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Modelling mortality and discharge of hospitalized stroke patients using a phase-type recovery model

Author

Listed:
  • Bruce Jones

    (Western University)

  • Sally McClean

    (Ulster University)

  • David Stanford

    (Western University)

Abstract

We model the length of in-patient hospital stays due to stroke and the mode of discharge using a phase-type stroke recovery model. The model allows for three different types of stroke: haemorrhagic (the most severe, caused by ruptured blood vessels that cause brain bleeding), cerebral infarction (less severe, caused by blood clots) and transient ischemic attack or TIA (the least severe, a mini-stroke caused by a temporary blood clot). A four-phase recovery process is used, where the initial phase depends on the type of stroke, and transition from one phase to the next depends on the age of the patient. There are three differing modes of absorption for this phase-type model: from a typical recovery phase, a patient may die (mode 1), be transferred to a nursing home (mode 2) or be discharged to the individual’s usual residence (mode 3). The first recovery phase is characterized by a very high rate of mortality and very low rates of discharge by the other two modes. The next two recovery phases have progressively lower mortality rates and higher mode 2 and 3 discharge rates. The fourth recovery phase is visited only by those who experience a very mild TIA, and they are discharged to home after a short stay. The novelty of our approach to phase representation is two-fold: first, it aligns the phases with labelled diagnosis states, representing stages of illness severity; second, the model allows us to obtain expressions for Key Performance Indicators that are of use to healthcare professionals. This allows us to use a backward estimation process where we leverage the fact that we know the phase of admission (the diagnosis), but not which phases are subsequently entered or when this happens; this strategy improves both computational efficiency and accuracy. The model has clear practical value as it yields length of stay distributions by age and type of stroke, which are useful in resource planning. Also, inclusion of the three modes of discharge permits analyses of outcomes.

Suggested Citation

  • 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.
    • Handle: RePEc:kap:hcarem:v:22:y:2019:i:4:d:10.1007_s10729-018-9446-6
    DOI: 10.1007/s10729-018-9446-6
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    References listed on IDEAS

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    1. S McClean & P Millard, 2007. "Where to treat the older patient? Can Markov models help us better understand the relationship between hospital and community care?," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 58(2), pages 255-261, February.
    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. Mark Fackrell, 2009. "Modelling healthcare systems with phase-type distributions," Health Care Management Science, Springer, vol. 12(1), pages 11-26, March.
    4. Jennifer Gillespie & Sally McClean & Bryan Scotney & Lalit Garg & Maria Barton & Ken Fullerton, 2011. "Costing hospital resources for stroke patients using phase-type models," Health Care Management Science, Springer, vol. 14(3), pages 279-291, September.
    5. 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.
    6. McGrory, C.A. & Pettitt, A.N. & Faddy, M.J., 2009. "A fully Bayesian approach to inference for Coxian phase-type distributions with covariate dependent mean," Computational Statistics & Data Analysis, Elsevier, vol. 53(12), pages 4311-4321, October.
    7. H. Xie & T. J. Chaussalet & P. H. Millard, 2005. "A continuous time Markov model for the length of stay of elderly people in institutional long‐term care," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 168(1), pages 51-61, January.
    8. 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.
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    Cited by:

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