Artificial intelligence

Health systems are hustling to implement artificial intelligence across their organizations, but how to measure returns on those investments isn’t always clear, experts said at the HLTH 2025 conference.

AI technology, which includes ambient documentation scribes, summarization tools and revenue cycle management products, costs money and time to put in place. And it’s not always obvious how AI produces hard-dollar ROI, which is important to justify expenses and measure success, experts say.

“The use of AI to simplify administration, the use of AI to assist with productivity is absolutely out there,” said Thom Bales, principal and health services advisory leader at PwC. “There still are questions around the ROI on it and hitting the bottom line.”

At the same time, health systems are also facing increased financial pressures. This summer, President Donald Trump signed a massive tax and policy bill that makes historic cuts to Medicaid, likely culling millions of people from the safety-net insurance program.

Meanwhile, more generous financial assistance for people who buy coverage on the Affordable Care Act exchanges — the issue at the center of a weeks-long government shutdown — is set to expire at the end of the year, also pushing more people off coverage.

More uninsured Americans means less revenue for providers and an increase in uncompensated care.

Still, the healthcare sector is hopeful that AI will move the needle on some of its biggest challenges, including workforce shortages, provider burnout and lengthy administrative tasks. Amid the challenging economic environment, healthcare companies are prioritizing AI investments most likely to improve profit margins and demonstrate clear returns, according to a survey published by Klas Research and Bain & Company.

But given those returns can be hard to show, hospitals may have to get creative in measuring the value of AI, including by looking at time savings for providers, stronger employee retention or improved patient satisfaction, experts say. Those softer measures could then be linked to financial improvement over time.

“I do think ultimately we can trace the majority of these things back to a bottom line, but you may have to connect more dots,” said Jennifer Goldsack, CEO of  industry association the Digital Medicine Society.

Piecing together ROI

Measuring financial returns is always tricky, given the complex interplay of factors that complicate attributing eventual savings to a specific product. This is also true when it comes to AI, where the direct financial impact of a product depends on the tool, the costs needed to adopt it and each provider’s specific priorities, experts said at HLTH.

“In healthcare, particularly in the healthcare delivery side, it’s really hard to apply line of sight to what we might think of as ROI,” Micky Tripathi, chief AI implementation officer at the Mayo Clinic, said during a Monday panel. “I don’t think that any of us should really expect you’re going to see a cost reduction that a CFO could look at and say, ‘Oh, I see it there.’”

Measuring financial returns is easier with certain AI products. For example, revenue cycle management tools might allow health systems to see metrics like time to collections improve, according to Sandra Johnson, senior vice president of client services at EHR vendor CliniComp.

But the impact of other AI tools is less obvious. There’s limited evidence that AI documentation assistants, which record physicians’ conversations with patients and create a clinical note, are affecting productivity and financial performance, according to a report published by the Peterson Health Technology Institute. 

However, the scribes are likely reducing clinician burnout, the report found — a significant concern that could be exacerbated by the amount of time and effort needed to document care and do other work in EHRs.

If AI scribes prevent a clinician from leaving the health system, that could be a major cost saver, experts say. The expense of replacing a physician can be two to three times the physician’s annual salary, studies suggest. 

And there are other potential financial and efficiency gains from ambient scribes, said Dr. Nele Jessel, the chief medical officer at health technology firm and EHR vendor Athenahealth.

For example, providers could complete notes more quickly and possibly see more patients. They also might be able to document services more accurately and bill for a higher level of services.

Patient satisfaction is another important metric for health systems. Patients tend to like ambient scribes in part because their doctors can be more present during appointments, instead of staring at a computer screen while they take notes. Documentation assistants might also help patients understand their care plans better, since physicians have to speak out loud during an exam to ensure the scribe picks up the information, Jessel said. 

“Providers are getting much better, for example, [at] vocalizing the physical exam, talking about the plan, right?” she said. “So it increases the transparency and also makes it a much more enjoyable experience for patients.”

How health systems are measuring AI’s impact

To implement AI successfully, health systems need to think critically about the biggest problems they want to solve and their main priorities, experts say. That plan should include how they’re going to evaluate the AI tool, which can help them determine if the project was a win — whether financially or by other metrics.

“To me, step zero is being very, very clear about, what is it that we’re doing? Why are we doing it? And what does success look like for that?” Mouneer Odeh, chief data and AI officer at Cedars-Sinai, said during a Sunday panel. 

Cleveland Clinic evaluates AI using two types of metrics, according to Sonja O’Malley, the general manager of business development and licensing at Cleveland Clinic Innovations, the system’s commercialization and innovation arm. 

Those include quantitative measures — like reducing no-show rates or cutting documentation time, compared to a baseline metric — as well as more qualitative criteria, like feedback on patient or clinician experience. 

Some tools will never be revenue generators. But they might do other important things, like allowing a clinician to spend time on tasks that require their full training and expertise, O’Malley said.

When an AI tool is brought before Ardent Health, metrics of success have to be clearly defined and measurable alongside a baseline, according to Anika Gardenhire, the health system’s chief digital and transformation officer. 

Financial return on investment is almost always important, but it might be correlative rather than direct causation, she said.

If a tool only breaks even, the system needs to consider other associated costs, such as implementation expenses or the time clinicians spent on the project when they could have been working elsewhere.

“If you get to a place where you’re just like, ‘Hey, this thing didn’t even break even. It doesn’t even pay for itself.’ Unless it’s doing something absolutely miraculous for patients, it’s out,” Gardenhire said.

Executives from some of the nation’s top health systems took to the stage at HLTH 2025 to share how their newly minted technology investments are improving operations ranging from capacity management to virtual specialty care. 

It’s a change from HLTH’s 2024 conference, when many major health systems’ more ambitious AI pilots and strategies were still in nascent stages and conference sessions on AI mostly featured technology companies.

In 2025, health systems including Risant Health, Advocate Health and Houston Methodist were eager to share data demonstrating that their forays into AI have been successful. 

Kelly Garrison, chief value officer at Atrium Health’s primary care network One Health, said Atrium had invested in AI solutions that cut expenses by identifying disease early, allowing the provider to implement lower cost solutions. For example, they might help a patient manage high blood pressure before they need medication. 

“We could all spend billions of dollars on new technologies, new tools that we all find interesting,” said Garrison. But such an investment strategy wouldn’t have the broad reach and cost savings of tools like the ones aimed at identifying early interventions, which can be used with 90% of patients.

Houston Methodist and Risant Health have leveraged AI to better manage capacity and coordinate patient care.

Capacity management has been a pain point for many overpacked health systems. Health systems are reluctant to build expensive new inpatient facilities, but they’re betting AI can help manage the churn of patients in the most appropriate and lowest cost setting. 

Houston Medical, for example, rolled out its “smart hospital” strategy this year. The initiative improves patient monitoring through the use of wearable technology and an AI-backed “care traffic control center” that helps direct and cue care.

The control center offers AI-powered “nudges” for clinicians when patients might need a higher level of care, helping move deteriorating patients more efficiently through care settings. Houston Methodist has seen a reduction in its average mortality rate since adopting the tools, according to President and CEO Dr. Mark Boom.

“Artificial intelligence, automation, remote monitoring and digital-first services are part of our future. They are the tools that will help make our care teams make faster, safer decisions,” said Boom during a panel.

The health system plans to apply the lessons learned from the inpatient setting to home health next, and expects to move 20% of its specialty care to virtual care settings over the next three years.

