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Waiting list behaviour and the consequences for NHS targets

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  • J Bowers

    (University of Stirling)

Abstract

The United Kingdom's National Health Service (NHS) is investing considerable resources in reducing patient waiting times for elective treatment. This paper describes the development of a waiting list model and its use in a simulation to assess management options. Simulation usually assumes that waiting is adequately described by simple queuing disciplines, typically first-in-first-out. However, waiting in the United Kingdom's NHS is a more complex phenomenon. The waiting list behaviour is explored through an analysis of the changes in waiting time distributions for elective orthopaedics in one Scottish Health Board, NHS Fife. The evolving distributions suggest that there have been substantial changes in priorities in response to the various NHS targets. However, in the short or medium term, the form of the distribution appears reasonably stable, providing a basis for estimating future waiting times in different scenarios. A model of the waiting behaviour and prioritization in the appointment allocations was embedded in a simulation of the complete elective orthopaedic patient journey from referral, through outpatients and diagnostics to surgery. The model has been used to explore the consequences of various management options in the context of the NHS target that no patient should wait more than 18 weeks between referral and treatment.

Suggested Citation

  • J Bowers, 2010. "Waiting list behaviour and the consequences for NHS targets," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 61(2), pages 246-254, February.
  • Handle: RePEc:pal:jorsoc:v:61:y:2010:i:2:d:10.1057_jors.2008.149
    DOI: 10.1057/jors.2008.149
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    References listed on IDEAS

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    Cited by:

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    3. Fabián Silva-Aravena & Eduardo Álvarez-Miranda & César A. Astudillo & Luis González-Martínez & José G. Ledezma, 2021. "Patients’ Prioritization on Surgical Waiting Lists: A Decision Support System," Mathematics, MDPI, vol. 9(10), pages 1-27, May.
    4. Fabián Silva-Aravena & Hugo Núñez Delafuente & César A. Astudillo, 2022. "A Novel Strategy to Classify Chronic Patients at Risk: A Hybrid Machine Learning Approach," Mathematics, MDPI, vol. 10(17), pages 1-17, August.
    5. Nguyen, Thu Ba T. & Sivakumar, Appa Iyer & Graves, Stephen C., 2018. "Capacity planning with demand uncertainty for outpatient clinics," European Journal of Operational Research, Elsevier, vol. 267(1), pages 338-348.

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