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

2015 ◽  
Vol 22 (e1) ◽  
pp. e13-e20 ◽  
Author(s):  
Gaurav Jay Dhiman ◽  
Kyle T Amber ◽  
Kenneth W. Goodman
Keyword(s):  
Decision Support ◽  
Clinical Decision Support ◽  
Conflicts Of Interest ◽  
Clinical Decision Support Systems ◽  
Clinical Decision ◽  
Sound Studies ◽  
Outcome Studies ◽  
Evidence Based ◽  
System Acquisition ◽  
Support Software

Abstract 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.

scholarly journals Evidence-based Clinical Decision Support Systems for the prediction and detection of three disease states in critical care: A systematic literature review

F1000Research ◽  
2019 ◽  
Vol 8 ◽  
pp. 1728
Author(s):  
Goran Medic ◽  
Melodi Kosaner Kließ ◽  
Louis Atallah ◽  
Jochen Weichert ◽  
Saswat Panda ◽  
...  
Keyword(s):  
Machine Learning ◽  
Critical Care ◽  
Decision Support ◽  
Respiratory Distress ◽  
Decision Support Systems ◽  
Clinical Decision Support ◽  
Clinical Decision Support Systems ◽  
Clinical Decision ◽  
Hemodynamic Instability ◽  
Evidence Based

Background: Clinical decision support (CDS) systems have emerged as tools providing intelligent decision making to address challenges of critical care. CDS systems can be based on existing guidelines or best practices; and can also utilize machine learning to provide a diagnosis, recommendation, or therapy course. Methods: This research aimed to identify evidence-based study designs and outcome measures to determine the clinical effectiveness of clinical decision support systems in the detection and prediction of hemodynamic instability, respiratory distress, and infection within critical care settings. PubMed, ClinicalTrials.gov and Cochrane Database of Systematic Reviews were systematically searched to identify primary research published in English between 2013 and 2018. Studies conducted in the USA, Canada, UK, Germany and France with more than 10 participants per arm were included. Results: In studies on hemodynamic instability, the prediction and management of septic shock were the most researched topics followed by the early prediction of heart failure. For respiratory distress, the most popular topics were pneumonia detection and prediction followed by pulmonary embolisms. Given the importance of imaging and clinical notes, this area combined Machine Learning with image analysis and natural language processing. In studies on infection, the most researched areas were the detection, prediction, and management of sepsis, surgical site infections, as well as acute kidney injury. Overall, a variety of Machine Learning algorithms were utilized frequently, particularly support vector machines, boosting techniques, random forest classifiers and neural networks. Sensitivity, specificity, and ROC AUC were the most frequently reported performance measures. Conclusion: This review showed an increasing use of Machine Learning for CDS in all three areas. Large datasets are required for training these algorithms; making it imperative to appropriately address, challenges such as class imbalance, correct labelling of data and missing data. Recommendations are formulated for the development and successful adoption of CDS systems.

scholarly journals Evidence-Based Clinical Decision Support

2013 ◽  
Vol 22 (01) ◽  
pp. 120-127
Author(s):  
A. Wright ◽  
R. N. Shiffman
Keyword(s):  
Decision Support ◽  
Best Practices ◽  
Decision Support Systems ◽  
Clinical Decision Support ◽  
Support Systems ◽  
Clinical Decision Support Systems ◽  
Clinical Decision ◽  
Quality Of Healthcare ◽  
Evidence Based ◽  
Full Potential

Summary Background: Clinical decision support (CDS) is a key tool for enabling evidence-based medicine and improving the quality of healthcare. However, effective CDS faces a variety of challenges, including those relating to knowledge synthesis, capture, transformation, localization and maintenance. If not properly addressed, these challenges can limit the effectiveness of CDS, and potentially risk inaccurate or inappropriate interventions to clinicians. Objectives: (1) To describe an approach to CDS development using evidence as a basis for clinical decision support systems that promote effective care; (2) To review recent evidence regarding the effectiveness of selected clinical decision support systems. Method: Review and analysis of recent literature with identification of trends and best practices. Results: The state-of-the-art in CDS has advanced significantly, and many recent trials have shown CDS to be effective, although the results are mixed overall. Issues related to knowledge capture and synthesis, problems in knowledge transformation at the interface between knowledge authors and CDS developers, and problems specific to local CDS design and implementation can interfere with CDS development. Best practices, tools and techniques to manage them are described. Conclusions: CDS, when used well, can be effective, but further research is needed for it to reach its full potential.

