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dc.contributor.authorShwartz, Michael
dc.contributor.authorKlimberg, Ronald K.
dc.contributor.authorKarp, Melinda
dc.contributor.authorIezzoni, Lisa I.
dc.contributor.authorAsh, Arlene S.
dc.contributor.authorHeineke, Janelle
dc.contributor.authorPayne, Susan M. C.
dc.contributor.authorRestuccia, Joseph D.
dc.date2022-08-11T08:10:41.000
dc.date.accessioned2022-08-23T17:16:53Z
dc.date.available2022-08-23T17:16:53Z
dc.date.issued1995-06-01
dc.date.submitted2010-07-01
dc.identifier.citationHealth Serv Res. 1995 Jun;30(2):359-76. <a href="http://www.ncbi.nlm.nih.gov/pmc/articles/PMC1070068/pdf/hsresearch00047-0093.pdf">Link to article on publisher's site</a>
dc.identifier.issn0017-9124 (Linking)
dc.identifier.pmid7782221
dc.identifier.urihttp://hdl.handle.net/20.500.14038/47502
dc.description.abstractOBJECTIVE: We describe an integer programming model that, for studies requiring repeated sampling from hospitals, can aid in selecting a limited set of hospitals from which medical records are reviewed. STUDY SETTING: The model is illustrated in the context of two studies: (1) an analysis of the relationship between variations in hospital admission rates across geographic areas and rates of inappropriate admissions; and (2) a validation of computerized algorithms that screen for complications of hospital care. STUDY DESIGN: Common characteristics of the two studies: (1) hospitals are classified into categories, e.g., high, medium, and low; (2) the classification process is repeated several times, e.g., for different medical conditions; (3) medical records are selected separately for each iteration of the classification; and (4) for budgetary and logistical reasons, reviews must be concentrated in a relatively small subset of hospitals. DATA COLLECTION/EXTRACTION METHODS. In each study, hospitals are ranked based on analysis of hospital discharge abstract data. CONCLUSIONS: The model is useful for identifying a subset of hospitals at which more intensive reviews will be conducted.
dc.language.isoen_US
dc.relation<a href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=pubmed&cmd=Retrieve&list_uids=7782221&dopt=Abstract">Link to Article in PubMed</a>
dc.relation.urlhttp://www.ncbi.nlm.nih.gov/pmc/articles/PMC1070068/pdf/hsresearch00047-0093.pdf
dc.subjectBias (Epidemiology)
dc.subjectData Interpretation, Statistical
dc.subjectDiagnosis-Related Groups
dc.subjectHealth Services Misuse
dc.subjectHealth Services Research
dc.subjectHospitals
dc.subjectMedical Records
dc.subjectModels, Statistical
dc.subjectOutcome Assessment (Health Care)
dc.subjectQuality of Health Care
dc.subject*Sampling Studies
dc.subjectSmall-Area Analysis
dc.subjectUnited States
dc.subjectBiostatistics
dc.subjectEpidemiology
dc.subjectHealth Services Research
dc.titleAn integer programming model to limit hospital selection in studies with repeated sampling
dc.typeJournal Article
dc.source.journaltitleHealth services research
dc.source.volume30
dc.source.issue2
dc.identifier.legacycoverpagehttps://escholarship.umassmed.edu/qhs_pp/638
dc.identifier.contextkey1378784
html.description.abstract<p>OBJECTIVE: We describe an integer programming model that, for studies requiring repeated sampling from hospitals, can aid in selecting a limited set of hospitals from which medical records are reviewed.</p> <p>STUDY SETTING: The model is illustrated in the context of two studies: (1) an analysis of the relationship between variations in hospital admission rates across geographic areas and rates of inappropriate admissions; and (2) a validation of computerized algorithms that screen for complications of hospital care.</p> <p>STUDY DESIGN: Common characteristics of the two studies: (1) hospitals are classified into categories, e.g., high, medium, and low; (2) the classification process is repeated several times, e.g., for different medical conditions; (3) medical records are selected separately for each iteration of the classification; and (4) for budgetary and logistical reasons, reviews must be concentrated in a relatively small subset of hospitals.</p> <p>DATA COLLECTION/EXTRACTION METHODS. In each study, hospitals are ranked based on analysis of hospital discharge abstract data.</p> <p>CONCLUSIONS: The model is useful for identifying a subset of hospitals at which more intensive reviews will be conducted.</p>
dc.identifier.submissionpathqhs_pp/638
dc.contributor.departmentDepartment of Quantitative Health Sciences
dc.source.pages359-76


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