GreatNorthCollective/greatnorth-us-federal-ai-inventory huggingface dataset:
US federal AI use case: Claims Processing Automation Quality Auditor POC
Agency: Department of Veterans Affairs - OIT: Office of Information & Technology
Problem addressed: In an automation-driven claims process, this tool will act as an entry point for quality reviews: Perform claims quality audits to identify and request NQA reviews or VCE corrections based on automated claims processing anomalies/errors. Function of the model: Perform claims quality audits to identify and request NQA reviews or VCE corrections based on automated claims processing anomalies
Purpose and benefits: Increased efficiency, improved benefit outcomes, faster decisions for Veterans.
Topic area: Benefits Processing
Development stage: Pre-deployment
Risk category: High-impact
US federal AI use case: GenAI for KM
Agency: Department of Veterans Affairs - VEO: Veterans Experience Office
Problem addressed: To provide agents/users content insight capabilities beyond simple keyword search currently available and improve response time & quality and incorporate updated source content in near real-time in future answers.
Purpose and benefits: Improve response time & quality information provided to customers
Topic area: Service Delivery
Development stage: Pre-deployment
US federal AI use case: Education Call Center (ECC) Next Gen POC
Agency: Department of Veterans Affairs - VBA: Veterans Benefits Administration
Problem addressed: To give easier access to information like approved, current procedures or helpful tips to call center employees that engage directly with Veterans. This is a high turnover position within VA so this would lighten the training barrier and supply our Veterans with better information.
Purpose and benefits: Increased efficiency and improved communication outcomes with Veterans about their benefits. Perhaps improve turnover at the call centers.
Topic area: Service Delivery
Development stage: Pre-deployment
US federal AI use case: No Missed Malignancy (NMM)
Agency: Department of Veterans Affairs - VHA: Veterans Health Administration
Problem addressed: Enhance clinical decision support capabilities and provide information not easily obtained through existing systems so that abnormal radiology findings are acted upon promptly—reducing risk of delayed cancer diagnoses and aligning with federal standards for patient safety and clinical accountability. The model has been successfully deployed to production.
Purpose and benefits: By summarizing pending and processed alerts and expanding support to additional stations, this solution can increase efficiency and provide clinicians with critical information not easily accessible through existing CPRS/VistA systems
Topic area: Health and Medical
Development stage: Deployed
Risk category: High-impact
US federal AI use case: AI Personal Assistance POC
Agency: Department of Veterans Affairs - VBA: Veterans Benefits Administration
Problem addressed: Help VBA Education Data Analysts create queries and interpret data which can be used for decision making.
Purpose and benefits: Increased efficiency
Topic area: Admin Functions
Development stage: Pre-deployment
US federal AI use case: AI in Digital Pathology
Agency: Department of Veterans Affairs - VHA: Veterans Health Administration
Problem addressed: Galen Second Read's key functionalities include image upload and analysis, flagging slides with high likelihood to contain AdC and displaying WSIs uploaded to the system along with the analysis results. Flagged findings constitute a recommendation for additional review by a pathologist.
Purpose and benefits: Increased productivity which will provide pathologists more time with complex cases
Topic area: Health and Medical
Development stage: Pre-deployment
Risk category: High-impact
US federal AI use case: Sleep Apnea
Agency: Department of Veterans Affairs - VHA: Veterans Health Administration
Problem addressed: Improve Veteran care by increasing access to diagnosis and treatment of Veterans at high risk for sleep apnea, a condition that is not recognized in an estimated 80-90% of people who have it but poses significant negative impact on quality of life, productivity, sleep satisfaction, daytime fatigue, motor vehicle accidents, hypertension, and possibly other comorbidities(associations via observational trials of sleep apnea and cardiovascular morbidity and mortality)

The AI solution predicts a patient's risk of Sleep Apnea. Clinicians use this information to increase access to diagnosis and treatment of patients at high risk for sleep apnea.  A data pipeline, not a person, gathers the data and a LLM summarizes the information.  Outputs are graded by humans to determine accuracy.
Purpose and benefits: Increased efficiency in delivering healthcare.
Topic area: Health and Medical
Development stage: Deployed
Risk category: High-impact
US federal AI use case: Coordinated Care Tracking System (CCTS) Generative AI Support
Agency: Department of Veterans Affairs - VHA: Veterans Health Administration
Problem addressed: To support the CCTS application we have created a generative AI pipeline that goes through all of the radiology and pathology notes for a day and extracts all references to all cancer related findings (tumors, nodules, masses, etc) and extracts all of the characteristics and comments of the findings.  These extracts will then be integrated in the CCTS application to ensure that proper diagnostic codes have been added to the scans so that a dangerous tumor is not missed.  There is no fine-tuning with this system.  Extracts from the notes are graded by humans to assess accuracy. This will provide insights to inform case follow up, scalable computing power for the CCTS clinical application​.
Purpose and benefits: Increased efficiency in healthcare delivery.
