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dc.contributor.authorIezzoni, Lisa I.
dc.contributor.authorShwartz, Michael
dc.contributor.authorAsh, Arlene S.
dc.contributor.authorHughes, John S.
dc.contributor.authorDaley, Jennifer
dc.contributor.authorMackiernan, Yevgenia D.
dc.date2022-08-11T08:10:41.000
dc.date.accessioned2022-08-23T17:16:54Z
dc.date.available2022-08-23T17:16:54Z
dc.date.issued1995-06-01
dc.date.submitted2010-07-01
dc.identifier.citationInt J Qual Health Care. 1995 Jun;7(2):81-94. <a href="http://intqhc.oxfordjournals.org/cgi/reprint/7/2/81">Link to article on publisher's site</a>
dc.identifier.issn1353-4505 (Linking)
dc.identifier.pmid7655814
dc.identifier.urihttp://hdl.handle.net/20.500.14038/47505
dc.description.abstractMortality rates are commonly used to judge hospital performance. In comparing death rates across hospitals, it is important to control for differences in patient severity. Various severity tools are now actively marketed in the United States. This study asked whether one would identify different hospitals as having higher- or lower-than-expected death rates using different severity measures. We applied 11 widely-used severity measures to the same database containing 9407 medically-treated stroke patients from 94 hospitals, with 916 (9.7%) in-hospital deaths. Unadjusted hospital mortality rates ranged from 0 to 24.4%. For 27 hospitals, observed mortality rates differed significantly from expected rates when judged by one or more, but not all 11, severity methods. The agreement between pairs of severity methods for identifying the worst 10% or best 50% of hospitals was fair to good. Efforts to evaluate hospital performance based on severity-adjusted, in-hospital death rates for stroke patients are likely to be sensitive to how severity is measured.
dc.language.isoen_US
dc.relation<a href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=pubmed&cmd=Retrieve&list_uids=7655814&dopt=Abstract">Link to Article in PubMed</a>
dc.relation.urlhttp://intqhc.oxfordjournals.org/cgi/reprint/7/2/81
dc.subjectAdolescent
dc.subjectAdult
dc.subjectAged
dc.subjectAged, 80 and over
dc.subjectAlgorithms
dc.subjectBias (Epidemiology)
dc.subjectCerebrovascular Disorders
dc.subjectDatabases, Factual
dc.subjectFemale
dc.subjectForecasting
dc.subject*Hospital Mortality
dc.subjectHospitals
dc.subjectHumans
dc.subjectMale
dc.subjectMiddle Aged
dc.subjectObserver Variation
dc.subjectOutcome Assessment (Health Care)
dc.subjectROC Curve
dc.subjectRetrospective Studies
dc.subject*Severity of Illness Index
dc.subjectUnited States
dc.subjectBiostatistics
dc.subjectEpidemiology
dc.subjectHealth Services Research
dc.titleUsing severity-adjusted stroke mortality rates to judge hospitals
dc.typeJournal Article
dc.source.journaltitleInternational journal for quality in health care : journal of the International Society for Quality in Health Care / ISQua
dc.source.volume7
dc.source.issue2
dc.identifier.legacycoverpagehttps://escholarship.umassmed.edu/qhs_pp/640
dc.identifier.contextkey1378786
html.description.abstract<p>Mortality rates are commonly used to judge hospital performance. In comparing death rates across hospitals, it is important to control for differences in patient severity. Various severity tools are now actively marketed in the United States. This study asked whether one would identify different hospitals as having higher- or lower-than-expected death rates using different severity measures. We applied 11 widely-used severity measures to the same database containing 9407 medically-treated stroke patients from 94 hospitals, with 916 (9.7%) in-hospital deaths. Unadjusted hospital mortality rates ranged from 0 to 24.4%. For 27 hospitals, observed mortality rates differed significantly from expected rates when judged by one or more, but not all 11, severity methods. The agreement between pairs of severity methods for identifying the worst 10% or best 50% of hospitals was fair to good. Efforts to evaluate hospital performance based on severity-adjusted, in-hospital death rates for stroke patients are likely to be sensitive to how severity is measured.</p>
dc.identifier.submissionpathqhs_pp/640
dc.contributor.departmentDepartment of Quantitative Health Sciences
dc.source.pages81-94


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