GreatNorthCollective/greatnorth-us-federal-ai-inventory huggingface dataset:
US federal AI use case: Enhanced Direct Enrollment Outlier Detection
Agency: Department of Health and Human Services - HHS/CMS/CCIIO
Problem addressed: (Marketplace) Enhanced Direct Enrollment (EDE) allows consumers to apply for and enroll in an exchange plan directly through an approved partner's UI, without being redirected through the Healthcare.gov application. These partner systems directly interface with the APIs developed by the FFE. As EDE partners gain more control over their application process, the FFE must ensure program integrity.
Purpose and benefits: Implement ML to identify anomalies/quality issues with partner-submitted person, application, and policy data?.
Topic area: IT
Development stage: Pre-deployment
Risk category: Presumed high-impact (determined not high)
Impact assessment: The Enhanced Direct Enrollment Outlier Detection use-case is not considered high-impact as it does not serve as the basis for decisions or actions that affect civil rights, liberties, privacy, critical resources, safety, or strategic assets. The AI models within this use-case use machine learning techniques to detect anomalous or inconsistent patterns relative to standard partner actions and other application channels outside of the EDE pathway. The findings are shared with the agency divisions in the form of different reports and tables. The data alone is not enough to determine fraud, however, can be used by CMS in tandem with other data to determine if CMS should take any corrective actions. CMS makes all determinations on actions.
US federal AI use case: IT System Utilization Optimization
Agency: Department of Health and Human Services - HHS/CMS/CCIIO
Problem addressed: (Marketplace) The FFE application usage patterns vary and are dependent on differing environment usage periods. CMS resources are currently manually scaled, not allowing immediate actions in correlation with usage changes.
Purpose and benefits: Implement ML to determine optimized infrastructure/application scaling to support system volume.
Topic area: IT
Development stage: Pre-deployment
US federal AI use case: Risk Adjustment Outlier Analysis
Agency: Department of Health and Human Services - HHS/CMS/CCIIO
Problem addressed: The risk adjustment (RA) program spreads the financial risk borne by Issuers due to offering a variety of plans meeting the need of the diverse population. RA payments are distributed based on population risk levels. CCIIO uses a distributed data solution (EDGE servers) to calculate plan average actuarial risk and associated RA transfers and must avoid potential program integrity risks to annual calculations.
Purpose and benefits: Implement ML to identify outliers in issuer data that may unduly influence risk adjustment transfers.
Topic area: IT
Development stage: Pre-deployment
US federal AI use case: Agent/Broker Fraud Analysis (ABCQI)
Agency: Department of Health and Human Services - HHS/CMS/CCIIO
Problem addressed: (Marketplace) Agents and brokers support the consumer enrollment and eligibility process. Because of this, they have learned the intricate details of the Federally-facilitated Exchange (FFE) for accessing applications, submitting eligibility determinations, and adding enrollments to their line of business, opening up the possibility of fraud.
Purpose and benefits: Implement Machine Learning (ML) to identify potential fraud/waste/abuse within Agent/Broker data.
Topic area: IT
Development stage: Pre-deployment
Risk category: Presumed high-impact (determined not high)
Impact assessment: The Agent/Broker Fraud use-case is not considered high-impact as it does not serve as the basis for decisions or actions that affect civil rights, liberties, privacy, critical resources, safety, or strategic assets. The AI models within this use-case use machine learning techniques to detect patterns that are inconsistent or anomalous relative to standard consumer, agent broker, and partner actions. Our findings are shared with the agency divisions in the form of reports and tables and can be used in combination with additional information derived outside of this AI tool to determine if CMS should take any corrective actions. All outcomes are internal facing. CMS makes all determinations on actions.
US federal AI use case: Help Desk Responses
Agency: Department of Health and Human Services - HHS/CMS/CCIIO
Problem addressed: (Marketplace) The Division of Issuer Management and Operations has talked with their contractor, LMI, about using an AI tool for the Help Desk contract.
Purpose and benefits: Generate responses to common questions from issuers and external organizations based on previously cleared material.
Topic area: IT
Development stage: Pre-deployment
US federal AI use case: Chatbot within Hub
Agency: Department of Health and Human Services - HHS/CMS/CCIIO
Problem addressed: (Other) Currently the following help support activities are handle by a human:
- Personalized FAQ?s
- Clarify questions with schema and onboarding, etc.
- Handle "Where is 'My file' or 'My Request'?" inquiries
- Provide a "Talk to Agent" feature
- Schedule a testing window
- Provide data summarization and reporting for internal stakeholders
- Report operational health of the system
- Include training materials, Q/A about the system
Purpose and benefits: Build a chatbot that will address some help support activities through:
1. Access to Internal Knowledge base Including retrieval augmented generation (RAG)
2. Personalized FAQs & contextual generation
3. Interaction with Live Agent
4. Chat & Talk Feature
5. Ability to query custom data source for File or Case status
Topic area: IT
Development stage: Pre-deployment
US federal AI use case: Independent Dispute Resolution (IDR) Eligibility Rules Engine
Agency: Department of Health and Human Services - HHS/CMS/CCIIO
Problem addressed: The current Independent Dispute Resolution (IDR) Technical Assistance (TA) process is very manual and time intensive which limits throughput.  The rules engine should significantly expedite processing and expand capacity. Automating more of the process may also increase consistency across recommendations and result in a more predictable timeframe for the workflow by better positioning disputes for analyst review.
