As the Veterans Administration (VA) continues to innovate in the realm of healthcare, artificial intelligence (AI) is playing an increasingly pivotal role in enhancing the quality and efficiency of care for veterans. AI technologies are being integrated across a range of applications, from advanced diagnostic tools to predictive analytics, addressing various aspects of veteran health and well-being. These innovations span diverse use cases including physical therapy, cardiac surgery, medication adherence, and mental health monitoring. By leveraging AI, the VA aims to provide more personalized, accurate, and timely care, ultimately improving patient outcomes and operational efficiencies. The following use cases highlight key AI-driven projects within the VA, showcasing how these technologies are transforming healthcare delivery and supporting the unique needs of veterans.
The Artificial Intelligence Physical Therapy App serves as a supportive tool for physical therapists, designed to be data source agnostic. It accepts input from various wearable sensors and analyzes the collected data to provide actionable feedback to therapists in a clear and understandable format. This app aims to enhance the effectiveness of physical therapy by enabling therapists to make informed decisions based on real-time data from their patients.
The Artificial Intelligence Coach in Cardiac Surgery is designed to identify misalignments in the mental models of team members during complex surgical tasks. This project focuses on safety-critical domains, such as healthcare and aviation, where discrepancies in shared mental models can result in preventable errors. By detecting these misalignments, the AI coach lays the groundwork for computer-assisted interventions that aim to enhance teamwork and support cognitive functions in the operating room, ultimately improving patient safety and surgical outcomes.
AICURE is a mobile application designed to monitor patient adherence to orally prescribed medications during clinical trials or pharmaceutical-sponsored drug studies. By tracking medication intake, the app aims to improve compliance and provide valuable data to researchers, enhancing the overall effectiveness of clinical studies and ensuring that patients receive the intended therapeutic benefits.
The Acute Kidney Injury (AKI) project is a collaborative effort with Google DeepMind aimed at developing AI solutions for the early detection of AKI, which can range from minor kidney function loss to complete kidney failure. The AI system is designed to identify cases of AKI that may arise as a complication of other illnesses, facilitating timely intervention and improving patient outcomes through enhanced monitoring and diagnosis.
The Assessing Lung Function in Health and Disease project utilizes artificial intelligence to help healthcare professionals identify predictors of both normal and abnormal lung function, as well as sleep parameters. By analyzing relevant data, the AI system aims to enhance the understanding of respiratory health, enabling more accurate assessments and interventions for patients with varying lung function profiles.
The Automated Eye Movement Analysis and Diagnostic Prediction project utilizes artificial intelligence to recursively analyze previously collected data, enhancing the quality and accuracy of automated algorithms. This system is designed to screen for markers of various neurological diseases, including traumatic brain injury, Parkinson’s disease, and stroke, thereby aiding in early diagnosis and intervention.
The Automatic Speech Transcription Engines project focuses on analyzing the cognitive decline of older Veterans Affairs (VA) patients through automated speech transcription. Digitally recorded speech responses are processed using multiple AI-based speech-to-text engines, and the resulting transcriptions are combined to minimize or eliminate the need for manual transcription. This automation streamlines the scoring of neuropsychological tests, improving efficiency and accuracy in assessing cognitive health.
CuraPatient is an innovative remote tool designed to empower patients in managing their health conditions without the necessity of in-person provider visits. Utilizing artificial intelligence, the platform enables users to create personalized profiles for tracking their health, enrolling in relevant programs, managing insurance details, and scheduling appointments. This tool aims to enhance patient engagement and streamline healthcare management.
The Digital Command Center initiative aims to centralize all data within a medical center and leverage predictive prescriptive analytics to enhance hospital performance. By consolidating information, healthcare leaders can make informed decisions that optimize operations, improve patient care, and increase overall efficiency within the facility.
The Disentangling Dementia Patterns project is a collaborative effort aimed at creating a deep learning framework to identify and predict various dementia patterns observed in MRI and EEG data. This initiative also explores the potential of these imaging modalities as biomarkers for different types of dementia and epilepsy disorders. The VA is conducting retrospective chart reviews to support this research, enhancing understanding of dementia and improving diagnostic capabilities.
The Machine Learning for Enhanced Diagnostic Error Detection project involves researchers conducting chart reviews to gather true/false positive annotations for patient records. This data is then used to create vector embeddings, enabling similarity-based retrieval of unlabeled records that are similar to labeled ones in a semi-supervised approach. The goal is to utilize machine learning as a filtering mechanism following rules-based retrieval, thereby improving diagnostic specificity. The inputs for embedding will include high-value structured data related to stroke risk and relevant prior text notes.
