Matt Cybulsky, A leader in the Healthcare AI, Value-Based Care and Product Innovation practice at LBMC, he has been researching and analyzing the digital healthcare market for years and advising companies on how to scale and profit in the face of changes in the financing landscape.
Cybulsky sat down with to discuss digital healthcare investment strategies and the role of AI in improving business profitability and patient outcomes.
MobiHealth News: How do you think the digital healthcare investment landscape has changed over the last few years?
Cybulski: A year and a half ago I was looking at CB Insights Statisticsand approximately $57 billion of investment capital was deployed in digital health, and since then we have seen a significant slowdown in deals and capital.
It was commensurate with macroeconomic pressures, obviously COVID, the dollar injection, inflationary pressures, and now the labor market is starting to respond to that. And so is the housing market. It’s not as relevant to digital health, but these are indices of what we would expect from investment transactions.
But that’s starting to change. I was at a JP Morgan conference in January and at some of the events I went to, a lot of the conversations revolved around, “What are you hearing? What are you seeing? How many deals? Who’s doing the deals? What’s happening macroeconomically to start these things opening up?”
So we’ve looked through this amazing treasure chest of fun, clever money, and now we have some more clever money.
Yet the pressure to deliver care to individuals’ doors remains unabated. It’s incredible shortage of doctors and nurseswhich is a huge problem. People want to talk about burnout, but to me that’s just a treadmill euphemism for the real problem, which is providing what we need while many people are infirmed and their illnesses are getting worse. It’s not going to go away, and as long as there’s pain, there’s a chance it’ll come back.
The engaging thing about healthcare is that there is a certain contradiction always goodwill, the nature of what medicine and healthcare is, against the business plan that makes it possible. So maybe we’re in a little bit of a reckoning. I started saying that behind schedule last year. I still think we are.
MHN: How has your strategy for advising companies on how to raise funds from investors changed in airy of these changes?
Cybulski: I don’t think that’s changed. I mean, there’s been a greater awareness, right? We talk to juvenile men or women about becoming pros in sports, if they’re in high school, you have to have a little bit of an open mind, but also a reality check. If they’re a starter in college, that’s a different conversation. But still, the odds are not great. And even if you make the team, are you going to play if you’re a pro? It’s the same here. If you want to be this gigantic, bad unicorn, you have to have talent, and you have to have a solid business plan.
We’re seeing some companies now that have had these incredible valuations and there’s a certain… reckoning, I guess that’s the right word. There are people who are looking at each other and saying, “We didn’t see that coming.”
So nothing has changed except the advice I give to any founder, board or team in an early stage or mid-market startup, an equity-backed company, which is that the business plan has to be really solid, given the research that we do about what the consumer can tolerate and what the market will pay for. Is it B2B? Is it B2C? How mighty are our market predictions? Let’s look at ITSELF. [serviceable addressable market]THERE [total addressable market]prices and value of what we offer.
MHN: You focus on AI in healthcare, value-based care and implementation, and product innovation. Does your advice differ for companies looking to invest in these areas?
Cybulski: Kind of like that, depending on whether it’s a payer side, a provider side, or a digital health company, I’ll modify my recommendation and what I present to them simply based on their model—like how I think they make money and how they’re telling me how they’re going to win at 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 do that because there’s a refund? That’s heartbreaking to me, but it’s also necessary if you’re going to keep your doors open.
There are three things I always tell companies that are my theses: the black box problem in AI, the “So what?” problem in data science and AI, and distinguishing the flowers from the weeds.
The black box problem is: How do you describe what AI is doing under the hood? What we’re really dealing with here is the myth of explanatory depth. I can say that AI is coming up with solutions and making predictive models, but if you ask me how it’s doing that, I’ll say, “Well, it’s these very specific tools, GPUs, and algorithms.” So, how do you do that? And soon I won’t be able to tell you how you do it. But at the same time, I have to present it to a group of executives or a company and say, “Use this. I promise you it works.” That’s the black box problem, and it’s challenging.
The other one I’m talking about is the “So what?” problem. So what could I predict from this data? So what could I retrospectively give you predictions and insights that people can’t? What do you do with it?
And finally, on the subject of where I give a lot of advice, and honestly, I’ve seen a lot of this, are you working on a flower product proposition or a weed product proposition? And sometimes the difference between flower and weed is marketing budget. And there are a lot of weeds.
MHN: With so many companies touting AI in their offerings, advertising their platforms as “AI-enabled,” has it reached a point where highlighting AI implementation as a selling point no longer reinforces the company’s value proposition to investors?
Cybulski: I think there’s fatigue, but there’s still a mighty desire to see how you’re going to operate AI. I mean, this market is way too gigantic. It’s a huge market; ignoring it is reckless.
So investors should be very curious about how AI can be used to augment the value of an investment or augment consumer acceptance, frequency of operate, etc. I think that’s exactly what’s happening.
I mean, humans can’t process the extensive amount of data that’s out there. There are so many stories that are being told that AI can uncover that we can’t. That’s the message. Not using AI means you’re missing out on products that you could sell as quickly as possible that you didn’t know you could, or you’re accelerating the production of labor. That basic integrity from revenue to expense, AI can bend that.
In addition, market sentiment analysis for investment is real, and valuation is often about future speculation about the value of a product. It’s not always about getting a K-1 and looking at EBITDA, cash flow, and expenses. It’s also about liking the company. Investing is all about perception. Never underestimate the power of the perception factor for the value of a product or market.