GreatNorthCollective/greatnorth-us-federal-ai-inventory huggingface dataset:
US federal AI use case: AMD-Platform Data Harmonization
Agency: Department of Health and Human Services - HHS/CDC
Problem addressed: Current Metadata may not match existing values, decreasing metadata quality.  The harmonization of submitted deviant metadata to best match our current values to ensure accurate metadata is the goal, with an example being Mtb is converted to Mycobacterium tuberculosis.
Purpose and benefits: Standardize datasets for analysis, increased quality of metadata and reduced processing time. Developed to reduce time for staff implementing Metadata submissions.
Topic area: IT
Development stage: Pre-deployment
US federal AI use case: Text embedding analysis tool
Agency: Department of Health and Human Services - HHS/CDC
Problem addressed: Reviewing large sets of documents can take hours to identify similar clusters or analysis. The tool helps non-technical staff explore large, text-based datasets by generating clusters of text and identifying similar documents.
Purpose and benefits: One benefit is being able to get a quick-but-principled analytic overview of a (potentially very large) text corpus's semantic content. This may yield time savings for tasks like responding to public inquiries and doing qualitative analyses of unstructured datasets.
Topic area: Health and Medical
Development stage: Deployed
First used: 2026-01-25
US federal AI use case: Agentic RAG Chatbot
Agency: Department of Health and Human Services - HHS/CDC
Problem addressed: The creation of accurate, detailed, and factually-grounded draft responses to public inquiries takes a large amount of time and could benefit from assistance from LLMs. Because of how complex the responses to the inquiries can be, LLMs alone did not perform well enough to be useful in production, so we decided to build a more advanced chatbot that uses a small team of agents to refine the inquiries, decide what source data to use for grounding, and generate higher-quality draft responses to the inquiries that staff can use as a starting place for writing their replies.
Purpose and benefits: Time savings, especially when programs are overwhelmed by acute increases in the volume of inquiries they receive after publishing a new guideline or regulation is the primary expected benefit.
Topic area: Health and Medical
Development stage: Pre-deployment
US federal AI use case: Internal Newsletter Formatter Tool
Agency: Department of Health and Human Services - HHS/CDC
Problem addressed: This project addresses the challenge of efficiently summarizing and formatting lengthy newsletter documents to provide leadership with clear, concise, and actionable information.
Purpose and benefits: Expected to take less time summarizing and disseminating important information for leadership.
Topic area: Admin Functions
Development stage: Deployed
First used: 2026-06-25
US federal AI use case: Determining if multiple isolates share the same antimicrobial resistant plasmids using short read whole genome sequencing data
Agency: Department of Health and Human Services - HHS/CDC
Problem addressed: Determining if multiple isolates share the same antimicrobial resistant plasmids using short read whole genome sequencing data.
Purpose and benefits: This will help in the early identification of outbreaks of antimicrobial resistant healthcare associated infections caused by the horizontal transmission of plasmids, which will help to increase the speed at which outbreaks are detected and addressed, potentially decreasing cases saving lives.
Topic area: Science
Development stage: Pilot
First used: 2026-12-23
US federal AI use case: Analyzing multidrug-resistant organism response data
Agency: Department of Health and Human Services - HHS/CDC
Problem addressed: Free text data are collected as part of internal tracking of Antimicrobial Resistance. These data are difficult to clean, categorize, and analyze.
Purpose and benefits: Cleaner and more accurate data on multidrug-resistant organism responses.
Topic area: Health and Medical
Development stage: Pre-deployment
US federal AI use case: Leveraging AI for the Creation of Synthetic Datasets
Agency: Department of Health and Human Services - HHS/CDC
Problem addressed: We aim to  generate synthetic datasets that will support training, testing, and software development improving the overall development process
Purpose and benefits: The use of an AI-assisted programming environment can significantly reduce the time required to write code for generating synthetic datasets. This efficiency allows us to quickly create the necessary variables and establish the relationships between them. The synthetic data produced through this project will enhance the training experience and streamline the software development process.
Topic area: Science
Development stage: Pilot
First used: 2026-04-25
US federal AI use case: DHP Data Repository and Dashboard Chatbot
Agency: Department of Health and Human Services - HHS/CDC
Problem addressed: HP uses data to drive action for preventing and addressing HIV in the United States (U.S.). Historically, DHP’s data sources have not been easily accessible internally due to a siloed organizational structure. The DHP Data Repository and Dashboard aims to break down silos by co-locating and visualizing data from across the Division. Currently the repository and dashboard focus on the management and presentation of numeric data. By integrating advanced analytics and natural language processing capabilities into the existing repository we can increase the efficiency, accessibility, and usability of narrative information that is collected in DHP.
