A conditional approach for modelling patient readmissions to hospital using a mixture of Coxian phase-type distributions incorporating Bayes' theorem
| Autor: | Adele H. Marshall, Andrew S. Gordon, Karen Cairns |
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| Rok vydání: | 2016 |
| Předmět: |
Statistics and Probability
Waiting time Gerontology 0209 industrial biotechnology Hospital readmission education.field_of_study Epidemiology business.industry Population 02 engineering and technology medicine.disease 01 natural sciences Phase (combat) humanities 010104 statistics & probability Health services Bayes' theorem 020901 industrial engineering & automation Care in the Community Medicine Elderly people Medical emergency 0101 mathematics business education |
| Zdroj: | Statistics in Medicine. 35:3810-3826 |
| ISSN: | 0277-6715 |
| DOI: | 10.1002/sim.6953 |
| Popis: | The number of elderly patients requiring hospitalisation in Europe is rising. With a greater proportion of elderly people in the population comes a greater demand for health services and, in particular, hospital care. Thus, with a growing number of elderly patients requiring hospitalisation competing with non-elderly patients for a fixed (and in some cases, decreasing) number of hospital beds, this results in much longer waiting times for patients, often with a less satisfactory hospital experience. However, if a better understanding of the recurring nature of elderly patient movements between the community and hospital can be developed, then it may be possible for alternative provisions of care in the community to be put in place and thus prevent readmission to hospital. The research in this paper aims to model the multiple patient transitions between hospital and community by utilising a mixture of conditional Coxian phase-type distributions that incorporates Bayes' theorem. For the purpose of demonstration, the results of a simulation study are presented and the model is applied to hospital readmission data from the Lombardy region of Italy. Copyright © 2016 John Wiley & Sons, Ltd. |
| Databáze: | OpenAIRE |
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