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RAAPID raises Series A funding with a Strategic “Industry Trifecta”. Read More

Day 2: Evolution of AI, From Machine Learning to Neuro-Symbolic

Why traditional AI fails in risk adjustment, and what the next generation actually looks like.

Most AI deployed in risk adjustment today is a black box. It pattern-matches against text, surfaces a code, and asks a coder to trust it. That worked when “more codes” was the goal. It doesn’t work when every diagnosis needs to survive a RADV audit.

The architecture matters. NLP and machine learning each break down at different points in the clinical reasoning chain. Neuro-Symbolic AI was built to close those gaps by combining neural networks with structured medical knowledge graphs.

This session walks through the evolution of AI in risk adjustment, why each approach fails or succeeds at clinical reasoning, and what the architectural differences mean for compliance exposure and vendor selection.

You’ll walk away with:

Why pattern-matching AI breaks down on clinical reasoning tasks like HCC validation
How traditional NLP and machine learning approaches create distinct types of black-box risk
What Neuro-Symbolic AI is, and how it combines neural networks with knowledge graphs
Why explainability and clinical evidence trails are non-negotiable in 2026
The architectural differences between AI that scales risk and AI that scales defense

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Panelists

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.

Chris brings over 30 years of healthcare operations expertise, including extensive experience in risk adjustment, revenue cycle management, and AI-powered coding solutions across industry-leading organizations. At RAAPID, he leads the implementation of advanced AI platforms that help healthcare organizations capture complete patient risk profiles while staying audit-defensible.

“In compliance, probably right is the same as wrong. You cannot train a keyword hunter to think like a coder, because coding is not about finding the entities, it’s about clinical reasoning. And if a system cannot reason, it cannot defend you in the audit.”

Who Should Attend

At organizations including Medicare Advantage plans, ACA health plans, ACOs, value-based provider groups, and third-party administrators

Chief Executive Officer (CEO)
Chief Information Officer (CIO)
Chief Technology Officer (CTO)
Chief Compliance Officer (CCO)
VP of Risk Adjustment
Director of Compliance
Risk Adjustment Manager
Coding Manager/Director
Director of Data Science
IT Director
Audit Manager
Operations Director

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