Meanwhile, Risant Health, the Kaiser Permanente-owned nonprofit health system launched in 2024, has integrated value-based care guides into its electronic health record to better direct care. The guides cover the most common conditions treated in primary care and include integrated orders a primary care doctor may want to order next based on evidence-based best practices. 

The primary goal of the guides is to standardize care across Kaiser Permanente and Risant facilities, which include Cone Health and Geisinger, Dr. Benjamin Hohmuth, chief medical informatics officer at Geisinger, said during a panel on Tuesday.

Risant’s parallel initiative, called Intelligent Triage, helps direct patients to the most appropriate care setting. The tool has resulted in about a 20% reduction in unnecessary emergency room encounters at Geisinger by prompting clinicians to consider lower acuity and more cost-effective options, according to Risant. Geisinger also used the tool to reduce unnecessary specialty referrals by 7% over the last year.

“It leads to faster resolution for patient concerns, lower cost sharing for patients and frees up specialty access,” said Hohmuth.

When specialists do need to be involved, Geisinger is pushing for virtual consults when possible, with Hohmuth noting “the specialist can do a lot more e-consults in an hour than they can do in person. It's a lot more convenient for the patient as well.”

Health systems also said they remain bullish on ambient AI and notetaking software to help reduce burnout.

Dr. Mark Collins, chief strategy officer at Atrium One Health, said the system’s investment in Microsoft's DAX Copilot, which automates notetaking, has helped give older providers “a new lease on caring for their patients.”

Meanwhile, Geisinger said physicians are saving approximately an hour per day through using ambient listening technology to assist in notetaking. 

Notetaking tools could be influential for recruiting talent too.

“If you don’t have this technology, people on the front lines may not want to come to your facility,” Amy Meister, senior vice president of UPMC and president of its community and ambulatory services division, said about documentation tools during a panel.

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The top priority of healthcare organizations is clear: Deliver the best care to the most people. However, the industry is struggling to maintain levels of care — both in quantity and quality — because the ratio of those needing care and those willing to deliver it is widening. Providers can’t keep up with the growing demand.

Between an aging patient population and a growing workforce shortage, there are often more jobs to be done than people to do them, forcing healthcare professionals to further allocate their time to a growing list of administrative tasks. Combine this with fragmented data, disparate applications, paper forms, and manual tasks, and it quickly leads to mounting inefficiencies and staff burnout.

Who pays the price? Patients, members, and customers — who must endure longer wait times, inaccessible or more expensive care protocols, disconnected experiences, conflicting information, and slower time to therapy. And providers struggle, too, as they’re constantly being pulled away from their most important job of providing care and answering questions.

Healthcare organizations must find ways to boost efficiency, and they’re turning to AI to do it. A 2024 Forrester Study indicated that more than 80% of healthcare leaders believe organizations that effectively adopt AI will be more efficient and agile. And according to Menlo Ventures’ research, 22% of healthcare organizations have already implemented domain-specific AI tools, a 7X increase over 2024 and 10X over 2023 — and twice the rate of the broader economy.

Until now, however, AI has taken a reactionary role, responding to rules and human prompts to complete tasks. But what if AI could act as a digital labor force, filling in the jobs that don’t have enough human workers? To ease the burden on healthcare workers and free up time for actual caregiving, AI agents would have to take autonomous actions, with human oversight, but far less human intervention than we’re accustomed to.

The next wave of AI is here

Chatbots opened the door to conversational AI, simplifying common tasks and personalizing patient interactions based on predefined rules. Then, copilots upped the ante with assistant-like automation that could answer questions and generate content using natural language grounded in customer data. But none of these solutions could reason or take action independently in digital workflows across systems.

AI’s third era? Agentic AI, joining the versatility and flexibility of large language models (LLMs) with the precision of traditional programming. AI agents achieve specific goals and even consider the consequences of their actions to make smarter decisions. This means AI can now be fully autonomous, working beside humans as a digital workforce to relieve administrative burdens without compromising trust.

From jumpstarting patient recruitment to scheduling appointments, agentic AI plays a pivotal role in enhancing efficiency, reducing errors, and personalizing patient care.

Agentic AI supports important healthcare use cases

With AI agents working alongside human teams, organizations can:

• Bring relief to a strapped workforce with intelligent assistance that works alongside humans to perform jobs in patient and member services, provider education, care coordination, sales and market engagement, and public health data management.
• Make smarter, faster decisions using natural language with AI agents, which can anticipate, plan, and reason, as well as adapt to new information, operating within predefined guardrails.
• Automate entire workflows and ensure seamless coordination and hand-off between AI agents and human employees, facilitating trusted collaboration across every department.

The future of agentic AI is built on trusted data

The data conversation has shifted from cleaning, breaking down silos, and storage to activation and interoperability, allowing different parts of the business and AI agents to collaborate with one another. A good agentic AI solution comes with prebuilt integration solutions to connect structured data with unstructured data, which now accounts for 90% of an enterprise’s information, according to IDC.

This shift in focus has paved the way for everyday use of AI, with healthcare leading the charge. Healthcare companies are expecting significant impact on productivity, efficiency, and outcomes. Specifically, Advocate Health, a North Carolina-based healthcare system, is expecting a 50% reduction in documentation time after implementing 40 AI use cases. And SimonMed, a large radiology group, is piloting solutions from more than 50 vendors, up from co-building only 10. It’s testing systems for intake, ambient scribing, and revenue cycle management, among others.

Healthcare leaders across all sectors are seeking opportunities to integrate AI into their daily operations. While initiating this transition can be challenging, delaying it puts competitive advantage at risk. The industry players that transform into agentic enterprises today are the trailblazers that will shape the future of healthcare.

Healthcare companies struggling to adopt artificial intelligence amid a lack of federal standards are unlikely to get any help from the Trump administration, placing the burden of responsible implementation squarely on the industry’s shoulders.

That mandate is becoming increasingly difficult as the technology grows more complex, experts said during the HIMSS healthcare conference in Las Vegas.

“One thing that’s clear is that this administration is not going to regulate AI. Good or bad, take that for what it is,” Tanay Tandon, the CEO of provider automation company Commure, said during a panel.

President Donald Trump’s hands-off approach to AI governance means hospitals turning to the tools to save money and give overworked clinicians some relief will likely be operating in a regulatory gray area for at least the next four years.

The president says his goal is to free up U.S. developers to innovate. But that comes with downsides for tech developers and healthcare companies desperate for guardrails given AI’s proclivity to make mistakes, degrade over time and exacerbate existing bias.

Not to mention, a lack of national standards might actually hamper AI development and adoption, according to some experts.

“When there isn’t a federal framework, it can just absolutely cause all kinds of problems,” said Leigh Burchell, the chair of the Electronic Health Records Association. “We all just want to know what our rules are. And then we can comply.”

Biden versus Trump on health AI

To date, a handful of federal agencies, including the HHS’ technology office, the CMS and the Food and Drug Administration, have published targeted rules around the use and quality of AI in healthcare. But neither Congress nor the executive branch have zeroed in on a comprehensive framework to regulate the models — despite some progress during the Biden administration, when an HHS task force was working to build a unified regulatory structure.

That task force unveiled a strategic plan in January 2025 — just 10 days before Trump’s inauguration. However, Trump nixed the blueprint in one of his first executive orders.

Meanwhile, federal employees working on AI oversight, including at the FDA, have been caught up in the Trump administration’s purge of the government’s workforce. Amid the turmoil, the future of the HHS office that oversees AI policy remains unclear.

As a result, what little momentum there was in Washington to create a concrete strategy for overseeing health AI appears to have stalled out, at least for now. In its place, Trump has announced the Stargate Project, a $500 billion investment deal with private companies to prioritize AI development and maintain U.S. supremacy in the space — a high-stakes bet that was immediately complicated by the release of DeepSeek, a high-performing and inexpensive open source model from China.

