on Domain Ontology and Rules Reasoning

    PubMed Central

    Bau, Cho-Tsan; Huang, Chung-Yi

    2014-01-01

    Abstract Objective: To construct a clinical decision support system (CDSS) for undergoing surgery based on domain ontology and rules reasoning in the setting of hospitalized diabetic patients. Materials and Methods: The ontology was created with a modified ontology development method, including specification and conceptualization, formalization, implementation, and evaluation and maintenance. The Protégé–Web Ontology Language editor was used to implement the ontology. Embedded clinical knowledge was elicited to complement the domain ontology with formal concept analysis. The decision rules were translated into JENA format, which JENA can use to infer recommendations based on patient clinical situations. Results: The ontology includes 31 classes and 13 properties, plus 38 JENA rules that were built to generate recommendations. The evaluation studies confirmed the correctness of the ontology, acceptance of recommendations, satisfaction with the system, and usefulness of the ontology for glycemic management of diabetic patients undergoing surgery, especially for domain experts. Conclusions: The contribution of this research is to set up an evidence-based hybrid ontology and an evaluation method for CDSS. The system can help clinicians to achieve inpatient glycemic control in diabetic patients undergoing surgery while avoiding hypoglycemia. PMID:24730353

  4. Integrating clinical decision support systems for pharmacogenomic testing into clinical routine - a scoping review of designs of user-system interactions in recent system development.

    PubMed

    Hinderer, Marc; Boeker, Martin; Wagner, Sebastian A; Lablans, Martin; Newe, Stephanie; Hülsemann, Jan L; Neumaier, Michael; Binder, Harald; Renz, Harald; Acker, Till; Prokosch, Hans-Ulrich; Sedlmayr, Martin

    2017-06-06

    Pharmacogenomic clinical decision support systems (CDSS) have the potential to help overcome some of the barriers for translating pharmacogenomic knowledge into clinical routine. Before developing a prototype it is crucial for developers to know which pharmacogenomic CDSS features and user-system interactions have yet been developed, implemented and tested in previous pharmacogenomic CDSS efforts and if they have been successfully applied. We address this issue by providing an overview of the designs of user-system interactions of recently developed pharmacogenomic CDSS. We searched PubMed for pharmacogenomic CDSS published between January 1, 2012 and November 15, 2016. Thirty-two out of 118 identified articles were summarized and included in the final analysis. We then compared the designs of user-system interactions of the 20 pharmacogenomic CDSS we had identified. Alerts are the most widespread tools for physician-system interactions, but need to be implemented carefully to prevent alert fatigue and avoid liabilities. Pharmacogenomic test results and override reasons stored in the local EHR might help communicate pharmacogenomic information to other internal care providers. Integrating patients into user-system interactions through patient letters and online portals might be crucial for transferring pharmacogenomic data to external health care providers. Inbox messages inform physicians about new pharmacogenomic test results and enable them to request pharmacogenomic consultations. Search engines enable physicians to compare medical treatment options based on a patient's genotype. Within the last 5 years, several pharmacogenomic CDSS have been developed. However, most of the included articles are solely describing prototypes of pharmacogenomic CDSS rather than evaluating them. To support the development of prototypes further evaluation efforts will be necessary. In the future, pharmacogenomic CDSS will likely include prediction models to identify patients who

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

    DTIC Science & Technology

    2014-10-01

    TITLE: Proposal for Development of EBM-CDSS (Evidence-based Clinical Decision Support System) to Aid Prognostication in Terminally Ill Patients...SUBTITLE Proposal for development of EBM-CDSS (Evidence-based Clinical Decision Support System) to aid prognostication in terminally ill patients 5a...to improve prognostication of the life expectancy of terminally ill patients to improve referral of patients to hospice. In addition, the EBM-CDSS

  6. A Decision Support System for Academic Scheduling.

    ERIC Educational Resources Information Center

    Burleson, Donald K.; Leivano, Rodrigo J.

    1986-01-01

    Describes the use of a decision support system to operate on a database for academic scheduling. Discusses the scheduling environment, database subsystem, dialog subsystem, modeling subsystem, and output formats. (JM)

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

    DTIC Science & Technology

    2010-10-01

    regarding continuation of life-sustaining vs. palliative care . Finally, using regret DCA, the optimal decision for the specific patient is suggested...is to develop an Evidence-based Clinical Decision Support (CDSS-EBM) system and make it available at the point of care to improve prognostication of...Analysis and Regret theory to compare multiple decision strategies based on the decision maker’s personal attitudes towards each strategy

  8. A multiresolution clinical decision support system based on fractal model design for classification of histological brain tumours.

    PubMed

    Al-Kadi, Omar S

    2015-04-01

    Tissue texture is known to exhibit a heterogeneous or non-stationary nature; therefore using a single resolution approach for optimum classification might not suffice. A clinical decision support system that exploits the subbands' textural fractal characteristics for best bases selection of meningioma brain histopathological image classification is proposed. Each subband is analysed using its fractal dimension instead of energy, which has the advantage of being less sensitive to image intensity and abrupt changes in tissue texture. The most significant subband that best identifies texture discontinuities will be chosen for further decomposition, and its fractal characteristics would represent the optimal feature vector for classification. The performance was tested using the support vector machine (SVM), Bayesian and k-nearest neighbour (kNN) classifiers and a leave-one-patient-out method was employed for validation. Our method outperformed the classical energy based selection approaches, achieving for SVM, Bayesian and kNN classifiers an overall classification accuracy of 94.12%, 92.50% and 79.70%, as compared to 86.31%, 83.19% and 51.63% for the co-occurrence matrix, and 76.01%, 73.50% and 50.69% for the energy texture signatures; respectively. These results indicate the potential usefulness as a decision support system that could complement radiologists' diagnostic capability to discriminate higher order statistical textural information; for which it would be otherwise difficult via ordinary human vision. Copyright © 2014 Elsevier Ltd. All rights reserved.

  9. Evaluation of a machine learning capability for a clinical decision support system to enhance antimicrobial stewardship programs.

    PubMed

    Beaudoin, Mathieu; Kabanza, Froduald; Nault, Vincent; Valiquette, Louis

    2016-03-01

    Antimicrobial stewardship programs have been shown to limit the inappropriate use of antimicrobials. Hospitals are increasingly relying on clinical decision support systems to assist in the demanding prescription reviewing process. In previous work, we have reported on an emerging clinical decision support system for antimicrobial stewardship that can learn new rules supervised by user feedback. In this paper, we report on the evaluation of this system. The evaluated system uses a knowledge base coupled with a supervised learning module that extracts classification rules for inappropriate antimicrobial prescriptions using past recommendations for dose and dosing frequency adjustments, discontinuation of therapy, early switch from intravenous to oral therapy, and redundant antimicrobial spectrum. Over five weeks, the learning module was deployed alongside the baseline system to prospectively evaluate its ability to discover rules that complement the existing knowledge base for identifying inappropriate prescriptions of piperacillin-tazobactam, a frequently used antimicrobial. The antimicrobial stewardship pharmacists reviewed 374 prescriptions, of which 209 (56% of 374) were identified as inappropriate leading to 43 recommendations to optimize prescriptions. The baseline system combined with the learning module triggered alerts in 270 prescriptions with a positive predictive value of identifying inappropriate prescriptions of 74%. Of these, 240 reviewed prescriptions were identified by the alerts of the baseline system with a positive predictive value of 82% and 105 reviewed prescriptions were identified by the alerts of the learning module with a positive predictive value of 62%. The combined system triggered alerts for all 43 recommendations, resulting in a rate of actionable alerts of 16% (43 recommendations of 270 reviewed alerts); the baseline system triggered alerts for 38 interventions, resulting in a rate of actionable alerts of 16% (38 of 240 reviewed

  10. Integration of heterogeneous clinical decision support systems and their knowledge sets: feasibility study with Drug-Drug Interaction alerts.

    PubMed

    Kam, Hye Jin; Kim, Jeong Ah; Cho, InSook; Kim, Yoon; Park, Rae Woong

    2011-01-01

    There exist limitations in both commercial and in-house clinical decision support systems (CDSSs) and issues related to the integration of different knowledge sources and CDSSs. We chose Standard-based Shareable Active Guideline Environment (SAGE) as a new architecture with knowledge integration and a centralized knowledge base which includes authoring/management functions and independent CDSS, and applied it to Drug-Drug Interaction (DDI) CDSS. The aim of this study was to evaluate the feasibility of the newly integrated DDI alerting CDSS into a real world hospital information system involving construction of an integrated CDSS derived from two heterogeneous systems and their knowledge sets. The proposed CDSS was successfully implemented and compensated for the weaknesses of the old CDSS from knowledge integration and management, and its applicability in actual situations was verified. Although the DDI CDSS was constructed as an example case, the new CDS architecture might prove applicable to areas of CDSSs.

  11. Performance of a clinical decision support system and of clinical pharmacists in preventing drug-drug interactions on a geriatric ward.

    PubMed

    Cornu, Pieter; Steurbaut, Stephane; Soštarić, Sabina; Mrhar, Aleš; Dupont, Alain G

    2014-06-01

    Drug-drug interactions (DDIs) can lead to adverse drug events and compromise patient safety. Two common approaches to reduce these interactions in hospital practice are the use of clinical decision support systems and interventions by clinical pharmacists. To compare the performance of both approaches with the main objective of learning from one approach to improve the other. Acute geriatric ward in a university hospital. Prospective single-centre, cohort study of patients admitted to the geriatric ward. An independent pharmacist compared the clinical decision support alerts with the DDIs identified by clinical pharmacists and evaluated their interventions. Contextual factors used by the clinical pharmacists for evaluation of the clinical relevance were analysed. Adverse drug events related to DDIs were investigated and the causality was evaluated by a clinical pharmacologist based on validated criteria. Number of alerts, interventions and the acceptance rates. Fifty patients followed by the clinical pharmacists, were included. The clinical pharmacists identified 240 DDIs (median of 3.5 per patient) and advised a therapy change for 16 of which 13 (81.2 %) were accepted and three (18.8 %) were not. The decision support system generated only six alerts of which none were accepted by the physicians. Thirty-seven adverse drug events were identified for 29 patients that could be related to 55 DDIs. For two interactions the causality was evaluated as certain, for 31 as likely, for ten as possible and for 12 as unlikely. Mainly intermediate level interactions were related to adverse drug events. Contextual factors taken into account by the clinical pharmacists for evaluation of the interactions were blood pressure, international normalised ratio, heart rate, potassium level and glycemia. Additionally, the clinical pharmacists looked at individual administration intervals and drug sequence to determine the clinical relevance of the interactions. Clinical pharmacists

  12. 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.

  13. A two-stage clinical decision support system for early recognition and stratification of patients with sepsis: an observational cohort study

    PubMed Central

    Lyons, Jason J; Greene, Tracy L; Haley, James M

    2015-01-01

    Objective To examine the diagnostic accuracy of a two-stage clinical decision support system for early recognition and stratification of patients with sepsis. Design 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. Setting Urban, 284 acute bed community hospital in the USA; 16,000 hospitalisations annually. Participants Data on 2620 adult patients were collected retrospectively in 2014 after the clinical decision support was implemented. Main outcome measure ‘Suspected infection’ was the established gold standard to assess clinical decision support clinimetric performance. Results 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. Conclusion A clinical decision support binary alarm system with cross-checking functionality improves early recognition and facilitates stratification of patients with sepsis. PMID:26688744

  14. What can Natural Language Processing do for Clinical Decision Support?

    PubMed Central

    Demner-Fushman, Dina; Chapman, Wendy W.; McDonald, Clement J.

    2009-01-01

    Computerized Clinical Decision Support (CDS) aims to aid decision making of health care providers and the public by providing easily accessible health-related information at the point and time it is needed. Natural Language Processing (NLP) is instrumental in using free-text information to drive CDS, representing clinical knowledge and CDS interventions in standardized formats, and leveraging clinical narrative. The early innovative NLP research of clinical narrative was followed by a period of stable research conducted at the major clinical centers and a shift of mainstream interest to biomedical NLP. This review primarily focuses on the recently renewed interest in development of fundamental NLP methods and advances in the NLP systems for CDS. The current solutions to challenges posed by distinct sublanguages, intended user groups, and support goals are discussed. PMID:19683066

  15. Adoption of clinical decision support systems in a developing country: Antecedents and outcomes of physician's threat to perceived professional autonomy.

    PubMed

    Esmaeilzadeh, Pouyan; Sambasivan, Murali; Kumar, Naresh; Nezakati, Hossein

    2015-08-01

    The basic objective of this research is to study the antecedents and outcomes of professional autonomy which is a central construct that affects physicians' intention to adopt clinical decision support systems (CDSS). The antecedents are physicians' attitude toward knowledge sharing and interactivity perception (about CDSS) and the outcomes are performance expectancy and intention to adopt CDSS. Besides, we include (1) the antecedents of attitude toward knowledge sharing-subjective norms, social factors and OCB (helping behavior) and (2) roles of physicians' involvement in decision making, computer self-efficacy and effort expectancy in our framework. Data from a stratified sample of 335 Malaysian physicians working in 12 public and private hospitals in Malaysia were collected to test the hypotheses using Structural Equation Modeling (SEM). The important findings of our research are: (1) factors such as perceived threat to professional autonomy, performance expectancy, and physicians' involvement in making decision about CDSS have significant impact on physicians' intention to adopt CDSS; (2) physicians' attitude toward knowledge sharing, interactivity perception and computer self-efficacy of physicians play a crucial role in influencing their perceived threat to professional autonomy; and (3) social network, shared goals and OCB (helping behavior) impact physicians' attitude toward knowledge sharing. The findings provide a comprehensive understanding of the factors that influence physicians' intention to adopt CDSS in a developing country. The results can help hospital managers manage CDSS implementation in an effective manner. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

  16. Critiquing Systems for Decision Support

    DTIC Science & Technology

    2006-02-01

    errors and deficiencies. An example of a comparative critic is the ATTENDING system ( anaesthesiology ), which first parses the user’s solution into a...design tools at the times when those tools are useful. 9. Experiential critics provide reminders of past experiences with similar designs or design...technique for hypertension rather than the broader field of anaesthesiology ; and (2) critiquing systems are most appropriate for tasks that require

  17. [Implementation of ontology-based clinical decision support system for management of interactions between antihypertensive drugs and diet].

    PubMed

    Park, Jeong Eun; Kim, Hwa Sun; Chang, Min Jung; Hong, Hae Sook

    2014-06-01

    The influence of dietary composition on blood pressure is an important subject in healthcare. Interactions between antihypertensive drugs and diet (IBADD) is the most important factor in the management of hypertension. It is therefore essential to support healthcare providers' decision making role in active and continuous interaction control in hypertension management. The aim of this study was to implement an ontology-based clinical decision support system (CDSS) for IBADD management (IBADDM). We considered the concepts of antihypertensive drugs and foods, and focused on the interchangeability between the database and the CDSS when providing tailored information. An ontology-based CDSS for IBADDM was implemented in eight phases: (1) determining the domain and scope of ontology, (2) reviewing existing ontology, (3) extracting and defining the concepts, (4) assigning relationships between concepts, (5) creating a conceptual map with CmapTools, (6) selecting upper ontology, (7) formally representing the ontology with Protégé (ver.4.3), (8) implementing an ontology-based CDSS as a JAVA prototype application. We extracted 5,926 concepts, 15 properties, and formally represented them using Protégé. An ontology-based CDSS for IBADDM was implemented and the evaluation score was 4.60 out of 5. We endeavored to map functions of a CDSS and implement an ontology-based CDSS for IBADDM.

  18. Using and Evaluating Administrative Decision Support Systems.

    ERIC Educational Resources Information Center

    King, William R.

    1981-01-01

    Computer technology is rapidly being integrated into decision support systems that far surpass the potential of other information systems. To take advantage of this potential, the administrator must be able to evaluate these systems in terms that are relevant to the organization. (Author/MLW)

  19. A Multidisciplinary Sepsis Program Enabled by a Two-Stage Clinical Decision Support System: Factors That Influence Patient Outcomes.

    PubMed

    Amland, Robert C; Haley, James M; Lyons, Jason J

    2016-11-01

    Sepsis is an inflammatory response triggered by infection, with risk of in-hospital mortality fueled by disease progression. Early recognition and intervention by multidisciplinary sepsis programs may reverse the inflammatory response among at-risk patient populations, potentially improving outcomes. This retrospective study of a sepsis program enabled by a 2-stage sepsis Clinical Decision Support (CDS) system sought to evaluate the program's impact, identify early indicators that may influence outcomes, and uncover opportunities for quality improvement. Data encompassed 16 527 adult hospitalizations from 2014 and 2015. Of 2108 non-intensive care unit patients screened-in by sepsis CDS, 97% patients were stratified by 177 providers. Risk of adverse outcome improved 30% from baseline to year end, with gains materializing and stabilizing at month 7 after sepsis program go-live. Early indicators likely to influence outcomes include patient age, recent hospitalization, electrolyte abnormalities, hypovolemic shock, hypoxemia, patient location when sepsis CDS activated, and specific alert patterns.

  20. A Novel Clinical Decision Support System Using Improved Adaptive Genetic Algorithm for the Assessment of Fetal Well-Being

    PubMed Central

    Jambek, Asral Bahari; Neoh, Siew-Chin

    2015-01-01

    A novel clinical decision support system is proposed in this paper for evaluating the fetal well-being from the cardiotocogram (CTG) dataset through an Improved Adaptive Genetic Algorithm (IAGA) and Extreme Learning Machine (ELM). IAGA employs a new scaling technique (called sigma scaling) to avoid premature convergence and applies adaptive crossover and mutation techniques with masking concepts to enhance population diversity. Also, this search algorithm utilizes three different fitness functions (two single objective fitness functions and multi-objective fitness function) to assess its performance. The classification results unfold that promising classification accuracy of 94% is obtained with an optimal feature subset using IAGA. Also, the classification results are compared with those of other Feature Reduction techniques to substantiate its exhaustive search towards the global optimum. Besides, five other benchmark datasets are used to gauge the strength of the proposed IAGA algorithm. PMID:25793009

  1. How Usability of a Web-Based Clinical Decision Support System Has the Potential to Contribute to Adverse Medical Events

    PubMed Central

    Graham, Timothy A.D.; Kushniruk, Andre W.; Bullard, Michael J.; Holroyd, Brian R.; Meurer, David P.; Rowe, Brian H.

    2008-01-01

    Introduction Clinical decision support systems (CDSS) have the potential to reduce adverse medical events, but improper design can introduce new forms of error. CDSS pertaining to community acquired pneumonia and neutropenic fever were studied to determine whether usability of the graphical user interface might contribute to potential adverse medical events. Methods Automated screen capture of 4 CDSS being used by volunteer emergency physicians was analyzed using structured methods. Results 422 events were recorded over 56 sessions. In total, 169 negative comments, 55 positive comments, 130 neutral comments, 21 application events, 34 problems, 6 slips, and 5 mistakes were identified. Three mistakes could have had life-threatening consequences. Conclusion Evaluation of CDSS will be of utmost importance in the future with increasing use of electronic health records. Usability engineering principles can identify interface problems that may lead to potential medical adverse events, and should be incorporated early in the software design phase. PMID:18998968

  2. Using Visualization in Cockpit Decision Support Systems

    NASA Technical Reports Server (NTRS)

    Aragon, Cecilia R.

    2005-01-01

    In order to safely operate their aircraft, pilots must make rapid decisions based on integrating and processing large amounts of heterogeneous information. Visual displays are often the most efficient method of presenting safety-critical data to pilots in real time. However, care must be taken to ensure the pilot is provided with the appropriate amount of information to make effective decisions and not become cognitively overloaded. The results of two usability studies of a prototype airflow hazard visualization cockpit decision support system are summarized. The studies demonstrate that such a system significantly improves the performance of helicopter pilots landing under turbulent conditions. Based on these results, design principles and implications for cockpit decision support systems using visualization are presented.

  3. The evidence-based clinical decision support guide: mucogingival/esthetics making clinical decisions in the absence of strong evidence.

    PubMed

    Merijohn, George K

    2007-09-01

    Although evidence-based decision-making in dentistry is quickly evolving, large gaps remain in our clinical knowledge base regarding every day decisions and procedures. Especially in the absence of strong evidence, as is the case with mucogingival conditions, risk assessment and identification are important components of the clinical decision-making process. Utilization of clinical decision support (CDS) guides, frameworks and systems enhances chairside decision-making and improves delivery of patient care. This article introduces an Evidence-Based Clinical Decision Support Guide for mucogingival/esthetic situations. This CDS guide delineates treatment strategies based upon evidence-based risk assessment and when possible, risk management. It provides the clinician with a framework that will support decision-making at the point of care. Recommendations for consultation, treatment and referral are reviewed.

  4. Impact of a computerized provider radiography order entry system without clinical decision support on emergency department medical imaging requests.

    PubMed

    Claret, Pierre-Géraud; Bobbia, Xavier; Macri, Francesco; Stowell, Andrew; Motté, Antony; Landais, Paul; Beregi, Jean-Paul; de La Coussaye, Jean-Emmanuel

    2016-06-01

    The adoption of computerized physician order entry is an important cornerstone of using health information technology (HIT) in health care. The transition from paper to computer forms presents a change in physicians' practices. The main objective of this study was to investigate the impact of implementing a computer-based order entry (CPOE) system without clinical decision support on the number of radiographs ordered for patients admitted in the emergency department. This single-center pre-/post-intervention study was conducted in January, 2013 (before CPOE period) and January, 2014 (after CPOE period) at the emergency department at Nîmes University Hospital. All patients admitted in the emergency department who had undergone medical imaging were included in the study. Emergency department admissions have increased since the implementation of CPOE (5388 in the period before CPOE implementation vs. 5808 patients after CPOE implementation, p=.008). In the period before CPOE implementation, 2345 patients (44%) had undergone medical imaging; in the period after CPOE implementation, 2306 patients (40%) had undergone medical imaging (p=.008). In the period before CPOE, 2916 medical imaging procedures were ordered; in the period after CPOE, 2876 medical imaging procedures were ordered (p=.006). In the period before CPOE, 1885 radiographs were ordered; in the period after CPOE, 1776 radiographs were ordered (p<.001). The time between emergency department admission and medical imaging did not vary between the two periods. Our results show a decrease in the number of radiograph requests after a CPOE system without clinical decision support was implemented in our emergency department. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

  5. Decision support systems for robotic surgery and acute care

    NASA Astrophysics Data System (ADS)

    Kazanzides, Peter

    2012-06-01

    Doctors must frequently make decisions during medical treatment, whether in an acute care facility, such as an Intensive Care Unit (ICU), or in an operating room. These decisions rely on a various information sources, such as the patient's medical history, preoperative images, and general medical knowledge. Decision support systems can assist by facilitating access to this information when and where it is needed. This paper presents some research eorts that address the integration of information with clinical practice. The example systems include a clinical decision support system (CDSS) for pediatric traumatic brain injury, an augmented reality head- mounted display for neurosurgery, and an augmented reality telerobotic system for minimally-invasive surgery. While these are dierent systems and applications, they share the common theme of providing information to support clinical decisions and actions, whether the actions are performed with the surgeon's own hands or with robotic assistance.

  6. Person centered prediction of survival in population based screening program by an intelligent clinical decision support system.

    PubMed

    Safdari, Reza; Maserat, Elham; Asadzadeh Aghdaei, Hamid; Javan Amoli, Amir Hossein; Mohaghegh Shalmani, Hamid

    2017-01-01

    To survey person centered survival rate in population based screening program by an intelligent clinical decision support system. Colorectal cancer is the most common malignancy and major cause of morbidity and mortality throughout the world. Colorectal cancer is the sixth leading cause of cancer death in Iran. In this survey, we used cosine similarity as data mining technique and intelligent system for estimating survival of at risk groups in the screening plan. In the first step, we determined minimum data set (MDS). MDS was approved by experts and reviewing literatures. In the second step, MDS were coded by python language and matched with cosine similarity formula. Finally, survival rate by percent was illustrated in the user interface of national intelligent system. The national intelligent system was designed in PyCharm environment. Main data elements of intelligent system consist demographic information, age, referral type, risk group, recommendation and survival rate. Minimum data set related to survival comprise of clinical status, past medical history and socio-demographic information. Information of the covered population as a comprehensive database was connected to intelligent system and survival rate estimated for each patient. Mean range of survival of HNPCC patients and FAP patients were respectively 77.7% and 75.1%. Also, the mean range of the survival rate and other calculations have changed with the entry of new patients in the CRC registry by real-time. National intelligent system monitors the entire of risk group and reports survival rates by electronic guidelines and data mining technique and also operates according to the clinical process. This web base software has a critical role in the estimation survival rate in order to health care planning.

  7. Person centered prediction of survival in population based screening program by an intelligent clinical decision support system

    PubMed Central

    Safdari, Reza; Maserat, Elham; Asadzadeh Aghdaei, Hamid; Javan Amoli, Amir hossein; Mohaghegh Shalmani, Hamid

    2017-01-01

    Aim: To survey person centered survival rate in population based screening program by an intelligent clinical decision support system. Background: Colorectal cancer is the most common malignancy and major cause of morbidity and mortality throughout the world. Colorectal cancer is the sixth leading cause of cancer death in Iran. In this survey, we used cosine similarity as data mining technique and intelligent system for estimating survival of at risk groups in the screening plan. Methods: In the first step, we determined minimum data set (MDS). MDS was approved by experts and reviewing literatures. In the second step, MDS were coded by python language and matched with cosine similarity formula. Finally, survival rate by percent was illustrated in the user interface of national intelligent system. The national intelligent system was designed in PyCharm environment. Results: Main data elements of intelligent system consist demographic information, age, referral type, risk group, recommendation and survival rate. Minimum data set related to survival comprise of clinical status, past medical history and socio-demographic information. Information of the covered population as a comprehensive database was connected to intelligent system and survival rate estimated for each patient. Mean range of survival of HNPCC patients and FAP patients were respectively 77.7% and 75.1%. Also, the mean range of the survival rate and other calculations have changed with the entry of new patients in the CRC registry by real-time. Conclusion: National intelligent system monitors the entire of risk group and reports survival rates by electronic guidelines and data mining technique and also operates according to the clinical process. This web base software has a critical role in the estimation survival rate in order to health care planning. PMID:28331566

  8. Decision support system for nursing management control

    SciTech Connect

    Ernst, C.J.

    1983-01-01

    A knowledge representation approach for expert systems supporting decision processes in business is proposed. A description of a knowledge representation schema using a logic programming metalanguage is described, then the role of such a schema in a management expert system is demonstrated through the problem of nursing management control in hospitals. 18 references.

  9. Microcomputer Use in Administrative Decision Support Systems.

    ERIC Educational Resources Information Center

    Brown, Kenneth G.; Droegemueller, Lee

    1983-01-01

    Some of the major problems facing higher education over the next decade and how microcomputer-based decision support systems can be used to address these as well as everyday administrative problems are discussed. Examples using electronic spreadsheets and database management systems are provided. (Author/MLW)

  10. Developing Academic Library Decision Support Systems.

    ERIC Educational Resources Information Center

    Chorba, Ronald W.; Bommer, Michael R. W.

    1983-01-01

    The approach to designing decision support systems for academic library management which is described explores online retrieval of profiles of user productivity, resource utilization, and resource availability. Database models, implementation considerations, database management systems, a sample application, and supplementing manager's judgement…

  11. Clinical Decision Support and Closed-Loop Control for Cardiopulmonary Management and Intensive Care Unit Sedation Using Expert Systems

    PubMed Central

    Gholami, Behnood; Bailey, James M.; Haddad, Wassim M.; Tannenbaum, Allen R.

    2013-01-01

    Patients in the intensive care unit (ICU) who require mechanical ventilation due to acute respiratory failure also frequently require the administration of sedative agents. The need for sedation arises both from patient anxiety due to the loss of personal control and the unfamiliar and intrusive environment of the ICU, and also due to pain or other variants of noxious stimuli. While physicians select the agent(s) used for sedation and cardiovascular function, the actual administration of these agents is the responsibility of the nursing staff. If clinical decision support systems and closed-loop control systems could be developed for critical care monitoring and lifesaving interventions as well as the administration of sedation and cardiopulmonary management, the ICU nurse could be released from the intense monitoring of sedation, allowing her/him to focus on other critical tasks. One particularly attractive strategy is to utilize the knowledge and experience of skilled clinicians, capturing explicitly the rules expert clinicians use to decide on how to titrate drug doses depending on the level of sedation. In this paper, we extend the deterministic rule-based expert system for cardiopulmonary management and ICU sedation framework presented in [1] to a stochastic setting by using probability theory to quantify uncertainty and hence deal with more realistic clinical situations. PMID:23620646

  12. Evaluating a Web-Based Clinical Decision Support System for Language Disorders Screening in a Nursery School

    PubMed Central

    Valero Duboy, Miguel Ángel; Torcal Loriente, Carmen; Pau de la Cruz, Iván

    2014-01-01

    Background Early and effective identification of developmental disorders during childhood remains a critical task for the international community. The second highest prevalence of common developmental disorders in children are language delays, which are frequently the first symptoms of a possible disorder. Objective This paper evaluates a Web-based Clinical Decision Support System (CDSS) whose aim is to enhance the screening of language disorders at a nursery school. The common lack of early diagnosis of language disorders led us to deploy an easy-to-use CDSS in order to evaluate its accuracy in early detection of language pathologies. This CDSS can be used by pediatricians to support the screening of language disorders in primary care. Methods This paper details the evaluation results of the “Gades” CDSS at a nursery school with 146 children, 12 educators, and 1 language therapist. The methodology embraces two consecutive phases. The first stage involves the observation of each child’s language abilities, carried out by the educators, to facilitate the evaluation of language acquisition level performed by a language therapist. Next, the same language therapist evaluates the reliability of the observed results. Results The Gades CDSS was integrated to provide the language therapist with the required clinical information. The validation process showed a global 83.6% (122/146) success rate in language evaluation and a 7% (7/94) rate of non-accepted system decisions within the range of children from 0 to 3 years old. The system helped language therapists to identify new children with potential disorders who required further evaluation. This process will revalidate the CDSS output and allow the enhancement of early detection of language disorders in children. The system does need minor refinement, since the therapists disagreed with some questions from the CDSS knowledge base (KB) and suggested adding a few questions about speech production and pragmatic

  13. Evaluating a web-based clinical decision support system for language disorders screening in a nursery school.

    PubMed

    Martín Ruiz, María Luisa; Valero Duboy, Miguel Ángel; Torcal Loriente, Carmen; Pau de la Cruz, Iván

    2014-05-28

    Early and effective identification of developmental disorders during childhood remains a critical task for the international community. The second highest prevalence of common developmental disorders in children are language delays, which are frequently the first symptoms of a possible disorder. This paper evaluates a Web-based Clinical Decision Support System (CDSS) whose aim is to enhance the screening of language disorders at a nursery school. The common lack of early diagnosis of language disorders led us to deploy an easy-to-use CDSS in order to evaluate its accuracy in early detection of language pathologies. This CDSS can be used by pediatricians to support the screening of language disorders in primary care. This paper details the evaluation results of the "Gades" CDSS at a nursery school with 146 children, 12 educators, and 1 language therapist. The methodology embraces two consecutive phases. The first stage involves the observation of each child's language abilities, carried out by the educators, to facilitate the evaluation of language acquisition level performed by a language therapist. Next, the same language therapist evaluates the reliability of the observed results. The Gades CDSS was integrated to provide the language therapist with the required clinical information. The validation process showed a global 83.6% (122/146) success rate in language evaluation and a 7% (7/94) rate of non-accepted system decisions within the range of children from 0 to 3 years old. The system helped language therapists to identify new children with potential disorders who required further evaluation. This process will revalidate the CDSS output and allow the enhancement of early detection of language disorders in children. The system does need minor refinement, since the therapists disagreed with some questions from the CDSS knowledge base (KB) and suggested adding a few questions about speech production and pragmatic abilities. The refinement of the KB will address

  14. A Four-Phase Model of the Evolution of Clinical Decision Support Architectures

    PubMed Central

    Wright, Adam; Sittig, Dean F.

