CMS Risk Adjustment Data Validation Audit Readiness by Martlet AI

Process millions of claims before submitting to CMS, run internal MOCK audits, or process historic data at scale to identify and flag coding inconsistencies:

Martlet AI’s RADV Audit Readiness Solution empowers healthcare organizations to proactively identify and address CMS audit risks through intelligent automation and explainable AI.

Developed by the team behind John Snow Labs, the system leverages healthcare-specific NLP and large language models to analyze clinical notes and coded diagnoses, flagging conditions that may not meet MEAT documentation standards. The engine generates detailed evidence packs and risk scoring that help compliance and audit teams prioritize reviews and defend coding accuracy.

Unlike SaaS products that require external data transfer, Martlet AI is deployed entirely within the client’s AWS environment, ensuring no PHI ever leaves your control.

Delivered as a professional services project, the Martlet AI team manages setup, configuration, and optimization of the RADV engine. The engagement includes environment validation, model calibration to your data, and user enablement for audit and compliance teams. Once implemented, the RADV system operates as a self-contained, in-house audit

intelligence layer, continuously analyzing documentation quality and surfacing potential risk areas before audit submission.

Key Benefits:

• Identify and correct documentation gaps that could trigger CMS RADV clawbacks.
• Automate MEAT validation and generate audit-ready evidence reports.
• Enhance compliance readiness while reducing manual audit effort.
• Gain end-to-end transparency into audit logic, evidence, and scoring.
• Retain full control of PHI by running the solution within your own AWS VPC.

Ideal Customers:

Medicare Advantage Organizations, ACOs, Medicaid Managed Care Plans, and internal compliance/audit teams focused on CMS RADV preparation.