Purpose and benefits: Expected benefits include easier and more automated processing of narrative information received in DHP leading to time saved by staff currently processing the information and more efficient and easier use of and access to the information. One of the overarching goals of the DHP repository and dashboard project is to create a mechanism for Division leadership to make informed decisions more easily through easier access to information across work areas. The chatbot is expected to support this by providing a combined source of narrative information with an approachable user interface.
Topic area: Health and Medical
Development stage: Pre-deployment
US federal AI use case: HIV Data Quality Score (DQS) Project
Agency: Department of Health and Human Services - HHS/CDC
Problem addressed: Currently, 60 health departments and 150 community-based organizations submit National HIV Monitoring and Evaluation (NHM&E) program data using a standard online form. However, this data often contains errors that require significant manual cleanup by CDC's HIV data managers. To address this issue, our project aims to create a large language model (LLM)-based data quality score capable of detecting errors in datasets and measuring dataset cleanliness levels. LLMs can also be utilized to automatically fix some detected errors.
Purpose and benefits: This project intends to enable HIV data managers to quickly identify errors, track trends in data quality by site, provide targeted technical assistance (TA), and automate some error corrections.
Topic area: Health and Medical
Development stage: Pre-deployment
US federal AI use case: NCHHSTP Social Media Modernization Strategy: Thought Leadership and Social Listening
Agency: Department of Health and Human Services - HHS/CDC
Problem addressed: To improve communication and ensure health messaging on social media platforms is responsive to the needs of the American people. AI-powered social listening tools are increasingly used in public health to analyze public conversations and audience sentiment in real time. We are using AI social listening tools to collect and analyze publicly available social media data. We then meet with a subject matter expert to discuss this information. Insights from the meeting with the SME and KPI data obtained natively on social platforms are used to draft, publish, analyze, and optimize social media content.
Purpose and benefits: We are demonstrating how integrating AI-powered social listening into a communication strategy enhances audience engagement by enabling the creation of targeted content that addresses audience concerns and contributes factual, clinical information to trending conversations. NCHHSTP messages informed by AI-driven social listening data already show a significantly higher engagement rate and impact than those created without this approach. Weekly and monthly social listening reports also indicate an increase in the positive market share of online conversations, particularly during the promotion of NCHHSTP’s updated guidelines and public comment periods. Notably, the engagement rate of one of our Center’s social media accounts significantly increased from 0.017% in 2023 to 1.92% in 2024, representing a 1,029% increase since this strategy was implemented.
Topic area: Emergency Mgmt
Development stage: Pre-deployment
US federal AI use case: Lectora AI Toolkit and Microbuilder authoring tools
Agency: Department of Health and Human Services - HHS/CDC
Development stage: Retired
US federal AI use case: Using LLM to optimize National Health Interview Survey (NHIS) case note information
Agency: Department of Health and Human Services - HHS/CDC
Problem addressed: The National Health Interview Survey (NHIS) employs Census field representatives (FRs) who use open text fields, referred to as ‘case notes,’ to document their interactions with households during screening and interview processes. These case notes serve as a valuable resource, offering insights into the nature of these interactions and aiding in the identification of ‘base cases’—instances that may reveal significant data quality issues. Currently, the review of case notes is performed manually on a case-by-case basis, which limits opportunities for optimization. The objective of this initiative is to explore how large language models (LLMs) can enhance the efficiency and effectiveness of the case notes review process.
Purpose and benefits: Utilizing large language models (LLMs) for case note reviews provides several advantages, including substantial time and cost savings, improved data quality post data collection, and the creation of more effective training programs. These enhancements not only optimize operational efficiency but also support the goals of public health organization by ensuring that high-quality data is readily available for informed decision-making.
Topic area: Health and Medical
Development stage: Pilot
First used: 2026-03-25
US federal AI use case: Auto-Suggest Journal Tool
Agency: Department of Health and Human Services - HHS/CDC
Problem addressed: It can be challenging and time-consuming for NCHS staff to research journals and gather the pertinent information to guide a decision of which journal to target for publication. Important information to consider when identifying a target journal includes the journal’s field or subject matter, word limits, formatting and submission guidelines, whether the journal is open access, the impact factor, and acceptance rate. Existing tools use keyword searching, term frequencies, and word similarity scoring to identify potential journal matches, but AI presents the potential for a more effective approach that can consider more factors.