Purpose and benefits: The AI tool will help automate the eligibility review process, reducing time-intensive manual steps and increasing consistency of results.
Topic area: Service Delivery
Development stage: Deployed
First used: 2026-03-25
US federal AI use case: Review Regulatory Comments
Agency: Department of Health and Human Services - HHS/CMS/CCIIO
Problem addressed: (Marketplace) Public comment analysis in the Letter to Issuers (LTI) and Notice of Benefits and Payment Parameters (NBPP) to streamline review of  comments.
Purpose and benefits: Identify trends in responses from commenters in policy and operational issues proposed by CMS.
Topic area: IT
Development stage: Pre-deployment
US federal AI use case: Improved Data Quality Checks
Agency: Department of Health and Human Services - HHS/CMS/CCIIO
Problem addressed: (Marketplace) Improve consumer experience by providing an asynchronous (not at the same time) solution to detect and provide near real time feedback via outreach to the consumer, shortening the overall return cycle time without requiring UI changes.
Purpose and benefits: Develop a POC classifier model to identify incorrect document upload types / low-quality images through use of optical character recognition (OCR).
Topic area: IT
Development stage: Pre-deployment
US federal AI use case: iVeri-Fi (Test)
Agency: Department of Health and Human Services - HHS/CMS/CCIIO
Problem addressed: In October 2024, Serco will begin utilizing iVeri-Fi, (a decision service platform) to perform automated processing of remote identity proofing (RIDP) verification tasks.  These tools are already in our Eligibility Workers Support System (EWSS) stack and have an existing Authority to Operate (ATO). We are not introducing any new technologies - we are just changing how and where the work is done through automation (before Task Inconsistency Processing System (TIPS) not integrated with TIPS).
Purpose and benefits: A decision service (Sapiens) will make the adjudication decision using Remote Identity Proofing (RIDP) business rules. This service integrates with Event-Based Processing (EBP) microservices, Sapiens Decision, and Rosette Name Indexer (RNI) for matching identity data. Sapiens Decision uses AI to ensure consumer and RIDP data match and will incorporate more machine learning in the future.
Topic area: IT
Development stage: Pre-deployment
Risk category: High-impact
US federal AI use case: Plan Justification Tool
Agency: Department of Health and Human Services - HHS/CMS/CCIIO
Problem addressed: (Marketplace) Identify trends/patterns within plan certification justification templates, containing free-form text data through use of natural language processing (NLP) and classification techniques.
Purpose and benefits: Build a supervised ML model using historical justification data and associated plan certification outcomes that can be recommended to CMS to build towards a more efficient review process.
Topic area: IT
Development stage: Pre-deployment
US federal AI use case: TIC URL Automation
Agency: Department of Health and Human Services - HHS/CMS/CCIIO
Problem addressed: (Marketplace) Issuers applying for  QHP (Qualified Health Plan) certification, including issuers offering off-Exchange SADPs (stand alone dental plans), must submit a Transparency in Coverage URL in MPMS that leads to a page on the issuer's website where required information is posted. The current review process requires reviewers to manually read and verify the language presented on each TIC URL for compliance, which is both time-consuming and labor-intensive.
Purpose and benefits: AI can expedite the process by enabling AI algorithms to rapidly scan URL content, to identify if the required language is present and compliant, reducing the review time compared to manual reading. Additionally, AI can manage and review large volumes of URL content, further accelerating the review process.
Topic area: IT
Development stage: Pre-deployment
US federal AI use case: Rx Data Integrity Review
Agency: Department of Health and Human Services - HHS/CMS/CCIIO
Problem addressed: The current process for conducting the Rx Data Integrity review is time intensive and requires reviewers to manually review online formularies in a condensed timeline.  The purpose of this proposal is to develop a new process, which will automate as much of the review as possible, using existing language models as well as large language models (LLM) to introduce efficiencies and increase data accuracy.
Purpose and benefits: A Python-based automation pipeline will download PDFs and extract required Rx information in appropriate format for review
Topic area: IT
Development stage: Pre-deployment
US federal AI use case: RA and RADV Predictive Modeling and Methodology Evaluation
Agency: Department of Health and Human Services - HHS/CMS/CCIIO
Problem addressed: (Marketplace) Risk adjustment data validation (RADV) is the audit of HHS-operated Risk Adjustment (RA).   KPMG, the main RADV contractor, seeks to conduct predictive modeling and simulations of proposed policy changes. KPMG also validates the RA models (ML-based) and evaluates the RADV methodology.
Purpose and benefits: Predictive modeling and simulations of policy changes are used to determine likelihood of various outcomes across markets, by individual issuer, etc. Model validation and methodology evaluation determine effectiveness, fairness, and impacts of the programs.
Topic area: IT
Development stage: Pre-deployment
US federal AI use case: RADV Medical Record Review
Agency: Department of Health and Human Services - HHS/CMS/CCIIO
Problem addressed: (Marketplace) RADV entails the review of enrollees' medical records to determine if diagnoses submitted to CCIIO for purposes of calculating RA transfers actually exist. This medical record review is time- and resource-intensive for KPMG (and therefore CCIIO).
Purpose and benefits: Improve efficiency of medical record review by flagging diagnoses for KPMG coders to review.
