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Long-run costs per nursing facility can be estimated using either cross-section or time-series methods. By relating total facility costs to the service levels provided by a number of hospitals, nursing homes, or out-patient care facilities during a specific period, useful cross-section estimates of total service costs are possible. If case mixes were to vary dramatically according to type of facility, then the type of facility would have to be explicitly accounted for in the regression model analyzed. Similarly, if patient mix or service-provider efficiency is expected to depend, at least in part, on the for-profit or not-for-profit organization status of the care facility, the regression model must also recognize this factor. These factors plus price-level adjustments for inflation would be accounted for in a time-series approach to nursing cost estimation.

To illustrate a regression-based approach to nursing cost estimation, consider the following cross-section analysis of variable nursing costs conducted by the Southeast Association of Hospital Administrators (SAHA). Using confidential data provided by 40 regional hospitals, SAHA studied the relation between nursing costs per patient day and four typical categories of nursing services. These annual data appear in Table 8.3 The four categories of nursing services studied include shots, intravenous (IV) therapy, pulse taking and monitoring, and wound dressing. Each service is measured in terms of frequency per patient day. An output of 1.50 in the shots service category means that, on average, patients received one and one-half shots per day. Similarly, an output of 0.75 in the IV service category means that IV services were provided daily to 75% of a given hospital's patients, and so on. In addition to four categories of nursing services, the not-for-profit or for-profit status of each hospital is also indicated. Using a "dummy" (or binary) variable approach, the profit status variable equals 1 for the eight for-profit hospitals included in the study and zero for the remaining 32 not-for-profit hospitals.

Cost estimation results for nursing costs per patient day derived using a regression-based approach are shown in Table 8.4.

A. Interpret the coefficient of determination (R2) estimated for the nursing cost function.

B. Describe the economic and statistical significance of each estimated coefficient in the nursing cost function.

C. Average nursing costs for the eight for-profit hospitals in the sample are only $120.94 per patient day, or $3.28 per patient day less than the $124.22 average cost experienced by the 32 not-for-profit hospitals. How can this fact be reconciled with the estimated coefficient of -2.105 for the for-profit status variable?

D. Would such an approach for nursing cost estimation have practical relevance for publicly funded nursing cost reimbursement systems?

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