    2008-01-01

    Background A large body of evidence over many years suggests that clinical decision support systems can be helpful in improving both clinical outcomes and adherence to evidence-based guidelines. However, to this day, clinical decision support systems are not widely used outside of a small number of sites. One reason why decision support systems are not widely used is the relative difficulty of integrating such systems into clinical workflows and computer systems. Purpose To review and synthesize the history of clinical decision support systems, and to propose a model of various architectures for integrating clinical decision support systems with clinical systems. Methods The authors conducted an extensive review of the clinical decision support literature since 1959, sequenced the systems and developed a model. Results The model developed consists of four phases: standalone decision support systems, decision support integrated into clinical systems, standards for sharing clinical decision support content and service models for decision support. These four phases have not heretofore been identified, but they track remarkably well with the chronological history of clinical decision support, and show evolving and increasingly sophisticated attempts to ease integrating decision support systems into clinical workflows and other clinical systems. Conclusions Each of the four evolutionary approaches to decision support architecture has unique advantages and disadvantages. A key lesson was that there were common limitations that almost all the approaches faced, and no single approach has been able to entirely surmount: 1) fixed knowledge representation systems inherently circumscribe the type of knowledge that can be represented in them, 2) there are serious terminological issues, 3) patient data may be spread across several sources with no single source having a complete view of the patient, and 4) major difficulties exist in transferring successful interventions from one

  15. Clinical decision support systems and computer-aided diagnosis in otology.

    PubMed

    Goggin, Leigh S; Eikelboom, Robert H; Atlas, Marcus D

    2007-04-01

    We reviewed the progress of the implementation of expert diagnostic systems in the field of otology. We conducted a review of the literature at a research institute. The utilization of expert diagnostic systems in otology is very limited. Previous applications focused primarily upon the diagnosis of vertiginous disorders with the use of deterministic algorithms and, more recently, with adaptive algorithms such as neural networks. Expert systems provide greater diagnostic accuracy to physicians across a wide range of medical specialties. The success of such a system depends upon the strength of its reasoning algorithm, the validity of its knowledge base, and its ease of use. There have been no attempts to develop an adaptive expert system for the full range of otological conditions. Such a tool may be of great use to physicians as a diagnostic aid and educational resource, particularly for those located in isolated sites.

  16. Formal Logic and Flowchart for Diagnosis Validity Verification and Inclusion in Clinical Decision Support Systems

    NASA Astrophysics Data System (ADS)

    Sosa, M.; Grundel, L.; Simini, F.

    2016-04-01

    Logical reasoning is part of medical practice since its origins. Modern Medicine has included information-intensive tools to refine diagnostics and treatment protocols. We are introducing formal logic teaching in Medical School prior to Clinical Internship, to foster medical practice. Two simple examples (Acute Myocardial Infarction and Diabetes Mellitus) are given in terms of formal logic expression and truth tables. Flowcharts of both diagnostic processes help understand the procedures and to validate them logically. The particularity of medical information is that it is often accompanied by “missing data” which suggests to adapt formal logic to a “three state” logic in the future. Medical Education must include formal logic to understand complex protocols and best practices, prone to mutual interactions.

  17. An analytics based clinical decision support system for CVD risk assessment and management.

    PubMed

    Anand, Shreya; Patil, Ravindra B; Krishnamoorthy, P

    2016-08-01

    Cardiovascular disease (CVD) is a major cause of disability and premature death throughout the world. Acute coronary events and other cardiovascular events frequently occur suddenly, and are often fatal before medical care can be given. Risk factor modification can reduce clinical events and premature death in people with established cardiovascular disease as well as in those who are at high cardiovascular risk due to one or more risk factors. In this work, India specific World Health Organization-International Society of Hypertension (WHO-ISH) guidelines has been implemented to stratify the subjects by their risk profile. It provides simplified approach to detect those people at high risk and provides guidance on what should be done for prevention of heart attack. Further, based on the risk stratification, lifestyle coaching, medication management and the next tests are advised to the subjects. This approach will help in early detection of cardiovascular risk subjects and provide necessary interventions at appropriate time frame. Also, it acts as motivation to the individuals to comply with recommended lifestyle changes.

  18. Dynamic clinical data mining: search engine-based decision support.

    PubMed

    Celi, Leo Anthony; Zimolzak, Andrew J; Stone, David J

    2014-06-23

    The research world is undergoing a transformation into one in which data, on massive levels, is freely shared. In the clinical world, the capture of data on a consistent basis has only recently begun. We propose an operational vision for a digitally based care system that incorporates data-based clinical decision making. The system would aggregate individual patient electronic medical data in the course of care; query a universal, de-identified clinical database using modified search engine technology in real time; identify prior cases of sufficient similarity as to be instructive to the case at hand; and populate the individual patient's electronic medical record with pertinent decision support material such as suggested interventions and prognosis, based on prior outcomes. Every individual's course, including subsequent outcomes, would then further populate the population database to create a feedback loop to benefit the care of future patients.

  19. Qualitative analysis of vendor discussions on the procurement of Computerised Physician Order Entry and Clinical Decision Support systems in hospitals.

    PubMed

    Cresswell, Kathrin M; Lee, Lisa; Slee, Ann; Coleman, Jamie; Bates, David W; Sheikh, Aziz

    2015-10-26

    We studied vendor perspectives about potentially transferable lessons for implementing organisations and national strategies surrounding the procurement of Computerised Physician Order Entry (CPOE)/Clinical Decision Support (CDS) systems in English hospitals. Data were collected from digitally audio-recorded discussions from a series of CPOE/CDS vendor round-table discussions held in September 2014 in the UK. Nine participants, representing 6 key vendors operating in the UK, attended. The discussions were transcribed verbatim and thematically analysed. Vendors reported a range of challenges surrounding the procurement and contracting processes of CPOE/CDS systems, including hospitals' inability to adequately assess their own needs and then select a suitable product, rushed procurement and implementation processes that resulted in difficulties in meaningfully engaging with vendors, as well as challenges relating to contracting leading to ambiguities in implementation roles. Consequently, relationships between system vendors and hospitals were often strained, the vendors attributing this to a lack of hospital management's appreciation of the complexities associated with implementation efforts. Future anticipated challenges included issues surrounding the standardisation of data to enable their aggregation across systems for effective secondary uses, and implementation of data exchange with providers outside the hospital. Our results indicate that there are significant issues surrounding capacity to procure and optimise CPOE/CDS systems among UK hospitals. There is an urgent need to encourage more synergistic and collaborative working between providers and vendors and for a more centralised support for National Health Service hospitals, which draws on a wider body of experience, including a formalised procurement framework with value-based product specifications. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a

  20. Qualitative analysis of vendor discussions on the procurement of Computerised Physician Order Entry and Clinical Decision Support systems in hospitals

    PubMed Central

    Cresswell, Kathrin M; Lee, Lisa; Slee, Ann; Coleman, Jamie; Bates, David W; Sheikh, Aziz

    2015-01-01

    Objectives We studied vendor perspectives about potentially transferable lessons for implementing organisations and national strategies surrounding the procurement of Computerised Physician Order Entry (CPOE)/Clinical Decision Support (CDS) systems in English hospitals. Setting Data were collected from digitally audio-recorded discussions from a series of CPOE/CDS vendor round-table discussions held in September 2014 in the UK. Participants Nine participants, representing 6 key vendors operating in the UK, attended. The discussions were transcribed verbatim and thematically analysed. Results Vendors reported a range of challenges surrounding the procurement and contracting processes of CPOE/CDS systems, including hospitals’ inability to adequately assess their own needs and then select a suitable product, rushed procurement and implementation processes that resulted in difficulties in meaningfully engaging with vendors, as well as challenges relating to contracting leading to ambiguities in implementation roles. Consequently, relationships between system vendors and hospitals were often strained, the vendors attributing this to a lack of hospital management's appreciation of the complexities associated with implementation efforts. Future anticipated challenges included issues surrounding the standardisation of data to enable their aggregation across systems for effective secondary uses, and implementation of data exchange with providers outside the hospital. Conclusions Our results indicate that there are significant issues surrounding capacity to procure and optimise CPOE/CDS systems among UK hospitals. There is an urgent need to encourage more synergistic and collaborative working between providers and vendors and for a more centralised support for National Health Service hospitals, which draws on a wider body of experience, including a formalised procurement framework with value-based product specifications. PMID:26503385

  1. Information integration in a decision support system.

    PubMed

    Hudson, D L; Cohen, M E; Deedwania, P C

    1994-01-01

    Electronic medical records pose a challenge because of the complex types of data which are included. Decision support systems must be able to deal effectively with these data types. In the expert system demonstrated here, a diversity of data types are included. These data are processed by three different methods. However, the different methods of processing are transparent to the user. An overall rule-based interface integrates the different methods into one comprehensive system.

  2. A health record integrated clinical decision support system to support prescriptions of pharmaceutical drugs in patients with reduced renal function: design, development and proof of concept.

    PubMed

    Shemeikka, Tero; Bastholm-Rahmner, Pia; Elinder, Carl-Gustaf; Vég, Anikó; Törnqvist, Elisabeth; Cornelius, Birgitta; Korkmaz, Seher

    2015-06-01

    To develop and verify proof of concept for a clinical decision support system (CDSS) to support prescriptions of pharmaceutical drugs in patients with reduced renal function, integrated in an electronic health record system (EHR) used in both hospitals and primary care. A pilot study in one geriatric clinic, one internal medicine admission ward and two outpatient healthcare centers was evaluated with a questionnaire focusing on the usefulness of the CDSS. The usage of the system was followed in a log. The CDSS is considered to increase the attention on patients with impaired renal function, provides a better understanding of dosing and is time saving. The calculated glomerular filtration rate (eGFR) and the dosing recommendation classification were perceived useful while the recommendation texts and background had been used to a lesser extent. Few previous systems are used in primary care and cover this number of drugs. The global assessment of the CDSS scored high but some elements were used to a limited extent possibly due to accessibility or that texts were considered difficult to absorb. Choosing a formula for the calculation of eGFR in a CDSS may be problematic. A real-time CDSS to support kidney-related drug prescribing in both hospital and outpatient settings is valuable to the physicians. It has the potential to improve quality of drug prescribing by increasing the attention on patients with renal insufficiency and the knowledge of their drug dosing. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

  3. Adoption of clinical decision support in multimorbidity: a systematic review.

    PubMed

    Fraccaro, Paolo; Arguello Casteleiro, Mercedes; Ainsworth, John; Buchan, Iain

    2015-01-07

    Patients with multiple conditions have complex needs and are increasing in number as populations age. This multimorbidity is one of the greatest challenges facing health care. Having more than 1 condition generates (1) interactions between pathologies, (2) duplication of tests, (3) difficulties in adhering to often conflicting clinical practice guidelines, (4) obstacles in the continuity of care, (5) confusing self-management information, and (6) medication errors. In this context, clinical decision support (CDS) systems need to be able to handle realistic complexity and minimize iatrogenic risks. The aim of this review was to identify to what extent CDS is adopted in multimorbidity. This review followed PRISMA guidance and adopted a multidisciplinary approach. Scopus and PubMed searches were performed by combining terms from 3 different thesauri containing synonyms for (1) multimorbidity and comorbidity, (2) polypharmacy, and (3) CDS. The relevant articles were identified by examining the titles and abstracts. The full text of selected/relevant articles was analyzed in-depth. For articles appropriate for this review, data were collected on clinical tasks, diseases, decision maker, methods, data input context, user interface considerations, and evaluation of effectiveness. A total of 50 articles were selected for the full in-depth analysis and 20 studies were included in the final review. Medication (n=10) and clinical guidance (n=8) were the predominant clinical tasks. Four studies focused on merging concurrent clinical practice guidelines. A total of 17 articles reported their CDS systems were knowledge-based. Most articles reviewed considered patients' clinical records (n=19), clinical practice guidelines (n=12), and clinicians' knowledge (n=10) as contextual input data. The most frequent diseases mentioned were cardiovascular (n=9) and diabetes mellitus (n=5). In all, 12 articles mentioned generalist doctor(s) as the decision maker(s). For articles reviewed

  4. Adoption of Clinical Decision Support in Multimorbidity: A Systematic Review

    PubMed Central

    Arguello Casteleiro, Mercedes; Ainsworth, John; Buchan, Iain

    2015-01-01

    Background Patients with multiple conditions have complex needs and are increasing in number as populations age. This multimorbidity is one of the greatest challenges facing health care. Having more than 1 condition generates (1) interactions between pathologies, (2) duplication of tests, (3) difficulties in adhering to often conflicting clinical practice guidelines, (4) obstacles in the continuity of care, (5) confusing self-management information, and (6) medication errors. In this context, clinical decision support (CDS) systems need to be able to handle realistic complexity and minimize iatrogenic risks. Objective The aim of this review was to identify to what extent CDS is adopted in multimorbidity. Methods This review followed PRISMA guidance and adopted a multidisciplinary approach. Scopus and PubMed searches were performed by combining terms from 3 different thesauri containing synonyms for (1) multimorbidity and comorbidity, (2) polypharmacy, and (3) CDS. The relevant articles were identified by examining the titles and abstracts. The full text of selected/relevant articles was analyzed in-depth. For articles appropriate for this review, data were collected on clinical tasks, diseases, decision maker, methods, data input context, user interface considerations, and evaluation of effectiveness. Results A total of 50 articles were selected for the full in-depth analysis and 20 studies were included in the final review. Medication (n=10) and clinical guidance (n=8) were the predominant clinical tasks. Four studies focused on merging concurrent clinical practice guidelines. A total of 17 articles reported their CDS systems were knowledge-based. Most articles reviewed considered patients’ clinical records (n=19), clinical practice guidelines (n=12), and clinicians’ knowledge (n=10) as contextual input data. The most frequent diseases mentioned were cardiovascular (n=9) and diabetes mellitus (n=5). In all, 12 articles mentioned generalist doctor(s) as the

  5. Geospatial decision support systems for societal decision making

    USGS Publications Warehouse

    Bernknopf, R.L.

    2005-01-01

    While science provides reliable information to describe and understand the earth and its natural processes, it can contribute more. There are many important societal issues in which scientific information can play a critical role. Science can add greatly to policy and management decisions to minimize loss of life and property from natural and man-made disasters, to manage water, biological, energy, and mineral resources, and in general, to enhance and protect our quality of life. However, the link between science and decision-making is often complicated and imperfect. Technical language and methods surround scientific research and the dissemination of its results. Scientific investigations often are conducted under different conditions, with different spatial boundaries, and in different timeframes than those needed to support specific policy and societal decisions. Uncertainty is not uniformly reported in scientific investigations. If society does not know that data exist, what the data mean, where to use the data, or how to include uncertainty when a decision has to be made, then science gets left out -or misused- in a decision making process. This paper is about using Geospatial Decision Support Systems (GDSS) for quantitative policy analysis. Integrated natural -social science methods and tools in a Geographic Information System that respond to decision-making needs can be used to close the gap between science and society. The GDSS has been developed so that nonscientists can pose "what if" scenarios to evaluate hypothetical outcomes of policy and management choices. In this approach decision makers can evaluate the financial and geographic distribution of potential policy options and their societal implications. Actions, based on scientific information, can be taken to mitigate hazards, protect our air and water quality, preserve the planet's biodiversity, promote balanced land use planning, and judiciously exploit natural resources. Applications using the

  6. An Intelligent Clinical Decision Support System for Patient-Specific Predictions to Improve Cervical Intraepithelial Neoplasia Detection

    PubMed Central

    Bountris, Panagiotis; Haritou, Maria; Pouliakis, Abraham; Margari, Niki; Kyrgiou, Maria; Spathis, Aris; Pappas, Asimakis; Panayiotides, Ioannis; Paraskevaidis, Evangelos A.; Karakitsos, Petros; Koutsouris, Dimitrios-Dionyssios

    2014-01-01

    Nowadays, there are molecular biology techniques providing information related to cervical cancer and its cause: the human Papillomavirus (HPV), including DNA microarrays identifying HPV subtypes, mRNA techniques such as nucleic acid based amplification or flow cytometry identifying E6/E7 oncogenes, and immunocytochemistry techniques such as overexpression of p16. Each one of these techniques has its own performance, limitations and advantages, thus a combinatorial approach via computational intelligence methods could exploit the benefits of each method and produce more accurate results. In this article we propose a clinical decision support system (CDSS), composed by artificial neural networks, intelligently combining the results of classic and ancillary techniques for diagnostic accuracy improvement. We evaluated this method on 740 cases with complete series of cytological assessment, molecular tests, and colposcopy examination. The CDSS demonstrated high sensitivity (89.4%), high specificity (97.1%), high positive predictive value (89.4%), and high negative predictive value (97.1%), for detecting cervical intraepithelial neoplasia grade 2 or worse (CIN2+). In comparison to the tests involved in this study and their combinations, the CDSS produced the most balanced results in terms of sensitivity, specificity, PPV, and NPV. The proposed system may reduce the referral rate for colposcopy and guide personalised management and therapeutic interventions. PMID:24812614

  7. Expert System Shells for Rapid Clinical Decision Support Module Development: An ESTA Demonstration of a Simple Rule-Based System for the Diagnosis of Vaginal Discharge.

    PubMed

    Kamel Boulos, Maged N

    2012-12-01

    This study demonstrates the feasibility of using expert system shells for rapid clinical decision support module development. A readily available expert system shell was used to build a simple rule-based system for the crude diagnosis of vaginal discharge. Pictures and 'canned text explanations' are extensively used throughout the program to enhance its intuitiveness and educational dimension. All the steps involved in developing the system are documented. The system runs under Microsoft Windows and is available as a free download at http://healthcybermap.org/vagdisch.zip (the distribution archive includes both the program's executable and the commented knowledge base source as a text document). The limitations of the demonstration system, such as the lack of provisions for assessing uncertainty or various degrees of severity of a sign or symptom, are discussed in detail. Ways of improving the system, such as porting it to the Web and packaging it as an app for smartphones and tablets, are also presented. An easy-to-use expert system shell enables clinicians to rapidly become their own 'knowledge engineers' and develop concise evidence-based decision support modules of simple to moderate complexity, targeting clinical practitioners, medical and nursing students, as well as patients, their lay carers and the general public (where appropriate). In the spirit of the social Web, it is hoped that an online repository can be created to peer review, share and re-use knowledge base modules covering various clinical problems and algorithms, as a service to the clinical community.

  8. A highly scalable, interoperable clinical decision support service

    PubMed Central

    Goldberg, Howard S; Paterno, Marilyn D; Rocha, Beatriz H; Schaeffer, Molly; Wright, Adam; Erickson, Jessica L; Middleton, Blackford

    2014-01-01

    Objective To create a clinical decision support (CDS) system that is shareable across healthcare delivery systems and settings over large geographic regions. Materials and methods The enterprise clinical rules service (ECRS) realizes nine design principles through a series of enterprise java beans and leverages off-the-shelf rules management systems in order to provide consistent, maintainable, and scalable decision support in a variety of settings. Results The ECRS is deployed at Partners HealthCare System (PHS) and is in use for a series of trials by members of the CDS consortium, including internally developed systems at PHS, the Regenstrief Institute, and vendor-based systems deployed at locations in Oregon and New Jersey. Performance measures indicate that the ECRS provides sub-second response time when measured apart from services required to retrieve data and assemble the continuity of care document used as input. Discussion We consider related work, design decisions, comparisons with emerging national standards, and discuss uses and limitations of the ECRS. Conclusions ECRS design, implementation, and use in CDS consortium trials indicate that it provides the flexibility and modularity needed for broad use and performs adequately. Future work will investigate additional CDS patterns, alternative methods of data passing, and further optimizations in ECRS performance. PMID:23828174

  9. Impact of a clinical decision support system for high-alert medications on the prevention of prescription errors.

    PubMed

    Lee, JaeHo; Han, Hyewon; Ock, Minsu; Lee, Sang-il; Lee, SunGyo; Jo, Min-Woo

    2014-12-01

    To evaluate the impact of a high-alert medication clinical decision support system called HARMLESS on point-of-order entry errors in a tertiary hospital. HARMLESS was designed to provide three kinds of interventions for five high-alert medications: clinical knowledge support, pop-ups for erroneous orders that block the order or provide a warning, and order recommendations. The impact of this program on prescription order was evaluated by comparing the orders in 6 month periods before and after implementing the program, by analyzing the intervention log data, and by checking for order pattern changes. During the entire evaluation period, there were 357,417 orders and 5233 logs. After HARMLESS deployment, orders that omitted dilution fluids and exceeded the maximum dose dropped from 12,878 and 214 cases to 0 and 9 cases, respectively. The latter nine cases were unexpected, but after the responsible programming error was corrected, there were no further such cases. If all blocking interventions were seen as errors that were prevented, this meant that 4137 errors (3584 of which were 'dilution fluid omitted' errors) were prevented over the 6-month post-deployment period. There were some unexpected order pattern changes after deployment and several unexpected errors emerged, including intramuscular or intravenous push orders for potassium chloride (although a case review revealed that the drug was not actually administered via these methods) and an increase in pro re nata (PRN; administer when required) orders for most drugs. HARMLESS effectively implemented blocking interventions but was associated with the emergence of unexpected errors. After a program is deployed, it must be monitored and subjected to data analysis to fix bugs and prevent the emergence of new error types. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

  10. A web-based clinical decision support system for gestational diabetes: Automatic diet prescription and detection of insulin needs.

    PubMed

    Caballero-Ruiz, Estefanía; García-Sáez, Gema; Rigla, Mercedes; Villaplana, María; Pons, Belen; Hernando, M Elena

    2017-06-01

    The growth of diabetes prevalence is causing an increasing demand in health care services which affects the clinicians' workload as medical resources do not grow at the same rate as the diabetic population. Decision support tools can help clinicians with the inspection of monitoring data, providing a preliminary analysis to ease their interpretation and reduce the evaluation time per patient. This paper presents Sinedie, a clinical decision support system designed to manage the treatment of patients with gestational diabetes. Sinedie aims to improve access to specialized healthcare assistance, to prevent patients from unnecessary displacements, to reduce the evaluation time per patient and to avoid gestational diabetes adverse outcomes. A web-based telemedicine platform was designed to remotely evaluate patients allowing them to upload their glycaemia data at home directly from their glucose meter, as well as report other monitoring variables like ketonuria and compliance to dietary treatment. Glycaemia values, not tagged by patients, are automatically labelled with their associated meal by a classifier based on the Expectation Maximization clustering algorithm and a C4.5 decision tree learning algorithm. Two finite automata are combined to determine the patient's metabolic condition, which is analysed by a rule-based knowledge base to generate therapy adjustment recommendations. Diet recommendations are automatically prescribed and notified to the patients, whereas recommendations about insulin requirements are notified also to the physicians, who will decide if insulin needs to be prescribed. The system provides clinicians with a view where patients are prioritized according to their metabolic condition. A randomized controlled clinical trial was designed to evaluate the effectiveness and safety of Sinedie interventions versus standard care and its impact in the professionals' workload in terms of the clinician's time required per patient; number of face

  11. 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.

  12. Management Decision Support Systems: From Theory to Practice.

    ERIC Educational Resources Information Center

    Wong, Simon C. H.

    1995-01-01

    A decision support system integrates individuals' intellectual resources with computer capabilities to improve decision-making quality. This paper presents the theoretical aspects of decision making and decision support and shows how the theories can be applied in developing an operational management decision-making support system for room booking…

  13. Executive Support Systems: An Innovation Decision Perspective

    DTIC Science & Technology

    1990-01-01

    account . The exception and annotation ability of MIDS alerted the executives to what was happening and prevented a ripple effect of overreactions...information directly to these executives, an executive support system (ESS) allows more effective analysis, control, planning, and decision making...Automated improve- ments to the management process have the potential to highly leverage the executive’s effectiveness . An ESS is a concept, a clustered IT

  14. A rule-based clinical decision model to support interpretation of multiple data in health examinations.

    PubMed

    Kuo, Kuan-Liang; Fuh, Chiou-Shann

    2011-12-01

    Health examinations can obtain relatively complete health information and thus are important for the personal and public health management. For clinicians, one of the most important works in the health examinations is to interpret the health examination results. Continuously interpreting numerous health examination results of healthcare receivers is tedious and error-prone. This paper proposes a clinical decision support system to assist solving above problems. In order to customize the clinical decision support system intuitively and flexibly, this paper also proposes the rule syntax to implement computer-interpretable logic for health examinations. It is our purpose in this paper to describe the methodology of the proposed clinical decision support system. The evaluation was performed by the implementation and execution of decision rules on health examination results and a survey on clinical decision support system users. It reveals the efficiency and user satisfaction of proposed clinical decision support system. Positive impact of clinical data interpretation is also noted.

  15. Screening Consolidated Clinical Document Architecture (CCDA) Documents for Sensitive Data Using a Rule-Based Decision Support System.

    PubMed

    Rocha, Beatriz H; Pabbathi, Deepika; Schaeffer, Molly; Goldberg, Howard S

    2017-02-08

    The Centers for Medicare & Medicaid Services' Stage 2 final rule requires that eligible hospitals provide a visit summary electronically at transitions of care in order to qualify for "meaningful use" incentive payments. However, Massachusetts state law and Federal law prohibit the transmission of documents containing "sensitive" data unless there is a new patient consent for each transmission. To describe the implementation and evaluation of a rule-based decision support system used to screen transition of care documents for sensitive data. We implemented a rule-based document screening system to identify transition of care documents that might contain sensitive data. The transmission of detected documents is withheld until a new patient consent is obtained. The documents that were flagged as containing sensitive data were reviewed in two different time periods to verify that the decision support system was not missing documents or withholding more documents than necessary. The rule-based screening system has been in regular production use for the past 18 months. During the first evaluation period, 3% of 5,841 documents were identified as containing sensitive data (true-positive rate of 44%). After additional enhancements to the rules, the system was evaluated a second time and 4.5% of 6,935 documents were identified as containing sensitive data (true-positive rate of 98.4%). The analysis of the system demonstrates that production rules can be used to automatically screen the content of transition of care documents for sensitive data. The utilization of the rule-based decision support system enabled our hospitals to achieve meaningful use and, at the same time, remain compliant with state and federal laws.

  16. A clinical decision support system for venous thromboembolism prophylaxis at a general hospital in a middle-income country *,**

    PubMed Central

    Fuzinatto, Fernanda; de Waldemar, Fernando Starosta; Wajner, André; Elias, Cesar Al Alam; Fernandez, Juliana Fernándes; Hopf, João Luiz de Souza; Barreto, Sergio Saldanha Menna

    2013-01-01

    OBJECTIVE: To determine the impact that implementing a combination of a computer-based clinical decision support system and a program of training seminars has on the use of appropriate prophylaxis for venous thromboembolism (VTE). METHODS: We conducted a cross-sectional study in two phases (prior to and after the implementation of the new VTE prophylaxis protocol) in order to evaluate the impact that the combined strategy had on the use of appropriate VTE prophylaxis. The study was conducted at Nossa Senhora da Conceição Hospital, a general hospital in the city of Porto Alegre, Brazil. We included clinical and surgical patients over 18 years of age who were hospitalized for ≥ 48 h. The pre-implementation and post-implementation phase samples comprised 262 and 261 patients, respectively. RESULTS: The baseline characteristics of the two samples were similar, including the distribution of patients by risk level. Comparing the pre-implementation and post-implementation periods, we found that the overall use of appropriate VTE prophylaxis increased from 46.2% to 57.9% (p = 0.01). Looking at specific patient populations, we observed that the use of appropriate VTE prophylaxis increased more dramatically among cancer patients (from 18.1% to 44.1%; p = 0.002) and among patients with three or more risk factors (from 25.0% to 42.9%; p = 0.008), two populations that benefit most from prophylaxis. CONCLUSIONS: It is possible to increase the use of appropriate VTE prophylaxis in economically constrained settings through the use of a computerized protocol adhered to by trained professionals. The underutilization of prophylaxis continues to be a major problem, indicative of the need for ongoing improvement in the quality of inpatient care. PMID:23670498

  17. Reducing Diagnostic Error with Computer-Based Clinical Decision Support

    ERIC Educational Resources Information Center

    Greenes, Robert A.

    2009-01-01

    Information technology approaches to delivering diagnostic clinical decision support (CDS) are the subject of the papers to follow in the proceedings. These will address the history of CDS and present day approaches (Miller), evaluation of diagnostic CDS methods (Friedman), and the role of clinical documentation in supporting diagnostic decision…

  18. Reducing Diagnostic Error with Computer-Based Clinical Decision Support

    ERIC Educational Resources Information Center

    Greenes, Robert A.

    2009-01-01

    Information technology approaches to delivering diagnostic clinical decision support (CDS) are the subject of the papers to follow in the proceedings. These will address the history of CDS and present day approaches (Miller), evaluation of diagnostic CDS methods (Friedman), and the role of clinical documentation in supporting diagnostic decision…

  19. Providers' Response to Clinical Decision Support for QT Prolonging Drugs.

    PubMed

    Sharma, Sunita; Martijn Bos, J; Tarrell, Robert F; Simon, Gyorgy J; Morlan, Bruce W; Ackerman, Michael J; Caraballo, Pedro J

    2017-09-02

    Commonly used drugs in hospital setting can cause QT prolongation and trigger life-threatening arrhythmias. We evaluate changes in prescribing behavior after the implementation of a clinical decision support system to prevent the use of QT prolonging medications in the hospital setting. We conducted a quasi-experimental study, before and after the implementation of a clinical decision support system integrated in the electronic medical record (QT-alert system). This system detects patients at risk of significant QT prolongation (QTc>500ms) and alerts providers ordering QT prolonging drugs. We reviewed the electronic health record to assess the provider's responses which were classified as "action taken" (QT drug avoided, QT drug changed, other QT drug(s) avoided, ECG monitoring, electrolytes monitoring, QT issue acknowledged, other actions) or "no action taken". Approximately, 15.5% (95/612) of the alerts were followed by a provider's action in the pre-intervention phase compared with 21% (228/1085) in the post-intervention phase (p=0.006). The most common type of actions taken during pre-intervention phase compared to post-intervention phase were ECG monitoring (8% vs. 13%, p=0.002) and QT issue acknowledgment (2.1% vs. 4.1%, p=0.03). Notably, there was no significant difference for other actions including QT drug avoided (p=0.8), QT drug changed (p=0.06) and other QT drug(s) avoided (p=0.3). Our study demonstrated that the QT alert system prompted a higher proportion of providers to take action on patients at risk of complications. However, the overall impact was modest underscoring the need for educating providers and optimizing clinical decision support to further reduce drug-induced QT prolongation.

  20. Relational Algebra in Spatial Decision Support Systems Ontologies.

    PubMed

    Diomidous, Marianna; Chardalias, Kostis; Koutonias, Panagiotis; Magnita, Adrianna; Andrianopoulos, Charalampos; Zimeras, Stelios; Mechili, Enkeleint Aggelos

    2017-01-01

    Decision Support Systems (DSS) is a powerful tool, for facilitates researchers to choose the correct decision based on their final results. Especially in medical cases where doctors could use these systems, to overcome the problem with the clinical misunderstanding. Based on these systems, queries must be constructed based on the particular questions that doctors must answer. In this work, combination between questions and queries would be presented via relational algebra.

  1. Usability evaluation of pharmacogenomics clinical decision support aids and clinical knowledge resources in a computerized provider order entry system: a mixed methods approach.

    PubMed

    Devine, Emily Beth; Lee, Chia-Ju; Overby, Casey L; Abernethy, Neil; McCune, Jeannine; Smith, Joe W; Tarczy-Hornoch, Peter

    2014-07-01

    Pharmacogenomics (PGx) is positioned to have a widespread impact on the practice of medicine, yet physician acceptance is low. The presentation of context-specific PGx information, in the form of clinical decision support (CDS) alerts embedded in a computerized provider order entry (CPOE) system, can aid uptake. Usability evaluations can inform optimal design, which, in turn, can spur adoption. The study objectives were to: (1) evaluate an early prototype, commercial CPOE system with PGx-CDS alerts in a simulated environment, (2) identify potential improvements to the system user interface, and (3) understand the contexts under which PGx knowledge embedded in an electronic health record is useful to prescribers. Using a mixed methods approach, we presented seven cardiologists and three oncologists with five hypothetical clinical case scenarios. Each scenario featured a drug for which a gene encoding drug metabolizing enzyme required consideration of dosage adjustment. We used Morae(®) to capture comments and on-screen movements as participants prescribed each drug. In addition to PGx-CDS alerts, 'Infobutton(®)' and 'Evidence' icons provided participants with clinical knowledge resources to aid decision-making. Nine themes emerged. Five suggested minor improvements to the CPOE user interface; two suggested presenting PGx information through PGx-CDS alerts using an 'Infobutton' or 'Evidence' icon. The remaining themes were strong recommendations to provide succinct, relevant guidelines and dosing recommendations of phenotypic information from credible and trustworthy sources; any more information was overwhelming. Participants' median rating of PGx-CDS system usability was 2 on a Likert scale ranging from 1 (strongly agree) to 7 (strongly disagree). Usability evaluation results suggest that participants considered PGx information important for improving prescribing decisions; and that they would incorporate PGx-CDS when information is presented in relevant and

  2. SMARTHealth India: Development and Field Evaluation of a Mobile Clinical Decision Support System for Cardiovascular Diseases in Rural India

    PubMed Central

    Patel, Anushka; Raghu, Arvind; Clifford, Gari D; Maulik, Pallab K; Mohammad Abdul, Ameer; Mogulluru, Kishor; Tarassenko, Lionel; MacMahon, Stephen; Peiris, David

    2014-01-01

    Background Cardiovascular disease (CVD) is the major cause of premature death and disability in India and yet few people at risk of CVD are able to access best practice health care. Mobile health (mHealth) is a promising solution, but very few mHealth interventions have been subjected to robust evaluation in India. Objective The objectives were to develop a multifaceted, mobile clinical decision support system (CDSS) for CVD management and evaluate it for use by public nonphysician health care workers (NPHWs) and physicians in a rural Indian setting. Methods Plain language clinical rules were developed based on standard guidelines and programmed into a computer tablet app. The algorithm was validated and field-tested in 11 villages in Andhra Pradesh, involving 11 NPHWs and 3 primary health center (PHC) physicians. A mixed method evaluation was conducted comprising clinical and survey data and in-depth patient and staff interviews to understand barriers and enablers to the use of the system. Then this was thematically analyzed using NVivo 10. Results During validation of the algorithm, there was an initial agreement for 70% of the 42 calculated variables between the CDSS and SPSS software outputs. Discrepancies were identified and amendments were made until perfect agreement was achieved. During field testing, NPHWs and PHC physicians used the CDSS to screen 227 and 65 adults, respectively. The NPHWs identified 39% (88/227) of patients for referral with 78% (69/88) of these having a definite indication for blood pressure (BP)-lowering medication. However, only 35% (24/69) attended a clinic within 1 month of referral, with 42% (10/24) of these reporting continuing medications at 3-month follow-up. Physicians identified and recommended 17% (11/65) of patients for BP-lowering medications. Qualitative interviews identified 3 interrelated interview themes: (1) the CDSS had potential to change prevailing health care models, (2) task-shifting to NPHWs was the central

  3. Feasibility study of a clinical decision support system for the management of multimorbid seniors in primary care: study protocol.

