Risk adjustment is the process CMS uses to align plan payments with the expected health costs of enrollees. It ensures fair compensation for plans that serve members with chronic or complex conditions. Accurate risk adjustment also prevents underpayment for sicker populations and supports high-quality care delivery.

Version 28 (V28) reflects newer ICD-10 codes, current medical utilization patterns, and introduces “constrained” HCC families — meaning some related conditions now share coefficients. It’s designed to improve model accuracy and fairness but changes how certain diagnoses impact RAFs compared to V24.

Each member’s Risk Adjustment Factor (RAF) is multiplied by a base capitation rate to calculate payments to Medicare Advantage plans. A higher RAF indicates higher expected costs, which increases payments; a lower RAF reduces them. Correct diagnosis capture directly influences financial accuracy.

CMS periodically updates models (every few years) to reflect new coding, clinical, and demographic data. The most recent major update, V28, began phased implementation in 2024–2026.

Normalization adjusts RAF scores to maintain average payment neutrality across the population. The coding pattern adjustment accounts for documentation differences between Medicare Advantage and Fee-for-Service populations, preventing systematic overpayment.

Our AI and NLP engines analyze structured and unstructured chart data to identify, validate, and code conditions into HCCs. The platform flags documentation gaps, verifies coding logic, and provides evidence trails for audits—all in real time.

Yes. We adhere to HIPAA, HITRUST, and SOC 2 standards. All data is encrypted in transit and at rest, with role-based access controls, detailed audit logs, and continuous monitoring.

Yes. Our API and file-based connectors integrate with major claims, EHR, and encounter data systems, including EDPS, RAPS, and FFS data sources. We support custom data feeds and secure SFTP exchanges for plan-specific workflows.

Absolutely. Our analytics are aligned with CMS’s Python implementation. We support both current SAS-based models and the upcoming Python-only releases to ensure seamless transition for payers and ACOs.

We combine domain expertise with an end-to-end SaaS platform — from chart ingestion to audit reconciliation — all powered by AI and built for CMS compliance. Our approach is transparent, measurable, and designed to improve both accuracy and efficiency.

Our analytics identify missed, invalid, or outdated HCCs and validate supporting documentation. Plans use these insights to optimize submissions, reduce denials, and protect revenue—all with audit-ready traceability.

Audit360 RADV Shield™ tracks every condition through the CMS submission and reconciliation lifecycle. It links each diagnosis to its source evidence and ensures audit response packages can be generated on demand.

Yes. Our platform supports benchmarking using public and proprietary CMS datasets. Clients can analyze RAF distributions, chronic condition prevalence, and trend deviations by contract or population.

Most plans see measurable improvements in data quality and RAF capture within one quarter of implementation, often translating to millions in recovered revenue or reduced RADV exposure.

Documentation must include clear diagnosis linkage to assessment, monitoring, and treatment (MEAT criteria). Our ChartCopilot AI identifies missing evidence and provides structured recommendations to strengthen chart completeness.

Yes. Our ChartCopilot Retro workflow automates retrospective reviews, detecting overlooked diagnoses and providing audit trails for provider validation before submission.

By automating chart abstraction, validation, and coding support, providers spend less time on manual reviews and more on clinical care—while improving coding accuracy.

Every data record passes through automated validation for completeness, hierarchy integrity, and format compliance. The system aligns with CMS EDPS and RAPS specifications to prevent submission rejections.

Our reconciliation process flags the error, identifies root causes, and provides corrected resubmission files. Clients receive detailed reports on correction and acceptance status for full transparency.

We continuously monitor HPMS memos, Advance Notices, and Rate Announcements. Updates to coefficients, mappings, and normalization factors are automatically applied to your instance upon CMS release.

Most clients go live within 4–8 weeks, depending on data readiness and integration complexity. Our team provides a structured onboarding plan and parallel validation period for accuracy.

Clients receive comprehensive onboarding, user documentation, and live training sessions. Ongoing support includes quarterly model updates, office hours, and access to our data science team for advanced questions.

Yes. Many clients use our analytics alongside other risk adjustment or chart review tools. Our modular architecture allows incremental adoption without disrupting current workflows.

We track key metrics like RAF lift, chart validation rates, and submission acceptance rates. Monthly performance dashboards keep leadership informed of progress and ROI.

We provide expertise in risk adjustment, encounter data management, CMS submissions, and RADV readiness, combining domain experience with analytics.

Both. We support project-based implementations and continuous data quality monitoring partnerships.

Our consultants monitor HPMS releases and implement model and normalization updates directly into your operational workflows.