Medicare HMOs: HCFA Can Promptly Eliminate Hundreds of Millions in Excess Payments
Released: 1997-05-13T00:00:00

Letter

Background

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.

How Medicare’s HMO Rate-Setting Method Can Lead to Excess Payments

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.

HCFA Could Improve Its Rate-Setting Method by Including HMO Enrollees in Its Calculations of County Average Cost

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.

Current County Rates Produce Substantial Excess Payments

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.)

Data Are Available to Enable HCFA to Promptly Adjust County Rates

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.

Conclusions

Medicare’s HMO rate-setting problems have prevented it from realizing the savings that were anticipated from enrolling beneficiaries in capitated managed care plans. In fact, enrolling more beneficiaries in managed care could increase rather than lower Medicare spending—unless Medicare’s method of setting HMO rates is revised.

Our method of calculating the county rate would have the effect of reducing payments more for HMOs in counties with higher excess payments and less for HMOs in counties with lower excess payments. In this way, our method represents a targeted approach to reducing excess payments and could lower Medicare expenditures by at least several hundred million dollars each year. Furthermore, because some proposals to reform Medicare HMO rate-setting rely on current county payment rates as a benchmark, correcting the current county rates would avoid locking in varying degrees of excess payments across counties for years to come.

Recommendation to the Secretary of Health and Human Services

We recommend that the Secretary of Health and Human Services direct the HCFA Administrator to incorporate the expected FFS costs of HMO enrollees into the methodology for establishing county rates using the method we explain in this report and adjust Medicare payment rates to risk contract HMOs accordingly.

Agency Comments

In commenting on a draft of this report, HHS agreed that, because Medicare HMO enrollees tend to be healthier than FFS beneficiaries, the current payment methodology may have resulted in Medicare’s overpaying HMOs substantially—according to HHS, by $1 billion in fiscal year 1996. HHS noted that the President’s fiscal year 1998 budget proposes to address the excess payment problem by lowering HMO capitation rates in calendar year 2000 and developing a new payment system to be phased in beginning in 2001. However, our recommended rate-setting change could be implemented much sooner and would continue to be useful after HCFA develops a new HMO payment system.

Although HHS did not question that our recommended rate-setting change would save hundreds of millions of dollars each year for Medicare and taxpayers, the Department doubted the change would be equitable and relatively easy to implement. However, our approach to reducing excess payments is equitable because it is targeted—in contrast to HHS’ proposed across-the-board cut—and would reduce payments only in those counties where HMOs receive excess payments. Furthermore, our recommended change should require very little additional HCFA staff time and no collection of new data. (See app. IV for the full text of HHS’ comments and our response.)

As arranged with your office, unless you publicly announce the contents of this report earlier, we plan no further distribution until 30 days after its issue date. At that time, we will send copies to the Secretary of Health and Human Services; the Director, Office of Management and Budget; the Administrator of the Health Care Financing Administration; and other interested parties. We will also make copies available to others upon request.

This work was done under the direction of William J. Scanlon, Director, Health Financing and Systems Issues. If you or your staff have any questions about this report, please contact Mr. Scanlon at (202) 512-7114. Other GAO contacts and staff acknowledgments are listed in appendix V.

Methodology

Despite evidence from a number of studies that health maintenance organization (HMO) enrollees tend to be healthier than demographically comparable fee-for-service (FFS) beneficiaries (“favorable selection”), the Health Care Financing Administration (HCFA) rate-setting method implicitly assumes that the health service needs of both groups are the same. To the extent that favorable selection occurs, HCFA’s assumption increases the capitation rates HCFA pays to risk HMOs and results in excess payments. This appendix describes how making more realistic assumptions concerning the health status of HMO enrollees can partially correct the excess payment problem. In essence, our approach determines the extent to which HCFA’s method overestimates average Medicare FFS costs and thus inflates the county rate—one component of HMO capitation payments. This appendix also briefly discusses a related method for estimating aggregate excess payments.

Method for Reducing Excess HMO Payments by Correcting Medicare’s County Rate

The basic steps HCFA takes to determine capitation payments can be described as follows.

HCFA calculates the per capita costs in Medicare FFS, or standard average cost (SAC). This is done for each county, partly to allow for geographic differences in medical prices.

The basic capitation rate, or county rate, is set at 95 percent of the county per capita cost. That is, COUNTY = 0.95

SAC.

Finally, payments for specific individuals are adjusted up or down on the basis of a limited set of demographic factors, or “risk factors.” These risk factors are intended to partially adjust for differences in expected health care costs of beneficiaries of different ages, gender, and so on.

Sources of Excess Payments to HMOs

Excess payments can occur if HMOs enroll a group of beneficiaries that is healthier than the average FFS beneficiary and the capitation rate is not sufficiently adjusted for the differences in health status. In HCFA’s current method, favorable selection can cause excess payments, partly because HCFA’s risk factors inadequately adjust for differences in beneficiaries’ health status and partly because SAC overstates the costs of serving HMO enrollees.

HCFA’s Risk Factors Are Rough Proxies for Expected Health Costs and Do Not Fully Adjust Payments for Favorable Selection

HCFA’s risk factors adjust for favorable selection using five characteristics (age, sex, Medicaid eligibility status, institutional status, and working status) that are relatively poor predictors of beneficiaries’ health care needs. Specifically, the risk factors are a set of weights—intended to reflect the relative health risk of each beneficiary—used to adjust the basic capitation rate up or down. For example, the weight assigned to 65- to 70-year-old males was .85 in 1995, implying that they had a greater health cost risk—higher expected health costs—than 65- to 70-year-old females, whose weight was .70. Beneficiaries with the same risk factor are assumed to have the same relative health service needs. However, if 70-year-old males enrolling in HMOs tend to be healthier than the 70-year-old males who remain in FFS, then the risk factor will overcompensate for the enrollees’ costs and the HMOs are said to have benefited from favorable selection.

