Running to Stand Still: EHR Efficiency and Workload Across Emergency Medicine Residency

Abstract

ABSTRACT Objectives The evolution of electronic health record (EHR) burden throughout emergency medicine (EM) residency remains poorly understood. We aimed to quantify EHR use, the composition of work performed outside scheduled hours, and the development of EHR efficiency across EM training. Methods Retrospective cohort study of EM residents (postgraduate year [PGY] 1–4) using 5.5 years of EHR audit log data (January 2020—June 2025) at a single academic institution. We analyzed EHR time per new patient encounter by PGY, categorized by activity and work during and after scheduled shifts. Results The analysis included 155,885 resident‐encounters from 133 residents across 14,811 shifts. Encounter‐attributed EHR time per encounter decreased by 51% from PGY‐1 to PGY‐4 (median 19.7 to 9.7 min, p < 0.001) while patient volume per shift increased 71% (median 7 to 12 encounters). Concurrently, documentation time decreased 69% (9.3 to 2.9 min). Total EHR time per shift increased across training. Per‐shift documentation time fell (69.2 to 37.0 min), while tracking board time nearly doubled (43.8 to 81.5 min), driven by on‐shift use rising from 41.0 to 78.6 min. Work occurring outside any scheduled shift was modest and did not increase monotonically with training (median 7.8, 13.7, 12.3, and 7.3 min per shift for PGY‐1 through PGY‐4; p = 0.027), and was predominantly documentation (41%–51% of unscheduled minutes). Counting all activity in the 48 h after a shift, without excluding time the resident was scheduled to work, yielded substantially higher estimates (61.9 to 103.5 min per shift). Conclusions EM residents achieve substantial gains in per‐encounter EHR efficiency, largest in documentation, but rising patient volume means total EHR time per shift increases rather than falls. Work performed outside scheduled hours is limited and is principally documentation, at least at this single institution. Monitoring these metrics may help programs identify struggling learners and evaluate interventions.

The authors' abstract, as published at the source. AEM Education and Training, 2026 · DOI ↗