If you need a shortlist for Medicare Advantage risk adjustment today, start with RAAPID, Datavant/Apixio, and Reveleer for end-to-end AI-assisted coding with strong audit support. Keep Cotiviti, Inovalon, Edifecs, and Episource in your evaluation for scale, Risk Adjustment Data Validation (RADV) tools, and services depth. 

This guide is designed to support a fast, defensible decision as the Centers for Medicare & Medicaid Services (CMS) accelerates RADV enforcement and the 2024 CMS-Hierarchical Condition Category (CMS-HCC) v28 model continues its phase-in.

Two forces shape your 2026 planning window. CMS now allows extrapolation of RADV audit findings, which sharply increases downside risk from unsupported diagnoses. The continued blending of the 2024 CMS-HCC risk adjustment model demands version-aware AI risk adjustment software that exports evidence with version 24 (v24) and version 28 (v28) lineage. Together, these realities make explainable, coder-validated AI a compliance imperative rather than a nice-to-have.

Clarify Who This Guide Serves and What Success Looks Like

This guide targets CFOs, COOs, VPs of Risk Adjustment, Compliance leads, and coding operations managers at Medicare Advantage (MA) plans, accountable care organizations (ACOs), and risk-bearing provider groups. Your primary job is increasing validated, defensible risk capture without adding headcount while reducing RADV exposure and time-to-billable.

Success in 2026 means coder-validated acceptance rates of 95% or higher and documentation that meets MEAT (Monitor, Evaluate, Assess/Address, Treat) criteria for each accepted hierarchical condition category (HCC). You want stable false-positive rates and RADV packet readiness with version control. Finance wants net validated risk adjustment factor (RAF) uplift and payback under six months, while Compliance needs audit packet completeness and immutable logs, and Operations tracks charts per coder hour and time-to-first-value.

You also care about coder satisfaction and provider abrasion. AI that floods coders with low-yield suggestions erodes trust and slows reviews. The right platform lifts validated revenue while keeping workload, denial rates, and provider complaints within agreed thresholds.

Evaluate AI Risk Adjustment Software With Audit-First Criteria

I weighted explainability and audit readiness more heavily than raw model claims because RADV extrapolation increases downside risk from poorly documented suggestions. Here is the criteria breakdown I used:

• Coder-validated accuracy (25%): Acceptance on blinded samples, precision and recall by HCC, post–quality assurance (QA) deltas
• Audit defensibility (25%): Evidence chains, MEAT markers, RADV export kits with versioning
• Workflow automation (15%): Inline evidence review, batch actions, QA tiers, role-based views
• Integration effort (15%): Bulk ingestion, optical character recognition (OCR) quality, application programming interfaces (APIs), v28-aware mappings
• Time-to-value (10%): Deployment speed, implementation staffing, first cohort under 60 days
• Return on investment (ROI) net of recoupments (10%): Uplift after deletes, coder-hour savings, modeled clawback risk

Treat coder-validated accuracy and audit defensibility as non-negotiable. Ask vendors for blinded test results by HCC, not just aggregate accuracy, and for examples of complete evidence packets that stood up to internal or external review.

Workflow automation and integration determine whether AI reduces or adds work. Prioritize inline evidence review, simple batch actions, and role-based views that match how your coders, auditors, and providers actually work. Integration scoring should reflect both technical effort and vendor willingness to partner with your internal teams.

Finally, insist on a rigorous ROI model. Include coder-hour savings, expected deletes, and clawback exposure, not just gross RAF gain. Solutions that cannot show payback inside a budgeting cycle will be hard to defend with your finance committee.

Let Regulatory Reality Shape Your Requirements

CMS describes Medicare Advantage RADV as its primary tool to address overpayments by verifying that diagnoses used for payment are supported in beneficiaries' medical records. The 2023 RADV Final Rule allows extrapolation beginning with Payment Year 2018 and removed the fee-for-service adjuster. For calendar year 2025, CMS continues blending 67% of the 2024 CMS-HCC version 28 model with 33% of the 2020 model.

These rules mean every suggested HCC must be backed by source-line evidence that meets MEAT criteria. Version-aware coding is mandatory, and vendors must export with v24 and v28 lineage and clear logic references. A U.S. Department of Health and Human Services Office of Inspector General (HHS-OIG) audit of MMM Healthcare identified unvalidated HCCs that led to estimated overpayments, underscoring why documentation-backed coding matters.

