Physicians Are People Too:
A Behavioral Science Approach to Market Research
A few years ago, we were fielding a study on a new BPH device, and the questionnaire included the question we thought we ultimately needed to know: "How likely would you be to use this device?" Nearly every urologist responded "highly likely." We can assume that translates into a huge commercial success, right? Well, not necessarily. Urologists love to tinker, and in a crowded market of BPH therapies - UroLift, Rezūm, Aquablation, HoLEP, TURP and a shelf of medications already competing for the same patients - a new technology is an easy thing to say yes to in a research setting. But "would you use this?" and "will you use this?" are two different questions. The real questions are: what does this device have to do clinically, and what has to surround it - training, support, reimbursement - for it to earn a real place in an already-crowded algorithm? A likelihood-to-use score doesn't answer either one.
That gap is what pushed me toward a different way of designing studies. Somewhere along the way I started saying it out loud to my team: "physicians are people too." It's tempting to treat them as purely rational actors weighing clinical evidence. But the same forces that shape everyone else's decisions - loss aversion, friction, choice overload - shape theirs as well.
Loss aversion shows up as a fear of what could go wrong, not just what could go right. A device with slightly lower long-term durability but zero risk to sexual function reads very differently to a urologist than one framed the other way around, and a direct efficacy question won't surface that trade-off. So we ask it as a trade-off instead: "Which scenario triggers more hesitation for you - a device with slightly lower durability but zero risk to sexual function, or one with bulletproof five-year durability but a small increase in transient incontinence risk?" We'll also probe what a physician stands to lose by changing course at all: "What's the biggest risk you feel you're taking by carving out time to learn this procedure instead of filling your schedule with the BPH cases you already know work?"
Friction shows up as the hidden operational cost of change - the parts of adoption that have nothing to do with clinical efficacy and everything to do with workflow. A longer catheter window, a harder conversation with a value-analysis committee, time carved out of a reliably full schedule to learn something new. We ask about it directly: "If a patient needs a catheter for even 24 hours longer than your current go-to procedure, how heavily does that weigh on your willingness to adopt this device?" And for capital equipment specifically: "Even if you're clinically sold on this technology, what friction do you expect when you try to get it through your hospital's value-analysis committee, or justify the expense to your partners?" We might also walk through the post-operative picture step by step: "If this device requires an extra follow-up visit, how much does that change your appetite for the procedure?"
And choice overload shows up simply because the algorithm is already full. A new device doesn't enter a vacuum - it has to displace something, and physicians need to know exactly what patient it owns. We ask it plainly: "With UroLift, Rezūm, Aquablation, HoLEP, and medications already in your toolkit, what prostate size or patient type does this device cleanly own? If it overlaps with several options you already have, how will you decide which one to reach for?" If a urologist can't answer that quickly, the device doesn't have a clear enough position yet - no matter how strong the clinical data looks.
Some of the most useful insights, though, are the ones physicians can’t – or won’t – give you when you ask directly. Ask a surgeon “are you worried you can’t master this technique?” and you’ll almost always hear a confident no; admitting doubt about their own skill runs against how they see themselves. So we reframe the question in the third person: “When you think about the average urologist in your community, what’s the steepest part of the learning curve going to be for them?” The hesitation they wouldn’t claim for themselves may show up more freely when it’s projected onto a peer. A related technique works the same way from the other direction: “Picture a urologist who refuses to adopt this device. Describe that physician. What are they worried about, and what does their practice look like?” People are far more candid describing someone else’s fears than naming their own.
None of these approaches are tricks - they're a more honest way of asking the question. Rationalized answers to direct questions tell you what a physician thinks they should say. Indirect questions, trade-off exercises, and projective techniques tell you what's actually driving the decision underneath it. And that distinction changes what a research report can tell a client: not just a likelihood-to-use score, but a clear read on what a device has to do clinically, what has to surround it operationally, and where it fits in an already-crowded treatment algorithm. It's the difference between telling a client their device tested well and telling them exactly what to fix before it launches.
That's usually the more useful output of a research study anyway - not a number to report up the chain, but a set of specific, actionable answers about what to build, message, and support around the device itself.
If you're wrestling with how to read your own market's real adoption drivers, let's set up time to talk through your most pressing business questions.