Most of what we called healthcare innovation over the last decade was an expensive lie we told ourselves.
Between 2015 and 2020, we poured billions into digitizing the status quo, making existing systems slightly more efficient, stretching legacy models to reach more people, and congratulating ourselves on it. We weren’t transforming healthcare. We were putting a fresh coat of paint on a building with a crumbling foundation and calling it a renovation.
I say this as someone inside the machine. As a corporate strategy professional, I spend my days assessing how the healthcare environment is shifting away from its traditional center of gravity. From where I sit, the difference between that last era and this one isn’t incremental. It’s categorical. Artificial intelligence isn’t optimizing the old system. It’s making the old system economically indefensible.
Yet our collective attention remains attuned to the cracks in the old foundation: a fragile financial landscape, skyrocketing premiums, and the instability of our safety nets amid impending Medicaid cuts. These are real crises. But while we obsess over the solvency of a model that was never going to survive, a fundamentally different structure is rising beside it, driven by AI and decentralized data that is changing the very definition of care.
The innovation shell game
Here is the uncomfortable truth that nobody in healthcare wants to say out loud: most of what we celebrate as “innovation” is actually a shell game.
Innovation that shifts costs from the provider’s balance sheet to the payer’s premium is not innovation. It is accounting. If an AI tool saves a hospital $1 million but imposes $1.2 million in new fees on the payer, we haven’t transformed anything. We’ve just moved the bill. Our industry has become remarkably skilled at celebrating “savings” that are actually someone else’s new expense.
True transformation requires lowering the total cost of the healthcare system, not merely redistributing. That’s the standard by which every new technology and model should be measured. By that standard, most of what’s on stage at our industry’s conferences doesn’t qualify. Paul Kusserow and David Johnson put it bluntly in The Coming Healthcare Revolution: the U.S. healthcare system is twice as expensive per capita as other high-income nations yet yields inferior results. Payers and the public are exhausted by the “more for more” model. When you’re drowning in a funding crisis, it’s hard to see the technological lifeboat floating right next to you.
The prophecy that arrived early
In 2020, Hemant Taneja and Dr. Stephen Klasko published UnHealthcare, a manifesto for what they called “Health Assurance,” a move away from hospital-centric sick care toward a proactive, healthcare-at-any-address system. At the time, the ideas were considered ambitious. Five years later, they look conservative. Even those authors, who were pushing the boundaries of what the industry was willing to imagine, underestimated how fast AI would make the hospital-centric model not just outdated but structurally obsolete.
Some early pioneers of this vision struggled to scale. Forward, for instance, bet on a niche market with limited reimbursement and couldn’t make the economics work. But the companies succeeding today prove the thesis was right — the business model just needed to catch up to the idea.
Look at Ro, which has built an “unscaled” direct to patient healthcare ecosystem that manages everything from diagnosis to the delivery of medication, or Transcarent, which provides a comprehensive health platform that simplifies the journey for employees and employers by aligning incentives. These aren’t more efficient versions of old clinics. They’re new models that bypass the hospital’s four walls entirely. And they’re growing because they’re actually reducing the total cost of care — not just shuffling it around.
Two forces that will make the annual physical obsolete
The revolution underway is driven by two AI-powered forces that, taken together, will render much of today’s routine healthcare infrastructure unnecessary within this decade.
The first is the democratization of biology. Health data is becoming a continuous stream rather than a clinical snapshot. Through continuous monitoring and frequent testing, individuals are gaining access to their own biological data in ways unimaginable five years ago. The increasing partnerships between large laboratory companies and fitness tracker manufacturers are a case in point: pairing the physiological data from a wearable—sleep, heart rate variability, activity — with the cellular-level insights from blood biomarkers. When you layer AI over this frequent monitoring, you don’t need to wait for a yearly checkup to discover something is wrong. You shift from reactive diagnosis to constant, personalized wellness. By 2030, the annual physical as we know it will be a relic. Continuous monitoring will catch what a once-a-year snapshot never could, and patients will wonder why they ever drove to an office to have someone listen to their heart for 30 seconds.
The second force is the rise of agentic AI in medical guidance. We’ve moved beyond simple chatbots. Companies like Hippocratic AI are deploying systems that don’t just answer questions — they act as autonomous coordinators of the care journey, proactively scheduling follow-up appointments, monitoring recovery after discharge, and explaining lab results in plain language, all without human intervention. Meanwhile, in the clinical realm, advanced genetic testing, particularly in cancer therapy selection and monitoring, is enabling us to abandon trial-and-error medicine for precision protocols tailored to a patient’s specific DNA. This isn’t a future state. It’s happening now in labs and clinics across the country, and it’s making the old model of “prescribe and hope” look medieval.
So who leads this?
The technology is here. The data is flowing. So who will force this non-incremental transformation? Will it be the consumer, finally armed with continuous biological data and the tools to interpret it? Will providers evolve into the distributed health hubs that Klasko and Taneja envisioned? Will payers stop processing claims long enough to become actual health partners? Will the government provide the necessary policy alignment to guide this transformation effectively?
I believe the consumer will lead this, not because they are demanding change yet, but because the economics will soon make a new path essential, leading to a pivotal moment of realization. When an individual with a wearable, a biomarker panel, and an AI health agent possesses more actionable intelligence than a standard primary care visit offers, the center of gravity in healthcare will permanently move toward the patient.
Here’s my prediction, with a date: by 2030, the employer that doesn’t offer continuous health monitoring as a benefit will struggle for talent as much as one without a 401(k) today. By 2032, at least a third of today’s primary care offices and freestanding urgent care centers will have been reimagined, replaced by virtual-first models backed by continuous data. The shift from incremental ‘efficiency theater’ to systemic transformation is already underway. The only question left is whether you’re building the new system or polishing the brass on a sinking ship?
Editor’s note: Neither the author nor his company have any relationship with the companies/products mentioned.
Image: champpixs, Getty Images
With nearly a decade of experience in healthcare strategy, Patrick Doolittle has held key leadership roles at both Jefferson Health and Quest Diagnostics. His expertise centers on rigorously assessing the external environment and analyzing emerging healthcare trends to shape long-term organizational priorities. By synthesizing complex market data, he provides the strategic foresight necessary to inform high-level decision-making and resource allocation. Currently, he continues to drive growth by identifying competitive tailwinds and navigating the shifting landscape of modern health systems. His views are solely his own, and do not represent the views of current or past employers.
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