Behavidence is a mental health tracking application designed for veterans. By downloading the app onto their smartphones, users can monitor their phone usage patterns, which are then compared to a digital phenotype representing individuals with confirmed mental health diagnoses. This comparison aims to provide insights into mental health status and promote awareness, potentially facilitating early intervention and support for veterans.
The Machine Learning Tools to Predict Outcomes of Hospitalized VA Patients project is an Institutional Review Board (IRB)-approved study that investigates the application of machine learning techniques to forecast health outcomes for VA patients. The study specifically targets predictions related to Alzheimer’s disease, rehospitalization rates, and Clostridioides difficile infections, aiming to enhance patient care and resource allocation through improved predictive analytics.
Nediser is an advanced artificial intelligence system designed to function as a “radiology resident,” continuously trained to assist radiologists in verifying X-ray properties within their reports. This AI tool can select normal templates, detect hardware issues, assess patella alignment, evaluate leg length discrepancies, and measure Cobb angles. By providing these capabilities, Nediser enhances the accuracy and efficiency of radiological assessments, supporting radiologists in their diagnostic processes.
The Precision Medicine PTSD and Suicidality Diagnostic and Predictive Tool is designed to analyze various real-time inputs to predict and diagnose episodes of post-traumatic stress disorder (PTSD) and suicidality. This model aims to provide early warnings for potential episodes, facilitating timely and accurate diagnoses. Additionally, it seeks to enhance understanding of the short- and long-term effects of stress, particularly in extreme situations, contributing to better management and treatment of PTSD among veterans.
The Prediction of Veterans’ Suicidal Ideation project employs machine learning techniques to identify factors that predict suicidal thoughts among veterans transitioning from military service. The analysis is based on data collected from a web-based survey that captures veterans’ experiences within three months of separation and continues every six months for the first three years post-service. This initiative aims to enhance understanding of the mental health challenges faced by veterans and inform preventive measures.
PredictMod is an innovative project that leverages artificial intelligence to explore the potential for predicting diabetes risk based on the composition of the gut microbiome. By analyzing microbiome data, the project aims to uncover relationships between gut health and diabetes, potentially leading to new preventive strategies and personalized treatment options for patients at risk.
The Predictor Profiles of Opioid Use Disorder (OUD) and Overdose project utilizes machine learning models to assess the interactions between established and emerging risk factors associated with OUD and overdose among Post-9/11 veterans. By employing various classification-tree modeling techniques, the project aims to develop comprehensive predictor profiles that can inform targeted interventions and support efforts to combat the opioid crisis within this population.
The Provider Directory Data Accuracy and System of Record Alignment project employs artificial intelligence to enhance identity resolution and linking within healthcare data systems. AI functions as a transactor, facilitating intelligent state reconstruction over time and enabling real-time discrepancy detection. Additionally, it synchronizes data through intelligent propagation and semi-automated resolution of discrepancies. The use of AI adapters for inference via OWL and logic programming further enhances data integration. The system also features long-term storage capabilities, akin to a “black box flight recorder,” supporting extensive machine learning and business intelligence applications.
The Seizure Detection from EEG and Video project utilizes machine learning algorithms to analyze electroencephalogram (EEG) and video data collected from a Veterans Health Administration (VHA) epilepsy monitoring unit. This system is designed to automatically identify seizure events without the need for human intervention, enhancing the efficiency and accuracy of seizure monitoring and diagnosis in patients with epilepsy.
The SoKat Suicidal Ideation Detection Engine (SSIE) employs natural language processing (NLP) techniques to enhance the identification of suicidal ideation among veterans. By analyzing survey data collected by the Office of Mental Health (OMH) through the Veteran Crisis Line (VCL) support team, the SSIE aims to improve the accuracy and timeliness of detecting veterans at risk of suicide, ultimately supporting better mental health interventions.
This project focuses on utilizing machine learning to develop predictive models that analyze the decision-making processes of perfusionists during critical moments in the cardiopulmonary bypass phase of cardiac surgery. The insights gained from this analysis may inform the creation of computerized clinical decision support tools that can be integrated into the operating room, enhancing patient safety and improving surgical outcomes through better-informed decision-making.