HCFA’s Capitation Rate Is Inflated by Favorable Selection

If HMOs’ enrollees tend to be healthier than the average beneficiary in FFS, then HCFA’s method will overestimate the expected cost of serving Medicare beneficiaries in FFS. The foundation of the rate-setting formula consists of the standard average cost to Medicare of a county’s FFS beneficiaries. (By standard, we mean this cost measure is normalized for differences in each county’s demographic composition, relative to the national average). HCFA calculates SAC from the costs of FFS program beneficiaries alone (SACFFS). However, to the extent that the health care costs of Medicare’s HMO enrollee population are lower, on average, than those of beneficiaries in FFS, the exclusion of HMO enrollees’ costs (that is, what they would have cost Medicare in FFS) causes SAC and, ultimately, the capitation rate, to be too high.

A better way to set Medicare HMO rates would be based on a SAC that reflected both the costs of beneficiaries in FFS (SACFFS) and what the costs of HMO enrollees would have been if they had been in FFS (SACHMO). Setting rates this way would lessen the amount of adjustment needed to reflect differences in health status because HMO enrollees’ expected FFS costs would already be included. The estimated average cost for all beneficiaries in the county could be calculated as a weighted average of SACFFS and SACHMO, where pFFS and pHMO are the proportions of county beneficiaries in FFS and HMOs, respectively. (See equation 2.)

However, because HCFA cannot directly observe what the FFS costs would have been for beneficiaries currently enrolled in HMOs (SACHMO), the agency assumes that the averages for the two groups are equal.

If relatively healthy beneficiaries enroll in HMOs while less healthy beneficiaries remain in Medicare FFS, however, SACHMO will be less than SACFFS. By assuming the two costs are equal, HCFA overstates the expected cost of serving HMO enrollees under FFS. This overestimate increases as the gap between SACFFS and SACHMO widens and can increase as the proportion of beneficiaries in HMOs (pHMO) increases. Because SAC forms one of the building blocks in the capitation rate formula, overestimating SAC leads to excess payments to HMOs.

The following examples illustrate how, in the presence of favorable selection, HCFA’s calculation of SAC and COUNTY results in excess payments to HMOs.

If a county had 10 demographically identical beneficiaries, 8 of whom cost Medicare nothing each year and 2 who cost $2,000 each, the county’s average per capita cost, or SACALL, would equal $400 ($4,000 divided by the 10 beneficiaries). If no beneficiaries were enrolled in HMOs, SACFFS would equal SACALL, or $400. In contrast, if two beneficiaries costing Medicare nothing had joined HMOs, SACFFS—on the basis of the eight remaining FFS beneficiaries—would equal $500 ($4,000 divided by eight).

Under HCFA’s method, COUNTY would be $500 .95—reflecting just the average costs of beneficiaries in the FFS sector—instead of $400 .95. Thus, Medicare would pay HMOs $100 .95 more than if capitation rates were based on the actual average expected FFS cost of all beneficiaries in the county.

Furthermore, the enrollment of additional beneficiaries with low costs in the county’s HMOs would widen the disparity between SACFFS and SACALL. For example, if six beneficiaries costing Medicare nothing had joined HMOs, SACFFS would equal $1,000 ($4,000 divided by the four beneficiaries still in FFS) or more than double SACALL’s value of $400. In this case, Medicare’s payments to HMOs would be based on a COUNTY equal to $1,000 .95 instead of the appropriate $400 .95.

Estimating Expected FFS Costs for HMO Enrollees

We developed a method to estimate the potential FFS costs for HMO enrollees that allows calculation of average FFS cost estimates based on all beneficiaries living in the county (SACALL). We identified the FFS cost experience of recent risk HMO enrollees prior to their HMO enrollment. Drawing on these prior-use cost data and data on changes in individuals’ health costs over time, we estimated the expected costs (on an FFS basis) of people who had been enrolled in an HMO for different periods of time. Finally, we combined these estimates to calculate SACHMO, which reflected the characteristics of the county’s HMO enrollees, including the length of time they had been HMO enrollees. This “prior-use” cost approach is necessary because no other relevant cost data are currently available to HCFA. After a beneficiary enrolls in an HMO, HCFA receives no information on the health care services provided to the beneficiary or their costs.

We made adjustments to respond to two major criticisms of previous studies that employed prior-use costs to estimate expected post enrollment costs. 1. Unadjusted prior-use estimates do not allow for the possibility that enrollees’ average expected costs can regress toward the mean cost of FFS beneficiaries. That is, as time passes, enrollees’ average costs can rise and approach the average costs of the FFS beneficiaries, rather than remain at their preenrollment levels. If this happens, the disparity between the prior-use costs of HMO enrollees and the costs of comparable FFS beneficiaries overstates the actual difference in cost that exists in years following enrollment.2. Unadjusted prior-use estimates underrepresent enrollees’ “death costs.” Unadjusted prior-use cost methodologies cannot take account of the full costs associated with death for enrollees, because beneficiaries must survive the prior year to enroll.

Not making these adjustments could result in an overestimate of excess Medicare HMO payments.

In developing our method to approximate SACHMO, we struck a balance between two potentially conflicting goals: (1) minimizing the computational burden and (2) maximizing the accuracy of the enrollees’ expected FFS cost estimate. The particular assumptions and modifications of our augmented prior-use methodology are detailed below. We recognize, however, that other approaches to approximating SACHMO could also result in slightly different, but equally plausible, estimates of enrollees’ expected FFS costs. Once we estimated SACHMO, we used the proportions of beneficiaries in FFS and HMOs to compute SACALL. (See equation 2.) Because we also knew actual HMO payments for each county, we could use our new estimates to compute estimates of county rate excess payments.