The Trump administration did issue a request for information in early February 2025 to get public input on a potential national AI action plan. However, the plan’s wording makes clear the administration’s priorities: to “sustain and enhance America’s AI dominance, and to ensure that unnecessarily burdensome requirements do not hamper private sector AI innovation.”

The revocation of the Biden-era AI plan was largely symbolic, as agencies hadn’t yet gotten around to imposing any requirements on developers or users.

But in “the current administration — the brakes have come off and the accelerator has come down,” said Brian Spisak, program director of AI and leadership at Harvard University's National Preparedness Leadership Initiative, at HIMSS. “There’s a lot of responsibility to leadership of health systems to find the optimal balance between innovation and speed and safety and tradition.”

A technological sea change

That responsibility — which also rests on AI developers creating the models, software vendors weaving them into health records and the clinicians using them — is not negligible.

Currently, even the most futuristic AI at healthcare institutions is being used to automate administration, and only peripherally touches patient care. But that appears to be changing: There’s growing interest among medical organizations in more clinical use cases for AI, like tailoring treatment plans or helping clinicians arrive at a diagnosis, according to a survey from HIMSS.

Many of those use cases involve generative AI, which can create original text and images. But such models are known to hallucinate, or provide answers that are factually incorrect or irrelevant. AI can leave important information out, an error known as omission. Models can also drift, a term for when an AI’s performance changes or degrades over time.

Given the increasing prevalence of AI in pulling data from EHR systems, transcribing doctor-patient visits and more, errors like these could interfere with clinicians’ ability to care for patients, experts say.

Meanwhile, the technology is advancing at a stunning rate. In 2024, the healthcare industry was just beginning to come to terms with governance for generative AI. But now, the conversation has already moved onto AI agents, which can complete complex tasks largely unsupervised by humans.

Commure’s Tanay equated the current moment to when America moved from kerosene to electricity at the end of the 19th century.

“The way that we did things six months ago is completely irrelevant,” he said.

Because of the breakneck speed of development, federal standards from any administration would likely need to be flexible, experts say. 

The Biden administration’s HHS task force suggested the government could create guidelines around testing and piloting tools, along with some support for adoption. However, it shied away from a prescriptive approach.

That’s in line with the wish list from many stakeholders. Numerous executives at tech companies and hospital systems said any federal standards should be stratified by the level of risk an AI model imposes — for example, stricter oversight for algorithms that help doctors diagnose diseases, and looser restrictions for algorithms that help hospital staff allocate patients to beds.

“We have to weigh the balance between underregulation, which can potentially increase risk, and overregulation, which will stunt innovation,” said Anthony Chang, the chief intelligence and innovation officer at the Children’s Hospital of Orange County in California, during a panel.

“The current administration is more likely to be on the underregulation side. So we have to be careful as a profession that we don’t allow that to happen,” Chang said.

States, industry groups filling the void

The lack of a playbook from Washington has left hospitals and medical groups scrambling to build their own internal controls amid a patchwork of state laws and voluntary standards released by industry groups.

States including Colorado, Utah and California have already enacted legislation establishing disclaimer requirements for AI systems. More states are considering similar laws: The Electronic Health Records Association is tracking 150 different state bills related to health AI, according to Burchell.

“There is a massive explosion of bills,” Burchell said.

But differing standards could stop health AI developers and software companies from rolling out products in specific states, potentially disadvantaging patients depending on where they live. More risk-averse software companies may avoid AI, or certain states, altogether, she added.

“Legislation of a lot of sizes and shapes at the state level is a risk to us, because it means we have to do all kinds of different development. We would rather develop one system that can be used broadly and accepted across the country,” Burchell said.

Health AI standards groups are also stepping into the gap left by the federal government. The groups — often made up of leading hospitals, digital health companies and tech giants — include the Health AI Partnership, an AI learning network for the industry, and the Coalition for Health AI, which recently launched an AI registry for hospitals.

”I think we’ll probably see more of these non-government organizations like Health AI Partnership and some of those emerge as maybe our north stars today, as here’s some leadership in the space,” said Rachel Wilkes, the corporate lead for generative AI initiatives at EHR vendor Meditech.

But standards from industry consortia hold little weight without the heft of the federal government behind them, experts say. Historically, voluntary standards aren’t particularly effective.

“There’s room for people to act in their own best interest, whatever that may be, without any sort of federal framework,” Wilkes said.

‘We still don’t quite know how to deal with it’

EHR vendors and hospital operators say they’re building up rigorous internal standards for AI tools, including validation and frequent auditing.

“Government oversight has its place, but I do think the way that clinical practice evolves tends to be more driven by what’s happening at a health system,” said Seth Howard, the executive vice president of research and development for Epic, the largest EHR company in the U.S.

In interviews, executives with Epic, Oracle, Meditech and eClinicalWorks said they’re making AI available to doctors with rigorous oversight, including back-end accuracy checks and ongoing monitoring.

However, tech leaders stressed that it’s also the hospital and the clinicians’ responsibility to make sure everything is working as planned.

“We are working in an industry that deals with human life. It cannot be trivial. It cannot be under-exaggerated on what guardrails and checks and balances and what discussions need to happen,” said Girish Navani, the CEO of eClinicalWorks.

Technology behemoths betting heavily on AI feel similarly. Google, for example, has worked with for-profit hospital giant HCA on an evaluation framework to catch any errors generated by its AI models and ensure their reliability, according to Aashima Gupta, the head of healthcare for Google Cloud.

“We provide those tools for an evaluation framework, and for all of this there’s a human in the loop capturing the feedback, and that feedback loop then makes the model more effective,” Gupta said. “That’s what gives me comfort.”

Though some in the private sector say they’ve got governance handled, modern AI is incredibly hard to oversee, according to AI engineers. The main advantage of generative AI — its creativity — also introduces subjectivity, making grading its outputs complicated.

For example, if two clinicians are tasked to summarize a patient’s medical history based on clinical notes, their results could be incredibly different, albeit still accurate. It’s the same with generative AI, experts say: How do you measure quality in a standardized way when there’s that degree of variability?

“AI governance is still very much a maturing process,” said Harvard’s Spisak.

Hospital executives say they’re tackling oversight carefully. But some research suggests governance systems for simpler predictive AI models already aren’t rigorous enough. Hospitals with explicit procedures for the use and assessment of AI tools are still struggling to identify and mitigate problems, according to a study published in 2024 in the New England Journal of Medicine.

Even some of the most well-resourced and tech-savvy systems are struggling.

The Cleveland Clinic has an AI governing body that includes stakeholders from across the academic medical center, according to Rohit Chandra, the Cleveland Clinic’s chief digital officer. The task force oversees AI’s impact on the organization’s patients and workers while ensuring clinical safety and discussing thorny questions of privacy, legality and bias.

But “I don’t think we’ve figured it all out,” Chandra said during a panel. “The term hallucination, for one, has just shown up in the last two, three years. And we still don’t quite know how to deal with it.”

Hospitals should try to make specific people accountable for the performance of the tools, as part of a larger governing body that includes executives, lawyers, doctors and nurses, according to Brenton Hill, the head of operations at standards group CHAI.

Hospitals need to decide how to monitor AI effectively and report that information, which can depend on the products they have in place. They also need to consider what resources the AI will use and develop appropriate data use agreements with AI vendors, Hill said during a panel.

But “there’s not one silver bullet governance structure that you can put out there that will solve all your problems,” Hill said.

