The American Journal of Managed Care June 2011
The Structure of Risk Adjustment for Private Plans in Medicare
The foregoing is strictly statistical, but there is also economic content in the results. One HCC that appears substantially more expensive in the MA-HMO sample is renal dialysis, HCC130. Patients who require renal dialysis often receive their dialysis in centers specializing in dialysis and from physicians in these centers who manage a range of the patients’ conditions. Most important for our purposes, the renal dialysis industry is concentrated into a small number of companies, which have market power in local markets. The MAHMO pays the market rate for these patients. We see a similar phenomenon with major organ transplants (HCC174), which also tend to be concentrated in a few major centers in the United States. As with renal dialysis, major organ transplant centers have market power. Therefore, these diagnoses seem expensive in the MA-HMO compared with the TM, where market power is not relevant.
We also tested whether the old adage that entities paid by capitation had incentives to select against sicker people still held with the new risk adjustment system. To do so, we computed the correlation between the difference of the TM and MA-HMO coefficients and the level of the TM coefficients. This correlation was −0.668 (P <.01) in 2006 and −0.281 (P = .05) in 2007 for the 46 and 45 HCCs listed in Tables 1 and 2. In other words, CMS-HCC with higher values seem unprofitable, appearing to suggest that the old adage continues to hold. However, closer inspection revealed that the values are driven by the outlier value for renal dialysis. If renal dialysis is omitted, the correlation for 2006 changes from −0.668 to −0.155 (not significant) and for 2007 from −0.281 to 0.171 (not significant). In other words, while the risk adjustment scheme has substantial errors for individual HCCs, there is no detectable pattern with respect to the size of the HCC weight, implying that the old adage likely no longer holds. In that sense, the CMS-HCC must be regarded as a success.
Medicare bases its reimbursement to MA health plans on the relative costliness of treating various maladies, but it computes that relative costliness using data from TM. Such data are likely to be in error when applied to health plans for many reasons, including that the treatment of some conditions is more amenable to the medical management techniques used by the plan than the treatment of other conditions and that plans face providers and suppliers with varying degrees of market power across diagnoses. Our results suggest not only that there are likely errors in the pricing structure Medicare usesbut also that those errors may well be large. Most important, these errors do not seem to be correlated with predicted risk, suggesting that simple strategies aimed at selecting lower-risk patients would not result in favorable selection.
Particularly notable are HCC130 (renal dialysis) and HCC174 (major organ transplants) because they are expensive in the MA-HMOs in our sample compared with TM. Whereas TM pays for renal dialysis and major organ transplants with administratively set take-it-or-leave-it prices, MA-HMOs pay market prices for these services. We believe that these diagnoses are unprofitable for the HMO because Medicare can exploit its ability to set prices to a greater degree for these diagnoses than for other diagnoses for which plans face a more competitive supplier market.
Crude selection strategies based simply on predicted risk (ie, skim the healthy) are unlikely to yield favorable selection not only because of the poor correlation between predicted risk and the payment distortions that we found but also because older persons often have multiple concurrent conditions. In other words, a plan enrolls the whole person, and for a beneficiary with multiple conditions, the reimbursement may be too generous for one condition but too skimpy for another.
Despite the mitigation that comes from having to enroll an entire patient, our results imply that the risk adjustment structure used by Medicare makes beneficiaries with certain conditions more or less profitable for MA plans than beneficiaries with other conditions. These distortions create incentives for plans to specialize in certain conditions, that is, to select for and against certain conditions in how they structure their networks and formularies with the objective of attracting or not attracting beneficiaries with specific types of conditions.
One can ask whether specialization by MA plans in certain conditions could be desirable in the sense that it might minimize social cost. There are at least 3 problems with this reasoning. One, as just mentioned, many Medicare beneficiaries have multiple conditions, and it does not seem optimal to have relatively good service or access for one condition but not for another. Second, a beneficiary may develop a new condition, in which case the beneficiary may want to change physicians or drugs and would need to change plans in the extreme case. This also does not seem optimal. Third, social cost is not the same as budget cost; in other words, to the degree that Medicare uses the same real resources to treat a condition but simply pays less because of its monopsony power, there is no necessary desirability in having TM specialize in that diagnosis. Inferences about economic efficiency in health care markets, however, are problematic for many reasons.20
Our work is subject to the obvious limitation that it comes from a single MA insurer, and we do not know to what degree these results would generalize to other insurers. However, even if on average MA plans replicated TM, any heterogeneity among insurers would leave problematic incentives for individual plans to engage in selection.
