For health plans, risk adjustment success depends on one non-negotiable requirement. Every ICD-10 and HCC submitted must be accurate, specific, complete, and fully defensible under audit.

As CMS oversight tightens and RADV exposure increases, payers can no longer evaluate risk adjustment technologies based solely on speed or automation claims. The real question is how completely a solution identifies diagnoses and whether those codes can be clearly substantiated when scrutiny increases.

Understanding the differences between today’s approaches is essential to choosing a solution that delivers confidence instead of cleanup.

The Three Common Approaches to Risk Adjustment Coding

Most health plans encounter one of three broad approaches when evaluating risk adjustment solutions.

Manual Coding and Review

Traditional manual coding relies on human reviewers using codebooks, guidelines, and reference tools to identify risk-adjustable diagnoses from medical records. While familiar, this approach is difficult to scale across large member populations and introduces variability between reviewers and the challenge of introducing human error.

Manual processes are time-consuming, costly, and often miss rare, complex, or combination diagnoses. Productivity is limited, consistency is hard to maintain, and audit outcomes vary depending on reviewer experience and time constraints.

Machine Learning AI Models

To address scale, many payers have adopted machine learning AI solutions. These systems analyze historical documentation patterns and “suggest” diagnoses based on probability.

While ML models can accelerate review, they introduce new challenges. Because they rely on statistical inference, their logic is often difficult to explain. Codes are suggested without a clear, traceable link to explicit clinical language in the record, lacking transparency and increasing denial and audit risk.

Typical accuracy rates fall in the 70 to 80 percent range. Less common and high-specificity ICDs and HCCs are frequently missed. ML models require ongoing retraining to accommodate annual CMS updates, which can introduce delays of weeks or months. During costly audits, risk adjustment teams remain responsible for validation, cleanup, and justification when suggested codes cannot be fully defended.

Deterministic, Rules-Based Precision

A fundamentally different approach focuses on deterministic accuracy rather than probability.

Precise Word Matching AI, with an accuracy rate of over 96%, evaluates the full medical record and assigns codes only when exact clinical language is present and supported. Instead of predicting diagnoses, this rules-based methodology matches all documented terms directly to the most specific ICD-10 and HCC codes, offering much higher completeness, specificity, and ROI than other models.

Every surfaced code is supported by extracted MEAT evidence. Logic is transparent. Results are explainable. Audit readiness is built into the process rather than addressed later, decreasing costs and greatly increasing compliance.

This approach consistently identifies rare, complex, and combination diagnoses that probabilistic models often overlook. It also adapts immediately to new ICD and HCC updates without retraining delays, reducing revenue risk and operational disruption.

What Truly Matters When Evaluating Risk Adjustment Solutions

When comparing risk adjustment technologies, health plans should prioritize criteria that hold up under audit pressure, not just during initial review.

Key evaluation questions include:

• Can every code be traced directly to explicit documentation?
• Is the logic transparent and explainable to auditors?
• Does the solution capture the full complexity of disease states, including rare and combination conditions?
• Does it avoid probabilistic suggestions that require manual validation?
• Can it adapt quickly to CMS updates without retraining delays?
• Does it reduce audit risk while maximizing RAF accuracy?

Solutions that rely on suggestions instead of certainty shift risk back to the organization. Solutions built on deterministic logic remove ambiguity and create confidence and trust.

Why Certainty Matters More Than Speed

Speed without defensibility creates exposure. Automation without transparency creates risk.

In an environment of heightened RADV enforcement and shrinking margins, health plans need more than faster review. They need certainty that every captured code is supported, compliant, and sustainable.

Deterministic, rules-based approaches eliminate gray areas. They reduce cleanup work, strengthen audit posture, and ensure that risk adjustment performance holds up when it matters most.

A Clear Direction for Risk Adjustment

As risk adjustment scrutiny increases, the distinction between suggested codes and substantiated codes becomes critical.

Machine learning AI may accelerate an initial review, but it introduces ambiguity and ongoing validation burden. Precise Word Matching AI delivers speed, accuracy, transparency, increased revenue and audit readiness at scale.

For health plans focused on long-term compliance, consistent outcomes, and defensible revenue, the path forward is clear. Certainty beats probability every time.