HOT SPRINGS, S.D. — Two of the nation’s most powerful health officials predict artificial intelligence will play a key role in solving rural America’s health challenges.
Health secretary Robert F. Kennedy Jr. told a panel of U.S. senators that AI nurses can provide “concierge care” to rural patients. Mehmet Oz, who leads the Centers for Medicare & Medicaid Services, has said “the best way to help some of these communities is going to be AI-based avatars” that connect rural patients to mental health services.
And many state health leaders agree. They are using some of their funding from the $50 billion federal Rural Health Transformation Program to expand AI among rural health organizations.
AI is computer technology that performs tasks that typically rely on human intelligence by finding patterns or generating words. It has the potential to improve the healthcare system by automating back-office work or identifying patients at risk, but several reports contend there’s little evidence AI can improve access to care and patient health in rural areas. It’s unclear how well states will track and share outcomes of the tech they invest in.
Meanwhile, some rural Americans are skeptical, according to interviews with people in Hot Springs, South Dakota, a city of about 3,400 residents at the southern end of the Black Hills.
“I get artificial intelligence for certain things, but for personal healthcare — no,” Tara Haffner said while standing outside the American Legion.
Haffner said she’s worried about AI making mistakes and wants healthcare to stay between her and her doctor.
But Phillip Mues, who oversees technology at Cherry County Hospital and Clinic in rural Valentine, Nebraska, said AI is already helping clinicians save time, reduce burnout, and focus more on patient care.
“I think it will help reduce burden on actual staffing,” he said. “It won’t replace people, but I think it will help in rural communities.”
Still, Mues said, AI can’t fix every challenge. Rural hospitals at risk of closing or ending certain services probably can’t use AI to save enough money to prevent those consequences, he said.
Congressional Republicans created the five-year Rural Health Transformation Program last summer as a last-minute sweetener to President Donald Trump’s signature One Big Beautiful Bill Act. The funding was intended to offset concerns about the outsize fallout anticipated in rural communities from the law, which is expected to reduce overall Medicaid spending by more than $900 billion over a decade.
The Word on the Street
Hot Springs, which has a 25-bed independent hospital and a Department of Veterans Affairs hospital, is known for its sandstone buildings, veterans’ services, and, yes, hot springs. Residents must drive at least an hour for more advanced care.
Six people interviewed there by KFF Health News said the biggest problem in rural healthcare is the cost or long wait times caused by staffing shortages.
Doug Nikkila, a heavy equipment operator, said AI and other technology come with benefits and risks.
“If it’s not utilized correctly, it becomes a burden,” he said.
Nikkila, who’s concerned about nursing home residents being neglected amid staffing shortages, said he thinks AI should send reminders to staff when their residents are due for diaper changes or other care. He also wondered whether AI-powered video monitors could send alerts when they detect falls or illness symptoms.
The healthcare industry is rapidly adopting AI despite the tools being “poorly evaluated,” according to a recent report from ARISE, a Stanford- and Harvard-led group that evaluates health-related AI. The report says that while some AI has been successful in controlled settings, there’s less evidence it can perform in the real world. It also said few studies track patient outcomes.
Evidence is especially lacking in rural areas. A recent academic paper found that only 26 peer-reviewed studies about AI in rural healthcare were published from 2010 through April 29, 2025. Few analyzed implementation or outcomes.
Despite the dearth of results, some states appear interested in bold experiments — such as using AI to suggest diagnoses or recommend treatments. Utah officials said in their application to the rural health program that they are interested in funding a controversial experiment in AI-powered prescription refill requests.
Even tools proven to work in urban settings may not work in rural ones, said Qian Huang, an assistant professor at the Center for Rural Health and Research at East Tennessee State University.
She said the technology is usually tested at large, academic hospitals and trained on data from urban patients, who may not have the same health issues and obstacles — such as a lack of transportation — as rural patients.
A KFF Health News review of states’ plans for the Rural Health Transformation Program shows they’re interested in using AI to automate time-consuming, behind-the-scenes tasks, such as medical charting, coding, referrals, and prior authorization requests. Some states also mentioned ways AI can save money, such as Washington, which discussed tools that “identify and recover” money it’s owed.
Mues said the Valentine clinic has been using AI scribes that record appointments and generate notes describing the visit. He said surveys of clinicians before and after they started using the technology show the scribes have helped reduce burnout by letting providers focus on patient care with “eye contact on the patient, not the computer.”
States also mentioned funding AI that directly affects patient care, such as tools that recommend possible diagnoses and treatment options to clinicians. Mississippi wants to use predictive AI algorithms to “guide” emergency medics with “triage, routing, and treatment decisions.”
Several states want to use AI to analyze patients’ medical charts and remote monitoring devices to identify immediate or future health risks. North Dakota’s plans mention AI to “detect early signs of chronic disease and behavioral health conditions,” while New Hampshire’s discusses AI that identifies patients “at high risk of adverse drug events.”
Some states plan to give patients access to chatbots or wearable devices that transmit data to their clinicians. Utah is interested in funding AI-powered fetal-monitoring devices, while Kentucky will explore using AI chatbots to “deliver personalized nudges and education” through “health coaching, gamified incentives, and rewards.”
Whether the technology appeals to consumers is another matter. Hot Springs resident Stephanie Keller wears a smartwatch to track her fitness but has no interest in an AI chatbot using her data to encourage her to reach her health goals.
“I don’t have the time to chat with AI every day. I mean, are you kidding me? I don’t want to spend my time on a cellphone,” she said.
Rural health facilities also face challenges in implementing AI.
Huang, who has written about AI in rural healthcare, said rural hospitals and clinics may not have the hardware or IT staff needed to support the technology. She said clinicians and staff may already be doing three jobs at once and not have time to go through AI training.
