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A decision support system for demand and capacity modelling of an accident and emergency department

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  • Muhammed Ordu
  • Eren Demir
  • Chris Tofallis

Abstract

Accident and emergency (A&E) departments in England have been struggling against severe capacity constraints. In addition, A&E demands have been increasing year on year. In this study, our aim was to develop a decision support system combining discrete event simulation and comparative forecasting techniques for the better management of the Princess Alexandra Hospital in England. We used the national hospital episodes statistics data-set including period April, 2009 – January, 2013. Two demand conditions are considered: the expected demand condition is based on A&E demands estimated by comparing forecasting methods, and the unexpected demand is based on the closure of a nearby A&E department due to budgeting constraints. We developed a discrete event simulation model to measure a number of key performance metrics. This paper presents a crucial study which will enable service managers and directors of hospitals to foresee their activities in future and form a strategic plan well in advance.

Suggested Citation

  • Muhammed Ordu & Eren Demir & Chris Tofallis, 2020. "A decision support system for demand and capacity modelling of an accident and emergency department," Health Systems, Taylor & Francis Journals, vol. 9(1), pages 31-56, January.
    • Handle: RePEc:taf:thssxx:v:9:y:2020:i:1:p:31-56
    DOI: 10.1080/20476965.2018.1561161
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    Cited by:

    1. Demir, Eren & Yakutcan, Usame & Page, Stephen, 2024. "Using simulation modelling to transform hospital planning and management to address health inequalities," Social Science & Medicine, Elsevier, vol. 347(C).

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