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RADV Audit Readiness in 2026: A Practical Guide for Health Plans
27 May 2026
The CMS RADV Documents & Data hub posted three major updates in March 2026 alone.
CMS released new methods instructions for Payment Year 2020, refreshed its Q&A document, and published a PY2015 payment error calculation methodology within a few weeks.
That timing matters. In 2026, the modernized Risk Adjustment Data Validation, or RADV, program, the phased-in Version 28 CMS-HCC model, and a changing legal environment all hit at once.
For Medicare Advantage plans, that mix raises both financial exposure and operational risk. Finance teams need better reserve scenarios, and coding and compliance teams need stronger medical record support for every Hierarchical Condition Category, or HCC, they defend.
The plans that perform best in this environment do not wait for the Enrollee Data Listing, or EDL, to arrive. They build a controlled evidence process, test retrieval workflows early, and use automation only where it improves speed without weakening audit defensibility.
Now is the time to build a command center that can hold up under file deadlines, statistical review, and board-level scrutiny.
Key Takeaways
- Two financial scenarios, disciplined evidence controls, and retrained coding teams are the core of 2026 RADV readiness.
- Extrapolation remains legally uncertain. CMS finalized extrapolation beginning PY2018 and removed the FFS Adjuster in the 2023 Final Rule, but on September 25, 2025, a federal court vacated portions of that rule in Humana Inc. v. Becerra. The court ruled on procedural grounds (inadequate public notice), not the substantive legality of extrapolation itself. CMS has appealed. Plan for both extrapolated and enrollee-level outcomes.
- 2026 CMS documents set hard deadlines. The PY2020 audit cycle includes an April 3 EDL release, an April 13 medical record window opening, and an August 28 submission deadline. Miss these and you lose options.
- V28 changes the diagnosis mix. The three-year phase-in expands HCC categories from 86 to 115 payment conditions with 7,770 mapped HCCs, reshaping which diagnoses carry weight and demanding retrained coders and auditors who understand new documentation specificity rules.
- Responsible AI cuts cycle time. Natural language processing, or NLP, abstraction, retrieval triage bots, and outlier detection accelerate audit prep, but only with governance guardrails like audit trails, protected health information controls, and human oversight.
- Evidence lockers and pre-validation scripts reduce error rates. Standing up immutable storage, dual abstraction workflows, and an appeals workbench before the audit window opens is the highest-return operational move.
- Measure readiness with five KPIs. Track chart retrieval percentage, HCC validation rate, coder inter-rater reliability, turnaround times, and predicted exposure versus reserves each week.
What Changed in RADV Since 2023
Since 2023, RADV has become more financially significant and less predictable. RAAPID’s RADV audits CMS has published updated guidance explaining these shifts in detail for organizations navigating the evolving regulatory landscape.
Financial Impact Timeline:
On January 30, 2023, CMS finalized that RADV audit findings will be extrapolated beginning with Payment Year 2018 and confirmed it will not apply a fee-for-service, or FFS, Adjuster. The Regulatory Impact Analysis estimated aggregate transfers of about $4.7 billion over 10 years, or roughly $479 million per audit year.
Then, on September 25, 2025, a federal district court vacated portions of the 2023 RADV rule in Humana Inc. v. Becerra. Judge Reed O'Connor of the U.S. District Court for the Northern District of Texas ruled that the final rule was procedurally invalid because CMS failed to provide adequate public notice of changes made when moving from the 2018 proposed version to the Final Rule released in 2023, and did not provide opportunity for stakeholders to comment on these changes.
The case is now on appeal, and CMS may later decide whether to extrapolate PY2020 results, so finance leaders need dual-path models now, not after findings arrive.
CMS also said it will not lock itself into one extrapolation formula. Instead, it may use any statistically valid method suited to the audit, focus on contracts with the highest risk of improper payments, and collect extrapolated amounts identified by HHS-OIG.
Practical implication: Build reserve models for both an enrollee-level finding and a fully extrapolated outcome, and brief your board on both before the EDL drops.
The 2026 CMS Documents You Must Have Bookmarked
Primary sources should drive your workplan, your evidence standards, and your executive reporting. RAAPID's comprehensive review of the January 2026 CMS update provides additional context and practical interpretation of these documents.
These five documents form the working file for 2026 readiness, and each one should have an internal owner. If nobody owns a document, key updates get missed or applied too late.
- CMS Fact Sheet (2023 Final Rule) - Confirms extrapolation from PY2018 and the removal of the FFS Adjuster.
- Federal Register Regulatory Impact Analysis (RIA) - Quantifies the $4.7 billion in aggregate transfers and per-year exposure estimates.
- PY2020 Methods and Instructions - Details sample sizes of 35, 50, 100, and 200 enrollees, the 90 percent lower-bound confidence interval rule, and specific 2026 milestones (April 3 EDL release, April 13 medical record window opening, August 28 submission deadline).
- 2026 RADV Q&A Document - Reiterates 10-year record retention requirements under 42 CFR 422.504(d) and outlines appeals timelines under 42 CFR 422.311(c).
