Measuring function for medicare inpatient rehabilitation payment

Health Care Financing Review, Spring, 2003 by Grace M. Carter, Daniel A. Relles, Gregory K. Ridgeway, Carolyn M. Rimes

Estimating Cost

The Hospital Cost Report Information System Files contain information on costs and charges by cost center, facility characteristics, and utilization. Each record covers a hospital fiscal year. In the analyses reported here, we used the latest cost report for each hospital that was available in July 2000. (5) We could not calculate costs for 2.4 percent of hospitals that were all-inclusive providers or otherwise were missing cost report data.

We used the departmental method to estimate the accounting cost of MEDPAR discharges. This method combines MEDPAR information about charges in each ancillary department with the departmental cost-to-charge ratio calculated from the cost report to estimate costs incurred by the patient in the department (Newhouse et al., 1989). Separate per diems for routine and special care days are combined with MEDPAR counts of such days to estimate routine and nursing costs. The per diems were inflated (or deflated) from the midpoint of the fiscal year to the day of discharge based on the observed rate of increase in hospital per diems (1.1 percent annually).

We use wage-adjusted cost per case as the dependent variable in our analyses. The wage adjustment affects 70.5 percent of costs, which is the labor share in the time period of our data. The hospital wage index used was prior to reclassification and reflects the elimination of teaching salaries.

Independent Variables

Our prediction of costs for cases discharged to the community is based on three sets of information: (1) RIC, (2) the 18 FIM[TM] items, and (3) patient age. (6) The RIC is a grouping of codes that describe the impairment that is the primary cause of the rehabilitation hospitalization (Carter et al., 2002; Federal Register, 2001. The codes for the primary impairment are identical in the UDSmr and HealthSouth data. RICs were created based on clinical criteria and, except for the miscellaneous group, do not group patients who are clinically different from one another in the same RIC. We began with the 20 RICs defined in version 2 of the FRGs (Stineman et al., 1997). We evaluated these RICs and updated them to include an additional RIC for burns and changed the assignment of the multiple fracture codes (Carter et al., 2000).

In addition to using the individual FIM[TM] items as variables, we use the FIM[TM] cognitive scale (the sum of the items on communication and social cognition) and a modification of the motor score that will be explained in the results section. Age is taken from the MEDPAR and is age in years on the day of admission.

Sample Definition and Size

Table 1 shows that there were 390,048 discharges from IRFs in 1999. Of these, we were able to match FIM[TM] records for 257,024, or 66 percent of the MEDPAR population. Most of the unmatched MEDPAR records were from hospitals that did not participate in either of our FIM[TM] data sources. We judged the quality of the match, compared with what was possible given our data, in two ways. First, we looked at MEDPAR records for providers that appeared in a FIM[TM] database throughout 1999 and calculated the fraction of the MEDPAR records that we were able to match to a FIM[TM] record. We were able to match 90.1 percent of such MEDPAR records in 1999. The second way we judged the quality of the match is the percent of FIM[TM] records for which Medicare is listed as the primary payer that we were able to match. In calendar year 1999 we matched 95.9 percent of such FIM[TM] records. Using both measures, the match rate was very similar for each FIM[TM] source. Hospitals and cases in the analysis sample are reasonably representative of the population (Carter et al., 2002).