A Two-Phase Framework Leveraging User Feedback and Systemic Validation to Improve Post-Live Clinical Decision Support

Despite the benefits of clinical decision support (CDS), concerns of potential risks arise amidst increasing reports of CDS malfunctions. Without objective and standardized methods to evaluate CDS in the post-live stage, CDS performance in a dynamic healthcare environment remains a black box from the user's perspective. In this study, we proposed a comprehensive framework to identify and evaluate post-live CDS malfunctions from the perspective of healthcare settings.We developed a two-phase framework to identify and evaluate post-live CDS system malfunctions: (1) real-time feedback from users in healthcare settings; (2) systematic validation through the use of databases that involve fundamental data flow validation and knowledge and rules validation. Identity, completeness, plausibility, and consistency across locations and time patterns were included as measures for systematic validation. We applied this framework to a commercial CDS system in 14 acute care facilities in Canada in a 2-year period.During this study, seven types of malfunctions were identified. The general rate of malfunctions was below 2%. In addition, an increase in CDS malfunctions was found during the electronic health record upgrade and implementation periods.This framework can be used to comprehensively evaluate CDS performance for healthcare settings. It provides objective insights into the extent of CDS issues, with the ability to capture low-prevalence malfunctions. Applying this framework to CDS evaluation can help improve CDS performance from the perspective of healthcare settings.