    PubMed

    Weltermann, Birgitta; Kersting, Christine

    2016-01-01

    Care for seniors is complex because patients often have more than one disease, one medication, and one physician. It is a key challenge for primary care physicians to structure the various aspects of each patient's care, to integrate each patient's preferences, and to maintain a long-term overview. This article describes the design for the development and feasibility testing of the clinical decision support system (CDSS) eCare*Seniors© which is electronic health record (EHR)-based allowing for a long-term, comprehensive, evidence-based, and patient preference-oriented management of multimorbid seniors. This mixed-methods study is designed in three steps. First, focus groups and practice observations will be conducted to develop criteria for software design from a physicians' and practice assistants' perspective. Second, based on these criteria, a CDSS prototype will be developed. Third, the prototype's feasibility will be tested by five primary care practices in the care of 30 multimorbid seniors. Primary outcome is the usability of the software measured by the validated system usability scale (SUS) after 3 months. Secondary outcomes are the (a) willingness to routinely use the CDSS, (b) degree of utilization of the CDSS, (c) acceptance of the CDSS, (d) willingness of the physicians to purchase the CDSS, and (e) willingness of the practice assistants to use the CDSS in the long term. These outcomes will be measured using semi-structured interviews and software usage data. If the SUS score reaches ≥70 %, feasibility testing will be judged successful. Otherwise, the CDSS prototype will be refined according to the users' needs and retested by the physicians and practice assistants until it is fully adapted to their requirements and reaches a usability score ≥70 %. The study will support the development of a CDSS which is primary care-defined, user-friendly, easy-to-comprehend, workflow-oriented, and comprehensive. The software will assist physicians and

  4. Medical decision support systems and therapeutics: The role of autopilots.

    PubMed

    Woosley, R L; Whyte, J; Mohamadi, A; Romero, K

    2016-02-01

    For decades, medical practice has increasingly relied on prescription medicines to treat, cure, or prevent illness but their net benefit is reduced by prescribing errors that result in adverse drug reactions (ADRs) and tens of thousands of deaths each year. Optimal prescribing requires effective management of massive amounts of data. Clinical decision support systems (CDSS) can help manage information and support optimal therapeutic decisions before errors are made by operating as the prescribers' "autopilot."

  5. Decision support system for drinking water management

    NASA Astrophysics Data System (ADS)

    Janža, M.

    2012-04-01

    The problems in drinking water management are complex and often solutions must be reached under strict time constrains. This is especially distinct in case of environmental accidents in the catchment areas of the wells that are used for drinking water supply. The beneficial tools that can help decision makers and make program of activities more efficient are decision support systems (DSS). In general they are defined as computer-based support systems that help decision makers utilize data and models to solve unstructured problems. The presented DSS was developed in the frame of INCOME project which is focused on the long-term stable and safe drinking water supply in Ljubljana. The two main water resources Ljubljana polje and Barje alluvial aquifers are characterized by a strong interconnection of surface and groundwater, high vulnerability, high velocities of groundwater flow and pollutant transport. In case of sudden pollution, reactions should be very fast to avoid serious impact to the water supply. In the area high pressures arising from urbanization, industry, traffic, agriculture and old environmental burdens. The aim of the developed DSS is to optimize the activities in cases of emergency water management and to optimize the administrative work regarding the activities that can improve groundwater quality status. The DSS is an interactive computer system that utilizes data base, hydrological modelling, and experts' and stakeholders' knowledge. It consists of three components, tackling the different abovementioned issues in water management. The first one utilizes the work on identification, cleaning up and restoration of illegal dumpsites that are a serious threat to the qualitative status of groundwater. The other two components utilize the predictive capability of the hydrological model and scenario analysis. The user interacts with the system by a graphical interface that guides the user step-by-step to the recommended remedial measures. Consequently, the

  6. 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.

  7. Workflow-driven clinical decision support for personalized oncology.

    PubMed

    Bucur, Anca; van Leeuwen, Jasper; Christodoulou, Nikolaos; Sigdel, Kamana; Argyri, Katerina; Koumakis, Lefteris; Graf, Norbert; Stamatakos, Georgios

    2016-07-21

    The adoption in oncology of Clinical Decision Support (CDS) may help clinical users to efficiently deal with the high complexity of the domain, lead to improved patient outcomes, and reduce the current knowledge gap between clinical research and practice. While significant effort has been invested in the implementation of CDS, the uptake in the clinic has been limited. The barriers to adoption have been extensively discussed in the literature. In oncology, current CDS solutions are not able to support the complex decisions required for stratification and personalized treatment of patients and to keep up with the high rate of change in therapeutic options and knowledge. To address these challenges, we propose a framework enabling efficient implementation of meaningful CDS that incorporates a large variety of clinical knowledge models to bring to the clinic comprehensive solutions leveraging the latest domain knowledge. We use both literature-based models and models built within the p-medicine project using the rich datasets from clinical trials and care provided by the clinical partners. The framework is open to the biomedical community, enabling reuse of deployed models by third-party CDS implementations and supporting collaboration among modelers, CDS implementers, biomedical researchers and clinicians. To increase adoption and cope with the complexity of patient management in oncology, we also support and leverage the clinical processes adhered to by healthcare organizations. We design an architecture that extends the CDS framework with workflow functionality. The clinical models are embedded in the workflow models and executed at the right time, when and where the recommendations are needed in the clinical process. In this paper we present our CDS framework developed in p-medicine and the CDS implementation leveraging the framework. To support complex decisions, the framework relies on clinical models that encapsulate relevant clinical knowledge. Next to

  8. Cost and economic benefit of clinical decision support systems for cardiovascular disease prevention: a community guide systematic review.

    PubMed

    Jacob, Verughese; Thota, Anilkrishna B; Chattopadhyay, Sajal K; Njie, Gibril J; Proia, Krista K; Hopkins, David P; Ross, Murray N; Pronk, Nicolaas P; Clymer, John M

    2017-05-01

    This review evaluates costs and benefits associated with acquiring, implementing, and operating clinical decision support systems (CDSSs) to prevent cardiovascular disease (CVD). Methods developed for the Community Guide were used to review CDSS literature covering the period from January 1976 to October 2015. Twenty-one studies were identified for inclusion. It was difficult to draw a meaningful estimate for the cost of acquiring and operating CDSSs to prevent CVD from the available studies ( n  = 12) due to considerable heterogeneity. Several studies ( n  = 11) indicated that health care costs were averted by using CDSSs but many were partial assessments that did not consider all components of health care. Four cost-benefit studies reached conflicting conclusions about the net benefit of CDSSs based on incomplete assessments of costs and benefits. Three cost-utility studies indicated inconsistent conclusions regarding cost-effectiveness based on a conservative $50,000 threshold. Intervention costs were not negligible, but specific estimates were not derived because of the heterogeneity of implementation and reporting metrics. Expected economic benefits from averted health care cost could not be determined with confidence because many studies did not fully account for all components of health care. We were unable to conclude whether CDSSs for CVD prevention is either cost-beneficial or cost-effective. Several evidence gaps are identified, most prominently a lack of information about major drivers of cost and benefit, a lack of standard metrics for the cost of CDSSs, and not allowing for useful life of a CDSS that generally extends beyond one accounting period.

  9. Renal Drug Dosing. Effectiveness of Outpatient Pharmacist-Based vs. Prescriber-Based Clinical Decision Support Systems.

    PubMed

    Vogel, Erin A; Billups, Sarah J; Herner, Sheryl J; Delate, Thomas

    2016-07-27

    The purpose of this study was to compare the effectiveness of an outpatient renal dose adjustment alert via a computerized provider order entry (CPOE) clinical decision support system (CDSS) versus a CDSS with alerts made to dispensing pharmacists. This was a retrospective analysis of patients with renal impairment and 30 medications that are contraindicated or require dose-adjustment in such patients. The primary outcome was the rate of renal dosing errors for study medications that were dispensed between August and December 2013, when a pharmacist-based CDSS was in place, versus August through December 2014, when a prescriber-based CDSS was in place. A dosing error was defined as a prescription for one of the study medications dispensed to a patient where the medication was contraindicated or improperly dosed based on the patient's renal function. The denominator was all prescriptions for the study medications dispensed during each respective study period. During the pharmacist- and prescriber-based CDSS study periods, 49,054 and 50,678 prescriptions, respectively, were dispensed for one of the included medications. Of these, 878 (1.8%) and 758 (1.5%) prescriptions were dispensed to patients with renal impairment in the respective study periods. Patients in each group were similar with respect to age, sex, and renal function stage. Overall, the five-month error rate was 0.38%. Error rates were similar between the two groups: 0.36% and 0.40% in the pharmacist- and prescriber-based CDSS, respectively (p=0.523). The medication with the highest error rate was dofetilide (0.51% overall) while the medications with the lowest error rate were dabigatran, fondaparinux, and spironolactone (0.00% overall). Prescriber- and pharmacist-based CDSS provided comparable, low rates of potential medication errors. Future studies should be undertaken to examine patient benefits of the prescriber-based CDSS.

  10. Randomized trial of a clinical decision support system: impact on the management of children with fever without apparent source.

    PubMed

    Roukema, Jolt; Steyerberg, Ewout W; van der Lei, Johan; Moll, Henriëtte A

    2008-01-01

    To assess compliance with a clinical decision support system (CDSS) for diagnostic management of children with fever without apparent source and to study the effects of application of the CDSS on time spent in the emergency department (ED) and number of laboratory tests. The CDSS was used by ED nursing staff to register children presenting with fever. The CDSS identified children that met inclusion criteria (1-36 months and fever without apparent source (FWS)) and provided patient-specific diagnostic management advice. Children at high risk for serious bacterial infection were randomized for the 'intervention' (n = 74) or the 'control' (n = 90) group. In the intervention group, the CDSS provided the advice to immediately order laboratory tests and in the control group the ED physician first assessed the children and then decided on ordering laboratory tests. Compliance with registration of febrile children was 50% (683/1,399). Adherence to the advice to order laboratory tests was 82% (61/74). Children in the intervention group had a median (25(th)-75(th) percentile) length of stay at the ED of 138 (104-181) minutes. The median length of stay at the ED in the control group was 123 (83-179) minutes. Laboratory tests were significantly more frequently ordered in the intervention group (82%) than in the control group (44%, p < 0.001, chi(2) test). Implementation of a CDSS for diagnostic management of young children with fever without apparent source was successful regarding compliance and adherence to CDSS recommendations, but had unexpected effects on patient outcome in terms of ED length of stay and number of laboratory tests. The use of the current CDSS was discontinued.

  11. Drug-related problems identified during medication review before and after the introduction of a clinical decision support system.

    PubMed

    Verdoorn, S; Kwint, H F; Hoogland, P; Gussekloo, J; Bouvy, M L

    2017-10-02

    To facilitate the identification of drug-related problems (DRPs) during medication review, several tools have been developed. Explicit criteria, like Beers criteria or STOPP (Screening Tool of Older Peoples' Prescriptions) and START (Screening Tool to Alert doctors to Right Treatment) criteria, can easily be integrated into a clinical decision support system (CDSS). The aim of this study was to investigate the effect of adding a CDSS to medication review software on identifying and solving DRPs in daily pharmacy practice. Pre- to post-analysis of clinical medication reviews (CMRs) performed by 121 pharmacies in 2012 and 2013, before and after the introduction of CDSS into medication review software. Mean number of DRPs per patient, type of DRPs and their resolution rates were compared in the pharmacies pre- and post-CDSS using paired t tests. In total, 9151 DRPs were identified in 3100 patients pre-CDSS and 15 268 DRPs were identified in 4303 patients post-CDSS. The mean number of identified DRPs per patient (aggregated per pharmacy) was higher after the introduction of CDSS (3.2 vs 3.6 P < .01). The resolution rate was lower post-CDSS (50% vs 44%; P < .01), which overall resulted in 1.6 resolved DRPs per patient in both groups (P = .93). After the introduction of CDSS, 41% of DRPs were detected by the CDSS. The resolution rate of DRPs generated by CDSS was lower than of DRPs identified without the help of CDSS (29% vs 55%; P < .01). The two most prevalent DRP types were "Overtreatment" and "Suboptimal therapy" in both groups. The prevalence of "Overtreatment" was equal in both groups (mean DRPs per patient: 0.84 vs 0.77; P = .22), and "Suboptimal therapy" was more frequently identified post-CDSS (mean DRPs per patient: 0.54 vs 1.1; P < .01). The introduction of CDSS to medication review software generated additional DRPs with a lower resolution rate. Structural assessment including a patient interview elicited the most relevant DRPs. Further

  12. Web Support System for Group Collaborative Decisions

    NASA Astrophysics Data System (ADS)

    Rigopoulos, George; Psarras, John; Askounis, Dimitrios Th.

    In this research, we present a Group Decision Support System (GDSS) based on web technology, which can be used in asynchronous mode from group members. It supports small collaborative groups in classification decisions, implementing a supervised multicriteria methodology. A facilitator, who defines necessary parameters and initiates the procedure, coordinates the entire operation. Next, members evaluate the proposed parameter set and express their preferences in numeric format. Aggregation of individuals` preferences is executed at the parameter level by utilization of OWA operator and a group parameter set is produced which is used as input for the classification algorithm. A multicriteria classification algorithm is used for the classification of actions (people, projects etc.). Finally, group members evaluate results and consensus as well as satisfaction indexes are calculated. In case of low acceptance level, parameters are redefined and aggregation phase is repeated. The system has been utilized effectively to solve group classification problems in business environment. The overall architecture as well the methodology is presented, along with a sample application. Empirical findings from GDSS application and the methodology provide evidence that it is a valid approach for similar decision problems in numerous business environments, including production, human resources and operations.

  13. Assessing the feasibility of a mobile health-supported clinical decision support system for nutritional triage in oncology outpatients using Arden Syntax.

    PubMed

    de Bruin, Jeroen S; Schuh, Christian; Seeling, Walter; Luger, Eva; Gall, Michaela; Hütterer, Elisabeth; Kornek, Gabriela; Ludvik, Bernhard; Hoppichler, Friedrich; Schindler, Karin

    2015-10-22

    Nutritional screening procedures followed by regular nutrition monitoring for oncological outpatients are no standard practice in many European hospital wards and outpatient settings. As a result, early signs of malnutrition are missed and nutritional treatment is initiated when patients have already experienced severe weight loss. We report on a novel clinical decision support system (CDSS) for the global assessment and nutritional triage of the nutritional condition of oncology outpatients. The system combines clinical and laboratory data collected in the clinical setting with patient-generated data from a smartphone application for monitoring the patients' nutritional status. Our objective is to assess the feasibility of a CDSS that combines the aforementioned data sources and describe its integration into a hospital information system. Furthermore, we collected patients' opinions on the value of the system, and whether they would regard the system as a useful aid in coping with their condition. The system implements the Patient-Generated Subjective Global Assessment (PG-SGA) to monitor nutritional status in the outpatient setting. A smartphone application is used to collect patient-generated data by performing weekly mini-surveys on patients concerning their eating habits, weight, and overall well-being. Data are uploaded on completion of each mini-survey and stored on a secure server at the Medical University of Vienna (MUV). The data are then combined with relevant clinical information from the Vienna General Hospital (VGH) information system. The knowledge base for the CDSS is implemented in medical logic modules (MLMs) using Arden Syntax. A three-month pilot clinical trial was performed to test the feasibility of the system. Qualitative questionnaires were used to obtain the patients' opinions on the usability and personal value of the system during the four-week test period. We used the existing separation between the scientific and clinical data domains

  14. Clinical Decision Support Knowledge Management: Strategies for Success.

    PubMed

    Khalifa, Mohamed; Alswailem, Osama

    2015-01-01

    Clinical Decision Support Systems have been shown to increase quality of care, patient safety, improve adherence to guidelines for prevention and treatment, and avoid medication errors. Such systems depend mainly on two types of content; the clinical information related to patients and the medical knowledge related to the specialty that informs the system rules and alerts. At King Faisal Specialist Hospital and Research Center, Saudi Arabia, the Health Information Technology Affairs worked on identifying best strategies and recommendations for successful CDSS knowledge management. A review of literature was conducted to identify main areas of challenges and factors of success. A qualitative survey was used over six months' duration to collect opinions, experiences and suggestions from both IT and healthcare professionals. Recommendations were categorized into ten main topics that should be addressed during the development and implementation of CDSS knowledge management tools in the hospital.

  15. LIMSI @ 2014 Clinical Decision Support Track

    DTIC Science & Technology

    2014-11-01

    UMLS semantics in mapping vocabularies. In Proceedings of the AMIA symposium, page 815. American Medical Informatics Association, 1998. [2] Aurélie...Elhadad, Carol Friedman, and Marianthi Markatou. Automated knowledge acquisition from clinical narrative reports. In AMIA Annual Symposium Proceedings

  16. Expert System Shells for Rapid Clinical Decision Support Module Development: An ESTA Demonstration of a Simple Rule-Based System for the Diagnosis of Vaginal Discharge

    PubMed Central

    2012-01-01

    Objectives This study demonstrates the feasibility of using expert system shells for rapid clinical decision support module development. Methods A readily available expert system shell was used to build a simple rule-based system for the crude diagnosis of vaginal discharge. Pictures and 'canned text explanations' are extensively used throughout the program to enhance its intuitiveness and educational dimension. All the steps involved in developing the system are documented. Results The system runs under Microsoft Windows and is available as a free download at http://healthcybermap.org/vagdisch.zip (the distribution archive includes both the program's executable and the commented knowledge base source as a text document). The limitations of the demonstration system, such as the lack of provisions for assessing uncertainty or various degrees of severity of a sign or symptom, are discussed in detail. Ways of improving the system, such as porting it to the Web and packaging it as an app for smartphones and tablets, are also presented. Conclusions An easy-to-use expert system shell enables clinicians to rapidly become their own 'knowledge engineers' and develop concise evidence-based decision support modules of simple to moderate complexity, targeting clinical practitioners, medical and nursing students, as well as patients, their lay carers and the general public (where appropriate). In the spirit of the social Web, it is hoped that an online repository can be created to peer review, share and re-use knowledge base modules covering various clinical problems and algorithms, as a service to the clinical community. PMID:23346475

  17. 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

  18. 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.

  19. 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:

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

    PubMed Central

    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. PMID:26958298

  1. 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.

  2. RespDoc: a new clinical decision support system for childhood asthma management based on Fraction of Exhaled Nitric Oxide (FeNO) measurements.

    PubMed

    Rigopoulou, Aikaterini V; Anthracopoulos, Michael B; Katsardis, Charis V; Lymberopoulos, Dimitrios K

    2013-01-01

    The use of Fraction of Exhaled Nitric Oxide (FeNO) for measurement of inflammation in the respiratory system is a good way to assess the level of asthma in children. In this paper we present a new Clinical Decision Support System (CDSS) for Childhood Asthma Management based on FeNO, which is named RespDoc. The core of RespDoc is a decision making algorithm and a patient's monitoring process that are extensively analyzed in the paper. The performance of RespDoc is tested through the process and assessment of archived patients' data.

  3. Naval Aviation Maintenance Decision Support System

    DTIC Science & Technology

    1989-03-01

    Processing FCF Functional Check Flight 99 FMC Fully Mission Capable JCN Job Control Number MCC Maintenance Contorl Chief MESM Mission Essential Support...DESIGN, EVALUATION AND EVOLUTION........................6 A. DECISION MAKING THEORY...............6 1. The Decision Making Process ...........7 2...87 2. Prototyping and Adaptive Design ...... 88 V. CONCLUSIONS AND RECOMMENDATIONS ... .......... 91 A. CONCLUSIONS

  4. Intelligent decision support systems for mechanical ventilation.

    PubMed

    Tehrani, Fleur T; Roum, James H

    2008-11-01

    An overview of different methodologies used in various intelligent decision support systems (IDSSs) for mechanical ventilation is provided. The applications of the techniques are compared in view of today's intensive care unit (ICU) requirements. Information available in the literature is utilized to provide a methodological review of different systems. Comparisons are made of different systems developed for specific ventilation modes as well as those intended for use in wider applications. The inputs and the optimized parameters of different systems are discussed and rule-based systems are compared to model-based techniques. The knowledge-based systems used for closed-loop control of weaning from mechanical ventilation are also described. Finally, in view of increasing trend towards automation of mechanical ventilation, the potential utility of intelligent advisory systems for this purpose is discussed. IDSSs for mechanical ventilation can be quite helpful to clinicians in today's ICU settings. To be useful, such systems should be designed to be effective, safe, and easy to use at patient's bedside. In particular, these systems must be capable of noise removal, artifact detection and effective validation of data. Systems that can also be adapted for closed-loop control/weaning of patients at the discretion of the clinician, may have a higher potential for use in the future.

  5. Development and evaluation of a comprehensive clinical decision support taxonomy: comparison of front-end tools in commercial and internally developed electronic health record systems

    PubMed Central

    Sittig, Dean F; Ash, Joan S; Feblowitz, Joshua; Meltzer, Seth; McMullen, Carmit; Guappone, Ken; Carpenter, Jim; Richardson, Joshua; Simonaitis, Linas; Evans, R Scott; Nichol, W Paul; Middleton, Blackford

    2011-01-01

    Background Clinical decision support (CDS) is a valuable tool for improving healthcare quality and lowering costs. However, there is no comprehensive taxonomy of types of CDS and there has been limited research on the availability of various CDS tools across current electronic health record (EHR) systems. Objective To develop and validate a taxonomy of front-end CDS tools and to assess support for these tools in major commercial and internally developed EHRs. Study design and methods We used a modified Delphi approach with a panel of 11 decision support experts to develop a taxonomy of 53 front-end CDS tools. Based on this taxonomy, a survey on CDS tools was sent to a purposive sample of commercial EHR vendors (n=9) and leading healthcare institutions with internally developed state-of-the-art EHRs (n=4). Results Responses were received from all healthcare institutions and 7 of 9 EHR vendors (response rate: 85%). All 53 types of CDS tools identified in the taxonomy were found in at least one surveyed EHR system, but only 8 functions were present in all EHRs. Medication dosing support and order facilitators were the most commonly available classes of decision support, while expert systems (eg, diagnostic decision support, ventilator management suggestions) were the least common. Conclusion We developed and validated a comprehensive taxonomy of front-end CDS tools. A subsequent survey of commercial EHR vendors and leading healthcare institutions revealed a small core set of common CDS tools, but identified significant variability in the remainder of clinical decision support content. PMID:21415065

  6. Clinical Decision Support for Early Recognition of Sepsis.

    PubMed

    Amland, Robert C; Hahn-Cover, Kristin E

    2016-01-01

    Sepsis is an inflammatory response triggered by infection, with a high in-hospital mortality rate. Early recognition and treatment can reverse the inflammatory response, with evidence of improved patient outcomes. One challenge clinicians face is identifying the inflammatory syndrome against the background of the patient's infectious illness and comorbidities. An approach to this problem is implementation of computerized early warning tools for sepsis. This multicenter retrospective study sought to determine clinimetric performance of a cloud-based computerized sepsis clinical decision support system (CDS), understand the epidemiology of sepsis, and identify opportunities for quality improvement. Data encompassed 6200 adult hospitalizations from 2012 through 2013. Of 13% patients screened-in, 51% were already suspected to have an infection when the system activated. This study focused on a patient cohort screened-in before infection was suspected; median time from arrival to CDS activation was 3.5 hours, and system activation to diagnostic collect was another 8.6 hours.

  7. Semantic technologies in a decision support system

    NASA Astrophysics Data System (ADS)

    Wasielewska, K.; Ganzha, M.; Paprzycki, M.; Bǎdicǎ, C.; Ivanovic, M.; Lirkov, I.

    2015-10-01

    The aim of our work is to design a decision support system based on ontological representation of domain(s) and semantic technologies. Specifically, we consider the case when Grid / Cloud user describes his/her requirements regarding a "resource" as a class expression from an ontology, while the instances of (the same) ontology represent available resources. The goal is to help the user to find the best option with respect to his/her requirements, while remembering that user's knowledge may be "limited." In this context, we discuss multiple approaches based on semantic data processing, which involve different "forms" of user interaction with the system. Specifically, we consider: (a) ontological matchmaking based on SPARQL queries and class expression, (b) graph-based semantic closeness of instances representing user requirements (constructed from the class expression) and available resources, and (c) multicriterial analysis based on the AHP method, which utilizes expert domain knowledge (also ontologically represented).

  8. Modeling decision support rule interactions in a clinical setting.

    PubMed

    Sordo, Margarita; Rocha, Beatriz H; Morales, Alfredo A; Maviglia, Saverio M; Oglio, Elisa Dell'Oglio; Fairbanks, Amanda; Aroy, Teal; Dubois, David; Bouyer-Ferullo, Sharon; Rocha, Roberto A

    2013-01-01

    Traditionally, rule interactions are handled at implementation time through rule task properties that control the order in which rules are executed. By doing so, knowledge about the behavior and interactions of decision rules is not captured at modeling time. We argue that this is important knowledge that should be integrated in the modeling phase. In this project, we build upon current work on a conceptual schema to represent clinical knowledge for decision support in the form of if then rules. This schema currently captures provenance of the clinical content, context where such content is actionable (i.e. constraints) and the logic of the rule itself. For this project, we borrowed concepts from both the Semantic Web (i.e., Ontologies) and Complex Adaptive Systems (CAS), to explore a conceptual approach for modeling rule interactions in an enterprise-wide clinical setting. We expect that a more comprehensive modeling will facilitate knowledge authoring, editing and update; foster consistency in rules implementation and maintenance; and develop authoritative knowledge repositories to promote quality, safety and efficacy of healthcare.

  9. A roadmap for national action on clinical decision support.

    PubMed

    Osheroff, Jerome A; Teich, Jonathan M; Middleton, Blackford; Steen, Elaine B; Wright, Adam; Detmer, Don E

    2007-01-01

    This document comprises an AMIA Board of Directors approved White Paper that presents a roadmap for national action on clinical decision support. It is published in JAMIA for archival and dissemination purposes. The full text of this material has been previously published on the AMIA Web site (www.amia.org/inside/initiatives/cds). AMIA is the copyright holder.

  10. A Roadmap for National Action on Clinical Decision Support

    PubMed Central

    Osheroff, Jerome A.; Teich, Jonathan M.; Middleton, Blackford; Steen, Elaine B.; Wright, Adam; Detmer, Don E.

    2007-01-01

    This document comprises an AMIA Board of Directors approved White Paper that presents a roadmap for national action on clinical decision support. It is published in JAMIA for archival and dissemination purposes. The full text of this material has been previously published on the AMIA Web site (www.amia.org/inside/initiatives/cds). AMIA is the copyright holder. PMID:17213487

  11. Using Clinical Decision Support Software in Health Insurance Company

    NASA Astrophysics Data System (ADS)

    Konovalov, R.; Kumlander, Deniss

    This paper proposes the idea to use Clinical Decision Support software in Health Insurance Company as a tool to reduce the expenses related to Medication Errors. As a prove that this class of software will help insurance companies reducing the expenses, the research was conducted in eight hospitals in United Arab Emirates to analyze the amount of preventable common Medication Errors in drug prescription.

  12. Interactive financial decision support for clinical research trials.

    PubMed

    Holler, Benjamin; Forgione, Dana A; Baisden, Clinton E; Abramson, David A; Calhoon, John H

    2011-01-01

    The purpose of this article is to describe a decision support approach useful for evaluating proposals to conduct clinical research trials. Physicians often do not have the time or background to account for all the expenses of a clinical trial. Their evaluation process may be limited and driven by factors that do not indicate the potential for financial losses that a trial may impose. We analyzed clinical trial budget templates used by hospitals, health science centers, research universities, departments of medicine, and medical schools. We compiled a databank of costs and reviewed recent research trials conducted by the Department of Cardiothoracic Surgery in a major academic health science center. We then developed an interactive spreadsheet-based budgetary decision support approach that accounts for clinical trial income and costs. It can be tailored to provide quick and understandable data entry, accurate cost rates per subject, and clear go/no-go signals for the physician.

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

    PubMed Central

    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. PMID:28269916

  14. 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

  15. A System Costs Planning Decision Support System

    DTIC Science & Technology

    1988-09-01

    York: Wiley and Sons, 1975, pp. 15-16. Lipsey , R. G., and Steiner, P. 0., Economics , New York: Harper and Row, 1969, ch. 11. Lloyd, C., Microeconomic...of the Highest Utility, an imposed spending ceiling, a minimum desired utility, or any possible combination of economic and/or political...Applica- tions," in M. Beckmann and H. P. Kunzi (eds.), Lecture Notes in Economics and Mathematical Systems, p. 208, Springer-Verlag, New York, 1981

  16. 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

  17. 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

  18. Pilot study to validate a computer-based clinical decision support system for dyslipidemia treatment (HTE-DLP).

    PubMed

    Zamora, A; Fernández de Bobadilla, F; Carrion, C; Vázquez, G; Paluzie, G; Elosua, R; Vilaseca, M; Martín-Urda, A; Rivera, A; Plana, N; Masana, L

    2013-12-01

    Pilot study to validate a Computerized Decision Support Systems (CDS) (HTE-DLP) for improving treatment of hyperlipidemia. HTE-DLP was programmed to offer automatic specific reminders for lipid treatment. Seventy-seven patients with high cardiovascular risk were randomized. 10 expert physicians in cardiovascular-risk management were recruited. We assessed number of patients at LDL <70 mg/dl after 12 weeks of treatment. A greater proportion of intervention group reached the LDL-C <70 mg/ml [55.0% vs 12.5%, p = 0.003; OR: 3.26 IC (1.16-9.15)]. "High potency statins" and combined therapy were used more frequently in the intervention group than the control group (p = 0.001). Seven adverse effects were documented in the intervention group and two in the control group. An acceptable relationship was observed with regard to costeffectiveness in the intervention group. Physicians expressed good agreement with HTE-DLP (86.1%) and comfortable ease-of-use (85%). Use of a CDSS in high-risk cardiovascular patients resulted in a significant reduction in LDL-C levels. Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.

  19. Developing the U.S. Wildland Fire Decision Support System

    Treesearch

    Erin Noonan-Wright; Tonja S. Opperman; Mark A. Finney; Tom Zimmerman; Robert C. Seli; Lisa M. Elenz; David E. Calkin; John R. Fiedler

    2011-01-01

    A new decision support tool, the Wildland Fire Decision Support System (WFDSS) has been developed to support risk-informed decision-making for individual fires in the United States. WFDSS accesses national weather data and forecasts, fire behavior prediction, economic assessment, smoke management assessment, and landscape databases to efficiently formulate and apply...

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

    NASA Astrophysics Data System (ADS)

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

    2011-03-01

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

  1. Clinical Decision Support for Immunizations (CDSi): A Comprehensive, Collaborative Strategy

    PubMed Central

    Arzt, Noam H.

    2016-01-01

    This article focuses on the requirements and current developments in clinical decision support technologies for immunizations (CDSi) in both the public health and clinical communities, with an emphasis on shareable solutions. The requirements of the Electronic Health Record Incentive Programs have raised some unique challenges for the clinical community, including vocabulary mapping, update of changing guidelines, single immunization schedule, and scalability. This article discusses new, collaborative approaches whose long-term goal is to make CDSi more sustainable for both the public and private sectors. PMID:27789956

  2. Barriers to implementation of a computerized decision support system for depression: an observational report on lessons learned in "real world" clinical settings

    PubMed Central

    2009-01-01

    Background Despite wide promotion, clinical practice guidelines have had limited effect in changing physician behavior. Effective implementation strategies to date have included: multifaceted interventions involving audit and feedback, local consensus processes, marketing; reminder systems, either manual or computerized; and interactive educational meetings. In addition, there is now growing evidence that contextual factors affecting implementation must be addressed such as organizational support (leadership procedures and resources) for the change and strategies to implement and maintain new systems. Methods To examine the feasibility and effectiveness of implementation of a computerized decision support system for depression (CDSS-D) in routine public mental health care in Texas, fifteen study clinicians (thirteen physicians and two advanced nurse practitioners) participated across five sites, accruing over 300 outpatient visits on 168 patients. Results Issues regarding computer literacy and hardware/software requirements were identified as initial barriers. Clinicians also reported concerns about negative impact on workflow and the potential need for duplication during the transition from paper to electronic systems of medical record keeping. Conclusion The following narrative report based on observations obtained during the initial testing and use of a CDSS-D in clinical settings further emphasizes the importance of taking into account organizational factors when planning implementation of evidence-based guidelines or decision support within a system. PMID:19159458

  3. 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

  4. How to improve vital sign data quality for use in clinical decision support systems? A qualitative study in nine Swedish emergency departments.

