Academic journal article Health Care Financing Review

Comparing Case-Mix Systems for Nursing Home Payment

Academic journal article Health Care Financing Review

Comparing Case-Mix Systems for Nursing Home Payment

Article excerpt

Comparing case-mix systems for nursing home payment

Introduction

A current trend in Federal and State payment for nursing home care has been toward systems in which the differences among residents in resource use are recognized explicitly. In such systems, some portion of the payment rate is associated with measures of the resources used in caring for a facility's residents, the facility's case mix. Operationally, residents are assessed and a case-mix measurement system is used to determine the relative measure of resources used in each facility. This relative measure is employed to determine rates either directly (e.g., by scaling payment for nursing services) or indirectly (e.g., by determining rate ceilings or peer groups of facilities). The mechanics of translating measures of case mix into rates are complex, especially considering the incentives and disincentives embodied in any payment system (Schneider et al., 1985; Liu et al., 1986; Holahan and Sulvetta, 1987). We focus here, however, on the more technical problem of deriving appropriate measures that predict resource use by nursing home residents. The issues in this latter focus are not fully separable from those in the former, in that the choice of a case-mix measurement system will in itself create incentives and disincentives when applied for payment. Recognizing this linkage, selecting an appropriate case-mix system is the precursor to developing an effective payment system. I sought to evaluate and compare several nursing home case-mix systems, to understand their commonalities and differences, and to examine their ability to predict accurately actual resource use.

Background and approach

Interest in case-mix measurement systems for nursing homes began in earnest in the mid-1970s with work at the Battelle Human Affairs Centers (McCaffree, Winn, and Bennett, 1976; McCaffree, Baker, and Perrin, 1979; and Winn, 1975) and The Johns Hopkins University (Cavaiola and Young, 1980, and Flagle et al., 1977). Since then, numerous case-mix systems have been developed. The criteria of some have been published: Deane and Cella, 1981 (in use in Maryland); Weissert et al., 1983; Fries and Cooney, 1985; Cameron, 1985; Morris et al., 1987; Arling et al., 1987; Arling, Zimmerman, and Updike, 1989; and Schneider et al., 1988 (in use in New York). Several other case-mix systems, unpublished, have been implemented as part of Medicaid State nursing home payment systems, such as those in Ohio, Illinois, West Virginia, and Minnesota. In all of these systems, resident characteristics are evaluated directly in determining case mix. Although considerable success has been achieved in understanding which institutional characteristics are predictive of costs for use in policy analysis (e.g., Birnbaum et al., 1981; Liu et al., 1986; and Sulvetta and Holahan, 1986), these appear to be less appropriate for setting payment than are patient characteristics. The residents for whom a facility cares rather than the characteristics of the facility should determine case mix.

Despite the plethora of case-mix systems available, little work has been done to contrast their operation and evaluate their ability to predict resources. A major effort in developing these systems is the customized collection of necessary data. Along with the effort to assess resident characteristics, this involves the substantial cost and difficulty of measuring actual resource use. Thus, it is not surprising that developers utilize each scarce, expensive data point to the fullest in the derivation of a system and, in reporting a new system's abilities, utilize the same data from which the system was originally derived. In this study. I compare several alternative resident classification systems to understand how they operate when applied to new populations without adaptive adjustments to improve their variance explanation (a measure of how well the system explains differences among residents in resource use) or other properties--that is, applied "out of the box. …

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