Risk stratification sounds like a data-science buzzword until you have to build the thing. For a Medicare Advantage plan, it's a concrete pipeline: take a population of members, score each one's clinical and financial risk, and rank them so care management and documentation teams know who to touch first. Here's how I'd architect it.
The core idea
Population health risk stratification = scoring + segmentation. You compute a per-member risk signal, then bucket members into tiers (e.g., rising-risk, high-risk, catastrophic) so finite resources go where they move outcomes and revenue most.
The mistake teams make is treating it as a single ML model. In practice you want a layered signal: a stable, explainable base (RAF + chronic conditions) plus optional predictive overlays. Explainability matters because care managers won't act on a black-box score, and auditors won't accept one.
Step 1: Build the member feature record
