Opportunity
SAM #270196
CMS RFI: AI Intelligent Coding Assistance Tool Integrated with Salesforce
Buyer
CMS Office of Acquisition and Grants Management
Posted
July 17, 2026
Respond By
August 20, 2026
Identifier
270196
NAICS
541519, 541512, 541511
The Centers for Medicare & Medicaid Services (CMS), part of the Department of Health and Human Services (HHS), is conducting market research for an AI-based Intelligent Coding Assistance Tool (ICAT) to support the HHS-Operated Risk Adjustment Data Validation (HHS-RADV) program. - Government Buyer: - Department of Health and Human Services (HHS) - Centers for Medicare & Medicaid Services (CMS) - Centers for Consumer Information and Insurance Oversight (CCIIO), Payment Policy & Financial Management Group (PPFMG) - Products/Services Requested: - AI Intelligent Coding Assistance Tool (ICAT) - Must be compatible with or integrated within Salesforce - Designed to consolidate discrete medical chart/record values from multiple sources for diagnosis abstraction - Supports enhanced and accurate first-level medical chart/record review, specifically for the HHS-RADV program's second validation audit (SVA) - No specific part numbers or quantities provided (market research only) - Unique/Notable Requirements: - Solution should be deployable within 6 months of award - Focus on reducing coder time, improving accuracy, and achieving return on investment (ROI) - Ongoing maintenance and enhancements expected after initial deployment - Emphasis on integration with Salesforce and medical data abstraction capabilities - No specific OEMs or vendors are named in the RFI; CMS is seeking information on available solutions in the market.
Description
The Centers for Medicare & Medicaid Services (CMS) is issuing this Request for Information (RFI) as a means of conducting market research to determine the availability and potential technical ability to procure an AI tool that is compatible with or integrated within Salesforce that consolidates from various sources discrete medical chart/record values for diagnosis abstraction.