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

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 13,935 visits (6,957 intervention; 6,978 concurrent controls), propensity score matching (6,949 pairs) estimated a boarding time reduction of 31.9 minutes (21.4%; p < 0.0001); interrupted time series analysis confirmed an immediate level change of 22.1 minutes at intervention onset (p = 0.010). Diagnostic testing per patient decreased by 8.4% (p = 0.022) and physician-level boarding burden declined by 12.0% (p = 0.013), with larger effects among lower-acuity patients and during the early-evening demand peak. Spillover analysis confirmed no progressive improvement in the concurrent control period. These findings demonstrate that coupling deep learning demand forecasting with optimization-based physician scheduling can produce measurable and multi-dimensional improvements in pediatric ED operations, directly addressing the translational gap between predictive model development and real-world clinical implementation.