Leveraging Individualized Electronic Health Record Learner Analytics to Improve Resident Inbasket Management.

BACKGROUND: Electronic health record (EHR) inbasket management is a large contributor to burnout among residents. Data-driven, personalized approaches may improve performance and reduce burnout; however, there are few published studies with objective resident outcomes. OBJECTIVE: To evaluate how inbasket EHR metrics change longitudinally among residents stratified by baseline inbasket performance after spaced, analytics-informed coaching. DESIGN: Using a stratified longitudinal analysis of EHR metrics along with pre- and post-intervention surveys, we conducted an evaluation of our inbasket coaching curriculum. PARTICIPANTS: Postgraduate year (PGY)-2 and PGY-3 internal medicine residents and continuity clinic attendings at a single, large Mid-Atlantic academic center. INTERVENTIONS: Our redesigned curriculum integrated individualized EHR resident analytic reports summarizing efficiency and practice-based quality metrics into structured, one-on-one feedback sessions using the Relationship, Reaction, Content, and Coaching (R2C2) model. MAIN MEASURES: Primary outcomes included measured change in "time in inbasket" and "turnaround time" across two training periods. Secondary outcomes included resident-reported confidence in managing inbasket tasks, perceived efficiency, and inbasket-related burnout. We also evaluated acceptability and perceived usefulness of the curriculum among residents and faculty. KEY RESULTS: Residents significantly reduced their turnaround time for patient calls by 2.2 days (p < 0.001). The mean time spent in patient calls significantly decreased (0.99 ± 0.53 to 0.74 ± 0.38 min/day, p = 0.03). Resident perceptions of inbasket-related burnout did not significantly change (p = 0.09). Neither perceived inbasket confidence (p = 0.18) nor efficiency (p = 0.56) improved after the intervention. Most faculty (6/7) felt EHR analytics data were at least moderately useful in helping coach residents and improved the quality of semiannual feedback. CONCLUSIONS: Integrating individualized EHR learning analytics into longitudinal coaching improves objective efficiency metrics and improves feedback quality. This model offers a structured approach to supervising inbasket management and promoting competency-based EHR education.