‘A pipe dream’

Though a road map from federal regulators would be helpful, stakeholders working to integrate AI tools into healthcare said they’re not holding their breath.

“While self-regulation is good, we don’t believe that’s sufficient. We believe AI is too important not to be regulated,” said Google’s Gupta.

But when asked what she expects from the Trump administration, Gupta was noncommittal. “It’s hard to say at this point. We are trying to figure out how we best work with them, share our best practices with them ... Too early to say. I think the entire healthcare community is waiting for that,” Gupta said.

Other experts said the Trump administration is a wake-up call for hospital executives who believed Washington could assume responsibility for overseeing AI.

Instead, the mandate should be on everyone touching the technology to ensure AI algorithms — given their shifting nature and innate opacity — perform as they’re designed, especially in an industry where any mistakes could impact patient health.

“The easy answer is, if this regulatory body says it’s safe, then I can trust it. I think people were hoping that might happen for AI,” said Aaron Neinstein, the chief medical officer of agentic AI company Notable. “I think that was a pipe dream.”

As hype grows around artificial intelligence in healthcare, a seemingly paradoxical question is setting off faint warning bells in the minds of skeptics and proponents alike: Could AI tools meant to reduce clinician burden actually increase it?

It’s too soon to say, given the technology’s nascency. But the answer — which has big implications for the future adoption of AI in the industry — depends in part on how intensely clinicians have to fact-check models, along with financial incentives in healthcare’s majority fee-for-service payment infrastructure, according to experts.

All told, AI products could add up to weaker time savings than developers promise, and any time saved could quickly be filled up by additional patient visits as hospitals and medical groups hustle to increase earnings.

“That’s absolutely a worry,” said Graham Walker, the co-director for advanced development at Kaiser Permanente’s medical group, during a panel at the HIMSS healthcare conference in Las Vegas. “The easiest way to get more revenue out of your healthcare system is by telling the doctors and [nurse practitioners] and pharmacists to go faster and see maybe just one more patient.”

‘The tools aren’t perfect’

The goal of many AI tools in healthcare is to reduce administrative friction by helping with rote tasks like documenting patient visits, looking up medical information or filing paperwork. Such tasks contribute to clinician burnout, which is a major problem in the industry and can lead physicians to exit the field.

About one-third of doctors responding to an American Medical Association survey in 2023 said they were interested in or planning to leave their jobs in the next few years. Many cited work overload as the reason: According to some research, healthcare workers spend the majority of their week working on administrative tasks.

Enter AI. Algorithms have been in use by healthcare companies for decades, but excitement around the tech has reached a fever pitch in the past few years with advancements like generative AI, which can create original text, and AI agents, which can perform tasks without human oversight.

A number of companies are folding the technology into sleekly-packaged products, touting time-saving metrics that may almost seem too good to be true to clinicians sick of after-hours ‘pajama time’ spent on documentation.

Abridge, a startup that uses ambient listening and generative AI to automate clinical notetaking, and Nabla, another ambient AI assistant, both claim to save providers about two hours each day.

Software giant Oracle has woven AI into its health records platform for providers, including a clinical AI assistant. Physicians using that tool see a 30% decrease in documentation time, according to the company.

Meanwhile, Microsoft says its AI documentation product saves doctors five minutes on average per patient visit.

“The capabilities that AI scribes bring to the practice of care are remarkable,” said Rohit Chandra, the chief digital officer at the Cleveland Clinic, during a panel. (The academic medical system is currently rolling out an AI documentation software from Ambience across its provider network.)

Documentation products tackle an acute pain point for doctors, while being relatively simple to use, easy to implement and safe, because they’re supervised by clinicians, Chandra said.

However, AI performance metrics such as time saved should be taken with a grain of salt, according to experts.

Clinicians still need to review the AI’s output to catch mistakes. That process, known as ‘human in the loop’, is key to ensure accuracy and build trust in AI, experts say.

Letting AI tools operate unsupervised could result in medical records containing made-up symptoms, or missing information that could be key to a patient’s health.

“That’s where I think we can get into trouble. The technology is going to get to the point where, at least in my opinion, I don’t think we’re going to need to worry as much. But as we think about early adoption, we do have to be cognizant that it is going to make mistakes,” Brenton Hill, head of operations at health AI standards group the Coalition of Health AI, said during a panel.

But deputizing clinicians to police what AI generates can cut significantly into time savings or, in some cases, erase them altogether.

“The tools aren’t perfect,” said Deborah Edberg, a family medicine physician with CVS-owned primary care chain Oak Street Health, during a panel. “We use AI to do our documentation and I do spend quite a bit of time going back and editing ... It can be a bit of a burden to make sure that what is recorded is accurate.”

To date, there have been no extensive, independent reviews of AI scribes in healthcare. But one recent study published in JAMA Network Open found Microsoft’s ambient scribe received mixed feedback from users.

“A recurring theme was the need for substantial editing and proofreading of the AI-generated notes, which sometimes offset the time saved,” the researchers wrote.

Time savings may not reach clinicians

Fee-for-service incentives could also prove a problem. The payment structure, which underpins the majority of healthcare spending in the U.S., rewards providers based on the volume of visits, tests and procedures they perform.

Meanwhile, despite a recovery in operating margins since the COVID-19 pandemic, costs are continuing to rise for hospital operators.

As a result, hospitals and other physician owners are incentivized to stuff their clinicians’ schedules as tightly as possible, to bring in as much revenue as possible. Any time freed up for doctors by AI might be filled with another visit, experts say.

“It could happen. And some organizations want to do that. And from our perspective, that’s an organizational decision,” Rachel Wilkes, the corporate lead for generative AI initiatives at EHR vendor Meditech, said. “If they want to schedule more patients with time savings, they can do that.”

Things that improve the productivity of the healthcare system — like reducing patient no-shows — often result in a heavier visit load for physicians, said Seth Howard, executive vice president of research and development at Epic.

“The conversation between the health system and the doctors need to be, ‘What do we do with those time savings?’ Does it go back into helping the doctor have some flexibility during the day? ... Or for doctors to see more patients?” Howard said.

Vendors are aware that the draw of AI products for hospitals includes the potential to increase revenue. 

Along with saving five minutes per encounter, Microsoft’s ambient AI frees up “13 additional appointment slots per provider, per month,” according to marketing materials.

The need to bolster flagging finances is one reason hospitals are ravenous for the tools, experts say. Demand has been skyrocketing, with the CEO of one company using AI to automate provider workflows likening it to “a fairy tale.”

“Our ambient product went from zero appointments a year two years ago, to a couple hundred thousand last year, to on track for 10 million this year,” said Tanay Tandon, the CEO of Commure, during a panel. “That sort of expansion and usage of AI in healthcare is incredibly exciting. That’s no longer your nerdy technical doctors playing around with it in the corner. This is meaningful use.”

Experts say like with any other technology, there are pros and cons when it comes to AI scribes, assistants and similar tools. In addition, other metrics besides time saved are important to physicians, like reducing their mental load by simplifying tasks or improving their job satisfaction by allowing them to focus on clinical care.

And anecdotal feedback from clinicians has been great, according to tech giants, AI scribes and the EHR vendors weaving the tech into their workflows.

Epic, for example, doesn’t have an average figure for how much time its own ambient documentation tool saves. But according to Howard, “physicians say, very commonly, I could never go back to how I did documentation before.”

North Country HealthCare is the “only game in town” for care delivery in some of the regions it serves, said Dr. Jennifer Cortes, quality and population health medical officer at the federally qualified health center. The provider operates 13 primary care clinics and two mobile units, caring for 55,000 people across rural Northern Arizona. 

