Risk adjustment software replaces manual workflows with AI-driven tools, enhancing efficiency, compliance, and patient care. In 2026, that definition carries new weight. A January 2026 CMS memo confirmed that RADV audits are now running on a quarterly cadence, with PY2020 audits launching in February 2026 [2]. The same month, Kaiser Permanente affiliates agreed to pay $556 million to settle False Claims Act allegations tied to unsupported diagnosis coding, the largest settlement of its kind [6]. And CMS proposed a net average payment increase of just 0.09% for 2027, erasing roughly $80 billion in managed care market value in a single trading session [8]. The message is clear: risk adjustment is no longer a revenue lever. It is a compliance and cost-control function. The era where a risk adjustment solution could focus on revenue capture alone is over.
Traditional healthcare systems relied on manual chart reviews and fragmented coding that led to decreased efficiency, high costs, and missed diagnoses. Modern risk adjustment software streamlines processes by analyzing patient data to predict healthcare costs, improve coding accuracy, and close risk gaps. This guide explains what the right platform must deliver for health plans, providers, and healthcare organizations, and how to build a program that protects your organization.
Autonomous Retrospective Risk Adjustment Solution
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What Is Risk Adjustment Software?
Risk adjustment software is a platform that analyzes patient data from medical records, claims, labs, and other clinical data to calculate accurate risk scores. These scores reflect the true health status of a population, determining appropriate reimbursement for health plans and healthcare providers under Medicare Advantage, ACO, and value-based care arrangements.
Risk adjustment is a method that identifies a person’s health status with a code or number, which is then evaluated to predict healthcare costs [4]. In Medicare Advantage, the government pays plans a higher monthly rate for members with higher risk scores. Equitable reimbursement ensures that providers treating sicker patients receive adequate funding without financial penalties for managing complex cases.
At its core, the main purpose of risk adjustment software is to improve coding accuracy and enhance efficiency in healthcare processes. The best platforms use clinically trained AI to capture clinical indicators from unstructured data within medical records, closing risk gaps and creating audit-ready documentation through an evidence validation engine. A strong risk adjustment solution serves as a single source of truth for member risk across coding, compliance, finance, and clinical operations.
Industry First RADV Audit Solution
AI-powered solution enables health plans to efficiently manage and streamline RADV audits
Why Risk Adjustment Matters for Health Plans, Providers, and Healthcare Organizations
Risk adjustment determines whether healthcare organizations receive appropriate reimbursement for the patients they serve. Accurate risk assessment ensures fair compensation for those caring for sicker populations. For health plans, providers, and ACOs alike, the ability to predict healthcare costs accurately determines both compliance posture and financial viability. Risk adjustment supports value-based care by aligning financial incentives with high-quality care delivery [4]. Risk adjustment also prevents discrimination by removing incentives for cherry-picking healthier patients, supporting access for those with chronic conditions.
Regulatory and Financial Pressure in 2026
The Risk Adjustment Data Validation (RADV) program has been expanding aggressively to recover overpayments caused by inaccurate coding [5]. In May 2025, CMS announced plans to audit all approximately 550 eligible Medicare Advantage contracts annually, up from roughly 60, citing federal estimates of $17 billion in overpayments and MedPAC estimates of $43 billion [1]. CMS also increased record samples from 35 to as many as 200 per plan, depending on contract size [2]. CMS signals for CY 2026-2027 include flat or limited Medicare Advantage payment growth, exclusion of unlinked chart review diagnoses, and population-level scrutiny of coding patterns [1]. Although a federal court vacated the 2023 RADV final rule on procedural grounds in September 2025 [3], industry experts view the ruling as a pause, not a reversal.
DOJ enforcement actions have escalated sharply alongside the audit expansion. The Kaiser Permanente settlement [6] is just the most visible example. The DOJ alleged Kaiser engaged in large-scale diagnosis “mining” and pressured physicians to add diagnoses via medical record addenda, sometimes months after patient visits. A Senate Judiciary Committee report accused UnitedHealth Group of “gaming” the risk adjustment system [7]. These cases demonstrate that systems designed to increase risk scores can be interpreted as an intent to inflate government payments. Accuracy alone is not enough. Process, intent, and evidence all matter.
Financial Risks Are Mounting
Health plans face mounting financial risks from escalating healthcare costs and regulatory audits. The 2027 rate proposal [8] sent shockwaves through the industry, and major insurers are now exiting underperforming Medicare Advantage markets and projecting continued margin pressure [9]. Healthcare organizations are rationalizing vendors and tightening compliance controls. The compliance risk of submitting unsupported codes now extends beyond audit penalties to DOJ action. Finance leaders no longer ask, “Does this maximize revenue?” They ask, “Does this reduce risk and improve financial sustainability?”
