Rural hospitals are adopting predictive AI at roughly half the rate of their urban counterparts, according to new research from Oklahoma State University that draws on data from more than 2,300 U.S. hospitals. The findings suggest rural facilities face distinct barriers to adoption - including cost, staffing and access to technical expertise - even as AI tools become more common in clinical and administrative settings.
Brian Whitacre, a professor and extension economist at Oklahoma State University, analyzed responses from the 2023 American Hospital Association's Information Technology Supplement, the first year that survey included questions about predictive AI use. His paper, currently under peer review, covers 1,301 urban and 1,018 rural nonfederal acute care hospitals.
The gap is substantial: 81% of urban hospitals reported adopting predictive AI, versus 51% of rural facilities.
Different tools, different priorities
The divide isn't just about whether hospitals adopt AI - it's also about how they use it. Rural hospitals are less likely than urban ones to deploy AI for clinical decision-making or treatment recommendations, but equally likely to use it for billing and other operational tasks.
Rural facilities are nearly twice as likely to rely on AI models built by their electronic health record provider than to develop their own or hire a third-party developer. Urban hospitals also lean heavily on their EHR providers, though they use third-party and self-developed models more often.
"A lot of the [electronic health records] are kind of driving the AI decision," Whitacre said.
One area stands out: rural hospitals are more likely than urban ones to use AI to identify high-risk outpatients for follow-up care. "It makes sense that rural are actually outperforming urban on that basis, because that's where their bread and butter is," Whitacre said. "They need that. That's a good chunk of their revenue coming in."
Factors such as system membership and stronger financial health are positively associated with AI adoption. Critical access hospitals and facilities under for-profit or government ownership are less likely to adopt the technology. High upfront or per-use costs, plus limited infrastructure and workforce capacity, remain the main barriers.
What rural adoption looks like in practice
Mercy Hospital Ada, a facility within a larger system, is using AI to compile patient information, suggest diagnoses, analyze imaging tests, and record clinical notes. Dr. Benjamin Lynch, the hospital's chief medical officer, said in a press release that system affiliation lets them access resources that independent rural hospitals lack.
Newman Memorial Hospital, a critical access facility in Shattuck, Oklahoma, is taking a different approach. CEO Tom Vasko said the hospital is partnering with the Mayo Clinic to provide advanced cardiac remote patient monitoring. The devices, prescribed to Newman Memorial patients, help predict cardiac events.
The hospital also implemented DAX AI, a tool that records conversations between providers and patients and turns them into clinical notes for provider review. Vasko said this creates more direct interaction time with patients, which matters in rural settings.
"In rural environments, it's so important to keep interaction face to face with the provider and the patient," Vasko said. "The patients want to feel like they're being heard."
The tool also helps with billing by ensuring providers meet payer requirements to avoid denials. "It's helping the bottom lines of a lot of the rural hospitals," Vasko said.
Some specialists visit Shattuck only a couple of times a month, so their schedules fill up quickly. Vasko said the AI documentation tool allows providers to see more patients, improving access to care.
Avo, a clinical intelligence platform serving around 30 rural hospitals, offers an ambient AI scribe and a billing optimization tool. Laurence Coman, the company's COO and co-founder, said a critical access hospital in Montana has received zero denials on claims submitted since launching the tools.
"There's so much inefficiency, and back and forth between the hospitals, and then insurance," Coman said. "[It removes] such a big headache from a critical access hospital that might have one biller, and that biller might be also doing IT work or other administrative work."
He acknowledged fears that AI could replace jobs, but argued these tools support rural healthcare workers and finances, especially amid cuts to federal Medicaid spending. Avo has calculated that AI tools for documentation and billing can help offset some of those losses.
"I think that's important framing for why implementing doesn't just have to be Avo and my company's technology, but why implementing AI is more critical than ever - or really any technology that can help them with these problems," Coman said. "It's just like a very critical juncture for rural hospitals."
Lessons from electronic health records
Whitacre sees parallels between AI adoption and the spread of electronic health records. Research in the early 2000s showed similar rural-urban gaps in EHR adoption. In 2009, the federal government allocated $27 billion through the Health Information Technology for Economic and Clinical Health Act to encourage adoption of EHR systems, with billions more for workforce training and setup assistance.
Whitacre argues for a similar federal program to support rural hospitals in adopting and evaluating AI tools.
"It's not going away. It seems like there's going to be winners, and probably the losers are going to be people that don't adopt it early," Whitacre said. "So maybe we need to help these laggards get it up and running - find the right case studies for their hospitals. So that's what the paper hopefully makes a case for."
His next research question is what happens once rural hospitals do adopt AI: whether it leads to revenue increases, better patient outcomes, or perhaps reduced hiring in communities that depend on hospital jobs. He has an open call for rural Oklahoma hospital officials interested in collaborating on the research.
Why this matters for healthcare professionals
For clinicians and administrators working in rural hospitals, the data points to a concrete takeaway: AI adoption is increasingly tied to operational efficiency and financial stability, and the tools most likely to deliver value are those connected to existing electronic health record systems. Billing and documentation tools, like those used at Newman Memorial and through platforms like Avo, address the administrative burden that rural facilities feel most acutely. Professionals who understand these tools - whether through AI for Healthcare training or specialized AI for Medical Billers courses - are better positioned to advocate for investments that match their facility's needs. The data is clear that adoption alone isn't the goal; the goal is using AI in ways that improve both patient care and the hospital's bottom line.
Your membership also unlocks:
