Performance dashboards are increasingly used by residency clinical competency committees (CCC) to aggregate, track, and visualize learner metrics. While qualitative data are often visualized on these dashboards, narrative feedback is not easily aggregated and summarized. The analysis and presentation of these qualitative data is typically done by individual faculty and program coordinators, which makes it prone to bias and consumes valuable residency administrator and faculty time and effort. As a finite resource, any time that can be saved can be reallocated to other creative and essential residency missions.
In this webinar, the presenters will demonstrate how the newest versions of generative AI can be used to analyze relevant information from clinical evaluations to create various reports that support the process of individual resident reporting including performance on core competencies and/or milestones. Program director and administrator faculty will discuss how AI-driven data analysis and interpretation led to increased efficiency for common residency tasks and targeted some sources of bias as well as highlight potential limitations and lessons learned. Presenters will explore the various types of narrative data commonly used for CCC evaluations that AI can tackle and demonstrate how to best query AI to analyze and summarize it.
Presented by:Danielle Jones, MDReena Hemrajani, MDMeghan Lane, MD
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