Prospective payment for Medicare inpatient psychiatric care: assessing the alternatives

Health Care Financing Review, Fall, 2004 by Philip G. Cotterill, Frederick G. Thomas

PAYMENT SIMULATIONS

The payment models were developed using the results of the cost regressions. Regression coefficients were converted to payment adjustment factors for categorical variables (the patient's DRG, comorbidities, age, and LOS) by treating each coefficient as an exponent of the base e (the base for natural logarithms). The resulting payment factors represent the proportional effect of each variable relative to a reference variable (omitted category). Non-categorical variables (the wage index, teaching intensity, facility size, and occupancy) were converted to payment variables by taking the antilog of the product of the regression coefficient and the value of the variable. This calculation is equivalent to raising the unlogged value of the variable to the exponential power represented by the regression coefficient.

For all payment models, aggregate payments were equated to aggregate costs, commonly referred to as making total payments "budget neutral" to aggregate costs. This approach differs from the statutory requirement in the BBRA, which would set estimated first year aggregate PPS payments equal to estimated aggregate TEFRA payments (in the absence of PPS). Our approach is more appropriate for the analysis presented in this article, whose objective is to assess the degree to which alternative payment models correspond to costs at the case and facility levels. It should also be noted that to strengthen our test of the alternative payment models, we used a different 20 percent sample of MedPAR claims for payment simulations and evaluation (the validation phase of the study) than was used for the cost regressions (the development phase).

Evaluation of Payment Models

As noted earlier, we used two methods to evaluate how well a payment system matches payments to costs (which we call the accuracy of the system). First, we ran linear regressions of cost per case on payments per case (Carter et al., 2000). As described in greater detail following, the slope coefficient and the [R.sup.2] from these regressions provide information about the relative accuracy of the payment models. These results are shown in Table 3. Second, we calculated payment to cost ratios for selected groups of facilities to determine whether particular types of facilities would likely be advantaged or disadvantaged under the various payment models. These results are shown in Table 4.

Since we have equated aggregate payments and aggregate costs for each system, the slope coefficient of the cost-payment regressions measures the relationship between the levels of per case costs and payments. Hence, for each payment model, the slope indicates whether there is a systematic bias toward over or under payment relative to cost. A slope greater than 1.0 indicates that for low levels of per case payment, payment tends to be less than cost, and for high levels of per case payment, payment tends to exceed cost. The converse is true for a slope less than 1.0: at low per case payment levels, payment tends to exceed cost, and at high per case payment levels, payment tends to be less than cost (Figure 1). As the slope coefficient pivots from either greater or less than 1.0 toward 1.0, the areas of systematic differences between cost and payment decrease. For systems where aggregate costs and payments are equal, the regression fines for alternative systems rotate through a common point of intersection, which represents the coordinates of the mean values of the cost and payment variables. More precisely, there will be a single point of intersection when the analysis is conducted at the case level. (3)