    PubMed

    Skyttberg, Niclas; Vicente, Joana; Chen, Rong; Blomqvist, Hans; Koch, Sabine

    2016-06-04

    Vital sign data are important for clinical decision making in emergency care. Clinical Decision Support Systems (CDSS) have been advocated to increase patient safety and quality of care. However, the efficiency of CDSS depends on the quality of the underlying vital sign data. Therefore, possible factors affecting vital sign data quality need to be understood. This study aims to explore the factors affecting vital sign data quality in Swedish emergency departments and to determine in how far clinicians perceive vital sign data to be fit for use in clinical decision support systems. A further aim of the study is to provide recommendations on how to improve vital sign data quality in emergency departments. Semi-structured interviews were conducted with sixteen physicians and nurses from nine hospitals and vital sign documentation templates were collected and analysed. Follow-up interviews and process observations were done at three of the hospitals to verify the results. Content analysis with constant comparison of the data was used to analyse and categorize the collected data. Factors related to care process and information technology were perceived to affect vital sign data quality. Despite electronic health records (EHRs) being available in all hospitals, these were not always used for vital sign documentation. Only four out of nine sites had a completely digitalized vital sign documentation flow and paper-based triage records were perceived to provide a better mobile workflow support than EHRs. Observed documentation practices resulted in low currency, completeness, and interoperability of the vital signs. To improve vital sign data quality, we propose to standardize the care process, improve the digital documentation support, provide workflow support, ensure interoperability and perform quality control. Vital sign data quality in Swedish emergency departments is currently not fit for use by CDSS. To address both technical and organisational challenges, we propose

  5. Clinical Decision Support for Vascular Disease in Community Family Practice

    PubMed Central

    Keshavjee, K; Holbrook, AM; Lau, E; Esporlas-Jewer, I; Troyan, S

    2006-01-01

    The COMPETE III Vascular Disease Tracker (C3VT) is a personalized, Web-based, clinical decision support tool that provides patients and physicians access to a patient’s 16 individual vascular risk markers, specific advice for each marker and links to best practices in vascular disease management. It utilizes the chronic care model1 so that physicians can better manage patients with chronic diseases. Over 1100 patients have been enrolled into the COMPETE III study to date.

  6. Decision support system for the diagnosis of schizophrenia disorders.

    PubMed

    Razzouk, D; Mari, J J; Shirakawa, I; Wainer, J; Sigulem, D

    2006-01-01

    Clinical decision support systems are useful tools for assisting physicians to diagnose complex illnesses. Schizophrenia is a complex, heterogeneous and incapacitating mental disorder that should be detected as early as possible to avoid a most serious outcome. These artificial intelligence systems might be useful in the early detection of schizophrenia disorder. The objective of the present study was to describe the development of such a clinical decision support system for the diagnosis of schizophrenia spectrum disorders (SADDESQ). The development of this system is described in four stages: knowledge acquisition, knowledge organization, the development of a computer-assisted model, and the evaluation of the system's performance. The knowledge was extracted from an expert through open interviews. These interviews aimed to explore the expert's diagnostic decision-making process for the diagnosis of schizophrenia. A graph methodology was employed to identify the elements involved in the reasoning process. Knowledge was first organized and modeled by means of algorithms and then transferred to a computational model created by the covering approach. The performance assessment involved the comparison of the diagnoses of 38 clinical vignettes between an expert and the SADDESQ. The results showed a relatively low rate of misclassification (18-34%) and a good performance by SADDESQ in the diagnosis of schizophrenia, with an accuracy of 66-82%. The accuracy was higher when schizophreniform disorder was considered as the presence of schizophrenia disorder. Although these results are preliminary, the SADDESQ has exhibited a satisfactory performance, which needs to be further evaluated within a clinical setting.

  7. Clinical Decision Support for Early Recognition of Sepsis

    PubMed Central

    Amland, Robert C.; Hahn-Cover, Kristin E.

    2014-01-01

    Sepsis is an inflammatory response triggered by infection, with a high in-hospital mortality rate. Early recognition and treatment can reverse the inflammatory response, with evidence of improved patient outcomes. One challenge clinicians face is identifying the inflammatory syndrome against the background of the patient’s infectious illness and comorbidities. An approach to this problem is implementation of computerized early warning tools for sepsis. This multicenter retrospective study sought to determine clinimetric performance of a cloud-based computerized sepsis clinical decision support system (CDS), understand the epidemiology of sepsis, and identify opportunities for quality improvement. Data encompassed 6200 adult hospitalizations from 2012 through 2013. Of 13% patients screened-in, 51% were already suspected to have an infection when the system activated. This study focused on a patient cohort screened-in before infection was suspected; median time from arrival to CDS activation was 3.5 hours, and system activation to diagnostic collect was another 8.6 hours. PMID:25385815

  8. Comparison of Overridden Medication-related Clinical Decision Support in the Intensive Care Unit between a Commercial System and a Legacy System.

    PubMed

    Wong, Adrian; Wright, Adam; Seger, Diane L; Amato, Mary G; Fiskio, Julie M; Bates, David

    2017-08-23

    Electronic health records (EHRs) with clinical decision support (CDS) have shown to be effective at improving patient safety. Despite this, alerts delivered as part of CDS are overridden frequently, which is of concern in the critical care population as this group may have an increased risk of harm. Our organization recently transitioned from an internally-developed EHR to a commercial system. Data comparing various EHR systems, especially after transitions between EHRs, are needed to identify areas for improvement. To compare the two systems and identify areas for potential improvement with the new commercial system at a single institution. Overridden medication-related CDS alerts were included from October to December of the systems' respective years (legacy, 2011; commercial, 2015), restricted to three intensive care units. The two systems were compared with regards to CDS presentation and override rates for four types of CDS: drug-allergy, drug-drug interaction (DDI), geriatric and renal alerts. A post hoc analysis to evaluate for adverse drug events (ADEs) potentially resulting from overridden alerts was performed for 'contraindicated' DDIs via chart review. There was a significant increase in provider exposure to alerts and alert overrides in the commercial system (commercial: n=5,535; legacy: n=1,030). Rates of overrides were higher for the allergy and DDI alerts (p<0.001) in the commercial system. Geriatric and renal alerts were significantly different in incidence and presentation between the two systems. No ADEs were identified in an analysis of 43 overridden contraindicated DDI alerts. The vendor system had much higher rates of both alerts and overrides, although we did not find evidence of harm in a review of DDIs which were overridden. We propose recommendations for improving our current system which may be helpful to other similar institutions; improving both alert presentation and the underlying knowledge base appear important.

  9. Issues Associated With the Use of Semantic Web Technology in Knowledge Acquisition for Clinical Decision Support Systems: Systematic Review of the Literature

    PubMed Central

    2017-01-01

    Background Knowledge-based clinical decision support system (KB-CDSS) can be used to help practitioners make diagnostic decisions. KB-CDSS may use clinical knowledge obtained from a wide variety of sources to make decisions. However, knowledge acquisition is one of the well-known bottlenecks in KB-CDSSs, partly because of the enormous growth in health-related knowledge available and the difficulty in assessing the quality of this knowledge as well as identifying the “best” knowledge to use. This bottleneck not only means that lower-quality knowledge is being used, but also that KB-CDSSs are difficult to develop for areas where expert knowledge may be limited or unavailable. Recent methods have been developed by utilizing Semantic Web (SW) technologies in order to automatically discover relevant knowledge from knowledge sources. Objective The two main objectives of this study were to (1) identify and categorize knowledge acquisition issues that have been addressed through using SW technologies and (2) highlight the role of SW for acquiring knowledge used in the KB-CDSS. Methods We conducted a systematic review of the recent work related to knowledge acquisition MeM for clinical decision support systems published in scientific journals. In this regard, we used the keyword search technique to extract relevant papers. Results The retrieved papers were categorized based on two main issues: (1) format and data heterogeneity and (2) lack of semantic analysis. Most existing approaches will be discussed under these categories. A total of 27 papers were reviewed in this study. Conclusions The potential for using SW technology in KB-CDSS has only been considered to a minor extent so far despite its promise. This review identifies some questions and issues regarding use of SW technology for extracting relevant knowledge for a KB-CDSS. PMID:28679487

  10. Issues Associated With the Use of Semantic Web Technology in Knowledge Acquisition for Clinical Decision Support Systems: Systematic Review of the Literature.

    PubMed

    Zolhavarieh, Seyedjamal; Parry, David; Bai, Quan

    2017-07-05

    Knowledge-based clinical decision support system (KB-CDSS) can be used to help practitioners make diagnostic decisions. KB-CDSS may use clinical knowledge obtained from a wide variety of sources to make decisions. However, knowledge acquisition is one of the well-known bottlenecks in KB-CDSSs, partly because of the enormous growth in health-related knowledge available and the difficulty in assessing the quality of this knowledge as well as identifying the "best" knowledge to use. This bottleneck not only means that lower-quality knowledge is being used, but also that KB-CDSSs are difficult to develop for areas where expert knowledge may be limited or unavailable. Recent methods have been developed by utilizing Semantic Web (SW) technologies in order to automatically discover relevant knowledge from knowledge sources. The two main objectives of this study were to (1) identify and categorize knowledge acquisition issues that have been addressed through using SW technologies and (2) highlight the role of SW for acquiring knowledge used in the KB-CDSS. We conducted a systematic review of the recent work related to knowledge acquisition MeM for clinical decision support systems published in scientific journals. In this regard, we used the keyword search technique to extract relevant papers. The retrieved papers were categorized based on two main issues: (1) format and data heterogeneity and (2) lack of semantic analysis. Most existing approaches will be discussed under these categories. A total of 27 papers were reviewed in this study. The potential for using SW technology in KB-CDSS has only been considered to a minor extent so far despite its promise. This review identifies some questions and issues regarding use of SW technology for extracting relevant knowledge for a KB-CDSS.

  11. A multiple-scenario assessment of the effect of a continuous-care, guideline-based decision support system on clinicians' compliance to clinical guidelines.

    PubMed

    Shalom, Erez; Shahar, Yuval; Parmet, Yisrael; Lunenfeld, Eitan

    2015-04-01

    To quantify the effect of a new continuous-care guideline (GL)-application engine, the Picard decision support system (DSS) engine, on the correctness and completeness of clinicians' decisions relative to an established clinical GL, and to assess the clinicians' attitudes towards a specific DSS. Thirty-six clinicians, including residents at different training levels and board-certified specialists at an academic OB/GYN department that handles around 15,000 deliveries annually, agreed to evaluate our continuous-care guideline-based DSS and to perform a cross-over assessment of the effects of using our guideline-based DSS. We generated electronic patient records that realistically simulated the longitudinal course of six different clinical scenarios of the preeclampsia/eclampsia/toxemia (PET) GL, encompassing 60 different decision points in total. Each clinician managed three scenarios manually without the Picard DSS engine (Non-DSS mode) and three scenarios when assisted by the Picard DSS engine (DSS mode). The main measures in both modes were correctness and completeness of actions relative to the PET GL. Correctness was further decomposed into necessary and redundant actions, relative to the guideline and the actual patient data. At the end of the assessment, a questionnaire was administered to the clinicians to assess their perceptions regarding use of the DSS. With respect to completeness, the clinicians applied approximately 41% of the GL's recommended actions in the non-DSS mode. Completeness increased to the performance of approximately 93% of the guideline's recommended actions, when using the DSS mode. With respect to correctness, approximately 94.5% of the clinicians' decisions in the non-DSS mode were correct. However, these included 68% of the actions that were correct but redundant, given the patient's data (e.g., repeating tests that had been performed), and 27% of the actions, which were necessary in the context of the GL and of the given scenario

  12. Comparative impact of guidelines, clinical data, and decision support on prescribing decisions: an interactive web experiment with simulated cases.

    PubMed

    Sintchenko, Vitali; Coiera, Enrico; Iredell, Jonathan R; Gilbert, Gwendolyn L

    2004-01-01

    The aim of this study was to compare the clinical impact of computerized decision support with and without electronic access to clinical guidelines and laboratory data on antibiotic prescribing decisions. A crossover trial was conducted of four levels of computerized decision support-no support, antibiotic guidelines, laboratory reports, and laboratory reports plus a decision support system (DSS), randomly allocated to eight simulated clinical cases accessed by the Web. Rate of intervention adoption was measured by frequency of accessing information support, cost of use was measured by time taken to complete each case, and effectiveness of decision was measured by correctness of and self-reported confidence in individual prescribing decisions. Clinical impact score was measured by adoption rate and decision effectiveness. Thirty-one intensive care and infectious disease specialist physicians (ICPs and IDPs) participated in the study. Ventilator-associated pneumonia treatment guidelines were used in 24 (39%) of the 62 case scenarios for which they were available, microbiology reports in 36 (58%), and the DSS in 37 (60%). The use of all forms of information support did not affect clinicians' confidence in their decisions. Their use of the DSS plus microbiology report improved the agreement of decisions with those of an expert panel from 65% to 97% (p=0.0002), or to 67% (p=0.002) when antibiotic guidelines only were accessed. Significantly fewer IDPs than ICPs accessed information support in making treatment decisions. On average, it took 245 seconds to make a decision using the DSS compared with 113 seconds for unaided prescribing (p<0.001). The DSS plus microbiology reports had the highest clinical impact score (0.58), greater than that of electronic guidelines (0.26) and electronic laboratory reports (0.45). When used, computer-based decision support significantly improved decision quality. In measuring the impact of decision support systems, both their

  13. Clinical decision support tools: analysis of online drug information databases

    PubMed Central

    Clauson, Kevin A; Marsh, Wallace A; Polen, Hyla H; Seamon, Matthew J; Ortiz, Blanca I

    2007-01-01

    Background Online drug information databases are used to assist in enhancing clinical decision support. However, the choice of which online database to consult, purchase or subscribe to is likely made based on subjective elements such as history of use, familiarity, or availability during professional training. The purpose of this study was to evaluate clinical decision support tools for drug information by systematically comparing the most commonly used online drug information databases. Methods Five commercially available and two freely available online drug information databases were evaluated according to scope (presence or absence of answer), completeness (the comprehensiveness of the answers), and ease of use. Additionally, a composite score integrating all three criteria was utilized. Fifteen weighted categories comprised of 158 questions were used to conduct the analysis. Descriptive statistics and Chi-square were used to summarize the evaluation components and make comparisons between databases. Scheffe's multiple comparison procedure was used to determine statistically different scope and completeness scores. The composite score was subjected to sensitivity analysis to investigate the effect of the choice of percentages for scope and completeness. Results The rankings for the databases from highest to lowest, based on composite scores were Clinical Pharmacology, Micromedex, Lexi-Comp Online, Facts & Comparisons 4.0, Epocrates Online Premium, RxList.com, and Epocrates Online Free. Differences in scope produced three statistical groupings with Group 1 (best) performers being: Clinical Pharmacology, Micromedex, Facts & Comparisons 4.0, Lexi-Comp Online, Group 2: Epocrates Premium and RxList.com and Group 3: Epocrates Free (p < 0.05). Completeness scores were similarly stratified. Collapsing the databases into two groups by access (subscription or free), showed the subscription databases performed better than the free databases in the measured criteria (p < 0

  14. Clinical decision support tools: analysis of online drug information databases.

    PubMed

    Clauson, Kevin A; Marsh, Wallace A; Polen, Hyla H; Seamon, Matthew J; Ortiz, Blanca I

    2007-03-08

    Online drug information databases are used to assist in enhancing clinical decision support. However, the choice of which online database to consult, purchase or subscribe to is likely made based on subjective elements such as history of use, familiarity, or availability during professional training. The purpose of this study was to evaluate clinical decision support tools for drug information by systematically comparing the most commonly used online drug information databases. Five commercially available and two freely available online drug information databases were evaluated according to scope (presence or absence of answer), completeness (the comprehensiveness of the answers), and ease of use. Additionally, a composite score integrating all three criteria was utilized. Fifteen weighted categories comprised of 158 questions were used to conduct the analysis. Descriptive statistics and Chi-square were used to summarize the evaluation components and make comparisons between databases. Scheffe's multiple comparison procedure was used to determine statistically different scope and completeness scores. The composite score was subjected to sensitivity analysis to investigate the effect of the choice of percentages for scope and completeness. The rankings for the databases from highest to lowest, based on composite scores were Clinical Pharmacology, Micromedex, Lexi-Comp Online, Facts & Comparisons 4.0, Epocrates Online Premium, RxList.com, and Epocrates Online Free. Differences in scope produced three statistical groupings with Group 1 (best) performers being: Clinical Pharmacology, Micromedex, Facts & Comparisons 4.0, Lexi-Comp Online, Group 2: Epocrates Premium and RxList.com and Group 3: Epocrates Free (p < 0.05). Completeness scores were similarly stratified. Collapsing the databases into two groups by access (subscription or free), showed the subscription databases performed better than the free databases in the measured criteria (p < 0.001). Online drug

  15. Real-Time Clinical Decision Support Decreases Inappropriate Plasma Transfusion.

    PubMed

    Shah, Neil; Baker, Steven A; Spain, David; Shieh, Lisa; Shepard, John; Hadhazy, Eric; Maggio, Paul; Goodnough, Lawrence T

    2017-08-01

    To curtail inappropriate plasma transfusions, we instituted clinical decision support as an alert upon order entry if the patient's recent international normalized ratio (INR) was 1.7 or less. The alert was suppressed for massive transfusion and within operative or apheresis settings. The plasma order was automatically removed upon alert acceptance while clinical exception reasons allowed for continued transfusion. Alert impact was studied comparing a 7-month control period with a 4-month intervention period. Monthly plasma utilization decreased 17.4%, from a mean ± SD of 3.40 ± 0.48 to 2.82 ± 0.6 plasma units per hundred patient days (95% confidence interval [CI] of difference, -0.1 to 1.3). Plasma transfused below an INR of 1.7 or less decreased from 47.6% to 41.6% (P = .0002; odds ratio, 0.78; 95% CI, 0.69-0.89). The alert recommendation was accepted 33% of the time while clinical exceptions were chosen in the remaining cases (active bleeding, 31%; other clinical indication, 33%; and apheresis, 2%). Alert acceptance rate varied significantly among different provider specialties. Clinical decision support can help curtail inappropriate plasma use but needs to be part of a comprehensive strategy including audit and feedback for comprehensive, long-term changes.

  16. Case-based reasoning in Intelligent Health Decision Support Systems.

    PubMed

    González, Carolina; López, Diego M; Blobel, Bernd

    2013-01-01

    Decision-making is a crucial task for decision makers in healthcare, especially because decisions have to be made quickly, accurately and under uncertainty. Taking into account the importance of providing quality decisions, offering assistance in this complex process has been one of the main challenges of Artificial Intelligence throughout history. Decision Support Systems (DSS) have gained popularity in the medical field for their efficacy to assist decision-making. In this sense, many DSS have been developed, but only few of them consider processing and analysis of information contained in electronic health records, in order to identify individual or population health risk factors. This paper deals with Intelligent Decision Support Systems that are integrated into Electronic Health Records Systems (EHRS) or Public Health Information Systems (PHIS). It provides comprehensive support for a wide range of decisions with the purpose of improving quality of care delivered to patients or public health planning, respectively.

  17. 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

  18. Decision support system based semantic web for personalized patient care.

    PubMed

    Douali, Nassim; De Roo, Jos; Jaulent, Marie-Christine

    2012-01-01

    Personalized medicine may be considered an extension of traditional approaches to understanding and treating diseases, but with greater precision. A profile of a patient's genetic variation can guide the selection of drugs or treatment protocols that minimize harmful side effects or ensure a more successful outcome. In this paper we describe a decision support system designed to assist physicians for personalized care, and methodology for integration in the clinical workflow. A reasoning method for interacting heterogeneous knowledge and data is a necessity in the context of personalized medicine. Development of clinical decision support based semantic web for personalized patient care is to achieve its potential and improve the quality, safety and efficiency of healthcare.

  19. A knowledge-based decision support system for payload scheduling

    NASA Technical Reports Server (NTRS)

    Floyd, Stephen; Ford, Donnie

    1988-01-01

    The role that artificial intelligence/expert systems technologies play in the development and implementation of effective decision support systems is illustrated. A recently developed prototype system for supporting the scheduling of subsystems and payloads/experiments for NASA's Space Station program is presented and serves to highlight various concepts. The potential integration of knowledge based systems and decision support systems which has been proposed in several recent articles and presentations is illustrated.

  20. Development of a clinical decision support system using genetic algorithms and Bayesian classification for improving the personalised management of women attending a colposcopy room.

    PubMed

    Bountris, Panagiotis; Topaka, Elena; Pouliakis, Abraham; Haritou, Maria; Karakitsos, Petros; Koutsouris, Dimitrios

    2016-06-01

    Cervical cancer (CxCa) is often the result of underestimated abnormalities in the test Papanicolaou (Pap test). The recent advances in the study of the human papillomavirus (HPV) infection (the necessary cause for CxCa development) have guided clinical practice to add HPV related tests alongside the Pap test. In this way, today, HPV DNA testing is well accepted as an ancillary test and it is used for the triage of women with abnormal findings in cytology. However, these tests are either highly sensitive or highly specific, and therefore none of them provides an optimal solution. In this Letter, a clinical decision support system based on a hybrid genetic algorithm - Bayesian classification framework is presented, which combines the results of the Pap test with those of the HPV DNA test in order to exploit the benefits of each method and produce more accurate outcomes. Compared with the medical tests and their combinations (co-testing), the proposed system produced the best receiver operating characteristic curve and the most balanced combination among sensitivity and specificity in detecting high-grade cervical intraepithelial neoplasia and CxCa (CIN2+). This system may support decision-making for the improved management of women who attend a colposcopy room following a positive test result.

  1. Understanding complex clinical reasoning in infectious diseases for improving clinical decision support design.

    PubMed

    Islam, Roosan; Weir, Charlene R; Jones, Makoto; Del Fiol, Guilherme; Samore, Matthew H

    2015-11-30

    Clinical experts' cognitive mechanisms for managing complexity have implications for the design of future innovative healthcare systems. The purpose of the study is to examine the constituents of decision complexity and explore the cognitive strategies clinicians use to control and adapt to their information environment. We used Cognitive Task Analysis (CTA) methods to interview 10 Infectious Disease (ID) experts at the University of Utah and Salt Lake City Veterans Administration Medical Center. Participants were asked to recall a complex, critical and vivid antibiotic-prescribing incident using the Critical Decision Method (CDM), a type of Cognitive Task Analysis (CTA). Using the four iterations of the Critical Decision Method, questions were posed to fully explore the incident, focusing in depth on the clinical components underlying the complexity. Probes were included to assess cognitive and decision strategies used by participants. The following three themes emerged as the constituents of decision complexity experienced by the Infectious Diseases experts: 1) the overall clinical picture does not match the pattern, 2) a lack of comprehension of the situation and 3) dealing with social and emotional pressures such as fear and anxiety. All these factors contribute to decision complexity. These factors almost always occurred together, creating unexpected events and uncertainty in clinical reasoning. Five themes emerged in the analyses of how experts deal with the complexity. Expert clinicians frequently used 1) watchful waiting instead of over- prescribing antibiotics, engaged in 2) theory of mind to project and simulate other practitioners' perspectives, reduced very complex cases into simple 3) heuristics, employed 4) anticipatory thinking to plan and re-plan events and consulted with peers to share knowledge, solicit opinions and 5) seek help on patient cases. The cognitive strategies to deal with decision complexity found in this study have important

  2. Lessons learned from implementing service-oriented clinical decision support at four sites: A qualitative study.

    PubMed

    Wright, Adam; Sittig, Dean F; Ash, Joan S; Erickson, Jessica L; Hickman, Trang T; Paterno, Marilyn; Gebhardt, Eric; McMullen, Carmit; Tsurikova, Ruslana; Dixon, Brian E; Fraser, Greg; Simonaitis, Linas; Sonnenberg, Frank A; Middleton, Blackford

    2015-11-01

    To identify challenges, lessons learned and best practices for service-oriented clinical decision support, based on the results of the Clinical Decision Support Consortium, a multi-site study which developed, implemented and evaluated clinical decision support services in a diverse range of electronic health records. Ethnographic investigation using the rapid assessment process, a procedure for agile qualitative data collection and analysis, including clinical observation, system demonstrations and analysis and 91 interviews. We identified challenges and lessons learned in eight dimensions: (1) hardware and software computing infrastructure, (2) clinical content, (3) human-computer interface, (4) people, (5) workflow and communication, (6) internal organizational policies, procedures, environment and culture, (7) external rules, regulations, and pressures and (8) system measurement and monitoring. Key challenges included performance issues (particularly related to data retrieval), differences in terminologies used across sites, workflow variability and the need for a legal framework. Based on the challenges and lessons learned, we identified eight best practices for developers and implementers of service-oriented clinical decision support: (1) optimize performance, or make asynchronous calls, (2) be liberal in what you accept (particularly for terminology), (3) foster clinical transparency, (4) develop a legal framework, (5) support a flexible front-end, (6) dedicate human resources, (7) support peer-to-peer communication, (8) improve standards. The Clinical Decision Support Consortium successfully developed a clinical decision support service and implemented it in four different electronic health records and four diverse clinical sites; however, the process was arduous. The lessons identified by the Consortium may be useful for other developers and implementers of clinical decision support services. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

  3. Clinical decisions support malfunctions in a commercial electronic health record.

    PubMed

    Kassakian, Steven Z; Yackel, Thomas R; Gorman, Paul N; Dorr, David A

    2017-09-06

    Determine if clinical decision support (CDS) malfunctions occur in a commercial electronic health record (EHR) system, characterize their pathways and describe methods of detection. We retrospectively examined the firing rate for 226 alert type CDS rules for detection of anomalies using both expert visualization and statistical process control (SPC) methods over a five year period. Candidate anomalies were investigated and validated. Twenty-one candidate CDS anomalies were identified from 8,300 alert-months. Of these candidate anomalies, four were confirmed as CDS malfunctions, eight as false-positives, and nine could not be classified. The four CDS malfunctions were a result of errors in knowledge management: 1) inadvertent addition and removal of a medication code to the electronic formulary list; 2) a seasonal alert which was not activated; 3) a change in the base data structures; and 4) direct editing of an alert related to its medications. 154 CDS rules (68%) were amenable to SPC methods and the test characteristics were calculated as a sensitivity of 95%, positive predictive value of 29% and F-measure 0.44. CDS malfunctions were found to occur in our EHR. All of the pathways for these malfunctions can be described as knowledge management errors. Expert visualization is a robust method of detection, but is resource intensive. SPC-based methods, when applicable, perform reasonably well retrospectively. CDS anomalies were found to occur in a commercial EHR and visual detection along with SPC analysis represents promising methods of malfunction detection.

  4. Evaluation of RxNorm for Medication Clinical Decision Support.

    PubMed

    Freimuth, Robert R; Wix, Kelly; Zhu, Qian; Siska, Mark; Chute, Christopher G

    2014-01-01

    We evaluated the potential use of RxNorm to provide standardized representations of generic drug name and route of administration to facilitate management of drug lists for clinical decision support (CDS) rules. We found a clear representation of generic drug name but not route of administration. We identified several issues related to data quality, including erroneous or missing defined relationships, and the use of different concept hierarchies to represent the same drug. More importantly, we found extensive semantic precoordination of orthogonal concepts related to route and dose form, which would complicate the use of RxNorm for drug-based CDS. This study demonstrated that while RxNorm is a valuable resource for the standardization of medications used in clinical practice, additional work is required to enhance the terminology so that it can support expanded use cases, such as managing drug lists for CDS.

  5. Evaluation of RxNorm for Medication Clinical Decision Support

    PubMed Central

    Freimuth, Robert R.; Wix, Kelly; Zhu, Qian; Siska, Mark; Chute, Christopher G.

    2014-01-01

    We evaluated the potential use of RxNorm to provide standardized representations of generic drug name and route of administration to facilitate management of drug lists for clinical decision support (CDS) rules. We found a clear representation of generic drug name but not route of administration. We identified several issues related to data quality, including erroneous or missing defined relationships, and the use of different concept hierarchies to represent the same drug. More importantly, we found extensive semantic precoordination of orthogonal concepts related to route and dose form, which would complicate the use of RxNorm for drug-based CDS. This study demonstrated that while RxNorm is a valuable resource for the standardization of medications used in clinical practice, additional work is required to enhance the terminology so that it can support expanded use cases, such as managing drug lists for CDS. PMID:25954360

  6. Using Machine Learning and Natural Language Processing Algorithms to Automate the Evaluation of Clinical Decision Support in Electronic Medical Record Systems.

    PubMed

    Szlosek, Donald A; Ferrett, Jonathan

    2016-01-01

    As the number of clinical decision support systems (CDSSs) incorporated into electronic medical records (EMRs) increases, so does the need to evaluate their effectiveness. The use of medical record review and similar manual methods for evaluating decision rules is laborious and inefficient. The authors use machine learning and Natural Language Processing (NLP) algorithms to accurately evaluate a clinical decision support rule through an EMR system, and they compare it against manual evaluation. Modeled after the EMR system EPIC at Maine Medical Center, we developed a dummy data set containing physician notes in free text for 3,621 artificial patients records undergoing a head computed tomography (CT) scan for mild traumatic brain injury after the incorporation of an electronic best practice approach. We validated the accuracy of the Best Practice Advisories (BPA) using three machine learning algorithms-C-Support Vector Classification (SVC), Decision Tree Classifier (DecisionTreeClassifier), k-nearest neighbors classifier (KNeighborsClassifier)-by comparing their accuracy for adjudicating the occurrence of a mild traumatic brain injury against manual review. We then used the best of the three algorithms to evaluate the effectiveness of the BPA, and we compared the algorithm's evaluation of the BPA to that of manual review. The electronic best practice approach was found to have a sensitivity of 98.8 percent (96.83-100.0), specificity of 10.3 percent, PPV = 7.3 percent, and NPV = 99.2 percent when reviewed manually by abstractors. Though all the machine learning algorithms were observed to have a high level of prediction, the SVC displayed the highest with a sensitivity 93.33 percent (92.49-98.84), specificity of 97.62 percent (96.53-98.38), PPV = 50.00, NPV = 99.83. The SVC algorithm was observed to have a sensitivity of 97.9 percent (94.7-99.86), specificity 10.30 percent, PPV 7.25 percent, and NPV 99.2 percent for evaluating the best practice approach, after

  7. Using Machine Learning and Natural Language Processing Algorithms to Automate the Evaluation of Clinical Decision Support in Electronic Medical Record Systems

    PubMed Central

    Szlosek, Donald A; Ferrett, Jonathan

    2016-01-01

    Introduction: As the number of clinical decision support systems (CDSSs) incorporated into electronic medical records (EMRs) increases, so does the need to evaluate their effectiveness. The use of medical record review and similar manual methods for evaluating decision rules is laborious and inefficient. The authors use machine learning and Natural Language Processing (NLP) algorithms to accurately evaluate a clinical decision support rule through an EMR system, and they compare it against manual evaluation. Methods: Modeled after the EMR system EPIC at Maine Medical Center, we developed a dummy data set containing physician notes in free text for 3,621 artificial patients records undergoing a head computed tomography (CT) scan for mild traumatic brain injury after the incorporation of an electronic best practice approach. We validated the accuracy of the Best Practice Advisories (BPA) using three machine learning algorithms—C-Support Vector Classification (SVC), Decision Tree Classifier (DecisionTreeClassifier), k-nearest neighbors classifier (KNeighborsClassifier)—by comparing their accuracy for adjudicating the occurrence of a mild traumatic brain injury against manual review. We then used the best of the three algorithms to evaluate the effectiveness of the BPA, and we compared the algorithm’s evaluation of the BPA to that of manual review. Results: The electronic best practice approach was found to have a sensitivity of 98.8 percent (96.83–100.0), specificity of 10.3 percent, PPV = 7.3 percent, and NPV = 99.2 percent when reviewed manually by abstractors. Though all the machine learning algorithms were observed to have a high level of prediction, the SVC displayed the highest with a sensitivity 93.33 percent (92.49–98.84), specificity of 97.62 percent (96.53–98.38), PPV = 50.00, NPV = 99.83. The SVC algorithm was observed to have a sensitivity of 97.9 percent (94.7–99.86), specificity 10.30 percent, PPV 7.25 percent, and NPV 99.2 percent for

  8. Automation and Accountability in Decision Support System Interface Design

    ERIC Educational Resources Information Center

    Cummings, Mary L.

