Background.Although the problem of adverse selection into more generous health insurance plans has been the focus of previous work, risk adjustment systems have only recently begun to be implemented to blunt its effect.Objectives.This study examines the ability of the leading risk adjustment systems to predict health care expenditures for people with chronic conditions, using claims and enrollment data from 2 large employers.Research design.Predictive errors and total financial losses/gains are compared for different risk adjustment approaches (primarily hierarchical condition categories [HCCs] and adjusted clinical groups) for several chronic conditions.Results.One of the best performing risk adjusteent systems was a regression-based HCC method, which had an average under-prediction error rate of 9% or 6%, depending on the employer. In comparison, more typical actuarial risk adjustments based on just age, gender, and prevailing area wages lead to a prediction error of at least 50%. We did not find evidence that payments for particular chronic conditions would be consistently and significantly under- or overestimated.Conclusion.The leading risk adjustment approaches substantially reduce the incentives for adverse se-lection but do not eliminate them.
Get full access to this article
View all access options for this article.
References
1.
1. Ash A, Porell F, Gruenberg L, Sawitz E, and Beiser A. Adjusting Medicare capitation payments using prior hospitalization data . Health Care Financing Review. 1989;(Summer): 17-29 .
2.
2. Weiner J, Tucker AM, Collins M, et al. The development of a risk-adjusted capitated payment system: the Maryland Medicaid model . Journal of Ambulatory Care Management. 1998;21(4): 29-52 .
3.
3. Wilson VM, Smith CA, Hamilton JM, et al. Case study: the Washington State Health Care Authority . Inquiry. 1998;32(2): 178-192 .
4.
4. Bertko J, Hunt S. Case study: the health insurance plan of California . Inquiry. 1998;32(2): 148-153 .
5.
5. Dunn DL. Applications of Health risk adjustment: what can be learned from experience to date? Inquiry. 1998;32(2): 132-147 .
6.
6. Knutson D. Case study: the Minneapolis Buyers Health Care Action Group . Inquiry. 1998;32(2): 171-177 .
7.
7. Tollen L, Rothman M. Case study: Colorado Medicaid HMO risk adjustment . Inquiry. 1998;32(2): 154-170 .
8.
8. Dunn DL, Rosenblatt A, Taira DA, et al. A comparative analysis of methods of health risk assessment. SOA Monograph M-HB91-1. Schaumburg, IL: Society of Actuaries ; October 1996.
9.
9. Weiner J, Dobson A, Maxwell AS, Coleman K, Starfield B, Anderson G. Risk-adjusted Medicare capitation rates using ambulatory and inpatient diagnoses . Health Care Financing Review. 1996;17(3): 77-99 .
10.
10. Ash A, Ellis R, Yu W, et al. Risk adjustment for the non-elderly. Report submitted to the Health Care Financing Administration under contract no. 18-C-90462/1-02, September 4, 1997.
11.
11. Ettner S, Frank R, Mark T, Smith M. Risk adjustment of capitation payments to behavioral health care carve-out: how well do existing methodologies account for psychiatric disability? Submitted to the Office of the Assistant Secretary of Planning and Evaluation, September 16, 1998.
12.
12. LaPlante M. Disability Risks of Chronic Illness and Impairments. Washington, DC: US Department of Health and Human Services ; 1989.
13.
13. Weiner J, Starfield B, Steinwachs D, Mumford L. Development and application of a population-oriented measure of ambulatory care case-mix . Med Care. 1991;29(5): 452-472 .
14.
14. Ellis RP, Pope G, Iezzoni LI, et al. Diagnosis-based risk adjustment for Medicare capitation payments . Health Care Financing Review. Spring, 1996.
