A method for risk-adjusting employer contributions to competing health insurance plans.
TL;DRAbstract
Biased selection can threaten the viability of multiple choice health systems unless payments to particular plans are adjusted to offset risk differences among employees. We report the results of a study designed to predict medical care utilization and expenditures for groups of fee-for-service plan (FFS) and health maintenance organization (HMO) enrollees, using characteristics commonly available in the personnel files of large employers. Simulation analyses indicate that the six-equation, maximum likelihood model predicts well for groups of 1,000 or more. Additional data are required to reduce prediction errors for smaller groups. This new methodology potentially allows risk-rating of employer contributions to competing health plans, based on the expected utilization of the individuals choosing each plan.
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Biased selection can threaten the viability of multiple choice health systems unless payments to particular plans are adjusted to offset risk differences among employees. We report the results of a study designed to predict medical care utilization and expenditures for groups of fee-for-service plan (FFS) and health maintenance organization (HMO) enrollees, using characteristics commonly available in the personnel files of large employers. Simulation analyses indicate that the six-equation, maximum likelihood model predicts well for groups of 1,000 or more. Additional data are required to reduce prediction errors for smaller groups. This new methodology potentially allows risk-rating of employer contributions to competing health plans, based on the expected utilization of the individuals choosing each plan.
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