Abstract:

Today, the demands for emergency health services show an extraordinary increase in cases such as epidemics, earthquakes, natural disasters, and explosions. The accurate estimation of the demand in question will facilitate the crisis management process for extraordinary situations, as it will enable the determination of the number of people who will apply to the emergency services and the effective realization of the relevant resource planning. In this study, it is aimed to estimate the number of applications to an emergency department. For the seasonal data, SARIMA, Holt-Winters and decomposition, which are among the time series analysis methods; Random tree and random forest techniques from machine learning methods are used. For this forecasting study, 396-day "number of patients admitted" data of a hospital located in Ankara is used. Forecasts in each method are performed for seven, fifteen, and thirty days. Correlation corrected square root and average absolute percentage error values are used to determine the most successful one among demand forecasting methods. In the analyzes made, it is observed that SARIMA method gives more effective results than others in forecasting the number of applications to the emergency department. In addition, because of the constantly changing and dynamic nature of the applications made to the emergency services, it is understood that the change in the forecasted number of days has a significant effect on the resulting forecast values.

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