Rural health facilities may not have fast-enough internet to use AI, while patients may have slow connections at home — if they have internet at all — or may not feel comfortable using AI, Huang said.
“In rural communities, trust and a personal relationship is essential,” she said.
Roy Ehlers, a Hot Springs resident, said he doesn’t trust AI in healthcare, or anywhere else.
“I’m old-fashioned. I don’t believe in it. Technology is not my forte,” Ehlers said.
Mues said that while some rural patients are “scared of AI,” most have let their clinicians at the Valentine facility use the scribing technology to record patients’ visits.
Will States Share AI Results?
Despite questions about implementation, the boom is on. Jordan Everson, an assistant professor at the Georgetown University Department of Family Medicine, said both urban and rural health facilities are rushing to use AI.
“The risk of signing contracts that rural healthcare organizations come to regret is pretty high,” said Everson, who previously worked in the information technology office at the U.S. Department of Health and Human Services.
Several states are addressing that risk by using their rural health funding to create groups that will help rural health facilities vet, select, or monitor AI tools while offering training, ongoing assistance, or funding for upfront costs.
CMS spokesperson Timothy Foster said the agency doesn’t have any AI-specific reporting requirements but is working on a form for states to report their overall progress and outcomes.
Abraham Pritzker, who works at Julota, a company that helps health organizations track data, said states should measure more than how often AI programs are used.
For example, states can measure whether the tech reduces falls, 911 calls, or hospital admissions, said Pritzker, a former paramedic. Huang said it’s also important to ask clinicians and patients about their experiences using AI.
Yet many states’ applications to the rural health program mention tracking only AI adoption metrics, not what happens after facilities deploy the tech. Some of these states may add further reporting requirements down the road.
Vermont spokespeople did not respond when asked why their state’s funding opportunity for AI scribes requires organizations to report only how many clinicians and patients are served by the tech, not how much time they save.
States requiring recipients to report outcomes include Connecticut, which will track how often AI-powered patient monitoring devices trigger accurate alerts. Texas will require organizations to track cost savings, while Wisconsin lists “patient outcomes” and “productivity and efficiencies” as possible metrics.
Huang said that after collecting results, states need to share them so other states and healthcare organizations can learn from their experiences.
“We do not have a lot of resources to waste on tools that don’t work in rural areas,” she said.
Facts Only
* Robert F. Kennedy Jr. stated AI nurses can provide “concierge care” to rural patients.
* Mehmet Oz stated AI-based avatars could connect rural patients to mental health services.
* State health leaders are using federal funding from the $50 billion Rural Health Transformation Program to expand AI in rural health organizations.
* AI is defined as computer technology performing tasks relying on human intelligence by finding patterns or generating words.
* Some reports contend there is little evidence AI improves access to care and patient health in rural areas.
* Tara Haffner expressed concern about AI making mistakes in personal healthcare and desires privacy between patients and doctors.
* Phillip Mues stated AI helps clinicians save time, reduce burnout, and focus on patient care by reducing the burden on staffing.
* Rural hospitals at risk of closing may not use AI to achieve sufficient cost savings.
* Studies on AI in rural healthcare are limited, with only 26 peer-reviewed studies published between 2010 and April 29, 2025 regarding this topic.
* States are exploring experiments using AI for tasks such as suggesting diagnoses or recommending treatments.
* States are interested in using AI to automate administrative tasks like charting, coding, referrals, and prior authorization requests.
* Some states plan to use predictive AI algorithms to guide emergency medics with triage, routing, and treatment decisions.
* Connecticut plans to track the frequency of accurate alerts from AI-powered patient monitoring devices.
* Texas plans to require organizations to track cost savings related to AI.
* Wisconsin lists “patient outcomes” and “productivity and efficiencies” as potential metrics for AI measurement.
Executive Summary
Full Take
The narrative presents a tension between the rapid adoption of AI by the healthcare industry and deep-seated skepticism among rural populations, complicated by systemic implementation gaps. The core conflict is framed around the promise of efficiency versus the reality of context-specific applicability. While entities like state governments and large health organizations push for AI integration using transformation funding, the lived experience in places like Hot Springs reveals profound distrust rooted in historical disparities—specifically concerning staffing shortages and the potential for technology to exacerbate existing inequities rather than solve them.
A critical pattern emerging is the divergence between high-level policy objectives (automating back-office tasks) and on-the-ground concerns (lack of infrastructure, training deficits, and a fundamental need for personal human connection in care). The push to measure outcomes via AI adoption metrics, as seen in state funding programs, risks prioritizing implementation status over demonstrated clinical efficacy or patient well-being. Furthermore, the academic limitation noted—the scarcity of real-world outcomes data specifically for rural settings—suggests that the current enthusiasm is outpacing the evidentiary basis required for responsible deployment, particularly when technologies developed in urban centers may fail in environments defined by transportation barriers and unique resource constraints. The emphasis on measuring time saved over actual patient outcomes highlights a potential systemic drift where efficiency becomes the primary metric, potentially sidelining essential human relational elements necessary for trust in rural healthcare delivery.
Bridge Questions: What mechanisms can be established to ensure that AI evaluation in rural health contexts prioritizes community-specific outcomes over generalized metrics? How can policies mandate infrastructure support and specialized training to address the disparities between urban and rural AI deployment? What is the long-term consequence of prioritizing quantifiable efficiency gains when the defining feature of rural healthcare quality remains personalized, relationship-based care?
Sentinel — Human
This article reads like a typical piece of feature journalism that effectively synthesizes expert opinions, policy details, and anecdotal evidence to explore the complexities of AI adoption in rural healthcare, balancing optimism with pragmatic skepticism.
