The Centers for Medicare & Medicaid Services (CMS) has laid the groundwork for a transformative shift in Medicare Advantage (MA) payment methodologies.
On January 10, 2025, CMS released the Calendar Year (CY) 2026 Advance Notice, proposing significant changes that could take effect as early as CY 2027. Among the most pivotal proposals is the potential transition to a risk adjustment model based on MA encounter data rather than traditional Fee-for-Service (FFS) claims data.
Why Move to an MA Encounter Data-Based Model?
CMS has long acknowledged the limitations of the current FFS-based risk adjustment model. The move toward an MA encounter data-based approach is designed to:
- Enhance Accuracy in Risk Scoring: MA encounter data, which includes diagnosis, treatment cost, and utilization data submitted by MA plans, could provide a more precise predictor of risk scores that better align with the member’s actual health expenditures than outdated FFS structures.
- Reduce Coding Intensity Adjustments: Coding intensity adjustments have been a contentious element of the current system. By leveraging MA encounter data, CMS may minimize the need for these adjustments, leading to a fairer payment model.
- Align Payments with Real-World Care Patterns: The transition would ensure that payments are more closely tied to how care is delivered within MA plans rather than being benchmarked against FFS data, which may not accurately reflect modern healthcare utilization. The clinical guidelines, diagnosis, and treatments observed in MA can guide the calibration of the risk scores. This could incentivize practices.
- More Accurate and Consistent Coding Practices: Expectations are that data quality, service delivery, and even care management will improve over time within MA.
While the theoretical benefits of this shift are compelling, implementing it is far from straightforward. CMS has received extensive feedback from industry stakeholders who caution against potential pitfalls, including inconsistencies in encounter data submissions and the need for greater transparency.
Industry Perspectives on the Proposed Shift
The healthcare industry has responded with a mix of optimism and concern. Many stakeholders see the move as a step toward a more equitable and data-driven approach to risk adjustment.
However, several pressing concerns remain:
- Insufficient Review Time: A 30-day comment period was seen as too brief for stakeholders to conduct a thorough analysis and provide meaningful input.
- Lack of Transparency: Industry experts have called for CMS to disclose the methodologies used for mapping. diagnoses to condition categories and for simulating potential impacts.
- Data Completeness Challenges: Historical inconsistencies in encounter data submissions raise questions about whether CMS has sufficient high-quality data to support the transition.
Key Questions for the Future
As CY 2027 approaches, several critical questions remain unanswered:
- Impact on Plan Payments and Risk Score Calculations:
Will payment adjustments fully reflect the nuances of the new model? - Validation and Oversight Mechanisms:
Will CMS introduce new oversight processes to ensure data accuracy and completeness? - Effect on Value-Based Care:
Could this shift accelerate the adoption of value-based care, emphasizing outcomes rather than service volume?
Preparing for the Transition
MA plans must take proactive steps to prepare for potential changes.
Key strategies include:
- Monitoring CMS Guidance: Staying informed about policy updates and CMS announcements is crucial for understanding how the new model will be structured.
- Ensuring Data Accuracy: MA plans should enhance their encounter data submission processes to minimize discrepancies and ensure compliance.
- Advocating for Transparent Cost Methodologies: Industry stakeholders should engage with CMS to push for fair and accurate cost reporting mechanisms.
- Invest in Data Quality and Infrastructure: Enhance EHR integration and data standardization.
- Training and Compliance: Provide education for providers and coders while incorporating regular internal audits. This allows for adjustments before the new model is fully implemented.
- Develop a contingency plan and financial buffer: Use analytics to simulate the potential impact of the new model and maintain a reserve for any short-term financial risks.
Final Thoughts
The CMS 2026 final Payment Policy Updates reinforces the shift towards data-driven payment methodologies in Medicare Advantage, officially endorsing and structuring the transition. By finalizing key payment policy update with a clear timeline and guidelines CMS solidifies the move toward an MA encounter data–based risk adjustment model that better aligns with real-world care patterns.
This regulation underscores the need for enhanced data accuracy and robust infrastructure, echoing calls for industry preparedness. While the transition holds promise for improved risk scoring and equitable payments, challenges such as incomplete data and legacy cost assumptions persist.
The Advance Notice of 2027 may provide greater clarity on how these reforms will mature, though the ultimate trajectory could hinge on decisions made by the current administration, which retains the authority to alter or rescind the proposals before finalization..
As the industry watches these developments unfold, a deeper look into the limitations of relying solely on MA encounter data is warranted. In our upcoming blog, we’ll explore the shortfalls of this model and how the Final Rule further shapes this narrative. Stay tuned as we continue to unpack the implications of this transition—and what it truly means for the future of risk adjustment.
References
CMS Finalizes 2026 Payment Policy Updates for Medicare Advantage and Part D Programs
About the author
Wynda Clayton
Director of Risk Adjustment Coding & Compliance
Wynda is a recognized leader with over 20 years of experience in risk adjustment, coding, and compliance. A seasoned RADV auditor and educator, she focuses on maximizing coding accuracy and maintaining regulatory standards. At RAAPID, Wynda spearheads AI-driven initiatives to enhance value-based care delivery and reimbursement accuracy.
