How Neuro-Symbolic AI Enables Defensible Coding in Risk Adjustment

Introduction: The Regulatory Shift That Changes Everything

The CMS HCC V28 model reached 100% implementation for payment year 2026, marking the most significant shift in Medicare Advantage risk adjustment in over a decade [1]. CMS announced plans to audit all 550 eligible MA contracts annually, with sample sizes increasing to 200 records per plan [2]. This expansion fundamentally changes decision-making for risk adjustment leaders.

DOJ enforcement actions targeting retrospective systems designed to increase raf scores have confirmed: process and intent matter as much as accuracy. When auditors review your organization, they evaluate whether your AI systems were designed to find truth or inflate RAF scores through legitimate clinical documentation.

This challenge is exactly what Neuro-Symbolic AI was built for. Health plans need AI models supporting decision-making with explanation and compliance, not just pattern recognition. The decision-making process must be transparent, auditable, and defensible.

Why General Purpose AI Fails in Risk Adjustment

Before understanding what Neuro-Symbolic AI does differently, it is worth understanding why organizations cannot plug ChatGPT or traditional natural language processing engines into risk adjustment workflows and expect defensible results.

A 2024 JMIR study found general AI models exhibited critical hallucination levels, with ChatGPT fabricating 20% of academic citations [5]. A Frontiers in AI study found hallucinations in 40% of AI-generated discharge summaries [6]. In risk adjustment, where every diagnosis code must link to verifiable clinical evidence, hallucination creates direct compliance exposure.

General purpose AI models lack built-in knowledge of CMS coding guidelines and MEAT requirements. A Health Science Reports review identified limitations, including inaccurate content and inconsistent accuracy, posing risks for clinical decision support [7]. These AI systems operate as black box models that cannot provide evidence trails. They are prediction engines, not compliance engines with model transparency.

This architectural limitation of large language models is why risk adjustment solutions require purpose-built artificial intelligence.

What Is Neuro-Symbolic AI and How Does Neural Symbolic Integration Work?

Neuro-Symbolic AI represents a hybrid architecture integrating two approaches to artificial intelligence. A 2024 systematic review confirmed that this neural symbolic integration addresses critical limitations: neural networks gain interpretability and logical reasoning capabilities, while symbolic AI systems gain the ability to learn from training data [4].

The Neuro Layer: The neural network component uses deep learning and machine learning models trained on clinical data. These AI models and neural networks understand context through object recognition, recognizing clinical patterns with human-like comprehension. Object recognition enables neural networks to identify relevant clinical information across varied document formats. This gives the system capabilities to process complex documentation that humans encounter daily.

The Symbolic Layer: The symbolic AI component applies knowledge representation through a structured knowledge graph containing over 60 million entity relationships mapping diagnoses, symptoms, procedures, and medications in knowledge bases. It embeds CMS coding guidelines and MEAT requirements directly into reasoning for compliant documentation. When neural networks identify potential conditions, symbolic reasoning validates whether the clinical context supports that medical diagnosis.

What Is Explainable AI and Why Does It Matter for Humans?

Explainable AI refers to AI systems making decision-making processes transparent and understandable to humans. In risk adjustment, explainability is a compliance requirement for human users who must verify every code recommendation.

A 2024 JMIR systematic review concluded that explainable AI models are necessary to foster healthcare workers’ trust in clinical decision support systems [9]. This differentiates glass box approaches from black box models, where humans cannot verify reasoning. Explainable AI enables better decision-making by showing the path from clinical evidence to code recommendation.

What Is an Explainable AI Example?

When Neuro-Symbolic AI processes a chart, coders see: evidence location with hyperlinks, human readable explanation of clinical reasoning, specific MEAT documentation, and compliance validation. This example of XAI techniques enables humans to make good decisions with AI assistance through model transparency. Coders can verify and defend every code because explainable AI showed its work.

Is ChatGPT an Explainable AI?

No. Large language models and neural networks generate text based on statistical patterns without tracing outputs to source evidence. They cannot guarantee compliance with coding rules. This difference matters in healthcare and criminal justice domains where explainable machine learning requirements apply: decisions affecting humans must be traceable [10]. Explainable ai provides the audit trail that black box models cannot.

What Does HCC Stand For and What Are Examples of HCC Conditions?

HCC stands for Hierarchical Condition Category, the CMS classification for diagnoses determining raf scores for Medicare Advantage payments [1]. Understanding what HCC means helps coders and providers grasp why accurate documentation directly affects raf scores and reimbursement.

Examples of HCC Conditions: Diabetes with complications (HCC 18), chronic kidney disease stage 4 (HCC 137), major depressive disorder (HCC 59), heart failure (HCC 85), and COPD (HCC 111). Each medical diagnosis maps to specific raf scores affecting health plans’ reimbursement calculations.

What Is HCC in Mental Health?

Mental health HCCs capture psychiatric conditions, including major depressive disorder, bipolar disorder, and schizophrenia. Accurate documentation is critical for patient care because these diagnoses lack objective markers. Proper documentation of mental health conditions significantly impacts raf scores.

What Does HCC Mean on MyChart?

When patients see HCC references, it indicates providers documented conditions mapping to disease categories for risk adjustment affecting health plans in value-based care models.

What Is Coding Defensively and What Are the 5 Principles?

Defensive coding means documenting and submitting diagnosis codes that withstand audit scrutiny through proper clinical validation. OIG audits consistently find that diagnosis codes do not comply with Federal requirements, with overpayment rates of 5 to 8 percent [11]. The key to defensibility is ensuring every code has documented clinical evidence.

The 5 Principles of Defensible Coding:

• Evidence-Based: Every code links to specific documentation with traceable reasoning
• MEAT Compliant: Documentation shows active monitoring, evaluation, assessment, or treatment as required by CMS standards
• Encounter Linked: Diagnoses connect to face-to-face provider visits with clinical support
• Bidirectional Review: AI systems identify codes to add AND remove for compliance
• Audit Ready: Complete evidence packages with clinical documentation exist before auditors request them

Two Common Defensive Coding Techniques: Prospective validation flags documentation issues during patient care encounters before claims submission, ensuring clinical requirements are captured. Retrospective cleanup reviews historical charts using AI to identify unsupported codes requiring removal and care gaps where evidence exists, but codes were not captured.

What is meant by Secure Coding?

Secure coding protects code recommendations through verifiable evidence trails while preventing unauthorized modifications, ensuring process integrity that auditors can verify through clinical documentation and AI assisted validation.