Beneficiaries Classified According to Enrollment Status

Because Medicare allows beneficiaries to switch among specific HMOs or between an HMO and FFS monthly, we classified beneficiaries according to the number of months they spent in a risk HMO or FFS during calendar years 1991 and 1992. We defined beneficiaries as enrollees (in risk HMOs) if they were Medicare eligible in 1991 and were enrolled in a risk contract HMO at least 7 months in 1992. We assigned beneficiaries who died in 1992 to the enrollee category if (1) they died while enrolled in a risk contract HMO and (2) it would have been feasible for them to have completed 7 months enrolled in an HMO in 1992 had they lived all 12 months of 1992.

To estimate SACHMO, we needed to develop FFS cost estimates for those beneficiaries soon to enroll in HMOs. Therefore, we created the category of joiners, a subset of enrollees. Joiners are beneficiaries who spent at least 6 months in FFS in 1991 and at least 7 months in a risk HMO in 1992.

To estimate SACFFS, we used FFS costs for beneficiaries who spent at least 6 months in FFS in both 1991 and 1992. Beneficiaries who died in 1992 and did not meet the criteria for inclusion in the enrollee category, but who were enrolled in FFS for at least 6 months in 1991, were assigned to the FFS category.

Prior-Year FFS Spending Used to Estimate Base-Year Costs for Each Beneficiary Category

We adjusted prior-year cost data of joiners to approximate average costs in the base year for enrollees because their costs (on an FFS basis) are unobserved while they are HMO enrollees. (See table I.1 for a summary of how we adjusted prior-use costs.) In each case, we constructed average monthly costs using total Medicare claims paid and months of FFS eligibility. The assumptions and adjustments we made to assign costs to the enrollee category of beneficiaries are described in the following sections.

1991 costs of people who joined an HMO in 1992 (joiners)

Costs increased to account for RTM effect 1991 costs of all FFS beneficiaries People who died within the sample year (1992)

Joiners’ Prior-Use Costs Used to Estimate All HMO Enrollees’ Costs

In estimating SACHMO, we used the prior-use costs of joiners as a baseline in estimating the (unobserved) expected FFS costs of all HMO enrollees. Adjusting these baseline costs for regression toward the mean and death costs translates the joiners’ costs into enrollees’ costs.

Our analysis of HMO enrollees from several years suggested that new HMO enrollees (joiners) in a given year tend to be similar—in terms of cost histories prior to joining an HMO—to longer-term HMO enrollees. Therefore, we assumed that enrollees’ costs could be estimated by adjusting joiners’ costs for expected cost changes after enrollment. This assumption enabled us to estimate costs for all HMO enrollees on the basis of a subset who had FFS costs in the prior year. (If the data had not supported this assumption, we would have had to collect FFS costs on all HMO enrollees prior to their enrollment. Because some enrollees had been HMO enrollees for several years while Medicare eligible, this more comprehensive task would have required complex adjustments to account for changes in price levels, medical practice patterns, and technology across years. In fact, such an approach would not have been possible for beneficiaries who enrolled in an HMO upon becoming Medicare eligible.)

We tested our assumption that joiners’ costs—with some adjustments—are representative of enrollees’ costs by examining joiners’ costs over several years. Noting that most enrollees were joiners in earlier years, we examined whether the relationship of joiners’ costs in the base year to average costs of those remaining in the FFS system was similar to the relationship of joiners’ costs in earlier years, relative to FFS beneficiaries’ costs. We found that the ratio of joiners’ to FFS beneficiaries’ costs remained relatively stable over time. Therefore, we concluded that joiners’ costs (in the base year) are representative of the just-prior-to-enrollment costs of enrollees from many years before the base year.

The ratio of joiners’ costs to FFS beneficiaries’ costs showed no trend and did not differ greatly from year to year. In fact, in all the years we examined, the ratio varied by less than 10 percent of its 3-year average.This suggests that, relative to FFS beneficiaries, soon-to-be HMO enrollees in 1992 and 1993 (who constituted about 25 percent of all HMO enrollees in 1994) were very similar to soon-to-be HMO enrollees in 1994. Ratios for each of three California counties for the years 1992 through 1994 are shown in table I.2.

Prior-Use Costs of Joiners Adjusted for Regression-Toward-the- Mean Effect

After a beneficiary joins an HMO, it is hypothesized that the beneficiary’s cost is likely to increase relative to his or her FFS costs in the year prior to enrolling. Such cost increases seem likely for two reasons. First, beneficiaries may postpone discretionary care in the months prior to joining an HMO so that they can take advantage of HMOs’ typically lower copayments. Second, beneficiaries may be more likely to join HMOs during a spell of unusually good health. This expectation that costs increase is known as “regression toward the mean” (RTM). To the extent that RTM occurs, unadjusted prior-use costs of joiners understate the initial average health care costs of new HMO enrollees, as well as the costs of all HMO enrollees.

HCFA’s method for determining HMO capitation rates implicitly assumes that RTM is full (100 percent) and immediate. That is, HCFA assumes that, upon enrolling in an HMO, joiners’ costs immediately increase to equal the average cost of FFS beneficiaries. Although it is reasonable to expect some RTM, no evidence supports a 100-percent effect that occurs so soon after enrollment.

We estimated the degree of RTM likely to occur and used this estimate to adjust joiners’ prior-use costs so they more accurately represented all enrollees’ costs. We derived our estimate of the regression effect, which we term the “regression-toward-the-mean adjustment factor” (RTMF), from actual FFS cost data for beneficiaries whose cost and demographic characteristics resembled those of joiners and from the actual distribution of enrollees’ HMO tenure. Our analysis of 1995 data suggested that the RTMF was about half of the maximum potential effect—50 percent, as opposed to the 100-percent RTMF that HCFA’s methodology implicitly assumes. (For further discussion of the RTMF, see app. II.)

