HHS · no agency ID · record R0768

Artificial Intelligence Use Case Pilot in Medical Review on Medicare Fee-For-Service (FFS) Improper Payment Measurement (Comprehensive Error Rate Testing Program) Data

Department of Health and Human Services · HHS/CMS/OFM

Overview

Development stage: In development or acquisitionSource: a) Pre-deployment – The use case is in a development or acquisition status.
High-impact designation: Not high-impactSource: c) Not high-impact
Impact justification: Not reported
Start date: Not reported
Withheld from public reporting: NoSource: a) No

Mission

Topic area: Information Technology
Operational functions: Fraud, waste and improper payments Derived by keyword rules; see methodology

Problem the AI is intended to solve

OFM is hoping to leverage cutting-edge AI technology to potentially introduce new efficiencies and accuracy in the Comprehensive Error Rate Testing (CERT) medical review process by moving away from manual medical review which is costly, sometimes inaccurate, and inefficient.

Expected benefits

Expected benefits include cost savings, new efficiencies and accuracy of clinical medical review decision making for reporting meaning Medicare FFS improper payment improper payments.

System outputs

Recommendation

Technology

AI classification: Generative AISource: Generative AI: AI that generates new or synthetic content (e.g., images, videos, audio, text, code).
System name(s): Not reported
Custom-developed code: Not reported
Public source code: Not reported

Sourcing

How it was built: Not reported
Vendor (as reported): Not reported
Vendors (standardized): None on the standard list

A vendor is the supplier named by the agency. It does not identify the underlying model or AI technology.

Data and privacy

Involves PII: Not reported
Privacy Impact Assessment: Not reported
Authorization to Operate: Not reported
Demographic features: Not reported
Training and evaluation data: Not reported
Federal Data Catalog: Not reported

Governance

0 of 8 minimum-practice questions answered. A blank answer means the agency reported nothing; it does not mean the practice is absent.

Pre-deployment testingNo answer reported

AI impact assessmentNo answer reported

Independent reviewNo answer reported

Ongoing monitoringNo answer reported

Operator trainingNo answer reported

Fail-safeNo answer reported

Appeal processNo answer reported

User and public consultationNo answer reported

Potential impacts and how they were identified

Not reported