Building the Future of Medicine: Dr. John Dayton on Upskilling, AI, and Physician-Led Innovation
John Dayton, MD, MBA - LinkedIn
Planetary Health First, Mars Next Podcast
In a world where artificial intelligence is reshaping every corner of society, healthcare stands both at the forefront of promise—and at risk of profound missteps. On a recent episode of Planetary Health First, Mars Next, we sat down with Dr. John Dayton, an emergency physician, health-tech advisor, and passionate advocate for upskilling clinicians to lead the next wave of innovation.
Dayton’s message is clear: Physicians must not be passive recipients of AI systems—they need to be active architects of them.
From Clinician to Innovator: Why Upskilling Matters
As Dr. Dayton shared, the journey of a physician is marked by intensive, highly specialized training. From competitive undergraduate programs to demanding residencies, physicians dedicate years to mastering patient care. But what many discover in practice is a gap between clinical training and the rapidly changing technological landscape.
“We get to practice and realize—wait—I spent all this time learning to care for patients, but I didn’t learn how to incorporate new technology into that care,” Dayton says.
This is not just an academic concern. The rollout of electronic health records (EHRs) offers a cautionary tale. Promised as a way to unify data and improve care, EHR systems were often designed around billing needs, implemented with little clinician input, and required hours of unpaid training. The result was widespread physician frustration, added administrative burden, and significant burnout—so much so that “pajama time” (late-night charting at home) became a standard part of the job.
For Dayton, the lesson is stark: physicians need a seat at the table when new technology is designed and deployed.
The Challenge—and Opportunity—of AI
Artificial intelligence is now poised to reshape healthcare far more dramatically than EHRs did. From ambient scribe technology that automatically generates clinical notes, to clinical decision support systems powered by large language models, AI promises to reduce documentation burden, improve operational efficiency, and enable more personalized care.
But it also raises serious risks. If hospitals or technology vendors roll out AI solutions without physician involvement, they risk repeating the mistakes of the EHR era—imposing tools that frustrate clinicians and fail to address real patient needs.
As Dayton put it:
“Physicians want to be in the driver's seat. They don’t want to be told: This is what you're using now, this fits our business model. They want to make sure new tools meet their pain points and actually improve patient care.”
He describes current AI adoption as still being in the early innings. Some applications—like ambient scribe tech—are already changing workflows, freeing clinicians from hours of charting. Clinical decision support tools like OpenEvidence are being used in emergency departments to guide rare or complex cases with up-to-date evidence.
But the real promise lies ahead: multimodal AI systems that can combine EHR text, imaging data, lab results, and remote patient monitoring to build living, dynamic patient records. These systems could help clinicians catch emerging trends in chronic conditions, coordinate care across providers, and support truly personalized medicine.
Empowering Physicians to Build and Lead
Recognizing these opportunities—and the risks of exclusion—Dayton and colleagues have worked to build resources for upskilling.
They’ve created curated documents to help physicians explore:
AI fundamentals, with both formal programs (e.g. MBAs with informatics or AI focus) and informal options (like free Coursera or Stanford online courses).
No-code and low-code platforms that let clinicians design solutions without traditional programming skills (for example, AvoMD, which lets doctors create evidence-based care pathways integrated into EHRs).
Networks and communities—like Slack groups or Health Tech Nerds—where clinicians can share lessons, challenges, and support.
Importantly, Dayton notes that many physicians are already getting involved on the investment and advisory side. Hospital venture arms like Intermountain Ventures actively invest in AI tools that improve operations, scheduling, billing, and clinical decision support. Angel groups made up of clinicians are backing early-stage solutions that meet real-world pain points.
And large venture funds like Andreessen Horowitz’s Bio + Health team have physicians on staff specifically to ensure they invest in solutions that make sense in the clinical workflow.
“The bar for starting a health-tech company is lower than ever,” Dayton explains. “A physician with a problem to solve can now partner with a data scientist or use no-code tools to build a real solution quickly—and get it to market with less capital and fewer people.”
Changing the Culture of Medicine
Yet while the technology is advancing, Dayton warns that cultural barriers remain. Medicine is famously hierarchical and conservative, with traditional career paths narrowly defined. Physicians are often told to “stay in their lane,” and many health systems haven’t invested in helping mid-career clinicians pivot into innovation or leadership.
Dayton sees that changing. Some residency programs, like those at Stanford and Cornell, now offer Innovation Fellowships alongside traditional subspecialty training. Hospitals are realizing that their venture arms and innovation initiatives work better when they include physician advisors who know the day-to-day realities of patient care.
He argues health systems should do more. Instead of letting mid-career physicians burn out, why not buy down some of their practice time to let them lead innovation, join design teams, or advise startups? Not only would that reduce turnover costs (which can be over $1 million to replace a burned-out doctor), but it would ensure technology is actually designed to work in practice.
Aligning Incentives: The Policy Challenge
Beyond culture, there’s the problem of incentives. Fee-for-service reimbursement still dominates, rewarding procedures and volume over preventive care. Even the best AI solutions will struggle to find a market if they don't fit the reimbursement model.
Dayton points to the success of companies like Omada Health and Hinge Health, which focused on demonstrating cost savings and securing reimbursement for preventive care and chronic disease management. For AI to truly transform care, payment models will need to evolve toward value-based care, where keeping patients healthy is financially sustainable.
As he puts it:
“If you want to change how care is practiced, you have to change how it’s reimbursed.”
A Call to Action
In the end, Dayton’s vision is both practical and inspiring. He sees AI as offering unprecedented tools to improve patient care, reduce burnout, and expand access. But he’s equally clear that this will only happen if physicians step up—to educate themselves, join discussions, build solutions, and shape policy.
Otherwise, we risk repeating the EHR debacle on an even larger scale.
“The worst scenario is an AI future designed without physicians—and without patients—in mind,” he warns.
As technology advances, the need for clinician leadership has never been more urgent.
Key Takeaways
✅ Physicians must upskill in AI and innovation to ensure new tech improves care instead of adding burdens.
✅ The EHR rollout showed the danger of excluding clinicians from design and decision-making.
✅ AI’s promise spans operational tools, scribe tech, clinical decision support, and future multimodal patient records.
✅ Cultural and reimbursement barriers must be addressed to enable adoption.
✅ New training paths, venture arms, and no-code tools are making it easier for doctors to help design solutions.
✅ Health systems should invest in physician leadership to reduce burnout and improve innovation.
✅ Value-based care models can help align incentives for better, preventive-focused healthcare.
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