Risk adjustment software analyzes patient data from medical records, claims, and labs to calculate accurate risk scores, validate every diagnosis against clinical evidence, and produce an audit-ready trail. In 2026, that last part is what separates a defensible platform from a liability. RADV audits now run on a quarterly cadence, with PY2020 audits launched in February 2026 [1]. In January 2026, Kaiser Permanente affiliates agreed to pay $556 million to settle False Claims Act allegations tied to unsupported diagnosis coding [2]. In March 2026, Aetna and CVS agreed to pay $117.7 million, in part because an add-only chart review program found new codes to submit but never deleted the unsupported ones its own reviewers flagged [3]. The lesson for buyers is direct: risk adjustment is no longer a revenue lever. It is a compliance and cost-control function, and the right platform has to prove the codes you keep.
Quick Answer
Risk adjustment software is a platform that turns scattered medical records into governed, encounter-linked HCC evidence and accurate risk scores. The best tools in 2026 do two-way retrospective review (add missed codes and remove unsupported ones), surface suspected diagnoses at the point of care, and link every HCC to MEAT evidence for RADV defense. RAAPID’s Clinical AI Platform, powered by Neuro-Symbolic AI, reaches 92% out-of-the-box coding accuracy and over 98% after human QA, cuts chart review to 8-12 minutes per chart, and improves coder productivity 60-80%*. Buyers should weigh defensibility, not raw code volume.
Key Takeaways
- Risk adjustment software calculates risk scores from medical records, claims, and clinical data, then validates each diagnosis against documented evidence.
- The single biggest compliance red flag in 2026 is add-only review: software that adds diagnoses but never removes unsupported ones.
- Two-way retrospective review (add plus remove) is now the buyer-side standard, and DOJ settlements against add-only programs prove why [3].
- CMS finalized exclusion of unlinked chart review diagnoses for CY2027, so encounter-linked documentation is no longer optional [4].
- Evaluate platforms on validation rates, evidence trails, and explainability, not on how many codes they capture.
Autonomous Retrospective Risk Adjustment Solution
One platform. Every HCC validated. Revenue secured.
What Is Risk Adjustment Software?
Risk adjustment software is a platform that analyzes patient data to calculate risk scores that reflect the true health status of a population. Those scores determine reimbursement for health plans and healthcare providers under Medicare Advantage, ACO, and value-based care arrangements. In Medicare Advantage, CMS pays plans a higher monthly rate for members with higher risk scores, so the math has to be accurate and supportable.
Risk adjustment is a method that assigns a code or number to a person’s health status, which is then used to predict healthcare costs [5]. Good software does more than tag codes. It captures clinical indicators from unstructured data inside medical records, closes coding gaps, and creates audit-ready documentation through an evidence validation engine. The strongest risk adjustment solutions act as a single source of truth for member risk across coding, compliance, finance, and clinical operations.
For the underlying coding rules behind these scores, see our guide to risk adjustment coding.
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Why Risk Adjustment Matters for Health Plans and Providers in 2026
Risk adjustment determines whether healthcare organizations get paid fairly for the patients they serve. Accurate risk assessment means plans that cover sicker populations receive adequate funding, and it removes the incentive to cherry-pick healthier members [5]. For health plans, providers, and ACOs, the ability to predict healthcare costs accurately now shapes both compliance posture and financial survival. That is why more buyers now judge risk adjustment solutions on defensibility first, not raw code volume.
Regulatory and financial pressure in 2026
The Risk Adjustment Data Validation (RADV) program has expanded to recover overpayments caused by inaccurate coding [6]. In 2025, CMS announced plans to audit all roughly 550 eligible Medicare Advantage contracts each year, up from about 60, citing federal estimates of $17 billion in overpayments [6]. MedPAC reported in March 2026 that Medicare pays Medicare Advantage plans about 14% more than equivalent fee-for-service care, roughly $76 billion in 2026 [10]. That gap is why coding accuracy now sits under a federal microscope. A January 27, 2026 CMS memo confirmed PY2020 audits beginning February 2026, a quarterly cadence, a restored five-month medical record submission window, and sample sizes of 35 to 200 records per plan [1]. A September 2025 federal court ruling paused extrapolation enforcement pending appeal, but the audits themselves continue [7]. Treat this as a preparation window, not a reprieve.
Enforcement has sharpened alongside the audits. The Kaiser settlement [2] and the Aetna/CVS settlement [3] both turned on the same pattern: programs that added diagnoses but ignored their own evidence of unsupported codes. An OIG audit of one Medicare Advantage organization (Report A-07-22-01207, March 2026) found that 247 of 271 sampled enrollee-years carried unsupported high-risk diagnosis codes, a 91% error rate [8]. The most common error was history-of conditions coded as active diagnoses. Accuracy alone is not enough. Process, intent, and evidence all matter.
The shift from capture to care
Risk adjustment has moved from a revenue function to a clinical, compliance, and enterprise AI discipline. Defensible coding is the foundation of that future. CMS finalized exclusion of unlinked chart review diagnoses for CY2027 [4], which means diagnoses that cannot be tied to a real encounter no longer count. For many traditional retrospective programs, that single change rewrites the ROI math. Software built to find more codes is now a risk. Software built to prove the right codes is the asset.
