title: Health-risk-assessment tools used to predict costs in defined populations.
abstract: With the Balanced Budget Act of 1997 mandating that the Health Care Financing Administration (HCFA) implement risk-adjusted payment mechanisms for Medicare managed care plans (Medicare + Choice) by January 2000, risk-adjustment tools will play an important role in future capitated reimbursement. This is because there is growing evidence that healthier-than-average beneficiaries select Medicare + Choice. The risk adjustment that HCFA has adopted is initially based on primary inpatient diagnosis from hospitalizations in the previous year. Other payers are likely to adopt similar payment mechanisms. This article reviews nineteen risk-adjustment research papers, including the tool adopted for Medicare + Choice, some of which are likely to form the basis for subsequent HCFA risk-adjustment methods. In general, claims-based models are more powerful in predicting total costs than survey-based or demographics-based models. Survey-based models, although expensive and not as powerful claims-based models, can be used when claims data are unavailable. One of the most popular survey-based tools, SF-36, is likely to become increasingly important because HCFA will be using it to measure quality outcomes from Medicare + Choice plans and will make the results public. All of the models reviewed have limitations, but can be expected to be building blocks for future risk-based capitated reimbursement.
authors.affiliation: Vanderbilt University Medical Center in Nashville, Tennessee, USA.
authors.forename: F
authors.initials: F
authors.lastname: FitzHenry
authors.forename: E K
authors.initials: EK
authors.lastname: Shultz
mesh_terms: D000368:Aged; D014487:Centers for Medicare and Medicaid Services, U.S.; D003710:Demography; D017048:Health Care Costs; D006305:Health Status Indicators; D006801:Humans; D015273:Insurance Selection Bias; D008329:Managed Care Programs / Q000191:economics* / Q000706:statistics & numerical data; D020398:Medicare Part C; D015233:Models, Statistical; D020379:Risk Adjustment; D018570:Risk Assessment; D014481:United States