Topic area: Health and Medical
Development stage: Deployed
Risk category: High-impact
US federal AI use case: BESSistant
Agency: Department of Veterans Affairs - VHA: Veterans Health Administration
Problem addressed: This use case will cut down the amount of time it takes an individual to search manuals for preventative maintenance and troubleshooting instructions - the AI chatbot returns answers to queries from uploaded maintenance documentation.
Purpose and benefits: Increased Efficiency
Topic area: Admin Functions
Development stage: Pre-deployment
US federal AI use case: Hospital Acquired Infections
Agency: Department of Veterans Affairs - VHA: Veterans Health Administration
Problem addressed: Automate the identification of hospital acquired infections to meet VA reporting requirements to reduce the reporting burden on health care provider staff.

We are creating a generative AI pipeline that will read through structured and unstructured data to identify the patients with central lines who got an infection during an in-patient stay at a VA facility.  There is no fine-tuning with this system. The patients identified are graded by humans and compared to known cases to assess accuracy.
Purpose and benefits: Increased efficiency in healthcare outcomes
Topic area: Health and Medical
Development stage: Deployed
US federal AI use case: SCORE (Structured Clinical Output Rating Engine)
Agency: Department of Veterans Affairs - VHA: Veterans Health Administration
Problem addressed: SCORE (Structured Clinical Output Rating Engine) uses o3-mini LLM to grade the quality of clinical notes that have either been created by a human or AI and provides feedback to the user. 
 
When a transcript of the patient encounter is available, SCORE employs embedding-based semantic analysis to enhance patient safety and documentation accuracy. Embeddings convert text into numerical vectors that capture meaning, enabling the system to measure how semantically similar—or different—statements are between the clinical note and the source transcript.
 
This capability powers two critical safety detectors:
 
Hallucination Detection identifies factual-sounding claims in the note that lack support in the transcript. This is essential for AI-generated documentation, where models may produce plausible but ungrounded statements about medications, allergies, diagnoses, or procedures. Claims with weak transcript support are flagged with risk levels (high/medium/low) based on their potential clinical impact.
 
Contradiction Detection finds instances where the note and transcript discuss the same clinical topic but present conflicting information—such as differing medication dosages, mismatched vital signs, or inverted symptom reports (e.g., "denies chest pain" versus "reports chest pain").
Purpose and benefits: Improve clinical documentation quality through standardized evaluation and scoring and create efficiency for documentation audit staff.
Topic area: Health and Medical
Development stage: Pre-deployment
US federal AI use case: Generative Annotations
Agency: Department of Veterans Affairs - VBA: Veterans Benefits Administration
Problem addressed: Veteran Service Representatives (VSRs) are required to annotate documents relevant to a Veteran claim so that subsequent reviewers are able to quickly reference information to make claim decisions. Currently in order to annotate documents, VSRs have to read large bodies of text and manually summarize them which is time consuming and increases cognitive load.
Purpose and benefits: Increased efficiency
Topic area: Benefits Processing
Development stage: Pre-deployment
US federal AI use case: Lambient
Agency: Department of Veterans Affairs - VHA: Veterans Health Administration
Problem addressed: Clinical burnout and decrease amount of late night time for documentation.
Purpose and benefits: Improve clinical staff satisfaction by letting them spend time with the patient and less time documenting the encounter. This would hopefully improve patient satisfaction.
Topic area: Health and Medical
Development stage: Pre-deployment
Risk category: High-impact
US federal AI use case: Lexis+
Agency: Department of Veterans Affairs - OGC: Office of General Counsel
Problem addressed: Improve the quality and efficiency of legal research, legal document review, and legal document preparation. The Lexis+ Document Analysis tool will improve efficiency and accuracy of legal services by giving OGC legal professionals the ability to have documents read and researched by the tool, reducing the time and legal skills required for legal research, legal writing, legal reviews, and trial preparation. The AI solution is preparing and synthesizing notes to be used by the legal teams; it is not preparing a conclusion or draft decision for legal teams.
Purpose and benefits: - Better outcomes from litigation
- Improve the timeliness and accuracy of legal advice
Topic area: Other
Development stage: Pre-deployment
Risk category: High-impact
US federal AI use case: Siemens Sequoia Crown
Agency: Department of Veterans Affairs - VHA: Veterans Health Administration
Problem addressed: The ACUSON Sequoia and Sequoia Select ultrasound imaging systems are intended to provide images of, or signals from, inside the body by an appropriately trained healthcare professional in a clinical setting for the following applications: Fetal, Abdominal, Pediatric, Neonatal Cephalic, Small Parts ,Obstetrics & Gynecology (OB/GYN) (useful for visualization of the ovaries, follicles, uterus and other pelvic structures), Cardiac, Pelvic, Vascular, Adult Cephalic, Musculoskeletal and Peripheral Vascular applications.
Purpose and benefits: Accelerate ultrasound workflows.
Topic area: Health and Medical
Development stage: Deployed
Risk category: High-impact
US federal AI use case: Heuristic Behavior Analytics
Agency: Department of Veterans Affairs - OIT: Office of Information & Technology
Problem addressed: Identify anomalous behaviors from established baselines for VA Accounts and Hosts. The output is a Risk score that is provided to CSOC Analysts to more quickly identify accounts and hosts that have been compromised after the initial authentications associated with a computational session
Purpose and benefits: Quicker identification, response, and mitigation of cybersecurity incidents.
