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Analysing data on lengths of stay of hospital patients using phase‐type distributions

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  • M. J. Faddy
  • S. I. McClean

Abstract

Phase‐type distributions which describe the time to absorption of a continuous‐time Markov chain are applied to analyse some data on lengths of stay of hospital patients. The phases (or transient states of the Markov chain) can be interpreted in terms of increased severity of any illnesses being treated. This leads to an identification of ‘short‐stay’, ‘medium‐stay’ and ‘long‐stay’ patients, with the phase‐type distribution interpreted as a mixture of such components. Differential effects of two covariates, age of patient at admission and year of admission, are shown on the different phases of the distribution. Copyright © 1999 John Wiley & Sons, Ltd.

Suggested Citation

  • M. J. Faddy & S. I. McClean, 1999. "Analysing data on lengths of stay of hospital patients using phase‐type distributions," Applied Stochastic Models in Business and Industry, John Wiley & Sons, vol. 15(4), pages 311-317, October.
    • Handle: RePEc:wly:apsmbi:v:15:y:1999:i:4:p:311-317
    DOI: 10.1002/(SICI)1526-4025(199910/12)15:43.0.CO;2-S
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    Cited by:

    1. Christina Chatzimichail & Pavlos Kolias & Alexandra Papadopoulou, 2024. "Cost Evaluation for Capacity Planning Based on Patients’ Pathways via Semi-Markov Reward Modelling," Mathematics, MDPI, vol. 12(10), pages 1-15, May.
    2. Yuta Kanai & Hideaki Takagi, 2021. "Markov chain analysis for the neonatal inpatient flow in a hospital," Health Care Management Science, Springer, vol. 24(1), pages 92-116, March.
    3. Nikolaos Stavropoulos & Alexandra Papadopoulou & Pavlos Kolias, 2021. "Evaluating the Efficiency of Off-Ball Screens in Elite Basketball Teams via Second-Order Markov Modelling," Mathematics, MDPI, vol. 9(16), pages 1-13, August.
    4. Eren Demir & Thierry Chaussalet, 2011. "Capturing the re-admission process: focus on time window," Journal of Applied Statistics, Taylor & Francis Journals, vol. 38(5), pages 951-960, December.

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