The Gait Signatures in Patients with Peripheral Artery Disease (PAD) project employs machine learning techniques to enhance the treatment of functional issues associated with PAD. By analyzing previously collected biomechanics data, the project aims to identify characteristic gait signatures of PAD patients. Additionally, it investigates the effectiveness of limb acceleration measurements in modeling significant biomechanical metrics related to PAD, ultimately improving patient assessment and treatment strategies.
The Medication Safety (MedSafe) Clinical Decision Support (CDS) system leverages electronic clinical data from the VA to analyze the management of conditions such as diabetes, hypertension, and chronic kidney disease. By providing patient-specific, evidence-based recommendations to primary care providers, the system enhances clinical decision-making. It utilizes knowledge bases that encode clinical practice guidelines and employs an automated execution engine to assess various factors, including comorbidities, laboratory results, medications, and past adverse drug events, to generate tailored recommendations for patient care.
The Prediction of Health Outcomes project utilizes electronic health records (EHR), incorporating both structured and unstructured data, to generate deep phenotypes and predict various health outcomes. These outcomes include suicide death, opioid overdose, and decompensated conditions related to chronic diseases. By analyzing comprehensive patient data, this tool aims to enhance risk assessment and inform preventive strategies, ultimately improving patient care and outcomes within the veteran population.
The VA-DoE Suicide Exemplar Project leverages artificial intelligence to enhance the Veterans Affairs (VA) capacity to identify veterans at risk for suicide. This initiative encompasses three interconnected projects that involve collaboration with the Department of Energy, aiming to develop more effective predictive models and interventions for veteran suicide prevention.
This project employs machine learning models to forecast the progression of hepatitis C virus (HCV) infection among veterans. By analyzing relevant patient data, the model aims to provide insights into disease trajectories, enabling timely interventions and improved management of HCV among the veteran population.
The Prediction of Biologic Response to Thiopurines project utilizes artificial intelligence to analyze data from the Computerized Patient Record System (CPRS) and Clinical Data Warehouse (CDW) to predict how veterans with irritable bowel disease (IBD) will respond to thiopurine medications. This predictive capability aims to optimize treatment plans and improve patient outcomes by tailoring therapies to individual responses.
The project focuses on predicting hospitalizations and corticosteroid use as indicators of irritable bowel disease (IBD) flare-ups among 20,368 patients diagnosed with IBD in the Veterans Health Administration (VHA) between 2002 and 2009. By utilizing longitudinal laboratory data and various predictors, random forest models are developed to enhance the understanding of IBD management and anticipate exacerbations, ultimately improving patient care.
The project aims to predict the likelihood of achieving corticosteroid-free endoscopic remission in patients with ulcerative colitis receiving Vedolizumab treatment. Utilizing random forest modeling, the study analyzes data from 594 patients to forecast outcomes at week 52, based on baseline characteristics and data collected during the first six weeks of therapy. This predictive modeling seeks to inform treatment decisions and improve patient outcomes in ulcerative colitis management.
This project employs machine learning techniques to analyze data from adult patients in the Veterans Integrated Service Networks (VISN) 10 cohort, focusing on demographics, medication usage, and longitudinal laboratory values collected between 2001 and 2015. The goal is to predict the likelihood of surgery for patients with Crohn’s disease and model potential surgical outcomes within one year, thereby enhancing clinical decision-making and patient management strategies.
This project utilizes reinforcement learning techniques to evaluate treatment policies for veterans with hepatitis C virus (HCV). By predicting disease progression, the model aims to inform and optimize treatment strategies, ultimately improving health outcomes for veterans affected by HCV.
The Predicting Hepatocellular Carcinoma project focuses on assessing the risk of developing hepatocellular carcinoma (HCC) in patients with hepatitis C virus (HCV)-related cirrhosis within the Veterans Health Administration. By analyzing data from patients with at least three years of follow-up, the study compares the performance of deep learning recurrent neural network (RNN) models, which utilize raw longitudinal data from electronic health records, against traditional regression models. The goal is to enhance predictive accuracy for HCC risk, facilitating early intervention and improved patient outcomes.
The Computer-Aided Detection and Classification of Colorectal Polyps project explores the application of artificial intelligence models to enhance the clinical management of colorectal polyps. The AI models analyze video frames from colonoscopy procedures in real time, focusing on two key tasks: detecting the presence of polyps in the frames and predicting their potential malignancy. This technology aims to improve diagnostic accuracy and support timely interventions for patients undergoing colonoscopy.
