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A Machine Learning Model to Improve Risk Adjustment Accuracy in Medicare

Daniel K. Shenfeld et al.

Health Services Research2026https://doi.org/10.1111/1475-6773.70093article
AJG 3ABDC A
Weight
0.50

What the paper says

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.

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https://doi.org/https://doi.org/10.1111/1475-6773.70093

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@article{daniel2026,
  title        = {{A Machine Learning Model to Improve Risk Adjustment Accuracy in Medicare}},
  author       = {Daniel K. Shenfeld et al.},
  journal      = {Health Services Research},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.1111/1475-6773.70093},
}

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A Machine Learning Model to Improve Risk Adjustment Accuracy in Medicare

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Evidence weight

0.50

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.