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 day days in advance. have an RMS 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 PHLS data on influenza like illnesses. We find that a period of high volatility, indicated by GARCH 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.
Jones
Simon Andrew
SA
School of Mathematics, Kingston University, Kingston-upon-Thames, Surrey, UK. s.jones@kingston.ac.uk
Joy
Mark Patrick
MP
Pearson
Jon
J
D004636:Emergency Service, Hospital / Q000706:statistics & numerical data*; D005544:Forecasting / Q000379:methods; D006301:Health Services Needs and Demand / Q000706:statistics & numerical data / Q000639:trends*; D006801:Humans; D007251:Influenza, Human / Q000453:epidemiology; D015233:Models, Statistical; D012621:Seasons; D006113:United Kingdom / Q000453:epidemiology