Rising–Risk and Risk Stratification
In the world of Accountable Care Organizations (ACOs), identifying high-risk patients is no longer enough. The real opportunity lies in recognizing rising-risk individuals—those who aren’t high-cost today but are trending toward costly, complex care tomorrow.
This is where risk stratification and predictive analytics become essential.
What is risk stratification?
Risk stratification is an essential concept in healthcare that allows providers to classify patients based on their risk levels for certain health conditions, complications, or outcomes. By assessing and categorizing patients clinicians can better allocate resources, tailor care plans, and focus on interventions that will lead to the best possible outcomes. Identifying risk in the population early and intervening effectively has the potential to save resources and improve health outcomes.2
What Is Rising Risk?
Rising-risk patients often have multiple chronic conditions, social determinants of health (SDOH) challenges, or inconsistent engagement with care. They may not yet trigger traditional high-risk flags, but without intervention, they’re likely to experience avoidable hospitalizations, complications, or cost spikes.
With rising-risk patients, successful models of care focus on managing risk factors, such as obesity, smoking and SDOH barriers, more than disease states. Identifying these risks enables staff to target the root causes of multiple conditions. 5
Rising-risk patients sit in the middle of the population health pyramid. They represent the largest segment that can be positively impacted by early intervention.
Rising-risk patients can benefit significantly from chronic disease management and evidence-based intensive intervention care programs that aim to slow, stop or even reverse disease progression by implementing structured, high-engagement behavior change.4
How Risk Stratification Works
BetterAi’s platform uses clinically engineered precision analytics to stratify risk across all healthcare conditions. Using clinical, behavioral, and utilization data to stratify risk and identify rising-risk cohorts for timely intervention, our AI engine integrates with existing systems to deliver real-time, actionable insights, including care gap identification and emerging risk prediction.
Previously recommendations and decisions were made based on inputs largely derived from the judgements of individual case managers and clinical reviewers. These recommendations, not always accurate, did not derive the benefit of lessons from past recommendations and their ultimate outcomes. By leveraging ML, we are able to both capture and scale the collective intelligence of past learning to improve future outcomes.
Why It Matters for ACOs
With CMS shifting toward whole-population quality reporting and tighter financial benchmarks, ACOs must be proactive—not reactive. Risk stratification helps ACOs:
- Allocate care team resources efficiently, prioritizing outreach and resources
- Match patients to appropriate care programs
- Reduce low-value care
- Improve performance on HEDIS and ACO quality measures
- Forecast utilization and cost trends
- Improve equity by identifying hidden risk factors
- Drive shared savings through smarter interventions
Care Management Impact
Precision analytics allow risk-bearing entities to prioritize workforce and decrease disease progression. A study in The American Journal of Managed Care6 showed that targeted care management for rising-risk Medicaid ACO patients led to a $243 monthly spending reduction and significant drops in ED visits and hospitalizations after just 6 months.1
In a Medicare ACO extending care management to the risking risk population reduced the number of patients who moved to the high-risk group by 12%, with a 10% decrease in overall costs.5
Key Takeaway
Rising-risk identification is the next frontier in population health. ACOs that invest in predictive analytics and dynamic risk stratification are better equipped to deliver smarter, more equitable care while achieving financial sustainability.
References
- The American Journal of Managed Care. Impact of Care Management on Rising-Risk Medicaid Patients. Published 2023. Accessed October 11, 2025. https://www.ajmc.com
- Blue Shield of California Foundation. Identifying and Managing Patients at Risk for High Utilization. JSI Research & Training Institute; 2018. Accessed October 11, 2025. https://blueshieldcafoundation.org/sites/default/files/covers/JSI%20Rising%20Risk%202018.pdf
- Centers for Medicare & Medicaid Services. Medicare Shared Savings Program Quality Reporting Requirements. Updated 2024. Accessed October 11, 2025. https://www.cms.gov
- Health IT Outcomes. Intervention Care For The Rising Risk — Before It’s Too Late. Published 2020. Accessed October 11, 2025. https://www.healthitoutcomes.com/doc/intervention-care-for-the-rising-risk-before-it-s-too-late-0001
- National Association of Community Health Centers. Risk Stratification Action Guide. Published January 2022. Accessed October 11, 2025. https://www.nachc.org/wp-content/uploads/2022/01/PHM_Risk-Stratification-AG-Jan-2022.pdf
- National Committee for Quality Assurance. Predictive Analytics in Value-Based Care. Published 2023. Accessed October 11, 2025. https://www.ncqa.org