AICES

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

Document processing and extraction, Risk scoring and triage, Claims and case processing, Clinical and medical Derived by keyword rules; see methodology

Problem the AI is intended to solve

1. Reduce the backlog and processing times, minimize unnecessary in-person exams, and address workflow inefficiencies. 2. Reduce the burden of travel on veterans and expedite the time required for claims processing. During the period of performance, this effort will demonstrate the effectiveness of the proposed solution to develop Disability Benefits Questionnaire (DBQs) through Acceptable Clinical Evidence (ACE).

Expected benefits

This initiative aims to: 1. Reduce the backlog and processing times by more efficiently using Acceptable Clinical Evidence (ACE), minimizing unnecessary in-person exams and addressing workflow inefficiencies. 2. Reduce VA costs due to reduced in-person exams. 3. Improve the Veteran experience by reducing the burden of travel on veterans and expediting the time required for claims processing.

System outputs

The system indexes structured, semi-structured, and unstructured Veteran health and service record data, including diagnosis, severity, and service connection evidence from eFolders, metadata, and lay evidence. It supports Disability Benefits Questionnaire (DBQ) processing to generate SMART Claims with recommendations. Objective 1: Reduce Unnecessary In-Person Examinations through Evidence-Driven Triage and Workflow Automation Goal: Demonstrate the accuracy of the solution to properly execute ACE exams. Metric: Demonstrate the ability to produce 5000 ACE exams during the period of performance within a 72-hour delivery window, through expert review. Objective 2: Enhance the accuracy of documentation for ACE-generated DBQs Goal: Validate the capability of advanced OCR and NLP tools to correctly extract and populate data. Metric: Achieve at least a 96% accuracy rating for all ACE DBQs.

Technology

AI classification

Agentic AISource: Agentic AI: AI systems that perform tasks or make decisions autonomously with minimal human intervention.

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