Prior-Use Costs Adjusted for Death Costs

Because new HMO enrollees, by definition, do not die during the period just prior to their enrollment, prior-use cost data understate the costs of HMO enrollees who die during the year. The costs associated with the final months of life—“death-related costs”—are typically substantial. Consequently, we accounted for them to avoid underestimating SACHMO. We assumed that the costs of an HMO enrollee who died equal the costs of an FFS beneficiary who died. To find the average cost estimate for the deceased, we divided the calendar year total costs of all FFS beneficiaries deceased in 1991 in each county by the number of months those beneficiaries were alive during the year.

Our adjustment was equivalent to imposing a 100-percent RTM effect on the costs of HMO enrollees who died during the base year. Because favorable selection can result in HMOs’ having lower mortality rates than FFS, we imputed death costs only for HMO enrollees who died during the year. This approach accounted for excess payments to HMOs in counties where mortality rates were lower in HMOs than in FFS.

Calculating County-Rate Excess Payments That Are Due to Using Only FFS Beneficiaries’ Experience to Set Rates

After estimating the average expected costs of serving all of a county’s beneficiaries in FFS (SACALL), we could estimate the excess capitation payments that resulted from HCFA’s method of calculating SAC and the county rate. The formula for determining capitation rates can be expressed as the following: However, HCFA estimates average costs using only beneficiaries actually in FFS, so that HCFA’s formula is actually this: Consequently, the excess capitation rate can be estimated by the following: The risk factor term is specific to individual beneficiaries. On the basis of their demographic characteristics, it can take on values greater or less than 1.0. The total of county rate excess payments for a given county is obtained by summing the individual level excess payment amounts, expressed by equation 5. We applied this methodology to California’s 58 counties to estimate county-rate excess payments for 1995, 1996, and 1997. Our estimates are presented in appendix III.

Method for Estimating Medicare’s Aggregate Excess Payments

This section describes the steps we followed to estimate aggregate excess payments to HMOs, that is, total excess payments caused by the full effect of favorable selection on the rate-setting formula. Our method compares what Medicare paid for risk contract HMO enrollees to what Medicare would have paid for the same enrollees had they not joined HMOs. Although this method establishes a benchmark for excess payments against which HMO payment reforms can be measured, we do not suggest that HCFA use the methodology described below to adjust capitation rates because it was not designed or tested as a rate-setting methodology.

We estimated the average cost of HMO enrollees (ACHMO) using the same prior-use approach described above. After our adjustments for RTM and death-related costs were applied, ACHMO was representative of the costs of a group of HMO enrollees with the demographic characteristics of new HMO enrollees (joiners).

We used HCFA’s method to calculate a county average capitation rate. Because ACHMO reflected the demographic characteristics of only joiners, we calculated the average capitation rate for the joiner population (CAP_RATEJAVG) so that it, too, reflected the demographic characteristics of only joiners. Specifically, we adjusted the 1995 county rate up or down according to the average risk factor of that county’s joiners.

We calculated the percent aggregate excess payment (PAEP) to risk contract HMOs in each county using the following formula: CAP_RATEJAVG and ACHMO reflect the demographic characteristics only of joiners, but the cost characteristics of all HMO enrollees. Because these terms affect both the numerator and denominator, PAEP is demographically neutral—that is, demographic characteristics are canceled out in the expression.

To find aggregate excess payments that corresponded to actual HMO enrollees, we multiplied PAEP by total payments to risk HMOs by county.

We applied this methodology to estimate aggregate excess payments to HMOs in California’s 58 counties in 1995. (See app. III.)

Adjustments for Regression Toward the Mean and Death-Related Costs in Estimating Excess Payments to Medicare HMOs

As explained in appendix I, establishing the Medicare capitation rate for HMOs on the basis of the cost of serving beneficiaries hinges on estimating the expected FFS costs of HMO enrollees (SACHMO). In turn, adequately estimating SACHMO requires adjusting HMO enrollees’ observed prior-use costs for the increases expected to occur after they enroll. This increase has been labeled regression toward the mean because enrollees’ average health costs, which are relatively low before joining the HMO, begin to rise over time and approach (“regress” toward) the average cost of similar beneficiaries who remain in FFS. This appendix describes our methodology to account for the RTM effect, including the high health care costs typically incurred during the last months of life. Although we drew on previous studies, available data required that we develop a new method of adjusting prior-use estimates of enrollees’ costs for RTM.

HCFA implicitly assumes than HMO enrollees’ costs fully regress (increase) to the mean of FFS immediately upon enrollment. Studies have generally found that, after a beneficiary enrolls in an HMO, his or her service use and costs rise. Nonetheless, HCFA’s assumption that RTM is full and immediate receives no empirical support in the literature. For example, Beebe found significant increases in the first year after enrollment and moderate increases thereafter. After 3 years, estimated costs of HMO enrollees were 94 percent of those of comparable FFS beneficiaries; by year 6, enrollees’ estimated costs had risen modestly to 96 percent of FFS beneficiaries’ costs. A more recent study by Hill and others found that RTM closed half the gap in costs between HMO joiners and FFS beneficiaries.

Methodology Allows RTM Factor to Vary by Beneficiary Survival Status

We allow our estimate of RTMF to differ between groups of beneficiaries, depending on whether they survived or died during the 4-year period that we analyzed. The association between mortality and average costs is well documented by previous studies. For example, Lubitz and others found that people in their last 12 months of life have costs that are significantly higher than those of other Medicare beneficiaries and account for a disproportionate share (about 28 percent) of health care expenditures. Similarly, average costs during the final 2 and 3 years of life, while not as large, are also considerably higher than the average for all beneficiaries.This pattern is illustrated in figure II.1.

