Becker Private Equity & Business Podcast
AI & Healthcare Innovation: Sarah Fuentes Molina on Digital Health Strategy 2-14-25
Fri, 14 Feb 2025
In this episode, Scott Becker speaks with Sarah Fuentes Molina, a healthcare and digital health leader specializing in AI strategy. They explore the intersection of AI and healthcare, the rise of open-source AI solutions, and how health systems can take control of their digital transformation.
Chapter 1: What is the background of Sarah Fuentes Molina?
My previous leadership roles span growth in marketing, essentially guiding healthcare enterprises from Fortune 500 companies to startups towards market expansion and revenue growth. I served as an entrepreneur in residence for venture-backed startups in the health tech space out of Stanford Healthcare's Innovation Lab.
And I've led the go-to-market launch for business lines at life sciences and medical services companies like Johnson & Johnson, Fitbit, which is now part of Google, and Kaya Health. Currently, I advise a select group of health companies and startups at the intersection of machine learning, artificial intelligence, and healthcare services, including surgical and digital health.
And the products that we're developing leverage health system and patient data to diagnose disease, achieve more precise surgical outcomes, and essentially realize the future of the four tenets of health tech, personalization, participation, precision, and prevention.
Chapter 2: How is AI transforming healthcare systems?
Thank you very, very much. And talk for a second about AI in health care, large learning models and so forth. What is some of your take on how these are going to impact health systems? I saw, for example, AdventHealth just hired a chief artificial intelligence officer. Is that something we're going to see more of? And where do you see some of the implications?
Oh, absolutely. That's a wonderful question. Thank you, Scott. So I would say we are going to see an exponential increase in AI leaning into healthcare. Currently, I'm closely watching the application of machine learning and AI, especially generative AI for healthcare data. Digital health and AI solutions are expanding rapidly.
exponentially, and our advancements over the last two years are set to significantly enhance our ability to expedite the development of new drugs and treatments, improve patient outcomes and streamline health care delivery. It's really quite astonishing and an exciting time to be in this space.
Chapter 3: What challenges do AI developers face in healthcare?
But to your question, while AI solution developers fully understand the technical fundamentals, many lack familiarity with established health principles, regulations, and standard clinical practices. And this gap can lead to the creation of solutions that have not been thoroughly evaluated for their impact, including safety for patients and health systems.
And that lack of understanding on both sides, I see leading to hesitation from healthcare leaders to invest. So hiring executives who come with that skill set and can build it in-house is going to be invaluable.
Chapter 4: Why is the intersection of AI and healthcare important?
And it's really that intersection. I love that point. It's that intersection of the software or technology background with the healthcare background so people could really make sense of how to put these things to work. I was talking to a CEO yesterday and he was explaining that he's got, you know, a relatively young AI person on the phone.
who just was able to command the entire call because that person knew everything about everything. And my colleague, who's closer to my age, felt sort of like, oh, my goodness, thank God that this person's with us because I just can't navigate this stuff. It's people who need help sort of navigating at that intersection of AI and health care, AI and whatever they're doing, don't they?
100%. And I think that juxtaposition, that nexus is the most interesting and exciting space in healthcare for me right now. And there are all sorts of applications that are going to be so important to establish that shared understanding. You may have seen in the news last week that DeepSeek AI just came out and that has essentially changed the game for open source AI.
Chapter 5: What innovations are emerging in open-source AI?
And what that means is AI that you can own on your own systems. And there are many obstacles for AI in healthcare and privacy and data control is one of the most important ones. And this now makes it possible for innovative players in the healthcare space to actually engage with AI on their own terms, as opposed to having to partner with companies like Anthropic.
And that increased amount of control that we have, that reduced long-term operational cost is going to mean that the field more people believe the fight is winnable and are going to be getting involved. And therefore, we are going to need more people at that nexus to help understanding from both sides and to foster that innovation.
Thank you very, very much. And Sarah, take a moment on what are you most focused on and excited about this year? Where are you most focused and excited?
Chapter 6: What are the future trends in digital health strategy?
I'm going to go right back to deep seek and open source AI. It's so exciting because deep seek and Open source generative AI is giving us so much more control and reducing long-term operational costs. It's dispelling the notion that only open AI and anthropic and the ethics of the world can do this.
And so that means it's worth for us as healthcare leaders to look for ways to increase control and decrease costs and not just let the big players accrue all of the value. On some level, if you're a health system, 10 years from now, you will be paying for AI to help in both clinical and non-clinical ways.
The exciting question that I see is, are you going to pay to develop these capabilities internally and in a way that's tailor-made to your population and your organization's needs? which is now a possible?
Or are you paying for a startup somewhere to achieve a venture scale outcome where they develop a generic solution that they can sell to many organizations and overcome a long competitive sales cycle, essentially higher cost for less value? That's the space that is most exciting for me now because we're going to be able to build this on our own terms.
But that's really an important point because there's many, many companies, small, midsize, even large, that don't want to sort of, you have to work entirely with huge, huge tech companies where it's often their way or the highway.
And obviously there's certain enterprise programs where you have to, but there's plenty of room where there's opportunities outside of that, but you have to make sure whoever you're working with outside of that is substantial enough could constantly improve enough and constantly get better. So you haven't made a bad choice in picking somebody that might be easier to work with, but not as large.
Absolutely. A common refrain that I hear from my peers and from other health executives is we are in the tech business and we can't get involved in that game. We're going to have to outsource that. But the reality is that you're in health care and this is what the industry will look like in 2035. Just as health care today includes, you know, big, impressive buildings.
We're not in the real estate business, but we still purchase land and design those buildings instead of attempting to perhaps retrofit a rented space. And that's a great analogy on how important it is to take the initiative and not wait for those giant enterprise companies like OpenAI to commercialize innovations.
And the pep talk that I give here is that we assume that a technology company is going to be better at health care than will be at tech. But medicine is not easy enough that they can develop that capability quicker and better than we can develop a tech capability ourselves. So get started now building this capability in-house.
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