Benefits Contention Classification Model
Department of Veterans Affairs · OIT: Office of Information & Technology
Overview
- Development stage
- In development or acquisitionSource: a) Pre-deployment – The use case is in a development or acquisition status.
- High-impact designation
- High-impactSource: a) High-impact
- Impact justification
- Not reported
- Start date
- Not reported
- Withheld from public reporting
- Not reported
Mission
- Topic area
- Government Benefits Processing
- Operational functions
- Claims and case processing Derived by keyword rules; see methodology
Problem the AI is intended to solve
The Benefits Contention Classification model is intended to improve the current benefits adjudication process by providing an automated classification for all benefits claims contentions. The model will do this by providing a classification for each contention that is not currently covered by the existing process. This is intended to make the overall benefits adjudication process more efficient by reducing the necessary time for VA rating representatives to choose the appropriate disability questionnaire for the the given contention.
Expected benefits
The expected benefit of this model is increasing efficiency in the benefits adjudication process by automating the internal classification required to select the disability benefits questionnaire that should be sent to the Veteran.
System outputs
The model outputs an internal classification (e.g., 3140) which is used to classify free text into a medical category.
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.
Potential impacts and how they were identified
Not reported