Matt Cybulski As LBMC’s practice leader in Healthcare AI, Value-Based Care and Product Innovation, he has spent years studying and analyzing the digital health market and advising companies on scaling and profitability as the funding environment changes.
Cybulski is MobiHealthNews We will discuss digital health investment strategies and the role of AI in improving both business profitability and patient outcomes.
MobiHealth News: How do you think the digital health investment landscape has changed over the past few years?
Cybulski: Two and a half years ago, I was looking at statistics from CB Insights and we saw that roughly $57 billion in investment capital was being pumped into digital health, but since then, we’ve seen a significant slowdown in deals and capital.
This is commensurate with macroeconomic pressures, obviously COVID, dollar injections, inflationary pressures, and now the labor market is starting to respond to that. The housing market as well. It’s not so much to do with digital health, but it’s an indicator of what to expect in investment transactions.
But things are starting to change. I was at a JPMorgan conference in January, and several of the events I’ve been to, a lot of the conversation revolved around, “What are you hearing? What are you seeing? How many deals are there? Who’s doing the deals? What’s happening macroeconomically as these deals start to reopen?”
So, we’ve looked into this wonderful treasure trove of funny, smart money, and now it’s time for some more smart money.
Yet the pressure to deliver care to individuals’ doorsteps remains the same. Shortage of doctors and nursesThis is a big problem. People like to talk about burnout, but to me that’s just a euphemism for the real problem, which is the supply of what we need, and that a lot of people are getting sick, and the numbers are growing. This isn’t going away. As long as there’s pain, there’s an opportunity for a comeback.
The interesting thing about health care is that there is conflict. There’s always a conflict between good intentions, the essence of medicine and healthcare, and the business plans that make it possible. So maybe we’re in for a bit of a moment of reflection. I started saying that late last year, and I still think so.
personal information: How have these changes led you to adjust your strategy when advising companies on how to approach investors for funding?
Cybulski: I don’t think much has changed. I mean, there’s just more awareness, right? When you talk to young men and women in high school about becoming a professional athlete, you’re open to a certain degree, but you’re also being realistic. If you get to be a starter in college, that’s a different story. But even then, it’s not likely. Even if you do make the team, will you play if you go pro? It’s the same thing here. It takes talent and a strong business plan to become this big, powerful unicorn.
Now we have some companies that have gotten incredible valuations and there’s been a kind of… regret is the best word. People are looking at each other and saying, “We never expected this.”
So nothing has changed except the advice I would give to every founder, executive, team at any early stage startup, mid-market, equity funded company: your business plan has to be really robust, based on the research that we’re doing about what consumers will tolerate, what the market will pay. Is it B2B, is it B2C, how strong are our projections for the market? Let’s look at SAM. [serviceable addressable market]T.A.M. [total addressable market]We’ll talk about the price and value of what we offer.
personal information: Your focus is on AI in healthcare, value-based care and its implementation, and product innovation. Is your advice different for companies looking to invest in these areas?
Cybulski: It’s a little bit different depending on whether it’s the payer side or the provider side or a digital health company. I modify my recommendations and what I present to them based on their model — how they think they make money, how they tell me they want to win with the problem they’re trying to solve.
It’s not necessarily as simple as money, money, money, but what problem are you solving in health care and can you make it work because you’re getting something in return, which is heartbreaking to me, but it’s also necessary to keep the doors open.
There are three things I always tell companies that are my arguments: the black box problem of AI, the “so what” problem of data analytics and AI, and the distinction between flowers and weeds.
The black box problem is, how do you explain what AI is doing under the hood? What’s really going on here is what I call the myth of explanatory depth. You can explain that AI comes up with solutions and creates predictive models, but when you ask how, you say, “It’s a very specific kind of tool and GPU and algorithm.” So how do they get made? Pretty soon, you can’t explain how. But at the same time, you have to take it to an executive group or a company and say, “Use this, I promise it will work.” It’s a black box problem, and it’s a hard problem.
The other question I talk about is the question of “so what?” What can we predict from this data? Can it provide predictions and insights retroactively that humans can’t? What will we do with it?
And finally, the question that I always advise, and frankly, I’ve seen this question asked a lot, is, are you marketing a flower product or a weed product? Sometimes the difference between flower and weed is the marketing budget. And there’s a lot of weed out there.
personal information: Many companies are advertising the use of AI in their products and touting their platforms as “AI-enabled.” Is highlighting the implementation of AI as a selling point no longer making a company more valuable to investors?
Cybulski: I think there’s a sense of fatigue, but there’s still a strong appetite to understand how to use AI. I mean, the market is just too big. So big. It would be foolish to ignore it.
So investors should be, and I think they are, very interested in how AI can be used to scale investments, increase consumer adoption, increase usage, etc.
That means humans can’t process the vast amounts of data available. There are many stories where AI can discover things humans can’t. That’s the message here. Not using AI means missing out on products that can be sold as quickly as possible, or missing out on the possibility of speeding up the production of your workforce. The basic integral from revenue to cost can be controlled with AI.
Also, investment market sentiment analysis is real and valuation is often about future guesses at the value of a product. It’s not just about getting a K-1 file and looking at EBITDA, cash flow and expenses. It’s also about liking the company. Investing is all about perceptions. Never underestimate the strength of perception coefficients on the value of a product or market.
