DOJ · DOJ-0071 · record R1536

Data & Analytics: Healthcare Fraud Detection

Department of Justice · Department of Justice / DEA

Overview

Development stage: DeployedSource: c) Deployed – The use case is being actively authorized or utilized to support the functions or mission of an agency.
High-impact designation: High-impactSource: a) High-impact
Impact justification: Not reported
Start date: 2024-01 (month precision)Source: 01/2024
Withheld from public reporting: NoSource: a) No

Mission

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

Problem the AI is intended to solve

DEA needs a way to facilitate analysis of health care fraud and abuse, especially those impacting government health programs. Federal government healthcare insurance programs include Medicare, Medicaid, TRICARE, VA, and others. The Health Care Fraud and Abuse Control Program (HCFAC) was created to unite DOJ and HHS in their efforts to combat fraud.

Expected benefits

Enhances the detection and prevention of health care fraud and abuse crimes within the context of DEA's mission.

System outputs

Outputs include data visualizations, as well as trend, benchmark, and link analyses.

Technology

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

Sourcing

How it was built: Contract and in-houseSource: c) Developed with both contracting and in-house resources
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: Yes
Privacy Impact Assessment: Not reported
Authorization to Operate: No
Demographic features: This AI use case utilizes one or more of the demographic variables listed in compliance with all federal laws and agency regulations.
Training and evaluation data: The case owner relied on DOJ AI governance practices to select and prepare data, as well as evaluate performance.
Federal Data Catalog: Not reported

Governance

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

Pre-deployment testingReported in progressSource: b) In-progress

AI impact assessmentReported in progressSource: b) In-progress

Independent reviewReported in progressSource: d) In-progress

Ongoing monitoringReported in progressSource: b) Development of monitoring protocols is in-progress

Operator trainingReported in progressSource: b) Establishment of sufficient and periodic training is in-progress

Fail-safeReported in progressSource: c) In-progress

Appeal processReported in progressSource: c) Establishment of an appropriate appeal process is in-progress

User and public consultationReported in progressSource: e) In-progress

Potential impacts and how they were identified

Consistent with Executive Orders and OMB guidance, the case owner relied on DOJ AI governance practices to evaluate impacts and risks.