HHS · no agency ID · record R0744

Risk Adjustment Outlier Analysis

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

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

The risk adjustment (RA) program spreads the financial risk borne by Issuers due to offering a variety of plans meeting the need of the diverse population. RA payments are distributed based on population risk levels. CCIIO uses a distributed data solution (EDGE servers) to calculate plan average actuarial risk and associated RA transfers and must avoid potential program integrity risks to annual calculations.

Expected benefits

Implement ML to identify outliers in issuer data that may unduly influence risk adjustment transfers.

System outputs

Maintain RA program integrity

Technology

AI classification: Classical / predictive MLSource: Classical/Predictive Machine Learning: Models trained on data to make predictions or classifications based on identified patterns or relatio…
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