Stratifying Populations by Clinical and Risk Signals

Effective population health management requires segmenting patients based on multiple dimensions:

  • Clinical complexity: Number and severity of chronic conditions
  • Risk scores: RAF or other predictive risk scores
  • Utilization patterns: ED visits, hospitalizations, and care intensity
  • Social determinants: Factors affecting health outcomes and access
  • Care gaps: Preventive care and chronic condition management needs
Key Principle: Multi-dimensional stratification enables more precise intervention targeting than single-variable approaches.

Identifying High-Risk and Rising-Risk Patients

Risk stratification identifies patients across the risk spectrum:

High-Risk Patients

Complex patients requiring intensive care management and coordination.

Rising-Risk Patients

Patients showing early signs of deterioration who may respond to intervention.

Moderate-Risk Patients

Patients with chronic conditions requiring ongoing monitoring and support.

Low-Risk Patients

Generally healthy patients benefiting from preventive care engagement.

Prioritizing Outreach and Interventions

Resource constraints require strategic prioritization:

  • Intervention matching: Align programs to patient needs and risk levels
  • ROI optimization: Focus on patients where intervention impact is highest
  • Capacity planning: Scale programs based on population distribution
  • Program evaluation: Measure effectiveness by risk tier

Measuring Movement Across Risk Tiers

Tracking how patients move between risk tiers indicates program effectiveness:

  • Positive movement: High-risk patients stabilizing to moderate risk
  • Prevention success: Rising-risk patients avoiding escalation
  • Health maintenance: Low-risk patients remaining healthy
  • Intervention gaps: Patients escalating despite programs

Connecting Stratification to RAF Impact

Risk stratification directly supports RAF optimization:

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