The U.S. Department of Veterans Affairs selected Abridge AI as an approved vendor on a new enterprise contract for ambient AI scribe technology, a deal with a total ceiling of $775.72 million over five years across all eligible vendors competing for task orders. The contract puts Abridge in position to expand its clinical documentation tools to VA medical centers nationwide as the department pushes to modernize its health records systems and reduce administrative burdens on clinicians.
Technology already in use across VA systems
Abridge said its platform is fully operational on both of the VA's electronic health record systems - VistA/CPRS and the Oracle Federal EHR - and already in clinical use at more than 75 VA medical centers. The company has built its AI to generate clinical documentation during patient visits, citing evidence-based sources and providing context-aware responses that draw from the patient chart, the ongoing conversation, and peer-reviewed medical literature.
"We've built Abridge to meet the scale and complexity that an enterprisewide deployment requires," said Dr. Shiv Rao, Abridge CEO and cofounder, in the announcement. Rao, who has treated veteran cardiology patients at VA medical centers in Ann Arbor, Michigan, and Pittsburgh, said AI-driven documentation could help improve care continuity for veterans who visit community care and virtual providers.
Clinical decision support draws on major medical journals
In April, Abridge announced it was incorporating more clinical evidence into its decision support tools, adding peer-reviewed research from the New England Journal of Medicine and the JAMA Network to insights from Wolters Kluwer Health's UpToDate platform. The integrated CDS tools cite these sources within existing clinical workflows, giving clinicians access to verified medical evidence as they document encounters.
The VA began exploring ambient scribe technology two years ago with a tech sprint focused on documenting clinical encounters and integrating community care data. Abridge received a fixed-price contract to evaluate its cloud-based software, and Nuance Communications was also tapped to pilot ambient listening tools. Other finalists in the competition included DeepScribe, Commure, Tali AI, and several government-focused contractors.
Expansion follows successful pilot program
Ambient dictation pilots proceeded under VA security controls, with veteran data handled within the department's infrastructure. By March, Rise8 and Thoughtworks Federal said they had been selected to expand the technology from a 10-site pilot to more than 130 medical centers. As of June, the Veterans Health Administration reported it had deployed ambient scribe to all VA Patient Aligned Care Team primary care providers after a phased rollout.
The VA said it would then expand ambient AI scribe to select outpatient specialty care providers and anticipated an enterprise contract extension later this year. The multiple-award contract structure means Abridge and other vendors will compete for individual task orders across the VA system.
"Abridge has proven its platform can support an integrated, consistent experience for VA clinicians and the veterans they serve," said Dr. David Shulkin, former VA Secretary and a consultant to Abridge.
Why this matters for government healthcare operations
The contract signals that ambient AI scribe technology is moving from pilot experiments to enterprise procurement inside the largest integrated healthcare system in the country. For operations leaders in government healthcare, the key takeaway is the procurement model itself: a multiple-award contract with a high ceiling that lets the VA evaluate vendors against specific task orders rather than locking into a single platform. That structure mirrors how large health systems manage technology risk - by maintaining a qualified vendor pool and directing spend based on performance at individual sites. The integration across both legacy VistA and the new Oracle Cerner EHR also shows that scribe tools must work across parallel record systems during long modernization transitions.
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