University of Texas System Scales AI Medical Scribes Across Eight Institutions After Pilot Shows 73% Clinician Adoption Surge
Following a successful trial, the UT System is deploying Abridge's ambient documentation platform enterprise-wide to support nearly 12 million annual patient visits.
Illustration: AI-generated
The University of Texas System is rolling out ambient AI clinical documentation across all eight of its health institutions, marking one of the largest enterprise-wide deployments of medical scribing technology in academic healthcare. The expansion follows a pilot program that demonstrated a 73% increase in recording clinicians and an 80% jump in monthly notes over 12 months.
The agreement, facilitated through UT REAL Health AI—an initiative established in 2025 to advance responsible AI adoption—will bring Abridge's platform to UT Southwestern Medical Center, UT Medical Branch, UT MD Anderson Cancer Center, UT Dell Medical School, UT Health Tyler, UTHealth Houston, UT Health San Antonio, and UT Health Rio Grande Valley. Together, these UT Health-owned and affiliated hospitals and clinics provided nearly 11.7 million outpatient visits and more than 2 million hospital days in the last year.
Pilot Results Drive System-Wide Commitment
The initial pilot deployed Abridge's technology across five locations, spanning outpatient, inpatient, and emergency department settings in more than 50 specialties. The substantial growth in both clinician participation and documentation volume over the 12-month trial period signaled strong acceptance among providers and effective workflow integration.
According to the company, the platform aims to reduce the "mental clutter of clerical overhead" that burdens clinicians during patient encounters. Abridge CEO and co-founder Shiv Rao emphasized the importance of the partnership in advancing responsible AI deployment at scale within complex academic health systems.
Balancing Governance with Local Flexibility
The enterprise framework established by UT REAL Health AI, chaired by Vice President Peter McCaffrey, M.D., enables systemwide access to the technology while preserving campus-level implementation autonomy. This approach allows individual institutions within the UT System to tailor deployment to their specific clinical environments and specialty needs while maintaining centralized governance around responsible AI use.
The model represents a growing trend in healthcare AI adoption, where large health systems seek to balance standardization benefits with the flexibility required for diverse clinical settings. The UT System's scale—spanning cancer centers, academic medical centers, and regional health facilities—provides a test case for whether ambient AI can effectively serve varied care delivery models under a unified enterprise agreement.
Expanding Market Presence
Abridge reports working with 300 of the largest U.S. health systems, with its platform expected to support more than 100 million patient-clinician conversations in 2026. The company recently secured a U.S. Department of Veterans Affairs enterprise contract for ambient AI in September 2024, further extending its reach into large-scale healthcare organizations.
The company has also expanded its AI capabilities beyond real-time documentation to include pre-bill review for clinical documentation integrity (CDI), coding, and revenue cycle teams, addressing downstream administrative processes that depend on accurate clinical notes.
Implications for Healthcare AI Adoption
The UT System deployment illustrates how academic health systems are moving from pilot projects to enterprise-wide AI integration. The emphasis on responsible AI frameworks and local decision-making authority appears to be emerging as a critical success factor for large-scale adoption, particularly in complex organizations serving diverse patient populations across multiple specialties and care settings.
For the nearly 12 million patients served annually by UT System facilities, the technology's potential impact centers on whether reducing clinician administrative burden translates to improved presence and attention during clinical encounters—a hypothesis that will likely be tested as the system-wide rollout progresses.
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