A Medicare Advantage (MA) organization receives an unexpected RADV audit notice from Centers for Medicare & Medicaid Services (CMS). The clinical team has just days to collect documentation for hundreds of patient records. What they don't know yet is that CMS's new aggressive audit strategy will examine all their contracts for payment years 2019 through 2023, not just a sample.

The stakes are higher than ever. CMS estimates that unsupported diagnoses may lead to $17 billion in annual overpayments to MA plans, with some reaching as high as $43 billion per year.

Since June 2025, CMS has started auditing all eligible plans annually, up from about 60 plans to 550 per year. Under this new strategy, CMS uses statistical sampling with extrapolation (Payment Year 2018 forward) to scale findings across the entire plan, increasing potential recoveries.

Many healthcare organizations still run reactively, addressing documentation gaps only when audit notices arrive. This opens doors to higher error rates, large recoupments, and heavy resource drain. The gap between what's documented and what's coded creates serious audit vulnerability.

With 8+ years of knowledge and adherence to federal and state compliance, HOM helps organizations avoid that scramble. Through AI-powered Clinical Documentation Improvement practices, healthcare payers can build audit readiness long before CMS sends that notice.

But before exploring how CDI prepares you for RADV audits, it's important to understand why they exist and how they impact healthcare organizations.

RADV Audits - Financial Impact and Why Traditional Approaches Fall Short

Risk Adjustment Data Validation (RADV) audits are CMS's primary way to address overpayments to Medicare Advantage Organizations. During these audits, CMS confirms that diagnosis codes submitted for risk adjustment accurately represent the patients' documented medical conditions. The process is simple but unforgiving. CMS selects a sample of enrollees and reviews their medical records. Any diagnosis code without proper documentation support is disallowed.

What's the Financial Impact?

CMS has implemented aggressive measures to recover the $17 billion of overpayments. If CMS identifies an unsupported diagnosis, MA plans may seek to recover those overpayments from providers. As a result, the financial impact often shifts to physicians and group practices, where repayment demands can disrupt cash flow and operational stability for months.

Why Traditional Approaches are Falling Short?

Retrospective CDI reviews happen after encounters are closed. The timing varies: inpatient retrospective CDI may occur during the coding phase before billing, allowing for queries and addenda. However, for outpatient services, claims are often submitted quickly, requiring later amendments. For risk adjustment purposes, retrospective CDI frequently targets closed charts after year-end to validate HCC diagnoses for the measurement year. 

The pace of these review processes can't always keep up with the volume. CDI analysts spend a substantial amount of their time sifting through lengthy charts. Besides, traditional methods also lack the analytics needed to identify risk patterns. This reactive approach ultimately creates burnout and ineffectiveness.

While many organizations have relied solely on retrospective CDI, forward-thinking payers are making strategic investments in prospective CDI. This proactive approach involves reviewing documentation before or during the patient encounter, allowing real-time corrections rather than post-submission fixes. This strategic shift requires both technology adoption and organizational commitment, but it fundamentally transforms audit readiness.

How AI-Driven CDI Helps Build Proactive Audit Readiness

The Clinical Documentation Improvement (CDI) process enhances the completeness and accuracy of medical records.  

Let’s see how AI-driven CDI helps create audit-ready documentation:

Pre-visit Chart Preparation

 AI-powered CDI practices involve reviewing patient records before appointments. Their tools enable analysts to scan medical histories to detect chronic conditions requiring immediate documentation updates, flag HCC codes that need support, and highlight gaps from previous encounters. This preparation allows providers to enter each visit with a clear documentation roadmap.

Evidence Mapping

AI-driven pattern recognition identifies documentation gaps that create (or could create) RADV audit vulnerabilities. The capability helps compare documented conditions with the required supporting clinical evidence. When a diagnosis lacks enough support, the module generates a targeted, actionable query for clarification before submission.

Automated Quality Checks

Before claim submission, improvement specialists perform automated quality validation. They review documentation for completeness, check provider signatures, verify date accuracy, ensure diagnosis specificity, and confirm that treatment notes align with coded conditions. This final review acts as a quality safeguard, allowing teams to correct discrepancies early in the process.

Targeted Query Management

Accurate, timely queries drive documentation improvement and provider engagement. Every query in an effective CDI practice is generated based on specific gaps in the medical record, with supporting evidence from medical records. Tracking provider responses allows teams to see where additional education might be needed. AI-driven CDI platforms can even help analyze query effectiveness and recommend ways to improve future communication.

Smart Chart Summaries

AI-driven CDI processes help organizations generate clear summaries of medical documents, highlighting sections most relevant to analysts and providers. It improves efficiency and reduces the time spent on less relevant parts of records.

Trend and Pattern Monitoring

Built-in dashboards in CDI tools monitor HCC distribution, documentation volume, and recurring query types. With this visibility, specialists can detect trends early and address systemic gaps before audits take place.

Predictive analytics help specialists identify charts that are more likely to be scrutinized by RADV before submission. Review historical error trends, documentation completeness, and code complexity to flag high-risk records. 

Streamlined Workflows and Audit Trails

CDI ecosystem maintains a complete audit trail for pre-visit and post-visit chart reviews, creating a structured workflow that ensures that every document is traceable and compliant for regulatory verification.

By implementing AI-driven CDI practices, healthcare organizations can move from last-minute documentation fixes to consistent, real-time quality improvements. Teams can move from fixing problems to preventing them.

Providers can receive immediate feedback that reinforces proper documentation habits. The focus shifts from audit response to audit readiness as a continuous state, enabling organizations to keep every chart audit-ready always.

HOM's CDI framework integrates these steps seamlessly through AI-assisted tools, structured reviews, and expert oversight.

Final Takeaway

RADV Audits aren't going away. With CMS now auditing all eligible Medicare Advantage plans annually, preparation isn't optional. The organizations that excel under increased scrutiny have moved from reactive to proactive documentation practices.

AI-powered CDI services make this shift possible by identifying gaps before submission, continuously validating HCC integrity, and providing real-time feedback to providers.

Having served 300+ providers globally in 15+ medical specialties, HOM brings together advanced AI capabilities with deep clinical expertise in CDI. Our in-house CDI platform enables organizations to monitor documentation quality in real-time, strengthen compliance, and maintain audit-ready records. Meanwhile, trained improvement specialists provide the human judgment that technology can't replicate. They have helped practices improve their productivity by 75% while maintaining close to 99% accuracy with a rapid turnaround time of 24 hours.

Request a free audit from HOM's experts to identify documentation gaps and understand how AI-driven CDI can strengthen your audit readiness through concurrent review, physician engagement, and continuous quality improvement.