Topic area: Cybersecurity
Development stage: Deployed
Risk category: High-impact
US federal AI use case: TherapyTrainer
Agency: Department of Veterans Affairs - VHA: Veterans Health Administration
Problem addressed: Simulation and feedback for training in evidence-based psychotherapies. This technology is intended to allow therapists to have just-in-time support and feedback when learning a new treatment. Problem to be solved: Problems scaling training; there are limited available experts to provide consultation and feedback.
Purpose and benefits: more rapid training and skill acquisition; more therapists available to provide high quality evidence-based care.
Topic area: Health and Medical
Development stage: Pre-deployment
US federal AI use case: Internal QMS Processes Revamp
Agency: Department of Veterans Affairs - VHA: Veterans Health Administration
Problem addressed: 1. Planning & Restructuring Assistant: Utilize GPT-4x (or more advanced available models with reasoning capabilities) to analyze current QMS structures, identify optimization opportunities, and generate actionable restructuring plans. 
2. Document Classification System: Implement GPT-4x with structured output to automatically classify, tag, and organize QMS documentation based on content, purpose, and relevance. 
3. Code Development Support: Assist developers with code generation, optimization. 
4. VBECS Knowledge Base: Build a Retrieval Augmented Generation (RAG) system using embeddings to create a searchable repository of VBECS system knowledge. 
5. Intelligent RAG Access Agent: Create an agentic interface using GPT-4x that can intelligently navigate the VBECS knowledge base to retrieve precise information and answer complex queries.

AI does not directly output SOPs. Instead, it provides information and guidance to assist a human in drafting the SOPs. The final output is created by a person based on the tool’s suggestions and support.
Purpose and benefits: Assists us with meeting regulatory deadlines in February 2026.
Topic area: Other
Development stage: Pre-deployment
US federal AI use case: Community Care Records Retrieval
Agency: Department of Veterans Affairs - VHA: Veterans Health Administration
Problem addressed: VA care teams often struggle to access Community Care documents in a timely manner. When access is granted, the process is labor-intensive, requires significant manual administration by clinical staff, and is not always successful. Veterans may also be asked to bring in a copy of their records when VA providers are unable to obtain them. This negatively impacts Veterans’ care coordination and makes them lose trust in VA.
Purpose and benefits: Increased efficiency in coordinating VA community care documentation, enhancing Veteran satisfaction with care and increasing quality of care and Veteran safety.
Topic area: Health and Medical
Development stage: Pre-deployment
Risk category: High-impact
US federal AI use case: CPT Worksheet Helper
Agency: Department of Veterans Affairs - VHA: Veterans Health Administration
Problem addressed: Patients struggle to complete Cognitive Processing Therapy (CPT) homework and want feedback beyond when they see their therapist. Research has shown that homework frequency and quality can support greater improvement.
Purpose and benefits: Patients may remain more engaged in treatment and experience greater improvement.
Topic area: Health and Medical
Development stage: Pre-deployment
Risk category: High-impact
US federal AI use case: AI-Assisted View Alert Management
Agency: Department of Veterans Affairs - VHA: Veterans Health Administration
Problem addressed: By analyzing and prioritizing alerts, this tool will reduce unnecessary notifications and help clinicians focus on what matters most, enhancing care for Veterans.
Purpose and benefits: - Reduce alert fatigue and clinician burnout by minimizing unnecessary distractions. 
- Improve clinical efficiency and workflows through targeted triage and routing of critical alerts. 
- Enhance patient safety and resource allocation by prioritizing meaningful and actionable view alerts. 
- Increase return on existing technology investments by optimizing the performance of the current alerting functionality.
Topic area: Health and Medical
Development stage: Pre-deployment
Risk category: High-impact
US federal AI use case: AI Transcription and Summary in Veteran Narrative Medicine Program
Agency: Department of Veterans Affairs - VHA: Veterans Health Administration
Problem addressed: 1) The MyLife MyStory program tends to be time intensive, often requiring about four hours of the interviewers' time per patient, which limits the number of Veterans who can receive the program. 
2) The narratives that are produced in MyLife MyStory are sometimes lengthy. When provided with an AI-generated summary, busy clinicians accessing the narrative in a patient's chart may come away with a clearer understanding of the patient's story.
Purpose and benefits: 1) Greater efficiency in the MyLife MyStory program with preserved acceptability, allowing for more Veterans to enjoy and benefit from the program.  
2) Due to the inclusion of patient-approved, AI-generated summaries, busy clinicians accessing MyLife MyStory narratives in patients' charts will more often come away with a clear understanding of the patients' experiences through their stories
Topic area: Health and Medical
Development stage: Pilot
US federal AI use case: CITC Automated Fax Relabeling
Agency: Department of Veterans Affairs - VHA: Veterans Health Administration
Problem addressed: Speed intake of outside medical records. Currently the VA system as a whole gets millions of pages of faxes a day. The subset of these related to care in the community (CITC) contain results of care that the VA itself has paid for in non-VA facilities. As CITC use increases, the burden of processing these faxes (uploading them and making them useful for VA physicians) is increasing. Delays in upload time lead to missing information about non-VA care.
