Workflow GuideGroup Practices

APCM Risk Stratification Workflow for Group Practices

Standardize APCM risk stratification across multi-physician groups with AI-driven workflows, automated outreach, and accurate provider attribution.

Implementing a standardized risk stratification workflow is essential for multi-physician groups looking to scale Advanced Primary Care Management (APCM). This guide outlines how to leverage AI-driven automation to identify, attribute, and enroll high-risk patients while maintaining consistency across multiple sites and providers without increasing administrative overhead.

The Challenge

Group practices often struggle with manual risk stratification that is inconsistent between providers, leading to missed APCM revenue, poor patient attribution, and administrative burnout when trying to manage thousands of chronic care patients across disparate clinical teams.

Step-by-Step Workflow

1

Centralized EHR Data Aggregation

Consolidate patient records across all group locations and EHR instances to identify patients with two or more chronic conditions eligible for APCM. This ensures a single source of truth for the entire organization.

Best Practices
  • Use automated SQL queries to pull ICD-10 codes for chronic conditions.
  • Ensure all site-specific EHR instances are synced to a central analytics dashboard.
Common Pitfalls
  • Failing to account for patients seen at multiple sites within the group.
2

Automated Provider Attribution Mapping

Apply logic to map every eligible patient to a specific billing provider within the group. Use encounter history and primary care designations to ensure revenue is correctly allocated and disputes are avoided.

Best Practices
  • Prioritize the provider with the most E/M visits in the last 12 months.
  • Automate the update of attribution logs monthly.
Common Pitfalls
  • Manually assigning patients, which leads to attribution errors and billing rejections.
3

AI-Driven Patient Screening Outreach

Deploy AI voice agents to conduct initial outreach calls to the high-risk cohort. The AI gathers data on social determinants of health (SDOH) and current symptom severity to refine clinical risk scores.

Best Practices
  • Program the AI to recognize common chronic care terminology.
  • Schedule calls during peak patient availability windows.
Common Pitfalls
  • Using generic robocalls that patients ignore rather than interactive AI agents.
4

Clinical Priority Scoring and Tiering

Calculate risk scores based on HCC codes, recent ER utilization, and AI screening results. Tier patients into high, medium, and low risk to prioritize care management resources effectively across the group.

Best Practices
  • Integrate MIPS quality measures into your scoring algorithm.
  • Review the top 5% of high-risk patients in a weekly APCM committee meeting.
Common Pitfalls
  • Treating all chronic patients with equal priority, which dilutes care quality.
5

Automated APCM Consent Capture

Use AI call handling to explain APCM benefits to eligible patients and capture compliant verbal consent. This step digitizes the enrollment process and updates the EHR automatically for billing readiness.

Best Practices
  • Ensure the AI script includes mandatory Medicare disclosure language.
  • Store audio recordings of consent within the patient's digital chart.
Common Pitfalls
  • Relying on busy front-desk staff to capture consent during check-in.
6

Standardized Care Plan Integration

Push stratification results and enrollment status back into the practice management system. Trigger standardized care plan templates that are uniform across all providers in the group practice.

Best Practices
  • Develop templates for the most common condition pairings, like COPD and Hypertension.
  • Use a shared task queue for care managers across different sites.
Common Pitfalls
  • Allowing each provider to use different care plan formats, complicating audits.
7

Revenue Attribution Reporting

Generate monthly reports that track stratification accuracy, enrollment rates, and revenue generated per physician. This transparency is vital for group practices with productivity-based compensation models.

Best Practices
  • Automate the delivery of these reports to practice administrators.
  • Link enrollment data directly to the billing software's APCM CPT codes.
Common Pitfalls
  • Failing to provide physicians with visibility into their specific APCM contributions.

Expected Outcomes

1

Increased APCM enrollment rates across all providers in the group.

2

Significant reduction in administrative time spent on manual patient screening.

3

Full compliance with Medicare provider attribution and consent regulations.

4

Standardized care delivery protocols for high-risk chronic patients group-wide.

5

Higher MIPS performance scores through proactive, data-driven management.

6

Transparent revenue allocation and tracking per physician NPI.

Frequently Asked Questions

Our workflow uses primary-encounter logic to attribute the patient to the physician who manages the majority of their chronic conditions, ensuring clear billing responsibility.

Yes, Tile’s AI voice agents are specifically programmed to explain the benefits, cost-sharing, and right to revoke APCM services, capturing fully compliant verbal consent for the EHR.

By automating the stratification and outreach phases, your clinical staff only intervenes once a patient is already screened and enrolled, allowing them to focus on care rather than admin.

The system is designed to aggregate data from multiple EHR instances, which is ideal for group practices that have grown through acquisitions or operate across different sites.

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APCM Risk Stratification Workflow for Group Practices | Tile Health