As AI becomes a buzzword in nearly every healthcare startup stadium, investors find it increasingly challenging to distinguish which ones are actually worth the hype.
That's why this question was raised in a panel discussion among venture capitalists at the Medcity Invest Digital Health conference in Dallas Thursday: What metrics do you want to see highlighting them more frequently when pitching, what is a red flag, and you question the effectiveness of their technology? The meeting was chaired by Neil Patel, head of venture capital redesigned health.
Here is what they must say:
Which founders should be highlighted
For Maddie Hilal, an investor in Oak HC/FT, it is important that startups have a strong net income retention rate, which measures the company's ability to retain revenue from existing customers.
“If we don’t necessarily understand these efforts [profit and loss] She said the impact point proves point, but your existing customer base is adding contracts and they are obviously excited. They see value. ”
Another investor is looking for companies with high-quality data.
“If you have better, higher quality data, you can solve the problem in a better way, [with] High predictability of the model. I think we are looking for it. What is that proprietary dataset? What training are you receiving? In which environment who was deployed? ” said Rohit Nuwal, partner at Telus Global Ventures.
Vickram Pradhan, vice president of Sopris Capital, hopes to see good clinical impacts from AI startups.
“People ask about clinical impacts in ways that weren’t asked about five years ago,” he said in the group. “I think the reason for this is that some of the reimbursement, finance and payment mechanisms in healthcare are a bit of a black box. … But if you know what you’re doing, it’s having a really meaningful clinical impact, that’s a good foundation to know that it’s going to be valuable and someone will pay for it.”
AI red flags
Hilal said many healthcare startups will use AI buzzwords on their pitch decks, but don’t support their claims with strong data and validation metrics. She said this is a major red flag.
Nuwal responded to Hilal's comment.
“I think in the machine learning problem that is basically trying to solve is essentially a lot of AI around it,” he said. “I don’t blame them, in this environment, the founders are having a hard time raising money, so you need to play some games. But I think it’s a long way to go about the authenticity of the problem that is to be solved.”
For Pradhan, a red flag income indicator is a “tight” income indicator. It is important for companies to stay realistic with investors.
“I think it’s common today, especially in some AI companies that are doing a lot of pilots talking about, ‘We have 10 million revenues.’ Then, when you go deeper a little deeper, it’s like, “Oh, that’s actually what it looks like in the third grade.” ” …The basis for realizing authenticity makes it more challenging,” he said.
