Effects of computerized clinical decision support systems on practitioner performance and patient outcomes: a systematic review.

By Amit X. Garg, Neill K. J. Adhikari, H. Mcdonald, M. Rosas-Arellano, P. J. Devereaux, Joseph Beyene, Justina J. Sam, R. B. Haynes. 2005

3,078 citations recorded by the source corpus.

Subfields: General Health Professions, Health Informatics, Health Information Management

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Citations in the corpus

Interval Coded Scoring: a toolbox for interpretable scoring systems (2018, background): “…to the increase of 42 computational power, but also due to the enormous amount of medical data that has become available in increase practitioner performance and improve patient outcome, while decreasing the costs at the same time (Johnston et al., 1994; Garg et al., 2005; Chaudhry et al., 2006).”
Automation bias and verification complexity: a systematic review (2017, background): “This suggests that (1) a higher level of verification complexity is required for automation bias to present in a single-task than a multitask setting, and (2) the cognitive demands of tasks are cumulative; ie, the addition of secondary tasks appears to increase demands on a user to the point where e”
Situation Awareness: Review of Mica Endsley's 1995 Articles on Situation Awareness Theory and Measurement (2008, background): “In this regard, the distinction between SA and action is analogous to the classic distinctions in decision theory between state-of-the-world estimation and choice (Edwards, 1961) and, in medicine, between diagnosis and treatment (Garg et al., 2005).”
eDoctor: machine learning and the future of medicine (2018, background): “Nomograms and decision aides have been presented for years, and many clinicians will be familiar with these and commonly use the online iterations of these [44].”
Humans: Still Vital After All These Years of Automation (2008, background): “The apparent failure to use many Stage 3 automation diagnostic aids by health care practitioners (Garg et al., 2005) appears to result in part from an alert rate that does not fully account for contextual factors (Patterson, Nguyen, Halloran, & Asch, 2004).”
The Impact of eHealth on the Quality and Safety of Health Care: A Systematic Overview (2011, background): “Evidence of the beneficial impact of such systems is limited, however, to a few academic clinical centres of excellence where the systems were developed in house, undergoing extensive evaluation with continual improvement, supported by a strong sense of local ownership by their clinical users [31,56”
Overview of Advanced Computer Vision Systems for Skin Lesions Characterization (2009, background): “According to a recent review [93], published studies of clinical machine intelligence systems are increasing rapidly, and their quality is improving.”
A survey of multiple classifier systems as hybrid systems (2014, background): “In Medicine, a specific research area since the inception of Artificial Intelligence is the construction of Computer Aided Diagnosis (CAD) systems or Clinical Decision Support Systems (CDSS) [207], which involve as the final step some kind of classifier predicting the subject’s disease or normal sta”
Medical Question Answering for Clinical Decision Support (2016, background): “Clinical Decision Support (CDS) systems have been designed to help physicians address the myriad of complex clinical decisions that might arise during a patient’s care [11].”
Some Unintended Consequences of Clinical Decision Support Systems (2007, positive): “Together, CPOE and CDS can decrease medical errors [4,5] and improve hospital efficiency [6] and practitioner performance [7].”