A second limitation is that we cannot exclude the possibility that some of the differences we found between MA costs and TM costs are attributable to diagnostic coding because coding has an element of endogeneity. Song et al21 recently showed that TM beneficiaries who moved to regions with greater intensity of services than their region of origin had substantially greater increases in CMS-HCC risk scores (ie, more coded diagnoses or higher-weighted diagnoses) than beneficiaries who moved to regions with the same or lower intensity and suggested that more diagnostic testing in regions of higher intensity led to additional diagnoses being recorded. Plans have an incentive to code diagnoses more completely than physicians treating TM beneficiaries (although less incentive to use a large number of tests to do so) because their reimbursement depends on the CMS-HCC score, whereas physician reimbursement in TM does not turn on a beneficiary’s diagnoses. However, hospital reimbursement in TM, like health plan reimbursement, does depend on the coding of diagnoses, and this should serve to lessen any coding differences between TM and MA.
A third limitation is that the hypothesis test of the null that the MA coefficients computed on MA data are the same as the TM coefficients had to make some assumptions that are known not to hold; these assumptions can be relaxed, and one can compute an exact test with TM claims data. Nonetheless, the violations of the assumptions seem sufficiently weak (given the degree to which the test statistics reject the null) that it seems unlikely an exact test would overturn these results. Finally, CMS required that the risk adjustment through 2006 remain budget neutral (ie, the total MA pie did not decrease because of the phase-in of the CMS-HCC risk adjustment approach), whereas starting in 2007, more of the MA pie reflected the actual risk of enrolled beneficiaries. This budget neutrality phase of the risk adjustment implementation could have mitigated some of the incentives to the extent that beneficiaries enrolled in MA differed from those enrolled in TM.
Part C of Medicare has the advantage relative to TM that CMS does not have to set thousands of prices for individual services that inevitably will diverge from cost, which is burdensome and subject to the political process. Rather, private plans negotiate prices with providers. Such prices will, of course, reflect the market power of the providers that treat patients with various HCCs in the plan’s local market (and the plan’s market power if it has a large market share), as indeed they appear to for renal dialysis and major organ transplants. Nonetheless, on balance, the negotiated prices between plansand providers may be closer to cost than the administratively set prices in TM are, a potential strength of Part C relative to TM. However, this work emphasizes that there is also an element of administered pricing in Part C because risk adjustment is a form of administratively set prices. Our results also suggest that the current methods for risk adjustment share a common problem of administratively set prices in that they can substantially depart from cost.
We thank James H. Ware for helpful discussions.
Author Affiliations: From Harvard University (JPN, JTH), Boston, MA; Division of Research (JH, VF, JTH), Kaiser Permanente, Oakland, CA; and University of California, San Francisco (RJB).
Funding Source: This study was funded by grant P01 032952 from the National Institute on Aging.
Author Disclosures: Dr Newhouse reports that he is a director of and holds equity in Aetna, which sells Medicare Advantage plans. Drs Huang and Fung are employed by Kaiser Permanente, which also sells Medicare Advantage plans. Dr Brand reports serving as a paid consultant for Kaiser Permanente Division of Research. Dr Hsu reports receiving grants from the NIH, whose research affects CMS.
Authorship Information: Concept and design (JPN, Dr Hsu); acquisition of data (JPN, Dr Hsu); analysis and interpretation of data (JPN, JH, RJB, VF, JH); drafting of the manuscript (JPN, Dr Hsu); critical revision of the manuscript for important intellectual content (JPN, JH, VF, JH); statistical analysis (JPN, RJB); obtaining funding (JPN); administrative, technical, or logistic support (JH, VF, JH); and supervision (Dr Hsu).
Address correspondence to: Joseph P. Newhouse, PhD, Harvard University, 180 Longwood Ave, Boston, MA 02115. E-mail: email@example.com.
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