D 4 RADV – AI RADV Audit Defense

RADV Audit Defense - Proactively Prepare, Validate, and Defend HCC Submissions

Built on D4RADV's proprietary intelligence engine, our platform performs member-level validation across ehr medical records with full MEAT compliance, ensuring that every HCC submitted is clinically supported and defensible during a radv audit.

As healthcare and digital transformation accelerates, D4RADV combines AI-powered validation with a fully integrated coder and auditor workflow, enabling continuous audit readiness at scale.

RADV audits have become a year-round operational priority. Automate chart review, evidence validation, and HCC reconciliation efficiently.

Identify documentation gaps early and generate audit-ready outputs at scale — before CMS auditors do.

Audit Defense Capabilities

99%

Diagnosis Code Detection

+99% accuracy for mapping diagnoses codes to the correct HCC

Unlimited RADV Projects

Create as many CMS or Mock RADV audit projects as you like. Complete control of chart volume and system throughput.

70%

Productivity Improvement

Realize up to 70% time reduction on each RADV project with automated setup, collaborative workflows and improved accuracy.

45%+

Per Chart Value Uplift

Expect >45% increase in chart value. AI assists by confirming diagnoses, flagging under-coded conditions and suggesting missed conditions.

The Challenge

Why RADV Readiness Matters

RADV audit scope and rigor continue to increase - making it essential to ensure each submitted HCC is supported by complete, defensible documentation and clear traceability.

Common pain points we solve:

Manual chart review that doesn't scale

Difficulty proving clinical evidence sufficiency

Inconsistent validation of documentation standards (e.g., MEAT gaps)

Risk of recoupments due to unsupported diagnoses

How It Works

Step 1

Ingest

Charts & coded diagnoses at scale

Step 2

AI Review

NLP/LLM documentation gap analysis

Step 3

Risk Scoring

HCC evidence packs & prioritization

Step 4

Audit-Ready

Mock audits & CMS submission

End-to-End RADV Readiness

Our Solution

What Our RADV Solution Delivers

A modern RADV audit readiness platform that helps you find issues before auditors do, automate evidence packets, and strengthen defensibility.

Automated Chart Review

Automated chart review and evidence validation to detect missing or non-defensible documentation at scale.

HCC Coding Reconciliation

Identify gaps and discrepancies between clinical documentation and HCC submissions before auditors find them.

Audit Scoring & Benchmarking

Audit-readiness reporting mapped to CMS criteria with cohort-level benchmarks for proactive risk management.

Evidence Packs

Explainable evidence packs and audit-ready reports to support review prioritization and audit defense.

MEAT Validation

Documentation gap detection including unsupported diagnoses and missing MEAT (Monitor, Evaluate, Assess, Treat) evidence.

Secure Deployment

Customer-controlled deployment (containerized) with HIPAA-aligned processing to maintain PHI control.

Process

How It Works

A simple, executive view of our RADV readiness workflow.

Ingest

Ingest charts and coded diagnoses from your environment at scale.

AI Review

AI reviews documentation using healthcare-trained NLP/LLMs to flag gaps.

Risk Scoring

Generate risk scoring and evidence packs so teams focus where it matters.

Audit-Ready

Produce audit-ready outputs supporting mock audits and CMS readiness.

Target Audience

Who It's For

Built for organizations operating in high-compliance risk adjustment environments.

Medicare Advantage Organizations

Risk Adjustment & Coding Teams

ACOs

Audit & Compliance Teams

Results

Outcomes You Can Expect

Reduced Exposure

Identify documentation gaps and unsupported diagnoses prior to audit submission.

Less Manual Effort

Automated review and evidence packet generation at scale.

Improved Defensibility

Explainable evidence packs, scoring, and audit-ready reporting.

Scalable Readiness

Support internal mock audits and ongoing monitoring workflows.

Get Started

Ready to operationalize RADV readiness with AI?