Deep Research for Public Health

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

2025-04 (month precision)Source: Apr-25

Withheld from public reporting

NoSource: a) No

Mission

Topic area

Science

Operational functions

No function tag matched Derived by keyword rules; see methodology

Problem the AI is intended to solve

Public health agencies like the CDC face challenges in efficiently processing large volumes of complex information, conducting evidence-based research, and producing timely, high-quality analyses and reports to inform decision-making. Traditional workflows for tasks such as literature review, data analysis, policy evaluation, and communications are often time-consuming and resource-intensive. The problem addressed by integrating agentic AI models, such as OpenAI’s Deep Research, is to enhance the efficiency, productivity, and rigor of these core public health functions by automating information retrieval, synthesis, and analysis, thereby enabling faster, more informed, and scalable decision-making while maintaining quality and transparency.

Expected benefits

Empirical evidence from the report shows that the AI compressed tasks that would normally take days or months, into a single automated workflow, with 92% of subject matter experts reporting substantial productivity gains. Quantitative analysis from an internal study found that 94% of prompts resulted in successful, high-quality reports (median rating “very good”), with most completed in under 30 minutes. The AI demonstrated strong effectiveness in information retrieval, data analysis, and strategic planning across a wide range of public health domains, enabling faster, more informed decision-making and allowing CDC staff to focus on higher-level work that benefits public health outcomes.

System outputs

The output from the AI system consists of detailed, report-style responses tailored to specific public health tasks and prompts. These reports typically include synthesized information from online sources, data analysis, summaries of scientific evidence, policy or legal analysis, and clear recommendations or findings, often with citations. The reports are structured, well-organized, and written in clear language, making them easy for CDC staff and subject matter experts to review and use. According to the evaluation, the outputs scored highly for clarity and reasoning transparency.

Technology

AI classification

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

System name(s)

EDAV (Enterprise Data Analytics and Visualization Platform)

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)

OpenAI

Vendors (standardized)

OpenAI

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

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