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Statistics in Medicine
Article . 2016 . Peer-reviewed
License: Wiley Online Library User Agreement
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zbMATH Open
Article . 2016
Data sources: zbMATH Open
image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao
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A conditional approach for modelling patient readmissions to hospital using a mixture of Coxian phase‐type distributions incorporating Bayes' theorem

A conditional approach for modelling patient readmissions to hospital using a mixture of Coxian phase-type distributions incorporating Bayes' theorem
Authors: Gordon, Andrew S.; Marshall, Adele H.; Cairns, Karen J.;

A conditional approach for modelling patient readmissions to hospital using a mixture of Coxian phase‐type distributions incorporating Bayes' theorem

Abstract

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.

Country
United Kingdom
Related Organizations
Keywords

330, readmission, Bayes Theorem, Patient Readmission, 004, Applications of statistics to biology and medical sciences; meta analysis, survival analysis, Europe, Hospitalization, Bayes' theorem, length of stay, Italy, Coxian phase-type distribution, Humans, Computer Simulation, Aged

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
6
Top 10%
Average
Average
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