    2006-01-01

    When the human element is introduced into decision support system design, entirely new layers of social and ethical issues emerge but are not always recognized as such. This paper discusses those ethical and social impact issues specific to decision support systems and highlights areas that interface designers should consider during design with an…

  9. Decision support system for health care resources allocation

    PubMed Central

    Sebaa, Abderrazak; Nouicer, Amina; Tari, AbdelKamel; Tarik, Ramtani; Abdellah, Ouhab

    2017-01-01

    Background A study about healthcare resources can improve decisions regarding the allotment and mobilization of medical resources and to better guide future investment in the health sector. Aim The aim of this work was to design and implement a decision support system to improve medical resources allocation of Bejaia region. Methods To achieve the retrospective cohort study, we integrated existing clinical databases from different Bejaia department health sector institutions (an Algerian department) to collect information about patients from January 2015 through December 2015. Data integration was performed in a data warehouse using the multi-dimensional model and OLAP cube. During implementation, we used Microsoft SQL server 2012 and Microsoft Excel 2010. Results A medical decision support platform was introduced, and was implemented during the planning stages allowing the management of different medical orientations, it provides better apportionment and allotment of medical resources, and ensures that the allocation of health care resources has optimal effects on improving health. Conclusion In this study, we designed and implemented a decision support system which would improve health care in Bejaia department to especially assist in the selection of the optimum location of health center and hospital, the specialty of the health center, the medical equipment and the medical staff. PMID:28848645

  10. Decision support system for health care resources allocation.

    PubMed

    Sebaa, Abderrazak; Nouicer, Amina; Tari, AbdelKamel; Tarik, Ramtani; Abdellah, Ouhab

    2017-06-01

    A study about healthcare resources can improve decisions regarding the allotment and mobilization of medical resources and to better guide future investment in the health sector. The aim of this work was to design and implement a decision support system to improve medical resources allocation of Bejaia region. To achieve the retrospective cohort study, we integrated existing clinical databases from different Bejaia department health sector institutions (an Algerian department) to collect information about patients from January 2015 through December 2015. Data integration was performed in a data warehouse using the multi-dimensional model and OLAP cube. During implementation, we used Microsoft SQL server 2012 and Microsoft Excel 2010. A medical decision support platform was introduced, and was implemented during the planning stages allowing the management of different medical orientations, it provides better apportionment and allotment of medical resources, and ensures that the allocation of health care resources has optimal effects on improving health. In this study, we designed and implemented a decision support system which would improve health care in Bejaia department to especially assist in the selection of the optimum location of health center and hospital, the specialty of the health center, the medical equipment and the medical staff.

  11. Checklist for Early Recognition and Treatment of Acute Illness (CERTAIN): evolution of a content management system for point-of-care clinical decision support.

    PubMed

    Barwise, Amelia; Garcia-Arguello, Lisbeth; Dong, Yue; Hulyalkar, Manasi; Vukoja, Marija; Schultz, Marcus J; Adhikari, Neill K J; Bonneton, Benjamin; Kilickaya, Oguz; Kashyap, Rahul; Gajic, Ognjen; Schmickl, Christopher N

    2016-10-03

    The Checklist for Early Recognition and Treatment of Acute Illness (CERTAIN) is an international collaborative project with the overall objective of standardizing the approach to the evaluation and treatment of critically ill patients world-wide, in accordance with best-practice principles. One of CERTAIN's key features is clinical decision support providing point-of-care information about common acute illness syndromes, procedures, and medications in an index card format. This paper describes 1) the process of developing and validating the content for point-of-care decision support, and 2) the content management system that facilitates frequent peer-review and allows rapid updates of content across different platforms (CERTAIN software, mobile apps, pdf-booklet) and different languages. Content was created based on survey results of acute care providers and validated using an open peer-review process. Over a 3 year period, CERTAIN content expanded to include 67 syndrome cards, 30 procedure cards, and 117 medication cards. 127 (59 %) cards have been peer-reviewed so far. Initially MS Word® and Dropbox® were used to create, store, and share content for peer-review. Recently Google Docs® was used to make the peer-review process more efficient. However, neither of these approaches met our security requirements nor has the capacity to instantly update the different CERTAIN platforms. Although we were able to successfully develop and validate a large inventory of clinical decision support cards in a short period of time, commercially available software solutions for content management are suboptimal. Novel custom solutions are necessary for efficient global point of care content system management.

  12. An update and further testing of a knowledge-based diagnostic clinical decision support system for musculoskeletal disorders of the shoulder for use in a primary care setting.

    PubMed

    Farmer, Nicholas

    2014-10-01

    A prototype diagnostic clinical decision support system (CDSS) was developed to assist primary care clinicians (general practitioners) in clinical decision making, aimed at reducing diagnostic errors. The prototype CDSS showed some promise with high levels of validity and reliability; however, issues regarding the underlying Bayesian belief network (BBN), small sample size and use of radiological imaging as a gold standard measure were highlighted that required further investigation before considering clinical testing. The prototype CDSS was reviewed and updated based on computer science literature and expert (orthopaedic consultant) opinion. The updated CDSS was tested by comparing its diagnostic outcome against the diagnosis of 93 case studies as determined by expert opinion combined with arthroscopy findings or radiological imaging. The updated CDSS showed significant high levels of sensitivity (91%), specificity (98%), positive likelihood ratio (53.12) and negative likelihood ratio (0.08) with a kappa value of 0.88 to a confidence level of 99% compared with expert diagnosis combined with arthroscopy findings or radiological imaging. The results suggest that the updated CDSS has addressed the issues highlighted from the initial research while maintaining high levels of validity and reliability. The updated CDSS is now ready for clinical testing. © 2014 John Wiley & Sons, Ltd.

  13. A Multi-criterial Decision Support System for Forest Management

    Treesearch

    Donald Nute; Geneho Kim; Walter D. Potter; Mark J. Twery; H. Michael Rauscher; Scott Thomasma; Deborah Bennett; Peter Kollasch

    1999-01-01

    We describe a research project that has as its goal development of a full-featured decision support system for managing forested land to satisfy multiple criteria represented as timber, wildlife, water, ecological, and wildlife objectives. The decision process proposed for what was originally conceived of as a Northeast Decision Model (NED) includes data acquisition,...

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

    NASA Astrophysics Data System (ADS)

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

    2017-08-01

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

  15. Cost-effectiveness of an electronic clinical decision support system for improving quality of antenatal and childbirth care in rural Tanzania: an intervention study.

    PubMed

    Saronga, Happiness Pius; Duysburgh, Els; Massawe, Siriel; Dalaba, Maxwell Ayindenaba; Wangwe, Peter; Sukums, Felix; Leshabari, Melkizedeck; Blank, Antje; Sauerborn, Rainer; Loukanova, Svetla

    2017-08-07

    QUALMAT project aimed at improving quality of maternal and newborn care in selected health care facilities in three African countries. An electronic clinical decision support system was implemented to support providers comply with established standards in antenatal and childbirth care. Given that health care resources are limited and interventions differ in their potential impact on health and costs (efficiency), this study aimed at assessing cost-effectiveness of the system in Tanzania. This was a quantitative pre- and post- intervention study involving 6 health centres in rural Tanzania. Cost information was collected from health provider's perspective. Outcome information was collected through observation of the process of maternal care. Incremental cost-effectiveness ratios for antenatal and childbirth care were calculated with testing of four models where the system was compared to the conventional paper-based approach to care. One-way sensitivity analysis was conducted to determine whether changes in process quality score and cost would impact on cost-effectiveness ratios. Economic cost of implementation was 167,318 USD, equivalent to 27,886 USD per health center and 43 USD per contact. The system improved antenatal process quality by 4.5% and childbirth care process quality by 23.3% however these improvements were not statistically significant. Base-case incremental cost-effectiveness ratios of the system were 2469 USD and 338 USD per 1% change in process quality for antenatal and childbirth care respectively. Cost-effectiveness of the system was sensitive to assumptions made on costs and outcomes. Although the system managed to marginally improve individual process quality variables, it did not have significant improvement effect on the overall process quality of care in the short-term. A longer duration of usage of the electronic clinical decision support system and retention of staff are critical to the efficiency of the system and can reduce the invested

  16. A Group Decision Support System for Staging of Cancer

    NASA Astrophysics Data System (ADS)

    Miranda, Miguel; Abelha, António; Santos, Manuel; Machado, José; Neves, José

    The TNM classification system was developed as a tool for physicians to stage different types of cancer based on standard criteria, according to a common language of cancer staging. Staging reports are usually performed by oncologists but sometimes are also done by physicians not specialized in this area. In this paper, it is presented a multi-agent system to support group decision that helps meeting participants to reach and to justify a solution. With the increasing use of web applications to perform the Electronic Medical Record on healthcare facilities, this system has the potential to be easily integrated in order to support the medical and clinical e-learning and to improve patient assistance. In fact, the usual need for documentation and specific information by the medical staff can be easily provided by these systems, making a new steep towards a paper free healthcare system.

  17. Evaluating Detection and Diagnostic Decision Support Systems for Bioterrorism Response

    PubMed Central

    Sundaram, Vandana; McDonald, Kathryn M.; Smith, Wendy M.; Szeto, Herbert; Schleinitz, Mark D.; Owens, Douglas K.

    2004-01-01

    We evaluated the usefulness of detection systems and diagnostic decision support systems for bioterrorism response. We performed a systematic review by searching relevant databases (e.g., MEDLINE) and Web sites for reports of detection systems and diagnostic decision support systems that could be used during bioterrorism responses. We reviewed over 24,000 citations and identified 55 detection systems and 23 diagnostic decision support systems. Only 35 systems have been evaluated: 4 reported both sensitivity and specificity, 13 were compared to a reference standard, and 31 were evaluated for their timeliness. Most evaluations of detection systems and some evaluations of diagnostic systems for bioterrorism responses are critically deficient. Because false-positive and false-negative rates are unknown for most systems, decision making on the basis of these systems is seriously compromised. We describe a framework for the design of future evaluations of such systems. PMID:15078604

  18. Decision support system for theater missile defense

    NASA Astrophysics Data System (ADS)

    Gonsalves, Paul; Burge, Janet; Popp, Ben

    2003-08-01

    Military services require C4I systems that support a full spectrum of operations. This is specifically relevant to the theatre missile defense (TMD) mission planning and analysis community where there have been several recent concept changes; advancements in information technology, sensors, and weapons; and expansion in the diversity and capabilities of potential adversaries. To fully support campaign development and analysis in this new environment, there is a need for systems and tools that enhance understanding of adversarial behavior, assess potential threat capabilities and vulnerabilities, perform C4I system trades, and provide methods to identify macro-level novel or emergent combat tactics and behavior derived from simpler micro-level rules. Such systems must also be interactive, collaborative, and semi-autonomous, providing the INTEL analyst with the means for exploration and potential exploitation of novel enemy behavior patterns. To address these issues we have developed an Intelligent Threat Assessment Processor (ITAP) to provide prediction and interpretation of enemy courses of actions (eCOAs) for the TMD domain. This system uses a combination of genetic algorithm-based optimization in tandem with the spatial analysis and visualization capabilities of a commercial-off-the-shelf (COTS) geographic information system to generate and evaluate potential eCOAs.

  19. A multi-faceted tailored strategy to implement an electronic clinical decision support system for pressure ulcer prevention in nursing homes: a two-armed randomized controlled trial.

    PubMed

    Beeckman, Dimitri; Clays, Els; Van Hecke, Ann; Vanderwee, Katrien; Schoonhoven, Lisette; Verhaeghe, Sofie

    2013-04-01

    Frail older people admitted to nursing homes are at risk of a range of adverse outcomes, including pressure ulcers. Clinical decision support systems are believed to have the potential to improve care and to change the behaviour of healthcare professionals. To determine whether a multi-faceted tailored strategy to implement an electronic clinical decision support system for pressure ulcer prevention improves adherence to recommendations for pressure ulcer prevention in nursing homes. Two-armed randomized controlled trial in a nursing home setting in Belgium. The trial consisted of a 16-week implementation intervention between February and June 2010, including one baseline, four intermediate, and one post-testing measurement. Primary outcome was the adherence to guideline-based care recommendations (in terms of allocating adequate pressure ulcer prevention in residents at risk). Secondary outcomes were the change in resident outcomes (pressure ulcer prevalence) and intermediate outcomes (knowledge and attitudes of healthcare professionals). Random sample of 11 wards (6 experimental; 5 control) in a convenience sample of 4 nursing homes in Belgium. In total, 464 nursing home residents and 118 healthcare professionals participated. The experimental arm was involved in a multi-faceted tailored implementation intervention of a clinical decision support system, including interactive education, reminders, monitoring, feedback and leadership. The control arm received a hard-copy of the pressure ulcer prevention protocol, supported by standardized 30 min group lecture. Patients in the intervention arm were significantly more likely to receive fully adequate pressure ulcer prevention when seated in a chair (F=16.4, P=0.003). No significant improvement was observed on pressure ulcer prevalence and knowledge of the professionals. While baseline attitude scores were comparable between both groups [exp. 74.3% vs. contr. 74.5% (P=0.92)], the mean score after the intervention was

  20. Development of a scalable pharmacogenomic clinical decision support service.

    PubMed

    Fusaro, Vincent A; Brownstein, Catherine; Wolf, Wendy; Clinton, Catherine; Savage, Sarah; Mandl, Kenneth D; Margulies, David; Manzi, Shannon

    2013-01-01

    Advances in sequencing technology are making genomic data more accessible within the healthcare environment. Published pharmacogenetic guidelines attempt to provide a clinical context for specific genomic variants; however, the actual implementation to convert genomic data into a clinical report integrated within an electronic medical record system is a major challenge for any hospital. We created a two-part solution that integrates with the medical record system and converts genetic variant results into an interpreted clinical report based on published guidelines. We successfully developed a scalable infrastructure to support TPMT genetic testing and are currently testing approximately two individuals per week in our production version. We plan to release an online variant to clinical interpretation reporting system in order to facilitate translation of pharmacogenetic information into clinical practice.

  1. Clinical decision support systems to improve utilization of thromboprophylaxis: a review of the literature and experience with implementation of a computerized physician order entry program.

    PubMed

    Adams, Paul; Riggio, Jeff M; Thomson, Lynda; Brandell-Marino, Renee; Merli, Geno

    2012-08-01

    A literature review was conducted of studies investigating the effectiveness of paper- and computer-based clinical decision support systems (CDSS) used with or without educational programs designed to increase the use of venous thromboembolism (VTE) prophylaxis. Medline was searched on August 9, 2010, without limits on publication year, but with restrictions to English-language articles only. The search terms used were "venous thromboembolism," "deep vein thrombosis," "pulmonary embolism," "prophylaxis," "thromboprophylaxis," "computerized," "computerised," "decision support," "alerts," "reminder," "paper system," "risk assessment," and "risk score." All types of studies regarding the effects of CDSS on VTE prophylaxis rates were included. Studies were included if ≥ 1 post-implementation outcome was measured, such as rates of VTE, rates of prophylaxis prescribing, or guideline-adherence measures. Studies evaluating paper-based CDSS used different strategies, including risk-assessment forms with prophylaxis recommendations, standard order sets, and preprinted sticker reminders on patient notes. Paper-based systems consistently improved prophylaxis rates; however, in most studies, there was still room for improvement. Furthermore, the effect of paper-based CDSS on VTE rates was not conclusively established. Studies evaluating computer-based systems used approaches including risk-assessment models integrated in the computerized physician order entry system, with or without alerts, and automatic reminders on operating schedules. Computerized systems are associated with substantial improvements in the prescribing of appropriate prophylaxis and reductions in VTE events, particularly in medical patients. More robust systems can be established with computer-based rather than paper-based CDSS. A drawback of computerized systems is that some hospitals may not have adequate information technology system resources.

  2. Studying the vendor perspective on clinical decision support.

    PubMed

    Ash, Joan S; Sittig, Dean F; McMullen, Carmit K; McCormack, James L; Wright, Adam; Bunce, Arwen; Wasserman, Joseph; Mohan, Vishnu; Cohen, Deborah J; Shapiro, Michael; Middleton, Blackford

    2011-01-01

    In prior work, using a Rapid Assessment Process (RAP), we have investigated clinical decision support (CDS) in ambulatory clinics and hospitals. We realized that individuals in these settings provide only one perspective related to the CDS landscape, which also includes content vendors and electronic health record (EHR) vendors. To discover content vendors' perspectives and their perceived challenges, we modified RAP for industrial settings. We describe how we employed RAP, and show its utility by describing two illustrative themes. We found that while the content vendors believe they provide unique much-needed services, the amount of labor involved in content development is underestimated by others. We also found that the content vendors believe their products are resources to be used by practitioners, so they are somewhat protected from liability issues. To promote adequate understanding about these issues, we recommend a "three way conversation" among content vendors, EHR vendors, and user organizations.

  3. Studying the Vendor Perspective on Clinical Decision Support

    PubMed Central

    Ash, Joan S.; Sittig, Dean F.; McMullen, Carmit K.; McCormack, James L.; Wright, Adam; Bunce, Arwen; Wasserman, Joseph; Mohan, Vishnu; Cohen, Deborah J.; Shapiro, Michael; Middleton, Blackford

    2011-01-01

    In prior work, using a Rapid Assessment Process (RAP), we have investigated clinical decision support (CDS) in ambulatory clinics and hospitals. We realized that individuals in these settings provide only one perspective related to the CDS landscape, which also includes content vendors and electronic health record (EHR) vendors. To discover content vendors’ perspectives and their perceived challenges, we modified RAP for industrial settings. We describe how we employed RAP, and show its utility by describing two illustrative themes. We found that while the content vendors believe they provide unique much-needed services, the amount of labor involved in content development is underestimated by others. We also found that the content vendors believe their products are resources to be used by practitioners, so they are somewhat protected from liability issues. To promote adequate understanding about these issues, we recommend a “three way conversation” among content vendors, EHR vendors, and user organizations. PMID:22195058

  4. The Organizational Impact of a Decision Support System.

    ERIC Educational Resources Information Center

    Pope, James A.; Cross, Edward M.

    1984-01-01

    The results of a follow-up evaluation of a decision support system installed 5 years ago at Guilford College are reported. Changes in the system, resulting procedures, and the organization are described. (Author/MLW)

  5. Evaluation of stream mining classifiers for real-time clinical decision support system: a case study of blood glucose prediction in diabetes therapy.

    PubMed

    Fong, Simon; Zhang, Yang; Fiaidhi, Jinan; Mohammed, Osama; Mohammed, Sabah

    2013-01-01

    Earlier on, a conceptual design on the real-time clinical decision support system (rt-CDSS) with data stream mining was proposed and published. The new system is introduced that can analyze medical data streams and can make real-time prediction. This system is based on a stream mining algorithm called VFDT. The VFDT is extended with the capability of using pointers to allow the decision tree to remember the mapping relationship between leaf nodes and the history records. In this paper, which is a sequel to the rt-CDSS design, several popular machine learning algorithms are investigated for their suitability to be a candidate in the implementation of classifier at the rt-CDSS. A classifier essentially needs to accurately map the events inputted to the system into one of the several predefined classes of assessments, such that the rt-CDSS can follow up with the prescribed remedies being recommended to the clinicians. For a real-time system like rt-CDSS, the major technological challenges lie in the capability of the classifier to process, analyze and classify the dynamic input data, quickly and upmost reliably. An experimental comparison is conducted. This paper contributes to the insight of choosing and embedding a stream mining classifier into rt-CDSS with a case study of diabetes therapy.

  6. Development, deployment and usability of a point-of-care decision support system for chronic disease management using the recently-approved HL7 decision support service standard.

    PubMed

    Lobach, David F; Kawamoto, Kensaku; Anstrom, Kevin J; Russell, Michael L; Woods, Peter; Smith, Dwight

    2007-01-01

    Clinical decision support is recognized as one potential remedy for the growing crisis in healthcare quality in the United States and other industrialized nations. While decision support systems have been shown to improve care quality and reduce errors, these systems are not widely available. This lack of availability arises in part because most decision support systems are not portable or scalable. The Health Level 7 international standard development organization recently adopted a draft standard known as the Decision Support Service standard to facilitate the implementation of clinical decision support systems using software services. In this paper, we report the first implementation of a clinical decision support system using this new standard. This system provides point-of-care chronic disease management for diabetes and other conditions and is deployed throughout a large regional health system. We also report process measures and usability data concerning the system. Use of the Decision Support Service standard provides a portable and scalable approach to clinical decision support that could facilitate the more extensive use of decision support systems.

  7. Effects of clinical decision-support systems on practitioner performance and patient outcomes: a synthesis of high-quality systematic review findings

    PubMed Central

    Smeulers, Marian; Vermeulen, Hester; Peute, Linda W

    2011-01-01

    Objective To synthesize the literature on clinical decision-support systems' (CDSS) impact on healthcare practitioner performance and patient outcomes. Design Literature search on Medline, Embase, Inspec, Cinahl, Cochrane/Dare and analysis of high-quality systematic reviews (SRs) on CDSS in hospital settings. Two-stage inclusion procedure: (1) selection of publications on predefined inclusion criteria; (2) independent methodological assessment of preincluded SRs by the 11-item measurement tool, AMSTAR. Inclusion of SRs with AMSTAR score 9 or above. SRs were thereafter rated on level of evidence. Each stage was performed by two independent reviewers. Results 17 out of 35 preincluded SRs were of high methodological quality and further analyzed. Evidence that CDSS significantly impacted practitioner performance was found in 52 out of 91 unique studies of the 16 SRs examining this effect (57%). Only 25 out of 82 unique studies of the 16 SRs reported evidence that CDSS positively impacted patient outcomes (30%). Conclusions Few studies have found any benefits on patient outcomes, though many of these have been too small in sample size or too short in time to reveal clinically important effects. There is significant evidence that CDSS can positively impact healthcare providers' performance with drug ordering and preventive care reminder systems as most clear examples. These outcomes may be explained by the fact that these types of CDSS require a minimum of patient data that are largely available before the advice is (to be) generated: at the time clinicians make the decisions. PMID:21422100

  8. Workflow Technology to Enrich a Computerized Clinical Chart with Decision Support Facilities

    PubMed Central

    Panzarasa, Silvia; Quaglini, Silvana; Cavallini, Anna; Micieli, Giuseppe; Pernice, Corrado; Pessina, Mauro; Stefanelli, Mario

    2006-01-01

    Literature results and personal experience show that intrusive modalities of presenting suggestions of computerized clinical practice guidelines are detrimental to the routine use of an information system. This paper describes a solution for smoothly integrating a guideline-based decision support system into an existing computerized clinical chart for patients admitted to a Stroke Unit. Since many years, the healthcare personnel were using a commercial product for the ordinary patients’ data management, and they were satisfied with it. Thus, the decision support system has been integrated keeping attention to minimize changes and preserve existing human-computer interaction. Our decision support system is based on workflow technology. The paper illustrates the middleware layer developed to allow communication between the workflow management system and the clinical chart. At the same time, the consequent modification of the graphical users' interface is illustrated. PMID:17238415

  9. Decision-support systems for forest management

    Treesearch

    H. Michael Rauscher

    2005-01-01

    The basic concept of sustainable development, formulated in the Brundtland report and applied to forest management by the Montreal Process, has focused attention on the need for formal decision processes (Brundtland. 1987). The application of decision theory is essential because meeting the needs of the present without compromising the ability of future generations to...

  10. Decision support system and medical liability.

    PubMed Central

    Allaërt, F. A.; Dusserre, L.

    1992-01-01

    Expert systems, which are going to be an essential tool in Medicine, are evolving in terms of sophistication of both knowledge representation and types of reasoning models used. The more efficient they are, the more often they will be used and professional liability will be involved. So after giving a short survey of configuration and working of expert systems, the authors will study the liabilities of people building and the using expert systems regarding some various dysfunctions. Of course the expert systems have to be considered only for human support and they should not possess any authority themselves, therefore the doctors must keep in mind that it is their own responsibility and as such keep their judgment and criticism. However other professionals could be involved, if they have participated in the building of expert systems. The different liabilities and the burden of proof are discussed according to some possible dysfunctions. In any case the final proof is inside the expert system by itself through re-computation of data. PMID:1482972

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

    ERIC Educational Resources Information Center

    Carney, Timothy Jay

    2012-01-01

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

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

    ERIC Educational Resources Information Center

    Carney, Timothy Jay

    2012-01-01

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

  13. Personalization and Patient Involvement in Decision Support Systems: Current Trends

    PubMed Central

    Sacchi, L.; Lanzola, G.; Viani, N.

    2015-01-01

    Summary Objectives This survey aims at highlighting the latest trends (2012-2014) on the development, use, and evaluation of Information and Communication Technologies (ICT) based decision support systems (DSSs) in medicine, with a particular focus on patient-centered and personalized care. Methods We considered papers published on scientific journals, by querying PubMed and Web of Science™. Included studies focused on the implementation or evaluation of ICT-based tools used in clinical practice. A separate search was performed on computerized physician order entry systems (CPOEs), since they are increasingly embedding patient-tailored decision support. Results We found 73 papers on DSSs (53 on specific ICT tools) and 72 papers on CPOEs. Although decision support through the delivery of recommendations is frequent (28/53 papers), our review highlighted also DSSs only based on efficient information presentation (25/53). Patient participation in making decisions is still limited (9/53), and mostly focused on risk communication. The most represented medical area is cancer (12%). Policy makers are beginning to be included among stakeholders (6/73), but integration with hospital information systems is still low. Concerning knowledge representation/management issues, we identified a trend towards building inference engines on top of standard data models. Most of the tools (57%) underwent a formal assessment study, even if half of them aimed at evaluating usability and not effectiveness. Conclusions Overall, we have noticed interesting evolutions of medical DSSs to improve communication with the patient, consider the economic and organizational impact, and use standard models for knowledge representation. However, systems focusing on patient-centered care still do not seem to be available at large. PMID:26293857

  14. Personalization and Patient Involvement in Decision Support Systems: Current Trends.

    PubMed

    Quaglini, S; Sacchi, L; Lanzola, G; Viani, N

    2015-08-13

    This survey aims at highlighting the latest trends (2012-2014) on the development, use, and evaluation of Information and Communication Technologies (ICT) based decision support systems (DSSs) in medicine, with a particular focus on patient-centered and personalized care. We considered papers published on scientific journals, by querying PubMed and Web of ScienceTM. Included studies focused on the implementation or evaluation of ICT-based tools used in clinical practice. A separate search was performed on computerized physician order entry systems (CPOEs), since they are increasingly embedding patient-tailored decision support. We found 73 papers on DSSs (53 on specific ICT tools) and 72 papers on CPOEs. Although decision support through the delivery of recommendations is frequent (28/53 papers), our review highlighted also DSSs only based on efficient information presentation (25/53). Patient participation in making decisions is still limited (9/53), and mostly focused on risk communication. The most represented medical area is cancer (12%). Policy makers are beginning to be included among stakeholders (6/73), but integration with hospital information systems is still low. Concerning knowledge representation/management issues, we identified a trend towards building inference engines on top of standard data models. Most of the tools (57%) underwent a formal assessment study, even if half of them aimed at evaluating usability and not effectiveness. Overall, we have noticed interesting evolutions of medical DSSs to improve communication with the patient, consider the economic and organizational impact, and use standard models for knowledge representation. However, systems focusing on patient-centered care still do not seem to be available at large.

  15. Promising adoption of an electronic clinical decision support system for antenatal and intrapartum care in rural primary healthcare facilities in sub-Saharan Africa: The QUALMAT experience.

    PubMed

    Sukums, Felix; Mensah, Nathan; Mpembeni, Rose; Massawe, Siriel; Duysburgh, Els; Williams, Afua; Kaltschmidt, Jens; Loukanova, Svetla; Haefeli, Walter E; Blank, Antje

    2015-09-01

    The QUALMAT project has successfully implemented an electronic clinical decision support system (eCDSS) for antenatal and intrapartum care in two sub-Saharan African countries. The system was introduced to facilitate adherence to clinical practice guidelines and to support decision making during client encounter to bridge the know-do gap of health workers. This study aimed to describe health workers' acceptance and use of the eCDSS for maternal care in rural primary health care (PHC) facilities of Ghana and Tanzania and to identify factors affecting successful adoption of such a system. This longitudinal study was conducted in Lindi rural district in Tanzania and Kassena-Nankana district in Ghana between October 2011 and December 2013 employing mixed methods. The study population included healthcare workers who were involved in the provision of maternal care in six rural PHC facilities from one district in each country where the eCDSS was implemented. All eCDSS users participated in the study with 61 and 56 participants at the midterm and final assessment, respectively. After several rounds of user training and support the eCDSS has been successfully adopted and constantly used during patient care in antenatal clinics and maternity wards. The eCDSS was used in 71% (2703/3798) and 59% (14,189/24,204) of all ANC clients in Tanzania and Ghana respectively, while it was also used in 83% (1185/1427) and 67% (1435/2144) of all deliveries in Tanzania and in Ghana, respectively. Several barriers reported to hinder eCDSS use were related to individual users, tasks, technology, and organization attributes. Implementation of an eCDSS in resource-constrained PHC facilities in sub-Saharan Africa was successful and the health workers accepted and continuously used the system for maternal care. Facilitators for eCDSS use included sufficient training and regular support whereas the challenges to sustained use were unreliable power supply and perceived high workload. However our

  16. 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…

  17. 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…

  18. Feasibility of integrating a clinical decision support tool into an existing computerized physician order entry system to increase seasonal influenza vaccination in the emergency department.

    PubMed

    Venkat, Arvind; Chan-Tompkins, Noreen H; Hegde, Gajanan G; Chuirazzi, David M; Hunter, Roger; Szczesiul, Jillian M

    2010-08-23

    While emergency department (ED) seasonal influenza vaccination programs are feasible, reported implementation barriers include added staffing requirements to identify eligible patients and getting busy ED personnel to order and provide vaccination. We present a prospective, observational trial of integrating a clinical decision support tool into an existing ED computerized physician order entry (CPOE) system to increase ED seasonal influenza vaccination without added staffing resources, the operational barriers identified to program implementation, the revenue generated and data on opportunities for future quality improvement. Compared to the comparable pre-protocol period, ED influenza vaccination rose by 17.5% with a resultant profit margin of 34.5%. Copyright 2010 Elsevier Ltd. All rights reserved.

  19. Protocol-Driven Decision Support within e-Referral Systems to Streamline Patient Consultation, Triaging and Referrals from Primary Care to Specialist Clinics.

    PubMed

    Maghsoud-Lou, Ehsan; Christie, Sean; Abidi, Samina Raza; Abidi, Syed Sibte Raza

    2017-09-01

    Patient referral is a protocol where the referring primary care physician refers the patient to a specialist for further treatment. The paper-based current referral process at times lead to communication and operational issues, resulting in either an unfulfilled referral request or an unnecessary referral request. Despite the availability of standardized referral protocols they are not readily applied because they are tedious and time-consuming, thus resulting in suboptimal referral requests. We present a semantic-web based Referral Knowledge Modeling and Execution Framework to computerize referral protocols, clinical guidelines and assessment tools in order to develop a computerized e-Referral system that offers protocol-based decision support to streamline and standardize the referral process. We have developed a Spinal Problem E-Referral (SPER) system that computerizes the Spinal Condition Consultation Protocol (SCCP) mandated by the Halifax Infirmary Division of Neurosurgery (Halifax, Canada) for referrals for spine related conditions (such as back pain). The SPER system executes the ontologically modeled SCCP to determine (i) patient's triaging option as per severity assessments stipulated by SCCP; and (b) clinical recommendations as per the clinical guidelines incorporated within SCCP. In operation, the SPER system identifies the critical cases and triages them for specialist referral, whereas for non-critical cases SPER system provides clinical guideline based recommendations to help the primary care physician effectively manage the patient. The SPER system has undergone a pilot usability study and was deemed to be easy to use by physicians with potential to improve the referral process within the Division of Neurosurgery at QEII Health Science Center, Halifax, Canada.

  20. Decision support system for predicting color change after tooth whitening.

    PubMed

    Thanathornwong, Bhornsawan; Suebnukarn, Siriwan; Ouivirach, Kan

    2016-03-01

    Tooth whitening is becoming increasingly popular among patients and dentists since it is a relatively noninvasive approach. However, the degree of color change after tooth whitening is known to vary substantially between studies. The present study aims to develop a clinical decision support system for predicting color change after in-office tooth whitening. We used the information from patients' data sets, and applied the multiple regression equation of CIELAB color coordinates including L*, a*, and b* of the original tooth color and the color difference (ΔE) that expresses the color change after tooth whitening. To evaluate the system performance, the patient's post-treatment color was used as "gold standard" to compare with the post-treatment color predicted by the system. There was a high degree of agreement between the patient's post-treatment color and the post-treatment color predicted by the system (kappa value=0.894). The results obtained have demonstrated that the decision support system is possible to predict the color change obtained using an in-office whitening system using colorimetric values. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

  1. Guidelines for maternal and neonatal "point of care": needs of and attitudes towards a computerized clinical decision support system in rural Burkina Faso.

