Authors : Sesi Mensah Gidisu; Millicent Naa Oye Boadu; Joseph Kobi

Volume/Issue : Volume 11 - 2026, Issue 9 - September

Google Scholar : https://tinyurl.com/45jykxbp

DOI : https://doi.org/10.38124/ijisrt/26sep1244

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : In the United States, emergency department congestion stealthily undermines the safety net of public hospitals for uninsured, underinsured, and medically complex populations, and the use of forecasting tools is limited where it is desired most. This research article proposes and tests a set of predictive modeling techniques for predicting the volume of patients in the emergency department (ED) and downstream hospital resource use for the next day or multiple days within public hospitals in the United States, based on 4 years of anonymized encounter data and calendar, weather, epidemiology, and hospital operational data. Different models, including naive, seasonal autoregressive integrated moving average with exogenous regressors (SARIMAX), Prophet, random forest, extreme gradient boosting (XGBoost), long short-term memory (LSTM), and attention-based transformer architectures were trained and evaluated using 1-day, 7-day, and 14-day forecasting horizons, and the results were converted to a discrete-event simulation of staffing and bed management.

Keywords : Emergency Department Overcrowding, Predictive Analytics, Machine Learning, Public Hospitals, Resource Allocation, Time-Series Forecasting.

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