Workflow GuideACOs (Accountable Care Organizations)

ACO Chronic Care Patient Risk Stratification Workflow Guide

Optimize ACO shared savings with our chronic care risk stratification workflow, aligning APCM with MSSP quality measures and total cost reduction.

Effective risk stratification is the engine of a successful Accountable Care Organization (ACO). By identifying high-risk beneficiaries and enrolling them in Advanced Primary Care Management (APCM), ACOs can proactively manage chronic conditions, improve quality scores, and maximize MSSP shared savings. Our AI-driven workflow leverages automated patient outreach to refine risk tiers and ensure ...

The Challenge

ACOs often struggle with static claims data that lags behind real-time patient health status, leading to inaccurate risk tiering. This results in missed opportunities for care gap closure, preventable hospitalizations, and depressed HCC scores that negatively impact the ACO's financial benchmarks.

Step-by-Step Workflow

1

Multi-Source Data Aggregation

Consolidate historical claims data, EMR records, and Social Determinants of Health (SDoH) indicators across the entire ACO network to create a unified longitudinal patient record.

Best Practices
  • Prioritize claims data from the last 12-24 months for HCC coding trends
  • Integrate SDoH data to identify barriers to care compliance
Common Pitfalls
  • Relying solely on EMR data which may lack out-of-network utilization insights
2

AI-Powered Predictive Modeling

Apply machine learning algorithms to identify 'rising risk' patients who may not yet meet high-utilizer criteria but show patterns of clinical decline or non-adherence.

Best Practices
  • Focus on patients with multiple chronic conditions that overlap with MSSP quality measures
  • Model the impact of APCM intervention on total cost of care
Common Pitfalls
  • Using overly simplistic age-based stratification instead of clinical complexity
3

Automated Clinical Outreach and HRA

Deploy AI-powered call center tools to conduct Health Risk Assessments (HRA). These automated calls capture real-time symptoms, medication changes, and social needs that claims data misses.

Best Practices
  • Use natural language processing to flag urgent clinical responses for immediate nurse intervention
  • Schedule calls during high-engagement windows to maximize completion rates
Common Pitfalls
  • Using manual staff calls for initial screening, which limits scalability across a large ACO
4

Dynamic Risk Tiering & APCM Enrollment

Assign patients to risk tiers (Low, Rising, High) based on the combined AI model and HRA results. Automatically trigger enrollment workflows for those eligible for APCM or CCM services.

Best Practices
  • Align risk tiers with CMS-defined APCM complexity levels for accurate billing
  • Communicate risk status changes to the primary care provider in real-time
Common Pitfalls
  • Failing to update risk tiers more than once per year
5

Quality Measure Alignment

Map stratified patient lists to specific ACO quality measures, such as HbA1c control or blood pressure management, to ensure high-risk patients receive targeted interventions.

Best Practices
  • Prioritize outreach for patients with open care gaps in the current reporting period
  • Use APCM documentation to support MIPS or APP quality reporting
Common Pitfalls
  • Treating risk stratification and quality reporting as siloed activities
6

Continuous Monitoring and Re-Stratification

Establish a feedback loop where AI call interactions and new clinical data points trigger a re-evaluation of the patient's risk tier, ensuring care intensity matches current needs.

Best Practices
  • Set up automated alerts for ER visits or hospital discharges to trigger immediate risk reassessment
  • Track the ROI of stratification by monitoring the reduction in total cost of care per beneficiary
Common Pitfalls
  • Ignoring 'soft' data like patient-reported lack of transportation in risk updates

Expected Outcomes

1

Significant reduction in preventable ER visits and hospital readmissions

2

Increased MSSP shared savings through lower total cost of care

3

Improved HCC coding accuracy and risk adjustment factor (RAF) scores

4

Streamlined APCM enrollment across the entire ACO network

5

Higher achievement levels on ACO-specific quality performance measures

Frequently Asked Questions

By identifying high-risk patients early, ACOs can deploy APCM resources to prevent high-cost acute events. Reducing these costs below the CMS-established benchmark directly increases the shared savings pool available to the ACO.

Yes. AI call center tools capture 'last-mile' health data, such as sudden weight gain or medication non-compliance, which serves as a leading indicator of risk that claims data cannot provide.

Our workflow integrates SDoH factors—like housing instability or food insecurity—into the predictive model, as these are often the primary drivers of health outcomes and total cost of care in vulnerable populations.

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ACO Chronic Care Patient Risk Stratification Workflow Guide | Tile Health