Alternative evaluation metrics for risk adjustment methods

Sungchul Park, Anirban Basu

Research output: Contribution to journalArticlepeer-review

13 Scopus citations


Risk adjustment is instituted to counter risk selection by accurately equating payments with expected expenditures. Traditional risk-adjustment methods are designed to estimate accurate payments at the group level. However, this generates residual risks at the individual level, especially for high-expenditure individuals, thereby inducing health plans to avoid those with high residual risks. To identify an optimal risk-adjustment method, we perform a comprehensive comparison of prediction accuracies at the group level, at the tail distributions, and at the individual level across 19 estimators: 9 parametric regression, 7 machine learning, and 3 distributional estimators. Using the 2013–2014 MarketScan database, we find that no one estimator performs best in all prediction accuracies. Generally, machine learning and distribution-based estimators achieve higher group-level prediction accuracy than parametric regression estimators. However, parametric regression estimators show higher tail distribution prediction accuracy and individual-level prediction accuracy, especially at the tails of the distribution. This suggests that there is a trade-off in selecting an appropriate risk-adjustment method between estimating accurate payments at the group level and lower residual risks at the individual level. Our results indicate that an optimal method cannot be determined solely on the basis of statistical metrics but rather needs to account for simulating plans' risk selective behaviors.

Original languageEnglish
Pages (from-to)984-1010
Number of pages27
JournalHealth Economics (United Kingdom)
Issue number6
StatePublished - Jun 2018

Bibliographical note

Publisher Copyright:
Copyright © 2018 John Wiley & Sons, Ltd.


  • individual-level prediction accuracy
  • models for health care expenditures
  • residual risk
  • risk adjustment
  • risk selection


Dive into the research topics of 'Alternative evaluation metrics for risk adjustment methods'. Together they form a unique fingerprint.

Cite this