The Medtronic GI Genius is an advanced artificial intelligence system designed to assist in the detection of colon polyps during gastrointestinal examinations. By leveraging AI technology, the GI Genius enhances the accuracy and efficiency of polyp detection, ultimately supporting healthcare providers in delivering better patient care during colonoscopy procedures.
The Extraction of Family Medical History from Patient Records project is a pilot initiative that utilizes TIU (Text Integration Utility) documentation to gather family medical history data specifically from African American veterans aged 45 to 50. The primary goal is to identify veterans who may be at increased risk for prostate cancer and have not yet undergone screening for the disease. This project aims to enhance early detection and preventive care for prostate cancer within this demographic.
The VA/IRB Approved Research Study for Finding Colon Polyps is an initiative that employs a randomized trial design to investigate the effectiveness of artificial intelligence in detecting colon polyps. This study, approved by the Institutional Review Board (IRB), aims to enhance the accuracy and efficiency of polyp detection during colonoscopy procedures, ultimately improving patient outcomes in colorectal cancer prevention.
The Interpretation/Triage of Eye Images project leverages artificial intelligence to assist in the triage of eye patients receiving care through telehealth services. The AI system interprets retinal images and evaluates health risks associated with various eye conditions, including glaucoma, macular degeneration, and diabetic retinopathy. The primary objective is to enhance diagnostic accuracy and facilitate timely interventions for patients with eye health concerns.
The Screening for Esophageal Adenocarcinoma project utilizes national Veterans Health Administration (VHA) administrative data to refine and adapt predictive tools that analyze electronic health records. The aim is to assess the risk of developing esophageal adenocarcinoma among veterans, thereby enabling early detection and intervention strategies for this serious condition.
The Social Determinants of Health Extractor project employs artificial intelligence to analyze clinical notes and extract information related to social determinants of health (SDOH). By identifying these variables, the project aims to facilitate health-related analyses that explore the impact of SDOH on disease risk and healthcare disparities. This initiative seeks to enhance understanding of how social factors contribute to health outcomes among veterans, ultimately informing targeted interventions and policy decisions.
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What Personal Data We Collect
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A cookie is a small file with information that your browser stores on your device. Information in this file is typically shared with the owner of the site in addition to potential partners and third parties to that business. The collection of this information may be used in the function of the site and/or to improve your experience.
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If we are involved in a merger, acquisition or asset sale, your personal information may be transferred. We will provide notice before your personal information is transferred and becomes subject to a different Privacy Policy. Under certain circumstances, we may be required to disclose your personal information if required to do so by law or in response to valid requests by public authorities (e.g. a court or a government agency).
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We do not knowingly collect Personal Data from children under the age of 18 Years.
Depending on your geographical location and citizenship, your rights are subject to local data privacy regulations. These rights may include:
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You have the right to obtain Personal Data we hold about you, in a structured, electronic format, and to transmit such Personal Data to another data controller, where this is (a) Personal Data which you have provided to us, and (b) if we are processing that data on the basis of your consent or to perform a contract with you or the third party that subscribes to services.
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Where the legal justification for our processing of your Personal Data is our legitimate interest, you have the right to object to such processing on grounds relating to your particular situation. We will abide by your request unless we have compelling legitimate grounds for processing which override your interests and rights, or if we need to continue to process the Personal Data for the establishment, exercise or defense of a legal claim.
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You understand and agree that your use of the website, its content, and any goods, digital products, services, information or items found or attained through the website is at your own risk. The website, its content, and any goods, services, digital products, information or items found or attained through the website are provided on an "as is" and "as available" basis, without any warranties or conditions of any kind, either express or implied including, but not limited to, the implied warranties of merchantability, fitness for a particular purpose, or non-infringement. The foregoing does not affect any warranties that cannot be excluded or limited under applicable law.
You acknowledge and agree that company or its respective directors, officers, employees, agents, service providers, contractors, licensors, licensees, suppliers, or successors make no warranty, representation, or endorsement with respect to the completeness, security, reliability, suitability, accuracy, currency, or availability of the website or its contents or that any goods, services, digital products, information or items found or attained through the website will be accurate, reliable, error-free, or uninterrupted, that defects will be corrected, that our website or the server that makes it available or content are free of viruses or other harmful components or destructive code.