How Risk Adjustment Software Works
AI-powered risk adjustment software follows a structured process from data collection through clinical analysis, code validation, and monitoring.
- Data collection. The software pulls patient data from EHRs, claims, labs, and health information exchanges. Advanced OCR extracts clinical indicators from unstructured data inside the medical record.
- Clinical analysis. Neuro-Symbolic AI (https://www.raapidinc.com/blogs/neuro-symbolic-ai-in-risk-adjustment/) combines neural networks with clinical reasoning and validates each diagnosis against MEAT-based evidence (https://www.raapidinc.com/blogs/simplify-hcc-coding-with-meat-criteria/) in the note.
- Code validation. Every suggested HCC passes through the evidence validation engine, which links each code to its source in the medical record and flags compliance risk, building a defensible audit trail.
- Quality assurance and monitoring. Continuous monitoring tracks performance, supports data-driven decisions, and drives healthcare analytics so teams can improve coding accuracy over time.
Essential Features to Look For
The strongest risk adjustment solutions share a short list of non-negotiable features. Use them as a baseline when you shortlist platforms.
Explainable AI with an evidence validation engine
Every HCC suggestion must link to MEAT-based evidence in the clinical note. That transparency turns audit defense from panic into process. RAAPID’s evidence validation engine creates a trail showing what was evaluated, when, and why, so a reviewer or a CMS auditor can follow the same path the AI did.
Two-way retrospective review
The biggest CMS red flag in retrospective risk adjustment today is software that only adds diagnoses and never removes unsupported ones. The DOJ cases against Kaiser [2] and Aetna [3] both cited the failure to delete unsupported diagnoses. Compliant software has to identify both unclaimed and overclaimed codes. Health plans using add-only tools carry growing compliance risk as CMS scrutinizes medical record review patterns.
Prospective support at the point of care
Cloud-based, integrated platforms help clinicians document suspected diagnoses during the visit. Prospective risk adjustment (https://www.raapidinc.com/blogs/prospective-risk-adjustment/) surfaces care gaps without coercing clinicians. The goal is decision support that respects physicians as physicians.
Real-time analytics and actionable insights
Risk teams need visibility into RAF accuracy trends, coding metrics, provider engagement, and risk gap closure. The platform should enable real-time data access and deliver actionable insights that support better patient care and informed decisions across the organization.
Add-Only vs Two-Way Coding
When you compare risk adjustment software, the first filter is simple: does the tool remove codes, or only add them? For health plans, that single question separates a compliant program from a costly one. Add-only review is the model regulators now treat as evidence of intent to inflate payments. Compliant risk adjustment solutions remove unsupported codes, not just add missed ones. Use the capability comparison below as a scorecard for every demo on your shortlist.
| Capability | Add-only software | Two-way software (RAAPID) |
| Adds missed HCCs | Yes | Yes |
| Removes unsupported HCCs | No | Yes |
| Encounter-linked evidence | Partial | Every code |
| Explainable, auditable AI | Rare | Built in |
| RADV and DOJ exposure | High | Low |
| OIG MA compliance alignment | Flagged as risky | Aligned |
Two-way software adds missed conditions and removes ones the record does not support. It is the difference between a clean RADV submission and a clawback. To compare named providers head to head, see our guides to risk adjustment companies and risk adjustment vendors.
Retrospective vs Prospective Risk Adjustment
Retrospective risk adjustment
Retrospective risk adjustment reviews medical records after encounters to find undercoded or overcoded diagnoses. Done right, with AI analysis, certified coder validation, and a QA audit at each level, it delivers strong productivity gains while holding high coding accuracy. Retrospective is no longer an offensive growth engine. It is a defensive safety layer that adds and removes codes to keep submissions clean. Effective retrospective risk adjustment is now a core part of staying compliant in value-based care. For a deeper look, see our breakdown of retrospective risk adjustment.
Prospective risk adjustment
Prospective risk adjustment happens before or during the visit. Pre-visit summaries surface recaptured conditions, suspected diagnoses, and emerging care gaps. At the point of care, physicians see condition summaries inside their EHR, which supports timely intervention and better documentation quality. CMS implicitly favors encounter-driven documentation, which makes prospective the safest growth path. The shorthand: retrospective protects and cleans, prospective grows safely.
How V28 Reshaped Risk Adjustment Programs
CMS-HCC V28 restructured how conditions map to HCCs, which changed risk score calculations across Medicare Advantage. The focus areas include restructured diabetes categories, expanded mental health categories, updated cardiovascular hierarchies, and new chronic kidney disease staging [9]. Many plans now run dual V24 and V28 logic and retrain their risk adjustment teams to handle the transition. Plans that delay face widening coding gaps and weaker risk scores. Software that handles both models in one workflow keeps risk score accuracy steady through the change.
