Jim VandeHei, co-founder and CEO of Axios, argues that most U.S. hospitals could deliver Mayo Clinic-level care by fully adopting AI - and he says the gap is far smaller than most healthcare leaders think.
Writing at Axios.com on Aug. 16, VandeHei describes the core of Mayo's success as "coordinated diagnostic brilliance based on real data, cases, lab results and patients" - precisely what modern AI systems can now replicate. Mayo Clinic already runs more than 12,000 clinical studies and is building its Mayo Clinic Platform to digitize that expertise. VandeHei suggests the model is portable: a community hospital doesn't need to recruit thousands of specialists if it can tap into a system that has them.
VandeHei built a personal AI agent to help guide care for his wife, who has three chronic conditions and has been in and out of emergency rooms and hospitals. "To this day, my personal AI agent has proven smarter than every doctor, other than Mayo's," he reports. His point stands on its own: the ambulatory care gap between a leading academic medical center and a typical community hospital is a data problem - not a headcount problem.
What Mayo does differently
VandeHei attributes Mayo's clinical performance, confirmed again this year, to three core practices that most hospitals could adopt:
- Doctors earn flat salaries. "No bonuses for more procedures, scans, tests or visits. They're paid to heal."
- Multidisciplinary teams, not lone specialists. "So gastroenterologists, liver specialists and surgeons all collaboratively review a complex case like Autumn's." (His wife.)
- A patient-first culture. "As someone who started and has run two companies, I can tell you: Cultures are controllable and scalable."
The C-suite AI requirement
Mayo Clinic President and CEO Gianrico Farrugia, MD, says he is pushing to show the federal government and other hospitals how to replicate the Mayo approach. Among the lessons he's learned about healthcare AI, one is particularly pointed for leadership teams:
"Require each member of the C-suite to have built at least one agent on their own and to use more than one LLM at work several times a week. Only then will they be able to make decisions right for AI for the institution."
That requirement has direct practical implications for executives who need to evaluate the flood of vendors and tools. Understanding the technology by using it - not just reviewing slide decks - is a different lens. It also aligns with broader competence building across the sector. Healthcare leaders who want to shorten the learning curve will find AI for Healthcare resources useful, while C-suite teams can look to AI for Executives & Strategy for governance-level guidance.
Why this matters for healthcare professionals
The business case for upskilling is now concrete. Mayo Clinic's approach suggests that the competitive difference in healthcare will soon be less about an organization's department lineup and more about how it integrates AI into the daily routine. It's not a future prospect - the gap between institutional leaders and followers is already visible, and the frontline physicians and nurses who learn to build and appropriately deploy agents will be more valuable to their employers. For staff who feel replaceable, the case to build the skill is direct: the alternative may be managing patients without the support Mayo's clinicians already have, and that's a margin no one wants to explain in a boardroom review.
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