Private Payers Serving Individuals with Disabilities and Chronic Conditions. G. Conclusions


In this study we tested the ability of the leading risk-adjustment systems to predict expenditures for people enrolled in private sector health plans with potentially disabling chronic conditions. The chronic conditions examined included asthma, cancer, psychiatric disorders, chronic obstructive pulmonary disorders, heart failure, diabetes, rheumatoid arthritis, seizure disorders, stroke, and ulcerative colitis. We also examined the ability of the models to predict costs for a group of chronic conditions defined by LaPlante (1989) as disabling on the basis of reported difficulties with activities of daily living.

Five different types of prospective risk-adjustment systems were examined: HCCs using weights (i.e., parameter estimates) that we created, HCCs using the weights provided with the DCG software, ACGs, ADGs, and a baseline model that included demographics, gender, and a wage index. Consistent with previous research, all four of the models that included health indicators were able to predict expenditures more closely than the baseline model. The best model was able to reduce the prediction error to less than 15 percent for all but one disabling chronic condition in one employer population. The largest loss incurred under the best model would have been for patients with heart failure, totaling $3 million for all patients with heart conditions treated by the employers' health plans.

The results from this study compare favorably with previous studies of the ability of risk-adjustment to predict expenditures for people with chronic conditions. Ash and colleagues (1997) found prediction errors of less than 10 percent for 13 of 26 conditions examined with DCGs. Wiener and colleagues (1996) found that ADGs lead to prediction errors of less than 10 percent for 9 out of the 15 conditions examined. In this paper, we found average prediction errors of less than 10 percent for 6-8 of 11 conditions examined for Employer A, depending on the system, and 6-9 of 11 for Employer B. There was no consistent pattern across the three studies in the types of conditions having the largest prediction errors, suggesting that prediction errors for particular conditions may vary depending on enrollee population characteristics and insurance benefits.

The absence of consistently large prediction errors for particular conditions argues against automatically carving out particular chronic conditions from capitated plans offered by private employers. A better strategy, given the difficulty of identifying consistently overestimated or underestimated expenditures for a particular condition, is for employers to monitor the performance of their risk-adjustment systems and the incentives they may create to avoid vulnerable populations.

This study finds that while risk-adjustment significantly reduces the incentives for adverse selection, it does not eliminate them. Ideally risk-adjustment would diminish the financial benefits of screening enough that it would not be cost-effective for firms to avoid people with high-cost conditions. For example, suppose that risk-adjustment would lead Medicaid to award a certain plan $2 million more than it would otherwise receive due to the plan's high proportion of cancer patients. Without risk-adjustment, the plan might attempt to avoid enrolling cancer patients through various means; with risk-adjustment, such actions would not be beneficial and presumably would not occur. Risk-adjustment systems are imperfect, however, and the cost of screening out potentially high-cost patients is unknown. It is difficult to determine, therefore, whether a given risk-adjustment system's performance is sufficient to discourage risk-avoidance behavior on the part of health plans.

If employers remain concerned about the potential for selection, then they may want to consider alternatives to full capitation. Under full capitation, health plans are paid 100 percent of the risk-adjusted capitation amount for each enrollee, regardless of what actual expenditures were. Two possible alternatives are sole source contracting and mixed payment. Under sole source contracting only one plan type is offered. This will eliminate adverse selection as long as the employee has no other option (such as coverage under a spouse's plan). The obvious disadvantage of sole source contracting is that employees cannot choose among competing plans in order to best fit their tastes, needs, and financial constraints. Under a mixed capitatedsystem, health plans are paid in part based on a risk-adjusted capitated rate and in part based on their actual expenditures. The advantage of this system is that it reduces the risk to plans and thus the incentives to avoid high-cost patients. The disadvantage is that basing reimbursement partly on actual expenditures lessens a plan's incentive to provide care efficiently.

In conclusion, this study indicates that the leading risk-adjustment models are a substantial improvement over simple age-sex adjustment that is now the norm. Additional simulation studies are needed using data from other employers. Moreover, the experiences of third party payers in using risk-adjustment systems under "real world" conditions will be an essential part of determining their future viability and value.

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