    PubMed

    Zakane, S Alphonse; Gustafsson, Lars L; Tomson, Göran; Loukanova, Svetla; Sié, Ali; Nasiell, Josefine; Bastholm-Rahmner, Pia

    2014-06-01

    In 2010, 245,000 women died due to pregnancy-related causes in sub-Saharan Africa and southern Asia. Our study is nested into the QUALMAT project and seeks to improve the quality of maternal care services through the introduction of a computerized clinical decision support system (CDSS) to help healthcare workers in rural areas. Healthcare information technology applications in low-income countries may improve healthcare provision but recent studies demonstrate unintended consequences with underuse or resistance to CDSS and that the fit between the system and the clinical needs does present challenges. To explore and describe perceived needs and attitudes among healthcare workers to access WHO guidelines using CDSS in maternal and neonatal care in rural Burkina Faso. Data were collected with semi-structured interviews in two rural districts in Burkina Faso with 45 informants. Descriptive statistics were used for the analysis of the quantitative part of the interview corresponding to informants' background. Qualitative data were analyzed using manifest content analysis. Four main findings emerged: (a) an appreciable willingness among healthcare workers for and a great interest to adapt and use modern technologies like computers to learn more in the workplace, (b) a positive attitude to easy access of guidelines and implementation of decision-support using computers in the workplace, (c) a fear that the CDSS would require more working time and lead to double-work, and (d) that the CDSS is complicated and requires substantial computer training and extensive instructions to fully implement. The findings can be divided into aspects of motivators and barriers in relation to how the CDSS is perceived and to be used. These aspects are closely connected to each other as the motivating aspects can easily be turned into barriers if not taken care of properly in the final design, during implementation and maintenance of the CDSS at point of care. Copyright © 2014 Elsevier

  2. Evaluating clinical decision support tools for medication administration safety in a simulated environment.

    PubMed

    Moss, Jacqueline; Berner, Eta S

    2015-05-01

    The specific aims of this study were to develop a methodology and tools for the design of clinical decision support systems to decrease the incidence of medication administration errors. A mixed-methods design was utilized in this study. First, observations of medication administration practice were used to inform the design of a simulated information system with a variety of decision support tools. Then, nurses were observed administering medications in a simulated environment using the simulated system. Finally, the nurses participated in focus groups to provide input into system tools design. Observations of nurses' use of the decision support tools as well as semi-structured focus groups were used to evaluate nurses' use and perceptions of the utility of the system decision support tools. Nurses' evaluation of the medication administration decision support tools as well as their actual performance revealed a tendency to underestimate their need for support. Their preferences were for decision support that was short, color coded, and easily accessed. Observations of medication administration showed that nurses exhibit a variety of work processes to prepare and administer medications to patients and access system decision support tools at a variety of points in this process. System design should allow flexibility of multiple points and types of information delivery to accommodate variations in workflow to minimize the tendency for system workarounds. This study was performed in one hospital and results may not generalize beyond this setting. However, this method used to design and test decision support could be transferred to other settings. Using simulation in this study provided a method for testing new information system design, related to a potentially dangerous procedure, in a manner that eliminated the hazards of potential unintended consequences for patients. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

  3. Designing a Decision-Support System for Enrollment Management.

    ERIC Educational Resources Information Center

    Glover, Robert H.

    1986-01-01

    The conceptual framework, design, and implementation plan for building a decision-support system for enrollment management at a private university are outlined, including information about the computer hardware and software used in implementation. (Author/MSE)

  4. A systematic review of near real-time and point-of-care clinical decision support in anesthesia information management systems.

    PubMed

    Simpao, Allan F; Tan, Jonathan M; Lingappan, Arul M; Gálvez, Jorge A; Morgan, Sherry E; Krall, Michael A

    2016-08-16

    Anesthesia information management systems (AIMS) are sophisticated hardware and software technology solutions that can provide electronic feedback to anesthesia providers. This feedback can be tailored to provide clinical decision support (CDS) to aid clinicians with patient care processes, documentation compliance, and resource utilization. We conducted a systematic review of peer-reviewed articles on near real-time and point-of-care CDS within AIMS using the Preferred Reporting Items for Systematic Review and Meta-Analysis Protocols. Studies were identified by searches of the electronic databases Medline and EMBASE. Two reviewers screened studies based on title, abstract, and full text. Studies that were similar in intervention and desired outcome were grouped into CDS categories. Three reviewers graded the evidence within each category. The final analysis included 25 articles on CDS as implemented within AIMS. CDS categories included perioperative antibiotic prophylaxis, post-operative nausea and vomiting prophylaxis, vital sign monitors and alarms, glucose management, blood pressure management, ventilator management, clinical documentation, and resource utilization. Of these categories, the reviewers graded perioperative antibiotic prophylaxis and clinical documentation as having strong evidence per the peer reviewed literature. There is strong evidence for the inclusion of near real-time and point-of-care CDS in AIMS to enhance compliance with perioperative antibiotic prophylaxis and clinical documentation. Additional research is needed in many other areas of AIMS-based CDS.

  5. New approaches for real time decision support systems

    NASA Technical Reports Server (NTRS)

    Hair, D. Charles; Pickslay, Kent

    1994-01-01

    NCCOSC RDT&E Division (NRaD) is conducting research into ways of improving decision support systems (DSS) that are used in tactical Navy decision making situations. The research has focused on the incorporation of findings about naturalistic decision-making processes into the design of the DSS. As part of that research, two computer tools were developed that model the two primary naturalistic decision-making strategies used by Navy experts in tactical settings. Current work is exploring how best to incorporate the information produced by those tools into an existing simulation of current Navy decision support systems. This work has implications for any applications involving the need to make decisions under time constraints, based on incomplete or ambiguous data.

  6. Decision Aids Can Support Cancer Clinical Trials Decisions: Results of a Randomized Trial.

    PubMed

    Politi, Mary C; Kuzemchak, Marie D; Kaphingst, Kimberly A; Perkins, Hannah; Liu, Jingxia; Byrne, Margaret M

    2016-12-01

    Cancer patients often do not make informed decisions regarding clinical trial participation. This study evaluated whether a web-based decision aid (DA) could support trial decisions compared with our cancer center's website. Adults diagnosed with cancer in the past 6 months who had not previously participated in a cancer clinical trial were eligible. Participants were randomized to view the DA or our cancer center's website (enhanced usual care [UC]). Controlling for whether participants had heard of cancer clinical trials and educational attainment, multivariable linear regression examined group on knowledge, self-efficacy for finding trial information, decisional conflict (values clarity and uncertainty), intent to participate, decision readiness, and trial perceptions. Two hundred patients (86%) consented between May 2014 and April 2015. One hundred were randomized to each group. Surveys were completed by 87 in the DA group and 90 in the UC group. DA group participants reported clearer values regarding trial participation than UC group participants reported (least squares [LS] mean = 15.8 vs. 32, p < .0001) and less uncertainty (LS mean = 24.3 vs. 36.4, p = .025). The DA group had higher objective knowledge than the UC group's (LS mean = 69.8 vs. 55.8, p < .0001). There were no differences between groups in intent to participate. Improvements on key decision outcomes including knowledge, self-efficacy, certainty about choice, and values clarity among participants who viewed the DA suggest web-based DAs can support informed decisions about trial participation among cancer patients facing this preference-sensitive choice. Although better informing patients before trial participation could improve retention, more work is needed to examine DA impact on enrollment and retention. This paper describes evidence regarding a decision tool to support patients' decisions about trial participation. By improving knowledge, helping patients clarify preferences for

  7. Group decision support system for customer-driven product design

    NASA Astrophysics Data System (ADS)

    Lin, Zhihang; Chen, Hang; Chen, Kuen; Che, Ada

    2000-10-01

    This paper describes the work on the development of a group decision support system for customer driven product design. The customer driven is to develop products, which meet all customer requirements in whole life cycle of products. A process model of decision during product primary design is proposed to formulate the structured, semi-structured and unstructured decision problems. The framework for the decision support system is presented that integrated both advances in the group decision making and distributed artificial intelligent. The system consists of the product primary design tool kit and the collaborative platform with multi-agent structure. The collaborative platform of the system and the product primary design tool kit, including the VOC (Voice of Customer) tool, QFD (Quality Function Deployment) tool, the Conceptual design tool, Reliability analysis tool and the cost and profit forecasting tool, are indicated.

  8. [Computerized decision support systems: EBM at the bedside].

    PubMed

    Capobussi, Matteo; Banzi, Rita; Moja, Lorenzo; Bonovas, Stefanos; González-Lorenzo, Marien; Liberati, Elisa Giulia; Polo Friz, Hernan; Nanni, Oriana; Mangia, Massimo; Ruggiero, Francesca

    2016-11-01

    One of the aims of Evidence-Based Medicine is to improve quality and appropriateness of care by the expedition of the knowledge transfer process. Computerized Decision Support Systems (CDSSs) are computer programs that provide alerts to the prescribing doctor directly at the moment of medical examination. In fact, alerts are integrated within the single patient electronic health record. CDSS based on the best available and updated evidence and guidelines may be an efficient tool to facilitate the transfer of the latest results from clinical research directly at the bedside, thus supporting decision-making. The CODES (COmputerized DEcision Support) trial is a research program funded by the Italian Ministry of Health and the Lombardy Region. It aims to evaluate the feasibility of the implementation of a CDSS at the hospital level and to assess its efficacy in daily clinical practice. The CODES project includes two pragmatic RCTs testing a CDSS (i.e. the EBMeDS - MediDSS) in two large Italian hospitals: the first is a general hospital in Vimercate (Lombardy), the second is an oncologic research center in Meldola (Emilia Romagna). The CDSS supports a full spectrum of decisions: therapy, drug interactions, diagnosis, and management of health care services are covered by a hundreds of reminders. However only few reminders are activated per patient, highlighting crucial problems in the delivery of high-quality care. The two trials have similar design and primary outcome, the rate at which alerts detected by the software are resolved by a decision of the clinicians. The project also includes the assessment of barriers and facilitators in the adoption of these new technologies by hospital staff members and the retrospective evaluation of the repeated risks in prescription habits. The trials are ongoing and currently more than 10,000 patients have been randomized. The qualitative analysis revealed a progressive shift in the perception of the tool. Doctors are now seeing it

  9. 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

  10. 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.…

  11. 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.…

  12. Integrated approach for designing medical decision support systems with knowledge extracted from clinical databases by statistical methods.

    PubMed Central

    Krusinska, E.; Babic, A.; Chowdhury, S.; Wigertz, O.; Bodemar, G.; Mathiesen, U.

    1991-01-01

    In clinical research data is often studied by a particular method without previous analysis of quality or semantic contents which could link clinical database and data analytical (e.g. statistical) procedures. In order to avoid bias caused by this situation, we propose that the analysis of medical data should be divided into two main steps. In the first one we concentrate on conducting the quality, semantic and structure analyses. In the second step our aim is to build an appropriate dictionary of data analysis methods for further knowledge extraction. Methods like robust statistical techniques, procedures for mixed continuous and discrete data, fuzzy linguistic approach, machine learning and neural networks can be included. The results may be evaluated both using test samples and applying other relevant data-analytical techniques to the particular problem under the study. PMID:1807621

  13. SANDS: An Architecture for Clinical Decision Support in a National Health Information Network

    PubMed Central

    Wright, Adam; Sittig, Dean F.

    2007-01-01

    A new architecture for clinical decision support called SANDS (Service-oriented Architecture for NHIN Decision Support) is introduced and its performance evaluated. The architecture provides a method for performing clinical decision support across a network, as in a health information exchange. Using the prototype we demonstrated that, first, a number of useful types of decision support can be carried out using our architecture; and, second, that the architecture exhibits desirable reliability and performance characteristics. PMID:18693950

  14. Periodicals collection management using a decision support system

    SciTech Connect

    Compton, M.L.; Moser, E.C.

    1993-12-31

    Sandia National Laboratories is a multiprogram national laboratory established in 1949. The Library currently uses DOBIS for its automated system, including the Periodicals Control function for periodical check-in. DOBIS performs processing and control functions adequately, but could not meet our reporting needs. Therefore the Library`s Periodicals Decision Team decided that they needed another ``system`` for collection management. A Periodicals Decision Support System was created using information downloaded from DOBIS and uploaded into dBASE IV. The Periodical Decision Support System functions as an information-processing system that has aided us in making collection management decisions for periodicals. It certainly allows us to do interactive ad-hoc analysis; although there are no modeling tools currently incorporated in the system. We hope that these modeling tools will come later. We have been gathering information and developing needed reports to achieve this goal.

  15. Decision Performance Using Spatial Decision Support Systems: A Geospatial Reasoning Ability Perspective

    ERIC Educational Resources Information Center

    Erskine, Michael A.

    2013-01-01

    As many consumer and business decision makers are utilizing Spatial Decision Support Systems (SDSS), a thorough understanding of how such decisions are made is crucial for the information systems domain. This dissertation presents six chapters encompassing a comprehensive analysis of the impact of geospatial reasoning ability on…

  16. Decision Performance Using Spatial Decision Support Systems: A Geospatial Reasoning Ability Perspective

    ERIC Educational Resources Information Center

    Erskine, Michael A.

    2013-01-01

    As many consumer and business decision makers are utilizing Spatial Decision Support Systems (SDSS), a thorough understanding of how such decisions are made is crucial for the information systems domain. This dissertation presents six chapters encompassing a comprehensive analysis of the impact of geospatial reasoning ability on…

  17. On conceptualization of a decision support system in health informatics.

    PubMed

    Nykänen, P

    2001-01-01

    A decision support system can be approached from two major disciplinary perspectives, those of information systems science (IS) and artificial intelligence (AI). We present in this study an extended ontology for a decision support system in health informatics, which is founded on experience from related research fields as well as being informed by our case studies. The ontology emphasises the need to cover environmental and contextual variables as an integral part of a decision support systems development methodology. With the addition of these variables, the focus in decision support systems development shifts from a task ontology towards a domain ontology. The results of this study help the system developers to take the system's context into account through the set of defined variables that are linked to the application domain. These variables explicate relevant constructs and present a vocabulary for a decision support system. However, applying the ontology requires a more thorough analysis of the domain and therefore more qualified resources for systems development. This indicates the need to focus more on education and training in health informatics.

  18. Therapy Decision Support Based on Recommender System Methods

    PubMed Central

    Gräßer, Felix; Beckert, Stefanie; Küster, Denise; Schmitt, Jochen; Abraham, Susanne; Malberg, Hagen

    2017-01-01

    We present a system for data-driven therapy decision support based on techniques from the field of recommender systems. Two methods for therapy recommendation, namely, Collaborative Recommender and Demographic-based Recommender, are proposed. Both algorithms aim to predict the individual response to different therapy options using diverse patient data and recommend the therapy which is assumed to provide the best outcome for a specific patient and time, that is, consultation. The proposed methods are evaluated using a clinical database incorporating patients suffering from the autoimmune skin disease psoriasis. The Collaborative Recommender proves to generate both better outcome predictions and recommendation quality. However, due to sparsity in the data, this approach cannot provide recommendations for the entire database. In contrast, the Demographic-based Recommender performs worse on average but covers more consultations. Consequently, both methods profit from a combination into an overall recommender system.

  19. Uncovering healthcare practitioners' information processing using the think-aloud method: From paper-based guideline to clinical decision support system.

    PubMed

    Kilsdonk, E; Peute, L W; Riezebos, R J; Kremer, L C; Jaspers, M W M

    2016-02-01

    To investigate whether the use of the think-aloud method with propositional analysis could be helpful in the design of a Clinical Decision Support System (CDSS) providing guideline recommendations about long-term follow-up of childhood cancer survivors. The think-aloud method was used to gain insight into healthcare professionals' information processing while reviewing a paper-based guideline. A total of 13 healthcare professionals (6 physicians and 7 physician assistants) prepared 2 fictitious patient consults using the paper-based guideline. Propositional analysis was used to analyze verbal protocols of the think-aloud sessions. A prototype CDSS was developed and a usability study was performed, again with the think-aloud method. The analysis revealed that the paper-based guideline did not support healthcare practitioners in finding patient-specific recommendations. An information processing model for retrieving recommendations was developed and used as input for the design of a CDSS prototype user interface. Usability analysis of the prototype CDSS showed that the navigational structure of the system fitted well with healthcare practitioners' daily practices. The think-aloud method combined with propositional analysis of healthcare practitioners' verbal utterances while they processed a paper-based guideline was useful in the design of a usable CDSS providing patient-specific guideline recommendations. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

  20. Decision-support and intelligent tutoring systems in medical education.

    PubMed

    Frize, M; Frasson, C

    2000-08-01

    One of the challenges in medical education is to teach the decision-making process. This learning process varies according to the experience of the student and can be supported by various tools. In this paper we present several approaches that can strengthen this mechanism, from decision-support tools, such as scoring systems, Bayesian models, neural networks, to cognitive models that can reproduce how the students progressively build their knowledge into memory and foster pedagogic methods.

  1. System Engineering and Evolution Decision Support

    DTIC Science & Technology

    2007-11-02

    invoked the applet. 5. LANGUAGE SUPPORT FOR INTELLIGENT SOFTWARE DECOYS We believe that Eiffel is a natural choice of programming languages for...implementing intelligent software decoys, at least for the purposes of initial experimentation with such decoys. In contrast to Ada, for example, Eiffel ...operations ensure postconditions invariant invariants end Moreover, Eiffel provides for inheritance of the assertions from ancestor classes by a descendant

  2. GELLO: An Object-Oriented Query and Expression Language for Clinical Decision Support

    PubMed Central

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

    2003-01-01

    GELLO is a purpose-specific, object-oriented (OO) query and expression language [1]. GELLO is the result of a concerted effort of the Decision Systems Group (DSG) working with the HL7 Clinical Decision Support Technical Committee (CDSTC) to provide the HL7 community with a common format for data encoding and manipulation. GELLO will soon be submitted for ballot to the HL7 CDSTC for consideration as a standard. PMID:14728515

  3. Examining perceptions of the usefulness and usability of a mobile-based system for pharmacogenomics clinical decision support: a mixed methods study.

    PubMed

    Blagec, Kathrin; Romagnoli, Katrina M; Boyce, Richard D; Samwald, Matthias

    2016-01-01

    Background. Pharmacogenomic testing has the potential to improve the safety and efficacy of pharmacotherapy, but clinical application of pharmacogenetic knowledge has remained uncommon. Clinical Decision Support (CDS) systems could help overcome some of the barriers to clinical implementation. The aim of this study was to evaluate the perception and usability of a web- and mobile-enabled CDS system for pharmacogenetics-guided drug therapy-the Medication Safety Code (MSC) system-among potential users (i.e., physicians and pharmacists). Furthermore, this study sought to collect data on the practicability and comprehensibility of potential layouts of a proposed personalized pocket card that is intended to not only contain the machine-readable data for use with the MSC system but also human-readable data on the patient's pharmacogenomic profile. Methods. We deployed an emergent mixed methods design encompassing (1) qualitative interviews with pharmacists and pharmacy students, (2) a survey among pharmacogenomics experts that included both qualitative and quantitative elements and (3) a quantitative survey among physicians and pharmacists. The interviews followed a semi-structured guide including a hypothetical patient scenario that had to be solved by using the MSC system. The survey among pharmacogenomics experts focused on what information should be printed on the card and how this information should be arranged. Furthermore, the MSC system was evaluated based on two hypothetical patient scenarios and four follow-up questions on the perceived usability. The second survey assessed physicians' and pharmacists' attitude towards the MSC system. Results. In total, 101 physicians, pharmacists and PGx experts coming from various relevant fields evaluated the MSC system. Overall, the reaction to the MSC system was positive across all investigated parameters and among all user groups. The majority of participants were able to solve the patient scenarios based on the

  4. Brief Survey of Operational Decision Support Systems

    DTIC Science & Technology

    1979-02-01

    aroumi the investigation of the life cycle evolution of the four specified experimental or operational DSS in terms of their operational...provided a rich source of raw- questionaire type data which reflects the opinions of individual users, technicians and managers (1). Further, a growing...four mature and widely publicized DSS over their individual system life cycles. This ap- - 10 - proach was adopted because the four systems of

  5. Introducing pharmacogenetic testing with clinical decision support into primary care: a feasibility study.

    PubMed

    Dawes, Martin; Aloise, Martin N; Ang, J Sidney; Cullis, Pieter; Dawes, Diana; Fraser, Robert; Liknaitzky, Gideon; Paterson, Andrea; Stanley, Paul; Suarez-Gonzalez, Adriana; Katzov-Eckert, Hagit

    2016-01-01

    Inappropriate prescribing increases patient illness and death owing to adverse drug events. The inclusion of genetic information into primary care medication practices is one solution. Our aim was to assess the ability to obtain and genotype saliva samples and to determine the levels of use of a decision support tool that creates medication options adjusted for patient characteristics, drug-drug interactions and pharmacogenetics. We conducted a cohort study in 6 primary care settings (5 family practices and 1 pharmacy), enrolling 191 adults with at least 1 of 10 common diseases. Saliva samples were obtained in the physician's office or pharmacy and sent to our laboratory, where DNA was extracted and genotyped and reports were generated. The reports were sent directly to the family physician/pharmacist and linked to an evidence-based prescribing decision support system. The primary outcome was ability to obtain and genotype samples. The secondary outcomes were yield and purity of DNA samples, ability to link results to decision support software and use of the decision support software. Genotyping resulted in linking of 189 patients (99%) with pharmacogenetic reports to the decision support program. A total of 96.8% of samples had at least 1 actionable genotype for medications included in the decision support system. The medication support system was used by the physicians and pharmacists 236 times over 3 months. Physicians and pharmacists can collect saliva samples of sufficient quantity and quality for DNA extraction, purification and genotyping. A clinical decision support system with integrated data from pharmacogenetic tests may enable personalized prescribing within primary care. Trial registration: ClinicalTrials.gov, NCT02383290.

  6. Prolog: A Practical Language for Decision Support Systems in Nursing?

    PubMed Central

    Ozbolt, Judy G.

    1987-01-01

    Developing decision support systems for nursing has been limited by difficulties in defining and representing nursing's knowledge base and by a lack of knowledge of how nurses make decisions. Recent theoretical and empirical work offers solutions to those problems. The challenge now is to represent nursing knowledge in a way that is comprehensible to both nurse and computer and to design decision support modalities that are accurate, efficient, and appropriate for nurses with different levels of expertise. This paper reviews the issues and critically evaluates Prolog as a tool for meeting the challenge.

  7. Using Visualization in Cockpit Decision Support Systems

    SciTech Connect

    Aragon, Cecilia R.

    2005-07-01

    Beamline 7.2 of the Advanced Light Source (ALS) at the Lawrence Berkeley National Laboratory (LBNL) is a beam diagnostics system that uses the synchrotron radiation emitted by a dipole magnet. It consists of two branches; in the first one the x-ray portion of the radiation is used in a pinhole camera system for measuring the transverse profile of the beam. The second branch is equipped with an x-ray beam position monitor (BPM) and with a multipurpose port where the visible and the far-infrared part of the radiation can be used for various applications such as bunch length measurements and IR coherent synchrotron radiation experiments. The pinhole system has been operating successfully since the end of 2003. The installation of the second branch has been completed recently and the results of its commissioning are presented in this paper together with examples of beam measurements performed at BL 7.2.

  8. Examining perceptions of the usefulness and usability of a mobile-based system for pharmacogenomics clinical decision support: a mixed methods study

    PubMed Central

    Blagec, Kathrin; Romagnoli, Katrina M.; Boyce, Richard D.

    2016-01-01

    Background. Pharmacogenomic testing has the potential to improve the safety and efficacy of pharmacotherapy, but clinical application of pharmacogenetic knowledge has remained uncommon. Clinical Decision Support (CDS) systems could help overcome some of the barriers to clinical implementation. The aim of this study was to evaluate the perception and usability of a web- and mobile-enabled CDS system for pharmacogenetics-guided drug therapy–the Medication Safety Code (MSC) system–among potential users (i.e., physicians and pharmacists). Furthermore, this study sought to collect data on the practicability and comprehensibility of potential layouts of a proposed personalized pocket card that is intended to not only contain the machine-readable data for use with the MSC system but also human-readable data on the patient’s pharmacogenomic profile. Methods. We deployed an emergent mixed methods design encompassing (1) qualitative interviews with pharmacists and pharmacy students, (2) a survey among pharmacogenomics experts that included both qualitative and quantitative elements and (3) a quantitative survey among physicians and pharmacists. The interviews followed a semi-structured guide including a hypothetical patient scenario that had to be solved by using the MSC system. The survey among pharmacogenomics experts focused on what information should be printed on the card and how this information should be arranged. Furthermore, the MSC system was evaluated based on two hypothetical patient scenarios and four follow-up questions on the perceived usability. The second survey assessed physicians’ and pharmacists’ attitude towards the MSC system. Results. In total, 101 physicians, pharmacists and PGx experts coming from various relevant fields evaluated the MSC system. Overall, the reaction to the MSC system was positive across all investigated parameters and among all user groups. The majority of participants were able to solve the patient scenarios based on the

  9. 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

  10. Developing a Decision Support System: The Software and Hardware Tools.

    ERIC Educational Resources Information Center

    Clark, Phillip M.

    1989-01-01

    Describes some of the available software and hardware tools that can be used to develop a decision support system implemented on microcomputers. Activities that should be supported by software are discussed, including data entry, data coding, finding and combining data, and data compatibility. Hardware considerations include speed, storage…

  11. A fuzzy expert system for diabetes decision support application.

    PubMed

    Lee, Chang-Shing; Wang, Mei-Hui

    2011-02-01

    An increasing number of decision support systems based on domain knowledge are adopted to diagnose medical conditions such as diabetes and heart disease. It is widely pointed that the classical ontologies cannot sufficiently handle imprecise and vague knowledge for some real world applications, but fuzzy ontology can effectively resolve data and knowledge problems with uncertainty. This paper presents a novel fuzzy expert system for diabetes decision support application. A five-layer fuzzy ontology, including a fuzzy knowledge layer, fuzzy group relation layer, fuzzy group domain layer, fuzzy personal relation layer, and fuzzy personal domain layer, is developed in the fuzzy expert system to describe knowledge with uncertainty. By applying the novel fuzzy ontology to the diabetes domain, the structure of the fuzzy diabetes ontology (FDO) is defined to model the diabetes knowledge. Additionally, a semantic decision support agent (SDSA), including a knowledge construction mechanism, fuzzy ontology generating mechanism, and semantic fuzzy decision making mechanism, is also developed. The knowledge construction mechanism constructs the fuzzy concepts and relations based on the structure of the FDO. The instances of the FDO are generated by the fuzzy ontology generating mechanism. Finally, based on the FDO and the fuzzy ontology, the semantic fuzzy decision making mechanism simulates the semantic description of medical staff for diabetes-related application. Importantly, the proposed fuzzy expert system can work effectively for diabetes decision support application.

  12. Decision Support Systems: A Preliminary Study,

    DTIC Science & Technology

    1977-09-01

    goal for data management research is an integrated data system -12- _ _...__ _ _ ... ’ . ENGLISH LOGIC FORMAL DATA LISP OR SUBSET (KOWALSKI LANG FOR...studies are indicated to determine if cannonical forms can be used to make vector operations out of operations like COND (from LISP ). Studies of the...W.W., Boyer, Robert S., and Henneman , William H., (1972), "Computer Proofs of Limits Theorems", A.I. Jour., 3, pp. 27-60. 12. Bledsoe, W.W. and

  13. The Contribution of a Decision Support System to Educational Decision-Making Processes

    ERIC Educational Resources Information Center

    Klein, Joseph; Ronen, Herman

    2003-01-01

    In the light of reports of bias, the present study investigated the hypothesis that administrative educational decisions assisted by Decision Support Systems (DSS) are characterized by different pedagogical and organizational orientation than decisions made without computer assistance. One hundred and ten high school teachers were asked to suggest…

  14. The Contribution of a Decision Support System to Educational Decision-Making Processes

    ERIC Educational Resources Information Center

    Klein, Joseph; Ronen, Herman

    2003-01-01

    In the light of reports of bias, the present study investigated the hypothesis that administrative educational decisions assisted by Decision Support Systems (DSS) are characterized by different pedagogical and organizational orientation than decisions made without computer assistance. One hundred and ten high school teachers were asked to suggest…

  15. Extracting clinical information to support medical decision based on standards.

    PubMed

    Gomoi, Valentin; Vida, Mihaela; Stoicu-Tivadar, Lăcrămioara; Stoicu-Tivadar, Vasile

    2011-01-01

    The paper presents a method connecting medical databases to a medical decision system, and describes a service created to extract the necessary information that is transferred based on standards. The medical decision can be improved based on many inputs from different medical locations. The developed solution is described for a concrete case concerning the management for chronic pelvic pain, based on the information retrieved from diverse healthcare databases.

  16. Application of GIS in foreign direct investment decision support system

    NASA Astrophysics Data System (ADS)

    Zhou, Jianlan; Sun, Koumei

    2007-06-01

    It is important to make decisions on how to attract foreign direct investment (FDI) to China and know how the inequality of FDI introduction by locational different provinces. Following background descriptions on China's FDI economic environments and FDI-related policies, this paper demonstrates the uses of geographical information system (GIS) and multi-criterion decision-making (MCDM) framework in solving a spatial multi-objective problem of evaluating and ranking China's provinces for FDI introduction. It implements a foreign direct investment decision support system, which reveals the main determinants of FDI in China and gives some results of regional geographical analysis over spatial data.

  17. On the heuristic nature of medical decision-support systems.

    PubMed

    Aliferis, C F; Miller, R A

    1995-03-01

    In the realm of medical decision-support systems, the term "heuristic systems" is often considered to be synonymous with "medical artificial intelligence systems" or with "systems employing informal model(s) of problem solving". Such a view may be inaccurate and possibly impede the conceptual development of future systems. This article examines the nature of heuristics and the levels at which heuristic solutions are introduced during system design and implementation. The authors discuss why heuristics are ubiquitous in all medical decision-support systems operating at non-trivial domains, and propose a unifying definition of heuristics that encompasses formal and ad hoc systems. System developers should be aware of the heuristic nature of all problem solving done in complex real world domains, and characterize their own use of heuristics in describing system development and implementation.

  18. Implementation problems of decision support system for nosocomial infection.

    PubMed

    Rems, M; Bohanec, M; Urh, B; Kramar, Z

    1997-01-01

    Decision support system for nosocomial infection therapy Ptah can reduce antibiotic misuse with data about bacteria resistance and antibiotic ineffectiveness. Resistance vectors in time series show epidemiological problems with resistant bacterias, named house-bacteria. Most important implementation factors are integrated hospital information system and doctors, nurses and managers interested in problems of nosocomial infection.

  19. Decision Support System for Disability Assessment and Intervention.

    ERIC Educational Resources Information Center

    Dowler, Denetta L.; And Others

    1991-01-01

    Constructed decision support system to aid referral of good candidates for rehabilitation from Social Security Administration to rehabilitation counselors. Three layers of system were gross screening based on policy guidelines, training materials, and interviews with experts; physical and mental functional capacity items derived from policy…

  20. Effects of a computerized provider order entry and a clinical decision support system to improve cefazolin use in surgical prophylaxis: a cost saving analysis.

    PubMed

    Okumura, Lucas M; Veroneze, Izelandia; Burgardt, Celia I; Fragoso, Marta F

    2016-01-01

    Computerized Provider Order Entry (CPOE) and Clinical Decision Support System (CDSS) help practitioners to choose evidence-based decisions, regarding patients' needs. Despite its use in developed countries, in Brazil, the impact of a CPOE/CDSS to improve cefazolin use in surgical prophylaxis was not assessed yet. We aimed to evaluate the impact of a CDSS to improve the use of prophylactic cefazolin and to assess the cost savings associated to inappropriate prescribing. This is a cross-sectional study that compared two different scenarios: one prior CPOE/CDSS versus after software implementation. We conducted twelve years of data analysis (3 years prior and 9 years after CDSS implementation), where main outcomes from this study included: cefazolin Defined Daily Doses/100 bed-days (DDD), crude costs and product of costs-DDD (cost-DDD/100 bed-days). We applied a Spearman rho non-parametric test to assess the reduction of cefazolin consumption through the years. In twelve years, 84,383 vials of cefazolin were dispensed and represented 38.89 DDD/100 bed-days or USD 44,722.99. Surgical wards were the largest drug prescribers and comprised >95% of our studied sample. While in 2002, there were 6.31 DDD/100 bed-days, 9 years later there was a reduction to 2.15 (p<0.05). In a scenario without CDSS, the hospital would have consumed 75.72 DDD/100 bed-days, which is equivalent to USD 116 998.07. It is estimated that CDSS provided USD 50,433.39 of cost savings. The implementation of a CPOE/CDSS helped to improve prophylactic cefazolin use by reducing its consumption and estimated direct costs.

  1. Effects of a computerized provider order entry and a clinical decision support system to improve cefazolin use in surgical prophylaxis: a cost saving analysis

    PubMed Central

    Veroneze, Izelandia; Burgardt, Celia I.; Fragoso, Marta F.

