hospitals and clinics can integrate with their own branded products and services to help patients with self-diagnosis and understanding treatment options.
If the company has success with that model, Le says it might consider trying to steer patients directly to Buoy. That shift would be similar to what has happened with the review website Yelp (NYSE: [[ticker:YELP]]), he says. Early on, many users ended up clicking through to Yelp’s site after running a Google search. But as more people came to learn about and trust Yelp, the service was increasingly able to cut out the middle man.
Of course, Le’s plan depends on Buoy demonstrating that its software consistently provides accurate and useful information to patients. It’s still early days for the startup—and the broader field of machine learning software in healthcare—so success is far from certain. But if Buoy can win the trust of patients and their caregivers, it has a shot at building a big business.
In the meantime, Le says he decided to return to medical school, and finished earlier this year. But for now, his focus is on Buoy, and trying to make an impact on large numbers of patients by encouraging adoption of the company’s digital tools.
“I graduated last May, just to make mom happy,” Le says. Returning to the physician track in the future is always a possibility, but for now Le says he is drawn to the work Buoy is doing because of what he says are the “implications for the number of people we can help.”
“It’s hard to justify seeing patients one by one when a tool like ours can potentially help so many people,” he says.