Except where such exclusions are prohibited by law, in no event shall the company nor its respective directors, officers, employees, agents, service providers, contractors, licensors, licensees, suppliers, or successors be liable under these terms of service to you or any third-party for any consequential, indirect, incidental, exemplary, special, or punitive damages whatsoever, including any damages for business interruption, loss of use, data, revenue or profit, cost of capital, loss of business opportunity, loss of goodwill, whether arising out of breach of contract, tort (including negligence), any other theory of liability, or otherwise, regardless of whether such damages were foreseeable and whether or not the company was advised of the possibility of such damages.
To the maximum extent permitted by applicable law, you agree to defend, indemnify, and hold harmless Company, its parent, subsidiaries, affiliates, and their respective directors, officers, employees, agents, service providers, contractors, licensors, suppliers, successors, and assigns from and against any claims, liabilities, damages, judgments, awards, losses, costs, expenses, or fees (including reasonable attorneys' fees) arising out of or relating to your breach of these Terms of Service or your use of the Website including, but not limited to, third-party sites and content, any use of the Website's content and services other than as expressly authorized in these Terms of Service or any use of any goods, digital products and information purchased from this Website.
At Company’s sole discretion, it may require you to submit any disputes arising from these Terms of Service or use of the Website, including disputes arising from or concerning their interpretation, violation, invalidity, non-performance, or termination, to final and binding arbitration under the Rules of Arbitration of the American Arbitration Association applying Ontario law. (If multiple jurisdictions, under applicable laws).
Any cause of action or claim you may have arising out of or relating to these terms of use or the website must be commenced within 1 year(s) after the cause of action accrues; otherwise, such cause of action or claim is permanently barred.
Your provision of personal information through the Website is governed by our privacy policy located at the "Privacy Policy".
The Website and these Terms of Service will be governed by and construed in accordance with the laws of the Province of Ontario and any applicable federal laws applicable therein, without giving effect to any choice or conflict of law provision, principle, or rule and notwithstanding your domicile, residence, or physical location. Any action or proceeding arising out of or relating to this Website and/or under these Terms of Service will be instituted in the courts of the Province of Ontario, and each party irrevocably submits to the exclusive jurisdiction of such courts in any such action or proceeding. You waive any and all objections to the exercise of jurisdiction over you by such courts and to the venue of such courts.
If you are a citizen of any European Union country or Switzerland, Norway or Iceland, the governing law and forum shall be the laws and courts of your usual place of residence.
The parties agree that the United Nations Convention on Contracts for the International Sale of Goods will not govern these Terms of Service or the rights and obligations of the parties under these Terms of Service.
If any provision of these Terms of Service is illegal or unenforceable under applicable law, the remainder of the provision will be amended to achieve as closely as possible the effect of the original term and all other provisions of these Terms of Service will continue in full force and effect.
These Terms of Service constitute the entire and only Terms of Service between the parties in relation to its subject matter and replaces and extinguishes all prior or simultaneous Terms of Services, undertakings, arrangements, understandings or statements of any nature made by the parties or any of them whether oral or written (and, if written, whether or not in draft form) with respect to such subject matter. Each of the parties acknowledges that they are not relying on any statements, warranties or representations given or made by any of them in relation to the subject matter of these Terms of Service, save those expressly set out in these Terms of Service, and that they shall have no rights or remedies with respect to such subject matter otherwise than under these Terms of Service save to the extent that they arise out of the fraud or fraudulent misrepresentation of another party. No variation of these Terms of Service shall be effective unless it is in writing and signed by or on behalf of Company.
No failure to exercise, and no delay in exercising, on the part of either party, any right or any power hereunder shall operate as a waiver thereof, nor shall any single or partial exercise of any right or power hereunder preclude further exercise of that or any other right hereunder.
We may provide any notice to you under these Terms of Service by: (i) sending a message to the email address you provide to us and consent to us using; or (ii) by posting to the Website. Notices sent by email will be effective when we send the email and notices we provide by posting will be effective upon posting. It is your responsibility to keep your email address current.
To give us notice under these Terms of Service, you must contact us as follows: (i) by personal delivery, overnight courier or registered or certified mail to Scry Analytics Inc. 2635 North 1st Street, Suite 200 San Jose, CA 95134, USA. We may update the address for notices to us by posting a notice on this Website. Notices provided by personal delivery will be effective immediately once personally received by an authorized representative of Company. Notices provided by overnight courier or registered or certified mail will be effective once received and where confirmation has been provided to evidence the receipt of the notice.
To request a copy for your information, unsubscribe from our email list, request for your data to be deleted, or ask a question about your data privacy, we've made the process simple: