DOJ · DOJ-0156 · record R1597
Medical Claims Adjudication
Department of Justice · Department of Justice / FBOP
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
- 2020-09 (month precision)Source: 09/2020
- Withheld from public reporting
- NoSource: a) No
Mission
- Topic area
- Health and Medical
- Operational functions
- Claims and case processing Derived by keyword rules; see methodology
Problem the AI is intended to solve
Uses Quantum Choice (QC) to adjudicate medical claims and analyze the data within the system.
Expected benefits
Cost savings and ensure compliance with billing regulations and contract pricing terms. It also provides data analysis to assist with the FBOP's mission.
System outputs
Medical Billing Payment decisions are made utilizing the AI.
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)
- Redacted for cybersecurity purposes.
- Custom-developed code
- No
- Public source code
- Not reported
Sourcing
- How it was built
- Purchased from vendorSource: a) Purchased from a vendor
- Vendor (as reported)
- Quantum Choice from Plexis
- Vendors (standardized)
- LexisNexis
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
- k) None of the above
- 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.