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

Risk Skimming

Cream skimming is based on information asymmetry. The sickness funds know more about the patients than the national health insurance program. Specifically, they know the patients' current utilization patterns, which are very strong predictors of future utilization, for both patients and providers. This implies that the Dutch national health insurance plan should impose requirements for regular (monthly) data transfers on ambulatory encounters and hospital admissions so that short-run forecasts of annual financial risks can be made and estimates of the magnitude of cream skimming can be made available to policymakers on a regular basis. This gives a sense of the degree of severity and concentration of cream skimming and adverse selection.

Initiatives to reduce risk skimming include imposing taxes on excess profits (operating surpluses); mandating health plans/sickness funds to provide additional benefits (reduction in copayments and coverage of uncovered services) to members when profits rise above a certain level; and requiring sickness funds to carry the liability for patients who disenroll for a defined time period, say, up to a year. Cream skimming is a sign of market failure. Policymakers should take steps to create a regulatory environment that is sensitive to and reacts quickly when evidence of cream skimming is imperative.

Other Issues

Prescription drug costs are omitted from Dr. Lamers' model because they are covered by the Dutch mandatory national health insurance program, not the sickness funds. The expenses for general practitioners (GPs) are also omitted because these providers are capitated. Thus, the model produces predictions of sickness fund liability, rather than overall health risk weighted by resource consumption from a societal perspective. In the case of the Dutch health insurance system, these omissions produce no distortions because patients face no additional liability for their medications and GP visits. Applying Lamers' model to another health system in which medication and physician costs are not covered on a first-dollar basis will create risk-based distortions. Sicker people will pay more than healthy people will. When some components of healthcare costs are excluded from the risk-adjustment system, patients face variations in out-of-pocket expense relative to their health status, thereby undermining the redistributive function of health insurance. Future advances on the Lamers model should include medication and GP costs on a person-specific level in the dependent variable. This enables sharing a greater proportion of the risk for overall healthcare expenditures with the sickness funds and increases the generalizability of her model.

Dr. Lamers mentions the problem with discretionary diagnoses in the diagnosis risk model. One of the major problems with the inpatient diagnosis approach to risk adjustment is that patients must be hospitalized in order to have their illnesses counted. Hospital-based risk-adjustment models penalize hospital-conserving styles of practice and reward hospital-intensive practice styles. Moreover, this type of model conveys a strong incentive to hospitalize patients whenever closer observation and acute care might improve safety and outcomes. Hence, discretionary diagnoses should certainly receive careful scrutiny before they are included in the risk-adjustment model. Another way to reduce inpatient bias is to count day treatment and same-day surgery diagnoses in the risk model. Of course, the locus of treatment bias can be avoided altogether by counting diseases wherever they are treated - another argument for collecting ambulatory diagnosis data and establishing disease registries with rigorous diagnostic criteria for case accrual.