Module 4
Risk Adjustment and Attribution
The foundation of every value-based contract: how risk scores make comparisons fair, and how attribution decides which patients count.
By the end of this module, you will be able to:
- Explain why unadjusted comparisons penalize providers who serve sicker patients
- Describe how a risk score is built from recorded diagnoses, and where coding intensity enters
- Compare attribution methods by their effect on which patients count
- Why Risk Adjustment Matters Fair payment and fair comparison both depend on accounting for how sick a population is. This lesson covers what breaks without that correction. About 4 min
- How Risk Models Work HCCs, risk scores, and coding intensity: how diagnoses turn into dollars, and how the process gets gamed. About 5 min
- Attribution: Whose Patient Is It? Before anyone can be accountable for a patient, someone must decide whose patient it is. The rules quietly shape every result. About 4 min
Module quiz
Answer all questions to see your score.
1. What are risk adjustment's two core jobs?
It pays more for predictably expensive patients so they stay financially welcome, and removes population differences so performance comparisons mean something.
2. Why is coding intensity a structural problem rather than a few bad actors?
Any model that pays on self-reported inputs will bend those inputs. The line between complete documentation and score inflation is genuinely blurry.
3. Prospective attribution's main trade-off is:
A list fixed before the year starts enables targeted outreach from January but includes patients who switch doctors mid-year. Retrospective lists have the mirror-image problem.