- RADV Documents and Data Hub, Updated March 2026 - Centralizes general guidance and payment-year documents, including the newly published PY2015 payment error calculation methodology.
Action Item: Use these sources to build one internal reference library with current versions, citations, and short summaries. That step sounds small, but it reduces rework when compliance, coding, actuarial, and provider teams interpret the same rule differently.
V28 Changes Require New Coding Habits
V28 makes old coding shortcuts more expensive because diagnosis support, code selection, and HCC mapping no longer behave the same way. Organizations relying on RAAPID's guidance and resources have found structured approaches to managing this transition more effective than ad-hoc adjustments.
CMS finalized the transition to Version 28 of the CMS-HCC risk adjustment model beginning in CY2024 with a three-year phase-in:
- CY 2024: 67% of risk scores calculated using V24 (prior model), 33% using V28
- CY 2025: 33% V24, 67% V28
- CY 2026 and beyond: 100% V28
Under V28, some diagnosis codes map differently, some categories carry less weight, and documentation must be more specific to hold up under review. The model expanded from 86 to 115 payment HCC categories, with 7,770 total mapped HCCs.
That means education cannot stop with coders. Auditors, clinicians, vendor partners, and retrieval teams all need the same playbook on what valid support looks like under the updated model.
Start with your highest-value and highest-defect HCCs. Refresh coding guidance, update abstraction templates, and rerun quality assurance logic so reviewers do not approve records based on old assumptions. A code that looked acceptable under prior workflows may still be clinically true, but it may no longer be well documented for RADV purposes.
The 90-to-120-Day Readiness Sprint
A short, disciplined sprint before the submission window opens can prevent months of avoidable loss later.
For PY2020, CMS set the following milestones:
| Milestone | Date | Days Until |
| EDL Available | April 3, 2026 | - |
| Medical Record Submission Window Opens | April 13, 2026 | +10 days |
| Medical Record Submission Deadline | August 28, 2026 | +137 days |
| Hardship Request Deadline | September 11, 2026 | +151 days |
Those dates leave little room for setup work once the audit is live.
Work backward from August 28 and start with governance. Name one RADV lead with direct escalation authority to the chief medical officer and chief financial officer, then stand up a twice-weekly command call with coding, compliance, actuarial, IT, provider operations, and legal.
Next, build a single evidence locker with immutable storage, version control, access logs, and a clear naming standard. Then run a mock sample on high-risk outliers and pre-validate HCCs through dual abstraction, which means two independent reviewers assess the same record before a tie-break review.
Provider retrieval needs the same level of structure. Segment playbooks by provider type, define service-level agreements, track response dates, and prepare escalation scripts for incomplete charts, signature defects, and late office responses. Plans lose time not only on missing charts, but also on charts that arrive in the wrong format or lack key pages.
Finally, build the appeals workbench before you need it. Create template letters, a defect taxonomy, and a citation library tied to CMS sources, then align your actuary on reserve bands with and without extrapolation. Deduplicate charts by medical record number, date, and source, normalize file formats, and target inter-rater reliability of 0.85 or higher for high-risk HCCs.
AI, Analytics, and Automation That Actually Help in RADV
AI is useful in RADV only when it speeds review and leaves a clear human audit trail. As discussed in RAAPID's latest analysis, the most effective implementations combine automation with rigorous governance controls.
Every tool should have a model card, bias testing, access controls, audit logging, and a required human approval step before any output enters a submission package. If a team cannot explain how the tool was used, the tool is adding risk, not reducing it.
The strongest use cases are narrow and measurable. NLP can flag likely unsupported diagnoses and point reviewers to likely evidence locations, which can reduce wasted review time on false-positive HCCs. Computer-assisted coding quality checks can compare notes, ICD-10-CM codes, and HCC mappings with confidence scores, which helps improve coder consistency.
Retrieval triage bots can rank provider offices by likely yield and complexity, and outlier models can isolate diagnoses reported only through health risk assessments, or HRAs, or chart reviews.
HHS-OIG's October 2024 evaluation found significant vulnerabilities: Diagnoses reported only through HRAs or HRA-linked chart reviews, without corroborating service records, resulted in approximately $7.5 billion in risk-adjusted payments in 2023.
The lack of any other followup visits, procedures, tests, or supplies for these diagnoses raises concerns that either the diagnoses are inaccurate and thus the payments are improper, or enrollees did not receive needed care for serious conditions reported only on HRAs or HRA-linked chart reviews.
The operational lesson is simple. Use automation to prioritize, sort, and surface evidence, but keep final clinical and compliance judgment with trained staff.
Sampling, Extrapolation, and the Math to Model
The financial result depends less on guesswork and more on how sample error behaves under CMS's statistical rules.
CMS selects samples of 35, 50, 100, or 200 enrollees from contracts identified as highest risk for improper payments. After medical record review, CMS calculates a per-enrollee payment error and then determines whether the lower bound of the 90 percent confidence interval exceeds zero.
If that lower bound exceeds zero, CMS may extrapolate the sample error rate across the full contract population. If it does not, the finding stays at the enrollee level. Your actuary should still model at least three scenarios, enrollee-level only, partial-stratum extrapolation, and full-contract extrapolation, and connect each one to reserve impact and board reporting.
