Essentially, HCFA’s calculation of its per-enrollee (capitation) rate in each county can be expressed as follows: Medicare pays risk HMOs a fixed amount per enrollee—a capitation rate—regardless of what each enrollee’s care actually costs. Medicare law stipulates that the capitation rate be set at 95 percent of the costs Medicare would have incurred for HMO enrollees if they had remained in FFS. In implementing the law’s rate-setting provisions, HCFA estimates a county’s average per-beneficiary cost and multiplies the result by 0.95.The product is the county adjusted average per capita cost rate. HCFA then applies a risk-adjustment factor to the county rate. Under HCFA’s risk-adjustment system, beneficiaries are sorted into groups according to their demographic traits (age; sex; and Medicaid, institutional, and working status). These traits serve as proxy measures of health status. HCFA calculates a risk factor for each group—the group’s average cost in relation to the cost of all beneficiaries nationwide. For example, in 1995 the risk factor for younger seniors (65- to 70-year-old males) was .85, whereas for older seniors (85-year-old or older males) it was 1.3. HCFA uses the risk factor to adjust the county rate, thereby raising or lowering Medicare’s per capita payment for each HMO enrollee, depending on the individual’s demographic characteristics. For HCFA’s rate-setting method to produce appropriate rates, the risk adjusters must reliably differentiate among beneficiaries with different health status. Much has been written about the inadequacy of Medicare’s risk adjuster to account for the tendency of HMOs to experience favorable selection. More than a decade of research has concluded that beneficiaries enrolling in HMOs are, on average, healthier than those remaining in FFS.Studies of pre-1990 data found that Medicare HMO enrollees—in a period just prior to their HMO enrollment—had health care costs that were from 20 percent to 42 percent lower than those of FFS beneficiaries with the same demographic characteristics. Studies of post-1990 data also showed costs of Medicare HMO enrollees ranging from 12 percent to 37 percent lower than those of their FFS counterparts. The problem for Medicare posed by favorable selection is that HMO enrollees are healthier than FFS beneficiaries within the same demographic group; for example, 70-year-old males in HMOs are, on average, healthier than 70-year-old males in FFS. Medicare’s risk adjuster is said to be inadequate because, while making broad distinctions among beneficiaries of different age, sex, and other demographic characteristics, it does not account for the significant health differences among demographically identical beneficiaries. The cost implications of health status differences can be dramatic for two demographically alike beneficiaries: one may experience occasional minor ailments while the other may suffer from a serious chronic condition. Devising a risk adjuster sensitive enough to capture health status differences, however, is such a technically complex and difficult task that years of independent research and HCFA-sponsored research have not yet produced an ideal risk adjuster. In reports issued in 1994 and 1995, we identified several promising, practical risk adjusters and suggested that HCFA implement an interim improvement. Independent of risk adjustment, modifying the method for calculating county rate would help reduce Medicare’s excess HMO payments. HCFA currently estimates the average Medicare costs of a county’s beneficiaries using the costs of only those beneficiaries in Medicare’s FFS sector. This method would be appropriate if the average health cost of FFS beneficiaries were the same as that of demographically comparable HMO enrollees. However, in counties where there are cost disparities between Medicare’s FFS and HMO enrollee populations, this method can either overstate the average costs of all Medicare beneficiaries and lead to overpayment or understate average costs and lead to underpayment. Suppose a county has 1,000 Medicare beneficiaries with identical demographic characteristics. Of these, 800 beneficiaries are in Medicare’s FFS program and cost Medicare on average $100 a month. The remaining 200 beneficiaries are enrolled in HMOs, but these beneficiaries would have cost an average of $75 a month had they remained in the FFS program. For all 1,000 beneficiaries, the county average cost would be $95 a month. HCFA’s method excludes the HMO enrollees with their lower costs from its calculations, producing a county average of $100 a month. Consequently, HCFA overestimates this county’s average monthly cost by $5, producing $1,000 a month in excessive Medicare payments to HMOs (200 beneficiaries times $5). The difficulty in correcting this problem comes from the inability to observe the costs HMO enrollees would have incurred if they had remained in the FFS sector. In the illustration above, HCFA needs a way to estimate that the beneficiaries enrolled in