Purpose and benefits: Reduction in researcher time spent searching through journal databases and websites to identify specific information about publication requirements.
Topic area: Admin Functions
Development stage: Pre-deployment
US federal AI use case: Use of National Language Processing/Machine Learning to Identify Personal Identifiers in Health Center EHR Medication Data
Agency: Department of Health and Human Services - HHS/CDC
Problem addressed: The NLP/ML processes being used are attempting to identify personal identifiers within EHR data fields that capture medication information such as name of medication and dosage of medication.
Purpose and benefits: The expected benefits are that with the use of these techniques to remove any personal identifiers, more medication data can be made available in restricted use data files for researchers and interested persons to analyze, which would ultimately allow more robust data for studying medications administered/present during visits to health centers.
Topic area: Health and Medical
Development stage: Pre-deployment
US federal AI use case: AI-Assisted Extraction of Circumstance Information from National Violent Death Reporting System Narratives
Agency: Department of Health and Human Services - HHS/CDC
Problem addressed: The National Violent Death Reporting System (NVDRS) compiles both quantitative and qualitative data on the circumstances surrounding violent deaths, including homicides and suicides, from three key data sources related to each death: the death certificate, the coroner/medical examiner report (including toxicology results), and the law enforcement report from the law enforcement agency that investigated the death.  

Data abstractors in state health departments across the U.S. abstract relevant information about each death from CME and LE reports, creating narratives that describe the most notable circumstances that contributed to the deaths captured in the system. Thus, much of the valuable contextual information about these deaths, such as details about chronic pain, interpersonal arguments, or other contributing factors, is embedded in free-text narratives and is not routinely abstracted into structured quantitative data fields. Manually extracting this information is labor-intensive, time-consuming, and subject to variability. The problem we are addressing with AI is the automated extraction of specific circumstance information from these unstructured narratives, enabling more comprehensive and systematic data analysis.
Purpose and benefits: Automating the extraction of circumstance information from NVDRS narratives using AI brings several important benefits. First, it significantly reduces the time required to process narrative data. While manual abstraction is resource-intensive and can take hours or days to review thousands of records, AI can accomplish this task in a matter of minutes. This efficiency is especially critical given the scale of the challenge: the NVDRS captures data on over 70,000 violent deaths annually, making manual analysis of detailed free-text information in the CME and LE narratives for each of these incidents impractical. In addition to saving time, AI improves data quality and consistency by applying uniform criteria across all records, which helps to minimize human error and variability. Furthermore, by extracting additional details, such as information about chronic pain or the presence of arguments, AI enhances the surveillance capabilities of public health officials. This richer data enables a better understanding of risk factors and circumstances surrounding violent deaths, which in turn informs more effective prevention strategies. Collectively, these outcomes directly support CDC’s mission to strengthen public health surveillance, guide prevention efforts, and ultimately reduce the incidence of violent deaths.
Topic area: Health and Medical
Development stage: Pre-deployment
US federal AI use case: Leveraging GenAI for Efficient Review of CDC Programmatic Reports
Agency: Department of Health and Human Services - HHS/CDC
Problem addressed: 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.
Purpose and 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.
Topic area: Health and Medical
Development stage: Pre-deployment
US federal AI use case: Bridging the gap: Leveraging natural language processing to identify reasons for buprenorphine discontinuation in Electronic Health Records
Agency: Department of Health and Human Services - HHS/CDC
Problem addressed: Life-saving treatment for opioid use disorder (OUD), such as the FDA-approved medication buprenorphine, remains underutilized. Buprenorphine has been shown to reduce illicit opioid use and risk of overdose mortality. Understanding treatment barriers can offer us opportunities for improved recovery.

The PanTher Electronic Health Records (EHR) data from OptumLabs are a unique and important data asset, containing structured variables, such as diagnoses and procedures, laboratory measures, and medication records, as well as semi-structured data derived from clinical notes through natural language processing (NLP). The NLP-derived data contain helpful contextual information but have been difficult to use thus far.
Purpose and benefits: The Data Science Upskilling Program advances a key focus of the agency’s Data Modernization Initiative, i.e., that CDC's mission is to give all people the information they need for decision-making and wellbeing. Through participation in the Data Science Upskilling Program (DSU), the DOP-DSU team was able to extract actionable insights from EHR, contextualized further by supplementing with NLP-derived data from clinical notes. They developed an algorithm identifying patients with OUD who discontinued buprenorphine and used it to characterize discontinuation reasons using EHR. This has helped provide a fuller understanding of the what and the why surrounding discontinuation of this life-saving treatment, underscoring the need for strategies that improve retention in treatment. The team also built important DOP capacity in working with EHR data and NLP-derived data, including assessing data quality, and linking, processing, analyzing, visualizing and interpreting these data.
