AI Use Cases for Clinical and Financial Operations in Rural Healthcare
National Rural Health Association CEO Alan Morgan says three common use cases for AI come up in his conversations with hospitals and health systems.
The first is deploying ambient AI to document patient appointments. This can help practitioners pay more attention to patients’ needs while alleviating the burden of note taking. “It’s amazing how much time this is freeing up,” Morgan says. “I think this is potentially the greatest benefit we may see coming from AI.”
Using AI to take notes improves the patient experience, Kwong explains, as practitioners no longer focus exclusively on their computer keyboard. Beyond the appointment, AI models can assess a patient’s records and flag issues worth a follow-up. For example, if a patient mentions in many visits that they’re having difficulty falling asleep, an AI model trained to detect patterns may flag that issue and prompt the health system to follow up.
“AI can help identify patterns that a doctor may not see at first, or that they may initially think is an offhand comment,” Kwong says. This scenario tends to come with little resistance, as it provides additional information to providers without explicitly telling them what to do.
The second is AI-based second opinions, which can help to reduce diagnostic errors. While hallucinations in AI models and biases in training data sets remain issues, the potential to access consultations in a matter of seconds has a clear benefit, Morgan notes. This is especially true in rural settings, where specialists may be hundreds of miles away or unavailable outside of normal business hours.
RELATED: Here are 13 ways AI enhances healthcare operations, patient care and treatments.
The third is streamlining billing and coding — a use case critical for the survival of rural organizations. Roughly one-third of rural hospitals are at risk of closure due to financial problems that stem from the cost of care delivery, the limitations of federal assistance and low financial reserves, according to a report from the Center for Healthcare Quality and Payment Reform.
Morgan adds that rural hospital leaders see AI in the revenue cycle as a response to insurers’ use of AI to assess claims — a practice facing class-action lawsuits.
In a podcast with the Rural Health Information Hub, Jordan Berg, director of the National Telehealth Technology Assessment Resource Center indicates applying AI to the revenue cycle is more than a matter of automating routine tasks. With the right AI tools, he says, organizations can ensure services are billed at the appropriate level, notify vendors and patients when bills are due, and identify opportunities for further revenue cycle optimization, “all with very minimal input from users and stakeholders.”
Additional use cases for AI in rural settings include optimizing workflows in the electronic health record (EHR), augmenting diagnosis and decision support, deploying mobile clinics with practitioners supported by AI agents, and improving scheduling and follow-up messaging. These examples work well because they don’t cause much friction.
“Patients are fine with getting an automated appointment reminder call,” Kwong says. “For organizations that have limited resources, a tool like that can be a good investment.”
READ MORE: Revolutionize prior authorizations with AI.
Lack of Policy Isn’t Hindering AI Adoption for Rural Healthcare
One wrinkle for rural, independent and community health systems looking for guidance on where and how to best use AI is the lack of direction from Capitol Hill. Recent regulations have closely defined the implementation of EHR systems, the acceptable use cases for telehealth, and the standards and infrastructure necessary to exchange health information, among other things.
Meanwhile, no such framework exists for AI in healthcare. Morgan says this isn’t surprising; given the typical long on-ramp for technology adoption in healthcare, policy moves at a slow pace. Right now, he adds, “it seems very hands-off.”
The Trump Administration’s proposed spending bill includes a 10-year pause on state or local AI laws in lieu of overarching federal regulation with few compliance hurdles. “It’s a very volatile time, and policymakers are still feeling their way around that,” Kwong says.
Even without the guardrails of federal policy — or a body of empirical research into how rural healthcare organizations are using AI — adoption appears to be taking off, Morgan says. “I’ve seen a lot of fads,” such as rolling out robots in hospitals, “but AI has such amazing potential, and we’re basically talking about utilization picking up in just the last year.”