Topic area: IT
Development stage: Pre-deployment
US federal AI use case: Issuer Expansion/Market Entry Prediction
Agency: Department of Health and Human Services - HHS/CMS/CCIIO
Problem addressed: CCIIO FO conducts targeted outreach to issuers likely to enter the markets or expand to other states
Purpose and benefits: Predictive models using AI/ML and over 300 signals derived from data  determine the likelihood of expansion or new entry into the markets in the future by issuer or parent-company
Topic area: IT
Development stage: Pre-deployment
US federal AI use case: SBC Content Review
Agency: Department of Health and Human Services - HHS/CMS/CCIIO
Problem addressed: (Marketplace) The SBC Content Review identifies cost-sharing discrepancies between the Plans and Benefits Template and SBC Form. If there are discrepancies in the numerical values input in the two data sources, reviewers must manually review the "Limitations, Exceptions and Other Information" in the SBC Form to assess if a true discrepancy exists.  The purpose of the proposal is to integrate large language models (LLM) to programmatically review qualitative data in the SBC Form.
Purpose and benefits: Improve efficiency of SBC Form review.
Topic area: IT
Development stage: Pre-deployment
US federal AI use case: MFT UI Chatbot
Agency: Department of Health and Human Services - HHS/CMS/CCIIO
Problem addressed: Hub/MFT team manually supports operations related to onboarding, Q/A, file tracking and technical inquiries. ?

Monthly Average of inquiries:
 ?
 ~800 Email inquires ?
~40 Jira/SNOW Tickets?

The Communication is manual via emails, Zoom Meetings, and Slack. This Current process is time-consuming, adds and degrades stakeholder experience.?
?
Typical operational hours are Monday through Friday, 8 a.m. to 6 p.m. ET (this doesn’t serve Mountain Time, West Coast, Hawaii, and Alaska), followed by XOC escalations. This means non-prod inquiries will be responded to the following business day.  ??

We have limited, Quality of Service (QoS) metrics to accurately assess consumer satisfaction at present time.
Purpose and benefits: Implement a chatbot to provide real-time file status updates for external users and tools for internal teams to generate reports and visualize key metrics.
Topic area: IT
Development stage: Pilot
First used: 2026-08-25
US federal AI use case: MLMS Upgrade MILA from CARLA to Druid
Agency: Department of Health and Human Services - HHS/CMS/CCIIO
Problem addressed: New chatbot proposed by CMS MLMS team will be integrated into the existing MLMS application. It aims to improve user experience, designed to maintain or boost deflection rates, and will be hosted within the CMS environment for smooth integration.
Purpose and benefits: - Empower MLMS operations team with full control over chatbot management
- Maintain or improve current deflection rate from MILA 1.0
- Reduce help desk costs through a more efficient support solution
- Tailor chatbot content to specific MLMS support requirements
- Deliver personalized user interactions to enhance experience
- Ensure smooth escalation to Tier 1 support when needed
Topic area: IT
Development stage: Pre-deployment
US federal AI use case: Interoperability URL Review Automation
Agency: Department of Health and Human Services - HHS/CMS/CCIIO
Problem addressed: Enable the team to save time and man hours, cost savings
Purpose and benefits: Improve timeliness, Minimize review inconsistencies, Improve quality
Topic area: IT
Development stage: Pre-deployment
US federal AI use case: Feedback Analysis Solution (FAS)
Agency: Department of Health and Human Services - HHS/CMS/CCSQ
Problem addressed: Speeds up the manual and time-consuming process of analyzing public comments on frequently posted regulations.gov and FDMS dockets by employing advanced natural language processing and machine learning technologies.
Purpose and benefits: Speeds up the manual and time-consuming process of analyzing public comments on frequently posted regulations.gov and FDMS dockets by employing advanced natural language processing and machine learning technologies. FAS help categorize stakeholder comments or feedback (collected in multiple venues), thereby enabling analysts to use the system to quickly identify comments that may impact program/policy decisions.
Topic area: IT
Development stage: Deployed
First used: 2026-09-21
US federal AI use case: CCSQ ServiceNow AI Search
Agency: Department of Health and Human Services - HHS/CMS/CCSQ
Problem addressed: The AI Search index stores data from ServiceNow AI Platform® records or external sources and makes that data available for users to search in multiple applications. Search query features use ServiceNow AI Platform technologies to improve the search user experience.
Purpose and benefits: CCSQ ServiceNow AI Search is a product inside ServiceNow (SaaS).  It replaces traditional Zing search tool (exact search), and enables users with more flexible searches to get relevant and actionable answers quickly.  * Improve search relevance * Promote self-service by empowering users to find information independently, and potential reduce number of cases
Topic area: IT
Development stage: Deployed
First used: 2026-04-24
US federal AI use case: Resource Library Assistant
Agency: Department of Health and Human Services - HHS/CMS/CCSQ
Problem addressed: Users being able to find information related to program participation.
Purpose and benefits: Improving access to program information and reducing service desk burden and improving user experience for searches.
Topic area: Service Delivery
Development stage: Pre-deployment
US federal AI use case: QPP Admin Bot
Agency: Department of Health and Human Services - HHS/CMS/CCSQ
Problem addressed: This AI is to help with Code complexity.
Purpose and benefits: In general we expect to see a decrease to lines of code, increase efficiency of code, increase developer output and reduced story points for work which in turn produces cost savings.
