Deep Learning-Based Forecasting of Boarding Patient Counts to Address ED Overcrowding
It addresses ED overcrowding for hospital management by providing a practical forecasting tool, though it is incremental as it applies existing deep learning methods to this specific problem.
This study tackled predicting emergency department boarding counts six hours in advance using operational and contextual data, achieving a mean absolute error of 4.30 and R2 of 0.79 with the TSTPlus model.
This study presents a deep learning-based framework for predicting emergency department (ED) boarding counts six hours in advance using only operational and contextual data, without patient-level information. Data from ED tracking systems, inpatient census, weather, holidays, and local events were aggregated hourly and processed with comprehensive feature engineering. The mean ED boarding count was 28.7 (standard deviation = 11.2). Multiple deep learning models, including ResNetPlus, TSTPlus, and TSiTPlus, were trained and optimized using Optuna, with TSTPlus achieving the best results (mean absolute error = 4.30, mean squared error = 29.47, R2 = 0.79). The framework accurately forecasted boarding counts, including during extreme periods, and demonstrated that broader input features improve predictive accuracy. This approach supports proactive hospital management and offers a practical method for mitigating ED overcrowding.