The relationship between the degree of RTM experienced by HMO enrollees and their proximity to death has not been addressed by previous studies. Nonetheless, it is possible that enrollees surviving different lengths of time after joining an HMO would experience different degrees of RTM. For example, it is plausible that HMO enrollees in their last year of life might experience complete RTM, while those many years from death might experience little.

In our analysis, we allowed for the possibility that the appropriate RTM adjustment for a group of beneficiaries may depend on their proximity to death. Table II.1 presents the definitions of the beneficiary categories and the percentage of HMO enrollees (for California in sample year 1992) in each category.

Method Used to Estimate the RTM Factor for Category I Enrollees

To estimate RTMF for enrollees who survive for 4 or more years (category I enrollees), we developed an approach that generally follows Beebe’s 1988 methodology. That is, we used 4 years of longitudinal data on a sample of the FFS Medicare population to track the cost experience over time of two proxy cohorts—one representing HMO joiners and one representing FFS beneficiaries. Our method involved four steps. 1. We randomly drew two samples—one reflecting the distribution of age, sex, and costs of new HMO enrollees (joiners) and the second reflecting the distribution of age, sex, and costs of beneficiaries who remained in FFS. 2. We then computed, for each of 4 years, the ratio of the average annual cost of the proxy HMO joiners to the cost of the proxy FFS beneficiaries. 3. Next, we used these cost ratios to estimate how rapidly and fully the costs of HMO joiners converged toward those of FFS beneficiaries. 4. Finally, we combined the cost ratios with data on HMO enrollees’ tenure within each county to produce a county-specific RTMF.

Description of FFS Beneficiary Data Set

We assembled a longitudinal data set that contained the claims for approximately 1.4 million California beneficiaries who were continuously enrolled in FFS Medicare between 1991 and 1994. Only beneficiaries who were eligible for part A and part B and who remained in the FFS sector for the entire 4-year period were included. People under age 65 who were eligible for Medicare because of a disability and people with end-stage renal disease were excluded.

Methodology for Constructing the Proxy HMO Joiner and Proxy FFS Cohorts

We constructed two proxy cohorts, one with the same demographic mix and 1991 service cost distribution as the Medicare HMO joiners, and the other with the demographics and cost distribution of continuing FFS beneficiaries. To do this, we divided the FFS data set into 10 age and sex subgroups and further divided each subgroup into 25 smaller strata according to the cost of services they received in 1991. We then selected two stratified random samples—one for each proxy cohort—from each demographic subgroup. We limited each sample to 20 percent of the size of its corresponding demographic subgroup within the FFS data set. The sample sizes within each cost stratum were determined by the actual cost distribution of HMO joiners and continuing FFS beneficiaries.

Table II.2 lists the cost strata for one demographic subgroup: females aged 65 to 69. Columns 2 and 3 show the percent distribution of the actual FFS and joiner populations across 25 cost categories. For example, among females aged 65 to 69, 19.2 percent of the FFS population and 39.9 percent of the joiner population had no Medicare charges in 1991.

Table II.2: 1991 Distribution Across Cost Categories of HMO Joiners and FFS Beneficiaries, 65- to 69-Year-Old Females Because of insufficient representation in the population, beneficiaries with costs in the first year of $100,000 or more were excluded from the analysis.

Ratio of Proxy HMO Joiners’ Costs to Proxy FFS Beneficiaries’ Costs

Within each demographic group, we calculated the ratio of the proxy HMO joiner cost average to the proxy FFS cost average for each of 4 years (1991 through 1994). The results are presented in figure II.2, which shows that the pattern of changes in the cost ratios over time displays a high degree of consistency across demographic groups.

The weighted average (across demographic groups) of these cost ratios is shown in table II.3. These ratios show how rapidly and fully the costs of the overall proxy HMO joiner cohort are likely to converge toward the costs of the proxy cohort in FFS.

Tenure in HMO (in years)

Year prior to enrollment (1991)

Year 1 (1992)

Year 2 (1993)

Year 3 (1994)

These cost ratios show that HMO enrollee costs (represented by proxy HMO joiners’ costs) are about two-thirds of comparable FFS beneficiary costs in the year before enrollment, suggesting significant favorable selection. However, once beneficiaries enroll, their costs are expected to increase significantly relative to FFS costs in the first year; the proxy HMO cohorts’ costs rose from 64 percent to 85 percent of FFS cost. In the second year of HMO enrollment, enrollees’ relative costs are expected to rise moderately, and they did—from 85 percent to 88 percent. In the third year, enrollees’ relative costs are expected to show a further, slight increase. By the end of the third year, enrollees’ expected costs—as represented by their proxy cohort’s costs—had regressed about 71 percent; the difference between enrollees’ costs and those of FFS beneficiaries had declined from 36 percent to 10 percent. The slight increases in the proxy enrollees’ costs (relative to the FFS beneficiaries’ costs) after the first year suggest that complete regression either will not occur or will take many years.

Calculating the RTMF From the Estimated Cost Ratios

We used the information on the joiners’ estimated cost increases over time (presented in table II.3) to construct an RTMF for each county. Table II.4 illustrates the calculations for a hypothetical county (based on California data). First, we used our estimates to calculate the increase in expected FFS costs of people who had been enrolled in an HMO for 1, 2, or 3 or more years—relative to their prior-use costs. (See table II.4, row 1.) Computing a weighted average of these increases—where the weights reflect the tenure distribution of HMO enrollees in a given county—yielded a county’s RTMF. (A tenure distribution representative of all California counties is presented in table II.4, row 2.) The RTMF of 1.40 combines information about how quickly and fully RTM occurs (row 1) with these data on the tenure of HMO enrollees.

