In the past, healthcare AI startups were able to raise funding or secure pilots based on their potential and the credibility of their founders—but now, the bar is higher. Investors, as well as health systems and payer customers, prefer startups that have proven value, the panel said.
Nick Culbertson, managing director of Techstars, an accelerator launched in partnership with Johns Hopkins University and CareFirst BlueCross BlueShield, said investors and customers have become increasingly skeptical of AI startups over the past few years, often demanding to see published research, case studies showing clear ROI and business traction data before committing. He made these comments during a panel discussion last month Medical City News” INVEST Digital Health Conference in Dallas.
“A lot of hospital systems are saying, 'Well, we want to be seen as innovative. We're willing to spend and invest in this program and hope it pays off. I think a lot of investors and a lot of health systems have been burned over time by companies that gave them too much leeway, and then it didn't work out,” Culbertson explained.
Artificial intelligence is having the most immediate and meaningful impact on administrative and compliance workflows, he said, noting that automating these back-office tasks can significantly reduce hospitals' labor costs and allow clinicians to focus more on patient care.
Dr. Ngoc-Anh Nguyen, vice president of research at the Houston Methodist Innovation Center, also believes that so far, the most obvious value of artificial intelligence in health care is management rather than clinical.
She noted that doctors already know how to provide care and trust their own medical judgment most over artificial intelligence. In her view, they need AI to simplify administrative burdens and compliance tasks, not to make treatment decisions.
Dr. Nguyen also noted that physicians want sophisticated, easy-to-use products.
“Physicians are already 110 percent full on providing patient care. PCPs are on a 10- to 15-minute schedule. We're seeing the patient, we're documenting, and then we have to comply — so the last thing we want is to spend more time learning to use another tool,” she declared.
Dr. Nguyen added that if a tool has a cumbersome interface or poor accuracy, mass adoption is unlikely, especially among older physicians who are resistant to new technologies.
Another panelist, Eric Levine, a principal at consulting firm Avalere Health, noted that the same scrutiny hospitals are putting on AI startups is also happening among payers.
For payers, value can be defined very differently depending on the line of business, such as Medicare Advantage, Medicaid, or commercial. Levine explained that improving star rating, risk adjustment accuracy or repurchase odds, for example, could be just as important as direct cost savings for a Medicare Advantage plan.
Overall, he noted that payers may be “harder to crack” for AI startups.
“[Payers] In a lot of areas, they can be very risk averse and they really expect a two to three times return on investment or they won't even get in with you,” commented Levine.
He noted that when trying to win over payers, startups must show evidence of their value, and that evidence must match the payer population. Many companies present data from studies that focus on narrow or high-risk populations, but the data does not reflect the payer's membership, damaging credibility.
Panelists agreed that the next wave of healthcare AI success stories won't come from the flashiest models or the biggest funding rounds, but from startups that can prove they can work in the messy realities of patient care and payer contracts.
Photo: Medical City News