scholarly journals Evidence-based Clinical Decision Support Systems for the prediction and detection of three disease states in critical care: A systematic literature review

F1000Research ◽  
2019 ◽  
Vol 8 ◽  
pp. 1728
Author(s):  
Goran Medic ◽  
Melodi Kosaner Kließ ◽  
Louis Atallah ◽  
Jochen Weichert ◽  
Saswat Panda ◽  
...  
Keyword(s):  
Machine Learning ◽  
Critical Care ◽  
Decision Support ◽  
Respiratory Distress ◽  
Decision Support Systems ◽  
Clinical Decision Support ◽  
Clinical Decision Support Systems ◽  
Clinical Decision ◽  
Hemodynamic Instability ◽  
Evidence Based

Background: Clinical decision support (CDS) systems have emerged as tools providing intelligent decision making to address challenges of critical care. CDS systems can be based on existing guidelines or best practices; and can also utilize machine learning to provide a diagnosis, recommendation, or therapy course. Methods: This research aimed to identify evidence-based study designs and outcome measures to determine the clinical effectiveness of clinical decision support systems in the detection and prediction of hemodynamic instability, respiratory distress, and infection within critical care settings. PubMed, ClinicalTrials.gov and Cochrane Database of Systematic Reviews were systematically searched to identify primary research published in English between 2013 and 2018. Studies conducted in the USA, Canada, UK, Germany and France with more than 10 participants per arm were included. Results: In studies on hemodynamic instability, the prediction and management of septic shock were the most researched topics followed by the early prediction of heart failure. For respiratory distress, the most popular topics were pneumonia detection and prediction followed by pulmonary embolisms. Given the importance of imaging and clinical notes, this area combined Machine Learning with image analysis and natural language processing. In studies on infection, the most researched areas were the detection, prediction, and management of sepsis, surgical site infections, as well as acute kidney injury. Overall, a variety of Machine Learning algorithms were utilized frequently, particularly support vector machines, boosting techniques, random forest classifiers and neural networks. Sensitivity, specificity, and ROC AUC were the most frequently reported performance measures. Conclusion: This review showed an increasing use of Machine Learning for CDS in all three areas. Large datasets are required for training these algorithms; making it imperative to appropriately address, challenges such as class imbalance, correct labelling of data and missing data. Recommendations are formulated for the development and successful adoption of CDS systems.

Taking Inventory of Medical Decision Support Software Development

1993 ◽  
Vol 32 (01) ◽  
pp. 9-11 ◽  
Author(s):  
R. A. Miller
Keyword(s):  
Decision Support ◽  
Software Development ◽  
Decision Support Systems ◽  
Clinical Decision Support ◽  
Support Systems ◽  
Clinical Decision Support Systems ◽  
Clinical Decision ◽  
Medical Decision ◽  
Medical Decision Support ◽  
Support Software

Abstract:Response to Heathfield HA, Wyatt J. Philosophies for the design and development of clinical decision-support systems. Meth Inform Med 1993; 32: 1-8.

Architectures for Architects

1993 ◽  
Vol 32 (01) ◽  
pp. 12-13 ◽  
Author(s):  
M. A. Musen
Keyword(s):  
Decision Support ◽  
Decision Support Systems ◽  
Clinical Decision Support ◽  
Support Systems ◽  
Clinical Decision Support Systems ◽  
Clinical Decision ◽  
Design And Development

Abstract:Response to Heathfield HA, Wyatt J. Philosophies for the design and development of clinical decision-support systems. Meth Inform Med 1993; 32: 1-8.

Sign in / Sign up

Export Citation Format

Share Document