Benchmark cost proportion: the cost ratio for each year divided by the cost ratio for the year prior to enrollmentTenure distribution: proportion of HMO enrollees for the county (from actual enrollment data)

Method Used to Estimate the RTM Factor for Category II Enrollees

We could not estimate an RTMF for category II enrollees with the method that we used for category I enrollees. That method requires constructing proxy cohorts of HMO joiners and FFS beneficiaries, but the number of category II enrollees—those who survive between 1 year and 4 years after enrollment—was insufficient to do so.

We chose to assume full RTM for the year a joiner died and to apply our estimate of RTMF for category I enrollees to category II enrollees prior to the year they died. Research indicates that individuals’ costs tend to rise most sharply in the months before death, so we assumed the costs of category II enrollees in their year of death regressed fully to the mean of FFS beneficiaries’ costs. With respect to the year or years before this last year of life, when individuals’ costs generally rise less sharply, we applied the category I RTMF estimate to category II enrollees, which represented a significant increase in prior-use costs. If these assumptions over- or underestimate the RTMF for category II enrollees, the effect on the estimate of the county adjusted average per capita cost (AAPCC) rate will be quite small, given the limited number of category II enrollees.

The RTM Factor for Category III Enrollees

The average costs of HMO joiners in the year of their death (in this case 1991) cannot be estimated. After all, joiners must live beyond the prior-use year (1991) to become HMO enrollees. This means that we lacked data to estimate the extent to which category III enrollees’ average costs (in the year of their death) might remain below the costs of comparable FFS beneficiaries. Consequently, to account for enrollees’ death-related costs that prior-use estimates cannot capture, we assigned to HMO enrollees who died in 1992 the costs of FFS beneficiaries with comparable demographic characteristics who died in 1991. Similarly, we used the costs of FFS beneficiaries who died in the prior-use year to approximate the costs of FFS beneficiaries who died in the sample year (1992). By setting the death-related costs of HMO enrollees equal to those of FFS beneficiaries, we assumed that, among category III enrollees, RTM in costs was complete.

Favorable Selection Indicated by Relatively Low HMO Death Rates

Although our method for estimating excess payments to HMOs assumed that no difference existed in death-related costs between HMO and FFS enrollees, it did not assume that the respective death rates were equal. As table II.5 shows, the death rates (per 100) of beneficiaries enrolled in HMOs are significantly lower than those of beneficiaries in FFS. This finding is consistent over time and across demographic groups. The lower death rates among HMO enrollees are a measure of favorable selection. Consequently, these lower death rates are partly responsible for the findings of excess payments to HMOs reported in appendix III.

Table II.5: Death Rates, per 100, of Aged Medicare Beneficiaries by Demographic Group and Year, 1992-94 To control for differences in the demographic composition of the FFS and HMO populations, population group means are weighted by the proportion of the FFS population in each demographic group.

Summary of Adjustments for RTM

We summarize below the source of empirical evidence we used to estimate the RTM experience for each category of enrollee, and how this evidence was used to arrive at a corresponding RTM adjustment factor.

Category I Enrollees

We used FFS data on cohorts of beneficiaries whose costs and demographic characteristics were comparable with those of HMO enrollees to simulate their RTM experience. On the basis of this simulation, we estimated an RTMF (a numerical factor) to adjust the average cost of category I enrollees upward.

Category II Enrollees

Because of insufficient sample size of cost strata, we could not conduct a simulation of proxy HMO enrollees’ costs to estimate an RTMF. However, research indicates that individuals’ costs tend to rise most sharply in the months before death. Consequently, we assumed these enrollees’ costs regressed fully to the mean of FFS beneficiaries’ costs. With respect to the year or years before the last year of life (when costs generally rise less sharply), we applied the category I RTMF estimate to category II enrollees.

Category III Enrollees

We could not conduct a category I-type simulation. Prior-use data provided only limited insight on the RTM experience for these enrollees. Consequently, we assumed that the costs of category III enrollees displayed complete RTM, that is, that their costs in the sample year were no different on average than costs for comparable FFS beneficiaries.

By making these RTM-related adjustments to our prior-use-based estimates of HMO enrollees’ costs, we significantly lowered our estimates of HMO excess payments from what they would have been otherwise. Appendix III presents estimates of excess payments affected by the RTM adjustments described above.

Estimates of Medicare Excess Payments to HMOs in California

This appendix discusses our estimates of the amount of excess payments Medicare has made to California HMOs that participate in its risk contract program, in order to indicate the size and significance of this problem in Medicare’s method of setting capitated rates. The appendix details the savings that could be realized by adopting our method to improve the county rate. These savings are implied by our estimates of county-rate excess payments for the years 1995, 1996, and 1997. The appendix also addresses aggregate excess payments to Medicare HMOs—the sum of county-rate and risk-adjuster-related excess payments—for 1995.

To reduce the computational burden, we limited our efforts to the 58 counties of California. Because risk contract program enrollees are concentrated in relatively few states, demonstrating the magnitude of excess payments did not require us to produce estimates for every county nationwide. We selected the counties of California because (1) about 36 percent of all risk contract enrollees reside there, (2) rates of beneficiary enrollment in risk HMOs vary substantially across the 58 counties, and (3) in recent years, California has experienced rapid growth in HMO enrollment. Although our estimates pertain to a large portion of the risk contract program, we cannot project our estimates nationwide or to other states with demographically similar counties.

