Emergency departments are vital units that operate 24/7, care for critically ill patients, and provide immediate emergency care in accordance with the triage code for admitted patients. The efficient operation of emergency services depends on adequate human and medical resources and on early planning. The increase in the intensity of emergency services due to COVID-19, a biological disaster, has limited the effective use of resources and planning efforts. Overcrowding in emergency rooms can disrupt services and endanger patients' lives. For this reason, it is essential to organize emergency service units according to patient estimates, provide service at an optimal level, facilitate planning and management, use medical and human resources effectively, and ensure patient satisfaction. This study was conducted to predict emergency department patient arrivals using meteorological data. In this study, hourly forecasting results are obtained using estimation methods, including the seasonal autoregressive integrated moving average (SARIMAX), an artificial neural network (ANN), and a nonlinear autoregressive with exogenous inputs (NARX) model. For the study, patient arrival data from a training and research hospital for December 2021 and meteorological data, including temperature, humidity, and wind, were used. Methods for predicting emergency department patient admissions were compared, and the SARIMAX model performed best. It is thought that predictions based on meteorological data will contribute to emergency department planning.
Coşkun et al. (Wed,) studied this question.