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Using risk-adjustment models to identify high-cost risks.

Richard T Meenan1, Michael J Goodman, Paul A Fishman

  • 1Center for Health Research, Northwest and Hawaii, Kaiser Permanente Northwest, Portland, Oregon 97227, USA. richard.meenan@kpchr.org

Medical Care
|October 30, 2003
PubMed
Summary
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Several risk adjustment models effectively identify high-cost individuals for case management. Models like Diagnostic Cost Groups (DCGs) and others show comparable performance, with some capturing more high-cost dollars.

Area of Science:

  • Health Services Research
  • Health Economics
  • Predictive Analytics

Background:

  • Evaluating the efficacy of various risk adjustment models in identifying high-cost individuals.
  • Utilizing multi-HMO administrative data for risk prediction analysis.

Purpose of the Study:

  • To compare the predictive accuracy of five publicly available risk models.
  • To determine which models best identify high-cost individuals and groups for potential intervention.

Main Methods:

  • Assessed five risk-adjustment models: GRAM, DCGs, ACGs, RxRisk, and Prior-expense.
  • Used a multi-HMO dataset of 1.5 million individuals (1995-1996) and a validation set of 106,000.
  • Calculated Area Under the ROC Curve (AUC) and proportion of high-cost dollars identified.

Related Experiment Videos

Main Results:

  • ACG, DCG, GRAM, and Prior-expense models demonstrated comparable discrimination (AUCs 0.83-0.86).
  • DCGs, GRAM, and Prior-expense captured more high-cost dollars at a 0.5% threshold.
  • DCGs showed superior performance for enrollees with asthma, diabetes, and depression.

Conclusions:

  • Risk models are effective tools for identifying high-cost enrollees who could benefit from case management.
  • The enhanced predictive performance of top risk models offers significant value for healthcare decision-makers.
  • Broader adoption of accurate risk models is encouraged for proactive cost management.