"Carving out" the financial risk and management of behavioral health care has become popular among both employers and state Medicaid programs. One of the important decisions facing purchasers of behavioral health care "carve-out" services is whether to contract with more than one vendor. For example, a number of states, including Arizona, Texas and New York, are considering contracting with several "carve-out" vendors to provide behavioral health services to Medicaid beneficiaries. The concern with having multiple capitated vendors competing with each other is that in the absence of a high-performing risk-adjustment methodology, the competition is likely to be on the basis of which vendor can enroll the healthiest patients, rather than which vendor can provide high quality in a cost-efficient manner. Unlike cost-based reimbursement, capitation payment allows vendors to profit by reducing the provision of services to clients. This study examined the degree to which risk-adjustment methodologies attenuate incentives for behavioral health care carve-out vendors to dump costly patients, with our particular concern being people with psychiatric disability.
Using mean absolute prediction errors as the basis of comparison, we found that two of the commercial risk-adjustment systems, the ADGs and HCCs, worked about equally well for risk-adjusting capitation payments to behavioral health care carve-out vendors. Interestingly, the more commonly used variant of the ACG methodology, the Ambulatory Care Groups, performed much more poorly than the ADGs and also performed worse than any of the other risk-adjustment models, with the exception of simple demographic adjustment. The difference in performance between ACGs and ADGs probably arises because the ADGs control for the exact types of diagnoses for people with multiple diagnoses, while the ACGs are more aggregated.
Both the ADG and HCC methodologies were clearly superior to simple demographic adjustments. However, adding separate controls for psychiatric disabilities further improved their performance, and even the best risk-adjustment models did not fully account for the higher behavioral health care expenditures of patients with psychiatric disability. Payments for patients with psychiatric disability would have covered only about 85 percent of actual expenditures, while MH/SA payments for patients without psychiatric disability would have exceeded actual expenditures by about 5 percent. The underprediction of expenditures for patients with psychiatric disability is similar to previous findings for patients with chronic medical conditions (Dunn et al., 1995) and probably arises because to some extent, risk-adjustment systems aggregate people with more and less severe disorders and pay the average cost of the entire group. Thus a risk-adjustment system might pay the same additional amount for a person with anxiety disorder as for a person with schizophrenia, even though only the latter might be truly disabled and expensive to care for. Although one could "over-adjust" payments for psychiatric disability in order to reduce the underpredictionproblem, taken to an extreme, this strategy reverts back to simple cost-based reimbursement.
One might expect the proportion of variance explained to have been higher within the carve-out population than the HMO population, due to the homogeneity of benefits and utilization review within carve-outs and their potentially greater ability to reduce the expenditures of high-cost patients (for example, through the use of specialty provider networks offering discounted rates and by using care management techniques tailored for behavioral health). However, our data showed no clear cut pattern to suggest that risk-adjustment worked better for patients with one type of coverage vs. another, perhaps because the HMO enrollees were more homogeneous in terms of their health status. The carve-out population appeared to be sicker on average and was likely to include more high-cost outliers than the HMO enrollees, which would make it more difficult to predict expenditures among this population. The results based on the HMO population are probably more relevant from a policy perspective, since most behavioral health care carve-outs involve management of care, regardless of whether behavioral health is carved out internally within the HMO or involves a carve-out vendor.
Comparisons with earlier research should be interpreted with caution, since the relative ranking of risk-adjustment methodologies found in this study may not necessarily generalize to populations with different characteristics and insurance benefits. Bearing this caveat in mind, our findings for privately insured adults support the conclusion of an earlier study of a limited number of risk-adjustment methodologies, based on data from the New Hampshire Medicaid program. ACGs were found to be worse at predicting MH/SA expenditures than relatively simple adjustments for classes of psychiatric diagnoses (Ettner and Notman, 1997).
Our results differed slightly from those of another study byEttner et al. (1998) using 1992-1993 data on privately insured employees and dependents to compare the performance of several risk-adjustment models in predicting MH/SA expenditures. The models compared were a basic demographic model, ACGs, ADGs, HCCs and a "comorbidity" model controlling for seven classes of MH/SA diagnoses and four interactions between psychiatric comorbidities. Among adults, the "comorbidity model" performed the best, with the ADGs a close second in terms of mean absolute prediction error. HCCs ranked third in terms of predictive ability but were clearly outperformed by the ADGs. The "value-added" of controlling for additional psychiatric diagnoses in the ADG and HCC specifications was not examined.
