Chronic Care Risk Stratification Workflow for Value-Based Care
Optimize patient risk stratification for VBC with our guide on APCM, CCM, and AI-driven population health management to increase shared savings.
Effective risk stratification is the cornerstone of Value-Based Care success. By identifying high-risk chronic patients through APCM and CCM frameworks, practices can proactively manage population health, improve HEDIS scores, and maximize shared savings. This guide outlines a data-driven workflow to categorize patients, allowing for targeted intervention and reduced total cost of care.
Many practices struggle with manual risk stratification, leading to missed care gaps, inaccurate risk adjustment scores (RAF), and reactive care that inflates total cost. Without automated outreach, high-risk patients fall through the cracks, jeopardizing VBC contract performance and shared savings.
Step-by-Step Workflow
Data Aggregation & Integration
Pull EHR data, claims history, and social determinants of health to create a comprehensive patient profile for APCM enrollment and initial risk assessment.
- Integrate pharmacy claims to identify medication non-adherence
- Include SDoH data to surface hidden barriers to care
- Relying solely on outdated EHR snapshots
Clinical Risk Scoring
Apply Hierarchical Condition Category (HCC) coding and clinical algorithms to assign risk scores, focusing on chronic condition complexity and acuity levels.
- Ensure all chronic conditions are documented annually for RAF accuracy
- Use weighted scores for multiple comorbidities
- Ignoring rising-risk patients who aren't yet high-cost
AI-Powered Outreach Prioritization
Use AI call handling to reach out to high-risk cohorts first, ensuring those with the highest probability of hospitalization receive immediate attention and CCM enrollment.
- Automate initial outreach to verify patient status
- Route complex cases to clinical staff immediately
- Treating all patients in a risk tier with the same urgency
Care Gap Identification
Cross-reference patient data against HEDIS measures and VBC contract requirements to identify specific clinical interventions needed for each stratified group.
- Focus on high-impact measures like A1c control and blood pressure
- Align gap closure with APCM monthly requirements
- Failing to link risk tiers to specific quality metrics
Stratified Care Plan Development
Create tailored CCM or APCM care plans based on risk tiers, allocating intensive resources to patients who impact the total cost of care the most.
- Set specific, measurable goals for high-risk patients
- Include the patient in the care planning process
- Using generic care plan templates for complex patients
Continuous Monitoring & Re-stratification
Utilize automated check-ins to monitor patient status in real-time, adjusting risk levels as health conditions evolve or care gaps are successfully closed.
- Schedule monthly AI check-ins for stable rising-risk patients
- Update risk scores quarterly based on new claims data
- Viewing risk stratification as a one-time annual event
Expected Outcomes
Improved Risk Adjustment Factor (RAF) accuracy
Reduction in avoidable ER visits and hospitalizations
Increased shared savings through better HEDIS performance
Enhanced patient engagement in chronic care programs
Streamlined administrative workflows via AI automation
Frequently Asked Questions
Accurate stratification ensures high-cost patients receive proactive care, reducing total cost of care and increasing the pool for shared savings by meeting quality benchmarks.
APCM provides the reimbursement framework and clinical structure for managing the chronic populations identified during the stratification process.
Yes, AI automates the outreach and data gathering process, ensuring risk scores are based on the most current patient information rather than stale EHR data.
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