AI Population Health Risk Stratification

Population-health risk stratification groups individuals or communities according to predicted health outcomes, care needs, or resource use. Organizations may use scores to prioritize case management, preventive care, or outreach. CMS distinguishes risk adjustment, which corrects quality measures for population characteristics, from risk stratification, which divides populations into groups for analysis. These concepts are related but not interchangeable. Prediction models can reflect disparities in healthcare access and prior utilization. A person with fewer recorded visits may have unmet needs rather than low risk. Scores also depend on when data are collected, how outcomes are defined, and whether social factors are represented. If a program uses a score to deny services or deprioritize people, it can reinforce inequity. Risk should guide supportive action, not replace individual assessment. Health systems should validate performance locally, assess calibration and subgroup errors, and track who receives interventions. Measure outcomes and access after deployment, not just predictive accuracy. Ensure patients can correct inaccurate information and care teams can override scores. Define how long risk classifications remain valid and how they are updated. Model use should align with privacy law, program policy, and clear clinical accountability. Explain to patients how scores may influence outreach and what options remain available regardless of classification. Review whether risks differ for groups with incomplete records or limited access to care. Revisit thresholds after program changes.