RADV Audit Readiness Is Now a Baseline Requirement
With CMS auditing eligible contracts on a quarterly cadence [1], RADV readiness is a baseline for every Medicare Advantage organization, regardless of where the 2023 final rule lands on appeal [7]. Every diagnosis needs an evidence trail tying it to encounter-based clinical data. Health plans and providers should run internal and mock audits to prepare. Purpose-built RADV management tools cut audit response time and improve validation rates by keeping evidence organized and retrievable.
How to Evaluate Risk Adjustment Software
Use these questions to separate defensible risk adjustment solutions from revenue engines. Bring them to every demo and ask for live answers, not slideware.
Technology
- “Show me the evidence trail for this suggested code.” If the vendor cannot link codes to MEAT criteria, they cannot support defensible coding.
- “Does your system identify both adds and deletes?” Two-way review is the compliance standard.
- “Is the AI explainable, auditable, and governable?” Non-negotiable for meeting regulatory requirements.
Performance
- “What is your actual validation rate in RADV audits?” Ask about validation when CMS auditors review real submissions, not accuracy in controlled tests.
- “How does your platform handle conflicting information in patient records?” Real medical records are messy, and the software has to resolve conflicts.
Integration
- “Does your platform support clean integration with existing EHRs and health systems?” Software that creates data silos defeats the purpose.
- “How do you handle provider engagement without abrasion?” Strong programs educate clinicians and support clinical reasoning.
Building a Risk Adjustment Program That Delivers
Start with your biggest pain point. If RADV audits are the worry, begin there. Health plans should use AI-powered clinical intelligence for routine reviews so experts focus on complex cases and quality assurance. Create feedback loops that give clinicians specific guidance at the point of care.
Measure what matters: chart review time, codes captured versus validated, provider documentation rates, and audit validation rates. RAAPID customers reach 92% out-of-the-box coding accuracy and over 98% after human-in-the-loop QA, with chart review at 8-12 minutes per chart and coder productivity up 60-80%*. Those numbers hold because every code carries evidence, not because the system chases volume.
Choosing the Right Platform
Any risk adjustment software you evaluate should deliver defensible coding with evidence trails, two-way retrospective review, prospective point-of-care support, and real-time analytics that drive informed decisions. For healthcare organizations managing fragmented data across many systems, the right platform turns scattered medical record information into governed, auditable evidence. Health plans, providers, and ACOs that invest now in compliance-first risk adjustment solutions protect themselves for 2026 and beyond.
Ready to pressure-test a platform against your own charts? Book a RAAPID demo and watch Neuro-Symbolic AI deliver defensible accuracy across retrospective, prospective, and RADV audit workflows, with an evidence trail behind every code.
Frequently Asked Questions
It is a platform that analyzes patient data and clinical records to calculate accurate risk scores. It helps health plans and healthcare organizations document member complexity, maintain coding accuracy, and meet regulatory requirements under Medicare Advantage and value-based care, while building an audit-ready evidence trail for every diagnosis.
Look for two-way retrospective review, an evidence validation engine that links each HCC to MEAT criteria, prospective point-of-care support, and explainable AI you can audit. Strong platforms also publish real RADV validation rates and integrate cleanly with your EHR, rather than creating new data silos.
The software improves coding accuracy by validating each diagnosis against documented clinical evidence and flagging unsupported codes for removal. AI analysis surfaces missed conditions in unstructured data, while certified coder review and QA audits confirm results, which reduces errors and produces defensible, encounter-linked submissions.
Retrospective risk adjustment reviews medical records after encounters to add missed codes and remove unsupported ones. Prospective risk adjustment works before or during the visit, surfacing suspected diagnoses and care gaps so clinicians document complexity in real time. Most programs use both for clean, complete coding.
These platforms help with RADV audits by linking every diagnosis to encounter-based evidence and keeping that documentation retrievable. Purpose-built RADV tools manage audit workflows, validate HCCs against the medical record, and cut response time, which improves validation rates when CMS auditors review real submissions.
Two-way coding matters because CMS and DOJ now treat add-only programs as evidence of intent to inflate payments. The Kaiser and Aetna settlements both cited failure to delete unsupported codes. Software that adds and removes diagnoses keeps submissions defensible and lowers RADV and False Claims Act exposure.
Source
5. Centers for Medicare & Medicaid Services, RADV HPMS memo, January 27, 2026.
8. Centers for Medicare & Medicaid Services, CY2027 Final Rate Announcement, April 6, 2026.
9. Centers for Medicare & Medicaid Services, Risk Adjustment overview.
10. Centers for Medicare & Medicaid Services, Medicare Advantage RADV program announcement, 2025.
11. U.S. District Court ruling on CMS RADV extrapolation methodology, September 2025.
13. Centers for Medicare & Medicaid Services, CMS-HCC Risk Adjustment Model (V28).
About the author
Wynda Clayton
Director of Risk Adjustment Coding & Compliance, RAAPID
Wynda Clayton, MS, RHIT, CRC, is Director of Risk Adjustment Coding and Compliance at RAAPID and a former CMS RADV auditor. She advises health plans and providers on defensible coding, RADV readiness, and compliance-first risk adjustment.
