Leveraging GenAI for Efficient Review of CDC Programmatic Reports

Overview

Development stage

In development or acquisitionSource: a) Pre-deployment – The use case is in a development or acquisition status.

High-impact designation

Not high-impactSource: c) Not high-impact

Impact justification

Not reported

Start date

Not reported

Withheld from public reporting

NoSource: a) No

Mission

Topic area

Health and Medical

Operational functions

No function tag matched Derived by keyword rules; see methodology

Problem the AI is intended to solve

Manual qualitative review of text data within programmatic reports, such as Annual Progress Reports (APRs) submitted by Injury Control Research Centers (ICRCs) and Drug Free Communities (DFCs), is resource and time intensive. Across multiple use cases, generative AI (GenAI) and natural language processing (NLP) are being leveraged to automate the analysis of programmatic data. This approach streamlines the review and evaluation of various programmatic documents, improves efficiency, and supports the assessment of performance, progress, and challenges in funded activities.

Expected benefits

By automating the extraction and analysis of critical information from programmatic data, generative AI is expected to significantly reduce the time required for manual coding and review. For example, initial applications have shown that AI can decrease manual review time from an estimated 35 hours to just 8 hours per topic, greatly enhancing efficiency. This time savings enables staff to focus on higher-level evaluations and strategic planning, improving the consistency and accuracy of assessments across multiple program areas.

System outputs

The output from the AI-based framework consists of automated analyses and summaries of insights and patterns extracted from programmatic reports, such as APRs. The AI system highlights critical barriers, challenges, key themes, and trends identified within the data, providing structured summaries and actionable information. These outputs can be compared with manual qualitative analysis outcomes for validation and further refinement. As the framework evolves, the AI will be expanded to analyze additional sections of programmatic reports, including progress toward goals, program impact, and other relevant metrics, supporting comprehensive evaluation and reporting.

Technology

AI classification

Natural language processingSource: Natural Language Processing: AI that processes, interprets, and shares information in human language.

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