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METHODS TO ESTIMATE AND ANALYZE MEDICAL CARE RESOURCE USE

An Example from Liver Transplantation

Published online by Cambridge University Press:  01 May 1999

Patricia P. Katz
Affiliation:
University of California
Jonathan A. Showstack
Affiliation:
University of California
John R. Lake
Affiliation:
University of California
Robert S. Brown, Jr.
Affiliation:
University of North Carolina at Chapel Hill
R. Adams Dudley
Affiliation:
University of California
M. Esther Colwell
Affiliation:
University of California
Russell H. Wiesner
Affiliation:
Mayo Clinic Foundation
Rowen K. Zetterman
Affiliation:
University of Nebraska Medical Center
James Everhart
Affiliation:
U.S. National Institute of Diabetes and Digestive and Kidney Diseases

Abstract

This paper describes a method to construct a standardized health care resource use database. Billing and clinical data were analyzed for 916 patients who received liver transplantations at three medical centers over a 4-year period. Data were checked for completeness by assessing whether each patient's bill included charges covering specified dates and for specific services, and for accuracy by comparing a sample of bills to medical records. Detailed services were matched to a standardized service list from one of the centers, and a single price list was applied. For certain services, clinical data were used to estimate service use or, if a match was not possible, adjusted charges for the services were used. Twenty-three patients were eliminated from the database because of incomplete resource use data. There was very good correspondence between bills and medical records, except for blood products. Direct matches to the standardized service list accounted for 69.3% of services overall; 9.4% of services could not be matched to the standardized service list and were thus adjusted for center and/or time period. Clinical data were used to estimate resource use for blood products, operating room time, and medications; these estimations accounted for 21.3% of services overall.

A database can be constructed that allows comparison of standardized resource use and avoids biases due to accounting, geographic, or temporal factors. Clinical data are essential for the creation of such a database. The methods described are particularly useful in studies of the cost-effectiveness of medical technologies.

Type
Research Article
Copyright
© 1999 Cambridge University Press

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