    2016-01-01

    Background: Computerized Provider Order Entry (CPOE) and Clinical Decision Support System (CDSS) help practitioners to choose evidence-based decisions, regarding patients’ needs. Despite its use in developed countries, in Brazil, the impact of a CPOE/CDSS to improve cefazolin use in surgical prophylaxis was not assessed yet. Objective: We aimed to evaluate the impact of a CDSS to improve the use of prophylactic cefazolin and to assess the cost savings associated to inappropriate prescribing. Methods: This is a cross-sectional study that compared two different scenarios: one prior CPOE/CDSS versus after software implementation. We conducted twelve years of data analysis (3 years prior and 9 years after CDSS implementation), where main outcomes from this study included: cefazolin Defined Daily Doses/100 bed-days (DDD), crude costs and product of costs-DDD (cost-DDD/100 bed-days). We applied a Spearman rho non-parametric test to assess the reduction of cefazolin consumption through the years. Results: In twelve years, 84,383 vials of cefazolin were dispensed and represented 38.89 DDD/100 bed-days or USD 44,722.99. Surgical wards were the largest drug prescribers and comprised >95% of our studied sample. While in 2002, there were 6.31 DDD/100 bed-days, 9 years later there was a reduction to 2.15 (p<0.05). In a scenario without CDSS, the hospital would have consumed 75.72 DDD/100 bed-days, which is equivalent to USD 116 998.07. It is estimated that CDSS provided USD 50,433.39 of cost savings. Conclusion: The implementation of a CPOE/CDSS helped to improve prophylactic cefazolin use by reducing its consumption and estimated direct costs. PMID:27785159

  2. The experience of physicians in pharmacogenomic clinical decision support within eight German university hospitals.

    PubMed

    Hinderer, Marc; Boeker, Martin; Wagner, Sebastian A; Binder, Harald; Ückert, Frank; Newe, Stephanie; Hülsemann, Jan L; Neumaier, Michael; Schade-Brittinger, Carmen; Acker, Till; Prokosch, Hans-Ulrich; Sedlmayr, Brita

    2017-06-01

    The aim of this study was to assess the physicians' attitude, their knowledge and their experience in pharmacogenomic clinical decision support in German hospitals. We conducted an online survey to address physicians of 13 different medical specialties across eight German university hospitals. In total, 564 returned questionnaires were analyzed. The remaining knowledge gap, the uncertainty of test reimbursement and the physicians' lack of awareness of existing pharmacogenomic clinical decision support systems (CDSS) are the major barriers for implementing pharmacogenomic CDSS into German hospitals. Furthermore, pharmacogenomic CDSS are most effective in the form of real-time decision support for internists. Physicians in German hospitals require additional education of both genetics and pharmacogenomics. They need to be provided with access to relevant pharmacogenomic CDSS.

  3. 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.

  4. Novel applications of intuitionistic fuzzy digraphs in decision support systems.

    PubMed

    Akram, Muhammad; Ashraf, Ather; Sarwar, Mansoor

    2014-01-01

    Many problems of practical interest can be modeled and solved by using graph algorithms. In general, graph theory has a wide range of applications in diverse fields. In this paper, the intuitionistic fuzzy organizational and neural network models, intuitionistic fuzzy neurons in medical diagnosis, intuitionistic fuzzy digraphs in vulnerability assessment of gas pipeline networks, and intuitionistic fuzzy digraphs in travel time are presented as examples of intuitionistic fuzzy digraphs in decision support system. We have also designed and implemented the algorithms for these decision support systems.

  5. Novel Applications of Intuitionistic Fuzzy Digraphs in Decision Support Systems

    PubMed Central

    Sarwar, Mansoor

    2014-01-01

    Many problems of practical interest can be modeled and solved by using graph algorithms. In general, graph theory has a wide range of applications in diverse fields. In this paper, the intuitionistic fuzzy organizational and neural network models, intuitionistic fuzzy neurons in medical diagnosis, intuitionistic fuzzy digraphs in vulnerability assessment of gas pipeline networks, and intuitionistic fuzzy digraphs in travel time are presented as examples of intuitionistic fuzzy digraphs in decision support system. We have also designed and implemented the algorithms for these decision support systems. PMID:25045752

  6. Web-LCCA: decision support system for military display acquisition

    NASA Astrophysics Data System (ADS)

    Binder, Michael L.; Calvo, Alberto B.; Gibson, Gregory J.

    2000-08-01

    This paper describes a Decision Support System for military display acquisition being developed under U.S. Display Consortium (USDC) sponsorship. The core of the system is a standard Life-Cycle Cost model. The system will use World Wide Web technology to make it widely accessible to Industry and Government Program Offices for use in the Display Acquisition Decision Process. Web-LCCA (Life-Cycle Cost Analyzer), a derivative of TASC's LCCATM, has been designed to aid in the evaluation of different Display System acquisition options. The target users of Web-LCCA are display vendors (Industry) and buyers (Government Program Offices). Web-LCCA will be USDC's standard tool for supporting cost tradeoffs and acquisition decisions among current operational displays and new flat panel display products.

  7. An integrated decision support system for diagnosing and managing patients with community-acquired pneumonia.

    PubMed Central

    Aronsky, D.; Haug, P. J.

    1999-01-01

    Decision support systems that integrate guidelines have become popular applications to reduce variation and deliver cost-effective care. However, adverse characteristics of decision support systems, such as additional and time-consuming data entry or manually identifying eligible patients, result in a "behavioral bottleneck" that prevents decision support systems to become part of the clinical routine. This paper describes the design and the implementation of an integrated decision support system that explores a novel approach for bypassing the behavioral bottleneck. The real-time decision support system does not require health care providers to enter additional data and consists of a diagnostic and a management component. Images Fig. 1 Fig. 2 Fig. 3 PMID:10566348

  8. Group Decision Support System applied to the medical pluri-disciplinary decision group: usability and efficacy.

    PubMed

    Degardin-Capon, Nathalie; Bricon-Souf, Nathalie; Beuscart-Zephir, Marie-Catherine; Beuscart, Régis

    2008-01-01

    This paper aims to study whether the application of a Group Decision Support System to medical collective decision committees is possible and to determine which GDSS specifications are convenient. We introduce the common knowledge about GDSS and define the process of the collective medical decision. An experimental GDSS has been tested in an actual medical collective decision committee. A usability analysis has been performed to precise usability and acceptability of the system and to highlight pro and cons of the various functionalities of the GDSS. Information sharing was conveniently supported by the GDSS. All the documents were available for the support of the discussion. But, the introduction of a GDSS in the decision committee added new constraints such as the necessity of an excellent preparation phase. Limits of the system have been revealed: lack of feedback on decision actors, lack of support to obtain the consensus and lack of memorisation. According to these results, we have proposed new GDSS features to improve the decision. Using a GDSS supporting the medical collective decision is realistic and may support the process of the consensual decision.

  9. The effect object paradigm--a means to support medication safety with clinical decision support.

    PubMed

    Patapovas, Andrius; Pfistermeister, Barbara; Tarkhov, Aleksey; Terfloth, Lothar; Maas, Renke; Fromm, Martin F; Kornhuber, Johannes; Prokosch, Hans-Ulrich; Bürkle, Thomas

    2014-01-01

    In many countries, officially approved drug information known as summary of product characteristics (SPC) is mostly available in text form, which cannot be used for Clinical Decision Support Systems (CDSS). It may be essential however to substantiate CDSS advice with such legally binding text snippets. In an attempt to link various drug data sources including SPC towards a CDSS to support medication safety in psychiatric patients we arrived at the notion of an effect object. A requirements analysis revealed data items and data structure which are needed from the patient and from the drug information source for the CDSS functionality. Published drug data modelling approaches were analyzed and found unsuitable. A conceptional database modeling approach using top down and bottom up modeling was performed. The schema based data model implemented within the django framework centered on SPC "effect objects" which comprise all SPC data required for the respective CDSS function such as search for contraindications in the proposed medication. Today six effect objects have been defined for contraindications and warnings, missing indications, adverse effects, drug-drug interactions, dosing and pharmacokinetics. The transformation of SPC data to a database-driven "effect objects" structure permits decoupling between the CDSS functions and different underlying data sources and supports the design of reusable, stable and verified CDSS functions.

  10. Systems Medicine for Multiple Myeloma: A Review on Decision Support Systems.

    PubMed

    Ganzinger, Matthias; Haux, Christian; Karmen, Christian; Wetter, Thomas; Knaup, Petra

    2015-01-01

    Systems medicine is a current approach trying to improve treatment for patients with complex diseases by analyzing as much phenotype and genotype data as possible for the disease in question. For individualized treatment decisions in clinical practice, this task has to be supported by an application system with decision support component. For a research project on systems medicine we reviewed methods for decision support. Criteria for selecting a method are derived from characteristics of the data and the diseases. They include, among others: dimensionality of data and existence of a priori models for diseases. As a result we decided to implement a prototype system with a case-based reasoning component for systems medicine on multiple myeloma.

  11. Clinical Decision Support using a Terminology Server to improve Patient Safety.

    PubMed

    Garcia-Jimenez, Alba; Moreno-Conde, Alberto; Martínez-García, Alicia; Marín-León, Ignacio; Medrano-Ortega, Francisco Javier; Parra-Calderón, Carlos L

    2015-01-01

    Clinical Decision Support Systems (CDSS) are software applications that support clinicians in making healthcare decisions providing relevant information for individual patients about their specific conditions. The lack of integration between CDSS and Electronic Health Record (EHR) has been identified as a significant barrier to CDSS development and adoption. Andalusia Healthcare Public System (AHPS) provides an interoperable health information infrastructure based on a Service Oriented Architecture (SOA) that eases CDSS implementation. This paper details the deployment of a CDSS jointly with the deployment of a Terminology Server (TS) within the AHPS infrastructure. It also explains a case study about the application of decision support to thromboembolism patients and its potential impact on improving patient safety. We will apply the inSPECt tool proposal to evaluate the appropriateness of alerts in this scenario.

  12. Consensus Recommendations for Systematic Evaluation of Drug-Drug Interaction Evidence for Clinical Decision Support

    PubMed Central

    Scheife, Richard T.; Hines, Lisa E.; Boyce, Richard D.; Chung, Sophie P.; Momper, Jeremiah; Sommer, Christine D.; Abernethy, Darrell R.; Horn, John; Sklar, Stephen J.; Wong, Samantha K.; Jones, Gretchen; Brown, Mary; Grizzle, Amy J.; Comes, Susan; Wilkins, Tricia Lee; Borst, Clarissa; Wittie, Michael A.; Rich, Alissa; Malone, Daniel C.

    2015-01-01

    Background Healthcare organizations, compendia, and drug knowledgebase vendors use varying methods to evaluate and synthesize evidence on drug-drug interactions (DDIs). This situation has a negative effect on electronic prescribing and medication information systems that warn clinicians of potentially harmful medication combinations. Objective To provide recommendations for systematic evaluation of evidence from the scientific literature, drug product labeling, and regulatory documents with respect to DDIs for clinical decision support. Methods A conference series was conducted to develop a structured process to improve the quality of DDI alerting systems. Three expert workgroups were assembled to address the goals of the conference. The Evidence Workgroup consisted of 15 individuals with expertise in pharmacology, drug information, biomedical informatics, and clinical decision support. Workgroup members met via webinar from January 2013 to February 2014. Two in-person meetings were conducted in May and September 2013 to reach consensus on recommendations. Results We developed expert-consensus answers to three key questions: 1) What is the best approach to evaluate DDI evidence?; 2) What evidence is required for a DDI to be applicable to an entire class of drugs?; and 3) How should a structured evaluation process be vetted and validated? Conclusion Evidence-based decision support for DDIs requires consistent application of transparent and systematic methods to evaluate the evidence. Drug information systems that implement these recommendations should be able to provide higher quality information about DDIs in drug compendia and clinical decision support tools. PMID:25556085

  13. A clinical decision support system for the diagnosis of probable migraine and probable tension-type headache based on case-based reasoning.

    PubMed

    Yin, Ziming; Dong, Zhao; Lu, Xudong; Yu, Shengyuan; Chen, Xiaoyan; Duan, Huilong

    2015-04-01

    The overlap between probable migraine (PM) and probable tension-type headache (PTTH) often confuses physicians in clinical practice. Although clinical decision support systems (CDSSs) have been proven to be helpful in the diagnosis of primary headaches, the existing guideline-based headache disorder CDSSs do not perform adequately due to this overlapping issue. Thus, in this study, a CDSS based on case-based reasoning (CBR) was developed in order to solve this problem. First, a case library consisting of 676 clinical cases, 56.95% of which had been diagnosed with PM and 43.05% of which had been diagnosed with PTTH, was constructed, screened by a three-member panel, and weighted by engineers. Next, the resulting case library was used to diagnose current cases based on their similarities to the previous cases. The test dataset was composed of an additional 222 historical cases, 76.1% of which had been diagnosed with PM and 23.9% of which had been diagnosed with PTTH. The cases that comprised the case library as well as the test dataset were actual clinical cases obtained from the International Headache Center in Chinese PLA General Hospital. The results indicated that the PM and PTTH recall rates were equal to 97.02% and 77.78%, which were 34.31% and 16.91% higher than that of the guideline-based CDSS, respectively. Furthermore, the PM and PTTH precision rates were equal to 93.14% and 89.36%, which were7.09% and 15.68% higher than that of the guideline-based CDSS, respectively. Comparing CBR CDSS and guideline-based CDSS, the p-value of PM diagnoses was equal to 0.019, while that of PTTH diagnoses was equal to 0.002, which indicated that there was a significant difference between the two approaches. The experimental results indicated that the CBR CDSS developed in this study diagnosed PM and PTTH with a high degree of accuracy and performed better than the guideline-based CDSS. This system could be used as a diagnostic tool to assist general practitioners in

  14. Integrating Biomarkers for Underlying Alzheimer's Disease in Mild Cognitive Impairment in Daily Practice: Comparison of a Clinical Decision Support System with Individual Biomarkers.

    PubMed

    Rhodius-Meester, Hanneke F M; Koikkalainen, Juha; Mattila, Jussi; Teunissen, Charlotte E; Barkhof, Frederik; Lemstra, Afina W; Scheltens, Philip; Lötjönen, Jyrki; van der Flier, Wiesje M

    2016-01-01

    Recent criteria allow biomarkers to provide evidence of Alzheimer's disease (AD) pathophysiology. How they should be implemented in daily practice remains unclear, especially in mild cognitive impairment (MCI) patients. We evaluated how a clinical decision support system such as the PredictAD tool can aid clinicians to integrate biomarker evidence to support AD diagnosis. With available data on demographics, cerebrospinal fluid (CSF), and MRI, we trained the PredictAD tool on a reference population of 246 controls and 491 AD patients. We then applied the identified algorithm to 211 MCI patients. For comparison, we also classified patients based on individual biomarkers (MRI; CSF) and the NIA-AA criteria. Progression to dementia was used as outcome measure. After a median follow up of 3 years, 72 (34%) MCI patients remained stable and 139 (66%) progressed to AD. The PredictAD tool assigned a likelihood of underlying AD to each patient (AUC 0.82). Excluding patients with missing data resulted in an AUC of 0.87. According to the NIA-AA criteria, half of the MCI patients had uninformative biomarkers, precluding an assignment of AD likelihood. A minority (41%) was assigned to high or low AD likelihood with good predictive value. The individual biomarkers showed best value for CSF total tau (AUC 0.86). The ability of the PredictAD tool to identify AD pathophysiology was comparable to individual biomarkers. The PredictAD tool has the advantage that it assigns likelihood to all patients, regardless of missing or conflicting data, allowing clinicians to integrate biomarker data in daily practice.

  15. Does clinical decision support reduce unwarranted variation in yield of CT pulmonary angiogram?

    PubMed

    Prevedello, Luciano M; Raja, Ali S; Ip, Ivan K; Sodickson, Aaron; Khorasani, Ramin

    2013-11-01

    The study objective was to determine whether previously documented effects of clinical decision support on computed tomography for pulmonary embolism in the emergency department (ie, decreased use and increased yield) are due to a decrease in unwarranted variation. We evaluated clinical decision support effect on intra- and inter-physician variability in the yield of pulmonary embolism computed tomography (PE-CT) in this setting. The study was performed in an academic adult medical center emergency department with 60,000 annual visits. We enrolled all patients who had PE-CT performed 18 months pre- and post-clinical decision support implementation. Intra- and inter-physician variability in yield (% PE-CT positive for acute pulmonary embolism) were assessed. Yield variability was measured using logistic regression accounting for patient characteristics. A total of 1542 PE-CT scans were performed before clinical decision support, and 1349 PE-CT scans were performed after clinical decision support. Use of PE-CT decreased from 26.5 to 24.3 computed tomography scans/1000 patient visits after clinical decision support (P < .02); yield increased from 9.2% to 12.6% (P < .01). Crude inter-physician variability in yield ranged from 2.6% to 20.5% before clinical decision support and from 0% to 38.1% after clinical decision support. After controlling for patient characteristics, the post-clinical decision support period showed significant inter-physician variability (P < .04). Intra-physician variability was significant in 3 of the 25 physicians (P < .04), all with increased yield post-clinical decision support. Overall PE-CT yield increased after clinical decision support implementation despite significant heterogeneity among physicians. Increased inter-physician variability in yield after clinical decision support was not explained by patient characteristics alone and may be due to variable physician acceptance of clinical decision support. Clinical decision support alone is

  16. Costs Associated with Implementation of Computer-Assisted Clinical Decision Support System for Antenatal and Delivery Care: Case Study of Kassena-Nankana District of Northern Ghana

    PubMed Central

    Dalaba, Maxwell Ayindenaba; Akweongo, Patricia; Williams, John; Saronga, Happiness Pius; Tonchev, Pencho; Sauerborn, Rainer; Mensah, Nathan; Blank, Antje; Kaltschmidt, Jens; Loukanova, Svetla

    2014-01-01

    Objective This study analyzed cost of implementing computer-assisted Clinical Decision Support System (CDSS) in selected health care centres in Ghana. Methods A descriptive cross sectional study was conducted in the Kassena-Nankana district (KND). CDSS was deployed in selected health centres in KND as an intervention to manage patients attending antenatal clinics and the labour ward. The CDSS users were mainly nurses who were trained. Activities and associated costs involved in the implementation of CDSS (pre-intervention and intervention) were collected for the period between 2009–2013 from the provider perspective. The ingredients approach was used for the cost analysis. Costs were grouped into personnel, trainings, overheads (recurrent costs) and equipment costs (capital cost). We calculated cost without annualizing capital cost to represent financial cost and cost with annualizing capital costs to represent economic cost. Results Twenty-two trained CDSS users (at least 2 users per health centre) participated in the study. Between April 2012 and March 2013, users managed 5,595 antenatal clients and 872 labour clients using the CDSS. We observed a decrease in the proportion of complications during delivery (pre-intervention 10.74% versus post-intervention 9.64%) and a reduction in the number of maternal deaths (pre-intervention 4 deaths versus post-intervention 1 death). The overall financial cost of CDSS implementation was US$23,316, approximately US$1,060 per CDSS user trained. Of the total cost of implementation, 48% (US$11,272) was pre-intervention cost and intervention cost was 52% (US$12,044). Equipment costs accounted for the largest proportion of financial cost: 34% (US$7,917). When economic cost was considered, total cost of implementation was US$17,128–lower than the financial cost by 26.5%. Conclusions The study provides useful information in the implementation of CDSS at health facilities to enhance health workers' adherence to practice guidelines

  17. A knowledge-based decision support system for payload scheduling

    NASA Technical Reports Server (NTRS)

    Tyagi, Rajesh; Tseng, Fan T.

    1988-01-01

    This paper presents the development of a prototype Knowledge-based Decision Support System, currently under development, for scheduling payloads/experiments on space station missions. The DSS is being built on Symbolics, a Lisp machine, using KEE, a commercial knowledge engineering tool.

  18. A Decision Support System for Solving Multiple Criteria Optimization Problems

    ERIC Educational Resources Information Center

    Filatovas, Ernestas; Kurasova, Olga

    2011-01-01

    In this paper, multiple criteria optimization has been investigated. A new decision support system (DSS) has been developed for interactive solving of multiple criteria optimization problems (MOPs). The weighted-sum (WS) approach is implemented to solve the MOPs. The MOPs are solved by selecting different weight coefficient values for the criteria…

  19. Decision support system for material handling and packaging design

    NASA Astrophysics Data System (ADS)

    Johnsson, Mats I.; Mazouz, Abdel K.; Han, Chingping

    1992-02-01

    The reliability of the materials handling process involving automated stacking of packages on a pallet or automated sorting of packages in a distribution system depends mainly on the design of the package and the material used for the package. Many problems can be eliminated that result in a higher utilization of the system if the package is designed not only for the product and its requirements but also for an automated handling system with different types of grasping devices. A decision support system is being developed to help the package designer select the most appropriate material and design to satisfy the requirements of the automated materials handling process. The decision support system is programmed in C++ which gives the flexibility and portability needed for this type of system. The user interface is using graphics to ease the understanding of different design options during the selection process.

  20. Behavior-aware decision support systems : LDRD final report.

    SciTech Connect

    Hirsch, Gary B.; Homer, Jack; Chenoweth, Brooke N.; Backus, George A.; Strip, David R.

    2007-11-01

    As Sandia National Laboratories serves its mission to provide support for the security-related interests of the United States, it is faced with considering the behavioral responses that drive problems, mitigate interventions, or lead to unintended consequences. The effort described here expands earlier works in using healthcare simulation to develop behavior-aware decision support systems. This report focuses on using qualitative choice techniques and enhancing two analysis models developed in a sister project.

  1. Ecological Assessment of Clinicians’ Antipsychotic Prescription Habits in Psychiatric Inpatients: A Novel Web- and Mobile Phone–Based Prototype for a Dynamic Clinical Decision Support System

    PubMed Central

    Barrigón, Maria Luisa; Brandt, Sara A; Nitzburg, George C; Ovejero, Santiago; Alvarez-Garcia, Raquel; Carballo, Juan; Walter, Michel; Billot, Romain; Lenca, Philippe; Delgado-Gomez, David; Ropars, Juliette; de la Calle Gonzalez, Ivan; Courtet, Philippe; Baca-García, Enrique

    2017-01-01

    Background Electronic prescribing devices with clinical decision support systems (CDSSs) hold the potential to significantly improve pharmacological treatment management. Objective The aim of our study was to develop a novel Web- and mobile phone–based application to provide a dynamic CDSS by monitoring and analyzing practitioners’ antipsychotic prescription habits and simultaneously linking these data to inpatients’ symptom changes. Methods We recruited 353 psychiatric inpatients whose symptom levels and prescribed medications were inputted into the MEmind application. We standardized all medications in the MEmind database using the Anatomical Therapeutic Chemical (ATC) classification system and the defined daily dose (DDD). For each patient, MEmind calculated an average for the daily dose prescribed for antipsychotics (using the N05A ATC code), prescribed daily dose (PDD), and the PDD to DDD ratio. Results MEmind results found that antipsychotics were used by 61.5% (217/353) of inpatients, with the largest proportion being patients with schizophrenia spectrum disorders (33.4%, 118/353). Of the 217 patients, 137 (63.2%, 137/217) were administered pharmacological monotherapy and 80 (36.8%, 80/217) were administered polytherapy. Antipsychotics were used mostly in schizophrenia spectrum and related psychotic disorders, but they were also prescribed in other nonpsychotic diagnoses. Notably, we observed polypharmacy going against current antipsychotics guidelines. Conclusions MEmind data indicated that antipsychotic polypharmacy and off-label use in inpatient units is commonly practiced. MEmind holds the potential to create a dynamic CDSS that provides real-time tracking of prescription practices and symptom change. Such feedback can help practitioners determine a maximally therapeutic drug treatment while avoiding unproductive overprescription and off-label use. PMID:28126703

  2. Ecological Assessment of Clinicians' Antipsychotic Prescription Habits in Psychiatric Inpatients: A Novel Web- and Mobile Phone-Based Prototype for a Dynamic Clinical Decision Support System.

    PubMed

    Berrouiguet, Sofian; Barrigón, Maria Luisa; Brandt, Sara A; Nitzburg, George C; Ovejero, Santiago; Alvarez-Garcia, Raquel; Carballo, Juan; Walter, Michel; Billot, Romain; Lenca, Philippe; Delgado-Gomez, David; Ropars, Juliette; de la Calle Gonzalez, Ivan; Courtet, Philippe; Baca-García, Enrique

    2017-01-26

    Electronic prescribing devices with clinical decision support systems (CDSSs) hold the potential to significantly improve pharmacological treatment management. The aim of our study was to develop a novel Web- and mobile phone-based application to provide a dynamic CDSS by monitoring and analyzing practitioners' antipsychotic prescription habits and simultaneously linking these data to inpatients' symptom changes. We recruited 353 psychiatric inpatients whose symptom levels and prescribed medications were inputted into the MEmind application. We standardized all medications in the MEmind database using the Anatomical Therapeutic Chemical (ATC) classification system and the defined daily dose (DDD). For each patient, MEmind calculated an average for the daily dose prescribed for antipsychotics (using the N05A ATC code), prescribed daily dose (PDD), and the PDD to DDD ratio. MEmind results found that antipsychotics were used by 61.5% (217/353) of inpatients, with the largest proportion being patients with schizophrenia spectrum disorders (33.4%, 118/353). Of the 217 patients, 137 (63.2%, 137/217) were administered pharmacological monotherapy and 80 (36.8%, 80/217) were administered polytherapy. Antipsychotics were used mostly in schizophrenia spectrum and related psychotic disorders, but they were also prescribed in other nonpsychotic diagnoses. Notably, we observed polypharmacy going against current antipsychotics guidelines. MEmind data indicated that antipsychotic polypharmacy and off-label use in inpatient units is commonly practiced. MEmind holds the potential to create a dynamic CDSS that provides real-time tracking of prescription practices and symptom change. Such feedback can help practitioners determine a maximally therapeutic drug treatment while avoiding unproductive overprescription and off-label use.

  3. Experience with a clinical decision support system in community pharmacies to recommend narrow-spectrum antimicrobials, nonantimicrobial prescriptions, and OTC products to decrease broad-spectrum antimicrobial use.

    PubMed

    Madaras-Kelly, Karl J; Hannah, Elizabeth Lyon; Bateman, Kim; Samore, Matthew H

    2006-06-01

    Overuse of antibiotics increases the incidence of bacterial resistance and contributes avoidable costs to the health care system. To determine the feasibility of a protocol-driven community pharmacy intervention that was designed to decrease broad-spectrum antimicrobial (BSA) use in patients with upper respiratory tract infections. The intervention involved pharmacists who conducted guided interviews regarding patient symptoms in a cohort of patients with BSA prescription visiting 2 rural community pharmacies during peak respiratory illness season. A clinical decision support system was provided to aid in pharmacist diagnosis and assist in determining if the BSA therapy was appropriate. Upon patient consent, pharmacists attempted to contact primary care providers (PCPs) to confirm the diagnosis and recommend appropriate alternative therapy. There were 192 subjects with prescriptions for BSAs and symptoms of respiratory tract infection. Only 3% of the patients who were approached declined to discuss their symptoms and treatment with the pharmacist. A mean of 3 minutes was required to collect symptom and treatment information from the patients. However, when patients were asked if the pharmacist could contact their PCP to recommend alternative therapy, only 7% (n=4) of patients agreed to the intervention. The PCPs who were contacted by pharmacists were receptive to altering the BSA to first-line antimicrobial therapy such as amoxicillin or doxycycline. Despite a description of the importance of the intervention, more than 90% of patients prescribed a BSA declined to permit the community pharmacist to contact the prescriber to discuss first-line therapeutic alternatives. This experience in a pilot study to explore the feasibility of pharmacist intervention at the point of dispensing of a BSA made clear that a successful community pharmacy intervention to reduce BSA use would require an alternative method, perhaps via a collaborative practice protocol that does not

  4. VisualDecisionLinc: a visual analytics approach for comparative effectiveness-based clinical decision support in psychiatry.

    PubMed

    Mane, Ketan K; Bizon, Chris; Schmitt, Charles; Owen, Phillips; Burchett, Bruce; Pietrobon, Ricardo; Gersing, Kenneth

    2012-02-01

    Comparative Effectiveness Research (CER) is designed to provide research evidence on the effectiveness and risks of different therapeutic options on the basis of data compiled from subpopulations of patients with similar medical conditions. Electronic Health Record (EHR) system contain large volumes of patient data that could be used for CER, but the data contained in EHR system are typically accessible only in formats that are not conducive to rapid synthesis and interpretation of therapeutic outcomes. In the time-pressured clinical setting, clinicians faced with large amounts of patient data in formats that are not readily interpretable often feel 'information overload'. Decision support tools that enable rapid access at the point of care to aggregate data on the most effective therapeutic outcomes derived from CER would greatly aid the clinical decision-making process and individualize patient care. In this manuscript, we highlight the role that visual analytics can play in CER-based clinical decision support. We developed a 'VisualDecisionLinc' (VDL) tool prototype that uses visual analytics to provide summarized CER-derived data views to facilitate rapid interpretation of large amounts of data. We highlight the flexibility that visual analytics offers to gain an overview of therapeutic options and outcomes and if needed, to instantly customize the evidence to the needs of the patient or clinician. The VDL tool uses visual analytics to help the clinician evaluate and understand the effectiveness and risk of different therapeutic options for different subpopulations of patients. Copyright © 2011 Elsevier Inc. All rights reserved.

  5. A decision support system to determine optimal ventilator settings.

    PubMed

    Akbulut, Fatma Patlar; Akkur, Erkan; Akan, Aydin; Yarman, B Siddik

    2014-01-10

    Choosing the correct ventilator settings for the treatment of patients with respiratory tract disease is quite an important issue. Since the task of specifying the parameters of ventilation equipment is entirely carried out by a physician, physician's knowledge and experience in the selection of these settings has a direct effect on the accuracy of his/her decisions. Nowadays, decision support systems have been used for these kinds of operations to eliminate errors. Our goal is to minimize errors in ventilation therapy and prevent deaths caused by incorrect configuration of ventilation devices. The proposed system is designed to assist less experienced physicians working in the facilities without having lung mechanics like cottage hospitals. This article describes a decision support system proposing the ventilator settings required to be applied in the treatment according to the patients' physiological information. The proposed model has been designed to minimize the possibility of making a mistake and to encourage more efficient use of time in support of the decision making process while the physicians make critical decisions about the patient. Artificial Neural Network (ANN) is implemented in order to calculate frequency, tidal volume, FiO2 outputs, and this classification model has been used for estimation of pressure support / volume support outputs. For the obtainment of the highest performance in both models, different configurations have been tried. Various tests have been realized for training methods, and a number of hidden layers mostly affect factors regarding the performance of ANNs. The physiological information of 158 respiratory patients over the age of 60 and were treated in three different hospitals between the years 2010 and 2012 has been used in the training and testing of the system. The diagnosed disease, core body temperature, pulse, arterial systolic pressure, diastolic blood pressure, PEEP, PSO2, pH, pCO2, bicarbonate data as well as the

  6. Clinical decision support software for diabetic foot risk stratification: development and formative evaluation.

    PubMed

    Schoen, Deborah E; Glance, David G; Thompson, Sandra C

    2015-01-01

    Identifying people at risk of developing diabetic foot complications is a vital step in prevention programs in primary healthcare settings. Diabetic foot risk stratification systems predict foot ulceration. The aim of this study was to explore the views and experiences of potential end users during development and formative evaluations of an electronic diabetic foot risk stratification tool based on evidence-based guidelines and determine the accuracy of the tool. Formative evaluation of the risk tool occurred in five stages over an eight-month period and employed a mixed methods research design consisting of semi-structured interviews, focus group and participant observation, online survey, expert review, comparison to the Australian Guidelines and clinical testing. A total of 43 healthcare practitioners trialled the computerised clinical decision support system during development, with multiple software changes made as a result of feedback. Individual and focus group participants exposed critical design flaws. Live testing revealed risk stratification errors and functional limitations providing the basis for practical improvements. In the final product, all risk calculations and recommendations made by the clinical decision support system reflect current Australian Guidelines. Development of the computerised clinical decision support system using evidence-based guidelines can be optimised by a multidisciplinary iterative process of feedback, testing and software adaptation by experts in modern development technologies.

  7. Decision Support Systems and Line Performance: Case of Gold Coast University Hospital.

    PubMed

    Connor, Martin; Ghapanchi, Amir Hossein; Blumenstein, Michael; Amrollahi, Alireza; Najaftorkaman, Mohammadreza

    2015-01-01

    Computer-based decision support information systems have been promoted for their potential to improve physician performance and patient outcomes and support clinical decision making. The current case study reported design and implementation of a high-level decision support system (DSS) which facilitated the flow of data from operational level to top managers and leadership level of hospitals. The results shows that development of a DSS improve data connectivity, timing, and responsiveness issues via centralised sourcing and storing of principal health-related information in the hospital. The implementation of the system has resulted in significant enhancements in outpatient waiting times management.

