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

In this issue, Dr. Leida Lamers has contributed an excellent, well-written, understandable article that adds significantly to our understanding of risk adjustment in capitation payment systems (in this case the Netherlands sickness fund system) (Lamers 1998). The Lamers model has high applicability to strategic planning for healthcare resource allocation systems in many public and private healthcare systems around the world.

The study clearly demonstrates the value of adding more health status information to improve the overall accuracy and goodness-of-fit of annual per capita expense prediction models. The author's results reinforce the independent contributions of demographics, diagnoses, and functional health status (FHS) to predicting annual per capita expenditures. In particular, the addition of information on hospital diagnoses during the previous year improves prediction over demographics only; adding information on hospital diagnoses over the previous three years improves prediction over hospital diagnoses for the past year only; and adding FHS information improves prediction over demographics and three years of inpatient diagnoses.

MEDICARE AND THE BALANCED BUDGET ACT OF 1997

One direct implication of this study for Medicare risk contracting in the United States is that the recent reforms contained in the Balanced Budget Act of 1997 can be improved by using three prior years of inpatient diagnoses in the Diagnostic Cost Groups (DCGs) instead of using only the immediate past year in the risk-adjustment model. If HCFA is not ready to move quickly to a model that includes both inpatient and ambulatory diagnoses, the next step should be to build a three-year DCG risk-adjustment model for the adjusted average per capita cost (AAPCC).

Ambulatory Diagnoses

Although Dr. Lamers' study does not show the relative importance of ambulatory diagnoses, self-reported use of medications for rheumatoid arthritis - one of the variables included from the health survey - does hint at this potential source of untapped information. We know from previous studies that ambulatory diagnoses carry considerable prediction information for next year's expenditures per person (Blough, Madden, and Hornbrook 1998; Ellis, Pope, Iezzoni, et al. 1996; Ellis, Pope, Iezzoni, et al. 1998; Kronick et al. 1996; Starfield et al. 1991; Weiner, Dobson, Maxwell, et al. 1996). Potential explanations of the prediction power of ambulatory diagnoses over inpatient diagnoses are that (1) inpatient diagnoses simply miss the considerable disease burden that is cared for outside the hospital; (2) random variation in ambulatory expense is less than that for inpatient expense; and (3) many chronic diseases are treated primarily in the ambulatory setting and involve continuing care patterns. As a result, ambulatory expenses are much more predictable than inpatient expenses, which are lumpy and infrequent on an individual basis. This implies that the Dutch sickness funds and other health systems concerned about selection bias and risk adjustment should make strategic investments in clinical information systems for ambulatory care providers. From a policy perspective, it is not sufficient to be content with the current state of healthcare information systems. Information systems should be viewed not as exogenous constraints but as strong policy instruments for the achievement of desired performance goals.

Health Status Surveys

Dr. Lamers contends that risk adjusters based on survey information are at present inappropriate in the Dutch context. She uses a health survey conducted by one of the Dutch sickness funds to test for bias and imprecision in the demographic/inpatient diagnoses risk-adjustment model rather than estimating a new version of a richer risk-adjustment model. Her results show that functional health status must be included in risk models. Dr. Lamers discusses the administrative costs of health surveys and their potential for gaming. Gaming will occur by providers and sickness funds on any information advantage they have over the national health insurance program. Policymakers and payers are concerned that providers will coach their patients to select survey responses that make them appear sicker. Whether this behavior is random or uniform will have little effect on the outcomes of the risk-adjustment system. If, however, some providers or funds are faster and better at coaching respondents, then selection bias will occur because the model will fail at detecting real differences in risks. This calls forth the following menu of policy responses:

1. Continue to develop risk models to include new risk factors and recalibrate the coefficients of existing factors to compensate for health status creep.

2. Embed health status surveys in the clinical context so that the primary sponsors and users of the results are the patients' physicians, not the payers.

3. Implement specific policies to penalize risk skimming (discussed further on).

4. Use health survey data for quality assessment purposes.