Topic area: Health and Medical
Development stage: Pre-deployment
US federal AI use case: Automating Influenza Vaccine Virus Data Processing
Agency: Department of Health and Human Services - HHS/CDC
Problem addressed: Information for candidate vaccine virus (CVV) data from public-facing websites is highly labor intensive to gather. Previously R scripts—text files with computer programming commands—automate some scraping processes but still rely on manual methods to extract data from PDFs.
Purpose and benefits: The automation from this AI use case reduced processing time for new data from about one week to one day, enabling faster access and analysis of CVV data to enhance CDC’s preparedness for upcoming flu seasons.
Topic area: Science
Development stage: Pilot
First used: 2026-04-25
US federal AI use case: Lineage Assignment by Extended Learning (LABEL)
Agency: Department of Health and Human Services - HHS/CDC
Problem addressed: Identifying, classifying, and annotating influenza sequences.
Purpose and benefits: Reliable and accurate clade assignment helps with downstream surveillance reporting and modeling. Accuracy is generally >98% and saves time through automation.
Topic area: Science
Development stage: Deployed
First used: 2026-01-14
US federal AI use case: Enhancing influenza A risk assessment rubrics: leveraging predictive correlates and machine learning from in vivo experiments.
Agency: Department of Health and Human Services - HHS/CDC
Problem addressed: The Pathogenesis Laboratory Team (Immunology and Pathogenesis Branch, Influenza Division, NCIRD) routinely performs influenza A virus (IAV) risk assessment studies in the ferret animal model, to assess IAV pathogenicity and transmissibility in a relevant small mammalian model. However, these studies are typically performed in isolation, with minimal efforts to comprehensively examine how each biological parameter obtained from the work correlates to disease severity and virus transmissibility and how these parameters can be used as a whole to improve risk assessment efforts. We have generated large sets of data collected from 25+ years of performing these in vivo studies, to identify predictive correlates associated with pathogenicity and transmissibility outcomes, and utilized machine learning approaches to better predict the potential public health risk posed by emerging influenza A viruses.
Purpose and benefits: CDC’s Influenza Risk Assessment Tool (IRAT) rubric is utilized to assess the pandemic potential of novel or emerging IAV that pose a threat to human health. A better understanding of which key quantifiable metrics of virus behavior in this species are most frequently correlated with virulence or transmissibility would greatly aid CDC leadership who score viruses in this rubric to ensure contributing data from the ferret model is rigorously and accurately contextualized within these risk assessments. As the project relies solely on previously collected in vivo data, it represents a valuable opportunity to support the 3 R’s of animal research (reduction, refinement, and replacement), gathering additional information from 25+ years of research in the ferret model already conducted at CDC, thus highlighting the agency’s commitment to responsible and ethical animal research. Numerous peer-reviewed publications have already resulted from this work, including development of predictive models of lethal disease and virus transmissibility, and assessment of which parameters and sample types collected during routine laboratory experimentation offer highest predictive value in these models. These first-in-field analyses also provide an analytic framework and template for subsequent studies with other data collected at CDC.
Topic area: Science
Development stage: Pilot
First used: 2026-05-23
US federal AI use case: RepoAnalysis
Agency: Department of Health and Human Services - HHS/CDC
Problem addressed: We are trying to generate a summary of code repositories that are insufficiently documented.
Purpose and benefits: This would allow people to search for code and reuse code that is not documented well enough for traditional search engines.
Topic area: IT
Development stage: Pilot
First used: 2026-01-25
US federal AI use case: Data Standardization with LLM
Agency: Department of Health and Human Services - HHS/CDC
Problem addressed: The purpose of this project is to enhance data standardization efforts by leveraging large language models. to improve data cleaning and standardization processes, ultimately enhancing the overall efficiency and accuracy of data
Purpose and benefits: Improve data cleaning and standardization processes within our data systems. ?
Enhance the accuracy and reliability of data stored.
Streamline workflows by automating certain tasks related to data cleaning and standardization. ?

More specifically, the ultimate scope of this project includes integrating  into the existing infrastructure of FluLIMS. The  data as well as rules . For example, sometimes we have misspelled locations or various ways of referring to the same place (i.e., ATL vs Atlanta) and we would like to standardize that. ?