Some communities are very remote — meaning patients may be forced to travel hours to reach specialty care — which makes recruiting providers a challenge, Cortes said. 

That’s one area artificial intelligence could help. Adopting an AI scribe, which typically records providers’ conversations with patients and drafts clinical documentation, could alleviate some of clinicians’ administrative work and decrease burnout, she said. 

“When ChatGPT first came out, I was like, ‘Oh my God, this might make things so much better for those of us working in this field,’” Cortes said. “I just want my job to not be so challenging all the time. It would be amazing if this works.”

But taking on an AI project isn’t easy for a safety-net provider. The technology can be labor intensive to implement, requiring technical expertise and oversight capabilities that many are unlikely to be able to easily access, experts say. 

And if health systems with the fewest resources — that often care for the most medically complex patients — aren’t able to realize the benefits of AI, they could fall even further behind larger or more affluent providers.

“If you look at the kinds of health systems that are actively deploying AI right now, those that can afford it are the ones that are more aggressively pursuing it,” said Brian Anderson, CEO of the Coalition for Health AI, an industry group developing guidelines for responsible AI use in healthcare. “Those that are in rural communities, for example, that don’t have the IT staff to deploy and configure different kinds of AI tools aren’t able to do that. That’s an example of the digital divide already being reinforced in the AI space.”

‘A ton of human effort’

Adopting AI products at health systems can require specialized human labor and technology resources to safely implement, creating significant barriers for cash-strapped safety-net providers, experts say. 

“People tend to talk about it or conceptualize it like you’re turning a light switch on,” said Paige Nong, assistant professor at the University of Minnesota School of Public Health. “It’s actually not that simple. These tools and these systems require a ton of human effort.”

Safety-net providers are likely to operate at slim margins, given their heavier reliance on Medicaid — a pressing challenge as the insurance program faces federal funding cuts — and higher uncompensated care demands. 

For example, the net margin at community health centers, which provide primary care to underserved populations, was just 1.6% in 2023, according to health policy research firm KFF. That fell from 4.5% in 2022, driven by inflation and the expiration of pandemic-era funding.

Many community health centers also face workforce concerns, with more than 70% reporting a primary care physician, nurse or mental health professional shortage last year, according to the Commonwealth Fund. Meanwhile, labor costs are a significant expense for many providers. 

And implementing AI will take plenty of work to manage. For example, health systems will need to set up AI governance structures that can evaluate products for safety and efficacy as well as maintain regulatory and legal compliance. Plus, providers should keep monitoring their AI tools, because the assumptions underlying the model, like patient characteristics, could change over time, potentially degrading its performance, experts say.

“It’s obvious when a scalpel gets rusty, you know you need to replace it or clean it,” CHAI’s Anderson said. “With a lot of these AI tools, we don’t necessarily know that yet. So how health systems can afford to do the kind of monitoring and management of these models over time is a real concern.”

Tech support

Additionally, providers will need IT staff with the technical expertise to handle the work needed to adopt AI tools, a particular challenge for cash-strapped and rural facilities that may struggle to attract the talent, experts say.

For example, many safety-net providers don’t have data scientists on staff, said Mark Sendak, population health and data science lead at the Duke Institute for Health Innovation. It likely doesn’t make sense for some of these care settings to employ them either, given the staff may command high salaries while not generating revenue from patient care, he added. 

Financial challenges can also make it hard to invest in the IT infrastructure needed to adopt AI tools, said Jennifer Stoll, chief external affairs officer at health IT provider and consultancy OCHIN.

“Compounding the challenges for community health organizations is that many have no choice but to rely on outdated, inefficient technology systems,” she said via email. “Not only are these outdated systems all they have access to, but some are not even capable of integrating AI tools—only exacerbating the technology divide.”

Meanwhile, AI might be at the bottom of an IT team’s to-do list. For example, at North Country, Wi-Fi at the clinics doesn’t always work well. And its legacy electronic health record won’t be supported by its vendor in a few years, so the provider will have to transition to a new one.

“We’re missing even the basics,” Cortes said. “Even if you’re not talking about AI, we’re behind.”

Missing out on AI

These constraints are likely already impacting how low-resource providers implement AI, experts say. 

For example, a study published early this year in Health Affairs found 61% of U.S. hospitals that used predictive AI models had evaluated them for accuracy using their own data, and just 44% locally evaluated their tools for bias — an important process to help health systems determine whether a tool will work well within their patient populations. 

Hospitals that developed their own predictive models, reported high operating margins and were part of a health system were more likely to locally evaluate their AI products. 

“You’ve got the resources, you’ve got an IT staff, you’ve got some data scientists who can design models or who can evaluate models from an EHR vendor,” said the University of Minnesota’s Nong, one of the study’s authors. “Resources were the critical necessary component for being able to really consistently conduct evaluation and also to design bespoke models.”

Without assistance, providers who lack the funds and technical capabilities to implement AI could miss out on the technology’s potential benefits or adopt AI without necessary safeguards. That could widen existing disparities between high- and low-resource providers and their patients, experts say.

For example, hiring and retaining staff is already a challenge for safety-net settings, Sendak said. When a new graduate from residency looks for a primary care job, would they rather take a position at a health system that could offer an AI documentation assistant — which could help alleviate provider burnout — or a clinic that’s unable to implement one?

Additionally, limiting AI uptake to the most well-resourced providers could reinforce biases inadvertently included in these tools, CHAI’s Anderson said. If data used to train algorithms continues to be only easily collected in urban, highly educated communities on the coasts, AI tools will miss out on information gathered from other groups. 

“I think our job as a society is to make sure that we are making that as easy as possible, so that we have AI that can serve the individual in rural Appalachia or in a farming community in Kansas just as well as it serves people in San Francisco or people in Boston,” he said.

Help wanted

Still, there are methods that could help small and low-resource providers adopt AI products — including mentorship and support models that have been used for other emerging technologies, experts say.

For example, the HITECH Act, enacted in 2009 to promote the use of EHRs, included funding for Regional Extension Centers, which provided on-the-ground technical assistance for small primary care practices, community health centers and critical access hospitals.

Similarly, the Health Resources and Services Administration funds Telehealth Resource Centers, a group of 12 regional and two national centers that offer education and resources to providers looking to implement virtual care. 

EHR vendors have a role to play as well, Nong said. Nearly 80% of the hospitals in her Health Affairs study used predictive models obtained through their EHR developer, so that could be an impactful point for helping providers safely deploy models, she added.

Larger health systems and academic medical centers could assist their small and resource-strapped counterparts too. The Health AI Partnership, a coalition that includes health systems like Duke Health and the Mayo Clinic, runs the Practice Network, which works with safety-net settings on adopting AI best practices with one-on-one support. 

North Country is one of the inaugural participants in the network. The program provides technical support to safety-net organizations, helping them work through AI procurement, evaluation and implementation, said Duke’s Sendak, who serves on HAIP’s leadership council. 

Still, the Practice Network currently works with five safety-net providers, while there are hundreds of federally qualified health centers across the country that may need support, he added. 

“There is a big difference between where all the conversation is about AI in healthcare and where a lot of the people who are delivering healthcare are in terms of ability to adopt and use it safely,” North Country’s Cortes said. “When they’re talking about all these huge investments going into AI, great, that’s exciting, but how are they going to bring us along too?”

State laws regulating artificial intelligence are on the rise, and industry leaders should step up to help lawmakers manage the technology, experts said at the HIMSS AI Leadership Strategy Summit. 