We constructed all our estimates from individual-level claims data, using data from two HCFA sources: (1) the Enrollment Database File (EDB) and (2) the HCFA claim files, which contain Medicare claims submitted by FFS providers. We combined individual expenditure information with EDB data to produce a single enrollment/expenditure file containing information on approximately 4.3 million California residents.

Estimates of County-Rate Excess Payments

Table III.1 presents estimates of county-rate excess payments in dollar amounts and as a percentage of risk contract program expenditures for each county. (The estimates are weighted averages of the excess payments in the rates for aged (parts A and B) and disabled (parts A and B).) The counties are ranked by excess payment amounts for 1997. We have included in table III.1 only those counties for which the number of new risk HMO enrollees exceeded 500 in the base year. With respect to the excluded counties, the county-rate excess payments (in each year) total less than 3 percent of total county-rate excess payments in the state.

County-rate excess payment amount (in millions) (continued)

County-rate excess payment amount (in millions)

Bullets indicate that the estimate was not sufficiently precise to be reported, because the county had fewer than 500 joiners during the base year.

These weighted average percentages are the ratios of total excess payments to risk contract program expenditures. Each weighted average pertains only to the counties listed. The weighted averages are not comparable across years because the number of counties differs from year to year. However, the percentages for a given county can be compared across years.

Table III.1 shows that, for California in 1996, the estimated excess payments solely attributable to the county rate are substantial. Consequently, elimination of this component of excess payments—in one state—would save Medicare several hundred million dollars annually. This potential saving equals about 5 percent of risk contract program expenditures in California.

As rates of risk HMO enrollment increase in future years, county-rate excess payments may increase as well. (As a result, the longer-term savings from eliminating county-rate excess payment could well exceed the immediate savings.) This conclusion follows from three premises: 1. Across counties in each year, the higher the HMO enrollment rate, the higher the county-rate excess payment as a share of risk contract outlays. (More technically, the relationship between the county-rate excess payment—as a share of risk contract outlays—and the share of Medicare beneficiaries in the county enrolled in a risk HMO is positive and statistically significant.) This premise implies that the degree of favorable selection in a county does not decline as enrollment rates rise—at least over their observed range of variation. 2. The enrollment rate for risk HMOs will increase nationwide and in California. 3. As the national and state enrollment rates increase, the number of counties with substantial risk HMO enrollment will increase.

In sum, in California, growing enrollment is likely to have two effects on excess payments. The more straightforward effect will be to raise excess payments because a given excess payment per enrollee will be multiplied by a larger number of enrollees. Less obvious, however, will be higher enrollment’s tendency to raise the excess payment per enrollee. That is, if favorable selection continues to occur while HMO enrollment increases, the average cost of beneficiaries remaining in FFS can also increase, leading to higher excess payments per HMO enrollee. As a result of these two effects, the statewide total estimate of county-rate excess payments will increase with HMO enrollment, between 1995 and 1997, from about $276 million to about $413 million.

Estimates of Aggregate Excess Payments

Table III.2 presents our estimates of aggregate excess payment by county. Only those counties for which the number of new HMO enrollees (joiners) exceeded 500 in 1995 are presented in the table. The counties are ranked by excess payment amounts. We estimated that aggregate excess payments totaled about $1 billion in 1995. This amount represents about 16 percent of Medicare’s payments to California HMOs under the risk contract program in 1995. Like county-rate excess payments, aggregate excess payments are concentrated in the five counties ranking highest in risk contract program enrollment. Together, these counties account for more than 75 percent of our estimate of statewide aggregate excess payments.

Aggregate excess payment amount (in millions)

A comparison of the percentages shown in tables III.1 and III.2 indicates that county-rate excess payments account for roughly one-quarter of aggregate excess payments. This result suggests that, even if the imprecision in the estimates of excess payment due to the county rate were substantial, correction of the county rate on the basis of those estimates would not lead Medicare to underpay HMOs as a group. In effect, the component of aggregate excess payment due to inadequate risk adjustment acts as a cushion for the county-rate correction.

Comments From the Department of Health and Human Services and Our Evaluation

The following is GAO’s comment on the Department of Health and Human Services’ letter dated March 26, 1997.

GAO Comment

In commenting on a draft of this report, HHS agreed that, because of favorable selection, the current payment method results in substantial overpayments to Medicare managed care plans. Moreover, HHS did not dispute that our recommended rate-setting revision would save money. However, HHS cited our proposed revision as potentially “inequitable,” possibly burdensome to implement, and “only an interim measure” until HCFA develops better health status adjusters. As discussed below, we believe that certain features make our recommended revision evenhanded, easy to implement, and important to adopt, regardless of the likely improvements to risk adjustment now under consideration. The details of our reasoning follow.

Recommended Revision Would Improve Payment Rate Accuracy and Target Excess Payments Reductions

HHS stated that our proposed revision is not equitable because it would differentially affect HMO payments based on the managed care penetration rate within each county. This is not accurate. Nothing in our proposed refinement to the Medicare payment method would tie HMO payments to HMO penetration rates. Our recommendation is to include an estimated FFS cost for HMO enrollees in the formula used to calculate the county rate. By making the estimate of a county’s average Medicare costs more accurate, this revision would reduce payments most in counties where cost disparities between the FFS and HMO beneficiaries are greatest. Our recommended approach would leave the county payment rate unchanged despite high managed care enrollment—if HMO and FFS beneficiaries in a county have the same average cost.

HHS also expressed concern that, with the adoption of our revision, counties with relatively low AAPCC rates but high Medicare managed care penetration rates could be “very adversely affected.” Our approach is targeted and would not reduce Medicare rates in counties with no cost disparities between the FFS and HMO beneficiaries. Under our approach, a county with a low AAPCC rate but no cost disparities would see no change in its county payment rate—even if the HMO penetration rate in that county was high. In contrast, an across-the-board payment rate cut—which, as HHS notes, is part of the administration’s fiscal year 1998 budget proposal—would affect high AAPCC and low AAPCC counties equally, regardless of how costly a county’s beneficiaries might be. Our proposed revision would reduce but not eliminate excess HMO payments.

