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Editor’s note: This is the second article in a series on CMS’ new LEAD model. Article one explored the new model in broad strokes. Future posts will explore CMS-administered risk arrangements and beneficiary enhancements.
One of the least discussed elements of CMS’ new LEAD model may turn out to be one of the most consequential.
CMS has signaled its intention to transition away from traditional HCC-based risk adjustment toward what it calls “AI-inferred” risk scores. The agency has shared very little detail about how this model will work, but it has been explicit about the timeline. Beginning in 2028, CMS will introduce the model in a shadow-testing phase alongside the existing methodology. By 2029, it will account for one-third of the risk score calculation. By 2031, it is expected to fully replace the current approach.
That timeline alone makes clear that this is not a marginal change. It represents a shift in how patient risk is defined, measured, and ultimately paid for.
How Risk Adjustment Works Today
To understand what is changing, it’s useful to start with how risk adjustment works today. The current HCC model relies on diagnosis codes submitted through claims. Those diagnoses are mapped to condition categories, each with an associated cost weight. A patient’s risk score is calculated as the sum of the weights, with demographic adjustments.
This approach is well understood, transparent, and deeply embedded in how organizations operate. At the same time, its limitations are well documented. It rewards coding completeness more than clinical nuance. It has a limited ability to account for social risk factors. It treats patients with the same diagnosis as having similar expected costs, even when their clinical trajectories differ significantly. And because it depends on claims, it often reflects what has already happened rather than what is happening in real time.