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Standardizing the hospital’s case load for diagnostic mix and resource use : a comparison of the RNI and information theory indices

University of British Columbia

Hospital cost containment is a priority for provincial governments in Canada and reimbursing agencies in the United States—a consequence of the dramatic rise in the rate of hospital cost increases. Studies have shown that this increase is mainly due to an increase in resource use per patient day—more intensive labour; more laboratory tests, drugs; more sophisticated technology. The level of resource use obviously varies for different cases, depending on the nature of the patient's illness. The problems facing reimbursing agencies are that of reducing the rate of growth of hospital expenditures and an equitable reimbursement of hospitals, taking adequate account of their diverse case mix. Simple measures of hospital output such as the number of cases or the number of patient days assume an homogeneous output. As a consequence, various standardization techniques have been developed to standardize for case mix variation, initially, output standardization was attempted using proxies for case mix such as the number of services and facilities available in a hospital. Later, diagnosis itself was used and weighted in a variety of different ways. The purpose of this study is to compare two approaches to case mix standardization—information theory and the Resource Need Index. These approaches rely on different assumptions. Hence the intention is to critically examine their conceptual bases and compare their relative ability to explain inter-hospital cost differences in Alberta. The empirical results show that the information theory measure performs better than the Resource Need Index in explaining hospital costs. An illustrative example is given to show how this technique can be built into the budget setting process in Alberta to effect a more equitable distribution of resources among hospitals.

Text

2010-04-13

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http://hdl.handle.net/2429/23373

Health Care and Epidemiology

University of British Columbia

Graduate