The Shift from Capture to Care
Risk adjustment has shifted from a revenue function to a clinical, compliance, and enterprise AI discipline. Defensible coding is the foundation of that future. Healthcare organizations must document real patient complexity, link diagnoses to real encounters, and ensure continuity in care plans. If CMS excludes diagnoses from unlinked chart reviews, the ROI math for many traditional retrospective programs changes fundamentally.
What Is a Good RAF Score?
A RAF (Risk Adjustment Factor) score quantifies expected healthcare costs relative to the average Medicare beneficiary. A score of 1.0 is average. Higher scores indicate patients with greater predicted costs, often due to chronic conditions. Health plans receive higher payments for covering sicker members [4]. There is no single “good” score. What matters is that the risk score accurately reflects actual clinical complexity, supported by encounter-linked evidence.
RAF accuracy depends on coding accuracy, documentation quality, and the ability to analyze patient data across encounters. Streamlining coding workflows reduces errors and boosts RAF accuracy and compliance. Risk score calculations must account for condition hierarchies under the CMS-HCC model, including V28 changes that restructured how conditions map to payment categories [10].
How Does Risk Adjustment Software Work?
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. Data collection involves gathering demographics and clinical data from claims and medical records. Advanced OCR extracts information from unstructured data.
- Clinical Analysis: Neuro-symbolic AI combines neural networks with clinical reasoning. It validates each diagnosis against MEAT evidence in the medical record. AI technology improves accuracy by enhancing diagnosis detection and coding compliance.
- Code Validation: Every suggested HCC passes through the evidence validation engine. The platform flags compliance risk, confirms coding accuracy, and links each code to its source in the medical record, creating a defensible audit trail.
- Quality Assurance and Monitoring: Risk adjustment is not a one-time event. Continuous monitoring tracks performance, supports data-driven decisions, and drives healthcare analytics for improvement. AI integration enhances visibility into coding progress and supports informed decision-making.
What Are a Few Essential Features of Risk Adjustment Software?
Explainable AI with Evidence Trails
Every HCC suggestion must link to MEAT-based evidence in the clinical note. This transparency transforms audit defense from panic into process. The evidence validation engine creates a defensible trail showing what was evaluated, when, and why.
Two-Way Retrospective Review
The biggest CMS red flag in retrospective risk adjustment today is programs that only ADD diagnoses and never REMOVE unsupported ones. The DOJ’s case against Kaiser Permanente specifically cited failure to delete unsupported diagnoses as part of the alleged scheme [6]. A compliant risk adjustment solution must identify both unclaimed and overclaimed codes. Health plans and healthcare providers using add-only tools face growing compliance risk as CMS scrutinizes medical record review patterns. Effective retrospective risk adjustment is key to success in value-based care, supporting fair reimbursement and financial sustainability.
Prospective Support at the Point of Care
Cloud-based and integrated risk adjustment software helps providers identify and document suspected diagnoses at the point of care. Prospective risk adjustment surfaces care gaps during encounters without coercing providers. The goal is decision support that respects clinicians as clinicians.
Real-Time Data and Advanced Analytics
Effective solutions rely on integration with robust EHRs and other data systems [11]. Risk adjustment software enables real-time data access, increasing transparency and closing risk gaps. Advanced analytics support quick clinical review, care coordination, and advanced technology adoption across the organization.
Built-In Quality Assurance
Risk adjustment solutions drive compliant, accurate results through in-depth expertise, advanced technology, and quality assurance processes. Accurate and efficient coding and data management are essential for compliance with regulatory audits. Quality processes must be built into every workflow step.
Actionable Insights for Informed Decisions
Risk teams need visibility into RAF accuracy trends, coding metrics, provider engagement, and risk gap closure. AI-powered software provides actionable insights that support better patient care and operational efficiency, enabling informed decisions across the organization.
Retrospective vs. Prospective Risk Adjustment
What Is Retrospective Risk Adjustment?
Retrospective risk adjustment reviews medical records after encounters to identify undercoded or overcoded diagnoses. When done properly with quality assurance at every level (AI analysis, certified coder validation, QA audit), it delivers significant productivity gains while maintaining high coding accuracy. Health plans need to acquire and share clinical data to support appropriate reimbursement and quality member care. Retrospective is no longer an offensive growth engine. It is a defensive safety layer that adds and removes codes to ensure compliance.
What Is Prospective Risk Adjustment?
Prospective risk adjustment happens before or during patient encounters. Pre-visit summaries surface recaptured conditions, suspected diagnoses, and emerging care gaps. At the point of care, physicians see condition summaries within their EHR, enabling timely interventions that improve documentation quality and patient outcomes. CMS implicitly favors encounter-driven documentation, making prospective the safest growth path for risk adjustment programs.