Topic area: Admin Functions
Development stage: Pre-deployment
US federal AI use case: CCSQ Now Assist for CSM
Agency: Department of Health and Human Services - HHS/CMS/CCSQ
Problem addressed: CCSQ Now Assist for CSM (Customer Service Management) is a product inside ServiceNow (SaaS).  It integrates Gen AI with CSM, and uses Now LLM (ServiceNow native Large Language Model) to generate contents based on machine learning (ML).  Now Assist for CSM helps agents to improve productivity and efficiency and deliver better services.    Improve agents' responsiveness and productivity * Quickly get familiar with a case/chat by getting case/chat summarization * Quickly resolve the case by using auto-generated resolution notes.
Purpose and benefits: CCSQ Now Assist for CSM (Customer Service Management) is a product inside ServiceNow (SaaS).  It integrates Gen AI with CSM, and uses Now LLM (ServiceNow native Large Language Model) to generate contents based on machine learning (ML).  Now Assist for CSM helps agents to improve productivity and efficiency and deliver better services.    Improve agents' responsiveness and productivity * Quickly get familiar with a case/chat by getting case/chat summarization * Quickly resolve the case by using auto-generated resolution notes.
Topic area: IT
Development stage: Deployed
First used: 2026-12-24
US federal AI use case: Citation Analysis and Survey Assistant (CASA - Nursing Home Survey CMS 2567)
Agency: Department of Health and Human Services - HHS/CMS/CCSQ
Problem addressed: CASA enhances the efficiency and effectiveness of monitoring and reviewing nursing home surveys across the US. It enhances how the Quality, Safety, and Oversight Group (QSOG) and Survey Operations Group (SOG) assess how State Survey Agencies (SSAs) are citing nursing home deficiencies, reported on CMS Form 2567, by employing advanced natural language processing and machine learning technologies.
Purpose and benefits: Speeds up the manual and time-consuming process of survey review and citing nursing home deficiencies, reported on CMS Form 2567, by employing advanced natural language processing and machine learning technologies.
Topic area: Health and Medical
Development stage: Deployed
First used: 2026-11-24
US federal AI use case: CCSQ Now Assist for Creator
Agency: Department of Health and Human Services - HHS/CMS/CCSQ
Problem addressed: Improve efficiencies regarding development withing the CCSQ ServiceNow program.
Purpose and benefits: Improve time to deliver customer value through improved efficiencies regarding development withing the CCSQ ServiceNow program.
Topic area: IT
Development stage: Deployed
First used: 2026-10-24
US federal AI use case: AI-assisted comment triaging tool
Agency: Department of Health and Human Services - HHS/CMS/CM
Problem addressed: To reduce labor hours of manual comment review we use AI to assist in comment review and triage, and in identifying form letters.
Purpose and benefits: Cost savings and time savings to the government.
Topic area: Admin Functions
Development stage: Deployed
First used: 2026-06-22
US federal AI use case: Docketscope Public Comment Processing
Agency: Department of Health and Human Services - HHS/CMS/CM
Problem addressed: Docketscope is the platform used to triage public comments submitted on the OPPS/ASC and ESRD PPS proposed rules.
Purpose and benefits: Allows division staff to compile, organize, and process thousands of comments that inform final rulemaking.   This AI functionality may contribute to greater efficiencies in reviewing public comments by automating the process of identifying identical comments and allowing for quicker processing of comments with similar themes.
Topic area: Admin Functions
Development stage: Deployed
First used: 2026-04-23
US federal AI use case: Health Plan Management System - Complaint Tracking Analysis
Agency: Department of Health and Human Services - HHS/CMS/CM
Problem addressed: Support the analysis of MA and Part D beneficiary complaints data.
Purpose and benefits: Enhance CMS' understanding of beneficiary issues with MA and Part D plans.
Topic area: IT
Development stage: Pre-deployment
US federal AI use case: Performance Metrics Database and Analytics (PMDA)
Agency: Department of Health and Human Services - HHS/CMS/CMCS
Development stage: Retired
US federal AI use case: T-MSIS Prima
Agency: Department of Health and Human Services - HHS/CMS/CMCS
Problem addressed: T-MSIS program objectives, facilitates data driven decisions, and IT acceleration
Purpose and benefits: T-MSIS Business Outcomes Acceleration: Team Sprint Velocity Acceleration, Faster IT feature delivery, High-level Task Automation, Better Customer Outcomes – Initially faster responses to customer assistance
Topic area: Admin Functions
Development stage: Pre-deployment
US federal AI use case: EPRO HTA Reporting
Agency: Department of Health and Human Services - HHS/CMS/EPRO
Development stage: Retired
US federal AI use case: CMS Labor Analysis Wizard (CLAW)
Agency: Department of Health and Human Services - HHS/CMS/OAGM
Problem addressed: CLAW provides semi-automated analysis and insights into contractor-proposed labor for evaluation and negotiation purposes, enabling OAGM to make more informed procurement decisions that optimize contract terms and enhance the efficiency of the federal acquisition process.
Purpose and benefits: CLAW enables  OAGM to make more informed procurement decisions that optimize contract terms and enhance the efficiency of the federal acquisition process.