More than 1,000 bills on AI have been introduced at the state level this year, including about 280 that relate to health technology, said Tina Joros, vice president of policy and innovation at health IT firm Veradigm.

That compares to around 500 to 700 bills in 2024, depending on how the legislation is counted, she said during a panel discussion. 

“Definitely an increase over three to five years ago,” Joros said. “It’s just a flood.” 

The rise comes as federal oversight over many aspects of health AI is relatively nascent, and the Trump administration has signaled it wants to pursue a deregulatory path to spur development of the technology in the U.S.

Meanwhile, the healthcare sector has moved to implement AI for a variety of tasks, including assisting with clinical documentation, triaging imaging results or data analysis. 

Still, the technology comes with risks, like the potential for inaccuracies or biases that could lead to patient harm. Some states are interested in filling the regulation gap — and the healthcare sector should weigh in on how those laws are crafted as they work to roll out these tools, experts said. 

State laws around healthcare AI have so far been thoughtful and helpful, said Daniel Smith, chief medical information officer at Michigan health system Corewell Health. However, if the regulations become too broad, AI use at the organization could be at risk. 

“So what we would hope for is that what’s getting built out in our legal frameworks are taking a more of a risk-based assessment, so those lower risk opportunities can still flourish quickly and not be held back,” Smith said. 

Where states are looking to regulate health AI

A significant portion of the health AI legislation this year is related to prohibiting or monitoring the use of AI in insurers’ utilization management processes, said Sarah Jaromin, a policy specialist in the health program at the National Conference of State Legislatures. 

Some insurers have already faced scrutiny from Congress for using AI to deny claims or prior authorization requests, where a provider has to receive approval before offering a drug or service.

In 2024, a Senate report criticized Medicare Advantage insurers UnitedHealthcare, Humana and CVS for allegedly using predictive algorithms to inappropriately refuse coverage for post-acute care.  

States are looking to regulate the practice too. For example, Arizona passed a law this spring that requires payers to have a person verify claims denials or rejected prior authorization requests. Maryland also passed a law this year overseeing insurers’ use of the technology in claims management. 

Another significant area for legislation is regulating the use of mental health chatbots, Jaromin said. For example, a law passed in New York would require AI chatbots to put safeguards in place to detect signs of suicidal ideation or self-harm. 

Proponents say AI mental health tools could provide quick and accessible support, but other experts argue they might not be effective enough to replace a human therapist — and could be unsafe. 

AI and mental health has also received more public attention in recent months amid news reports on teens who died by suicide after developing deep relationships with chatbots.  

“Sometimes it’s like what you see in the news, and then you'll see some state regulation there,” Jaromin said. “And I think that’s what's happening with the payers and the mental health chatbots as well.”

Engaging with lawmakers

State lawmakers likely don’t have expertise in healthcare or AI, experts said. 

Additionally, many of the legislators who are making decisions on AI policy likely have limited knowledge on what they’re voting on, largely because no one in the community has come to speak to them about the impact, said State Sen. Bo Watson, R-Tenn.

Healthcare leaders should meet with legislators and offer themselves as a resource to policymakers, he said. And though it’s a challenging task in politics, they should approach lawmakers without bias or self-interest. 

“State government is the infrastructure of our political system,” he said. “It is critically important that you engage yourself in your state government, and you engage yourself with your state legislators, because ultimately they will be making 95% to 98% of the decisions that will have impact on your business model.”

The healthcare industry isn’t known for quick adaption to new technologies. But as the sector faces mounting challenges — like stressed providers, high costs and staffing shortages — executives are hopeful artificial intelligence could offer some relief.

Still, even as leaders look to adopt AI, the implementation process isn’t easy. And health systems will need to get their workforces on board to ensure success, experts said at the HIMSS AI Leadership Strategy Summit in Chicago. 

The technology tends to look like a black box to some healthcare professionals, said Dr. Everett Weiss, medical director for health informatics at Rochester Regional Health, during a panel discussion. Doctors might not understand how AI works or generates recommendations.

That could prove challenging for some providers, given concerns that AI tools could offer inaccurate, misleading or biased information, potentially putting patient care at risk.

Clinicians’ credibility is on the line — so ensuring they can trust the tools is key, he added.

“Being able to build up that trust is hugely important,” Weiss said. “It is very easy to tear down trust. It is very hard to build that back up.”

That means health systems need to make sure their AI tools are reliable, through plenty of testing before the AI is fully adopted and educating users on how the products work, experts said.

Keeping a “human in the loop” — ensuring a person is involved in an AI workflow to double check its work — can help too, said Susan Fenton, vice dean for education for the department of clinical and health informatics at the University of Texas Health Science Center at Houston.

“No AI is licensed to practice. None, zero,” she said. 

Where AI can succeed

One method for considering potentially successful use cases for AI is finding areas where the technology can complement human strengths, said Sagar Parikh, vice president of operations excellence and innovation at revenue cycle management company Ensemble Health Partners.

For example, an AI tool is probably much faster than a human worker at sifting through an entire electronic health record and pulling data points to support a response to a claim denial. But a nurse is likely better at providing the clinical context and choosing which facts are the most important, he said. 

“That’s where I see language models and AI generally to be helpful,” Parikh said. “Doing the things that a human is historically not great at, and pairing it with things that humans are historically great at.”

Alleviating some of clinicians’ heavy administrative burdens is another common use case for AI, experts said. Providers have said they spend long hours on documentation and other EHR tasks, taking time away from patient care and eating into after-work hours. 

AI documentation assistants are perhaps the “poster child” for improving clinician experience, Fenton said. But during a notetaking pilot in some clinics at UT Health Houston, the assistant only saved about nine minutes per day per provider — not a huge return on investment, she noted.

Still, the system decided to deploy the AI tool across the institution, because patient and provider satisfaction with the documentation assistant was high. That highlights the value of a pilot that measures metrics outside of finances, she added.

“It’s actually truly now improving that patient and provider experience,” Fenton said. “So I think that those are the things that we need to look for.”

Not all healthcare organizations need to be trailblazers when it comes to choosing an AI project either, Weiss said. If an AI project is doing well at other health systems, that could be a good place to start.

Additionally, organizations can look at places where AI has already been in use for a while, like using the technology in radiology to triage cases that need more immediate attention, he said. 

Still, health systems should consider that not all AI tools work for every clinician. For example, while an AI notetaking assistant was popular with 9 out of 10 providers at his system, some clinicians didn’t see efficiency gains, Weiss said. 

“We should not be forcing technology on our providers. These are tools to help them do their jobs even better,” he said. “We talk about the joy of medicine a lot in our organization, and so we want technology to be able to bring back that joy for all of our providers.” 

Health records companies are racing to weave artificial intelligence into multiple segments of their software, hoping that by streamlining clunky experiences they can attract clients and keep up with competitors doing the same.

Some of the largest EHR vendors, including Epic and Oracle, have announced or accelerated modernization efforts in recent months. The companies say they aim to increase the productivity of a healthcare system facing rising costs — while improving job satisfaction for doctors and nurses, increasing the quality of patient care and advancing the state of the art of medicine.

The shift represents a sea change for a software often cited by providers as a significant source of frustration.

“Doctors are not looking for AI to act as a doctor, to step in their place,” said Leigh Burchell, chair of the Electronic Health Records Association. “They really want to take the things that are administrative burdens, or that take them time after a visit to document, and get help with that. So those are really the areas where our members are most focused.”

