OpenEvidence Lands NYU Langone, Deepening Its Grip on New York's Hospital Systems

OpenEvidence has secured another marquee enterprise client, and the pattern is starting to look less like a string of wins and more like market consolidation.

The company announced a partnership with NYU Langone Health that embeds its AI-powered clinical evidence platform directly into the health system's Epic electronic health record workflows. The integration covers NYU Langone's hospitals, its ambulatory network, the Family Health Centers, and extends to trainees at the NYU Grossman School of Medicine and the NYU Grossman Long Island School of Medicine. That's a system with more than 12 million outpatient visits annually, 421,000 emergency department visits, seven inpatient locations, and over 400 outpatient sites. Staffing alone totals more than 6,000 physicians and faculty, over 2,000 residents and fellows, and 54,000 employees.

Nader Mherabi, NYU Langone's executive vice president and vice dean and chief digital and information officer, framed the deal in terms of controlled scale. The system wants the technology "available to our clinicians in a safe and practical environment at scale," he said in a statement.

That's the right framing for a decision-support tool touching millions of patient encounters. OpenEvidence built its reputation on a medical search engine and generative AI chatbot designed exclusively for doctors, summarizing peer-reviewed literature and, per the company, now layering in "patient-aware clinical intelligence" that reads evidence against a patient's own chart. NYU Langone clinicians will be able to pose natural-language clinical questions and get answers grounded in both literature and patient records without leaving Epic.

Daniel Nadler, OpenEvidence's founder and CEO, didn't undersell the moment. "New York has always been where American medicine goes to prove itself at scale," he said. "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."

New York as a Proving Ground

The NYU Langone deal doesn't stand alone. It builds on an enterprise rollout across NewYork-Presbyterian's 10 hospitals and nearly 350 care sites, a systemwide deployment at Mount Sinai, and a specialized precision oncology partnership with Memorial Sloan Kettering Cancer Center. Add partnerships with the city's leading medical schools around training and research, and OpenEvidence now sits inside nearly every major academic medical center in New York City. For a company still scaling its enterprise motion, that kind of geographic density is a credibility signal as much as a revenue one.

The underlying usage numbers support the pitch. OpenEvidence says roughly two-thirds of U.S. physicians now use its platform, with 1.2 million medical license-verified U.S. clinicians, including physicians and nurses, as of October. Verified clinicians logged 45 million queries in October alone. Nadler also pointed to specialty care penetration: with roughly 300 million physical specialist visits occurring annually in the U.S., he said OpenEvidence handles about one query per specialist visit nationwide. That's a striking utilization rate for a three-year-old product, and it's the kind of number that tends to get a sales team into the room at the next health system.

Independent data backs the trajectory. A study published in JAMA, led by researchers from Harvard Medical School and Brigham and Women's Hospital, tracked nearly 1 million U.S. clinicians submitting 271 million AI-enabled clinical queries through OpenEvidence between January 2024 and June 2026. More than half the sample, 556,200 users, were physicians, submitting an average of 37 queries per month. Early-career physicians and those in oncology, hematology, internal medicine, and family medicine submitted a higher-than-average share. Most queries, 51.9%, concerned clinical decision-making, particularly treatment and management, with diagnosis accounting for 13.9%.

Retention is the number that should matter most to anyone evaluating the durability of this business. Monthly active users grew from 355 in January 2024 to 703,265 by June 2026. Queries per active user rose from 18 to 39.5 monthly. Month-to-month retention hit roughly 90% in 2026, with 92.5% of active users classified as returning rather than brief adopters. That's not a novelty tool getting tried once and abandoned. It's becoming embedded habit.

Anupam Jena, the study's corresponding author and a Harvard Medical School professor, flagged the more cautionary finding. "An enormous number of physicians are using AI for clinical care and they are often using it in areas where they don't have specific expertise," he said. He also noted the limits of current data: usage volume and query type are now measurable, but impact on patient outcomes remains an open question. "We have limited information on how many doctors are using it, what they are using it for, and how it's impacting patients," Jena said.

That gap matters for anyone pricing risk into this sector. High query volume in specialties where physicians lack deep expertise is exactly the scenario clinical decision-support tools are designed to address, but it's also where errors, if they occur, carry the highest cost. Health systems adopting these platforms at scale are making a bet that literature-grounded AI reduces variance in care quality. The JAMA data confirms adoption is real and sticky. It doesn't yet confirm the clinical payoff, and regulators, malpractice insurers, and competing vendors will be watching that distinction closely.

Investment Implications

OpenEvidence's expansion strategy reads as deliberate: win individual clinicians first, then convert that grassroots usage into enterprise EHR contracts with the health systems employing them. New York City is now effectively a closed loop for the company, with NewYork-Presbyterian, Mount Sinai, Memorial Sloan Kettering, and now NYU Langone all locked in. That concentration reduces near-term customer acquisition friction in subsequent cities, where OpenEvidence can point to a dense reference network of peer institutions. The risk sits downstream: as outcomes data lags adoption data, any high-profile clinical miss could slow the enterprise pipeline faster than usage metrics suggest. Watch for outcome-focused follow-up studies and whether competitors like Regard, which is pushing from clinical decision support into case management workflows at systems like Sentara Health, start encroaching on OpenEvidence's enterprise territory rather than just the point-of-care niche.