  8. Engineering of a Clinical Decision Support Framework for the Point of Care Use

    PubMed Central

    Wilk, Szymon; Michalowski, Wojtek; O’Sullivan, Dympna; Farion, Ken; Matwin, Stan

    2008-01-01

    Computerized decision support for use at the point of care has to be comprehensive. It means that clinical information stored in electronic health records needs to be integrated with various forms of clinical knowledge (elicited from experts, discovered from data or summarized in systematic reviews of clinical trials). In order to provide such comprehensive support we created the MET-A3Support framework for constructing clinical applications aimed at various medical conditions. We employed the multiagent system paradigm and the O-MaSE methodology to define an engineering process involving three main activities: requirements engineering, analysis and design. Then we applied the process to build MET-A3Support. The paper describes the engineering process and its results, including models representing selected elements of our framework. PMID:18999068

  9. Engineering of a clinical decision support framework for the point of care use.

    PubMed

    Wilk, Szymon; Michalowski, Wojtek; O'Sullivan, Dympna; Farion, Ken; Matwin, Stan

    2008-11-06

    Computerized decision support for use at the point of care has to be comprehensive. It means that clinical information stored in electronic health records needs to be integrated with various forms of clinical knowledge (elicited from experts, discovered from data or summarized in systematic reviews of clinical trials). In order to provide such comprehensive support we created the MET-A3Support framework for constructing clinical applications aimed at various medical conditions. We employed the multiagent system paradigm and the O-MaSE methodology to define an engineering process involving three main activities: requirements engineering, analysis and design. Then we applied the process to build MET-A3Support. The paper describes the engineering process and its results, including models representing selected elements of our framework.

  10. A Decision Support System for Optimum Use of Fertilizers

    SciTech Connect

    R. L. Hoskinson; J. R. Hess; R. K. Fink

    1999-07-01

    The Decision Support System for Agriculture (DSS4Ag) is an expert system being developed by the Site-Specific Technologies for Agriculture (SST4Ag) precision farming research project at the INEEL. DSS4Ag uses state-of-the-art artificial intelligence and computer science technologies to make spatially variable, site-specific, economically optimum decisions on fertilizer use. The DSS4Ag has an open architecture that allows for external input and addition of new requirements and integrates its results with existing agricultural systems' infrastructures. The DSS4Ag reflects a paradigm shift in the information revolution in agriculture that is precision farming. We depict this information revolution in agriculture as an historic trend in the agricultural decision-making process.

  11. 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.

  12. Assessment and improvement of HIV screening rates in a Midwest primary care practice using an electronic clinical decision support system: a quality improvement study.

    PubMed

    Marcelin, Jasmine R; Tan, Eugene M; Marcelin, Alberto; Scheitel, Marianne; Ramu, Praveen; Hankey, Ronald; Keniya, Pritesh; Wingo, Majken; Rizza, Stacey A; North, Frederick; Chaudhry, Rajeev

    2016-07-04

    Universal human immunodeficiency virus (HIV) screening remains low in many clinical practices despite published guidelines recommending screening for all patients between ages 13-65. Electronic clinical decision support tools have improved screening rates for many chronic diseases. We designed a quality improvement project to improve the rate of universal HIV screening of adult patients in a Midwest primary care practice using a clinical decision support tool. We conducted this quality improvement project in Rochester, Minnesota from January 1, 2014 to December 31, 2014. Baseline primary care practice HIV screening data were acquired from January 1, 2014 to April 30, 2014. We surveyed providers and educated them about current CDC recommended screening guidelines. We then added an HIV screening alert to an existing electronic clinical decision support tool and post-intervention HIV screening rates were obtained from May 1, 2014 to December 31, 2014. The primary quality outcome being assessed was change in universal HIV screening rates. Twelve thousand five hundred ninety-six unique patients were eligible for HIV screening in 2014; 327 were screened for HIV. 6,070 and 6,526 patients were seen before and after the intervention, respectively. 1.80 % of eligible patients and 3.34 % of eligible patients were screened prior to and after the intervention, respectively (difference of -1.54 % [-2.1 %, -0.99 %], p < 0.0001); OR 1.89 (1.50, 2.38). Prior to the intervention, African Americans were more likely to have been screened for HIV (OR 3.86 (2.22, 6.71; p < 0.001) than Whites, but this effect decreased significantly after the intervention (OR 1.90 (1.12, 3.21; p = 0.03). These data showed that an electronic alert almost doubled the rates of universal HIV screening by primary care providers in a Midwestern practice and reduced racial disparities, but there is still substantial room for improvement in universal screening practices. Opportunities for

  13. Journal club: Requiring clinical justification to override repeat imaging decision support: impact on CT use.

    PubMed

    O'Connor, Stacy D; Sodickson, Aaron D; Ip, Ivan K; Raja, Ali S; Healey, Michael J; Schneider, Louise I; Khorasani, Ramin

    2014-11-01

    The purpose of this study was to determine the impact of requiring clinical justification to override decision support alerts on repeat use of CT. This before and after intervention study was conducted at a 793-bed tertiary hospital with computerized physician order entry and clinical decision support systems. When a CT order is placed, decision support alerts the orderer if the patient's same body part has undergone CT within the past 90 days. The study cohort included all 28,420 CT orders triggering a repeat alert in 2010. The intervention required clinical justification, selected from a predetermined menu, to override repeat CT decision support alerts to place a CT order; otherwise the order could not be placed and was dropped. The primary outcome, dropped repeat CT orders, was analyzed using three methods: chi-square tests to compare proportions dropped before and after intervention; multiple logistic regression tests to control for orderer, care setting, and patient factors; and statistical process control for temporal trends. The repeat CT order drop rate had an absolute increase of 1.4%; 6.1% (682/11,230) before to 7.5% (1290/17,190) after intervention, which was a 23% relative change (7.5 - 6.1)/6.1 × 100 = 23%; p < 0.0001). Orders were dropped more often after intervention (odds ratio, 1.3; 95% CI, 1.1-1.4; p < 0.0001). Statistical control analysis supported the association between the increase in the drop rate with intervention rather than underlying trends. Adding a requirement for clinical justification to override alerts modestly but significantly improves the impact of repeat CT decision support (23% relative change), with the overall effect of preventing one in 13 repeat CT orders.

  14. 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

  15. 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.

  16. A Computerized Decision Support System for Depression in Primary Care

    PubMed Central

    Kurian, Benji T.; Trivedi, Madhukar H.; Grannemann, Bruce D.; Claassen, Cynthia A.; Daly, Ella J.; Sunderajan, Prabha

    2009-01-01

    Objective: In 2004, results from The Texas Medication Algorithm Project (TMAP) showed better clinical outcomes for patients whose physicians adhered to a paper-and-pencil algorithm compared to patients who received standard clinical treatment for major depressive disorder (MDD). However, implementation of and fidelity to the treatment algorithm among various providers was observed to be inadequate. A computerized decision support system (CDSS) for the implementation of the TMAP algorithm for depression has since been developed to improve fidelity and adherence to the algorithm. Method: This was a 2-group, parallel design, clinical trial (one patient group receiving MDD treatment from physicians using the CDSS and the other patient group receiving usual care) conducted at 2 separate primary care clinics in Texas from March 2005 through June 2006. Fifty-five patients with MDD (DSM-IV criteria) with no significant difference in disease characteristics were enrolled, 32 of whom were treated by physicians using CDSS and 23 were treated by physicians using usual care. The study's objective was to evaluate the feasibility and efficacy of implementing a CDSS to assist physicians acutely treating patients with MDD compared to usual care in primary care. Primary efficacy outcomes for depression symptom severity were based on the 17-item Hamilton Depression Rating Scale (HDRS17) evaluated by an independent rater. Results: Patients treated by physicians employing CDSS had significantly greater symptom reduction, based on the HDRS17, than patients treated with usual care (P < .001). Conclusions: The CDSS algorithm, utilizing measurement-based care, was superior to usual care for patients with MDD in primary care settings. Larger randomized controlled trials are needed to confirm these findings. Trial Registration: clinicaltrials.gov Identifier: NCT00551083 PMID:19750065

  17. A Decision Support System for Concrete Bridge Maintenance

    NASA Astrophysics Data System (ADS)

    Rashidi, Maria; Lemass, Brett; Gibson, Peter

    2010-05-01

    The maintenance of bridges as a key element in transportation infrastructure has become a major concern for asset managers and society due to increasing traffic volumes, deterioration of existing bridges and well-publicised bridge failures. A pivotal responsibility for asset managers in charge of bridge remediation is to identify the risks and assess the consequences of remediation programs to ensure that the decisions are transparent and lead to the lowest predicted losses in recognized constraint areas. The ranking of bridge remediation treatments can be quantitatively assessed using a weighted constraint approach to structure the otherwise ill-structured phases of problem definition, conceptualization and embodiment [1]. This Decision Support System helps asset managers in making the best decision with regards to financial limitations and other dominant constraints imposed upon the problem at hand. The risk management framework in this paper deals with the development of a quantitative intelligent decision support system for bridge maintenance which has the ability to provide a source for consistent decisions through selecting appropriate remediation treatments based upon cost, service life, product durability/sustainability, client preferences, legal and environmental constraints. Model verification and validation through industry case studies is ongoing.

  18. Do false positive alerts in naïve clinical decision support system lead to false adoption by physicians? A randomized controlled trial.

    PubMed

    Tsai, Chung-You; Wang, Shi-Heng; Hsu, Min-Huei; Li, Yu-Chuan Jack

    2016-08-01

    False positive alerts in patient-safety-related clinical decision support systems (CDSS) are defined as alerts which incorrectly prompt when no-risk patients are encountered. It is an unfavorable condition which may potentially mislead physicians. The aim is to investigate physician responses toward false positive (FP) and true positive (TP) alerts in CDSS for the prevention of contrast-induced nephropathy (CIN). A two-arm cluster randomized controlled trial was conducted in university hospitals. Eligible physicians were randomized to receive alert intervention or no intervention (groups 1 and 2, respectively). The alert system was embedded with a deliberately non-specific risk detection tool in order to generate TP and FP alerts. The naïve alert system would alert the physician to cancel the order regardless of the patient being at-risk or not at-risk. CIN risk was stratified as at-risk and no-risk according to a patient's pre-existing renal function. Contrast imaging order-cancellation rate was measured as primary outcome. 3802 contrast-enhanced examination orders from 66 physicians were analyzed. Demographic data and risk distributions of patients were similar and well-balanced between two groups. In the intervention group, a total of 1892 alerts were generated (332 TP alerts and 1560 FP alerts). Order-cancellation rates were 5.1% versus 1.4% in groups 1 and 2 for at-risk patients (relative risk [RR] = 3.69) from TP alerts, and 1.0% versus 1.4% for no-risk patients (RR = 0.71) from FP alerts. Using generalized linear model with generalized estimating equation, the FP alerts had no order-cancellation effect when compared to the control arm (adjusted RR = 0.69; 95%CI, 0.36-1.32). The TP alerts had a larger order-cancellation effect than that of the control arm (adjusted RR = 2.95; 95%CI, 0.94-9.27), which revealed a marginal trend toward significance. However, the effect was not statistically significant (adjusted RR = 1.24; 95%CI, 0

  19. Encounter Decision Aid vs. Clinical Decision Support or Usual Care to Support Patient-Centered Treatment Decisions in Osteoporosis: The Osteoporosis Choice Randomized Trial II.

    PubMed

    LeBlanc, Annie; Wang, Amy T; Wyatt, Kirk; Branda, Megan E; Shah, Nilay D; Van Houten, Holly; Pencille, Laurie; Wermers, Robert; Montori, Victor M

    2015-01-01

    Osteoporosis Choice, an encounter decision aid, can engage patients and clinicians in shared decision making about osteoporosis treatment. Its effectiveness compared to the routine provision to clinicians of the patient's estimated risk of fracture using the FRAX calculator is unknown. Patient-level, randomized, three-arm trial enrolling women over 50 with osteopenia or osteoporosis eligible for treatment with bisphosphonates, where the use of Osteoporosis Choice was compared to FRAX only and to usual care to determine impact on patient knowledge, decisional conflict, involvement in the decision-making process, decision to start and adherence to bisphosphonates. We enrolled 79 women in the three arms. Because FRAX estimation alone and usual care produced similar results, we grouped them for analysis. Compared to these, use of Osteoporosis Choice increased patient knowledge (median score 6 vs. 4, p = .01), improved understanding of fracture risk and risk reduction with bisphosphonates (p = .01 and p<.0001, respectively), had no effect on decision conflict, and increased patient engagement in the decision making process (OPTION scores 57% vs. 43%, p = .001). Encounters with the decision aid were 0.8 minutes longer (range: 33 minutes shorter to 3.0 minutes longer). There were twice as many patients receiving and filling prescriptions in the decision aid arm (83% vs. 40%, p = .07); medication adherence at 6 months was no different across arms. Supporting both patients and clinicians during the clinical encounter with the Osteoporosis Choice decision aid efficiently improves treatment decision making when compared to usual care with or without clinical decision support with FRAX results. clinical trials.gov NCT00949611.

  20. A Decision Support System for Optimum Use of Fertilizers

    SciTech Connect

    Hoskinson, Reed Louis; Hess, John Richard; Fink, Raymond Keith

    1999-07-01

    The Decision Support System for Agriculture (DSS4Ag) is an expert system being developed by the Site-Specific Technologies for Agriculture (SST4Ag) precision farming research project at the INEEL. DSS4Ag uses state-of-the-art artificial intelligence and computer science technologies to make spatially variable, site-specific, economically optimum decisions on fertilizer use. The DSS4Ag has an open architecture that allows for external input and addition of new requirements and integrates its results with existing agricultural systems’ infrastructures. The DSS4Ag reflects a paradigm shift in the information revolution in agriculture that is precision farming. We depict this information revolution in agriculture as an historic trend in the agricultural decision-making process.

  1. Middle Mississippi River decision support system: user's manual

    USGS Publications Warehouse

    Rohweder, Jason J.; Zigler, Steven J.; Fox, Timothy J.; Hulse, Steven N.

    2005-01-01

    This user's manual describes the Middle Mississippi River Decision Support System (MMRDSS) and gives detailed examples on its use. The MMRDSS provides a framework to assist decision makers regarding natural resource issues in the Middle Mississippi River floodplain. The MMRDSS is designed to provide users with a spatially explicit tool for tasks, such as inventorying existing knowledge, developing models to investigate the potential effects of management decisions, generating hypotheses to advance scientific understanding, and developing scientifically defensible studies and monitoring. The MMRDSS also includes advanced tools to assist users in evaluating differences in complexity, connectivity, and structure of aquatic habitats among river reaches. The Environmental Systems Research Institute ArcView 3.x platform was used to create and package the data and tools of the MMRDSS.

  2. Decision Support Systems for Research and Management in Advanced Life Support

    NASA Technical Reports Server (NTRS)

    Rodriquez, Luis F.

    2004-01-01

    Decision support systems have been implemented in many applications including strategic planning for battlefield scenarios, corporate decision making for business planning, production planning and control systems, and recommendation generators like those on Amazon.com(Registered TradeMark). Such tools are reviewed for developing a similar tool for NASA's ALS Program. DSS are considered concurrently with the development of the OPIS system, a database designed for chronicling of research and development in ALS. By utilizing the OPIS database, it is anticipated that decision support can be provided to increase the quality of decisions by ALS managers and researchers.

  3. A knowledge authoring tool for clinical decision support.

    PubMed

    Dunsmuir, Dustin; Daniels, Jeremy; Brouse, Christopher; Ford, Simon; Ansermino, J Mark

    2008-06-01

    Anesthesiologists in the operating room are unable to constantly monitor all data generated by physiological monitors. They are further distracted by clinical and educational tasks. An expert system would ideally provide assistance to the anesthesiologist in this data-rich environment. Clinical monitoring expert systems have not been widely adopted, as traditional methods of knowledge encoding require both expert medical and programming skills, making knowledge acquisition difficult. A software application was developed for use as a knowledge authoring tool for physiological monitoring. This application enables clinicians to create knowledge rules without the need of a knowledge engineer or programmer. These rules are designed to provide clinical diagnosis, explanations and treatment advice for optimal patient care to the clinician in real time. By intelligently combining data from physiological monitors and demographical data sources the expert system can use these rules to assist in monitoring the patient. The knowledge authoring process is simplified by limiting connective relationships between rules. The application is designed to allow open collaboration between communities of clinicians to build a library of rules for clinical use. This design provides clinicians with a system for parameter surveillance and expert advice with a transparent pathway of reasoning. A usability evaluation demonstrated that anesthesiologists can rapidly develop useful rules for use in a predefined clinical scenario.

  4. Cost of installing and operating an electronic clinical decision support system for maternal health care: case of Tanzania rural primary health centres.

    PubMed

    Saronga, Happiness Pius; Dalaba, Maxwell Ayindenaba; Dong, Hengjin; Leshabari, Melkizedeck; Sauerborn, Rainer; Sukums, Felix; Blank, Antje; Kaltschmidt, Jens; Loukanova, Svetla

    2015-04-02

    Poor quality of care is among the causes of high maternal and newborn disease burden in Tanzania. Potential reason for poor quality of care is the existence of a "know-do gap" where by health workers do not perform to the best of their knowledge. An electronic clinical decision support system (CDSS) for maternal health care was piloted in six rural primary health centers of Tanzania to improve performance of health workers by facilitating adherence to World Health Organization (WHO) guidelines and ultimately improve quality of maternal health care. This study aimed at assessing the cost of installing and operating the system in the health centers. This retrospective study was conducted in Lindi, Tanzania. Costs incurred by the project were analyzed using Ingredients approach. These costs broadly included vehicle, computers, furniture, facility, CDSS software, transport, personnel, training, supplies and communication. These were grouped into installation and operation cost; recurrent and capital cost; and fixed and variable cost. We assessed the CDSS in terms of its financial and economic cost implications. We also conducted a sensitivity analysis on the estimations. Total financial cost of CDSS intervention amounted to 185,927.78 USD. 77% of these costs were incurred in the installation phase and included all the activities in preparation for the actual operation of the system for client care. Generally, training made the largest share of costs (33% of total cost and more than half of the recurrent cost) followed by CDSS software- 32% of total cost. There was a difference of 31.4% between the economic and financial costs. 92.5% of economic costs were fixed costs consisting of inputs whose costs do not vary with the volume of activity within a given range. Economic cost per CDSS contact was 52.7 USD but sensitive to discount rate, asset useful life and input cost variations. Our study presents financial and economic cost estimates of installing and operating an

  5. Integrated Clinical Decision Support Systems Promote Absolute Cardiovascular Risk Assessment: An Important Primary Prevention Measure in Aboriginal and Torres Strait Islander Primary Health Care.

    PubMed

    Matthews, Veronica; Burgess, Christopher P; Connors, Christine; Moore, Elizabeth; Peiris, David; Scrimgeour, David; Thompson, Sandra C; Larkins, Sarah; Bailie, Ross

    2017-01-01

    Aboriginal and Torres Strait Islander Australians experience a greater burden of disease compared to non-Indigenous Australians. Around one-fifth of the health disparity is caused by cardiovascular disease (CVD). Despite the importance of absolute cardiovascular risk assessment (CVRA) as a screening and early intervention tool, few studies have reported its use within the Australian Indigenous primary health care (PHC) sector. This study utilizes data from a large-scale quality improvement program to examine variation in documented CVRA as a primary prevention strategy for individuals without prior CVD across four Australian jurisdictions. We also examine the proportion with elevated risk and follow-up actions recorded. We undertook cross-sectional analysis of 2,052 client records from 97 PHC centers to assess CVRA in Indigenous adults aged ≥20 years with no recorded chronic disease diagnosis (2012-2014). Multilevel regression was used to quantify the variation in CVRA attributable to health center and client level factors. The main outcome measure was the proportion of eligible adults who had CVRA recorded. Secondary outcomes were the proportion of clients with elevated risk that had follow-up actions recorded. Approximately 23% (n = 478) of eligible clients had documented CVRA. Almost all assessments (99%) were conducted in the Northern Territory. Within this jurisdiction, there was wide variation between centers in the proportion of clients with documented CVRA (median 38%; range 0-86%). Regression analysis showed health center factors accounted for 48% of the variation. Centers with integrated clinical decision support systems were more likely to document CVRA (OR 21.1; 95% CI 5.4-82.4; p < 0.001). Eleven percent (n = 53) of clients were found with moderate/high CVD risk, of whom almost one-third were under 35 years (n = 16). Documentation of follow-up varied with respect to the targeted risk factor. Fewer than 30% with abnormal blood

  6. Disaster Management with a Next Generation Disaster Decision Support System

    NASA Astrophysics Data System (ADS)

    Chen, Y.

    2015-12-01

    As populations become increasingly concentrated in large cities, the world is experiencing an inevitably growing trend towards the urbanisation of disasters. Scientists have contributed significant advances in understanding the geophysical causes of natural hazards and have developed sophisticated tools to predict their effects; while, much less attention has been devoted to tools that increase situational awareness, facilitate leadership, provide effective communication channels and data flow and enhance the cognitive abilities of decision makers and first responders. In this paper, we envisioned the capabilities of a next generation disaster decision support system and hence proposed a state-of-the-art system architecture design to facilitate the decision making process in natural catastrophes such as flood and bushfire by utilising a combination of technologies for multi-channel data aggregation, disaster modelling, visualisation and optimisation. Moreover, we put our thoughts into action by implementing an Intelligent Disaster Decision Support System (IDDSS). The developed system can easily plug in to external disaster models and aggregate large amount of heterogeneous data from government agencies, sensor networks, and crowd sourcing platforms in real-time to enhance the situational awareness of decision makers and offer them a comprehensive understanding of disaster impacts from diverse perspectives such as environment, infrastructure and economy, etc. Sponsored by the Australian Government and the Victorian Department of Justice (Australia), the system was built upon a series of open-source frameworks (see attached figure) with four key components: data management layer, model application layer, processing service layer and presentation layer. It has the potential to be adopted by a range of agencies across Australian jurisdictions to assist stakeholders in accessing, sharing and utilising available information in their management of disaster events.

  7. Impact of an electronic clinical decision support system on workflow in antenatal care: the QUALMAT eCDSS in rural health care facilities in Ghana and Tanzania

    PubMed Central

    Mensah, Nathan; Sukums, Felix; Awine, Timothy; Meid, Andreas; Williams, John; Akweongo, Patricia; Kaltschmidt, Jens; Haefeli, Walter E.; Blank, Antje

    2015-01-01

    Background The implementation of new technology can interrupt established workflows in health care settings. The Quality of Maternal Care (QUALMAT) project has introduced an electronic clinical decision support system (eCDSS) for antenatal care (ANC) and delivery in rural primary health care facilities in Africa. Objective This study was carried out to investigate the influence of the QUALMAT eCDSS on the workflow of health care workers in rural primary health care facilities in Ghana and Tanzania. Design A direct observation, time-and-motion study on ANC processes was conducted using a structured data sheet with predefined major task categories. The duration and sequence of tasks performed during ANC visits were observed, and changes after the implementation of the eCDSS were analyzed. Results In 24 QUALMAT study sites, 214 observations of ANC visits (144 in Ghana, 70 in Tanzania) were carried out at baseline and 148 observations (104 in Ghana, 44 in Tanzania) after the software was implemented in 12 of those sites. The median time spent combined for all centers in both countries to provide ANC at baseline was 6.5 min [interquartile range (IQR) =4.0–10.6]. Although the time spent on ANC increased in Tanzania and Ghana after the eCDSS implementation as compared to baseline, overall there was no significant increase in time used for ANC activities (0.51 min, p=0.06 in Ghana; and 0.54 min, p=0.26 in Tanzania) as compared to the control sites without the eCDSS. The percentage of medical history taking in women who had subsequent examinations increased after eCDSS implementation from 58.2% (39/67) to 95.3% (61/64) p<0.001 in Ghana but not in Tanzania [from 65.4% (17/26) to 71.4% (15/21) p=0.70]. Conclusions The QUALMAT eCDSS does not increase the time needed for ANC but partly streamlined workflow at sites in Ghana, showing the potential of such a system to influence quality of care positively. PMID:25630707

  8. Impact of an electronic clinical decision support system on workflow in antenatal care: the QUALMAT eCDSS in rural health care facilities in Ghana and Tanzania.

    PubMed

    Mensah, Nathan; Sukums, Felix; Awine, Timothy; Meid, Andreas; Williams, John; Akweongo, Patricia; Kaltschmidt, Jens; Haefeli, Walter E; Blank, Antje

    2015-01-01

    The implementation of new technology can interrupt established workflows in health care settings. The Quality of Maternal Care (QUALMAT) project has introduced an electronic clinical decision support system (eCDSS) for antenatal care (ANC) and delivery in rural primary health care facilities in Africa. This study was carried out to investigate the influence of the QUALMAT eCDSS on the workflow of health care workers in rural primary health care facilities in Ghana and Tanzania. A direct observation, time-and-motion study on ANC processes was conducted using a structured data sheet with predefined major task categories. The duration and sequence of tasks performed during ANC visits were observed, and changes after the implementation of the eCDSS were analyzed. In 24 QUALMAT study sites, 214 observations of ANC visits (144 in Ghana, 70 in Tanzania) were carried out at baseline and 148 observations (104 in Ghana, 44 in Tanzania) after the software was implemented in 12 of those sites. The median time spent combined for all centers in both countries to provide ANC at baseline was 6.5 min [interquartile range (IQR) =4.0-10.6]. Although the time spent on ANC increased in Tanzania and Ghana after the eCDSS implementation as compared to baseline, overall there was no significant increase in time used for ANC activities (0.51 min, p=0.06 in Ghana; and 0.54 min, p=0.26 in Tanzania) as compared to the control sites without the eCDSS. The percentage of medical history taking in women who had subsequent examinations increased after eCDSS implementation from 58.2% (39/67) to 95.3% (61/64) p<0.001 in Ghana but not in Tanzania [from 65.4% (17/26) to 71.4% (15/21) p=0.70]. The QUALMAT eCDSS does not increase the time needed for ANC but partly streamlined workflow at sites in Ghana, showing the potential of such a system to influence quality of care positively.

  9. Impact of an electronic clinical decision support system on workflow in antenatal care: the QUALMAT eCDSS in rural health care facilities in Ghana and Tanzania.

    PubMed

    Mensah, Nathan; Sukums, Felix; Awine, Timothy; Meid, Andreas; Williams, John; Akweongo, Patricia; Kaltschmidt, Jens; Haefeli, Walter E; Blank, Antje

    2015-01-01

    Background The implementation of new technology can interrupt established workflows in health care settings. The Quality of Maternal Care (QUALMAT) project has introduced an electronic clinical decision support system (eCDSS) for antenatal care (ANC) and delivery in rural primary health care facilities in Africa. Objective This study was carried out to investigate the influence of the QUALMAT eCDSS on the workflow of health care workers in rural primary health care facilities in Ghana and Tanzania. Design A direct observation, time-and-motion study on ANC processes was conducted using a structured data sheet with predefined major task categories. The duration and sequence of tasks performed during ANC visits were observed, and changes after the implementation of the eCDSS were analyzed. Results In 24 QUALMAT study sites, 214 observations of ANC visits (144 in Ghana, 70 in Tanzania) were carried out at baseline and 148 observations (104 in Ghana, 44 in Tanzania) after the software was implemented in 12 of those sites. The median time spent combined for all centers in both countries to provide ANC at baseline was 6.5 min [interquartile range (IQR) =4.0-10.6]. Although the time spent on ANC increased in Tanzania and Ghana after the eCDSS implementation as compared to baseline, overall there was no significant increase in time used for ANC activities (0.51 min, p=0.06 in Ghana; and 0.54 min, p=0.26 in Tanzania) as compared to the control sites without the eCDSS. The percentage of medical history taking in women who had subsequent examinations increased after eCDSS implementation from 58.2% (39/67) to 95.3% (61/64) p<0.001 in Ghana but not in Tanzania [from 65.4% (17/26) to 71.4% (15/21) p=0.70]. Conclusions The QUALMAT eCDSS does not increase the time needed for ANC but partly streamlined workflow at sites in Ghana, showing the potential of such a system to influence quality of care positively.

  10. Malaria elimination: moving forward with spatial decision support systems.

    PubMed

    Kelly, Gerard C; Tanner, Marcel; Vallely, Andrew; Clements, Archie

    2012-07-01

    Operational challenges facing contemporary malaria elimination have distinct geospatial elements including the need for high-resolution location-based surveillance, targeted prevention and response interventions, and effective delivery of essential services at optimum levels of coverage. Although mapping and geographical reconnaissance (GR) has traditionally played an important role in supporting malaria control and eradication, its full potential as an applied health systems tool has not yet been fully realised. As accessibility to global positioning system (GPS), geographic information system (GIS) and mobile computing technology increases, the role of an integrated spatial decision support system (SDSS) framework for supporting the increased operational demands of malaria elimination requires further exploration, validation and application; particularly in the context of resource-poor settings. Copyright © 2012 Elsevier Ltd. All rights reserved.

  11. Healthcare decision support system for administration of chronic diseases.

    PubMed

    Woo, Ji-In; Yang, Jung-Gi; Lee, Young-Ho; Kang, Un-Gu

    2014-07-01

    A healthcare decision-making support model and rule management system is proposed based on a personalized rule-based intelligent concept, to effectively manage chronic diseases. A Web service was built using a standard message transfer protocol for interoperability of personal health records among healthcare institutions. An intelligent decision service is provided that analyzes data using a service-oriented healthcare rule inference function and machine-learning platform; the rules are extensively compiled by physicians through a developmental user interface that enables knowledge base construction, modification, and integration. Further, screening results are visualized for the self-intuitive understanding of personal health status by patients. A recommendation message is output through the Web service by receiving patient information from the hospital information recording system and object attribute values as input factors. The proposed system can verify patient behavior by acting as an intellectualized backbone of chronic diseases management; further, it supports self-management and scheduling of screening. Chronic patients can continuously receive active recommendations related to their healthcare through the rule management system, and they can model the system by acting as decision makers in diseases management; secondary diseases can be prevented and health management can be performed by reference to patient-specific lifestyle guidelines.

  12. A framework for evaluating the appropriateness of clinical decision support alerts and responses.

    PubMed

    McCoy, Allison B; Waitman, Lemuel R; Lewis, Julia B; Wright, Julie A; Choma, David P; Miller, Randolph A; Peterson, Josh F

    2012-01-01

    Alerting systems, a type of clinical decision support, are increasingly prevalent in healthcare, yet few studies have concurrently measured the appropriateness of alerts with provider responses to alerts. Recent reports of suboptimal alert system design and implementation highlight the need for better evaluation to inform future designs. The authors present a comprehensive framework for evaluating the clinical appropriateness of synchronous, interruptive medication safety alerts. Through literature review and iterative testing, metrics were developed that describe successes, justifiable overrides, provider non-adherence, and unintended adverse consequences of clinical decision support alerts. The framework was validated by applying it to a medication alerting system for patients with acute kidney injury (AKI). Through expert review, the framework assesses each alert episode for appropriateness of the alert display and the necessity and urgency of a clinical response. Primary outcomes of the framework include the false positive alert rate, alert override rate, provider non-adherence rate, and rate of provider response appropriateness. Application of the framework to evaluate an existing AKI medication alerting system provided a more complete understanding of the process outcomes measured in the AKI medication alerting system. The authors confirmed that previous alerts and provider responses were most often appropriate. The new evaluation model offers a potentially effective method for assessing the clinical appropriateness of synchronous interruptive medication alerts prior to evaluating patient outcomes in a comparative trial. More work can determine the generalizability of the framework for use in other settings and other alert types.

  13. A framework for evaluating the appropriateness of clinical decision support alerts and responses

    PubMed Central

    Waitman, Lemuel R; Lewis, Julia B; Wright, Julie A; Choma, David P; Miller, Randolph A; Peterson, Josh F

    2011-01-01

    Objective Alerting systems, a type of clinical decision support, are increasingly prevalent in healthcare, yet few studies have concurrently measured the appropriateness of alerts with provider responses to alerts. Recent reports of suboptimal alert system design and implementation highlight the need for better evaluation to inform future designs. The authors present a comprehensive framework for evaluating the clinical appropriateness of synchronous, interruptive medication safety alerts. Methods Through literature review and iterative testing, metrics were developed that describe successes, justifiable overrides, provider non-adherence, and unintended adverse consequences of clinical decision support alerts. The framework was validated by applying it to a medication alerting system for patients with acute kidney injury (AKI). Results Through expert review, the framework assesses each alert episode for appropriateness of the alert display and the necessity and urgency of a clinical response. Primary outcomes of the framework include the false positive alert rate, alert override rate, provider non-adherence rate, and rate of provider response appropriateness. Application of the framework to evaluate an existing AKI medication alerting system provided a more complete understanding of the process outcomes measured in the AKI medication alerting system. The authors confirmed that previous alerts and provider responses were most often appropriate. Conclusion The new evaluation model offers a potentially effective method for assessing the clinical appropriateness of synchronous interruptive medication alerts prior to evaluating patient outcomes in a comparative trial. More work can determine the generalizability of the framework for use in other settings and other alert types. PMID:21849334

  14. Use of decision support systems as a drought management tool

    USGS Publications Warehouse

    Frevert, D.; Lins, H.; ,

    2005-01-01

    Droughts present a unique challenge to water managers throughout the world and the current drought in the western United States is taxing facilities to the limit. Coping with this severe drought requires state of the art decision support systems including efficient and accurate hydrologic process models, detailed hydrologic data bases and effective river systems management modeling frameworks. This paper will outline a system of models developed by the Bureau of Reclamation, the US Geological Survey, the University of Colorado and a number of other governmental and university partners. The application of the technology to drought management in several key western river basins will be discussed.