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dc.contributor.authorDemir, Eren
dc.contributor.authorChaussalet, Thierry
dc.contributor.authorAdeyemi, Shola
dc.contributor.authorToffa, Sam
dc.date.accessioned2013-01-09T15:29:11Z
dc.date.available2013-01-09T15:29:11Z
dc.date.issued2012-11
dc.identifier.citationDemir , E , Chaussalet , T , Adeyemi , S & Toffa , S 2012 , ' Profiling hospitals based on emergency readmission: A multilevel transition modelling approach ' , Computer Methods and Programs in Biomedicine , vol. 108 , no. 2 , pp. 487-499 . https://doi.org/10.1016/j.cmpb.2011.03.003
dc.identifier.issn1872-7565
dc.identifier.otherPURE: 561092
dc.identifier.otherPURE UUID: ccc573f5-d7a8-4698-a923-fe9db9f0572b
dc.identifier.otherPubMed: 21612839
dc.identifier.otherWOS: 000310828200004
dc.identifier.otherScopus: 84867403118
dc.identifier.urihttp://hdl.handle.net/2299/9536
dc.descriptionCopyright © 2011 Elsevier Ireland Ltd. All rights reserved.
dc.description.abstractEmergency readmission is seen as an important part of the United Kingdom government policy to improve the quality of care that patients receive. In this context, patients and the public have the right to know how well different health organizations are performing. Most methods for profiling estimate the expected numbers of adverse outcomes (e.g. readmission, mortality) for each organization. A number of statistical concerns have been raised, such as the differences in hospital sizes and the unavailability of relevant data for risk adjustment. Having recognized these statistical concerns, a new framework known as the multilevel transition model is developed. Hospital specific propensities of the first, second and further readmissions are considered to be measures of performance, where these measures are used to define a new performance index. During the period 1997 and 2004, the national (English) hospital episodes statistics dataset comprise more than 5 million patient readmissions. Implementing a multilevel model using the complete population dataset could possibly take weeks to estimate the parameters. 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. Analysing the output from the full 1000 sample, we noticed that 4 out of the 5 worst performing hospitals treating cancer patients were in London. These hospitals are known to be the leading NHS Trusts in England, providing diverse range of services to complex patients, and therefore it is inevitable to expect higher numbers of emergency readmissions.en
dc.format.extent13
dc.language.isoeng
dc.relation.ispartofComputer Methods and Programs in Biomedicine
dc.subjectSURGICAL-PROCEDURES
dc.subjectSTATISTICAL-MODELS
dc.subjectMultilevel transition model
dc.subjectMORTALITY-RATES
dc.subjectCARE
dc.subjectHEART-FAILURE
dc.subjectProfiling hospitals
dc.subjectACUTE MYOCARDIAL-INFARCTION
dc.subjectLEAGUE TABLES
dc.subjectGrid
dc.subjectEmergency readmission
dc.subjectCABG SURGERY
dc.subjectSMALL-AREA VARIATIONS
dc.subjectPATIENT PREDICTORS
dc.titleProfiling hospitals based on emergency readmission: A multilevel transition modelling approachen
dc.contributor.institutionStatistical Services Consulting Unit
dc.contributor.institutionHertfordshire Business School
dc.contributor.institutionSocial Sciences, Arts & Humanities Research Institute
dc.contributor.institutionDepartment of Marketing and Enterprise
dc.contributor.institutionHealth Services and Medicine
dc.contributor.institutionCentre for Research on Management, Economy and Society
dc.contributor.institutionHealthcare Management and Policy Research Unit
dc.description.statusPeer reviewed
rioxxterms.versionVoR
rioxxterms.versionofrecordhttps://doi.org/10.1016/j.cmpb.2011.03.003
rioxxterms.typeJournal Article/Review
herts.preservation.rarelyaccessedtrue


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