Purpose and benefits: Enhancing patient outcomes (results of care that we are paying for is made more quickly available within the VA); increased efficiency (reduce or eliminate tedious nursing work of manually renaming faxes).
Topic area: Health and Medical
Development stage: Pilot
Risk category: High-impact
US federal AI use case: Ephesoft Fax Automation
Agency: Department of Veterans Affairs - VHA: Veterans Health Administration
Problem addressed: Ensure timely healthcare by increasing speed of processing incoming faxes so Request For Services (RFS) and medical records can be clinically reviewed and uploaded to patient electronic health records.
Purpose and benefits: Estimated cost savings of $2 million while enhancing the timeliness of care, standardizing processes and reducing human error.
Topic area: Health and Medical
Development stage: Pre-deployment
Risk category: High-impact
US federal AI use case: Natural Language Online Workflow (NOW)
Agency: Department of Veterans Affairs - VHA: Veterans Health Administration
Problem addressed: - Information Gaps and Misinformation: Veterans and staff often have trouble finding accurate, consistent answers about vasectomy procedures, eligibility, risks, and next steps. Policies and contacts change and not everyone knows where to look.
- Inefficient Consult Request Process: Requesting a consult can be confusing, requiring multiple phone calls, paper forms, or follow-ups that delay care. Veterans may give up or receive the wrong info, which slows down the process and frustrates everyone.
- Inefficient Use of Staff Time on Routine Questions: Clinical and administrative staff spend a lot of time answering the same questions or redirecting Veterans to the right place. This eats up resources that could be used for more complex cases.
- Missed or Delayed Care: If eligibility is unclear, or the Veteran doesn’t know how to proceed, appointments and procedures get delayed. This can lead to worse outcomes and more work down the line.
- Inconsistent Communication: Without a standard source, Veterans might get different answers depending on who they ask or what site they visit.
Purpose and benefits: - Centralizes reliable information in one place and it is always up-to-date.
- Removes ambiguity for users by guiding them through eligibility and next steps.
- Streamlines the consult request process, making it easy to start and track the process.
- Reduces repetitive work for staff, so they can focus on higher-priority needs.
- Flags new or complex questions for human follow-up, so nothing falls through the cracks.
Topic area: Service Delivery
Development stage: Pre-deployment
Risk category: High-impact
US federal AI use case: Sharing RVU Provider Metrics
Agency: Department of Veterans Affairs - VHA: Veterans Health Administration
Problem addressed: Next Fiscal year, the VA will be focusing heavily on RVUs to measure provider productivity. However, the data currently can only be found in Pyramid Analytics, which is very difficult to use and very few people have access to it. This leaves all frontline employees, and many supervisors, in the dark as far as whether they are meeting their performance metrics.
Purpose and benefits: This AI model will read the data from the RVU report and send an individual email to each employee, with their supervisor CC'ed, so that they know where they stand on their RVU metrics.
Topic area: Admin Functions
Development stage: Pilot
US federal AI use case: Use of Large Language Models to improve classification of identified text snippets from the CLEVER natural language processing pipeline
Agency: Department of Veterans Affairs - VHA: Veterans Health Administration
Problem addressed: Use of text matching approaches like CLEVER to identify clinical information of interest in free-text clinical notes is an efficient approach but can be over-inclusive (e.g. including terms when used with an alternative meaning, for example, a search for “arms” may pick up mentions of forearms and firearms).  A variety of approaches can be used to further filter identified text snippets to those more likely to be of interest for a given use case.  Here we use large language model queries to classify text snippets as being relevant to a concept of interest.
Purpose and benefits: Improved classification of text snippets from clinical notes will increase clinician and management efficiency in use of decision support to summarize and review information extracted from free-text clinical notes.  For example, improved filtering of text snippets for clinical risk factors of interest will reduce clinician time in obtaining clinically needed information about risk factors of interest by filtering out text snippets that are unlikely to be meaningful to their immediate clinical decision.  In short, the clinician will have fewer candidate text snippets to read through to get the information they need.  Likewise, summary views of identified possible concept mentions will be more accurate, reducing noise in views to support strategic decisions.
Topic area: Health and Medical
Development stage: Deployed
US federal AI use case: Redseal AI Usage
Agency: Department of Veterans Affairs - OIT: Office of Information & Technology
Problem addressed: The cyber risk analytics AI is designed to provide enhanced risk prioritization and likelihood of exposure, validating impact through AI orchestrated workflows and automation.
Purpose and benefits: Increased efficiency by reducing incident response guesswork and accelerating remediation.
Topic area: Cybersecurity
Development stage: Pre-deployment
US federal AI use case: Ambient Scribing with Patient Chart Exploration
Agency: Department of Veterans Affairs - VHA: Veterans Health Administration
Problem addressed: The typical clinical workflow in primary care is complex, with numerous interruptions and areas of friction. I want to understand how AI could augment my flow by replacing the lower-tier cognitive work and allowing me to focus on making decisions and providing counseling. Much of this cognitive load is now invisible, even to myself. For example, for a patient with diabetes, I may spend time hunting down the appropriate labs and medication history as well as summarizing the subjective information they've shared over the years about the difficulties they've had in adherence. If this could be done for me, similar to what a clinical assistant or medical trainee may do, I would be better able to care for my patient in the here and now.

