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A simulation-based decision support tool for informing the management of patients with Parkinson’s disease

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  • Eren Demir
  • Christos Vasilakis
  • Reda Lebcir
  • David Southern

Abstract

We describe a decision support toolkit that was developed with the aim of assisting those responsible with the management and treatment of Parkinson’s disease (PD) in the UK. Having created a baseline model and established its face validity, the toolkit captures the complexity of PD services at a sufficient level and operates within a user-friendly environment; that is, an interface was built to allow users to specify their own local PD service and input their own estimates or data of service demands and capacities. The main strength of this decision support tool is the adoption of a team approach to studying the system, involving six PD specialist nurses across the country, ensuring that variety of views and suggestions are taken as well as systems modelling and simulations. The tool enables key decision-makers to estimate the likely impact of changes, such as increased use of community services on activity, cost, staffing levels, skill-mix and utilisation of resources. Such previously unobtainable quantitative information can be used to support business cases for changes in the increased use of community services and its impact on clinical outcomes (disease progression), nurse visits and costing.

Suggested Citation

  • Eren Demir & Christos Vasilakis & Reda Lebcir & David Southern, 2015. "A simulation-based decision support tool for informing the management of patients with Parkinson’s disease," International Journal of Production Research, Taylor & Francis Journals, vol. 53(24), pages 7238-7251, December.
    • Handle: RePEc:taf:tprsxx:v:53:y:2015:i:24:p:7238-7251
    DOI: 10.1080/00207543.2015.1029647
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    References listed on IDEAS

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    1. J B Jun & S H Jacobson & J R Swisher, 1999. "Application of discrete-event simulation in health care clinics: A survey," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 50(2), pages 109-123, February.
    2. H Pilgrim & P Tappenden & J Chilcott & M Bending & P Trueman & A Shorthouse & J Tappenden, 2009. "The costs and benefits of bowel cancer service developments using discrete event simulation," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 60(10), pages 1305-1314, October.
    3. Brailsford, Sally & Schmidt, Bernd, 2003. "Towards incorporating human behaviour in models of health care systems: An approach using discrete event simulation," European Journal of Operational Research, Elsevier, vol. 150(1), pages 19-31, October.
    4. Robert W. Klein & Robert S. Dittus & Stephen D. Roberts & James R. Wilson, 1993. "Simulation Modeling and Health-care Decision Making," Medical Decision Making, , vol. 13(4), pages 347-354, December.
    5. M S Rauner & S C Brailsford & S Flessa, 2005. "Use of discrete-event simulation to evaluate strategies for the prevention of mother-to-child transmission of HIV in developing countries," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 56(2), pages 222-233, February.
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    Cited by:

    1. Richard M Wood & Christopher J McWilliams & Matthew J Thomas & Christopher P Bourdeaux & Christos Vasilakis, 2020. "COVID-19 scenario modelling for the mitigation of capacity-dependent deaths in intensive care," Health Care Management Science, Springer, vol. 23(3), pages 315-324, September.
    2. Eren Demir & David Southern, 2017. "Enabling better management of patients: discrete event simulation combined with the STAR approach," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 68(5), pages 577-590, May.
    3. Bokhorst, Jos A.C. & van der Vaart, Taco, 2018. "Acute medical unit design – The impact of rearranged patient flows," Socio-Economic Planning Sciences, Elsevier, vol. 62(C), pages 75-83.

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