Matt Cybulsky, practice leader for healthcare AI, value-based care and product innovation at LBMC, has studied and analyzed the digital health market for years and advised companies on scaling and profitability as funding landscapes have shifted.
Cybulsky sat down with MobiHealthNews to discuss strategies for digital health investment and AI’s role in improving both business profitability and patient outcomes.
MobiHealthNews: How have you seen the digital health investment landscape change over the past several years?
Cybulsky: Two and a half years ago, I was looking at statistics from CB Insights, and something like $57 billion of investment capital was going into digital health, and since that time, we’ve seen deals and capital slow significantly.
That’s been commensurate with macroeconomic pressure, obviously COVID, dollar injection, inflationary pressure, and now the labor market is starting to respond to that. So is the residential housing market. It’s not that relevant to digital health, but what it is are indexes to what we could expect with investment deals.
That’s starting to change, though. I was at JP Morgan’s conference in January, and at some of the events I went to a lot of the conversation revolved around, “What are you hearing? What are you seeing? How many deals? Who’s doing deals? What’s going on macroeconomically for those things to start opening again?”
So, we’ve gone through this incredible treasure chest of funny, smart money, and now it’s a little bit more shrewd money.
Still, the pressure on getting care to the doorstep of individuals is not changing. There’s this incredible shortage of clinicians and nurses, which is a huge problem. People want to talk about burnout, but to me that’s just a euphemism treadmill to the real issue, which is supply for what we need, with a lot of people being sick and increasing in their sickness. That’s not going to go away, and as long as there’s pain, there’s opportunity for return.
The interesting thing about healthcare is there’s this loggerhead always of goodwill, the nature of what medicine and healthcare is, against a business plan to make that possible. So maybe we’re in a little bit of a reckoning. I started saying that at the end of last year. I still think we are.
MHN: Due to those changes, how has your strategy adjusted when advising companies on how to approach investors for funding?
Cybulsky: I don’t think it has changed much. I mean, there’s been more of a realization, right? We speak to a young man or woman about going pro in a sport, if they’re in high school, you have somewhat of an open mind, but also a reality check. If they’re a starter in college, it’s a different conversation. But still the odds aren’t great. And even if you make the team, are you going to play if you’re pro? The same is true here. If you’re going to be this big, bad unicorn, you have to have the talent and you have to have a business plan that’s strong.
We’re seeing some companies now that had these incredible valuations, and there’s some … reckoning I guess would be the word. There are some folks looking at each other and saying, “We didn’t anticipate this.”
So, nothing’s really changed outside of the advisory I give with every founder or board or team at an early-stage startup or middle market, an equity-backed company, which is the business plan has to be really sound with the research we’re doing on what the consumer can tolerate, and what the market will pay for. Is it B2B? Is it B2C? How strong are our predictions on the market? Let’s look at the SAM [serviceable addressable market], the TAM [total addressable market], the pricing and the value of what we’re offering.
MHN: You focus on AI within healthcare, value-based care and implementation, and product innovation. Does your advice for companies seeking investment in those areas differ from each other?
Cybulsky: It does slightly, depending on if it’s payer or provider side or if it’s a digital health company. I will modify my recommendation and what I present to them just based on their model—like how I think they make money and how they tell me how they want to win with the problem they’re trying to solve.
It’s not always reductive, like money, money, money, but it’s definitely about what problem are you solving in healthcare, and then can we make that work because there is a return? That is heart-wrenching to me, but it’s also necessary if you’re going to keep the doors open.
There are three things I always tell firms that are theses of mine: The black box problem in AI, the “So what?” problem in data analytics and AI, and distinguishing flowers from weeds.
The black box problem is: How do I describe what AI is doing under the hood? What we really have here is what I call the myth of explanatory depth. I can tell you that AI comes up with solutions and creates forecasted models, but you ask me how, and then I say, “Well, it’s these very specific sort of tools and GPUs and algorithms.” Well, how are those made? And pretty soon, I can’t tell you anymore about how that’s done. But at the same time, I’ve got to take it to a group of executives or a firm and say, “Use this. I promise you it works.” That’s a black box problem and it’s a tough one.
The other one I talk about is the “So what?” problem. So what I could forecast this data? So what I could retrospectively give you predictions and insights that humans can’t? What do you do with it?
And then, lastly, the one I advise on a lot, and frankly I’ve seen a lot of this, is are you working on a pitch for a flower product or a weed product? And sometimes the difference between a flower and a weed is the marketing budget. And there’s a lot of weeds out there.
MHN: So many companies are touting the use of AI in their offerings, advertising their platforms as being “AI-enabled.” Has it come to a point where highlighting AI implementation as a selling point no longer amplifies a company’s value for investors?
Cybulsky: I think there is fatigue, but there’s still a strong desire to see how you’re going to use AI. I mean, that market is way too ginormous. It’s an enormous market; to ignore it is foolhardy.
So, investors ought to be very curious about how you can use AI to scale the dollar of investment or increase consumer adoption, frequency of use, et cetera, and I think they are.
I mean, humans can’t digest the enormity of data that’s available. There are so many stories being told that AI can uncover that we cannot. That’s the message here. Not using AI means you miss out on the products you can sell as fast as possible that you didn’t know you could, or speed up the production of a workforce. That basic integral from revenue to expense, AI can bend it.
Also, the sentiment analysis of markets for investing is real, and so often valuation is about the future speculation of the value of a product. It’s really not always getting the K-1 file and looking at the EBITDA, cash flows and expenses. It’s also about liking the company. Investing is all perception. Never undervalue the strength of coefficient of perception for the value of a product or a market.
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