By implementing this proposed project, we anticipate significant improvements in data cleaning and standardization processes within FluLIMS, leading to enhanced efficiency, accuracy, and overall effectiveness in managing flu-related information.? This will most likely lead to up to at least 50% reduction in time and effort to clean data. 
This can be used for other cleaning other data or other processes that LLM would be useful for.
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: Using Databricks Genie for Routine Immunization Data Insights
Agency: Department of Health and Human Services - HHS/CDC
Problem addressed: The volume of immunization data reported quarterly is approximately 5 billion records. Analyzing this data to gather high-level insights requires complex coding and manipulation. By using a Large Language Model (LLM) based approach, the solution offers a plain language query capability that generates code to provide high-level insights without the need to move or create specific views for program needs. This approach is cost-effective, timely, and provides insights that can be used to improve data quality and inform data management planning by programs.
Purpose and benefits: The use of a Large Language Model (LLM) based approach for generating code to analyze immunization data offers several promising benefits for the CDC's immunization program staff and data operation team:
1.	Enhanced Efficiency and Time Savings: By enabling plain language queries, this approach significantly reduces the time and effort required for complex data manipulation and coding. This will allow program and data ops staff to focus on more critical tasks, potentially plan the data management tasks efficiently.
2.	Improved Data Quality and Management: The insights generated can help identify data quality issues and inform better data management practices. This will lead to potentially more accurate and reliable data.
3.	Cost-Effectiveness: Simplifying the analysis process reduces the need for extensive manual labor and specialized coding skills and compute costs.
4.	Scalability: Handling approximately 5 billion records quarterly, this approach can scale to meet the demands of large datasets, ensuring timely and comprehensive analysis.
Topic area: Science
Development stage: Pilot
First used: 2026-06-25
US federal AI use case: Vaccine Tracking System (VTrckS) Conversational AI Pilot
Agency: Department of Health and Human Services - HHS/CDC
Problem addressed: The VTrckS Conversational AI module will provide a user-friendly prompt driven approach to gaining insights into the VTrckS datasets. Current approach involves utilizing the SAP HANA’s reporting modules and custom dashboards. The conversational AI approach will allow for a more human friendly metto  quick insights without having to learn about the underlying data models.
Purpose and benefits: This AI pilot aims to add efficiency for CDC awardees and CDC program staff in gathering insights from the VTrckS order, distribution, shipping, transfers and provider information available in the NCIRD’s Advanced Business Intelligence Platform (NABIP). This tool will allow awardees to rapidly gather insights with no coding required both during routine operations and local or national emergency response. Some examples of data insights users gather from the data includes: 
-	Assess active providers – Determine if provider is complaint to obtain vaccines through VFC or 317 programs. 
-	Provider Management – Validate address and scope of provider’s offering to public. 
-	Determine providers with specific vaccine availability – Determine provider vaccine inventory unique to vaccines distributed. 
-	Assess provider locations against vulnerable populations – Quickly respond to routine or emergent request for information related to provider locals with key vaccine inventory.
Topic area: Health and Medical
Development stage: Pilot
First used: 2026-10-25
US federal AI use case: Beryllium exposure reconstruction
Agency: Department of Health and Human Services - HHS/CDC
Problem addressed: Using machine learning to extract information about job title and task and assign relevant exposure codes.
Purpose and benefits: Previously exposure codes were assigned manually by researchers. This method would increase consistency and accuracy of exposure coding and substantially reduce the amount of time needed to assign exposure codes and reduce exposure misclassification.
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: Site Audit AI Support (SAAIS) App
Agency: Department of Health and Human Services - HHS/CDC
Problem addressed: NIOSH conducts numerous post-market activities to ensure that respirator configurations approved by NIOSH remain protective. Among these activities are audits of the manufacturing sites where the approved products are produced. These audits involved detailed evaluations of sites against quality assurance plans approved by NIOSH. Site are audited every few years and, thus, previous reports of nonconformances may be of particular interest to auditors. 

SAAIS is a prototype application being developed as an AI use case to inform:

1. CDC Office of the Chief Information Officer (OCIO)’s implementation strategy for cloud-based enterprise services standalone AI tools and AI-enhanced systems 

2. The specific enterprise approach, AI tools, and training techniques to be used by Respirator Approval System (RAS) developers when AI enhancements are eventually added following the completion of the base system within the Power Platform enterprise system.
