Journal article
Alternative evaluation metrics for risk adjustment methods
Health economics, v 27(6), pp 984-1010
01 Jun 2018
PMID: 29577489
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
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.
Metrics
Details
- Title
- Alternative evaluation metrics for risk adjustment methods
- Creators
- Sungchul Park - Department of Health Services, University of Washington, Seattle, WA, USA.Anirban Basu - University of Washington
- Publication Details
- Health economics, v 27(6), pp 984-1010
- Publisher
- Wiley
- Number of pages
- 27
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Health Management and Policy
- Web of Science ID
- WOS:000431646700005
- Scopus ID
- 2-s2.0-85046342233
- Other Identifier
- 991019296579404721
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InCites Highlights
Data related to this publication, from InCites Benchmarking & Analytics tool:
- Web of Science research areas
- Economics
- Health Care Sciences & Services
- Health Policy & Services