From Microscopes to AI: PathologyWatch’s Framework for Selling Innovation to Risk-Averse Healthcare Providers
Healthcare providers aren’t known for rapidly adopting new technology – and for good reason. When patient care is on the line, caution isn’t just prudent, it’s essential. In a recent Category Visionaries episode, PathologyWatch CEO Daniel Lambert shared how they overcame this inherent resistance by building trust through a carefully structured adoption process.
Starting with the Status Quo
Before proposing any changes, PathologyWatch needed to deeply understand the existing workflow. As Daniel explains, “If you ever go into a doctor to get a biopsy, what’s typically done is that tissue is sent out to a lab, like quest or labcore or one of the really big labs, and there they do some tissue. A pathologist looks at it under a microscope, makes a best guess as to what that cancer is, and then they typically fax the report back to the clinic or the hospital.”
This process, while functional, felt “25 or 30 years in the past.” But rather than criticizing it, PathologyWatch used this understanding to design a transition path that felt manageable to healthcare providers.
The Three-Pillar Trust Framework
PathologyWatch developed three core arguments for adoption, each building on the previous one:
- Safety and Improved Patient Care: “It’s simply better patient care if the dermatologist can see the image and they can see the notes from the physician, at the same time, they can actually show the patient the case. Less room for error, more ability to talk, to show the patient.”
- Operational Benefits: “Our average clinic saves 25 hours a month, but at the time, we kind of just did some guesswork and said, we think that we’re going to save you ten or 15 hours a month of just clerical work because we’re nicely integrated with your system.”
- Proven Results: “We have several published studies that shows exactly what the AI got right and what the AI got wrong. In the study, it’s several thousand patient cases. So you can get a pretty clear idea, or it’s a statistically significant picture of what the results are.”
The Gradual Adoption Strategy
Instead of pushing for immediate full adoption, PathologyWatch allowed customers to test the waters. As Daniel notes, their early customers “didn’t commit the whole volume up front. They wanted to test us, and we delivered on those early cases, and then they ended up sending more volume over time.”
This approach proved particularly effective because it allowed providers to validate the technology’s reliability on their own terms.
Transparency About AI’s Role
Rather than overselling AI’s capabilities, PathologyWatch took a measured approach to introducing their technology. Daniel explains their findings transparently: “The AI is extremely good at picking up those very small melanomas and early melanomas that are easy to miss because it’s picking up patterns of a melanoma that might not even be fully formed yet.”
They also publish their limitations: “We also published all of our flaws, too. Yeah, I think that’s a really important part, especially in healthcare, so that others can learn both through our methodology and where the AI falls a little bit short.”
Building Long-Term Trust
This strategy of transparency and gradual adoption has paid off. PathologyWatch has grown to serve 180 clinics and three hospital partnerships in just two and a half years of active selling. The key, according to Daniel, was understanding that in healthcare, you “sometimes can’t move at lightning speed. We face a wall of regulations, some things that were made for other parts of healthcare that end up impacting Pathology.”
For founders selling innovation into regulated industries, PathologyWatch’s experience offers valuable lessons about the importance of meeting customers where they are, providing clear evidence of benefits, and allowing them to validate technology at their own pace. Sometimes the key to driving rapid adoption is being willing to move slowly at first.