Purpose and benefits: Reduced time and errors and greater consistency related to audits of manufacturing sites used to produce NIOSH Approved respirators.
Topic area: Service Delivery
Development stage: Pre-deployment
US federal AI use case: Respirator Selection Logic (RSL) Copilot
Agency: Department of Health and Human Services - HHS/CDC
Problem addressed: Workers rely on NIOSH Approved respirators to protect them from inhaling high-consequence particulate, gas, and vapor hazards. Some examples of these respiratory hazards include:
•	Wildfire, structural, or surgical smoke 
•	Mold during post-flood remediation efforts 
•	Infectious diseases such as tuberculosis
•	Chemicals used to clean or disinfect
•	Particles liberated when cutting rock in industries such as construction and mining 
Selecting the correct respirator to protect workers requires knowledge of the hazard or hazards present, the job task, and the environment. NIOSH’s Respirator Selection Logic (RSL) is a state-of-the-art tool designed to guide the selection of appropriate respiratory protection devices based on specific workplace hazards and conditions. The RSL requires users to enter detailed, task- and environment-specific information at multiple decision points to execute its logic correctly.
Purpose and benefits: No current AI tool operationalizes the RSL while addressing the challenge of gathering and validating highly specific input information required at each decision point. The absence of such assistance leads to errors in respirator selection that can cause hazardous exposures, regulatory violations, and adverse health outcomes. Developing Ally to close this gap will improve the effectiveness of respiratory protection programs by ensuring that users supply accurate, relevant data to the RSL, thereby enhancing the quality and traceability of respiratory protection decisions aligned with established federal guidance.
Topic area: Service Delivery
Development stage: Pre-deployment
US federal AI use case: PPE Concerns Copilot
Agency: Department of Health and Human Services - HHS/CDC
Problem addressed: The PPE Concerns Mailbox received inquiries that NIOSH/NPPTL staff must review and respond to. To date, over 10,000 questions have been received with answers provided. Responding to an inquiry requires a multi-step process that staff must complete manually, including
•	Reading the inquiry
•	Logging the inquiry and information about the inquiry and inquirer in an Excel spreadsheet
•	Searching through the spreadsheet of past questions
•	Reviewing prior responses for relevance
•	Drafting a reply based on similar previous responses or researching and composing a new answer from scratch if no match is found
•	Sending response to additional staff if further subject matter expert review is needed
•	Obtaining Executive Leadership review and approval, if needed
•	Updating the spreadsheet with the finalized response, reply date, and staff who assisted with the response
This process can be time-consuming, repetitive, and inconsistent, especially when multiple team members are handling and categorizing inquiries or when there are high volumes.
Purpose and benefits: The Copilot could handle the initial steps—reviewing the question, searching past responses, and drafting a reply—which currently take the most time. For straightforward or simple, repeat questions, this could reduce staff time from 30–60 minutes to under 10 minutes, with staff only needing to review and finalize the AI-generated draft. Even for more complex inquiries, having a well-structured starting point and a searchable interface would significantly cut down manual effort and improve turnaround time across the board.
Additionally, the Copilot would improve time savings when a new staff member is assigned to managing the mailbox due to events such as staffing changes. The Copilot would allow a more seamless transition of the mailbox, whereas the current process requires months of on-the-job training to effectively navigate the spreadsheet and learn the proper standard responses to use for specific inquiries. Additionally, the Copilot would remove the burden of relying on memory recall to determine where a previous response is in the spreadsheet when staff receive specific, repeat questions.
Topic area: Service Delivery
Development stage: Pre-deployment
US federal AI use case: Assessing public comment responses to draft NIOSH wildland fire smoke document
Agency: Department of Health and Human Services - HHS/CDC
Problem addressed: Efficiently compile and synthesize the public comments received for a draft NIOSH document.
Purpose and benefits: Make the NIOSH response to public comments more efficient and effective and reduce the time needed to review public comments.
Topic area: Admin Functions
Development stage: Pre-deployment
US federal AI use case: Mining.AI
Agency: Department of Health and Human Services - HHS/CDC
Problem addressed: Mining.AI is an innovative initiative led by a small team at NIOSH tasked to develop a domain-specific generative LLM AI platform focused on improving health and safety in the mining industry. The project consolidates NIOSH's latest peer-reviewed research and safety knowledge to support decision-making, hazard prevention, and incident response.
Purpose and benefits: •Accelerates Access to Critical Safety Knowledge
Mining.AI enables instant retrieval of NIOSH mining research, technical reports, and best practices, replacing slow manual searches through multiple archives.