How V28 Changes Your Risk Adjustment Program
CMS-HCC V28 restructured how conditions map to HCCs, affecting risk score calculations across Medicare Advantage. Focus areas include restructured diabetes categories, expanded mental health categories, updated cardiovascular hierarchies, and new chronic kidney disease staging [12]. The healthcare industry is adapting as health plans retrain their risk adjustment teams and update their risk adjustment solution to handle dual V24/V28 logic. Health plans that delay this transition face growing risk adjustment compliance gaps.
RADV Audit Readiness: A Baseline Requirement
With CMS pushing to audit all eligible contracts on a quarterly cadence [2], RADV readiness is a baseline requirement for every Medicare Advantage organization, regardless of the current legal status of the 2023 final rule [3]. Every diagnosis needs an evidence trail linking it to encounter-based clinical data. Health plans and providers should conduct internal and mock audits to prepare for regulatory requirements. Purpose-built RADV management tools can significantly reduce audit response times and improve validation rates.
How to Evaluate Risk Adjustment Software
Technology Questions
- “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 retrospective review is the compliance standard.
- “Is the AI explainable, auditable, and governable?” Non-negotiable for meeting regulatory requirements.
Performance Questions
- “What is your actual validation rate in RADV audits?” Not accuracy in controlled settings, but validation when CMS auditors review real submissions.
- “How does your platform handle conflicting information in patient records?” Real medical records are messy. Clinical intelligence must resolve conflicts.
Integration Questions
- “Does your platform support seamless integration with existing EHRs and health systems?” Software that creates data silos defeats the purpose.
- “How do you handle provider engagement without abrasion?” Successful programs educate providers and support clinical reasoning.
What Is RAF and HCC Coding?
RAF and HCC coding are the building blocks of risk adjustment in Medicare Advantage. HCC coding assigns diagnosis codes to categories representing clinical conditions. These feed into the risk score model determining CMS payments. Each HCC must be supported by MEAT evidence documented during a face-to-face encounter.
Improving coding accuracy is essential for navigating risk adjustment complexity and regulatory compliance. Health plans and providers that invest in AI-powered tools, provider education, and quality processes see better health outcomes and accurate compensation for patient complexity. The associated costs of poor coding, including audit penalties and lost revenue, far outweigh any technology investment. Health plans using a dedicated risk adjustment solution with advanced technology to predict healthcare costs from medical record data outperform those relying on manual chart review.
Building a Risk Adjustment Program That Delivers Results
Start with your biggest pain point. If RADV audits are the concern, begin there. AI-driven tools streamline risk adjustment processes by replacing manual workflows, increasing efficiency, and reducing costs. Use AI-powered clinical intelligence with advanced technology for routine reviews so experts focus on complex cases and quality assurance.
Create feedback loops that deliver specific guidance to providers at the point of care. Measure what matters: chart review time, codes captured vs. validated, provider documentation rates, and audit validation rates. Accurate cost predictions improve when these metrics drive continuous improvement and future revenue adjustments.
Next Steps: From Evaluation to Action
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 multiple systems, the right risk adjustment solution transforms scattered medical record information into governed, auditable evidence. Utilizing technology for real-time data access enhances transparency and closes care gaps. Patient engagement improves when care quality and documentation quality align. Health plans, providers, and ACOs that invest now in compliance-first platforms position themselves for financial sustainability in 2026 and beyond.
Schedule a demo to see how RAAPID’s Neuro-Symbolic AI delivers defensible accuracy across retrospective, prospective, and RADV audit workflows.
Frequently Asked Questions
Risk adjustment solutions are platforms that analyze patient data and clinical records to calculate accurate risk scores. They help healthcare organizations capture appropriate reimbursement, maintain coding accuracy, and meet regulatory requirements under Medicare Advantage and value-based care.
A Medicare Advantage member with diabetes, chronic kidney disease, and heart failure is a high-risk patient with higher expected costs. Risk adjustment assigns HCC codes to each condition, calculates a risk score, and ensures the health plan receives fair reimbursement for the associated costs of care.
CMS’s HCC model assigns coefficients to demographic factors and HCC categories. The calculations account for condition hierarchies, interaction terms, and demographic profiles. V28 updated many of these coefficients.
Specialized risk adjustment software combines AI-powered clinical analysis, an evidence validation engine, coding workflow automation, and analytics dashboards. The software ensures compliance with CMS regulations. Look for platforms with AI-powered neuro-symbolic technology that deliver explainable, defensible outputs.
A risk assessment tool evaluates patient data to identify individuals with higher predicted costs and unmet care needs. These tools analyze medical records, claims, and clinical data to surface risk gaps, support care coordination, and help organizations make data-driven decisions about patient care.
