Commentary: improving risk-adjustment models for capitation payment and global budgeting - Methods - response to article by Leida Lamers in this issue, p. 1727

Health Services Research, Feb, 1999 by Mark C. Hornbrook

An apparently contradictory finding from this study was that profits were higher for good risks with a previous hospital admission than for good risks without an earlier admission. This seems to suggest that having the additional diagnosis risk cells enables sickness funds to obtain higher profits from cream skimming relative to profits from persons grouped only by demographic variables. Readers should be reminded that the "good" and "bad" risks were defined on the basis of knowing actual expenses for the future. Hence, the profit estimates represented maximum amounts if the sickness funds were omniscient, and the estimates were not necessarily indicators of a flawed risk model. In this case, persons with a previous hospital admission had higher expected overall healthcare costs compared to persons without a previous admission. With a higher mean expense, the returns to omniscience will be absolutely greater than for lower mean expense.

CONCLUSION

This study makes a significant contribution to the field. The model is useful both for global budgeting (to assure equitable resource allocation across localities) and for managed competition (to assure equitable resource allocation across health plans or sickness funds). With the available menu of demographic, diagnosis, and functional health status risk-adjustment models now available, risk adjustment should become an integral component of all healthcare resource allocation systems. A major challenge to researchers and policymakers is presented by the fact that static risk-adjustment models do not reward health plans/sickness funds for cost-effective disease prevention efforts or for investing in the improved health status of their members. We need to devise means to reward plans/sickness funds for improving health outcomes for their members. This is no simple task because the normal trajectory of health status for a defined population is downward. Hence, as the population ages, we are faced with providing incentives for slowing down the rate of decrease in health status and for maintaining functional abilities over longer time periods. The immediate challenges to researchers are to develop measures of population health status trajectories to serve as the dependent variable in a dynamic risk-adjustment model and then to estimate models to adjust for exogenous factors that affect providers' ability to alter health status trajectories. We have only just begun to define the field of risk adjustment in healthcare. We applaud Dr. Lamers' contributions to the field.

REFERENCES

Blough, D. K., C. W. Madden, and M. C. Hornbrook. 1998, in press. "Modeling Risk Using Generalized Linear Models." Journal of Health Economics.

Ellis, R. P., G. C. Pope, L. I. Iezzoni, J. Z. Ayanian, D. W. Bates, H. Burstin, and A. S. Ash. 1996. "Diagnosis-based Risk-Adjustment for Medicare Capitation Payments." Health Care Financing Review 17:101-28.

Ellis, R. P., G. C. Pope, L. I. Iezzoni, J. Z. Ayanian, D. W. Bates, H. Burstin, D. A. Dayhoff, and A. S. Ash. 1998. "Risk Adjustment of Medicare Payments Using the Hierarchical Coexisting Conditions Model." Unpublished manuscript, Boston University.