There are many people working on optimizing ambient scribing and AI summarization/querying. My goal is not to create a state of the art version of either, but to focus on understanding how basic versions of these tools could help me. That may include context-aware changes in data presentation, the use of visualization, and improvements in search and browsing.
Purpose and benefits: This tool could help replace the lower-tier cognitive work and allow me to focus on making decisions, providing counseling, and overall, be better able to care for my patients. The hope would be to create a framework that would be available through Clinical Decision Support (CDS) or another internal VA platform, allowing clinicians to customize chart data for their own workflows. Improved scribing, natural language processing and AI summarization could be swapped in as they develop further over time.
Topic area: Health and Medical
Development stage: Pre-deployment
Risk category: High-impact
US federal AI use case: Program Evaluation for Commission on Collegiate Nursing Education (CCNE)
Agency: Department of Veterans Affairs - VHA: Veterans Health Administration
Problem addressed: To better understand and manage large volumes of written data, that assist in identifying student trends and curriculum gaps, and evaluating the program, utilizing CCNE standards and expected outcomes.
Purpose and benefits: - Improved patient safety by enhancing learner outcomes and assisting and guiding nursing resident progress from advanced beginner to competent professional.
- Increased efficiency by reducing the labor hours spent evaluating large volumes of data. The saved time can be shifted to curriculum development, resident oversight, and program improvement.
- Assisting in attaining Registered Nurse Transition-to-Practice (RNTTP) program CCNE accreditation.
Topic area: Other
Development stage: Pilot
US federal AI use case: eDiscovery (VASI 2460): Predictive Coding
Agency: Department of Veterans Affairs - OIT: Office of Information & Technology
Problem addressed: Document review teams train an AI model to identify positive or negative content within a universe of documents.
Purpose and benefits: Prioritizes and surfaces relevant documents early to speed review and improve accuracy.
Topic area: Admin Functions
Development stage: Deployed
US federal AI use case: Using LLMs to assist with data extraction
Agency: Department of Veterans Affairs - VHA: Veterans Health Administration
Problem addressed: We are investigating the ability of Large Language Models (LLMs) to automate the extraction of discrete data elements from clinical notes. The National Radiation Oncology Program (NROP) office is using Quality Measures to evaluate the quality of care to veterans receiving radiotherapy inside of the VA and in the community. While a much value was found in the data collected in prior projects, the manual data extraction process was expensive, time consuming and still error prone. The purpose of this work is to determine the feasibility of using modern AI technology to reduce the burden of collecting this information from clinical notes.

AI will be used to automate data extraction from free text notes which is not possible at scale because of the cost and the level of human labor required.
Purpose and benefits: Extracting this discrete data will help to track changes in treatment quality over time, identify care gaps and provide data which could later be used for building predictive models to improve care and become a resource for VA researchers.
Topic area: Health and Medical
Development stage: Pre-deployment
US federal AI use case: Concept Clustering
Agency: Department of Veterans Affairs - OIT: Office of Information & Technology
Problem addressed: Electronic discovery (e-discovery) refers to discovery in legal proceedings such as litigation, government investigations, or Freedom of Information Act (FOIA) requests, where the information sought is in electronic format. As data volumes and the number of civil cases continue to rise, so does the need tools to help government agencies manage electronically stored information used for discovery for litigation, investigations, and FOIA requests. This AI model analyzes the content of documents to identify contextually similar clusters of text. It groups similar documents automatically to reveal themes, without predefined keywords. Runs once on workspace text and metadata to group documents by similarity. No external data or ongoing training.
Purpose and benefits: The expected benefits include reduced manual effort in organizing and identifying related documents, streamlined information retrieval, and improved efficiency for VA staff. Emphasis is placed on cost savings through reduction of labor-intensive processes, while also enhancing user experience by improving search and discovery.
Topic area: Admin Functions
Development stage: Deployed
Risk category: High-impact
US federal AI use case: JLV Booster
Agency: Department of Veterans Affairs - VHA: Veterans Health Administration
Problem addressed: It is difficult for physicians to figure out the details of care that occurred outside the VA. Even when the data exists and can be viewed in Joint Legacy Viewer (JLV), the process of viewing records is time consuming. AI can help summarize the existing external data such that physicians have an idea of what information exists and where to find the full details (no use of AI), if needed.
Purpose and benefits: Improved patient outcomes and increased physician efficiency
Topic area: Health and Medical
Development stage: Pre-deployment
Risk category: High-impact
US federal AI use case: Smart Pension Automation
Agency: Department of Veterans Affairs - VBA: Veterans Benefits Administration
Problem addressed: Reduce the overall average of days to complete a pension related benefit claim. Currently, there is a batch claim processing system in place with Pension Automation that leverages Camunda and is currently only able to process 300 claims/night due to constraints with the original solution. Due to the constraints, a large number of claims are offboarded each night for manual resolution.
