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dc.contributor.authorDemir, Eren
dc.contributor.authorChaussalet, Thierry J.
dc.contributor.authorWeingarten, Noam
dc.contributor.authorKiss, Tamas
dc.contributor.editorMcClean, S.
dc.contributor.editorMillard, P.
dc.contributor.editorEl-Darzi, E.
dc.contributor.editorNugent, C. D.
dc.identifier.citationDemir , E , Chaussalet , T J , Weingarten , N & Kiss , T 2009 , A Grid implementation for profiling hospitals based on patient readmissions . in S McClean , P Millard , E El-Darzi & C D Nugent (eds) , Intelligent Patient Management . Studies in Computational Intelligence , vol. 189 , Springer Nature , pp. 127-146 .
dc.identifier.otherPURE: 563117
dc.identifier.otherPURE UUID: 045baa23-5ec7-4d16-841c-45af3d60539b
dc.identifier.otherScopus: 62249172247
dc.description.abstractGenerally, high level of readmission is associated with poor patient care, hence, its relation to the quality of care is plausible. Frequent patient readmissions have personal, financial and organisational consequences. This has motivated healthcare commissioners in England to use emergency readmission as an indicator in the performance rating framework. A statistical model, known as the multilevel transition model was previously developed, where individual hospitals propensity for first readmission, second readmission, third (and so on) were con-sidered to be measures of performance. Using these measures, we defined a new performance index. During the period 1997 and 2004, the national (England) hos-pital episodes statistics dataset comprise more than 5 million patient readmissions. Implementing a statistical model using the complete population dataset could possibly take weeks to estimate the parameters. Moreover, it is not statistically sound to utilise the full population dataset. To resolve the problem, we extract 1000 random samples from the original data, where each random sample is likely to lead to differing hospital performance measures. For computational efficiency a Grid implementation of the model is developed. Using a stand-alone computer, it took approximately 500 hours to estimate 1000 samples, whereas in the Grid implementation, the full 1000 samples were analysed in less than 24 hours. From the 167 National Health Service Acute and Foundation Trusts in England, 4 out of the 5 worst performing hospitals treating cancer patients were in London.en
dc.publisherSpringer Nature
dc.relation.ispartofIntelligent Patient Management
dc.relation.ispartofseriesStudies in Computational Intelligence
dc.titleA Grid implementation for profiling hospitals based on patient readmissionsen
dc.contributor.institutionStatistical Services Consulting Unit
dc.contributor.institutionHertfordshire Business School
dc.contributor.institutionSocial Sciences, Arts & Humanities Research Institute
dc.contributor.institutionHealth Services and Medicine
dc.contributor.institutionCentre for Research on Management, Economy and Society
dc.contributor.institutionDepartment of Marketing and Enterprise
dc.description.statusNon peer reviewed

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