For executives, the practical impact is tighter error tolerances. Retrospective sweeps that once aimed to maximize revenue now need explicit loss caps and RADV-ready documentation standards. Prospective programs must support provider education and real-time feedback so documentation quality improves before claims ever reach CMS.

Define What Explainability Means in Daily Coding Practice

Explainability means sentence-level evidence chains per HCC showing MEAT elements, provider attribution, date-of-service, and encounter type. American Academy of Family Physicians (AAFP) and American Health Information Management Association (AHIMA) guidance reinforces these MEAT and TAMPER criteria for defensible coding. Opaque model outputs increase audit risk, delay QA, and drain coder time.

For each suggested HCC, you should see exact source text, MEAT elements, provider credentials, date of service (DOS), and face-to-face status. Exports need document type, facility identifiers, and versioned ICD-10-to-HCC mappings. Implement validation checks that flag missing MEAT elements before acceptance.

A practical standard is that a new congestive heart failure HCC should surface with the full office note sentence, associated problem list entry, medication list, and cardiology consult if present. Coder screens should let reviewers accept, reject, or pend each suggestion with a single click while seeing all supporting evidence in one view. When auditors can read the same chain of evidence that the AI used, disputes shrink and cycle times shorten.

Choose Vendor #1: RAAPID for Neuro-Symbolic AI and Audit-Ready Risk Capture

RAAPID stands out for audit-first teams by combining knowledge graphs and clinical rules with machine learning to produce sentence-level, MEAT-complete evidence chains per HCC. This neuro-symbolic AI technology delivers explainable, audit-ready results that traditional black-box models cannot match. For busy executives building a longlist and for a broader scan of risk adjustment vendors, see RAAPID's own roundup; we referenced it while comparing platform approaches.

RAAPID reports 98%+ coding accuracy once tuned with coder validation and 60-80% productivity gains for coding teams. Exports are audit-ready, with evidence mapped to each HCC and organized for RADV packet assembly. The platform supports retrospective gap closure, prospective reviews, and RADV packet preparation with an inline coder experience and rapid acceptance workflows.

Deployment options include Azure-tenant and customer-cloud configurations. The platform handles bulk ingestion from electronic health record (EHR) systems, claims, and PDFs with OCR, plus version-aware ICD-10-to-HCC mappings. Role-based audit logs and export vaulting support legal hold and RADV timelines. In your request for proposal (RFP), ask for per-HCC precision and recall under v28, sample evidence screenshots, and references from plans achieving 95%+ coder acceptance.

Consider Vendor #2: Datavant/Apixio for Retrieval at Scale and AI Coding

Datavant/Apixio combines large-scale medical record retrieval with AI-assisted coding and auditing workflows. Apixio materials state that coding teams can conduct chart reviews in 80% less time than manual reviews, with higher accuracy. The platform now integrates with Datavant's Connected Care Platform, tying retrieval, coding, and submission workflows together.

This combination works well for high-volume programs needing integrated retrieval-to-submission pipelines. Multi-level QA and mature tooling support both retrospective and prospective programs. Verify current acceptance rates under v28, coder-hours saved per 10,000 charts, and audit export fidelity with evidence chains before committing.

For MA plans that already rely on Datavant for chart retrieval, the tight linkage can reduce vendor management overhead. Just ensure that retrieval service-level agreements and AI coding performance are evaluated separately so weaknesses in one area do not get hidden by strengths in the other.

Assess Vendor #3: Reveleer for High-Throughput Retrieval and Evidence Validation

Reveleer cites up to 99% accuracy in identifying and mapping potential missed diagnoses to HCCs, with customers achieving three times ROI. The end-to-end platform includes retrieval, AI-assisted coding, audit support, and submissions suitable for high-volume programs with metrics dashboards for oversight.

Test precision on complex chronic conditions like heart failure and COPD. Confirm RADV export structure and governance capabilities. Insist on coder-validated acceptance rates from independent QA rather than relying solely on vendor-reported model accuracy.

Leverage Vendor #4: Cotiviti for Enterprise-Scale Coding and QA Depth

Cotiviti reports over 10 million charts coded in 2023, greater than 97% average coding accuracy, and over $2 billion in appropriate incremental MA risk adjustment revenue delivered annually. The platform offers mature QA tiers, second-level review, and enterprise-scale operations suited for national plans needing predictable throughput.

Evaluate natural language processing (NLP) assist capabilities and evidence export depth carefully. Check v28 tuning, sentence-level evidence availability, and acceptance rates by HCC family under independent QA. Request QA sampling methodology and reviewer calibration documentation.

