GRU-Based Forecasting of Emergency Department Patient Volume for Hospital Operations and Supply Chain Planning

Emergency departments (EDs) experience highly variable patient arrivals that complicate operational planning and resource coordination.Accurate demand forecasting is therefore essential to support timely decision-making in hospital operations.This study developed a gated recurrent unit (GRU)-based time series forecasting model to predict ED patient volumes using high-resolution hourly arrival data collected over four years.The model is evaluated across short-, medium-, and longterm forecasting horizons to assess its ability to capture daily, weekly, and seasonal demand patterns.The results indicate that the GRU effectively captures non-linear temporal dynamics and produces stable forecasts across time scales.Rather than positioning forecasting accuracy as an isolated objective, this study explicitly frames patient volume prediction as an operational input for hospital supply chain planning, particularly in workforce scheduling, facility utilization, and logistics preparation.By linking deep learning-based forecasting with hospital operational decision contexts, this research provides an application-oriented contribution to the healthcare analytics literature.The findings suggest that GRU models can serve as practical decision-support tools in emergency care environments and highlight the need for future validation across multiple hospital settings.