By the end of 2025, roughly half of all nonfederal acute care hospitals in the United States were on track to be running generative AI somewhere inside their systems. That number sounds like healthcare finally catching up to the rest of the economy. It also hides something that matters more than the headline figure: some hospitals got there fast, and a specific kind of hospital barely moved at all.
The Number Everyone Quotes, and What It Skips
A survey study published in JAMA Network Open tracked 2,174 nonfederal acute care hospitals through the 2024 American Hospital Association IT Supplement. It found that 31.5% were already using generative AI tied to their electronic health records, with another 24.7% planning to adopt within a year. Add those together and you get the “half of hospitals by 2025” line that made headlines.
The remaining 43.7% had no clear plans at all, or weren’t sure when they’d move. That’s not a rounding error. It’s a hospital, roughly two in five nationally, sitting on the sidelines while its peers integrate a technology that’s already reshaping documentation, imaging review, and discharge planning elsewhere in the system.
It’s worth pausing on what “generative AI tied to the EHR” actually means in practice, since the phrase can sound abstract. In the hospitals already using it, the most common applications are far less dramatic than an AI making diagnoses. A nurse finishes a shift and an AI tool drafts the handoff summary from the notes already entered. A radiologist dictates a finding and the system produces a structured report ready for review instead of a blank page. None of this replaces clinical judgment. It replaces the twenty minutes a clinician used to spend typing.
Who’s Actually Racing Ahead
The researchers behind the study didn’t find a uniform pace of adoption. Independent hospitals and those with a high share of Medicaid discharges were far more likely to report no implementation plans whatsoever. Hospitals affiliated with a larger health system, and especially major teaching hospitals, moved considerably faster.
That split points to something the framing of “hospitals are slow” gets wrong. It isn’t slowness. It’s resources. A hospital plugged into a system with dedicated IT staff, existing vendor relationships, and budget flexibility can integrate generative AI into an EHR workflow in a way a standalone rural hospital simply can’t, regardless of how curious its staff might be about the technology.
Think about what integration actually requires. Someone has to vet the tool for accuracy and bias. Someone has to train staff on when to trust its output and when to double check it. Someone has to negotiate a contract with the vendor, and someone has to monitor the system after it’s live to catch problems before they reach a patient. A 400-bed hospital inside a ten-hospital system can spread that work across a dedicated informatics team. A 40-bed independent hospital in a rural county often has one IT person handling everything from the EHR to the parking lot cameras.
This is why the adoption gap isn’t likely to close on its own. Left alone, it tends to widen, because system-affiliated hospitals compound their advantage with every new tool they adopt, while independent hospitals fall further behind the learning curve each year they wait.
The Physicians Are Already Ahead of Their Own Hospitals
There’s a second layer to this that rarely makes it into the adoption headlines. The American Medical Association’s 2026 Physician Survey on Augmented Intelligence found that 81% of physicians now use AI in some form in their practice, more than double the 38% who said the same in 2023.
That’s a strange contrast to sit next to a hospital-level adoption rate stuck near 55%. Individual physicians can start using an AI tool for summarizing research or drafting a discharge note without waiting for their hospital’s IT department to formally integrate anything into the EHR. The technology reaches clinicians well before it reaches the institution’s official infrastructure, which makes hospital-level adoption numbers look slower than what’s actually happening on the ground.
A physician at an independent hospital with no formal AI policy can still open a general purpose AI tool on a personal device to summarize a research paper between appointments. That’s not sanctioned by the hospital, isn’t tracked in any survey about institutional adoption, and carries its own risks around patient privacy and unverified output. But it happens anyway, because the gap between what a clinician wants to use and what their employer has formally approved rarely stays closed for long.
This creates a strange two-track reality inside the same building. The hospital’s official systems might show zero AI integration. Meanwhile, half the medical staff is already using it informally, just not through any channel the hospital’s own IT department controls or even knows about.
None of this shows up in a hospital-level survey, because surveys like the JAMA study measure what a hospital’s IT department formally reports, not what individual clinicians quietly do on their own devices. That’s a real limitation of the data, not a flaw in the researchers’ methodology. It just means the true adoption picture, counting both official integration and unofficial personal use, is almost certainly further along than any single number suggests.
Good Places to Keep Watching How This Plays Out
Stories like this, where a headline adoption number hides a much more specific reality underneath, show up constantly as new technology spreads through established institutions. The Decoder, active in AI-specific reporting since 2022 and part of heise medien since 2024, delivers daily coverage without chasing the hype cycle. Metamandrill, active for 4 years, explains how emerging technology like AI is reshaping everyday life, including its AI in healthcare coverage. Interesting Engineering, founded in 2010, covers engineering, science, and technology broadly, with one of the largest readerships in its category. Freethink, a solutions-focused outlet, covers the people and technology shaping the future in daily published stories. Futurism, founded in 2017 and now part of Recurrent Ventures, publishes daily coverage of breakthroughs across AI, space, and robotics.
The gap between what a hospital reports and what its physicians are actually doing day to day is likely to keep widening before it narrows. Watching where the next survey lands, and which hospitals show up on which side of it, tells you more about how healthcare actually adopts technology than any single adoption percentage ever will.
