Literature DB >> 12437281

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

A H Marshall1, S I McClean, C M Shapcott, P H Millard.   

Abstract

A fundamental aspect of health care management is the effective allocation of resources. This is of particular importance in geriatric hospitals where elderly patients tend to have more complex needs. Hospital managers would benefit immensely if they had advance knowledge of patient duration of stay in hospital. Managers could assess the costs of patient care and make allowances for these in their budget. In this paper. we tackle this important problem via a model which predicts the duration of stay distribution of patients in hospital. The approach uses phase-type distributions conditioned on a Bayesian belief network.

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Year:  2002        PMID: 12437281     DOI: 10.1023/a:1020394525938

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


  1 in total

1.  Developing a Bayesian belief network for the management of geriatric hospital care.

Authors:  A H Marshall; S I McClean; C M Shapcott; I R Hastie; P H Millard
Journal:  Health Care Manag Sci       Date:  2001-02
  1 in total
  7 in total

1.  Short term hospital occupancy prediction.

Authors:  Steven J Littig; Mark W Isken
Journal:  Health Care Manag Sci       Date:  2007-02

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

Authors:  Adele H Marshall; Sally I McClean
Journal:  Health Care Manag Sci       Date:  2004-11

Review 3.  Length of stay-based patient flow models: recent developments and future directions.

Authors:  Adele Marshall; Christos Vasilakis; Elia El-Darzi
Journal:  Health Care Manag Sci       Date:  2005-08

4.  Addressing bed costs for the elderly: a new methodology for modelling patient outcomes and length of stay.

Authors:  Adele H Marshall; Sally I McClean; Peter H Millard
Journal:  Health Care Manag Sci       Date:  2004-02

5.  Understanding Dynamic Status Change of Hospital Stay and Cost Accumulation via Combining Continuous and Finitely Jumped Processes.

Authors:  Yanqiao Zheng; Xiaobing Zhao; Xiaoqi Zhang
Journal:  Comput Math Methods Med       Date:  2018-06-10       Impact factor: 2.238

6.  Comparison of hospital charge prediction models for gastric cancer patients: neural network vs. decision tree models.

Authors:  Jing Wang; Man Li; Yun-tao Hu; Yu Zhu
Journal:  BMC Health Serv Res       Date:  2009-09-14       Impact factor: 2.655

7.  Comparison of hospital charge prediction models for colorectal cancer patients: neural network vs. decision tree models.

Authors:  Seung-Mi Lee; Jin-Oh Kang; Yong-Moo Suh
Journal:  J Korean Med Sci       Date:  2004-10       Impact factor: 2.153

  7 in total

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