By CRAIG HAUBEN
Ask anyone outside healthcare who resists clinical AI and you’ll get a confident answer. The older doctors. The ones who spent thirty years building expertise and now see a machine coming for it. The story writes itself, which should have been the first clue it was wrong.
I’ve spent thirty years in healthcare, and I now run a company that builds and runs AI inside provider and payer organizations. At Clutch we use AI’s data analysis to solve engagement challenges. Who is the patient today? What message will land with them? When do they want to read it? Get those right and you can drive the kind of sustained behavior change that moves clinical outcomes like drug adherence, care plan adherence, and gap closure.
So I’m not working from theory. I watch this land in real workflows, and here’s what I see. The clinicians most enthusiastic about AI are usually the ones who’ve done the job the longest. The resistance comes from somewhere else. If you run a health system, that difference should change how you plan your next deployment.
Start with the adoption numbers, because they already break the resistance story. The AMA’s latest survey found four in five physicians now use AI in practice, up from 38 percent in 2023. That’s not a profession digging in against a threat. That’s a profession that found something useful.
Now the veterans. A doctor with three decades in a specialty can see, better than anyone, what these systems are good at. Pattern recognition at scale. Catching the thing that should have been flagged two visits ago. Surfacing what was already sitting in the data: the missed finding in last year’s imaging, the lab trend across eighteen months that looked unremarkable one value at a time, the three ED visits in six weeks nobody had the time to connect.
This isn’t hypothetical. The Nature study of Google’s breast cancer screening system showed a 9.4 percent drop in false negatives for US patients, the cancers human readers missed. The largest NHS evaluation to date, across 175,000 women, found AI caught more invasive cancers with fewer false positives than human readers. The harm these systems go after, information that existed and never got connected, is one experienced clinicians know cold. They’ve spent careers watching its absence hurt people.
Here’s one from our own work. We’re working with a national government programs payer on some of their hardest members to engage, the high intensity ones who need contact four or five times a day for six months or more. We got engagement to 95 percent, measured by the customer, and adherence to 93 percent. The result was a 0.8 average drop in HbA1c and an 18 percent reduction in symptoms.
When a system takes the mechanical load off so the judgment work gets more attention, the thirty-year clinician doesn’t feel threatened. They feel relieved. Their expertise is the judgment, not the data retrieval, and they’ve always known the difference.
Now look at where the fear actually lives. It comes from the middle.
The people who built careers on being the synthesizer, the translator between systems, the one who pulled information from six places and assembled it into a picture. That role is under real pressure, not clinical judgment. Anthropic’s labor market research points the same way, finding AI exposure concentrated in exactly this kind of assembly work rather than in judgment-heavy roles. The synthesizer is scared. And the synthesizer is right to be, because synthesis is what these systems do best.
Both responses are rational. That’s the point. Your workforce isn’t split into the enlightened and the fearful. It’s split by what people do all day, and the line doesn’t run where the conventional wisdom says it does.
If you run a health system, that has three consequences.
First, your deployment champions aren’t who your consultants think. The standard playbook recruits young physicians as AI ambassadors, on the theory that digital natives adapt faster. Recruit the thirty-year veterans instead. They have the credibility, they can say exactly where the system helps and where it can’t be trusted, and their word carries different weight in the staff lounge. A skeptical senior clinician turned into a precise, conditional advocate is worth ten enthusiastic residents.
Second, the people in the synthesis layer deserve honesty, not slogans. Telling a care coordinator or a utilization review nurse that AI will simply make their job easier is how you destroy your own credibility, because they can see the mechanism as clearly as you can. The honest conversation is about which parts of the role are moving into the machine, what the role becomes after that, and what the institution will do to carry people across the gap. Most organizations aren’t having that conversation. The ones that do will keep their best people. The ones that don’t will lose them at exactly the wrong time.
Third, stop measuring adoption and start measuring trust, in both directions. Nearly every Fortune 500 company now tracks employee AI usage, and healthcare is copying the habit. Usage is the wrong metric. A system clinicians use reluctantly under mandate is a risk. A system clinicians trust past its real performance envelope is a bigger one. The AMA’s sentiment data captures the right posture better than any dashboard. Roughly two in five physicians say they’re equally excited and concerned, and that ambivalence isn’t a problem to manage away. It’s the right response to a powerful tool with uneven performance, and it’s exactly the disposition good governance should be built on.
The veterans are your asset here too. The clinicians most excited about these tools are often the most precise about their limits, because real expertise includes knowing what the tool can’t do. Build your oversight around that precision instead of around utilization dashboards, and you get an early warning system staffed by the people best qualified to run it.
The coverage of AI in medicine keeps offering two stories. The machine that replaces doctors, or the machine that destroys medicine. People who run things don’t get to live in either one. The real version is more granular, in places really good, and it starts with noticing that the people we expected to resist this are the ones quietly showing us how to use it well.
That gap, between the two clean stories and what happens on the floor, is what got me writing the book. The AI: Migration is a novel about how AI is disrupting work. Every AI system and clinical event in it is drawn from the documented record, so while the characters are fiction, the AI stories are real.
Craig Hauben is CEO of Clutch and has spent thirty years as a healthcare operator and executive. His novel The AI: Migration publishes in July 2026.
Categories: Health Tech