Consequently, substantial excess payments would probably remain to cushion HMOs from any resulting reduction in the county rate. (See p. 49.)

To illustrate what HHS believes is the potential for our modified payment method to produce inequitable results, HHS constructed an example involving two hypothetical counties. HHS contends that the example shows a paradoxical result: under our modified method, HHS asserts, HMOs in county A would receive higher capitation payments than HMOs in county B even though HMO enrollees in county A are healthier than those in county B. As explained below, this conclusion is incorrect.

Our recommendation would yield HMO payment rates in line with Medicare law, because they would be set on the basis of the estimated average FFS cost of all beneficiaries in a county. HHS did not acknowledge that under the current method both counties’ HMOs receive the same rate even though county A HMOs serve healthier beneficiaries than county B HMOs. Our method would reduce excess payments to HMOs in both counties, although HMOs would still receive payments exceeding their enrollees’ expected per capita costs. Moreover, our method would increase payments to HMOs in counties experiencing adverse selection—that is, in instances where a county’s HMOs have enrollees whose expected costs exceed those of FFS users.

HHS’ example also runs counter to the experience of the counties we examined. Our data show that counties with low HMO penetration rates tend to have low excess payments relative to counties with high penetration rates. For example, excess HMO payments are lower in Sacramento, which had 5.6 percent of its Medicare beneficiaries enrolled in HMOs, than in Los Angeles, which had 25.5 percent enrolled in HMOs. Nonetheless, HHS’ example assumes excess payments and HMO penetration are inversely related (higher penetration rate, lower excess payments). Though some counties may display this pattern, the counties we examined do not.

In discussing its example, HHS seemingly endorses the current method of paying Medicare HMOs as an interim strategy and, consequently, considers it appropriate to ignore the problem of large excess payments in counties like A, at least for several years. In contrast, our recommended modification of the current method would reduce excess payments significantly and promptly. While it is true that HMOs in B would be paid less than in A, correcting such discrepancies is the role of improved health status adjusters.

Recommended Revision Could Be Readily Implemented

HHS commented that our modification to the current payment method may be difficult to implement, citing both conceptual issues and resource requirements. For example, HHS suggested that “the issue of when to begin counting for the regression (toward the mean) effect is problematic” because many beneficiaries switch plans or switch between managed care and FFS. To overcome this potential difficulty, HCFA could consider time spent in various HMOs with brief spells in FFS as continuous enrollment in managed care. If the beneficiary spent a significant length of time in FFS, HCFA could reset the regression effect for that beneficiary to zero. This approach would be conservative in that it would tend to increase the estimated FFS costs of HMO enrollees and thus yield rates favorable to HMOs.

In addition, HHS expressed concern that “if separate [RTM factor] estimates are required for each county the burden could be very great.” Separate estimates of RTM factors for each county are not needed. We estimated the RTM factor using statewide data, although we used HMO tenure levels at the county level in conjunction with the RTM factor to adjust county costs.

HHS believes that implementing our refinement to the current method would require a significant amount of resources. Given the modest resources (two analysts) that we used in conducting our analysis, and that our proposed change would not entail collecting new data, we believe that the additional resources needed to implement our refinement would be small. Moreover, the likely benefits greatly outweigh such costs. As our report indicates, the payoff from this effort would probably be hundreds of millions of dollars in Medicare savings each year.

Recommended Revision Is Fundamental to Fixing Excess Payment Problem

HHS states that our payment method revision is an interim solution to the HMO overpayment problem. HHS also notes that HCFA is working to develop a new payment methodology incorporating health status adjusters that might be phased in starting in calendar year 2001. Together, these assertions could imply that our approach is unnecessary.

Our revision, however, is not an interim solution. It is an important first step toward—and most likely will be a component of—a comprehensive solution. By addressing the effect of favorable selection in the county rate, our revision makes an essential adjustment to the rate on which the rest of an HMO’s capitation payment is based. The revision could be implemented as early as calendar year 1998. This would allow the government, at the very least, 3 years to make partial reductions in excess HMO payments—amounting to saving hundreds of millions of taxpayer dollars in each of those years. Moreover, our recommended correction of the county rate would complement improved health status adjusters to provide the foundation for a more efficient, accurate, and equitable redesign of Medicare’s method of HMO payment.

GAO Contacts and Staff Acknowledgments

GAO Contacts

Staff Acknowledgments

The following team members also made important contributions to this report: James Cosgrove, Assistant Director; Thomas Dowdal, Assistant Director; Craig Winslow, Senior Attorney; George M. Duncan, Senior Evaluator; and Hannah F. Fein, Senior Evaluator.

The first copy of each GAO report and testimony is free. Additional copies are $2 each. Orders should be sent to the following address, accompanied by a check or money order made out to the Superintendent of Documents, when necessary. VISA and MasterCard credit cards are accepted, also. Orders for 100 or more copies to be mailed to a single address are discounted 25 percent.

U.S. General Accounting Office P.O. Box 6015 Gaithersburg, MD 20884-6015 Room 1100 700 4th St. NW (corner of 4th and G Sts. NW) U.S. General Accounting Office Washington, DC Orders may also be placed by calling (202) 512-6000 or by using fax number (301) 258-4066, or TDD (301) 413-0006.

Each day, GAO issues a list of newly available reports and testimony. To receive facsimile copies of the daily list or any list from the past 30 days, please call (202) 512-6000 using a touchtone phone. A recorded menu will provide information on how to obtain these lists.