The R2 values in the Ettneret al. (1998) study were somewhat lower than those found here, perhaps because the data in this study were from an employer located in a single geographic region. In particular, the model controlling only for three types of severe psychiatric disability did as well in this study as the full-fledged "comorbidity" model did in the earlier study. As seen in this study as well, Ettner et al. showed that natural selection into health plans gives rise to the potential for large profits or losses to be made under full capitation, regardless of which risk-adjustment model is used. However, the earlier study did not look at predictive ability separately for people with and without psychiatric disabilities or examine alternatives to full capitation for paying carve-outs.
Previous research has looked at whether risk-adjustment of capitation payments to HMOs adjusts adequately for the higher costs of enrollees with particular medical conditions, concluding that all of the existing methodologies substantially underpredicttotal expenditures for these groups (Dunn et al., 1995). This finding has a counterpart in our study, which demonstrates that people with psychiatric disability may become targets of "dumping" by competing behavioral health care carve-outs under full capitation unless risk-adjustment systems can be substantially improved. In the absence of better data and given the need for any risk-adjustment system to be easily implemented, it seems unlikely that such improvements will be forthcoming. The likelihood that carve-outs will compete on the basis of patient selection depends on the administrative costs of avoiding sick patients and whether the insurer is concerned about the possibility of negative publicity and subsequent loss of patient goodwill that might result from overt "cream-skimming" and "dumping" behavior.
Given the financial incentives to engage in such behavior, if the deterrents are weak, then purchasers may want to consider alternatives to competition and full capitation, such as sole source contracting, "soft" capitation or "mixed" payment systems. With sole source contracting, "dumping" of patients with psychiatric disability would not be a source of concern, because a single behavioral health care carve-out vendor would be responsible for financing the MH/SA care of the entire beneficiary population. Furthermore, given the current trend towards consolidation in the behavioral health care carve-out market through mergers and takeovers (Oss et al., 1997), practical considerations may sometimes dictate that purchasers rely on a single vendor. The disadvantage of this approach is that the purchaser must rely on the rebidding process to provide incentives for the carve-out vendor to provide high-quality care. Furthermore, carve-outs observed in the real world typically exclude HMO enrollees, applying only to beneficiaries who remain in a fee-for-service system. Thus selection remains a potential problem unless MH/SA services are carved out of the HMOs as well, so that all beneficiaries have the same insurer for these services, regardless of their medical plan.
In order to examine alternative payment methodologies, we calculated the per-enrollee profits or losses that would be incurred by behavioral health care carve-outs experiencing selection corresponding to the empirical patterns of patient enrollment among the seven HMOs in our sample. In our example, use of a mixed payment methodology would have reduced (but not eliminated) the variability in financial performance of the models, relative to full capitation.
Soft capitation based on plan averages would also lead to greater equality of financial performance across carve-out vendors. However, it does less to attenuate incentives for selective enrollment of healthy patients by vendors than soft capitation implemented at the individual level. Contracts using individualized target amounts and risk corridors would substantially reduce the risk to vendors associated with enrolling high-cost patients with psychiatric disabilities. The downside is that if purchasers do not impose budget neutrality under soft capitation contracts, the purchaser ends up incurring a higher proportion of excess costs when payments are calculated separately for each individual.
Even if post hoc budget neutrality is written into the soft capitation contract, the disadvantage still remains that plans have less incentive to do a good job of managing the care of high-cost patients. Soft capitation is similar to mixed payment, except that under soft capitation, the purchaser tends to retain 100 percent of the risk of expenditures falling outside of the risk corridors. Thus, depending on how the contract is written, patient-level soft capitation is closer to cost-based reimbursement than any of the other payment methodologies, including mixed payment. The tradeoff between giving carve-out vendors incentives to be cost-efficient while limiting their incentives to "dump" patients with psychiatric disabilities should be considered by purchasers in choosing the most appropriate methodology for paying behavioral health care carve-out vendors.