Forecasting of emergency department attendances in a tourist region with an operational time horizon

Abstract

Hospital patient waiting times and length of stay are indicators of the quality of emergency department (ED) services, factors that are a ected by the number of patient arrivals. It is necessary to ac- curately estimate ED patient arrivals in order to manage resources e ectively. Prediction models, however, are conditioned by the hospi- tal population and its placement (i.e. meteorological conditions). In the particular case of a tourist region, the population has an impor- tant amount of variability which challenge EDs. This paper aims to address ED attendances predictions for an hospital placed in a tourist region by means of a new approach that combines multiple linear re- gression with arti cial neural networks and regression tree ensembles, looking for dealing for ED variability and prediction for a week time horizon that enables operational reaction to the ED responsible. The methodology uses exogenous variables such as calendar, weather and socio-economic data to improve the accuracy of these forecasts. Pre- diction models are built on data for 11-years and the predictions are tested over 1-year. The results showed that the proposed methodol- ogy is capable to perform weekly predictions with an error about 5%, demonstrating that it could be used by EDs