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Examining the feasibility of using a modelling tool to assess resilience across a health-care system and assist with decisions concerning service reconfiguration

Author

Listed:
  • Sonya Crowe

    (Clinical Operational Research Unit, University College London, London, UK)

  • Christos Vasilakis

    (The Information, Decisions and Operations Group, School of Management, University of Bath, UK)

  • Andrew Skeen

    (Clinical Operational Research Unit, University College London, London, UK)

  • Phil Storr

    (NHS Resilience Project, Department of Health, London, UK)

  • Peter Grove

    (Health Protection Analytical Team, Department of Health, London, UK)

  • Steve Gallivan

    (Clinical Operational Research Unit, University College London, London, UK)

  • Martin Utley

    (Clinical Operational Research Unit, University College London, London, UK)

Abstract

Changes in medical practice, demographic shifts and financial pressures are all examples of factors that may contribute to demand for periodic changes in the configuration of health services. When reconfiguring a service, health planners often take into account projected demand for services, patient access criteria and budgetary constraints (among other things), but typically give little consideration regarding its resilience to deliver services during and after external disruptions to its capability to deliver. In this paper we discuss a study conducted in response to a direct request from the National Health Service (NHS) Resilience Project within the Department of Health to explore the feasibility of assessing resilience across local services within the NHS and developing a computer software tool to assess resilience of different service reconfigurations. We give an account of the modelling process used, including the analytical framework we developed using both optimisation and heuristic methods, and an illustrative example of usage of a prototype software tool. We also highlight the key lessons that emerged during this project, which may be helpful to OR analysts working on similar projects regarding resilience in the public sector.

Suggested Citation

  • Sonya Crowe & Christos Vasilakis & Andrew Skeen & Phil Storr & Peter Grove & Steve Gallivan & Martin Utley, 2014. "Examining the feasibility of using a modelling tool to assess resilience across a health-care system and assist with decisions concerning service reconfiguration," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 65(10), pages 1522-1532, October.
    • Handle: RePEc:pal:jorsoc:v:65:y:2014:i:10:p:1522-1532
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    Cited by:

    1. Priscila Silva & Mariana Hidalgo & Mindy Hotchkiss & Lasitha Dharmasena & Igor Linkov & Lance Fiondella, 2024. "Predictive Resilience Modeling Using Statistical Regression Methods," Mathematics, MDPI, vol. 12(15), pages 1-30, July.
    2. Tippong, Danuphon & Petrovic, Sanja & Akbari, Vahid, 2022. "A review of applications of operational research in healthcare coordination in disaster management," European Journal of Operational Research, Elsevier, vol. 301(1), pages 1-17.

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