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dc.contributor.authorDemir, Eren
dc.contributor.authorChaussalet, Thierry J.
dc.contributor.authorXie, Haifeng
dc.date.accessioned2013-11-26T09:29:54Z
dc.date.available2013-11-26T09:29:54Z
dc.date.issued2007-06-25
dc.identifier.citationDemir , E , Chaussalet , T J & Xie , H 2007 , Determining readmission time window using mixture of generalised Erlang distribution . in Procs 20th IEEE Int Symposium on Computer-Based Medical Systems : CBMS 2007 . IEEE , pp. 21-26 , 20th IEEE International Symposium on Computer-Based Medical Systems , Maribor , Slovenia , 20/06/07 . https://doi.org/10.1109/CBMS.2007.39
dc.identifier.citationconference
dc.identifier.isbn0-7695-2905-4
dc.identifier.isbn1063-7125
dc.identifier.otherPURE: 635967
dc.identifier.otherPURE UUID: 1f638b4e-10c5-448d-8d86-bce73e6f1bd3
dc.identifier.otherScopus: 34748867711
dc.identifier.urihttp://hdl.handle.net/2299/12194
dc.descriptionErin Demir, et al, 'Determining Readmission Time Window Using Mixture of Generalised Erlang Distribution', in Proceedings of the 20th IEEE International Symposium on Computer-Based Medical Systems, June 2007, doi: https://doi.org/10.1109/CBMS.2007.39.
dc.description.abstractThe absence of a unified definition of readmissions has motivated the development of a modelling approach, to systematically tackle the issue surrounding the appropriate choice of a time window which defines readmission. The population of discharged patients can be broadly divided in two groups - a group at high risk of readmission and a group at low risk. This approach extends previous work by the authors, without restricting the number of stages, that patients may experience in the community. Using the national data (UK), we demonstrate its usefulness in the case of chronic obstructive pulmonary disease (COPD) which is known to be one of the leading causes of readmission. We further investigate variability in the definition of readmission among 10 strategic health authorities (SHAs) in England and observe that there are differences in the estimated time window across SHAs. The novelty of this modelling approach is the ability of capturing time to readmission that exhibit a non-zero mode and to estimate an appropriate time window based on evidence objectively derived from operational data.en
dc.language.isoeng
dc.publisherIEEE
dc.relation.ispartofProcs 20th IEEE Int Symposium on Computer-Based Medical Systems
dc.titleDetermining readmission time window using mixture of generalised Erlang distributionen
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.relation.schoolHertfordshire Business School
dcterms.dateAccepted2007-06-25
rioxxterms.versionofrecordhttps://doi.org/10.1109/CBMS.2007.39
rioxxterms.typeOther
herts.preservation.rarelyaccessedtrue


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