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Title: Using phase-type models to cost stroke patient care across health, social and community services
Authors: McClean, Sally
Gillespie, Jennifer
Garg, Lalit
Barton, Maria
Scotney, Bryan
Kullerton, Ken
Keywords: Cost effectiveness
Decision making
Markov processes
Medical care
Issue Date: 2014
Publisher: Elsevier BV
Citation: McClean, S., Gillespie, J., Garg, L., Barton, M., Scotney, B., & Kullerton, K. (2014). Using phase-type models to cost stroke patient care across health, social and community services. European Journal of Operational Research, 236(1), 190-199.
Abstract: Stroke disease places a heavy burden on society, incurring long periods of time in hospital and community care, and associated costs. Also stroke is a highly complex disease with diverse outcomes and multiple strategies for therapy and care. Previously a modeling framework has been developed which clusters patients into classes with respect to their length of stay (LOS) in hospital. Phase-type models were then used to describe patient flows for each cluster. Also multiple outcomes, such as discharge to normal residence, nursing home, or death can be permitted. We here add costs to this model and obtain the Moment Generating Function for the total cost of a system consisting of multiple transient phase-type classes with multiple absorbing states. This system represents different classes of patients in different hospital and community services states. Based on stroke patients’ data from the Belfast City Hospital, various scenarios are explored with a focus on comparing the cost of thrombolysis treatment under different regimes. The overall modeling framework characterizes the behavior of stroke patient populations, with a focus on integrated system-wide costing and planning, encompassing hospital and community services. Within this general framework we have developed models which take account of patient heterogeneity and multiple care options. Such complex strategies depend crucially on developing a deep engagement with the health care professionals and underpinning the models with detailed patient-specific data.
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