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Assessing risk-adjustment approaches under non-random selection.

Harold S Luft1, R Adams Dudley

  • 1Institute for Health Policy Studies, School of Medicine, University of California, San Francisco 94118, USA.

Inquiry : a Journal of Medical Care Organization, Provision and Financing
|September 29, 2004
PubMed
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Comparing health plan risk adjustment methods, concurrent diagnosis-based and hybrid approaches significantly outperform prospective or demographic adjustments. Simulations reveal these methods better manage risk selection and financial stability.

Area of Science:

  • Health economics
  • Health services research
  • Biostatistics

Background:

  • Health plans face challenges in adjusting for enrollee risk.
  • Risk-selection strategies (dumping, skimming, stinting) impact enrollment and plan finances.
  • Existing risk adjustment methods have limitations.

Purpose of the Study:

  • To evaluate the performance of different risk adjustment strategies.
  • To assess the impact of risk selection on health plans.
  • To identify optimal risk adjustment methods for various selection scenarios.

Main Methods:

  • Simulated three types of enrollee risk selection: dumping, skimming, and stinting.
  • Compared concurrent diagnosis-based risk adjustment with prospective and demographic adjustments.

Related Experiment Videos

  • Evaluated a hybrid adjustment model (concurrent and prospective).

    • Main Results:

    • Concurrent diagnosis-based and hybrid risk adjustment models showed superior performance.
    • These methods improved R-squared values and reduced unwarranted financial gains/losses for plans.
    • Prospective and demographic adjustments were less effective under simulated selection pressures.

    Conclusions:

    • Concurrent diagnosis-based and hybrid risk adjustment strategies are more effective.
    • Simulation modeling is a valuable tool for assessing risk adjustment methods.
    • These findings can inform policy and practice in health plan risk adjustment.