Healthcare Domain Expertise

RAF & HCC Coding AI

$10M+ in risk adjustment improvements. 90%+ diagnosis accuracy. 20,000+ patients across 7 states with 245 providers.

Risk adjustment determines how you get paid in Medicare Advantage, ACO REACH, and value-based contracts. Accurate HCC coding isn't just about revenue-it's about getting reimbursed appropriately for the patients you actually serve.

Assess Your RAF Opportunity

$10M+

Risk Adjustment Improvements

90%+

Diagnosis Accuracy

20K+

Patients Served

States in Production

Understanding RAF & HCC

Your RAF score determines your payment. HCC coding determines your RAF.

The Risk Adjustment Factor (RAF) is CMS's way of adjusting payments based on how sick your patients are. Sicker patients with more chronic conditions get higher RAF scores, which means higher payments to cover their expected costs.

Hierarchical Condition Categories (HCCs) are the diagnosis groupings that drive your RAF score. Each HCC has a specific coefficient-conditions like diabetes with complications, heart failure, and COPD add to your RAF and increase your payment.

The key: HCCs must be documented and coded annually. A patient with diabetes last year doesn't automatically count this year-the condition must be documented in the current measurement period. That's where most organizations leave money on the table.

Why RAF Accuracy Matters

Medicare Advantage

MA plans receive capitated payments based on member RAF scores. Under-coded members mean you're paid less than required to care for them.

ACO REACH

Risk adjustment determines your benchmark. Low RAF scores mean artificially low targets-making shared savings harder to achieve.

Value-Based Contracts

Commercial payers increasingly use risk adjustment. Accurate coding ensures fair payment for the complexity you manage.

Every missed HCC is money left on the table-and inaccurate risk representation to payers.

The Problem

Chronic conditions disappear from documentation. Revenue follows.

30-40%

HCC Recapture Gap

Known chronic conditions documented in prior years often don't get re-documented annually. Diabetes, heart failure, COPD-conditions don't resolve, but documentation gaps make them invisible to risk adjustment.

15-25%

Suspected Conditions

Clinical evidence suggests conditions that were never formally diagnosed. Medications, lab values, and symptoms point to HCCs hiding in plain sight.

$5-15K

Per Patient Impact

Annual revenue impact of accurate vs. incomplete RAF coding. For a 10,000-patient population, that's $50M-$150M in risk adjustment accuracy at stake.

The Documentation Challenge

Annual Recapture Requirement

HCCs must be documented every measurement year. A diabetic patient from 2024 needs 2025 documentation to count toward 2025 RAF-regardless of how chronic the condition is.

Provider Cognitive Load

Primary care physicians manage 15-25 patients daily with 10-15 chronic conditions each. Remembering which HCCs need documentation is impossible without system support.

Specificity Requirements

Generic diagnosis codes often miss HCC opportunities. "Diabetes" isn't enough-the type and complications must be specified for proper risk adjustment.

Audit Exposure

CMS audits risk adjustment accuracy. Over-coding creates compliance risk; under-coding leaves money on the table. You need accuracy, not just volume.

Our Solution

AI that identifies HCC opportunities with explainable evidence.

We built our RAF optimization platform using peer-reviewed CMS methodologies and clinical NLP. Every recommendation links to source documentation-creating audit-defensible accuracy, not black-box suggestions.

HCC Recapture

Our system continuously monitors for known HCCs from prior years that need current-year documentation. It alerts providers before visits, identifies documentation gaps, and tracks recapture status across your entire population.

Example: "Patient has HCC 18 (Diabetes with Chronic Complications) from 2024. No 2025 documentation found. Recommend addressing at upcoming visit."

Suspected Condition Identification

AI analyzes medications, lab results, and clinical notes to identify conditions that may be present but never formally diagnosed. Every suspicion includes supporting evidence for clinical review.

Example: "Patient on Metformin + Glipizide with A1C of 8.2%-no diabetes diagnosis coded. Consider HCC 19 evaluation."

Specificity Enhancement

Generic codes often miss HCC opportunities. Our system identifies where diagnosis specificity could improve risk adjustment accuracy-differentiating between types, complications, and severity levels.

Example: "E11.9 (Type 2 Diabetes without complications) coded, but neuropathy symptoms documented. Consider E11.42 for HCC 18."

Audit-Ready Documentation

Every AI recommendation links directly to supporting documentation-labs, notes, medication lists. This creates an audit trail that protects your revenue while ensuring clinical accuracy.

Result: 90%+ diagnosis accuracy with full documentation linkage for CMS audit defense.

Responsible AI

90%+ accuracy built on peer-reviewed CMS methodologies.

Our RAF optimization isn't a black box. We use peer-reviewed CMS scoring models and clinical NLP trained on millions of healthcare documents. Every recommendation is explainable-you can see exactly why the AI flagged a condition.

This explainability matters for three reasons: clinicians trust it, auditors accept it, and you can validate accuracy before acting on recommendations.

The result: 90%+ diagnosis accuracy across 20,000+ patients, with documentation linkage that stands up to CMS scrutiny.

Under the Hood

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CMS HCC Model V28: Current risk adjustment methodology with proper coefficient mapping

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Clinical NLP: Trained on millions of healthcare documents for condition extraction

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Multi-Source Integration: EHR, claims, labs, medications-holistic patient view

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Confidence Scoring: Each recommendation includes confidence level and supporting evidence strength

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Audit Trail: Complete documentation linkage for every flagged condition

Proven Results

$10M+ improvements across 20,000+ patients in 7 states.

Production deployment at scale. Not a pilot. Not a projection. Real results from value-based care organizations managing real patient populations.

Value-Based Care Provider

Multi-state value-based care organization managing Medicare Advantage and ACO REACH populations. They needed RAF optimization that could scale across 245 providers while maintaining audit-ready accuracy.

Risk Adjustment Improvements $10M+ (cumulative)

Diagnosis Accuracy 90%+

Patients Covered 20,000+

Providers Using 245

States in Production 7

HCC Recapture Rate

Improved annual recapture of known chronic conditions from 60-65% to 85-90%-closing the documentation gap.

Suspected Conditions

Identified 15-20% additional HCC opportunities from clinical evidence that hadn't been formally diagnosed.

Audit Performance

Documentation linkage and explainability enabled confident audit defense with minimal findings.

EHR Integration

Integrated with PointClickCare, Gehrimed, Acclivity, and more.

Our RAF platform integrates with the EHR systems value-based care organizations actually use. We pull clinical documentation, labs, medications, and claims data-creating a holistic patient view that manual review can't match.

Recommendations flow back into provider workflows at the point of care. Alerts before visits, embedded decision support during documentation, and tracking dashboards for population management.

For organizations with multiple EHRs across their network, we handle the complexity-normalizing data from different sources into unified RAF insights.