•Supports Faster, Safer Decision-Making
Frontline professionals, engineers, and safety managers can query the AI to get tailored responses grounded in NIOSH-validated scientific evidence.
•Preserves Expertise
Encodes decades of institutional knowledge into a reusable, interactive platform, mitigating the impact of staff turnover and retirements.
•Promotes Research Translation
Converts dense, technical research into actionable language accessible to a wider range of users, including mine operators and workers.
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: CSB MCP AI
Agency: Department of Health and Human Services - HHS/CDC
Problem addressed: Reduce required manhours and improve accuracy of reports on CSB infrastructure and by assisting infrastructure administrators with tasks and duties.
Purpose and benefits: Expected to generate reports for and answer questions posed by senior staff about CSB infrastructure, improving accuracy and freeing infrastructure administrators from these tasks. Assist infrastructure administrators with tasks and duties, improving accuracy and completion time.
Topic area: IT
Development stage: Pre-deployment
US federal AI use case: Support for MCP and AI APIs in Digital Gateway
Agency: Department of Health and Human Services - HHS/CDC
Problem addressed: Digital Gateway (CDC's API Platform) can support MCP and AI APIs. Given MCPs are a new addition to the ecosystem, the goal is to explore what it takes to implement MCPs internally.
Purpose and benefits: Centralized Management: The Digital Gateway can act as a centralized point for managing interactions between AI agents and MCP servers. This simplifies the integration process and ensures consistent policy enforcement across all tools and data sources.

Enhanced Security: By routing all requests through the Digital Gateway, the CDC can enforce security policies, access controls, and rate limiting. This helps protect sensitive health data and ensures compliance with regulatory requirements.

Improved Data Governance: The Gateway can provide visibility into data usage and interactions, helping the CDC maintain robust data governance practices. This includes monitoring access, usage patterns, etc.
Topic area: IT
Development stage: Pre-deployment
US federal AI use case: Delegations Repository Assistant
Agency: Department of Health and Human Services - HHS/CDC
Problem addressed: The Delegations Library, which houses critical guidance on authorities and approvals, has been selected as the pilot use case due to its centralized structure and high relevance. Staff frequently report difficulty locating the right documents, resulting in delays and inefficiencies. This project will use CDC’s internal EDAV platform to develop a chatbot assistant that helps users retrieve existing content more easily. The proof of concept will test technical feasibility, user experience, and potential for broader application to support CDC’s operational mission.
Purpose and benefits: Staff currently spend considerable time searching for this type of guidance, which slows administrative actions and diverts focus from core public health work. By streamlining access to internal policies and procedures, this proof of concept supports greater operational efficiency. While the chatbot does not directly impact health outcomes, it enables staff to redirect time toward critical public health priorities and lays the groundwork for applying similar tools across other business functions that support CDC’s mission.
Topic area: Admin Functions
Development stage: Pre-deployment
US federal AI use case: OFR Robotics and Process Automation (ORPA)
Agency: Department of Health and Human Services - HHS/CDC
Problem addressed: Reduction in human workload and increase in quality through automation of repetitive tasks.
Purpose and benefits: Multiple Robotics and Process Automation (RPA) bots are in use.  Most are simple automations of basic tasks (conversation of documents/file management). The two most significant bots focus on automating the sorting and distribution of thousands of invoices annually (Invoice Mailbox Management) and conducting government purchase card use validations monthly (PCARD Line Item Review).  The result of the two bots are greater consistency/quality in the outputs and a reduction in human workload.

The Invoice Mailbox Management bot processes >15,000 emails annually and the PCARD Line Item Review bot processes 3,000 – 5,000 transactions monthly against more than 200 unique business rules.
Topic area: Procurement and Finance Mgmt
Development stage: Deployed
First used: 2026-05-24
US federal AI use case: FERRET
Agency: Department of Health and Human Services - HHS/CDC
Problem addressed: High throughput processing of unstructured data necessary for mandated reporting.
Purpose and benefits: Usage of programs to automate identification, extraction, and re-structuring of data will significantly decrease human involvement and processing time. Once deployed the time savings is anticipated to be on the scale of weeks to months.
Topic area: Health and Medical
Development stage: Pre-deployment
US federal AI use case: Deep Research for Public Health
Agency: Department of Health and Human Services - HHS/CDC
Problem addressed: 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.
Purpose and 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.
