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Risk Adjustment Changes: AI, V28, and compliance
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Things You'll Get From This Course
The Regulatory Convergence
From Pattern Matching to Reasoning
AI That Explains Itself
Defensible by Design
Meet Your Instructors
Wynda Clayton
Wynda Clayton knows what makes documentation hold up under scrutiny because she spent years reviewing it for CMS. As a former RADV auditor, she examined medical records for Medicare Advantage plans under federal review. She saw which organizations passed and which faced clawbacks. More importantly, she learned to recognize the documentation patterns that separated them long before a final determination came down. After transitioning to health plan operations, she led teams through RADV audits from the other side. She understands the pressure of receiving an enrollee list and having a five-month window to gather medical records and validate years of HCC submissions.
Christopher Lally
Chris brings over 30 years of healthcare operations expertise, including extensive experience in risk adjustment, revenue cycle management, and AI-powered coding solutions across multiple industry-leading organizations.
As Vice President of Operations at RAAPID, he spearheads the implementation of advanced AI technology platforms that help healthcare organizations capture complete patient risk profiles and optimize financial performance.
In this course, Chris will share practical insights on operationalizing defensible coding workflows, including what successful implementations look like and where organizations commonly stumble.
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Raxit Goswami
Raxit is a healthcare AI researcher with over 15 years of experience in clinical NLP, knowledge graphs, and machine learning. A published author with 70+ academic citations, he focuses on building explainable AI systems that deliver audit-defensible coding accuracy.
At RAAPID, Raxit leads the development of Neuro-Symbolic AI solutions that enhance risk adjustment precision and regulatory compliance.
In this course, Raxit will break down how Neuro-Symbolic AI works under the hood, why it produces defensible outputs where traditional NLP falls short, and what organizations should look for when evaluating AI technology for risk adjustment.
Course Syllabus & Schedule
Module 1
Day 1
The Changing Landscape of Medicare Advantage
(4/7, 12-130 PM ET)
Module 2
Day 2
Evolution of AI: From Machine Learning to Neuro-Symbolic
(4/8, 12-130 PM ET)
Module 3
Day 3
Building compliant workflows and evaluating AI
(4/9, 12-130 PM ET)
Module 4
Day 4
Module 5
Day 5
Module 6
Day 6
Module 7
Day 7
Module 8
Day 8
Module 9
Day 9
Module 10
Day 10
Frequently Asked Questions
Who is this course for?
Anyone responsible for Medicare Advantage revenue integrity. That includes coders, risk adjustment directors and VPs, compliance officers, finance leaders managing audit exposure, operations executives overseeing coding workflows, and IT leaders evaluating AI vendors. Whether you're reviewing charts daily or setting organizational strategy, you'll walk away with actionable insights.
Do I need to attend every session?
We recommend it, but recordings will be shared with all registered participants. Each session is about 90 minutes. If you miss one, you can catch up before the next, but you'll get the most value from the live Q&A.
Will I be a risk adjustment expert after this course?
You'll walk away understanding how the regulatory environment has changed, what makes AI outputs defensible vs. risky, and what questions to ask when evaluating vendors or building internal processes. Whether you're a coder, compliance officer, or executive, you'll have the context to make informed decisions and ask the right questions.
Is there a lot of work?
No heavy lifting required. There's some light pre-reading we'll send before the course, and optional prep materials if you want to go deeper. The sessions themselves are designed to be practical and engaging, not homework-heavy.
I have another question not answered here
Reach out to the RAAPID team at info@raapidinc.com, and we'll get back to you.