Buoy Snags $6.7M for Chatbot, as A.I. Pushes Further Into Healthcare
While finishing up his third year at Harvard Medical School in 2013, Andrew Le started to notice a pattern. Le was working in an emergency room as part of a clinical rotation, and says that he spoke with patient after patient who had run Google searches on the symptoms they were experiencing before deciding to go to the hospital.
“They were guessing what they had,” Le says. “Some patients were coming in when they shouldn’t have. Some were coming in way too late.”
Le says he opted to take a leave from medical school in order to create a digital tool to help patients find credible information about conditions related to particular symptoms. And, perhaps just as importantly, the software tries to determine which healthcare setting—the emergency room, an urgent care clinic, or a doctor’s office, for instance—seems most appropriate for assessing and treating the symptoms.
Investors think Le’s company, Buoy Health, is on to something. On Wednesday, the Boston-based startup announced it raised $6.7 million in a Series A funding round led by F-Prime Capital Partners, a Cambridge, MA-based venture capital firm affiliated with Fidelity Investments. Other investors in the round include New York-based FundRx and individual investors. Le says Buoy has now raised a total of $9.2 million from investors.
Le says Buoy will use some of the new proceeds to hire more employees. The startup’s current headcount is 10, and Le says Buoy aims to double that in the next year.
The company sits at the intersection of several trends, including the proliferation of software tools geared toward helping patients be more proactive in their care, and a push to apply artificial intelligence-related technologies to healthcare.
After launching in 2014, Le says he and his three fellow co-founders reviewed thousands of clinical papers. Buoy fed some of the information in these papers into its machine learning-powered software, which Le says is aimed at connecting symptom information shared by patients with contextual information, such as where they live, what they do for work, and what medications they take.
Nearly three-quarters of patients in the U.S. first turn to Google’s search engine after they begin experiencing noticeable health symptoms and want to get more information, Le says. That often leads them to websites such as WebMD or the Mayo Clinic. (In late July, WebMD announced it had been acquired by Internet Brands, a portfolio company of the private equity firm KKR, in a deal valued at $2.8 billion.)
Le says that articles on WebMD and competing websites are often “too generic” and fail to take patient-specific considerations into account. That’s one area where Le believes Buoy’s technology can do better.
He also says that the business model of some health knowledge websites like WebMD involves “selling eyeballs to pharma,” so that patients reading about a particular condition will see ads for drugs aimed at treating it.
Buoy’s approach is to provide a more personalized and interactive experience, Le says. Typically, users spend two to three minutes answering prompts, and the software ultimately serves up what it determines are the three most likely diagnoses, and recommends where the patient should go to receive further care (or the software could advise that a trip to a clinic or hospital isn’t necessary).
Buoy launched its product in March. Le says that, to date, more than 300,000 people have visited its website or used its mobile app.
Buoy intends to sell its software as a white-label product that insurers and networks of 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: 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.