Forecast-Driven Dynamic Physician Staffing in a Pediatric Emergency Department: A Prospective Quasi-Experimental Pilot Study.

Published in Journal of medical systems. Abstract: Emergency department crowding is a persistent threat to acute care quality, yet predictive models for ED demand have rarely been translated into prospective operational staffing interventions. Here we report a prospective single-center quasi-experimental pilot study evaluating forecast-driven dynamic physician scheduling in the pediatric ED of Hacettepe University Ihsan Dogramaci Children's Hospital (December 2024 to May 2025). Using a deep learning demand forecasting model (TiDE-RIN) combined with linear programming, we determined daily physician counts (range 3 to 6) for evening shifts (16:00 to 24:00) during days 1 to 15 of each month; days 16 to end of month maintained the institution's standard fixed four-physician schedule. Among after-hours visits with valid disposition timestamps (n = 9,626), mean post-evaluation length of stay (PE-LOS) was 175.9 min in the intervention arm and 184.0 min in the control arm (unadjusted difference 8.2 min; propensity-score-matched 7.9 min, median 10.0; stabilised inverse-probability weighting 8.5 min). Because staffing was allocated by calendar day, inference was clustered on the 182 study days: the two-way fixed-effects arm contrast was a reduction of 8.9 min (95% CI - 22.5 to + 4.8; p = 0.20) and the fully covariate-adjusted contrast 11.0 min (95% CI - 24.4 to + 2.5; p = 0.11). All design-consistent specifications placed the reduction between 4 and 11 min, none excluded no effect under day-level clustering, and the pilot was not power...