Purpose and benefits: The AI solution is expected to increase the number of claims awarded on a daily basis by 10% with the minimum viable product (MVP) solution and then continue to improve with iterative deliveries after the MVP solution.
Topic area: Benefits Processing
Development stage: Pre-deployment
Risk category: High-impact
US federal AI use case: Veterans Life Tailored
Agency: Department of Veterans Affairs - VHA: Veterans Health Administration
Problem addressed: The VLT (Veterans Life Tailored) project, branded as VLT, leverages advanced AI technologies to tailor benefits and support services specifically for veterans. This project harnesses the power of artificial intelligence to analyze vast amounts of data, providing personalized recommendations and streamlined access to a wide array of veteran benefits. Through the use of AI, this product will provide additional information to veterans based on service information. We will suggest benefits with the goal of supporting the application process in the future.
Purpose and benefits: - Improved Veteran and family/beneficiary experience
- Increased number of applications and approvals for benefits
Topic area: Benefits Processing
Development stage: Pre-deployment
Risk category: High-impact
US federal AI use case: Claims Adjudication and Financial Decision-Making
Agency: Department of Veterans Affairs - VHA: Veterans Health Administration
Problem addressed: VA staff spend large amounts of time gathering and reviewing information to process payments and recover revenue. This slows down financial decisions and delays service to Veterans and providers. The AI platform brings the necessary data together, highlights key issues, and streamlines next steps so staff can work faster and focus on higher value decisions.
Purpose and benefits: It will deliver cost savings and efficiency gains, strengthening VA's overall financial stewardship.
Topic area: Procurement and Finance Mgmt
Development stage: Pre-deployment
Risk category: High-impact
US federal AI use case: Mindray TE X
Agency: Department of Veterans Affairs - VHA: Veterans Health Administration
Problem addressed: The TEX20/TEX20 Pro/TEX20S/TEX20T/TEX20 Exp/TEX20 Elite/TEX10/ TEX10 Pro/TEX10S/TEX10T/TEX10 Exp/TEX10 Elite/TE X/TE X Lite Diagnostic Ultrasound System is applicable for adults, pregnant women, pediatric patients and neonates. It is intended for use in Ophthalmic, fetal, abdominal, Intra-operative (abdominal, thoracic, and vascular), Laparoscopic, pediatric, small organ(breast, thyroid, testes), neonatal and adult cephalic, trans_x0002_rectal, trans-vaginal, musculo-skeletal(conventional, superficial), Thoracic/Pleural (For detection of fluid and pleural motion/sliding.), adult and pediatric cardiac, trans-esoph. (Cardiac), peripheral vessel, and urology exams.
Purpose and benefits: Accelerate ultrasound workflows.
Topic area: Health and Medical
Development stage: Pre-deployment
Risk category: High-impact
US federal AI use case: Executive Spend Analysis
Agency: Department of Veterans Affairs - VHA: Veterans Health Administration
Problem addressed: AI is used to enrich Integrated Funds Control, Accounting, and Procurement (IFCAP) purchase order data by categorizing line item purchases into pre-defined spend categories that are more analytically useful. It offers insights into spending patterns and helps executives monitor and manage procurement expenditures effectively.
Purpose and benefits: Enhanced visibility into spending patterns and trends which allow VISN- and VHA-level leadership to make informed and strategic decisions about areas to drive spending efficiencies.
Topic area: Procurement and Finance Mgmt
Development stage: Deployed
US federal AI use case: ASKA: Automated Support and Knowledge Assistance
Agency: Department of Veterans Affairs - VBA: Veterans Benefits Administration
Problem addressed: ASKA is an internal support tool for VBA Public Contact staff. It connects approved policy and procedural content from SharePoint to a searchable question and answer bot. Staff can ask common questions about public contact, FOIA, congressional inquiries, and other customer service duties. ASKA does not use or process PII, claims data, or make any automated decisions, it simply returns standard reference answers an directs staff to relevant SOP or job aids. We are currently using many different websites to obtain information which delays services to the public, Veterans and Service members. Having a tool available to provide this information will help provide this service at a faster rate.
Purpose and benefits: Increase efficiency and productivity for public services
Topic area: Other
Development stage: Pre-deployment
US federal AI use case: CSOC AI Analyst Accelerator (Andesite AI)
Agency: Department of Veterans Affairs - OIT: Office of Information & Technology
Problem addressed: VA Cybersecurity Operations Center (CSOC) requires new tools and capabilities to meet increasing demands for monitoring and securing the VA’s complex information technology (IT) environment, which currently involve time-consuming and manual processes.
Purpose and benefits: Andesite AI has the potential to be a significant part of VA’s overall defensive security strategy which will provide comprehensive visibility by enabling the cybersecurity team to transform and accelerate investigations, connect data silos, and decrease time to response. Hosted in VAEC AWS, Andesite AI will connect via API to several endpoints such as Splunk, SOAR, ThreatQ and the VA Network to seamlessly integrate information to assist Cybersecurity Analysts with their investigations.
