An integer programming model to limit hospital selection in studies with repeated samplingQuantitative Health Sciences Publications and Presentations
UMMS AffiliationDepartment of Quantitative Health Sciences
SubjectsBias (Epidemiology); Data Interpretation, Statistical; Diagnosis-Related Groups; Health Services Misuse; Health Services Research; Hospitals; Medical Records; Models, Statistical; Outcome Assessment (Health Care); Quality of Health Care; *Sampling Studies; Small-Area Analysis; United States
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.
SourceHealth Serv Res. 1995 Jun;30(2):359-76. Link to article on publisher's site
Related ResourcesLink to Article in PubMed
Citation InformationMichael Shwartz, Ronald K. Klimberg, Melinda Karp, Lisa I. Iezzoni, et al.. "An integer programming model to limit hospital selection in studies with repeated sampling" Vol. 30 Iss. 2 (1995) ISSN: 0017-9124 (Linking)
Available at: http://works.bepress.com/arlene_ash/59/