10
Loading video...
By Alexis Kayser
Healthcare Editor
0
Share
Newsweek is a Trust Project member
See more of our trusted coverage when you search.Prefer Newsweek on Googleto see more of our trusted coverage when you search.
With at least 71 percent of U.S. hospitals integrating predictive AI into their electronic health records, the debate over whether AI belongs in health care is becoming outdated.
Now, health system leaders tell Newsweek that AI is here to stay. But they're still figuring out how to make longstanding workflows, processes and job descriptions jibe with the new tech. AI in all forms—agentic, generative, predictive—still requires human oversight and sign-off in a high-stakes industry like health care.
How does the "human in the loop" keep pace when AI is changing not only the tools they use but the work they're expected to do?
Read More on Health Care
Say an algorithm can identify that a patient is becoming septic before the signs are obvious. Someone still needs to decide what to do with that warning. Or say AI can help a primary care doctor manage conditions that once required a specialist. The technology may increase capacity, but it cannot create a nephrologist where there isn't one.
Those questions will be part of the conversation at Newsweek and Investment Reports' AI Health Summit on September 23 in New York City, where hospital executives and health tech leaders will discuss AI deployment in clinical decision-making, research, distributed care, governance and cybersecurity.
In briefing calls ahead of the Summit, speakers repeatedly returned to the fact that AI can change what is possible without automatically changing how work gets done. Health care leaders are now responsible for creating an ecosystem where AI can deliver on its promises, and it's not a simple task. A 2026 survey from the Scottsdale Institute and Deloitte Center for Health Solutions found a substantial gap between health systems' enthusiasm for transformation and their ability to make it operational. Every surveyed health system said care-delivery transformation was either a top enterprise priority (63 percent) or very important (37 percent), but organizations scored themselves just 2.7 out of five on their ability to turn transformation efforts into scaled, AI-enabled operations. Deloitte specifically describes sustained operational change as remaining “difficult—and slow.”
...
Tampa General Hospital offers one example of how that work is taking shape. There, AI has not replaced the people responsible for patient care. Instead, leaders have focused on changing how clinicians use the information it provides.
John Couris, president and CEO of Tampa General, told Newsweek that his organization has taken what he calls a “very practical approach” to AI and predictive analytics. The health system now has more than 110 AI applications running around the clock, but those tools haven't led to layoffs.
“We're not deploying AI to reduce FTEs [full-time equivalents],” Couris said. "AI is allowing us to do more work with the same amount of people, and that's the journey we're on.”
Sepsis care shows what that looks like in practice. Tampa General uses AI and predictive analytics to monitor changes in patients' physiology. When the technology detects a potential problem, it directs caregivers to the medical record. Clinicians confirm whether the concern is legitimate and, if it is, bring the appropriate teams to the bedside.
Couris said Tampa General's 48-hour mortality rate for sepsis is three percent (well below the national average of 20 percent). While advanced technology had a hand in that result, he attributes much of the success to the workforce, who act effectively on the information it provides.
Couris added that he would rather see AI additions done right than done quickly, ensuring clinicians are comfortable with their new copilots.
“We have to deploy technology that actually makes a difference in the lives of the people who are responsible for caring for patients, and in the lives of the patients that we care for,” he said.
But changing how people work becomes more complicated when there are not enough people to begin with.
That is the problem Dr. David Carmouche, chief medical and commercial officer at Lumeris, sees in primary care.
“There's a little bit of a problem in that we just don't have enough human beings to deliver primary care to all Americans, and yet there's this notion that we don't want to let go of the fact that humans have been part of health care,” Carmouche said. “But how do we then take a constrained workforce and also meet all of the needs of the population?”
In that context, AI could do more than make an existing task faster by changing who is equipped to perform it.
Carmouche said contextual clinical decision support could help primary care physicians manage some conditions that might otherwise require specialists. Similar tools could give nurse practitioners, physician assistants, social workers and care coordinators more support in their existing roles.
That could help a constrained workforce do more, although reorganizing work inside the health system does not guarantee that patients outside of it will benefit.
“The challenge is [getting] these tools in the hands of people who need it if they're in the cracks of health care delivery, if they're in the shadows?" Carmouche said. “We haven't solved for that yet.”
The same problem takes a different form in clinical research. There, AI may be able to accelerate individual tasks, but the benefit is limited if the processes surrounding those tasks continue to take months.
At MD Anderson Cancer Center, Chief Clinical Research Officer Dr. Jennifer Litton is looking beyond whether AI can help write a clinical trial protocol. She wants to know whether it can properly connect the disjointed processes that come next.
A trial needs a coverage determination, a budget and a contract. It also needs order sets built into the electronic medical record. Each represents another step between designing a study and enrolling a patient.
“Could it get us the coverage determination, the budget, the contract, and put together the electronic medical record order sets, so that something that can take six months could get down to 60 days?” Litton asked.
That would require the industry to reconsider a sequence of work that has traditionally moved through different people and systems.
And the workflow does not end when the trial opens. Litton said wearables and telemedicine could move parts of clinical research closer to patients' homes. MD Anderson is also working with national network partners on a clinical trial program that could open studies across multiple sites, including for patients who might never travel to the cancer center.
Paradigm Health CEO Kent Thoelke pointed to related work his company is doing with the FDA on what he called “real-time clinical trials.”
“It's a combination of clinical trial matching, but also how we collect data,” Thoelke said.
As AI changes how clinical work is organized, it is also increasing health systems' dependence on the technology underneath that work, leading to yet another operational question: What happens when the technology goes down?
The number of patient records affected by reported health care data breaches climbed from about 6 million in 2010 to 170 million in 2024, according to a recent study published in JAMA Network Open. Hacking or IT incidents accounted for 91 percent of affected records in 2024.
At Jefferson Health, Chief Information Officer Luis Taveras said that threat environment has already forced the organization to reconsider something as routine as installing a security patch.
Traditionally, Jefferson would wait for third-party vendors to send patches, bundle them together and install them during scheduled monthly downtime. Taveras said that approach no longer moves quickly enough. Jefferson now uses same-day or immediate patching depending on the vulnerability.
The shift illustrates the same tension playing out elsewhere in health care AI. New technology can move quickly, but organizations built around older processes do not automatically move with it.
Taken together, these examples point to a familiar management problem, a less futuristic side of health care's AI transition. The technology may be new, but many of the questions it is raising are not: Who does what? In what order? Who makes the final decision? And what needs to change when the old way of working no longer fits?
As Carmouche told Newsweek, "It's not really a technology story here."
View 0 Comments
Submit Correction
View Editorial & AI Guidelines