Defensibility is an Urgent Mandate

In our recent article¹, we discussed that the Medicare Advantage (MA) financial model has fundamentally shifted. The CFO mandate is no longer revenue maximization; it is defensible accuracy. Regulatory tightening, aggressive DOJ enforcement, and the V28 risk model transition have converged to make audit-readiness a C-level financial priority.

The urgency is measurable. In March 2026, HHS OIG published three MA risk adjustment compliance audits simultaneously, with findings that revealed unsupported diagnosis error rates of 91%, 84%, and 81% across the reviewed plans.² Deploying AI that compounds documentation risk in that environment is not a strategic miscalculation. It is an operational threat.

The CMS submission mechanics only add to the issue of urgency. CMS’s HPMS memo issued April 29, 2026, confirmed that once the final risk adjustment data submission deadline passes, CMS processes only diagnosis deletions, not new additions, per 42 CFR 422.310(g).³ A missed code after that window closes is lost revenue with no path to recovery. For health plans, this makes the architecture of their AI tools a direct financial variable: a platform that can only add codes is only useful before the deadline. Defensibility requires operating accurately in both directions.

Three Ways Standalone Generative AI Fails the Defensibility Test

Generative AI has genuine strengths in healthcare documentation. Large Language Models (LLMs) read and summarize physician notes, synthesize multi-visit records, and handle unstructured clinical language at scale. However, stand-alone LLMs, such as ChatGPT, fail in several areas when it comes to defensible risk adjustment.

Hallucination risk
A 2023 Mayo Clinic Proceedings: Digital Health study found that ChatGPT fabricated 69% of medical reference citations in a controlled test; a separate Nature Communications Medicine study testing if LLM models elaborated on a single fabricated clinical detail (error) reported hallucination rates ranging from 50 – 82%.^{4, 5} In risk adjustment, a hallucinated diagnosis code is not a performance anomaly, it is a false claim.

Opaque reasoning
Black-box models cannot trace a code recommendation back to specific MEAT (Monitoring, Evaluation, Assessment, Treatment) evidence within the encounter. They produce outputs, not explanations. When a CMS RADV auditor asks why a specific HCC was submitted, “the model suggested it” is not a defensible answer.

Guideline misalignment
LLMs are trained on historical data and do not inherently embed current movements in CMS coding guidelines, V28 HCC model updates, or MEAT documentation requirements. Without a structured validation layer, outputs can quietly reflect rules that are months or years out of date.

A 2025 JMIR systematic review concluded that explainable AI models are necessary to foster healthcare workers’ trust in clinical decision support systems and enable this through factors such as transparency, usability, and clinical reliability.⁶

Neuro-Symbolic AI: The Guardrails for Defensible Risk-adjustment

Neuro-Symbolic AI is not a single model. It is a two-layer architecture. The neural layer, the “neuro” half, applies deep language understanding to unlock data from clinical documentation and medical charts. This is where LLM capability is genuinely valuable: dealing with unstructured data and extracting patterns intuitively. The symbolic layer, the “symbolic” half, then validates each patient condition against a structured medical knowledge graph, CMS coding guidelines, and MEAT criteria before surfacing a recommendation.

For this type of purpose, Neuro-Symbolic AI overcomes failures seen with standard 3^rd party LLMs. Hallucinations are caught by knowledge graph validation. Reasoning is traceable through the symbolic chain via an audit trail (the clinical evidence from the encounter, the knowledge graph path, and the coding guideline that supports the recommendation). Guideline misalignment is managed at the rule layer rather than the model layer, when CMS updates coding guidelines or HCC mappings, the rule base is updated without retraining the entire model. Current requirements are encoded in the logic, not inferred from historical training data.

RAAPID: Operationalizing Revenue Resiliency

RAAPID’s approach to revenue resiliency leverages a three-layer architecture: a foundational layer, DocumentAI, intelligence layer, Neuro-Symbolic AI, and an activation layer that delivers results to downstream systems. DocumentAI ingests unstructured data to make it machine readable for use by Neuro-Symbolic AI.

Every HCC recommendation that surfaces by RAAPID’s Neuro-Symbolic AI includes the reasoning chain, the clinical evidence from the encounter mapped to MEAT criteria, and the coding guideline validation, making the output audit-ready by design. The platform identifies unclaimed codes with grounded evidence (adds) and flags codes that lack sufficient documentation (deletes), leveraging a two-way architecture, providing the revenue resiliency that is so important to health plans with the current macro environment.

RAAPID’s credentials reflect the healthcare data security and compliance standards the market requires: HIPAA compliant; SOC 2 Type 2 and HITRUST i1 certified; Microsoft Solutions Partner with Healthcare AI Certified software designation; and more than 20 years of organizational expertise in healthcare AI.⁷

The Last Word

Generative AI has changed what is computationally possible in risk adjustment. But raw capability without the right guardrails is not an upgrade, it is compliance exposure at scale. Health plans that deploy AI without a validation layer built on clinical rules and coding standards are taking on several risks at once: codes the AI invented that have no clinical basis, recommendations it cannot explain to an auditor, and outputs built on CMS guidelines that may be out of date. And once the final submission deadline passes, a missed code is simply lost revenue, CMS will not accept new diagnosis additions after that window closes, only deletions. This scenario can be prevented with the right AI technology approach.

Deploy generative AI without symbolic guardrails, and you are building exposure. Deploy it with them, and you are building resilience.
In the next installment of this blog series, we examine how accuracy alone does not equal defensibility, and what compliance leaders must demand for any AI system used in risk adjustment.

Endnotes

1. Ruppar, D. (2026). “How Medicare Advantage CFOs Are Turning Coding Defensibility Into Their Strongest Revenue Strategy.” Frost & Sullivan Blog Series: Defensible Risk Adjustment in the Era of AI, Blog 1. February 2026. Last accessed May 2026, at https://www.frost.com/growth-opportunity-news/healthcare/healthcare-it/how-medicare-advantage-cfos-are-turning-coding-defensibility-into-their-strongest-revenue-strategy/
2. HHS Office of Inspector General (2026). Medicare Advantage Risk Adjustment Compliance Audits. Published March 2026. U.S. Department of Health and Human Services. Washington, D.C.
3. Centers for Medicare & Medicaid Services (April 29, 2026). Subject: Deadline for Submitting Risk Adjustment Data for Use in Risk Score Calculation – Payment Years 2026, 2027, and 2028. Per 42 CFR 422.310(g), after the final submission deadline CMS only processes diagnosis deletions; new diagnosis additions are not accepted.
4. Gravel, J., et al. (2023). “Learning to Fake It: Limited Responses and Fabricated References Provided by ChatGPT for Medical Questions.” Mayo Clinic Proceedings: Digital Health. Last accessed May 2026, at https://www.mcpdigitalhealth.org/article/S2949-7612(23)00036-6/fulltext
5. 5 Omar, M., et al. (2025). “Multi-modal assurance analysis showing large language models are highly vulnerable to adversarial hallucination attacks during clinical decision support.” Nature Communications Medicine. Last accessed May 2026 at https://www.nature.com/articles/s43856-025-01021-3
6. Tun, H. M., et al. (2025). “Trust in Artificial Intelligence–Based Clinical Decision Support Systems Among Health Care Workers: Systematic Review.” Journal of Medical Internet Research. Last accessed May 2026 at https://www.jmir.org/2025/1/e69678
7. 7. RAAPID, Inc. (2026). Company credentials and certifications. Last accessed May 2026, at https://www.raapidinc.com.