Literature DB >> 15717813

Using Coxian phase-type distributions to identify patient characteristics for duration of stay in hospital.

Adele H Marshall1, Sally I McClean.   

Abstract

Coxian phase-type distributions are a special type of Markov model that describes duration until an event occurs in terms of a process consisting of a sequence of latent phases. This paper considers the use of Coxian phase-type distributions for modelling patient duration of stay for the elderly in hospital and investigates the potential for using the resulting distribution as a classifying variable to identify common characteristics between different groups of patients according to their (anticipated) length of stay in hospital. The identification of common characteristics for patient length of stay groups would offer hospital managers and clinicians possible insights into the overall management and bed allocation of the hospital wards.

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Year:  2004        PMID: 15717813     DOI: 10.1007/s10729-004-7537-z

Source DB:  PubMed          Journal:  Health Care Manag Sci        ISSN: 1386-9620


  2 in total

1.  Multi-phased bed modelling.

Authors:  J Sorenson
Journal:  Health Serv Manage Res       Date:  1996-02

2.  Modelling patient duration of stay to facilitate resource management of geriatric hospitals.

Authors:  A H Marshall; S I McClean; C M Shapcott; P H Millard
Journal:  Health Care Manag Sci       Date:  2002-11
  2 in total
  5 in total

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Authors:  Jaakko Riihimäki; Reijo Sund; Aki Vehtari
Journal:  Health Care Manag Sci       Date:  2010-06

2.  Costing hospital resources for stroke patients using phase-type models.

Authors:  Jennifer Gillespie; Sally McClean; Bryan Scotney; Lalit Garg; Maria Barton; Ken Fullerton
Journal:  Health Care Manag Sci       Date:  2011-06-22

3.  A two-stage approach to the joint analysis of longitudinal and survival data utilising the Coxian phase-type distribution.

Authors:  Conor Donnelly; Lisa M McFetridge; Adele H Marshall; Hannah J Mitchell
Journal:  Stat Methods Med Res       Date:  2017-06-20       Impact factor: 3.021

4.  Predicting Intracerebral Hemorrhage Patients' Length-of-Stay Probability Distribution Based on Demographic, Clinical, Admission Diagnosis, and Surgery Information.

Authors:  Li Luo; Xueru Xu; Yan Jiang; Wei Zhu
Journal:  J Healthc Eng       Date:  2019-01-27       Impact factor: 2.682

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

Authors:  Bruce Jones; Sally McClean; David Stanford
Journal:  Health Care Manag Sci       Date:  2018-05-01
  5 in total

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