HMOs would have cost $75 a month in the FFS sector rather than $100. Therefore, we developed a method to estimate HMO enrollees’ expected FFS costs using information available to HCFA. Our method consists of two main steps: First, we computed the average costs of new HMO enrollees during the year before they enrolled—that is, while they were still in FFS Medicare. These FFS costs are available through HCFA’s claims data. Next, we adjusted this amount to reflect the expectation that an enrollee’s use of health services will, over time, rise. Having completed these steps, we combined the result with an estimate of the average cost of FFS beneficiaries. This new average produced a county rate that reflected the costs of all Medicare beneficiaries. Thus, our method helps prevent biasing HMO payments with either overgenerous estimates of enrollees’ initial health costs or low estimates that fail to compensate for the likelihood of rising health costs over time. The technical details of this approach are discussed in appendix I. To illustrate the effect of our approach, we analyzed data for counties with different shares of beneficiaries enrolled in HMOs. We found that our method could have reduced excess payments by more than 25 percent. Substantially better risk adjustment, which appears to be years away from implementation, would target the remaining 75 percent of excess payments. Specifically, for the counties that we analyzed, we estimated that total excess payments in 1995 amounted to about $1 billion of the roughly $6 billion in total Medicare payments to risk HMOs in the state. (App. III discusses excess payment estimates in further detail.) Applying our method for setting county rates would have reduced the excess by about $276 million. We also found that the excess payments attributable to inflated county rates were concentrated in 12 counties with large HMO enrollment and ranged from less than 1 percent to 6.6 percent of the counties’ total HMO payments, representing between $200,000 and $135.3 million. (See table 1.) Despite the size of these amounts, the application of our method would have produced relatively small changes in the monthly, per-beneficiary capitation payments, ranging from $3 to $38. The excess payments shown in table 1 reflect the difference between Medicare’s county rates and rates calculated by our method. As shown in the table, five counties accounted for more than 90 percent of the state’s county-rate excess payments. Our analysis did not support the hypothesis, put forward by the HMO industry and others, that the excess payment problem will be mitigated as more beneficiaries enroll in Medicare managed care and HMOs progressively enroll a more expensive mix of beneficiaries. Our data—from counties with up to a 39-percent HMO penetration—indicated that excess payments as a percentage of total HMO payments were higher in counties with higher Medicare penetration. For example, as seen in figure 1, the four counties with the highest rates of excess payment, ranging from 5.1 to 6.6 percent, were also among the counties with the highest enrollment rates. If the relationship between enrollment and excess payments we found for California in 1995 persists, excess payments are likely to grow. The recent trend in Medicare HMO enrollment suggests continued growth in the next several years. Therefore, some counties with moderate enrollment today may experience higher enrollment rates in the future, exacerbating the excess payment problem. (See app. III, table III.1, for estimates of future excess HMO payments in California based on projected enrollment.) Because the data we used to estimate HMO enrollees’ costs come from data that HCFA compiles to update HMO rates each year, our method has two important advantages. First, HCFA’s implementation of our proposal could be achieved in a relatively short time. The time element is important, because the prompt implementation of our method would avoid locking in a current methodological flaw that would persist in any adopted changes to Medicare’s HMO payment method that continued to use either current county rates as a baseline or FFS costs to set future rates. Second, the availability of the data would also make our proposal economical: we believe that the savings to be achieved from reducing county-rate excess payments would be much greater than the administrative costs of implementing our modification. We recognize that for counties with little or no HMO enrollment, HCFA’s current method of estimating the county rate would yield virtually the same result as our method because the small number of HMO enrollees is overwhelmed by the large number of FFS beneficiaries and has only a minimal effect on average FFS costs. Thus, HCFA could decide to use a beneficiary enrollment threshold for computing revised county rates. Medicare’s HMO rate-setting problems have prevented it from realizing the savings that were anticipated from enrolling beneficiaries in capitated managed care plans.