CPT Governance Is Healthcare's Next Payment Integrity Frontier

Last week's Washington Post report on Senator Bill Cassidy's probe into the American Medical Association (AMA) signals a major policy pivot.

For decades, we have treated payment integrity as a downstream problem, catch the fraudulent claim, deny the upcoded procedure, flag the unbundled service.

But what happens when the integrity risk is baked into the system itself?

That's the question now being raised on Capitol Hill, where lawmakers are scrutinizing how the American Medical Association sets and licenses CPT codes, the very foundation of how healthcare services are defined, documented, and reimbursed.

If the system itself creates misaligned incentives, it requires a new strategy for payment integrity.

The Governance Gap

The reality is that CPT codes serve as more than neutral technical descriptors; they also act as economic signals that influence provider behavior, specialty compensation, and the allocation of substantial Medicare funding each year.

The AMA's CPT Editorial Panel, primarily representatives from physician specialty societies, determines which procedures receive codes, how those codes are valued, and the criteria for bundling or unbundling. Consequently, specialties with favorable coding outcomes often play a significant role in these decisions.

Restructuring evaluation and management (E/M) codes can result in substantial financial shifts between primary care and specialist providers. Similarly, the introduction or delay of new codes can impact entire market segments. Changes to bundling rules affect payer claims adjudication across the healthcare system.

The Senate's review underscores the growing awareness that ensuring payment integrity extends beyond monitoring claims; it requires a comprehensive understanding of how regulatory frameworks establish incentive structures that may not always align with broader policy objectives.

Why Traditional Payment Integrity Tools Fall Short

Most payment integrity solutions are built for a different problem. PI today is optimized to detect anomalies against a baseline, finding the claim that doesn't fit the pattern, the provider whose billing profile looks unusual, the code combination that triggers an edit.

But what if the baseline itself is the problem?

Rules-based systems can't question the rules they are built on. Machine learning models trained on historical claims data learn to replicate existing patterns, including patterns that reflect structural coding biases.

Neither approach is designed to ask: "Does this code's relative value accurately reflect the resource consumption and clinical complexity involved?"

This is the fundamental limitation of treating payment integrity as a pattern-matching exercise. When the patterns encode systematic distortions, finding outliers misses the point.

The Case for Neuro-Symbolic AI in Coding Governance

At Nēdl Labs, we've been building a neuro-symbolic AI platform that combines neural network pattern recognition with the logical reasoning of symbolic systems.

Why does this matter for CPT governance? Because understanding coding integrity requires both capabilities:

Neural networks can process vast amounts of unstructured clinical documentation, identifying how procedures are actually described in physician notes, operative reports, and clinical narratives. They can surface the semantic relationships between clinical activities that may or may not align with how codes categorize them.

Symbolic reasoning can encode the explicit logic of coding rules, definitions, inclusions, and exclusions, bundling hierarchies, and relative value methodologies. It can trace the logical implications of how codes are structured and identify internal inconsistencies or misalignments with stated policy objectives.

The combination creates something neither can achieve alone: the ability to reason about whether coding structures accurately represent clinical reality and whether payment flows align with intended policy outcomes.

What is Payment Integrity in Healthcare?

What This Looks Like in Practice

Consider a practical application: analyzing whether code families reflect actual clinical complexity gradients.

A traditional payment integrity system might flag that a provider is using higher-level codes more frequently than peers. But it can't tell you whether the code levels themselves make clinical sense---whether the distinctions between levels correspond to meaningful differences in resource consumption, or whether they create arbitrary thresholds that incentivize upcoding.

A neuro-symbolic approach can:

1. Extract clinical semantics from documentation across thousands of encounters, building a data-driven picture of what actually varies between cases coded at different levels
2. Map those variations against the code definitions and valuation methodologies, identifying where the symbolic structure aligns or diverges from empirical clinical patterns.
3. Trace payment implications through the logical chain from code assignment to reimbursement, quantifying how structural coding choices translate into financial flows
4. Generate explainable findings that connect specific coding constructs to specific payment outcomes---not just correlations, but causal reasoning chains that can inform policy discussions.

This isn't about replacing human judgment on coding policy. It's about giving policymakers, payers, and regulators the analytical foundation to ask better questions and evaluate structural alternatives.

From Claim-Level Detection to System-Level Intelligence

Senate scrutiny of CPT reflects a shift from identifying fraud to evaluating payment systems. Now, tools must assess not only claim compliance but also whether the rules achieve their goals and support better policy decisions.

Historically, payment integrity has focused on countering rule exploitation, but significant risks may stem from flawed rules that introduce unintended biases. Neuro-symbolic AI is designed to tackle these challenges.

The Bottom Line

While CPT governance concerns are longstanding, increased political attention and advanced analytics bring new possibilities. Payers need broader strategies that emphasize analytical infrastructure to understand the coding impacts on payments before regulatory changes occur. For the industry, effective payment integrity requires examining if the system itself operates as intended, not just detecting bad claims.

The Senate is pushing for answers; leaders must ensure they have the right tools to respond.