Buoy Health raises $6.7M for smarter triage tool aimed at hospitals and payers

The funding will be used to expand the staff from 10 to 19 in the next 12 months and add to its customer base of hospitals and payers beyond Boston


Symptom checkers or automated triage tools? Whatever you call them, the accuracy of these programs is often faulty because the advice tends to rely on the assumptions buried in the questions it triggers, according to Boston-based Buoy Health Cofounder and CEO Dr. Andrew Le. He believes his business is taking a smarter approach

He believes Buoy Health is taking a smarter approach than the likes of, say, WebMD by feeding its program 18,000 clinical papers. The idea is to trigger more thoughtful follow-up questions and reach more informed recommendations for what to do next, Le said in a phone interview. The goal is to do a better job of triaging patients by recommending urgent care facilities for minor cases and to ensure that patients who should be in the emergency room get there.

Buoy Health has closed a $6.7 million Series A round led by F-Prime Capital Partners, formerly Fidelity Biosciences, with participation from FundRx and angel investors including Jack Connors, according to a news release. The funding will be used to expand the staff of 10 to 19 with additional clinical researchers, engineers, and marketers in the next 12 months. The business also wants to add to its customer base of hospitals and payers beyond Boston. They offer a white labeled version of the company’s symptom tracker with their own brand. The company has raised $9.2 million to date.

The company’s symptom checker, which has been live since March 8, is also available to consumers. Depending on the diagnoses, Buoy Health has eight triage level recommendations from staying at home to calling an ambulance. Some other recommendations include seeking primary care, urgent care, a nurse triage line or telemedicine.

The questions Buoy Health’s program asks, based on patient search criteria, stem from the most common diagnoses. Each question from the patient causes follow up questions to be “re-ranked”.  Le likened the machine learning software its program uses to the way Netflix’s app makes recommendations based on the films users watch.

He noted that the goal is to figure out which questions will reduce the number of diagnoses the most. 

“By creating that granular understanding of medicine, the more patients we see, the better we get,” Le said.

Photo: Topp_Yimgrimm, Getty Images