Marrying medicine and technology

EHR software allows clinicians to access and update a patient’s complete medical history electronically, including diagnoses, lab results and medications. Healthcare providers in the U.S. were required to adopt EHRs more than one decade ago in order to continue receiving full funds from federal programs like Medicare and Medicaid. Use of the software spiked, with 78% of physicians and 96% of hospitals using an EHR in 2021, according to government data.

However, doctors often express annoyance with EHRs. The systems can be tricky to use, cluttered with pop-ups and screens and requiring significant time for clinicians to enter and find data. Another common criticism is that EHRs were designed more for billing purposes than for clinical decisionmaking. Many doctors say the systems distract from patient care as a result.

Major EHR vendors have long said they’re working to make their products simpler and easier to use. That crusade is being significantly accelerated by recent advances in AI: like generative AI, which can understand and create original text and images; ambient listening, which marries microphones and machine learning to produce transcripts of clinician-patient encounters in real time; and agentic AI, which can complete complex tasks largely without human oversight.

In the past, navigating EHRs required a lot of data entry and “persistent screen after screen navigation,” Girish Navani, the CEO of EHR vendor eClinicalWorks, said. “If you look at the last two years, the advent and the rapid rise of generative AI makes a lot of those paradigms obsolete.”

Epic, the largest EHR vendor in the U.S., has “been working to incorporate in particular generative AI across the software,” said Seth Howard, Epic’s executive vice president of research and development.

The Wisconsin-based company holds more than one-third of the hospital market, trailed by Oracle Health, at 22%, and Meditech, with 13%, according to health analytics company Definitive Healthcare.

More than 3,100 hospitals use Epic, the company says. And about two-thirds of its customer base are live with the vendor’s generative AI products, according to Howard. Those include algorithms that help doctors write notes and summarize patient information for clinicians prior to a visit.

Epic integrated ambient listening software from a startup called Abridge into its workflow in 2024, but is working to make its software compatible with more products that can transcribe patient visits in real time, Howard said. The company is also working to make its documentation tools multimodal, so they can understand videos, images and other types of information beyond text.

Over the past year, Epic has also started building a framework around agentic AI into its software, to see how digital assistants that can summarize information, interact with patients, schedule followups and more can work together, according to Howard.

Overall, Epic has 125 AI use cases either in development or live, he said.

Similarly, in October 2024 Oracle announced that it plans to release a new EHR platform this year that incorporates its clinical AI agent, voice-activated navigation and search capabilities. The clinical agent, which can record and transcribe patient visits and draft a note in the EHR for the clinician’s approval, is currently available for more than 30 medical specialties, the company said.

Bharat Sutariya, Oracle’s chief health officer, called the refresh “nothing short of a complete reinvention of the EHR.”

“We are investing massively to ensure we rapidly innovate and get it right at the same time,” he said.

Meanwhile Meditech and eClinicalWorks, a company that offers cloud-based EHR software currently in use at 70,000 care sites, are pursing a partnership approach.

Meditech has long worked with Google to accelerate clinicians’ access to data and with Microsoft to automate scribing, including deploying Microsoft’s ambient listening and clinical documentation tools into its EHR.

Meditech also plans to include Microsoft’s latest clinical assistant as it rolls out in 2025, according to Rachel Wilkes, Meditech’s corporate lead for generative AI initiatives. A variety of other AI scribes are also available to Meditech’s customers through integrations with the company’s software, she said.

“We want to keep our existing customer base engaged,” Wilkes said. But “yes, we are looking to attract new customers as well by having a platform that is innovative, scalable, and a really good alternative to maybe other competitors that are out there.”

eClinicalWorks has also partnered with Microsoft to use machine learning to match patient records, and with a company called Healow to create an AI medical scribe and an AI contact center.

AI checks and balances

EHR vendors are remaking their software with AI despite dogged concerns about quality, privacy, bias and a lack of federal regulation around the technology.

Generative AI in particular is tricky to monitor. The models are known to hallucinate, or provide factually incorrect or irrelevant answers, or omit information altogether. Their performance can also degrade over time, in a phenomenon called model drift.

Still, records companies say that they, along with their clients, are carefully supervising AI to make sure they’re not making up any information — or leaving anything out.

Leaders with eClinicalWorks, Meditech and Epic said they’ve invested heavily in risk management and have stood up stringent guardrails, including ongoing assessment by clinicians.

There should always be a human in the loop when it comes to ambient documentation and generative AI products, said Meditech’s Wilkes.

Meditech also includes disclaimer language on any records created by AI, sharing that it was created by an algorithm and reviewed by medical staff. The company is working to put more formal citations into the workflow to clarify what’s generated and where the model pulled relevant information from, Wilkes said.

Epic applies the same principles to ensuring AI quality as it does to any other software to make sure it works well and isn’t causing harm, according to Howard.

But “the techniques you might use to understand, ‘is it working well?’ are different,” Howard said.

Still, despite concerns with the models, hospital customers are full steam ahead on adoption, vendors say. According to a survey from HIMSS conducted in the fall, 86% of respondents already leverage AI in their medical organizations, mostly for administrative tasks, though there’s growing interest in more clinical use cases.

“Our customers are very warm to it. More warm over the past 12 months,” said Wilkes. “We’ve gone from doing a lot of education about the impact, to — now the question we’re getting is, ‘When? When can I get it? How much is it going to cost? What’s the plan?’”

“There’s always going to be the early adopters, the mid adopters and the lagging adopters of technology. It’s happened with smartphones, it happened with color TV, it’s happened with every product,” said eClinicalWorks’ Navani. “All I can tell you today is we are seeing incredible multiples of customers who you would call early adopters.”

But “this is not just being done because modern is new and it’s good,” Navani continued. “It’s got a clear efficiency benefit written all over it. That’s driving the change.”

Tech companies are trying to get healthcare companies to adopt artificial intelligence tools, hoping they can pull in revenue by appealing to the industry’s need to reduce costs and tackle clinician burden.

Google is one such company. The search and cloud computing giant has unveiled a number of health AI products in the past few years, including a large language model called Med-PaLM trained specifically on medical data, generative AI products for healthcare organizations and a platform to help companies create their own AI agents.

Google also offers a tool that allows clinicians to search for and answer questions about information in patient notes and other medical documentation. That product, called Vertex AI Search for Healthcare, has quickly ramped up after launching in March 2024. Google announced that Vertex AI Search was multimodal earlier in March 2025, meaning it can now understand images as well as text, allowing the tool to scrub charts and scans for relevant information.

Google has inked deals with major EHR vendors and leading health systems to integrate its AI into their workflows. But not everyone is gung ho about AI, especially as the models become more evolved and the potential for mistakes grows.

Concerns include hallucinations, when AI makes up a response; omissions, when AI leaves important information out; and model drift, when AI becomes less reliable over time. Meanwhile, fledgling oversight and a lack of regulation are also hampering adoption as the industry confronts weighty questions of accuracy, privacy and bias.

Healthcare Dive sat down with Aashima Gupta, the head of healthcare for Google Cloud, to chat about Google’s work in health AI and the future of the technology — including how AI is evolving from a task-based helper to a collaborator for clinicians, uncertainty from the Trump administration and why she’s excited about agentic AI.

HEALTHCARE DIVE: Vertex AI Search for healthcare can now understand images. Why was this a necessary update?

AASHIMA GUPTA: Healthcare information is scattered in different forms and types. During an annual diabetes foot exam, for example, when a physician is looking for ulcers, they mark on a diagram of a foot where it’s callous, pre-ulcer and ulcer, using different symbols. Healthcare is full of examinations like this. Now, with multimodal, you’re able to see and contextualize that diagram of a foot and extract that information, including any potential ulcers, and put it into the medical record automatically. That saves clinicians time, because they don’t need to interpret this themselves.

