HHS · no agency ID · record R0639

Automated Analysis of Injury Control Research Center (ICRC) Annual Progress Reports (APRs) using Large Language Models

Department of Health and Human Services · HHS/CDC

Overview

Development stage: PilotSource: b) Pilot – The use case has been deployed in a limited test or pilot capacity.
High-impact designation: Not high-impactSource: c) Not high-impact
Impact justification: Not reported
Start date: 2023-08 (month precision)Source: Aug-23
Withheld from public reporting: NoSource: a) No

Mission

Topic area: Administrative Functions
Operational functions: No function tag matched Derived by keyword rules; see methodology

Problem the AI is intended to solve

The AI is designed to streamline the review process of Annual Progress Reports (APRs) submitted by Injury Control Research Centers (ICRCs), improve efficiency, and support the evaluation of the performance and progress of ICRC-funded activities.

Expected benefits

The AI will help quickly and efficiently identify key challenges and insights from ICRC APRs, enabling more effective decision-making in the review process. By automating the extraction and analysis of critical information, the AI allows the ICRC team to focus on higher-level evaluation and strategic planning. This will reduce the time and resources needed for manual review, improve the consistency and accuracy of assessments, and facilitate faster responses to ICRC needs. Ultimately, this will support ICRCs in overcoming challenges and achieving their research and injury control goals, benefiting the public health system as a whole.

System outputs

The AI analyzes the textual content of APRs, focusing initially on sections detailing the challenges faced by ICRCs. It identifies key themes, trends, and critical information that may require further attention. The AI methodology extracts insights and patterns from the data, which can then be compared with manual qualitative analysis outcomes. In subsequent stages, the AI will be expanded to analyze other sections of the APRs, such as progress toward goals and program impact.

Technology

AI classification: Natural language processingSource: Natural Language Processing: AI that processes, interprets, and shares information in human language.
System name(s): EDAV (Enterprise Data Analytics and Visualization Platform)
Custom-developed code: No
Public source code: Not reported

Sourcing

How it was built: Developed in-houseSource: b) Developed in-house
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: No
Privacy Impact Assessment: Not reported
Authorization to Operate: Yes
Demographic features: k) None of the above
Training and evaluation data: Injury Control Research Center Annual Progress Reports
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