Forecasting demand of emergency care

This paper describes a model that can forecast the daily number of occupied beds due to emergency admissions in an acute hospital. Out of sample forecasts 32 days in advance, have a Root Mean Square error of 3% of the mean number of beds used for emergency admissions. We find that the number of occupied beds due to emergency admissions is related to both air temperature and UK Public Health Laboratory Service data on influenza-like illnesses. We find that a period of high volatility, indicated by Generalised Autoregressive Conditional Heteroskedasticity errors, will result in an increase in waiting times in the A&E Department. Furthermore, volatility gives more warning of waiting times in A&E than total bed occupancy.