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Modelling the impact of COVID-19 on elective waiting times

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  • Richard M Wood

Abstract

In an early stage of the COVID-19 outbreak, hospitals in England were asked to postpone elective treatments in order to accommodate the expected demand for COVID-19 related admissions. This study aims to forecast the extent to which waiting times could increase as a result of these measures, and estimate the level of effort required to restore performance to pre COVID-19 levels. A time-driven simulation is configured and calibrated based upon conditions in England as of February 2020. As a worst case scenario, where restrictions on elective care extend to twelve months and elective treatment rates are halved, results suggest performance could drop to levels not seen since 2007 and the size of the waiting list could double. Restoring performance would take two years assuming additional capacity injections of 12.5%, costing an estimated £14.7b. The modelling presented here offers clinicians and managers an insight into the outcomes that could result under a range of scenarios considered plausible at the early stage of the outbreak. Freely available as open source code, the model may be locally-calibrated for regional healthcare systems and used more widely in countries where similar elective performance measures exist.

Suggested Citation

  • Richard M Wood, 2022. "Modelling the impact of COVID-19 on elective waiting times," Journal of Simulation, Taylor & Francis Journals, vol. 16(1), pages 101-109, January.
    • Handle: RePEc:taf:tjsmxx:v:16:y:2022:i:1:p:101-109
    DOI: 10.1080/17477778.2020.1764876
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    Cited by:

    1. Richard M Wood, 2022. "Supporting COVID-19 elective recovery through scalable wait list modelling: Specialty-level application to all hospitals in England," Health Care Management Science, Springer, vol. 25(4), pages 521-525, December.

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