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dc.contributor.authorDemir, Eren
dc.contributor.authorChaussalet, Thierry J
dc.date.accessioned2013-01-09T16:29:06Z
dc.date.available2013-01-09T16:29:06Z
dc.date.issued2011
dc.identifier.citationDemir , E & Chaussalet , T J 2011 , ' Capturing the readmission process : the focus on the time window ' , Journal of Applied Statistics , vol. 38 , no. 5 , pp. 951 – 960 . https://doi.org/10.1080/02664761003692415
dc.identifier.issn0266-4763
dc.identifier.otherPURE: 563025
dc.identifier.otherPURE UUID: 8ac06748-1652-450b-9eb1-edc1b572f7b4
dc.identifier.otherScopus: 79952721150
dc.identifier.urihttp://hdl.handle.net/2299/9537
dc.description.abstractIn the majority of studies on patient re-admissions, a re-admission is deemed to have occurred if a patient is admitted within a time window of the previous discharge date. However, these time windows have rarely been objectively justified. We capture the re-admission process from the community using a special case of a Coxian phase-type distribution, expressed as a mixture of two generalized Erlang distributions. Using the Bayes theorem, we compute the optimal time windows in defining re-admission. From the national data set in England, we defined re-admission for chronic obstructive pulmonary disease (COPD), stroke, congestive heart failure, and hip- and thigh-fractured patients as 41, 9, 37, and 8 days, respectively. These time windows could be used to classify patients into two groups (binary response), namely those patients who are at high risk (e.g. within 41 days for COPD) and low risk of re-admission group (respectively, greater than 41 days). The generality of the modelling framework and the capability of supporting a broad class of distributions enables the applicability into other domains, to capture the process within the field of interest and to determine an appropriate time window (a cut-off value) based on evidence objectively derived from operational data.en
dc.language.isoeng
dc.relation.ispartofJournal of Applied Statistics
dc.titleCapturing the readmission process : the focus on the time windowen
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.statusPeer reviewed
dc.relation.schoolHertfordshire Business School
dcterms.dateAccepted2011
rioxxterms.versionVoR
rioxxterms.versionofrecordhttps://doi.org/10.1080/02664761003692415
rioxxterms.typeJournal Article/Review
herts.preservation.rarelyaccessedtrue


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