INFORMS Journal Manufacturing & Service Operations Management New Study Key Takeaways:
- Medicare Advantage (MA), the largest healthcare capitation program in the U.S., unintentionally incentivizes health plans to cherry-pick profitable patients from traditional Medicare. Less profitable patients can be left behind.
- Research paper proposes a modified medical loss ratio mechanism to address the risk selection problem in MA.
- Study calls for practitioners and policymakers to change their views of seeing risk adjustment as a pure statistical and machine learning problem.
BALTIMORE, MD, January 4, 2022 – New research in the INFORMS journal Manufacturing & Service Operations Management finds that Medicare Advantage (MA), the largest healthcare capitation program in the U.S., unintentionally incentivizes health plans to cherry-pick profitable patients from traditional Medicare (TM). “Capitation” is the annual fee paid to a healthcare practice by each participant in a health plan.
“Contrary to popular belief, big data and machine learning alone cannot address this problem. We propose a modified medical loss ratio mechanism to address the risk selection problem in MA,” says Turgay Ayer of the Georgia Institute of Technology.
Ayer, alongside co-authors, Zhaowei She of the Singapore Management University and Daniel Montanera of Grand Valley State University, say this observed risk selection in the MA market has historically been attributed to data limitations and low explanatory power of the current risk adjustment design. Because of this, many believe that risk selection would gradually disappear over time with increased availability of big data.
The study, “Can Big Data Cure Risk Selection in Healthcare Capitation Program? A Game Theoretical Analysis,” shows that even if the current MA risk adjustment design became informationally perfect through increased availability of big data, incentives would continue to persist for risk selection, primarily because of the way the current risk adjustment model is designed.
“This work addresses a critical design problem in the capitation model, an emerging healthcare payment model rapidly adopted by payers around the world,” says Ayer.
Capitation payment models unintentionally incentivize cross-subsidization behaviors, where healthcare providers conduct risk selection through strategically subsidizing some subgroups of patients using capitation payments collected from other subgroups.
“To address risk-selection problems, payers should modify their current capitation mechanisms to consider the cross-subsidization behaviors in healthcare capitation programs so that less profitable patients aren’t treated as second class,” says Ayer.
“No generic risk adjustment algorithm can solve the strategic prediction problem in risk adjustment without explicitly taking into account the underlying mechanism in healthcare capitation programs,” adds She.
This study calls for practitioners and policymakers to change their views of seeing risk adjustment as a pure statistical and machine learning problem and to look more comprehensively at the human impact.
About INFORMS and Manufacturing & Service Operations Management
INFORMS is the largest association for decision and data sciences. Manufacturing & Service Operations Management, one of 17 journals published by INFORMS, is a premier academic journal that covers the production and operations management of goods and services including technology management, productivity and quality management, product development, cross-functional coordination, and practice-based research. More information is available at www.informs.org or @informs.
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Manufacturing and Service Operations Management
Method of Research
Subject of Research
"Can Big Data Cure Risk Selection in Healthcare Capitation Program? A Game Theoretical Analysis"