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Forecasting Demand of Emergency Care

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Abstract

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.

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Authors and Affiliations

  1. School of Mathematics, Kingston University, Penrhyn Road, Kingston-upon-Thames, Surrey, KT1 2EE, UK

    Simon Andrew Jones & Mark Patrick Joy

  2. Applied Research Unit, Bromley Hospitals NHS Trust, Farnborough Hospital, Farnborough Common, Orpington, Kent, BR6 8ND, UK

    Jon Pearson

Authors
  1. Simon Andrew Jones
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  2. Mark Patrick Joy
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  3. Jon Pearson
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Correspondence to Simon Andrew Jones.

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Jones, S.A., Joy, M.P. & Pearson, J. Forecasting Demand of Emergency Care. Health Care Management Science 5, 297–305 (2002). https://doi.org/10.1023/A:1020390425029

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  • DOI: https://doi.org/10.1023/A:1020390425029

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