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

By Kensaku Kawamoto, C. Houlihan, A. Balas, D. Lobach. 2006

1,926 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/e5b9c8427cef62bfd91a5baaa9e64e9247b48b0b

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Citations in the corpus

Risk prediction models: II. External validation, model updating, and impact assessment (2012, background): “41 42 Availability in routine care of electronic health records that can automatically give predictions for individual patients, improves implementation and, accordingly, impact analysis of predictive models.(42) 43”
Using AI-generated suggestions from ChatGPT to optimize clinical decision support (2023, background): “Clinical decision support (CDS) provides information and recommendations to healthcare professionals and patients at the point of care.(1) As electronic health record (EHR) adoption has increased, in part due to more than $34 billion of government spending,(2) the use of CDS has also increased.”
Overview of Clinical Decision Support Systems (2016, background): “In addition to the individual studies, Kawamoto et al.(53) examined factors associated with CDSS success across a variety of studies.”
Translating Clinical Research into Clinical Practice: Impact of Using Prediction Rules To Make Decisions (2006, background): “We think that beneficial impact is more likely when decision aids provide specific recommendations, provide them automatically as part of clinicians’ routine workflow, and use computer-based decision support (63).”
Unremarkable AI: Fitting Intelligent Decision Support into Critical, Clinical Decision-Making Processes (2019, background): “Interestingly, almost all these tools have failed when migrating from research labs to clinical practice in the past 30 years [5, 7, 8].”
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].”
Effect of Clinical Decision-Support Systems (2012, background): “A CDSS is “any electronic system designed to aid directly in clinical decision making, in which characteristics of individual patients are used to generate patientspecific assessments or recommendations that are then presented to clinicians for consideration” (1).”
A Mathematical Model for Interpretable Clinical Decision Support with Applications in Gynecology (2012, background): “Kawamoto [8] identified four features which are crucial for CDS systems: the system should (i) be provided to clinicians in an automatic way, without interfering with the workflow, (ii) provide decision support at the time and location of decision making, (iii) provide a recommendation and (iv) be…”
What can Natural Language Processing do for Clinical Decision Support? (2009, background): “Several features are correlated with decision support systems’ ability to improve patient care: automatically and proactively providing decision support as part of clinician workflow; providing recommendations rather than assessments; and providing decision support at the time and location of decisi”