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CMS announces potential shift in Risk Adjustment - using MA encounter data to create the next MA risk adjustment models. #riskadjustment #medicareadvantage I tried to post this yesterday, but it was too long and I had to post it in two parts, and I messed it up. So, here is the whole post in PDF format.

What’s Next? With CY 2027 now in sight, this potential shift raises important questions for MA plans: ?? How will the transition impact plan payments and risk score calculations? ?? Will CMS introduce new oversight to validate encounter data quality? ?? Could this move accelerate a broader shift toward value-based care integration? In Conclusion CMS’s proposal to build a risk adjustment model using MA encounter data represents a potential paradigm shift in how risk scores are calculated. While this change would bring risk adjustment closer to reflecting real-world MA care patterns, the lack of cost data in EDPS remains a critical challenge. Unless CMS finds an alternative to FFS cost data—such as requiring MA plans to report financial data—the new model may still rely on FFS cost structures, even if it better aligns with MA utilization trends. For MA plans, now is the time to prepare: ? Monitor upcoming CMS guidance for more details on how this transition will unfold. ? Ensure encounter data submissions are complete and accurate, as they could play a larger role in future risk scores. ? Advocate for clear cost methodologies to ensure fair and accurate payment models. The next Advance Notice in 2027 will provide the first real glimpse into this potential transformation. Until then, expect continued debate and policy refinements as CMS explores this fundamental shift in risk adjustment methodology. #riskadjustment #medicareadvantage 2/2

CMS Signals a Potential Shift in Risk Adjustment—Using MA Encounter Data When CMS released the 2026 Advance Notice on January 10, one detail that has flown under the radar is a brief yet significant mention on page 56 under the heading: "Risk Adjustment Model Development Using MA Encounter Data." Yes, you read that right—CMS is considering using Encounter Data Processing System (EDPS) data as the foundation for Medicare Advantage (MA) risk adjustment. And they’re signaling that this change could be implemented as early as CY 2027. That means by the time the 2027 Advance Notice is released next year, we could see the first draft of this new risk adjustment model. Why This Is a Big Deal Historically, CMS has based MA risk adjustment (RA) models on Fee-for-Service (FFS) data, simply because, when risk adjustment first began, there wasn’t enough MA-specific data to build a reliable model. The previous Risk Adjustment Processing System (RAPS) data lacked the necessary depth to support a financial model. Using MA encounter data to build an MA-specific risk adjustment model makes logical sense. It would likely enhance accuracy, eliminating the need for the MA coding pattern adjustment—a long-standing concern for many in the industry. However, there’s one major gap: cost data. The Missing Piece: Where Will Cost Data Come From? While EDPS captures utilization data, it does not include the cost of services—a critical factor in any risk adjustment model. This raises the question: How will CMS determine cost weights without FFS data? Right now, the only clear answer would be to continue using FFS data for costs while leveraging EDPS for utilization patterns. This hybrid approach would improve the model’s ability to reflect MA-specific care utilization, rather than relying on FFS-derived assumptions. By shifting toward MA-based utilization, CMS could introduce: ? More plan-specific adjustments (e.g., differences between PPOs and HMOs) ? Regional refinements (e.g., adjusting for geographic variations in care patterns) ? Closer alignment with value-based care (VBC), potentially integrating quality measures and utilization data directly into risk adjustment Could MA Financial Data Be Incorporated? CMS already collects financial data from MA plans via bids and Medical Loss Ratio (MLR) reports. However, this data is highly aggregated and lacks the granularity needed to establish precise condition-based cost estimates for a risk adjustment model. One potential future step? CMS could require MA plans to submit financial data alongside EDPS data, incorporating: - Allowed amounts - Paid amounts - Patient cost-sharing data If CMS were to implement such a requirement, it could enable a fully MA-specific risk adjustment model—one that is more accurate and better tailored to actual MA spending patterns. However, there is no indication yet that this is in the works. #riskadjustment #medicareadvantage 1/2

Our new website is live. https://kirsha.health

It’s a beautiful day in Orlando here at #flaaco. I’d love to talk to you and learn about you and what you do. #MedicareAdvantage #ACO #RiskAdjustment

?? Tomorrow’s the Day! Ready to Level Up Your Healthcare Strategy? ?? Join us for our exclusive webinar, “Preventing Audit Risks and Planning Financial Targets on Risk Adjustment” ?? Gain the insights and tools you need to drive impactful results! ?? Nov 15 ? 11:30 AM EST Don’t miss out on actionable strategies, expert guidance, and new knowledge! ??? Let’s get ready to tackle the challenges of healthcare together. ?? . . . Dawn Carter MHA, CPC, CRC, CPMA, CDEO, CPCO,AAPC Fellow Kirk Shanks Kirsha LLC #Vivega #RiskAdjustment #ManagedCare #starratings #HealthcareLeadership #ValueBasedCare #MedicareAdvantage #RevenueIntegrity #HealthcareWebinar

Happy to share that I'll be speaking at Practical Insights on Improving Risk Adjustment Performance with Subbu Ramalingam and Dawn Carter MHA, CPC, CRC, CPMA, CDEO, CPCO,AAPC Fellow! Make sure to attend it tomorrow.

I'm excited to announce that I will be taking part in a webinar on November 15th with Subbu Ramalingam and Dawn Carter MHA, CPC, CRC, CPMA, CDEO, CPCO,AAPC Fellow focusing on risk adjustment. I hope to see many of you there. #RiskAdustment #MAPD #MedicareAdvantage

The Department of Health and Human Services Office of Inspector General (HHS-OIG) has implemented new extrapolation algorithms in their audit findings. As a result, Humana was found to owe $274,000 for 2017 and $6.5 million for 2018. The analytic methods used to identify these potential discrepancies are relatively straightforward and should be adopted by all health plans and at-risk provider organizations. #riskadjustment #MAPD #Humana https://lnkd.in/d8ibz-Kp

Very excited to accomplish this goal!