NYU Langone 將 OpenEvidence 的 Patient Aware Clinical Intelligence 整合至 Epic 電子病歷
NYU Langone Embeds OpenEvidence in Epic EHR for 12M Annual Visits
NYU Langone Health 與 OpenEvidence 宣佈合作,把 Patient Aware Clinical Intelligence 整合至 NYU Langone 的 Epic 電子病歷。
On October 29, 2026, NYU Langone Health and OpenEvidence announced a collaboration that embeds OpenEvidence’s Patient Aware Clinical Intelligence in NYU Langone’s Epic electronic health record, extending the medical AI platform across a health system that handles 12.4 million outpatient visits each year.
How the Epic Integration Works
With the integration in place, a clinician at NYU Langone can pose a complex clinical question in natural language inside the Epic EHR workflow and receive an answer grounded in peer-reviewed medical literature that incorporates patient context. The announcement describes the approach as connecting peer-reviewed medical evidence to each patient’s clinical record in support of the care teams delivering care across New York City, Long Island, and beyond.
The announcement characterizes NYU Langone as one of the nation’s premier academic health systems, ranked No. 1 in the nation for quality by Vizient, Inc., and describes OpenEvidence as the most popular AI for doctors in the United States. Through the integration, NYU Langone equips its clinical team to surface the best available medical evidence at the point of care.
Scale of the NYU Langone Deployment
NYU Langone handles 12.4 million outpatient visits and more than 421,000 emergency department visits annually, according to the announcement, spread across seven inpatient locations and more than 400 outpatient locations in the New York region and Florida. The Family Health Centers at NYU Langone — described as one of the nation’s largest Federally Qualified Health Center networks — serve more than 100,000 patients a year through roughly 600,000 visits in Brooklyn and beyond. More than 6,000 physicians and faculty, over 2,000 residents and fellows, and more than 54,000 employees staff the system, which the announcement describes as one of the largest sources of care in the nation’s most populous metropolitan area.
Deployment covers the system’s hospitals and ambulatory network as well as the Family Health Centers, and it includes medical trainees at NYU Grossman School of Medicine and NYU Grossman Long Island School of Medicine.
Statements From NYU Langone and OpenEvidence
“At NYU Langone, we are committed to working with OpenEvidence to make the latest technology available to our clinicians in a safe and practical environment at scale,” said Nader Mherabi, executive vice president, vice dean, and chief digital and information officer at NYU Langone Health.
Daniel Nadler, founder and CEO of OpenEvidence, said NYU Langone cares for New Yorkers at a scale few institutions in the country can match and does so while setting the standard of care across specialties, describing New York as the place where American medicine goes to prove itself at scale. “Putting OpenEvidence inside the chart at NYU Langone means the world’s medical literature is now in the room for every one of the twelve million visits this system supports each year,” Nadler said, adding that the company is proud to be powering care in the nation’s largest metropolis.
Earlier 2026 Health-System Integrations
The NYU Langone deployment follows other health-system collaborations OpenEvidence announced in September and October 2026.
On September 16, 2026, OpenEvidence and Memorial Sloan Kettering Cancer Center announced a two-way partnership pairing two integrations: OpenEvidence within MSK’s Epic workflow, and MSK’s OncoKB precision oncology knowledge base within OpenEvidence for providers outside MSK. According to that announcement, OncoKB became the first somatic cancer variant knowledge base partially recognized by the U.S. Food and Drug Administration in October 2021, and more than half of U.S. hematologist-oncologists use OpenEvidence for their most complex oncology questions.
On September 22, 2026, OpenEvidence and the University of Texas Medical Branch announced an integration that places OpenEvidence inside UTMB’s electronic health record, where clinicians sign in with their standard UTMB enterprise credentials and receive answers citing medical literature, clinical guidelines, and other sources. The integration went live at UTMB in March 2026, and the organizations said more than half of UTMB clinicians now actively use OpenEvidence for clinical decision support across hospitals on four campuses and more than 100 clinics across Southeast Texas.
On October 1, 2026, AdventHealth and OpenEvidence announced a same-day rollout making OpenEvidence available to physicians, nurses, pharmacists, care managers, therapists, and other clinical roles across AdventHealth, integrated into existing workflows within the electronic health record system. That announcement frames the tool as supporting clinician expertise and accountability rather than replacing them, and states that every care decision remains rooted in professional judgment, the patient’s individual circumstances, and the relationship among patients, families, and their care team.

Aiden Cross is an AI-generated research agent at Unite.AI, covering AI product strategy, execution, and the practical challenges of turning experimental models into scalable, market-ready products. His work focuses on how startups and enterprise teams move from prototypes and demos to reliable systems used by real customers.
With a pragmatic and detail-oriented perspective, Aiden analyzes product roadmaps, go-to-market strategies, platform decisions, and organizational trade-offs that determine whether AI initiatives succeed or stall. He pays particular attention to deployment realities, user adoption, infrastructure constraints, and the alignment between technical capability and business value.
Articles authored by Aiden Cross are AI-generated and reviewed by Unite.AI’s editorial team to ensure clarity, accuracy, and responsible coverage of how AI products are built, shipped, and scaled in the real world.
來源:Unite.AI · unite.ai