Topic area: Procurement and Finance Mgmt
Development stage: Deployed
First used: 2026-05-25
US federal AI use case: AI Code Assistance – WETG GitHub CoPilot Proof-of-Concept
Agency: Department of Health and Human Services - HHS/CMS/OC
Problem addressed: Coding assistance
Purpose and benefits: Cost savings, improved efficiency and quality
Topic area: IT
Development stage: Pilot
First used: 2026-08-25
US federal AI use case: AI Search in Google Vertex (discovery)
Agency: Department of Health and Human Services - HHS/CMS/OC
Problem addressed: Improved search
Purpose and benefits: Improved customer experience
Topic area: IT
Development stage: Pre-deployment
US federal AI use case: AI in Slack
Agency: Department of Health and Human Services - HHS/CMS/OC
Problem addressed: Improved Slack
Purpose and benefits: Improved operations
Topic area: IT
Development stage: Deployed
First used: 2026-07-25
US federal AI use case: AI in Figma Make
Agency: Department of Health and Human Services - HHS/CMS/OC
Problem addressed: Rapid prototyping
Purpose and benefits: Improved speed and efficiency
Topic area: IT
Development stage: Deployed
First used: 2026-07-25
US federal AI use case: AI Language Translation Support in Smartling
Agency: Department of Health and Human Services - HHS/CMS/OC
Problem addressed: Improved translation
Purpose and benefits: Cost savings, improved efficiency and quality
Topic area: IT
Development stage: Deployed
First used: 2026-09-24
US federal AI use case: AI Web Content Search Engine Optimization (SEO) in Drupal
Agency: Department of Health and Human Services - HHS/CMS/OC
Problem addressed: Search engine optimization
Purpose and benefits: Cost savings, improved efficiency and quality
Topic area: IT
Development stage: Pre-deployment
US federal AI use case: Medicare AI Customer Insights
Agency: Department of Health and Human Services - HHS/CMS/OC
Problem addressed: Improved customer insights
Purpose and benefits: Cost savings, improved efficiency and quality
Topic area: IT
Development stage: Pilot
First used: 2026-08-25
US federal AI use case: Medicare AI Drug Search
Agency: Department of Health and Human Services - HHS/CMS/OC
Problem addressed: Improved customer experience
Purpose and benefits: Improved access to government benefits
Topic area: Benefits Processing
Development stage: Pilot
First used: 2026-08-25
US federal AI use case: Marketplace Qualified Health Plan Benefit AI Assistant (discovery)
Agency: Department of Health and Human Services - HHS/CMS/OC
Problem addressed: Improved customer experience
Purpose and benefits: Improved benefits selection
Topic area: Benefits Processing
Development stage: Pre-deployment
US federal AI use case: AI Automation within the WETG Web Help Service Desk (discovery)
Agency: Department of Health and Human Services - HHS/CMS/OC
Problem addressed: Improved help desk service
Purpose and benefits: Cost savings, improved efficiency and quality
Topic area: IT
Development stage: Pre-deployment
US federal AI use case: Artificial Intelligence Use Case Pilot in Medical Review on Medicare Fee-For-Service (FFS) Improper Payment Measurement (Comprehensive Error Rate Testing Program) Data
Agency: Department of Health and Human Services - HHS/CMS/OFM
Problem addressed: OFM is  hoping to leverage cutting-edge AI technology to potentially introduce new efficiencies and accuracy in the Comprehensive Error Rate Testing (CERT) medical review process by moving away from manual medical review which is costly, sometimes inaccurate, and inefficient.
Purpose and benefits: Expected benefits include cost savings, new efficiencies and accuracy of clinical medical review decision making for reporting meaning Medicare FFS improper payment improper payments.
Topic area: IT
Development stage: Pre-deployment
US federal AI use case: Knowledge Management Solution
Agency: Department of Health and Human Services - HHS/CMS/OFM
Problem addressed: This AI-driven system addresses a critical challenge in government operations - preserving institutional knowledge.
Purpose and benefits: Key benefits include knowledge preservation by capturing and maintaining complex expertise, and resource development that helps onboard new staff and upskill existing team members. Additionally, operational efficiency is enhanced by enabling teams to accomplish more with existing resources. Continuity is ensured by reducing knowledge loss when experienced staff transition.
Topic area: IT
Development stage: Pre-deployment
US federal AI use case: ASSIST Tool
Agency: Department of Health and Human Services - HHS/CMS/OFM
Problem addressed: This tool focuses on strategic alignment and mission effectiveness across the team.
Purpose and benefits: This tool focuses on strategic alignment and mission effectiveness by ensuring that work activities directly support CMS's strategic objectives through Strategic Framework Integration. It maintains organizational direction and priorities with a Mission Focus. Furthermore, it drives performance improvements and best practices through Operational Excellence and demonstrates how daily work contributes to broader CMS goals with accountability.
Topic area: IT
Development stage: Pre-deployment
US federal AI use case: Executive Order Gap Analysis Tool
Agency: Department of Health and Human Services - HHS/CMS/OFM
Problem addressed: A specialized compliance and analysis solution that analyzes Executive Orders and program impact.
Purpose and benefits: The solution assists the program in compliance and analysis. It systematically reviews new Executive Orders and compares them against existing business and technical requirements. It identifies gaps where current processes might need adjustments and supports compliance to ensure CMS operations align with federal mandates.
Topic area: IT
Development stage: Pre-deployment
US federal AI use case: Contract Invoice Analyzer Assistant
Agency: Department of Health and Human Services - HHS/CMS/OFM
Problem addressed: Uses AI and automation to provide a more comprehensive review of contract invoices, mitigating waste, fraud, and abuse.
Purpose and benefits: Contract Invoice Analyzer is an AI-powered financial oversight tool designed to provide a comprehensive analysis of contract invoices. It automates part of the review process, identifying potential patterns of waste, fraud, and abuse. Additionally, it helps optimize contract spending and oversight.
