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Discrete Conditional Phase-type models utilising classification trees: Application to modelling health service capacities

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  • Harper, P.R.
  • Knight, V.A.
  • Marshall, A.H.

Abstract

Discrete Conditional Phase-type models (DC-Ph) consist of a process component (survival distribution) preceded by a set of related conditional discrete variables. This paper introduces a DC-Ph model where the conditional component is a classification tree. The approach is utilised for modelling health service capacities by better predicting service times, as captured by Coxian phase-type distributions, interfaced with results from a classification tree algorithm. To illustrate the approach, a case-study within the healthcare delivery domain is given, namely that of maternity services. The classification analysis is shown to give good predictors for complications during childbirth. Based on the classification tree predictions, the duration of childbirth on the labour ward is then modelled as either a two or three-phase Coxian distribution. The resulting DC-Ph model is used to calculate the number of patients and associated bed occupancies, patient turnover, and to model the consequences of changes to risk status.

Suggested Citation

  • Harper, P.R. & Knight, V.A. & Marshall, A.H., 2012. "Discrete Conditional Phase-type models utilising classification trees: Application to modelling health service capacities," European Journal of Operational Research, Elsevier, vol. 219(3), pages 522-530.
    • Handle: RePEc:eee:ejores:v:219:y:2012:i:3:p:522-530
    DOI: 10.1016/j.ejor.2011.10.035
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    References listed on IDEAS

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    1. P R Harper & A K Shahani, 2002. "Modelling for the planning and management of bed capacities in hospitals," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 53(1), pages 11-18, January.
    2. Etzioni, Ruth D. & Feuer, Eric J. & Sullivan, Sean D. & Lin, Danyu & Hu, Chengcheng & Ramsey, Scott D., 1999. "On the use of survival analysis techniques to estimate medical care costs," Journal of Health Economics, Elsevier, vol. 18(3), pages 365-380, June.
    3. 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.
    4. Harper, Paul R. & Winslett, David J., 2006. "Classification trees: A possible method for maternity risk grouping," European Journal of Operational Research, Elsevier, vol. 169(1), pages 146-156, February.
    5. Harper, Paul R., 2005. "A review and comparison of classification algorithms for medical decision making," Health Policy, Elsevier, vol. 71(3), pages 315-331, March.
    6. Mark Fackrell, 2009. "Modelling healthcare systems with phase-type distributions," Health Care Management Science, Springer, vol. 12(1), pages 11-26, March.
    7. Paul Harper & Evandro Leite Jr., 2008. "TreeWorks: Advances in Scalable Decision Trees," International Journal of Healthcare Information Systems and Informatics (IJHISI), IGI Global, vol. 3(4), pages 53-68, October.
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    Cited by:

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    2. McClean, Sally & Gillespie, Jennifer & Garg, Lalit & Barton, Maria & Scotney, Bryan & Kullerton, Ken, 2014. "Using phase-type models to cost stroke patient care across health, social and community services," European Journal of Operational Research, Elsevier, vol. 236(1), pages 190-199.
    3. 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.
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    5. 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).
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