The State of Digital Health Investments, Part 2: LBMC

Matt Cybulsky, Practice Leader for AI in Healthcare, Value-Based Care, and Product Innovation at LBMC, has spent years studying and analyzing the digital health market and advising companies on how to scale and drive profitability as financing landscapes evolve.

Cybulsky sat down with MobiHealthNews to discuss strategies for investing in digital health and the role of AI in improving both business profitability and patient outcomes.

MobiHealthNews: How have you seen the digital health investment landscape change over the past few years?

Cybulsky: Two and a half years ago, I looked at the CB Insights statistics. They showed that there was about $57 billion of investment capital going into digital health. Since then, we’ve seen a significant slowdown in the outflow of deals and capital.

That’s been commensurate with macroeconomic pressures, obviously COVID, dollar injection, inflationary pressures, and now the labor market is starting to respond to that. And the housing market too. It’s not as relevant to digital health, but they are indexes of what we can expect from investment deals.

That’s starting to change though. I was at the JP Morgan conference in January and at some of the events I went to, the conversation was very much about, “What are you hearing? What are you seeing? How many deals? Who’s doing deals? What’s happening macroeconomically to open those things back up?”

So we’ve been through the incredible treasure trove of funny and smart money, and now it’s a little smarter money.

Yet the pressure to get care to individuals’ doorsteps doesn’t change. 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 problem, which is the delivery of what we need, with a lot of people who are sick and getting sicker. That’s not going to go away, and as long as there’s pain, there’s a chance of it coming back.

The interesting thing about health care is that there is a conflict always of good will, the nature of what medicine and health care is, versus a business plan to make that happen. So maybe we’re in a bit of a reckoning. I started saying that late last year. I still think so.

MHN: How has your strategy adapted when advising companies on how to approach investors for financing, given these changes?

Cybulsky: I don’t think it’s changed that much. I mean, there’s more awareness, right? We talk to a young man or woman about going pro in a sport, if they’re in high school, you have an open mind, but also a reality check. If they’re a college freshman, it’s a different conversation. But the odds are still not great. And even if you make the team, are you going to play when you’re pro? Same thing here. If you’re going to be this big, bad unicorn, you’ve got to have the talent and you’ve got to have a strong business plan.

We’re now seeing companies that had these incredible valuations, and there’s some … reckoning, I guess, is the right word. There are people looking at each other and saying, “We didn’t expect this.”

So nothing has really changed other than the advice that I give to any founder or board or team at an early stage startup or a mid-market, an equity company, which is that the business plan has to be really solid with the research that we do 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 in healthcare, value-driven care and implementation, and product innovation. Does your advice for companies looking to invest in those areas differ from each other?

Cybulsky: It does to some extent, depending on whether it’s the payer side or the provider side or a digital health company. I’ll tailor my recommendation and what I present to them 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 reductionist, like money, money, money, but it’s absolutely about what problem are you solving in health care, and can we make that work because there’s a return? That’s heartbreaking to me, but it’s also necessary if you want to keep the doors open.

There are three things I always tell companies that mirror my theses: the black box problem in AI, the “So what?” problem in data analytics and AI, and telling 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 is coming up with solutions and creating predictive models, but when you ask me how, I say, “Well, it’s these very specific tools and GPUs and algorithms.” Well, how do they create those? And pretty soon I won’t be able to tell you how that’s done. But at the same time, I have to take it to a group of executives or a company and say, “Use this. I promise you it’ll work.” That’s a black box problem, and it’s a hard problem.

The other one I talk about is the “So what?” So what could I predict from this data? So what could I give you predictions and insights after the fact that humans can’t? What do you do with it?

And then, lastly, which I advise a lot on, and honestly I’ve seen this a lot, is whether you’re 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 weed.

MHN: So many companies are touting the use of AI in their offerings and advertising their platforms as ‘AI-enabled’. Has the point been reached where highlighting AI implementation as a selling point no longer increases a company’s value to investors?

Cybulsky: I think there is fatigue, but there is still a strong desire to see how you are going to use AI. I mean, that market is way too huge. It’s a huge market; ignoring it is reckless.

So investors should be very curious about how you can use AI to increase investment size or increase consumer adoption, frequency of use, and so on. And I think they are.

I mean, humans can’t process the enormity of the data that’s out there. There are so many stories that AI can tell that we can’t. That’s the message here. If you’re not using AI, you’re missing out on products that you can sell as fast as you can that you didn’t know you could, or you’re accelerating the production of a workforce. That basic integral of revenue to expenditure, AI can bend it.

Also, sentiment analysis of markets for investments is real, and often valuation is about future speculation about the value of a product. It’s not always about getting the K-1 file and looking at EBITDA, cash flows, and expenses. It’s also about liking the company. Investing is all about perception. Never underestimate the power of the perception coefficient for the value of a product or a market.