Topic area: Cybersecurity
Development stage: Pre-deployment
Risk category: High-impact
US federal AI use case: Research and Development Computer Center (RDCC) System Transformation AI Supported Reporting
Agency: Department of Veterans Affairs - OIT: Office of Information & Technology
Problem addressed: The goal of our AI use case is to manage grants and supporting reporting related to the grants allocation.
Purpose and benefits: Cost savings, increased efficiency
Topic area: Other
Development stage: Pilot
US federal AI use case: Modeling to Learn (MTL)
Agency: Department of Veterans Affairs - VHA: Veterans Health Administration
Problem addressed: Modeling to Learn is designed to address the limited start, flow, and dose of evidence-based psychotherapy and evidence-based pharmacotherapy for Post Traumatic Stress Disorder (PTSD), depression, alcohol use disorder and opioid use disorders.
Purpose and benefits: Improved patient safety and recovery and efficient use of staff and financial resources to deliver timely access to VHA’s highest quality behavioral healthcare.
Topic area: Service Delivery
Development stage: Pre-deployment
Risk category: High-impact
US federal AI use case: Exam Verification Agent
Agency: Department of Veterans Affairs - OIT: Office of Information & Technology
Problem addressed: The process for a Veteran Service Representative (VSR) to determine if an exam is warranted for a claim for increase (claims with end product [EP] 020) for a given contention requires that the VSR expend considerable effort and time to review extensive documentation within a Veteran or Claimant's eFolder. VSR needs review this documentation to identify whether sufficient evidence already exists for the claim to proceed to a rating or if an exam should be ordered to be able to rate the claim. The AI will reduce the time needed to review the evidence by providing a list of related evidence to the contention and a recommendation on whether an exam is needed.
Purpose and benefits: The Exam Verification Agent system will reduce the time needed to develop a claim, increasing efficiency in benefits processing. It transforms the complex and time-intensive process of determining medical exam necessity for disability claims for increase by deploying multiple specialized AI agents. The agents autonomously evaluate claim evidence and provide a recommendation and a summary of the reasoning for the recommendation to the user.
Topic area: Benefits Processing
Development stage: Pre-deployment
Risk category: High-impact
US federal AI use case: Ingestion of TRM and VASI
Agency: Department of Veterans Affairs - OIT: Office of Information & Technology
Problem addressed: Lack of connection between the Technical Reference Model (TRM) and instances of the technology in use in the TRM.
Purpose and benefits: Ease identification of the use of specific technology at the VA and reduce time spent identifying how and where technology is being used.
Topic area: Procurement and Finance Mgmt
Development stage: Pre-deployment
US federal AI use case: Nursing Career Path Tool
Agency: Department of Veterans Affairs - VHA: Veterans Health Administration
Problem addressed: It will solve several critical leadership challenges within the VA system, including:
- Identification of leadership potential
- Workforce optimization
- Centralized training and development resources properly matched with different levels of leadership stages
- Support for leadership talent identification (qualified leaders to assume detail position)
- Enhanced veteran centric care and operational efficiency 
- Cost-efficiency (recruitment, retention)

The tool aims to systematically review staff qualifications, identify leadership levels, collate VA-wide resources, and assist leadership in talent identification and assignment for vacant leadership positions. By leveraging AI technology, the VA can ensure the right nursing talent is developed and effectively placed in leadership roles, ultimately improving service delivery and staff satisfaction.
Purpose and benefits: Provide a structured and data-driven approach to nursing career development and succession planning within the VA. This tool aims to create a well-organized and efficient process for identifying, developing, and placing nursing (potential) leaders within the VA, ultimately enhancing patient care, reducing costs, and improving overall operational efficiency. It will also better align staff qualifications with leadership roles, improve succession planning, reduce gaps in leadership positions, and increased staff engagement and satisfaction through clear career progression paths.
Topic area: HR
Development stage: Pre-deployment
Risk category: High-impact
US federal AI use case: Scriptpro Ticket Report
Agency: Department of Veterans Affairs - VHA: Veterans Health Administration
Problem addressed: Tabulating the scriptpro report is time consuming. It is a report they use to track pharmacy traffic volume and it was previously tabulated manually. They now use AI to automate for efficiency.
Purpose and benefits: Having the AI tabulate the report saves staff time
Topic area: Admin Functions
Development stage: Deployed
US federal AI use case: Predictive Claims Processing Capability POC
Agency: Department of Veterans Affairs - VBA: Veterans Benefits Administration
Problem addressed: Faster claims decisions for Veterans by predicting/performing claims adjudication without the need for a rules engine
Purpose and benefits: Increased efficiency, faster decisions for veterans, and improved veteran experience.
Topic area: Benefits Processing
Development stage: Pre-deployment
Risk category: High-impact
US federal AI use case: Pre-COVA
Agency: Department of Veterans Affairs - VHA: Veterans Health Administration
Problem addressed: Incorrectly or unmatched products are not automatically migrated to the new federal EHR upon go-live, requiring facility staff to manually review and enter to prescriptions to be available for Veterans to request.  This would improve the efficiency and accuracy of that matching.
