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An integer programming model to limit hospital selection in studies with repeated sampling

Shwartz, Michael
Klimberg, Ronald K.
Karp, Melinda
Iezzoni, Lisa I.
Ash, Arlene S.
Heineke, Janelle
Payne, Susan M. C.
Restuccia, Joseph D.
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Abstract

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.

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.

Source

Health Serv Res. 1995 Jun;30(2):359-76. Link to article on publisher's site

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7782221
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