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Related Experiment Videos

Risk adjusting capitation: applications in employed and disabled populations.

C W Madden1, B P Mackay, S M Skillman

  • 1Department of Health Services, University of Washington, Seattle 98195, USA. wmadden@u.washington.edu

Health Care Management Science
|April 26, 2000
PubMed
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Risk adjustment models can reduce bias in managed care payments. A two-part logistic/GLM model showed promise, especially for populations with low healthcare service utilization.

Area of Science:

  • Health Services Research
  • Health Economics
  • Biostatistics

Background:

  • Selection bias is a concern in managed care, potentially leading to inequitable payments.
  • Risk adjustment aims to link payments to enrollee health status and expected costs.
  • Evaluating different risk adjustment models is crucial for effective healthcare payment systems.

Purpose of the Study:

  • To compare the performance of various risk adjustment models.
  • To assess models in diverse populations: public employees and low-income individuals with disabilities.
  • To identify the most suitable statistical approach and health status measures for risk adjustment.

Main Methods:

  • Utilized two statistical approaches: a two-part logistic/GLM model and another unspecified model.

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  • Employed three distinct health status measures for comparison.
  • Applied models to two distinct populations in Washington State.

    • Main Results:

    • The two-part logistic/GLM statistical model demonstrated superior performance, particularly in populations with a high proportion of non-users of health services.
    • Successful implementation was achieved in the employed population.
    • The managed care program for the publicly insured population was discontinued before risk adjustment could be fully implemented.

    Conclusions:

    • The two-part logistic/GLM model is a potentially effective tool for risk adjustment, especially in specific population types.
    • The selection of health status measures should align with purchaser principles and desired outcomes.
    • Further research and implementation are needed to fully realize the benefits of risk adjustment in diverse managed care settings.