Purpose and benefits: It is intended to improve product matching efficiency and accuracy, improving patient safety and availability of prescriptions post Federal EHR go-live.
Topic area: Health and Medical
Development stage: Pre-deployment
US federal AI use case: HTM112 Tutor
Agency: Department of Veterans Affairs - VHA: Veterans Health Administration
Problem addressed: While current VA and Healthcare Technology Management (HTM) courses offer tailored content, the HTM112 Tutor elevates the learning experience by providing individualized and personal support that adapts to each learner's unique pace, style and specific knowledge gaps. It serves as a learning enhancement tool, not a replacement for instruction, by increasing accessibility to information and fostering an individualized learning journey. This ultimately boosts course efficiency and overall training effectiveness.
Purpose and benefits: The HTM112 AI Tutor delivers measurable benefits across training effectiveness, operational efficiency, and patient safety outcomes:
• Accelerated Learning: Reduces time spent searching course materials by over 80%
• Personalized Support: Provides individualized assistance tailored to each learner
• Improved Retention: Offers immediate clarification and reinforcement, Adds ability to create learning aids to improve recall and retention back on the job
• 24/7 Accessibility: Ensures continuous learning support
• Instructor Resource Optimization: Frees instructors from repetitive questions
• Reduced Training Administration: Streamlines content delivery and reduces manual support requests
• Scalable Knowledge Management: Provides consistent, verified information access across HTM staff 
• Cost-Effective Continuous Education: Reduces need for refresher training
• Improved Technical Competency: Better-trained HTM professionals enhance the reliability and safety 
• Faster Issue Resolution: On-demand access to networking knowledge reduces equipment downtime and improves patient care continuity
• Standardized Best Practices: Ensures consistent application of networking procedures
• Risk Mitigation: Reduces potential for networking-related equipment failures 
• Knowledge Preservation: Maintains institutional knowledge for future HTM professionals
• Continuous Professional Development: Supports career advancement
• Enhanced Workforce Readiness: Ensures HTM staff remain current with VA standards
Topic area: Other
Development stage: Deployed
US federal AI use case: PestScore VA: AI-Driven Pest Risk Forecasting
Agency: Department of Veterans Affairs - VHA: Veterans Health Administration
Problem addressed: VA facilities currently rely on fragmented, inconsistent pest control documentation methods, ranging from hand written logs to variable digital records. This make it difficult to detect early warning signs, track pest trends or ensure system-wide compliance with IPM standards. Additionally, leadership lacks real-time visibility into pest activity across departments or sites. PestScore VA solves this by creating a standardized AI enhanced system that enables predictive insights and uniform pest risk documentation, enhancing transparency, documentation, and proactive care.
Purpose and benefits: 1. Predictive Insight: The AI forecasts pest activity using hybrid logic (trends plus probability), helping teams act before outbreaks escalate.

2. Standardized Reporting: Uniform, clean reports help facilities meet regulatory and environmental standards more easily.

3. Operational Efficiency: Technicians enter familiar data (notes, sightings), and  AI handles analysis, saving time without increasing workload.

4. Environmental Health: Supports less toxic, preventive pest strategies that align with patient safety and VA sustainability goals.

5. Leadership Visibility: Allows decision makers to compare zones or hospitals at a glance, helping allocate resources intelligently.
Topic area: Science
Development stage: Pre-deployment
US federal AI use case: Benefits Contention Classification Model
Agency: Department of Veterans Affairs - OIT: Office of Information & Technology
Problem addressed: The Benefits Contention Classification model is intended to improve the current benefits adjudication process by providing an automated classification for all benefits claims contentions. The model will do this by providing a classification for each contention that is not currently covered by the existing process. This is intended to make the overall benefits adjudication process more efficient by reducing the necessary time for VA rating representatives to choose the appropriate disability questionnaire for the the given contention.
Purpose and benefits: The expected benefit of this model is increasing efficiency in the benefits adjudication process by automating the internal classification required to select the disability benefits questionnaire that should be sent to the Veteran.
Topic area: Benefits Processing
Development stage: Pre-deployment
Risk category: High-impact
US federal AI use case: Ansible Lightspeed
Agency: Department of Veterans Affairs - OIT: Office of Information & Technology
Problem addressed: Lack of adoption of automation, or expertise in coding knowledge to automate a remediation/code change. Ansible LightSpeed helps create automations faster, making teams more efficient and productive in a shorter amount of time than if they needed to write automations on their own.
Purpose and benefits: Quicker time to service and resolution
Topic area: IT
Development stage: Pre-deployment
US federal AI use case: Philips 7300 C
Agency: Department of Veterans Affairs - VHA: Veterans Health Administration
Problem addressed: AI-assisted Smart Collimation Thorax algorithm, developed by Philips, adjusts detector height and proposes collimation for each chest upright patient individually, based upon data from a 3D Camera.
Purpose and benefits: Smart Collimation Thorax reduces exam time by up to 35 seconds, resulting in potential daily time savings of 20 minutes for the medical team.
Topic area: Health and Medical
Development stage: Deployed
Risk category: High-impact