Guiding risk adjustment models toward machine learning methods

Administrative risk adjustment models are widely used in health care and substantially affect spending, allocation of resources, and health equity. In the Medicare program, quality reporting, value-based and alternative payment models, and Medicare Advantage payments all rely on the same underlying model. However, despite advances in machine learning methods, here referring both to complex modeling approaches and to rigorous statistical practices developed by computer scientists and biostatisticians, the risk adjustment models used in Medicare have not been meaningfully updated. This Viewpoint reviews the current state of these models in the Medicare program and makes concrete suggestions for their modernization.

Gary E Weissman, Karen E Joynt Maddox