Deep Learning-Based Forecasting of Boarding Patient Counts to Address ED Overcrowding

This study develops deep learning models to forecast the number of patients in the emergency department (ED) boarding phase six hours in advance, aiming to support proactive operational decision-making using only non-clinical, operational, and contextual features. Data were collected from five sources: ED tracking systems, inpatient census records, weather reports, federal holiday calendars, and local event schedules. After feature engineering, the data were aggregated at an hourly level, cleaned, and merged into a unified dataset for model training. Several time series deep learning models, including ResNetPlus, TSTPlus, TSiTPlus (from the tsai library), and N-BEATSx, were trained using Optuna and grid search for hyperparameter tuning. The average ED boarding count was 28.7, with a standard deviation of 11.2. N-BEATSx achieved the best performance, with a mean absolute error of 2.10, mean squared error of 7.08, root mean squared error of 2.66, and a coefficient of determination of 0.95. The model maintained stable accuracy even during periods of extremely high boarding counts, defined as values exceeding one, two, or three standard deviations above the mean. Results show that accurate six-hour-ahead forecasts are achievable without using patient-level clinical data. While strong performance was observed even with a basic feature set, the inclusion of additional features improved prediction stability under extreme conditions. This framework offers a practical and generalizable approach for hospital systems to anticipate boarding levels and help mitigate ED overcrowding.