Across the world, healthcare systems are under stress and this has been hugely exacerbated by the COVID pandemic. Key Performance Indicators (KPIs), usually in the form of time-series data, are used to help manage that stress. Making reliable predictions of these indicators, particularly for emergency departments (ED), can facilitate acute unit planning, enhance quality of care and optimise resources. This motivates models that can forecast relevant KPIs and this paper addresses that need by comparing the Autoregressive Integrated Moving Average (ARIMA) method, a purely statistical model, to Prophet, a decomposable forecasting model based on trend, seasonality and holidays variables, and to the General Regression Neural Network (GRNN), a machine learning model. The dataset analysed is formed of four hourly valued indicators from a UK hospital: Patients in Department; Number of Attendances; Unallocated Patients with a DTA (Decision to Admit); Medically Fit for Discharge. Typically, the data exhibit regular patterns and seasonal trends and can be impacted by external factors such as the weather or major incidents. The COVID pandemic is an extreme instance of the latter and the behaviour of sample data changed dramatically. The capacity to quickly adapt to these changes is crucial and is a factor that shows better results for GRNN in both accuracy and reliability.
Predicting emergency department (ED) indicators in time series may benefit hospital planning, improving quality of care and optimising resources. It motivates analysis of models that can forecast relevant KPIs (Key Performance Indicators) for identifying future pressure. This paper analyses the Autoregressive Integrated Moving Average (ARIMA) method in comparison to the analysis of Prophet, an autoregressive forecasting model based on Re-current Neural Networks. The dataset analysed is formed by hourly valued hospital indicators, composed by Wait to be Seen Major in ED, Number of Attendances Major in ED, Unallocated Patients in ED with a DTA and Number of Beds Available on Medical Acute Unit. A comparison of predictions models ARIMA and Prophet is the focus. Each model is designed to provide better predictions for different time series characteristics. Measurements of best prediction for each indicator are based in accuracy, reliability bands and indicator meta information.
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