Healthcare, as an industry, has a lot of paperwork. We talk about burnout. These are the type of innovations we want to add to Search to reduce it. Last year, we said Search was ‘semantic’, meaning it knows what people mean when they say ‘diabetes’ or ‘A1C’ — it knows clinical concepts and how they’re related. Now we’re taking that and applying that to forms in an exam room that have different pictures. So our results are much more accurate and helpful.

What other inputs might Google want to add to the search-and-answer tool — sound?

You’re right. We will continue to add different modalities here. Sound and videos, as an example.

What else is Google working on in healthcare that you’re excited about?

We are very excited about agentic AI. The last few years have been a lot about generative AI, which is task-based — ‘Give me a discharge summary. Give me a nurse handoff. Write me a referral.’

AI agents are a leap forward, because they can think multiple steps ahead. They can plan unique steps for the goal in mind.

Imagine this in healthcare workflows. Let’s say I want to figure out, in my revenue cycle, where there’s variability by payer type, market, for a certain CPT code — that’s a multistep process. And that’s what agentic AI offers. Imagine hundreds of agents helping nurses and physicians do their jobs.

Given the number of companies offering AI agents, what’s Google’s elevator pitch?

We believe there’s a need for centralized coordination and management, and that’s the platform we provide, and we will give agents the tools.

So if a company is building their first-party agent, they have three things: An ability to use [Google’s family of large language models] Gemini, our search functionality based on clinical knowledge and an orchestration layer.

We believe that people have choice. Some people they really want to build themselves. Some want a partner. How do you organize all of that? That’s where we want to be.

Google doesn’t publicly share the number of healthcare organizations using its AI. How would you characterize uptake?

We have relationships with Basset Health, Highmark Health, Mayo Clinic, HCA — customers using generative AI to streamline prior authorization submissions, close screening gaps, enhance radiology workflows and more.

Healthcare used to be a laggard in technology adoption. But this AI is different — this is actually going into the back office, the revenue cycle, claims, to tackle burnout. You are seeing this move much, much faster. You are seeing generative AI pilots move from experimentation to scaling.

As for agentic AI, I’d say healthcare companies are definitely adapting to it. But it’s a newer space. 

Generative AI is becoming more integrated into healthcare workflows, despite the technology’s tendency to make mistakes. Does Google track error rates for its AI products?

Not only us — customers are building guardrails. At HCA, for example, we worked with them on a very robust evaluation framework, not just to catch errors but also — Is this model reliable? Is it hallucinating? Did it omit something? So we provide those tools for an evaluation framework, and for all of this there’s a human in the loop capturing the feedback, and that feedback loop then makes the model more effective. That’s what gives me comfort. That’s best practices being deployed.

But no data around accuracy that you’ll share? Is it accurate enough that you’re comfortable with increasing adoption?

Our health systems have put humans in the loop, and there’s change management around that. Technology by itself is not enough. You need robust change management, and to have a human in the loop and make sure they’re evaluating this. These processes are in place before the AI is brought in.

There are checks like grounding, or citing where the AI pulled information from. That helps combat hallucination. For omission, when the AI leaves something out, this is where we’re seeing evaluation frameworks. We’ll say, ‘Okay, did this result omit something pertinent?’ And eventually, after that feedback loop goes a few times with human oversight and the model learns, the next situation is better.

Also, use cases matter. This is why AI is being used more in administrative tasks.

The Biden administration was working to create a federal governance framework for health AI, but Trump has canceled those efforts and is instead focusing on deregulation. What are your thoughts on that approach?

At Google, we have our own AI principles and processes. But while self-regulation is good, we don’t believe that it’s sufficient. We believe AI is too important to not be regulated. And I will stop at that.

Do you think we’ll see anything from the Trump administration around oversight?

It’s hard to say at this point. I think we are trying to figure out how we best work with them, share our best practices with them. Too early to say. I think the entire healthcare community is waiting for that.

Lightning-fast evolution in artificial intelligence and growing adoption of the models is giving rise to concerns that AI could exacerbate existing divides between technological haves and have-nots in the healthcare industry.

But all types of providers — regardless of location or finances — can adopt AI, experts said in March 2025 at the HIMSS conference in Las Vegas.

It’s just a question of knowing how.

Rural hospitals, smaller systems and health clinics without a massive IT infrastructure should reach out to AI companies to discuss potential partnerships, said Graham Walker, co-director of advanced development at Kaiser Permanente’s medical group, during a panel.

Providers can get access to AI products in return for use of their facility as a testing ground, Walker said. That’s valuable for AI developers, who want to train and test their models and prove that they actually work in the real world.

“A lot of the startups and smaller companies want some access to health systems. It’s much harder to get into large health systems than a solitary hospital. And so you can very well imagine a world where there’s some mutual benefit there,” Walker said.

Smaller providers can also band together to research and share information on tools that work for them, said Brenton Hill, head of operations at the Coalition for Health AI, a health AI standards consortium. They can explore joint purchasing arrangements, or benefit by knowing which AI products have already been validated by other hospitals with more resources.

“The more people that you can get to buy into a concept, come together and do it, the better price you’re going to get, the more leverage you’re going to have,” Hill said. “We have 1000-plus hospitals that need a solution. Can we identify what they need, prioritize that and then partner with a vendor and come together and do this so it’s mutually beneficial?”

And smaller providers almost benefit from their lack of existing partnerships with tech companies, said Tanay Tandon, the CEO of provider automation company Commure. Without a relationship with a vendor, no one is nudging them toward a specific product or fix. Instead, they’re unencumbered to explore a variety of solutions and find what works best for them.

“Some of the best AI tools out there are free and accessible online,” Tandon said. Because of that,“we’ve seen physicians all over the world in rural clinics get access to ambient documentation,” which listens and records patient visits.

Similarly, price shouldn’t be a barrier for smaller providers much longer, according to Tandon.

The introduction of more low-cost AI models — including DeepSeek, a Chinese model with performance metrics akin to those released by leading U.S. companies, but offered at a fraction of the cost — means these AI tools will likely get cheaper, the CEO said.

Yet it’s important that providers put in the work ahead of time to have a clear view of the problem they’re trying to solve, whether that’s improving billing processes, prior authorizations or patient triage, said Brian Spisak, program director of AI and leadership at Harvard University’s National Preparedness Leadership Initiative. That way, providers can be ready to jump on inexpensive and helpful AI products when they emerge.

As for AI vendors, companies should also be open to exploring deals with smaller providers — and should view those as a partnership, not a sales relationship, given the importance of ameliorating the numerous struggles providers face, experts said.

Current challenges for U.S. providers include labor shortages, inflation driving up the cost of medical supplies and inadequate insurance rates for some patient care. Most of these are trickier for rural facilities to navigate. And such facilities are already used to being left behind when it comes to technological advances — though some research suggests the digital divide may be shrinking, mostly due to improved access to the Internet in rural areas, the cost of technology, insufficient broadband access and poorer-performing devices have proved perniciously difficult to overcome.

As a result — and because of rural regions’ less-than-stellar history with technological innovations putting many Americans out of jobs — the tech industry should treat facilities in rural areas with extreme care and tact, according to Tandon.

“When you’re in Silicon Valley, the approach to technology and to some extent the conversations about technology come from a place of rapid iteration, speed, hope — you know, this is a force for good. When you go out to middle America one of their core interactions with technology was losing the manufacturing industry. Automation — it wrecked the region,” Tandon said. With AI, “we have to get it right.”