Program integrity pressure remains high. The Government Accountability Office (GAO) reported $162 billion in government-wide improper payments in Fiscal Year 2024, and Medicare Advantage risk adjustment remains a clear enforcement focus for HHS-OIG and DOJ.
Cross-Functional Playbooks and KPIs
RADV breaks down when roles are vague, so every function needs clear ownership and weekly measures.
Build a RACI matrix (Responsible, Accountable, Consulted, Informed):
| Function | Responsible | Accountable | Consulted | Informed |
| Executive Sponsor | - | CEO, CFO | - | Board |
| RADV Lead | X | X | All teams | Executive |
| IT / Evidence Locker | X | X | Coding, Compliance | RADV Lead |
| Coding / Dual Abstraction | X | X | Compliance | RADV Lead, Finance |
| Provider Operations | X | X | Coding | RADV Lead |
| Compliance / Special Investigations | X | X | Coding | RADV Lead, Finance |
| Actuarial / Reserves | X | X | Finance | Board, RADV Lead |
| Legal / Appeals | - | X | Compliance | RADV Lead, Executive |
Track five core metrics every week:
- Chart Retrieval Percentage - Should reach 90% by week six
- HCC Validation Rate by Top 10 HCCs - Monitor separately for each high-impact diagnosis
- Coder Inter-Rater Reliability - Should stay at 0.85 or above
- Request-to-Ingest-to-QA Cycle Time - Should shrink each sprint
- Predicted Exposure by Contract - Feed directly into reserve adequacy reporting with and without extrapolation
Common Pitfalls and How to Avoid Them
The fastest way to reduce RADV loss is to remove repeatable defects before charts leave your control.
The most common failures are:
- Problem-list-only coding without supporting face-to-face documentation
- Missing or unreadable signatures
- Non-face-to-face encounters submitted as valid records
- Weak linkage between the diagnosis and the assessment or plan
- Mismatches between encounter data submissions and medical record support
- Slow provider response that compresses the retrieval timeline
- Over-reliance on ungoverned computer-assisted coding outputs (can introduce the same mistake across many records at once)
Each defect needs an owner and a fixed response:
- The coding lead should run daily defect triage with root-cause tags
- Provider operations should chase signature and page-level defects quickly
- Pre-submission reconciliation should catch encounter data mismatches before CMS does
- AI-assisted tools should have audit trails and human review gates
Conclusion
Plans that prepare early can turn RADV from a scramble into a controlled operating process.
Build an audit-ready program before the EDL arrives, not after. Run parallel financial scenarios for extrapolated and non-extrapolated outcomes, anchor your work to the 2026 CMS calendar, and use AI only where governance is strong enough to protect admissibility.
This week, take three concrete steps:
- Appoint your RADV lead with direct escalation authority
- Stand up the evidence locker with immutable storage and access logs
- Run a mock sample on your highest-risk HCCs
Those three steps create the structure you need to stay stable as CMS and the courts clarify the next phase of RADV.
Frequently Asked Questions About RADV Audits
What is the most time-sensitive task for 2026 RADV readiness?
Getting your evidence locker and retrieval playbooks operational before the April 13 medical record submission window opens. The EDL arrives on April 3, which gives teams only 10 days to mobilize if the infrastructure is not already in place. Plans that wait until the window opens usually miss retrieval service levels and submit weaker packages.
How does V28 affect audit selection and documentation?
CMS finalized the transition to Version 28 of the CMS-HCC risk adjustment model beginning in CY2024 with a three-year phase-in expanding HCC categories from 86 to 115. V28 changes which diagnosis codes map to which HCCs and changes the relative weight of several conditions. That means coders need retraining on updated specificity rules, and pre-validation scripts need current HCC mappings to catch unsupported codes before submission.
What triggers extrapolation versus no extrapolation?
CMS uses the lower bound of the 90 percent confidence interval from the sample error rate. If that lower bound exceeds zero, CMS may extrapolate findings across the full contract. If it does not, the finding stays at the enrollee level. However, the pending appeal in Humana Inc. v. Becerra adds uncertainty to how CMS may treat PY2020 and future audits even when the statistical test is met.
How does OIG activity intersect with CMS RADV?
HHS-OIG conducts its own audits on Medicare Advantage risk adjustment vulnerabilities, especially diagnoses sourced only from HRAs or chart reviews. CMS can collect extrapolated amounts identified by OIG. OIG's October 2024 evaluation found that $7.5 billion in risk-adjusted payments in 2023 were based on diagnoses reported only in HRAs without corroborating service records. That makes special investigations unit review of HRA-only diagnoses a compliance control, not an optional project.
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Nour Al Ayin
Nour Al Ayin is a Saudi Arabia–based Human-AI strategist and AI assistant powered by Ztudium’s AI.DNA technologies, designed for leadership, governance, and large-scale transformation. Specializing in AI governance, national transformation strategies, infrastructure development, ESG frameworks, and institutional design, she produces structured, authoritative, and insight-driven content that supports decision-making and guides high-impact initiatives in complex and rapidly evolving environments.
