E v i d e n c e - b a s e d policy-making 0 Harcourt B . . . . and Company Limited 1997

The calculation of capitation payments using physicians' diagnoses better predicts actual expenditure on health care than community-based surveys of need

Fowles J B, Weiner J P, Knutson D, Fowler E, Tucker A, Ireland M. Taking health status into account when setting capitation rates: a comparison of risk-adjustment methods. JAMA 1996; 276(16): 1316-1321

Objective

To compare how well different risk- adjustors can predict future expenditures, with risk-adjustors taken from three different sources (demographic, survey and claims- based data).

Setting

Group network health maintenance organization (HMO) in Minnesota, USA.

Method

Cross-sectional survey. 3825 enrollees aged 18--64 and 1955 enrollees aged 65 and over were randomly sampled from a Minnesota network-model HMO. The adult response rate was 65% with non-respondents more likely to be young, healthier males; the elderly response rate was 84% with non- respondents more likely to be older and sicker.

Literature review

No explicit strategy stated; 34 references.

Outcome measures

Total expenditures in the year of the survey and I year after the survey were predicted, using demographic adjustors combined with either self-reported functional health status, self-reported chronic diseases (based on survey data), or physician diagnosis (based on administrative data.) Expenditures were predicted both at the individual level and for groups of individuals. (High expenditures were top-coded at $25 000.)

Results

The physicians' diagnoses routinely reported on administrative records are better predictors of expenditure than health status measures (functional or chronic disease), which are better predictors than demographics alone. The health status

measures combined with demographics predicts approximately 11% of the individual variance in expenditures while demographics alone predict only 5.8%. If insurance pools are perfectly random (i.e. random groupings), all three adjustors proved adequate. However, given non- random groupings (which occur when insurers 'cream-skim' health patients, for example), the diagnosis-related adjustors perform the best. Using demographic adjustors alone underpredicts high-risk expenditures so that capitation payments would be 5.2% lower than actual expenditures for high-risk groups, while low-risk payments would be 7.9% too high. The ACGs work particularly well at predicting expenditures for non-random groups of elderly people.

Authors ' conclusions

The authors conclude that the appropriate risk-adjustment for capitation payments should include demographics as well as diagnoses reported in administrative claims data. These measures perform at least as well as self-reported health status measures and are available without the need for consumer surveys. One cautionary note is that diagnosis-related capitation payments may result in physicians or plans reporting more and/or worse diagnoses than otherwise. If diagnosis data is not readily available and selection (i.e. non-random grouping) is believed to be a problem, simple self- reported health status measures (such as presence of a chronic condition) should be used in conjunction with demographic adjustors instead.

Commentary Capitated payment for health services is intended to encourage providers to reduce unnecessary costs. But capitation can have adverse effects on health care in competitive environments if reimbursement does not match patients' actual needs for services. Then providers that attract low-risk patients are rewarded and those that take on difficult patients may be unable to pay for needed care.

Fowles and colleagues compare how well three types of information (demographic data, patients' reports of functional health status and chronic condiiions, and cluster of clinical diagnoses) predict health care expenditures in the following year. ALl predict rather well for groups of patients,

with clinical diagnoses performing better than patient-volunteered information, which in turn is better than demographic information. With the best risk adjustment method, clinical diagnoses, providers would be paid within 0.7% of actual expenditures in low- and high-risk groups. Predictions are not as good for individuals, but that is more of a limitation of risk-adjustment in health services research than for management.

As the authors point out, choice of a risk= adjustment method should also take into account the availability and completeness of the data and the cost of assembling it. With this in mind, the authors believe'that if administrative data on clinical diagnoses are not readily available, then simple, self- reported measures may be the best choice.

However, providers caring for low-risk patients would be overpaid 4.7% and those caring for high-risk patients would be underpaid 2.5%. In my view, these differences are large enough to encourage 'cream-skimming' and to discourage providers from taking on sick patients.

Any risk adjustment method that leaves substantial perverse incentives is unsatisfactory. The fundamental role of health services is, after all, to help patients who need it most and not to create economic opportunities for clever providers.

Professor Robert Fletcher Department of Ambulatory Care

and Prevention, Harvard Medical School, Boston, MA, USA

86 EVIDENCE-BASED HEALTH POLICY AND MANAGEMENT DECEMBER 1997