This study explored the role of machine-learning based clinical decision support (CDS) algorithms to support (rather than replace) human decision-making and the impact on diagnostic performance. This systematic review of 37 studies found limited evidence that the use of machine learning-based CDS systems contributes to improved diagnostic performance among clinicians. Interobserver agreement, user feedback, and clinician override were the most commonly reported outcomes. The authors emphasize the importance of further evaluation of human-computer interaction.
Kostopoulou O, Tracey C, Delaney BC. J Am Med Inform Assoc. 2021;28:1461-1467.
In addition to being used for patient-specific clinical purposes, data within the electronic health record (EHR) may be used for other purposes including epidemiological research. Researchers in the UK developed and tested a clinical decision support system (CDSS) to evaluate changes in the types and number of observations that primary care physicians entered into the EHR during simulated patient encounters. Physicians documented more clinical observations using the CDSS compared to the standard electronic health record. The increase in documented clinical observations has the potential to improve validity of research developed from EHR data.
Delvaux N, Piessens V, Burghgraeve TD, et al. Implement Sci. 2020;15:100.
Clinical decision support systems (CDSS) and computerized physician order entry (CPOE) have the potential to improve patient safety. This randomized trial evaluated the impact of integrating CDSS into CPOE among general practitioners in Belgium. The intervention improved appropriateness and decreased volume of laboratory test ordering and did not show any increases in diagnostic errors.
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