Evaluating informatics applications - clinical decision support systems literature review

By Bonnie Kaplan. Int. J. Medical Informatics, 2001

388 citations recorded by the source corpus.

Subfields: Health Information Management, Organizational Behavior and Human Resource Management

View Paper: https://www.semanticscholar.org/paper/92340e1aa28c4eef884d62be895be7d226450ffc

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In Health Information Management

In Organizational Behavior and Human Resource Management

Citations in the corpus

Overview of Clinical Decision Support Systems (2016, background): “Several published reviews of CDSS have emphasized the dearth of evidence of similar effectiveness on a broader scale and have called for more research, especially qualitative research, that elucidates the factors which lead to success outside the development environment.(41,42) Studies of the Leeds ”
Evaluation of health information systems - problems and challenges (2003, neutral): “[70—73]).”
The Impact of eHealth on the Quality and Safety of Health Care: A Systematic Overview (2011, background): “An additional 55 supplementary reviews provided context to the findings [68–122], aiding in their interpretation [123].”
Qualitative Research Methods for Evaluating Computer Information Systems (2005, background): “rates, retrievability, usage rates, user satisfaction, and clinician behavior changes [2, 3 ].”
An evaluation framework for Health Information Systems: human, organization and technology-fit factors (HOT-fit) (2008, background): “[2] B.”
Effects of computerized clinical decision support systems on practitioner performance and patient outcomes: a systematic review. (2005, methodology): “Supplementary methods of finding studies included a review of article reference lists, articles citing included studies as listed in the Science Citation Index, PubMed related articles feature, informatics conference proceedings, information provided by primary study authors, and other recent review”
Biomedical Informatics for Computer-Aided Decision Support Systems: A Survey (2013, background): “Along these lines, Kaplan reviews the literature related to clinical decision support systems with an emphasis on evaluation criteria [50].”
EHRs Connect Research and Practice: Where Predictive Modeling, Artificial Intelligence, and Clinical Decision Support Intersect (2012, background): “For example, guidelines may recommend the same treatment for everyone (i.e., one-size-ﬁts-all), or prescribe a rigorous, standardized sequence of treatment options [3,4].”
A Holistic Framework to Improve the Uptake and Impact of eHealth Technologies (2011, neutral): “Some authors [30,48,60,80,84,85,94] have expressed the need for mixed methods using both quantitative and qualitative designs in order to better measure the uptake and impact of eHealth technologies.”
Better decision support through exploratory discrimination-aware data mining: foundations and empirical evidence (2014, background): “The evaluation practice in specific domains such as medical decision support therefore suggests that laboratory studies are useful and necessary as a first step on the way to evaluation in more naturalistic settings (Kaplan 2001).”
Towards the development of a decision support system for water resource management (2005, background): “…made to evaluate DSS (Brooksbank, 2001; Finlay and Wilson, 1997; Harrison and Pelletier, 1998; Harrison and Pelletier, 2001; Kanungo et al., 2001; Kaplan, 2001; Keil et al., 1995; Lu et al., 2001; Mennecke et al., 1997; Newman et al., 1999; Poon and Wagner, 2001; Potts et al., 2001; Wyatt and…”
Stubborn Reliance on Intuition and Subjectivity in Employee Selection (2008, background): “Physicians, aware of this lay bias against ‘‘cookbook medicine,’’ grossly underutilize these valuable technologies in practice (Kaplan, 2001).4 Hastie and Dawes (2001) noted that relying on 4.”