Guiding Risk Adjustment Models Toward Machine Learning Methods

Gary E Weissman; Karen E Joynt Maddox

doi:10.1001/jama.2023.12920

Guiding Risk Adjustment Models Toward Machine Learning Methods

JAMA. 2023 Sep 5;330(9):807-808. doi: 10.1001/jama.2023.12920.

Authors

Gary E Weissman^{1

2}, Karen E Joynt Maddox^{3

4}

Affiliations

¹ Palliative and Advanced Illness Research (PAIR) Center, Department of Medicine, University of Pennsylvania Perelman School of Medicine, Philadelphia.
² Leonard Davis Institute of Health Economics, University of Pennsylvania, Philadelphia.
³ Cardiovascular Division, John T. Milliken Department of Internal Medicine, and Center for Advancing Health Services, Policy & Economics Research (CAHSPER), Institute for Public Health, Washington University in St Louis, St Louis, Missouri.
⁴ Associate Editor, JAMA.

PMID: 37566405
DOI: 10.1001/jama.2023.12920

No abstract available

Plain language summary

This Viewpoint reviews the history of administrative risk adjustment models used in health care and provides recommendations for modernizing these models to promote their safe, transparent, equitable, and efficient use.

MeSH terms

Computer Simulation
Machine Learning*
Risk Adjustment*