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Nancy Brown used to wear a t-shirt that featured a shiny object and the word “no.” It’s the rule she lives by when investing in artificial intelligence in health tech.
“Honestly, there’s very few AI companies we don’t take a meeting with, because we’re always keeping an eye on everything,” Brown, a general partner at health tech venture firm Oak HC/FT, told Endpoints News. “We don’t want to assume there’s not new use cases or new ways to think about things.”
But Brown, who formerly served as an executive at healthcare giants McKesson and Athenahealth, said she’s looking in the increasingly crowded space for startups that are truly unique and tackle the biggest problems in healthcare. One of those problems: the loads of money and time that healthcare providers spend on administrative tasks.
Brown said she sees AI as a tool that, rather than inventing something new, can free up time for providers so they can innovate. One of Oak HC/FT’s first AI investments was in Regard, which earlier this month said it raised a $61 million Series B. The startup saves doctors time when diagnosing patients by using AI to analyze and present health data.
Regard has a strong return on investment for both clinical and financial outcomes, Brown said, and increases efficiency and productivity by giving doctors actionable insights in real time during appointments with patients. That’s different from other AI companies that may provide those insights only after appointments, she said.
AI has made great progress in cutting down on the administrative burden for providers by getting rid of tiresome manual tasks such as cleaning data and organizing contracts, Brown said. Still, at a time when many startups market themselves as AI-powered, it’s important to pick out ones that are able to use it efficiently, she said.
“I think we’re going to go through the shiny object phase, and we’ll go to the actual pure results phase,” she said about the current AI hype cycle.
Founders of health tech startups using AI should be able to pinpoint the problem they’re solving, why it’s important, who they’re selling to and why they’d buy it. They also need to prove how they’re going to differentiate their tool with all the other AI products coming into the market, Brown said.
“Any AI tools right now need to be able to be very clear about what [they’re] doing behind the scenes,” she said.