Show simple item record

dc.contributor.authorKulinskaya, E.
dc.contributor.authorKornbrot, D.
dc.contributor.authorGao, H.
dc.date.accessioned2011-08-08T14:01:14Z
dc.date.available2011-08-08T14:01:14Z
dc.date.issued2005
dc.identifier.citationKulinskaya , E , Kornbrot , D & Gao , H 2005 , ' Length of stay as a performance indicator : robust statistical methodology ' , IMA Journal of Management Mathematics , vol. 16 , no. 4 , pp. 369-381 . https://doi.org/10.1093/imaman/dpi015
dc.identifier.issn1471-678X
dc.identifier.otherPURE: 193962
dc.identifier.otherPURE UUID: b3b0a263-56f5-4488-87bf-13c3730e846c
dc.identifier.otherdspace: 2299/242
dc.identifier.otherScopus: 26444552252
dc.identifier.urihttp://hdl.handle.net/2299/6062
dc.descriptionThis is a pre-copy-editing, author produced PDF of an article accepted for publication in IMA Journal of Management Mathematics following peer review. The definitive publisher-authenticated version [Kulinskaya, E. , Kornbrot, D. and Gao, H. (2005) 'Length of stay as a performance indicator : robust statistical methodology'. IMA Journal of Management Mathematics 16 (4) pp.369-381] is available online at : http://imaman.oxfordjournals.org/archive/index.dtl . --Copyright Institute of Mathematics and its Applications-- --DOI : 10.1093/imaman/dpi015
dc.description.abstractLength of stay (LOS) is an important performance indicator for costing and hospital management and a key measure of efficiency of NHS. However, LOS is difficult to analyse because its statistical distribution is non-normal and LOS data habitually have many outliers. Furthermore, the usefulness of LOS for improving NHS performance is undermined because no adjustments are made for some key factors. This paper addresses both these problems. Health episodes statistics data from the UK NHS for 1997/98, and 1998/99 are analysed to investigate the effects of five key variables: admission method, discharge destination, provider (hospital) type, speciality and NHS region. All are found to influence LOS. The effects of some factors are substantial, and were not previously known, and so are not included in planned future NHS performance measures, e.g. LOS is at least 25% longer for patients transferred from other hospitals rather than admitted as an emergency; and LOS for patients discharged to private institutions is more than twice that for patients discharged to NHS institutions or their own home. The problem of finding the most appropriate statistical analysis for data of the LOS type is addressed by comparing standard general linear model methods with an advanced robust method called truncated maximum likelihood (TML). The TML methods are shown to have several advantages over standard methods, in terms of model fit and accuracy of parameter estimation. Implications of these findings for future use of LOS are considered.en
dc.language.isoeng
dc.relation.ispartofIMA Journal of Management Mathematics
dc.rights/dk/atira/pure/core/openaccesspermission/open
dc.titleLength of stay as a performance indicator : robust statistical methodologyen
dc.contributor.institutionDepartment of Psychology
dc.description.statusPeer reviewed
rioxxterms.versionofrecordhttps://doi.org/10.1093/imaman/dpi015
rioxxterms.typeJournal Article/Review
herts.preservation.rarelyaccessedtrue
herts.rights.accesstyperestrictedAccess


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record