How many of you have felt like a flea in a jar?
If you put fleas in a jar, they immediately jump out. Put a lid on it, and they hit their heads — once, twice — and learn not to jump so high. Then something remarkable happens.
You remove the lid, and they never jump out again. Not because they can’t, but because they’ve learned not to try.
Healthcare workers often feel like that jar.
Across hospitals and health systems, clinicians and front-line teams generate ideas every day — ideas to improve care, reduce harm and expand access. Yet too often those ideas never see the light of day. Not because they lack merit, but because the system cannot test them quickly, measure them reliably or scale them effectively. Over time, we learn not to jump.
Artificial intelligence is about to change that.
Economist Tyler Cowen argues that sustained growth is the engine of human flourishing. At its core, growth is driven by productivity — the ability to do more with less. Healthcare has long been constrained by scarcity: limited time, limited workforce, limited capacity. Every day requires trade-offs — who gets seen, who waits, who receives attention.
AI begins to break those constraints.
Across industries, AI is compressing the distance between data, decision and action. In healthcare, this is already visible. AI is answering patient calls, optimizing scheduling, drafting documentation, identifying coding errors and detecting early clinical deterioration. This is not incremental improvement. It is a redesign of how work happens.
From our shared seat as physicians who have spent decades in clinical practice, we see the promise of achieving goals previously considered outside the jar: A shift from triage to imaging when it looks like care is abundant. The nurse who knows exactly which post-discharge patients need a follow-up call. The pharmacist who can identify every patient leaving the hospital without filling prescriptions and actually reach out to them with a clinical conversation about their care. The quality officer who sees where readmissions cluster and can provide care that gets ahead of a crisis. As clinicians, they have always known what to do. AI gives them the scale to finally do it.
Take University Hospitals as an example. UH has developed and initiated three initial use cases focused on helping people get access to care and guiding them on their care journey.
One of these efforts is already seeing early success in helping patients fill their prescriptions, and the team is working to expand this approach across more points in the care journey. UH also held brainstorming sessions to identify additional opportunities, with a goal of driving $100 million in cost savings or new revenue. Through this work, the team identified 40 potential projects and is prioritizing those expected to have the greatest impact.
The strategic implications are profound. Insurers, technology companies and new entrants are moving faster. They have more capital, more integrated data pipelines and stronger incentives to optimize decisions at scale. If providers move cautiously while others industrialize intelligence, the balance of power will shift — and it may not shift back.
This is not about buying a chatbot. It is about building organizations that learn faster than competitors.
AI can reveal variation in care, surface hidden risks and shorten feedback loops from months to days. But technology alone does not create value. Culture does. The systems that will lead will pair AI with a belief that every employee is capable of improvement — and give them the tools to act on it.
We need to unleash the ideas of our clinicians, not have them feel like fleas in a jar because it is the creativity of clinical leaders that will determine where AI creates the most value. It is the CNO who knows which post-op patients fall through the cracks. The pharmacist who sees revenue walking out the door. The care manager who can pinpoint where follow-up fails.
AI gives these leaders a way to act on what they have always known at a scale they could never achieve with human labor alone. The question is not whether AI works. It is how clinical leaders will step forward to direct it.
To translate promise into impact, three priorities matter.
First, expand access and outreach. AI should proactively identify patient needs and connect them to care before crises occur.
Second, eliminate administrative waste. Utilization management, call centers and back-office processes should be largely automated, freeing clinicians to focus on care.
Third, advance clinical AI with discipline. We have seen what happens when technology outpaces value. Models must perform well, integrate into workflows and improve outcomes. A model that predicts but does not change care changes nothing.
There is also a critical trust gap. Clinicians must trust the model, the company and the organization deploying it. Today, that trust is fragile. Without trust, adoption stalls, impact disappears and value is never realized. Building trust is not optional; it is strategic.
The deeper opportunity is not efficiency alone. It is unlocking ideas at scale.
For decades, healthcare has been full of trapped ideas — front-line insights that never spread because testing them was too slow or too costly. AI changes the economics of experimentation. Ideas can be tested faster, measured in real time and scaled across systems. The lid comes off the jar.
AI is not a technology; it is a mirror. AI will amplify whatever system we build around it. If we build systems that diminish, it will scale diminishing. If we build systems of separation, it will scale separation. But if we build systems that elevate and connect, that believe every person is capable, that learn continuously, and that are accountable for results, AI becomes far more powerful.
Not artificial intelligence. But augmented humanity. In that world, healthcare is no longer a system of scarcity. It becomes what it was always meant to be: A human flourishing machine.
The question for healthcare leaders is simple: Will we design AI around virtues? Will we keep jumping low or finally realize the lid is gone?
Peter Pronovost, MD, PhD, is chief quality and clinical transformation officer at University Hospitals in Cleveland.
Meenesh Bhimani, MD, MHA, is chief medical officer of Hippocratic AI
David Sylvan is chief strategy, innovation and marketing officer at University Hospitals
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