Use Vendor #5: Inovalon for Converged Record Review With RADV Focus

Inovalon announced an AI solution in partnership with AWS to help health plans meet evolving RADV audit requirements, expanding capabilities in its Converged Record Review platform. This positions Inovalon well for standardizing RADV readiness across lines of business with enterprise deployment patterns.

Validate sentence-level evidence linkage, coder acceptance rates, and automated RADV dry-run tooling. Show evidence screenshots tied to each suggestion and demonstrate packet automation with exception handling for missing MEAT or attribution issues.

Select Vendor #6: Edifecs for Unified Risk Adjustment and Compliance Operations

Edifecs, with Health Fidelity heritage, offers an AI-powered suite spanning risk adjustment (RA) coding, compliance, submissions, and audit support. The platform provides value where integration with the broader Edifecs stack matters across retrospective reviews, pre-submission checks, and audit workflows.

Confirm code-level explainability and evidence exports meet your standards. Verify time-to-value on high-volume retrospective projects and secure commitment to deployment timelines with measurable coder productivity gains.

Engage Vendor #7: Episource for Modular NLP Assist and Services Coverage

Episource promotes 99.5% accuracy for NLP identification logic and case studies showing measurable reimbursement uplift even where baseline coding accuracy was 98%. The modular approach pairs NLP assist with retrieval and submission services if you prefer consolidating vendors.

Verify coder-validated precision and recall by HCC family, RADV export features, and evidence depth under v28. Request per-HCC confusion matrices and packet samples with MEAT and provenance documentation.

Apply a Comparison Rubric to Score Vendors With Clear Pass-Fail Thresholds

Use this scoring approach to drive structured consensus across Finance, Compliance, and Operations:

Collect standardized evidence packets and KPI reports from each vendor before scoring. Have stakeholders score independently, then reconcile variances in a live session.

Run a 90-Day Pilot Blueprint With Gated Decision Points

Run a gated pilot with explicit milestones and decision gates tied to validated accuracy, workflow impact, and audit readiness:

• Days 0-15: Finalize data feeds, lock MEAT-based gold-standard rubric, define KPIs with Finance and Audit
• Days 16-45: Run blinded dual-track coding, capture acceptance and precision and recall by HCC
• Days 46-75: Sample accepted HCCs, generate RADV packets, conduct internal mock audits
• Days 76-90: Compute net validated RAF uplift, model clawback risk, present payback and internal rate of return (IRR) to the CFO

Keep vendors on a single scoring template during the pilot so results remain comparable. Avoid expanding scope midstream; instead, use change requests to log new ideas for phase two once a vendor proves it can meet baseline targets.

Set Integration and Security Requirements That Match Enterprise Risk

Great models fail without clean pipelines. Require bulk ingestion from EHR systems, claims, and labs with de-duplication, document provenance, and robust OCR for scanned PDFs. Insist on API access, identity resolution, and versioned ICD-10-to-HCC maps with v28 awareness.

Prefer customer-cloud deployments with HITRUST or SOC 2 certification. Require single sign-on (SSO), Security Assertion Markup Language (SAML) integration, role-based access control (RBAC), and field-level encryption. Maintain an evidence export vault with legal hold capabilities aligned to RADV timelines.

Clarify where protected health information (PHI) will reside, who can access it, and how long evidence will be retained. Align those policies with your broader information security and legal hold standards so RADV work does not create surprise obligations or gaps.

Plan Next Steps to Down-Select, Pilot, and Scale With Confidence

Down-select to two or three vendors based on this rubric. Run the 90-day pilot requiring 95%+ coder-validated acceptance and RADV packet readiness. Choose the platform maximizing validated accuracy with explainable evidence and the shortest path to audit-ready exports.

Your 30-60-90 plan starts now: finalize your RFP and shortlist within 30 days, launch pilot cohorts and RADV dry-runs by day 60, and secure CFO sign-off with scale plan and governance by day 90. RADV extrapolation and v28 blending make explainable, coder-validated AI a compliance imperative. Move now to align Finance, Compliance, and Operations on the rubric and require exportable, MEAT-complete evidence for every accepted HCC.

Invest early in coder and auditor involvement so the chosen platform becomes a productivity tool, not a perceived threat. When frontline users see that leadership is tying adoption to quality and compliance outcomes, skepticism about AI gives way to practical improvement ideas.