Topic area: Procurement and Finance Mgmt
Development stage: Pre-deployment
US federal AI use case: AI-Powered Meeting Notes for MAG Hearings and AIRC Sessions
Agency: Department of Health and Human Services - HHS/CMS/OHI
Problem addressed: The AI is intended to address process redundancy by streamlining repetitive tasks, reducing manual effort, and minimizing duplication across workflows. This allows teams to operate more efficiently and focus on higher-value activities.
Purpose and benefits: The goal is to reduce manual note-taking, improve accuracy, and ensure timely documentation for case management and compliance.
Topic area: Admin Functions
Development stage: Pre-deployment
US federal AI use case: AI-Generated Pre-Briefs for MAG Hearings and AIRC Sessions
Agency: Department of Health and Human Services - HHS/CMS/OHI
Problem addressed: Utilize an AI system to compile structured pre-briefs for MAG Hearings and AIRC sessions.
Purpose and benefits: The goal is to provide Hearing Officers with a concise, data rich overview of the appeal case, enabling more focused and efficient sessions.
Topic area: Admin Functions
Development stage: Pre-deployment
US federal AI use case: Knowledge Management Solution for Appeal Case Workers
Agency: Department of Health and Human Services - HHS/CMS/OHI
Problem addressed: Utilize an AI solution to create a centralized, AI-enhanced Knowledge Management system to support case workers by providing quick access to relevant SOPs, policy guidance, workflows, forms, and call scripts
Purpose and benefits: The goal is to improve consistency, reduce research time, and enhance the quality of appeal processing.
Topic area: Admin Functions
Development stage: Pre-deployment
US federal AI use case: AI Workspace
Agency: Department of Health and Human Services - HHS/CMS/OIT
Problem addressed: Hosted development env with access to AI tooling, including LLMs for use in early stage exploration.
Purpose and benefits: AI Workspace provides a hosted development environment with access to AI tooling including LLMs, enabling CMS teams to conduct early-stage AI exploration and experimentation that can lead to innovative solutions
Topic area: IT
Development stage: Deployed
First used: 2026-04-25
US federal AI use case: CMS Chat
Agency: Department of Health and Human Services - HHS/CMS/OIT
Problem addressed: Workforce productivity and operational efficiency
Purpose and benefits: The expected benefits include significant productivity gains and enhanced operational efficiency through streamlined work.
Topic area: IT
Development stage: Deployed
First used: 2026-12-24
US federal AI use case: Content Analysis POC
Agency: Department of Health and Human Services - HHS/CMS/OIT
Problem addressed: Content analysis POC for the automated analysis of qualitative data including comments, complaints, stakeholder interviews, surveys, etc.
Purpose and benefits: The content analysis POC will enhance productivity and operational efficiency by automating the analysis of qualitative data from comments, complaints, stakeholder interviews, and surveys, enabling CMS staff to process large volumes of feedback more quickly and systematically to improve healthcare programs and services.
Topic area: IT
Development stage: Pilot
First used: 2026-07-25
US federal AI use case: Enterprise Architecture LLM for CMS Regulatory Content
Agency: Department of Health and Human Services - HHS/CMS/OIT
Problem addressed: Enhance the Enterprise Architecture (EA) Knowledgebase by creating a centralized, searchable repository of CMS regulatory knowledge, and make complex regulatory information available within the EA environment.
Purpose and benefits: Provides insight into how a law, regulation, policy or guidance will impact CMS’ programs, business functions, stakeholders, and systems.
Topic area: IT
Development stage: Pre-deployment
US federal AI use case: Text Network Analysis POC
Agency: Department of Health and Human Services - HHS/CMS/OIT
Development stage: Retired
US federal AI use case: RFI Comment Analysis POC
Agency: Department of Health and Human Services - HHS/CMS/OIT
Problem addressed: Addresses the challenge of efficiently analyzing and extracting insights from large volumes of comments in response to an RFI.
Purpose and benefits: The AI solves the challenge of efficiently conducting qual data analysis on RFI comments.
Topic area: IT
Development stage: Pilot
First used: 2026-07-25
US federal AI use case: AI Agent Orchestrator POC
Agency: Department of Health and Human Services - HHS/CMS/OIT
Problem addressed: The AI Agent Orchestrator solves the problem that current chatbots are limited to question-and-answer interactions and cannot orchestrate complex, multi-step analytical workflows that require calling external data science notebooks and tools in the correct sequence to solve sophisticated problems.
Purpose and benefits: The AI Agent Orchestrator will transform CMS's analytical capabilities by enabling any staff member to execute sophisticated, multi-step data science workflows through natural language interactions, eliminating the current barrier of requiring specialized technical expertise to access complex analytical tools and notebooks.
Topic area: IT
Development stage: Pilot
First used: 2026-07-25
US federal AI use case: MCP Server Registry and Integration Platform
Agency: Department of Health and Human Services - HHS/CMS/OIT
Problem addressed: The MCP Server Registry solves the problem that CMS teams currently lack a centralized, standardized platform to develop, share, and integrate specialized AI tools and capabilities across the organization
Purpose and benefits: The MCP registry and integration platform will support agentic AI tool development across CMS by enabling any team to build and share specialized capabilities through standardized MCP servers
Topic area: IT
Development stage: Pre-deployment
US federal AI use case: Agentic Web Search
Agency: Department of Health and Human Services - HHS/CMS/OIT
Problem addressed: The AI-powered web search agent solves the problem that CMS's custom chatbot, CMS Chat currently lacks internet access and is limited to training data with a cutoff date, preventing staff from accessing real-time information
Purpose and benefits: The web search agent will significantly enhance the chatbot's capabilities by providing access to real-time information
Topic area: IT
Development stage: Pre-deployment
US federal AI use case: Deep Research Multi-Agent System
Agency: Department of Health and Human Services - HHS/CMS/OIT
Problem addressed: The Deep Research multi-agent system solves the problem that CMS employees currently cannot conduct research directly within CMS Chat.
