Forecasting Emergency Department Crowding using Data Science Techniques

The provision of insufficient resources during periods of high demand can lead to overcrowding in emergency departments. This issue has been extensively addressed through time series forecasting and regression problems. Despite the fact the increasing number of studies, accurate forecasting of demand remains a challenge. Thus, the purpose of this study was to develop a tool to predict the future evolution of emergency department occupancy in order to anticipate overcrowding episodes, avoid their negative effects on health and improve efficiency. This article presents a novel approach under the premise that the ability of the system to drain patients is the most determining factor in overcrowding episodes as opposed to previous approaches focused on patient demand. The forecasts model were based on the hourly number of patients occupying the general Emergency Department of Insular University Hospital of Gran Canaria Island, mainly given data of the flow of patients through the emergency department as well as performance indicators from other areas of the hospital extracted from the information system.