Predictive Analytics in Emergency Services: Evaluating Forecast Periods for Sustainability

This study seeks to identify the most effective forecasting period and methods for predicting demand in an Accident & Emergency (A&E) department at a mid-sized hospital in England. Utilizing the National Hospital Episode Statistics (HES) dataset, that covers a 36-month period from February 2010 to January 2013, the research evaluates four commonly used forecasting methods: Autoregressive Integrated Moving Average (ARIMA), exponential smoothing, stepwise linear regression (SLR), and Seasonal and Trend decomposition using Loess (STLF). Forecast accuracy is assessed using the Mean Absolute Scaled Error (MASE). The MASE values for the best forecasting methods across different periods were 0.7834 for daily, 0.9354 for weekly, and 0.5259 for monthly estimates. The study found that the SLR model was the most effective predictive method, with monthly estimation emerging as the optimal period. Contrary to past studies that favoured daily estimates, this research indicated that daily A&E demand forecasts might not be the most accurate.