Purpose and benefits: The Deep Research system will enable CMS employees to conduct, multi-faceted research directly within CMS Chat by automatically decomposing complex queries into targeted subqueries, searching across web and internal data sources simultaneously
Topic area: IT
Development stage: Pre-deployment
US federal AI use case: 508 Compliance Review MCP Server
Agency: Department of Health and Human Services - HHS/CMS/OIT
Problem addressed: The 508 Compliance Review MCP server solves the problem that CMS staff currently must manually review documents for Section 508 accessibility compliance, which requires specialized knowledge of accessibility standards, is time-consuming, and may result in inconsistent evaluations
Purpose and benefits: The 508 Compliance Review system will enable CMS staff to automatically evaluate documents for accessibility compliance through CMS Chat and other systems
Topic area: IT
Development stage: Pre-deployment
US federal AI use case: PubMed Literature Review MCP Server
Agency: Department of Health and Human Services - HHS/CMS/OIT
Problem addressed: The PubMed Literature Review MCP server solves the problem that CMS teams need to carry out various types of literature reviews, commonly using PubMed data, but lack an internal tool for doing this at greater scale than manual effort allows
Purpose and benefits: Enable CMS teams to conduct literature reviews at scale through an internal tool that automates PubMed data analysis and synthesis
Topic area: IT
Development stage: Pilot
First used: 2026-08-25
US federal AI use case: SAM.gov RFI Comment Analysis POC
Agency: Department of Health and Human Services - HHS/CMS/OIT
Problem addressed: The SAM.gov RFI Comment Analysis POC solves the problem that CMS procurement staff currently must manually review and score vendor responses to Requests for Information (RFI) posted on SAM.gov, which is time-intensive, especially when there are many questions and many vendors responding.
Purpose and benefits: The RFI Comment Analysis POC will enable CMS teams to systematically evaluate vendor responses using AI-powered analysis, improving the efficiency of assessments.
Topic area: IT
Development stage: Pilot
First used: 2026-08-25
US federal AI use case: C3PO: CMS Comprehensive Cybersecurity and Privacy Optimization
Agency: Department of Health and Human Services - HHS/CMS/OIT
Problem addressed: Reduce the burden of reviewing lengthy OMB, NIST and HHS policy documents, quicker review of SORN and PIAs
Purpose and benefits: Reducing the response time to new or emerging policies and technologies, the ability to adequately review privacy agreements in the event of incident
Topic area: IT
Development stage: Pilot
First used: 2026-10-24
US federal AI use case: Integrated Data Repository (IDR) Customer Analytic Environment (CAE)
Agency: Department of Health and Human Services - HHS/CMS/OIT
Problem addressed: Provides an AI/ML environment connected to the IDR that enables CAE customers to focus on their AI use cases and business outcomes, without the burden of setting up or maintaining their own IT infrastructure or AI/ML labs or environments.
Purpose and benefits: The CAE provides CMS Centers and Offices with secure, fully managed AI/ML workspaces directly integrated with the IDR, eliminating the need to build or maintain separate infrastructure. This accelerates AI innovation, shortens time-to-insight, and enables scalable adoption of advanced analytics across the agency.
Topic area: IT
Development stage: Pilot
First used: 2026-07-25
US federal AI use case: IDR Support Bot
Agency: Department of Health and Human Services - HHS/CMS/OIT
Problem addressed: The IDR Support Bot is an AI-powered chatbot that makes it easier for CMS users to get the IDR information they need, when they need it. It answers questions about IDR terminology, tools and services, onboarding steps, available data, training content, key contacts, and more—without the hassle of submitting a support request or digging through pages of documentation.
Purpose and benefits: Any CMS user with an active ID can use the IDR Support Bot to gain a foundational understanding of the IDR, its data offerings, and how to navigate its resources. The IDR Support Bot leverages retrieval-augmented generation (RAG) technology to provide accurate and contextually relevant responses to user queries. By combining a structured knowledge base with real-time document retrieval, IDR Support Bot ensures that users receive up-to-date and comprehensive information about the IDR.
Topic area: IT
Development stage: Deployed
First used: 2026-07-25
US federal AI use case: Eligibility & Enrollment Medicare Online (ELMO) Chatbot
Agency: Department of Health and Human Services - HHS/CMS/OIT
Problem addressed: Assist Medicare caseworkers in navigating a fairly complex application
Purpose and benefits: More efficient caseworkers
Topic area: IT
Development stage: Deployed
First used: 2026-07-25
US federal AI use case: Production Operations Anomaly Analysis
Agency: Department of Health and Human Services - HHS/CMS/OIT
Problem addressed: Identify potential production issues proactively and refer for human analysis
Purpose and benefits: Early identification of issues and improved response time, ensure accuracy of bills
Topic area: IT
Development stage: Pilot
First used: 2026-06-25
US federal AI use case: Case Management Tool Case Creation & Automation
Agency: Department of Health and Human Services - HHS/CMS/OIT
Problem addressed: Automate and optimize manual case creation for incoming documents/requests and simplify manual tasks
Purpose and benefits: Improved case completion volume, higher caseworker productivity
Topic area: IT
Development stage: Pilot
First used: 2026-07-25
US federal AI use case: Complaint Analysis
Agency: Department of Health and Human Services - HHS/CMS/OPOLE
Problem addressed: Conduct high volume case analysis to identify root causes/trends that can be validated by SMEs and act as a workforce multiplier.