Topic area: Science
Development stage: Pilot
First used: 2026-04-25
US federal AI use case: Federal Select Agents Program (FSAP) Customer Agent
Agency: Department of Health and Human Services - HHS/CDC
Problem addressed: FSAP users need support on the use of the FSAP system. Completing forms, managing entities, agents, and toxins. Updating permits, correcting data, conducting inspections, and related activities. The FSAP Customer Agent will provide answers to questions as users navigate the FSAP process.
Purpose and benefits: Support will be provided to users without additional staffing requirements. Users will get fast and accurate solutions to questions instantly.
Topic area: IT
Development stage: Pilot
First used: 2026-03-25
US federal AI use case: Publication Portfolio Analytics
Agency: Department of Health and Human Services - HHS/CDC
Problem addressed: The Data Strategy and Analytics Team (DSAT) within OS employs natural language processing (NLP) topic modeling techniques to help programs identify common themes within their publication data. By combining these efforts with bibliometric analysis, we standardize the reporting of media attention, as well as policy and academic citations, by theme and/or CDC organization. Automating these efforts helps the CDC library to optimize their allocation of resources and avoid duplication of effort.
Purpose and benefits: OS DSAT utilizes NLP to generate organization-specific reports and maintain an agency-wide dashboard. In the case of the agency-wide publication impact dashboard, NLP topic modeling was used to identify common publishing themes for the agency using 10+ years of CDC publication data. This allows users to see trends in publishing topics have changed over time and, when connected to media attention and citation data, allows communication teams, leadership, and scientists interested in assessing the impact of their program’s publications. 
This dashboard has proven to be impactful, with 105 unique CDC staff across several CIOs and divisions using the dashboard between 7/1/2025 and 8/1/2025.
Topic area: Admin Functions
Development stage: Deployed
First used: 2026-06-23
US federal AI use case: CDC Vault and Stacks Metadata Extraction
Agency: Department of Health and Human Services - HHS/CDC
Problem addressed: We are attempting to speed up the process of generating a digital metadata record for objects that will be curated and stored into either CDC Public Access Platform (Stacks) or CDC Vault.   These two systems are built using the same software stack but one is for public data and the other is for non-public data.   To create a metadata record solely with a human, the process takes about an hour per document.   We are looking to improve the process to use AI to prepare the metadata record and reduce the human time to under 5 minutes.  A secondary objective is to have a non-human process for the non-public data that will go into CDC Vault.
Purpose and benefits: There are two primary paths and uses for the AI assisted pre-processing.   The first is to improve the speed and effectiveness of human catalogers/librarians.  Long term, we need to be able to process more data and require AI to improve this process so that humans are only working on critical steps and validation of the AI.  This process is going from 60 minutes per document to <5 minutes per document. The second is to process federal records prior to a record being entered into CDC Vault and copied to NARA. This process will not have a human review as the final disposition is not public but we need to process a large number of files (100s of thousands to millions).   This is simply not realistic to do via humans so this is a novel opportunity.
Topic area: Admin Functions
Development stage: Deployed
First used: 2026-04-25
US federal AI use case: AIP Assist
Agency: Department of Health and Human Services - HHS/CDC
Problem addressed: AIP assist supports user engagement with the Palantir Platform. It is an LLM-powered support tool that is able to answer inquiries about the platform and can also guide users in developing their own applications by supporting users to write their own code or pointing the user to the right tool for the task. It supports assistance for platform navigation, coding tasks, documentation support, and problem solving for users.
Purpose and benefits: AIP Assist benefits the agency and general public by enabling users to quickly understand the platform which allows them to quickly ramp up the development of new applications.  It supports assistance for platform navigation, coding tasks, documentation support, and problem solving for users when working on applications in many roles including data science, data engineering, machine learning, and AI.
Topic area: Admin Functions
Development stage: Deployed
First used: 2026-10-24
US federal AI use case: Center for Forecast and Analytics (CFA) Model Studio
Agency: Department of Health and Human Services - HHS/CDC
Problem addressed: This tool is intended to provide streamlined infrastructure around allowing users to bring their own models or discover existing ones and evaluate them in a streamlined way. For example, users can develop their models in their local machine, containerize them and deploy them using CFA Model Studio in the platform for further experimentation, parameter exploration and registry.
Purpose and benefits: This tool is intended to extend the Natice Modeling Objectives capabilities on platform to reduce the effort and burden around bringing models on platform and especially allow for R based models for fine evaluation. Additionally,  adds flexibility for users to utilize their preferred modelling language and tools
Topic area: Health and Medical
Development stage: Pilot
First used: 2026-01-25