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Modelling nationwide hospital length of stay: opening the black box

Author

Listed:
  • C Vasilakis

    (University of Westminster)

  • A H Marshall

    (Queen's University of Belfast)

Abstract

Hospital length of stay is considered to be a reliable and valid proxy for measuring the consumption of hospital resources. Average length of stay, however, albeit easy to quantify and calculate, does not suitably reflect the nature of such underlying distributions and may therefore mask the effects that the different streams of patients have on the system. This paper uses routinely collected and readily available nationwide data on stroke-related patients, aged 65 years and over, who were discharged from English hospitals over a 1-year period. This will be the basis for a running example illustrating the alternative methods of analysis and models of patients' length of stay. The methods include statistical methods: survival analysis, mixed exponential and phase-type distributions; and decision modelling techniques: compartmental and simulation models. The paper concludes by summarizing these various modelling techniques and by highlighting the similarity of the estimated parameters of patient flow as calculated by the phase-type distribution and compartmental modelling techniques.

Suggested Citation

  • C Vasilakis & A H Marshall, 2005. "Modelling nationwide hospital length of stay: opening the black box," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 56(7), pages 862-869, July.
    • Handle: RePEc:pal:jorsoc:v:56:y:2005:i:7:d:10.1057_palgrave.jors.2601872
    DOI: 10.1057/palgrave.jors.2601872
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    Citations

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    Cited by:

    1. Amir Elalouf & Dmitry Tsadikovich & Sharon Hovav, 2021. "A simulation-based approach for improving the clinical blood sample supply chain," Health Care Management Science, Springer, vol. 24(1), pages 216-233, March.
    2. Azcarate, Cristina & Esparza, Laida & Mallor, Fermin, 2020. "The problem of the last bed: Contextualization and a new simulation framework for analyzing physician decisions," Omega, Elsevier, vol. 96(C).
    3. Harris, Shannon L. & May, Jerrold H. & Vargas, Luis G., 2016. "Predictive analytics model for healthcare planning and scheduling," European Journal of Operational Research, Elsevier, vol. 253(1), pages 121-131.
    4. Bruce Jones & Sally McClean & David Stanford, 2019. "Modelling mortality and discharge of hospitalized stroke patients using a phase-type recovery model," Health Care Management Science, Springer, vol. 22(4), pages 570-588, December.
    5. J P Oddoye & M A Yaghoobi & M Tamiz & D F Jones & P Schmidt, 2007. "A multi-objective model to determine efficient resource levels in a medical assessment unit," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 58(12), pages 1563-1573, December.
    6. Mark Fackrell, 2009. "Modelling healthcare systems with phase-type distributions," Health Care Management Science, Springer, vol. 12(1), pages 11-26, March.
    7. Fermín Mallor & Cristina Azcárate & Julio Barado, 2015. "Optimal control of ICU patient discharge: from theory to implementation," Health Care Management Science, Springer, vol. 18(3), pages 234-250, September.
    8. Fermín Mallor & Cristina Azcárate & Julio Barado, 2016. "Control problems and management policies in health systems: application to intensive care units," Flexible Services and Manufacturing Journal, Springer, vol. 28(1), pages 62-89, June.
    9. Fermín Mallor & Cristina Azcárate, 2014. "Combining optimization with simulation to obtain credible models for intensive care units," Annals of Operations Research, Springer, vol. 221(1), pages 255-271, October.
    10. John Bowers, 2013. "Balancing operating theatre and bed capacity in a cardiothoracic centre," Health Care Management Science, Springer, vol. 16(3), pages 236-244, September.
    11. N C Proudlove & S Black & A Fletcher, 2007. "OR and the challenge to improve the NHS: modelling for insight and improvement in in-patient flows," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 58(2), pages 145-158, February.
    12. Willoughby, Keith A. & Chan, Benjamin T.B. & Marques, Shauna, 2016. "Using simulation to test ideas for improving speech language pathology services," European Journal of Operational Research, Elsevier, vol. 252(2), pages 657-664.
    13. B Shaw & A H Marshall, 2007. "Modelling the flow of congestive heart failure patients through a hospital system," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 58(2), pages 212-218, February.
    14. Asaduzzaman, Md & Chaussalet, Thierry J., 2014. "Capacity planning of a perinatal network with generalised loss network model with overflow," European Journal of Operational Research, Elsevier, vol. 232(1), pages 178-185.

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