A Machine Learning Model to Improve Risk Adjustment Accuracy in Medicare
Franklin is an ML risk adjustment model that significantly improves risk-adjustment accuracy for Medicare beneficiaries compared to HCC. Franklin could generate improvement in payment accuracy, reduction in selection incentives, and financial savings to Medicare. Clarifying the equity impacts of more accurate risk adjustment is necessary.
Authors
- Eli Silvert (ORCID: https://orcid.org/0000-0003-0445-4395)
- Amol S. Navathe (ORCID: https://orcid.org/0000-0001-7182-4988)
- Daniel K. Shenfeld
- Ravi B. Parikh
- Matthew Guido
- Karen Joynt Maddox
- Maggie Makar
- Ezekiel J Emanuel
- Lindsay Warrenburg
Institutions
- Emory University (US)
- Washington University in St. Louis (US)
- University of Michigan (US)
- Michigan Medicine (US)
- University of Pennsylvania (US)
Publication Details
- Journal
- Health Services Research
- Published
- 2026-03-05
- DOI
- https://doi.org/10.1111/1475-6773.70093
- Primary Topic
- Machine Learning in Healthcare
- Type
- article
- Field-Weighted Citation Impact
- 0.00