AI Will Make Your Doctors Busier, Not Less

Meet the super-informed patient.

Apr 29, 2026

Last week at my annual physical, I caught two errors before my doctor did.

A lab order that conflicted with a medication I was already on. A duplicate test that had been run six weeks earlier at another visit. Neither was catastrophic. Both were the kind of thing that, ten years ago, would have gone unnoticed by everyone in the room, including me.

What changed isn’t my doctor. What changed is that I walked in with AI.

A new category of patient

I’m not the only one. According to a late-2025 West Health and Gallup poll, roughly one in four U.S. adults used an AI tool for health information in the past 30 days. When OpenAI launched ChatGPT Health in January 2026, it crossed 40 million daily users within weeks. Dr. Angelo Volandes, a Dartmouth physician and professor at Geisel, captured the dynamic in STAT in December: “Our patients aren’t waiting. They have already consulted ChatGPT or other AI chatbots before they arrive at appointments. They ask questions that assume their physician has considered options that the doctor has never encountered.”

I’d call this category the super-informed patient. Three behaviors define them:

Pre-visit differential. They arrive with a working hypothesis, a drug interaction screen, or relevant studies already pulled.
Real-time reconciliation. They check medications, lab orders, and care plans against AI during the encounter.
Post-visit verification. They re-examine notes, results, and recommendations after the visit, often surfacing follow-up questions that drive new messages, calls, or appointments.

This is not a fringe phenomenon. It is happening in your exam rooms today.

The productivity paradox, round two

Healthcare executives are planning for AI as a productivity multiplier for physicians. That frame is incomplete, and it ignores a lesson the industry already learned the hard way.

The electronic health record was sold as a productivity tool. What it delivered was higher patient volume, not more time per patient. The seminal Sinsky study in Annals of Internal Medicine (co-authored, notably, by clinicians at Dartmouth) found that for every hour of direct patient care, physicians spent nearly two additional hours on EHR and desk work. Subsequent research by Arndt and colleagues, published in Annals of Family Medicine in 2024, showed that EHR time increased another 7.8% from 2019 to 2023, with patient inbox messages rising 24%, even as the early benefits of AI scribes were already being rolled out at major systems.

The mechanism is well understood. Efficiency in healthcare gets absorbed by volume, not returned as time. Panels grow. Schedules tighten. The work that was supposed to disappear gets replaced by new work made possible by the gain.

Now repeat that pattern with AI.

Why this round is worse

The EHR productivity paradox played out on one side of the encounter. The clinician got a tool. The patient did not.

AI is different. For the first time in medical history, the patient has access to the same cognitive tool as the clinician, and the patient has far more time to use it.

Provider AI is being deployed to optimize throughput: ambient scribes, ordering automation, summarization, decision support. Patient AI is being deployed to optimize advocacy: differential generation, error detection, second-opinion validation. Same underlying technology, opposite vectors.

These vectors do not cancel each other out. They compound.

What this means for health system leaders

Plan for three consequences in 2026 and 2027.

First, encounter intensity rises. Even if documentation gets faster, the encounter itself becomes denser. More evidence is presented. More questions are asked. Decisions that used to be accepted are now negotiated.

Second, error surfacing shifts upstream to the patient. Patients are catching conflicts, duplications, and outdated guidance in real time. This changes quality signals, medicolegal exposure, and the cadence of post-visit communication. Inbox volume, already growing, will accelerate.

Third, physician work categories expand. Defending decisions to AI-prepared patients, documenting clinical reasoning at higher fidelity, and managing rework triggered by patient challenges are emerging as real categories of physician time. None of them are on most cFTE models. None of them appear in any standard time study.

You cannot manage what you cannot forecast

Every health system planning an AI strategy is making implicit assumptions about how physician time will be reallocated. Most of those assumptions are wrong, because they are based on data that does not exist. Annual time studies cannot capture monthly shifts. Productivity benchmarks cannot capture changes in encounter composition. RVU trends cannot capture the time spent defending decisions or responding to AI-prepared inbox messages.

Continuous, accurate physician activity data is the foundation, not a deliverable. Without it, you cannot forecast the reallocation that’s already underway. You can only be surprised by it.

The question for 2026

Every CMO and CFO is being asked some version of this by their board: what is our AI strategy, and what return do we expect?

Here is the better question to answer first: Is our AI strategy planning for a busier physician or a less busy one, and what evidence are we using to decide?

If the answer is “we expect AI to give time back to our physicians,” ask what data supports that expectation, and what your plan is if it doesn’t.

The super-informed patient is already in your exam room. The question is whether your strategy has accounted for them.

Sources

Paul J. Swider is CEO & Chief AI Officer at RealActivity, a Microsoft Partner specializing in mission-critical AI for healthcare systems. He has 30+ years in healthcare technology, has trained over 3,000 engineers across GE, IDX, and Microsoft, and is the founder of BOSHUG, the Boston Healthcare Cloud & AI Community spanning 50+ countries.

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