Purpose and benefits: Reduce repeat issues delaying benefits or access to care, and improve health plan compliance with federal rules and regulations.
Topic area: Health and Medical
Development stage: Pilot
First used: 2026-02-24
US federal AI use case: Medicare Part C/D Marketing Material Review
Agency: Department of Health and Human Services - HHS/CMS/OPOLE
Problem addressed: Expand the volume of materials reviewed while providing consistent insights into trends and issues with materials received.
Purpose and benefits: reduce cycle time and increase volume reviewed
Topic area: Health and Medical
Development stage: Pre-deployment
US federal AI use case: AI to Improve Public Access to the Administrative Appeals Process
Agency: Department of Health and Human Services - HHS/DAB/OS
Problem addressed: More effectively leverage online resources to better assist the public and reduce the number of misfiled appeals submitted to the Departmental Appeals Board (DAB) via electronic filing.
Purpose and benefits: DAB adjudicatory divisions rely heavily on electronically filed (E-filed) appeals. Most appellants access E-filing through the DAB's website. A chatbot on the website will reduce filing errors and improve customer experience by directing appellants to the correct DAB adjudicatory division responsible for deciding their appeal.
Topic area: Service Delivery
Development stage: Pre-deployment
US federal AI use case: AI Use Policy Tool
Agency: Department of Health and Human Services - HHS/DAB/OS
Problem addressed: Ensure public confidence in the integrity of DAB decisions by maximizing quality review standards and reducing errors.
Purpose and benefits: The DAB is responsible for issuing fair, impartial, legally correct and defensible determinations which serve as the final decision of the DHHS Secretary. The AI quality review large language model (LLM) tool will use an LLM to create algorithms that run behind our case tracking system to randomly select certain DAB decisions to identify potential quality review issues. The LLM will scan DAB decisions to ensure compliance with quality review standards (e.g., protect PHI, PII and FTI). Benefits include more effective quality review and faster identification of data trends that may require additional analysis.
Topic area: Other
Development stage: Pre-deployment
US federal AI use case: Resources to Assist the Advisory Board In Identifying AI Tools for Use In An Adjudication Environment
Agency: Department of Health and Human Services - HHS/DAB/OS
Problem addressed: Agency adjudication activities require the analysis of large quantities of data to conduct docket analysis, identify efficiencies in case processing, and conduct 508 compliance required to make decisions available to the public.
Purpose and benefits: Use AI to analyze data received from appellants and interested parties to identify trends, increase efficiency in case processing and improve adjudication outcomes.
Topic area: Other
Development stage: Pre-deployment
US federal AI use case: LLM-Assisted VAERS Analyses
Agency: Department of Health and Human Services - HHS/FDA/CBER
Problem addressed: Extraction of relevant information, tabulation of data, find patterns across adverse events reports, and generate hypotheses for further investigations.
Purpose and benefits: Build capacity for and assess the application of a LLM to VAERS (Vaccine Adverse Events Reporting System) to provide reviewers adhoc VAERS queries and efficiently generate customized query outputs. Efficiently generate customized query outputs of VAERS queries for reviewers.
Topic area: Health and Medical
Development stage: Pre-deployment
US federal AI use case: Process Large Amount of Submitted Docket Comments
Agency: Department of Health and Human Services - HHS/FDA/CBER
Problem addressed: Deduplication of public comments, and generating draft sentiment analysis and grouping of comments
Purpose and benefits: To enhance the automatic process of dockets, we have created an AI/ML tool in CBER/HIVE that automatically download dockets and process them to accelerate the review of docket comments, significantly improving the efficiency and accuracy of our regulatory processes. Efficiently generate customized query outputs of VAERS queries for reviewers.
Topic area: IT
Development stage: Pilot
First used: 2026-06-23
US federal AI use case: HIVE AI Pilot
Agency: Department of Health and Human Services - HHS/FDA/CBER
Problem addressed: The AI is intended to assist in solving several problems related to the review process for INDs.  Specifically, it aims to address issues of inefficiency, delays in identifying deficiencies and information overload.  By providing recommendations for review disciplines, it helps reviewers to quickly identify potential review disciplines that are required.  By identifying grossly deficient submissions early on, it reduces the workload and highlighting key data helps reviewers to focus on higher-level tasks that require their expertise.
Purpose and benefits: Overall, is designed to improve efficiency and effectiveness of the regulatory review process, allowing for quicker and well-informed decision making
Topic area: Health and Medical
Development stage: Pilot
First used: 2026-07-25
US federal AI use case: AI and Vaccine Labeling
Agency: Department of Health and Human Services - HHS/FDA/CBER
Problem addressed: The app is designed to streamline vaccine label review processes, offering several key features to simplify and improve efficiency. It includes MedDRA integration for term searches, comprehensive search across all vaccine documents, and vaccine-specific information retrieval. Additionally, the app provides lookup functionality for approval timelines and active ingredients, as well as tools for detecting duplicates and comparing section content using AI-enhanced technology.
Purpose and benefits: Enhance the vaccine label review process, making it more efficient and effective.
Topic area: Health and Medical
Development stage: Pre-deployment