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Strategic Communications Advisory For Visionary Founders
Actionable
Takeaways
Find revenue where barriers are lowest:
When Savana discovered hospitals weren't ready for their solution, they pivoted to pharmaceutical companies who had immediate needs and budgets. This kept them alive until the market matured. B2B founders should identify alternative buyers who can provide early revenue while waiting for their primary market to develop.
Regulatory challenges can create opportunities:
Savana turned Europe's strict data privacy regulations into an advantage by developing compliant solutions that could scale as regulations evolved. B2B founders should view regulatory constraints as potential differentiators rather than just obstacles.
Crisis can accelerate market readiness:
COVID-19 transformed European attitudes toward healthcare data sharing, creating new opportunities. B2B founders should stay prepared for external events that can suddenly accelerate market adoption of their solution.
Product-market fit requires cultural alignment:
Savana's initial doctor-focused tool failed because it conflicted with medical culture around evidence-based decision-making. B2B founders must deeply understand not just customer needs, but also their professional values and decision-making frameworks.
Healthcare requires different ROI calculations:
Traditional market efficiency metrics often don't apply in healthcare due to political and social considerations. B2B founders entering healthcare must account for non-financial factors in their value proposition and sales strategy.
Conversation
Highlights
When Doctors Told Him Machine Learning Would Never Work in Medicine
In a recent episode of Category Visionaries, Ignacio Medrano, Founder and Chief Medical Officer of Savana, shared how he went from practicing neurologist to healthcare tech founder – and why his first product for doctors was a complete disaster that nearly killed the company.
Ten years ago, Ignacio stood in his hospital cafeteria explaining machine learning to his fellow neurologists. Their response wasn’t encouraging. “I remember telling this story and they were looking at me like, yeah, that’s cool stuff, but it’s never going to work,” Ignacio recalls. “I mean, we’re in science, we’re in proper statistics, you need proper methods. And maybe a hundred years from now that will happen.”
But Ignacio saw something his colleagues didn’t. While treating patients with Alzheimer’s and stroke, he realized that healthcare was sitting on a goldmine of untapped data. “Everybody is trying to predict things with data,” he explains. “You know, like the statistics is the past and probability is the future. And the more data you have, the more you can predict.” The question was whether all that information written in medical records every day could be fed into machine learning models to predict patient outcomes.
The Pivot That Almost Didn’t Happen
Savana’s original vision was elegant: create a tool that could extract data from medical records using natural language processing and transform free text into structured databases. The technology worked. The problem was finding someone to use it.
“When we launched this tool that is able to go to medical records, apply a lot of validated and scientific natural language processing,” Ignacio explains, “the idea was great, but then who would use it? And we found that it was no one’s job to use this kind of tool.”
Facing extinction, Savana pivoted to pharmaceutical companies. “We stayed there as a company for five years. We survived. We’re incredibly close to die, as probably every entrepreneur would tell you,” Ignacio admits. They took the same NLP technology, partnered with hospitals to access de-identified patient data, and sold insights to pharma companies eager to understand real-world disease progression.
It wasn’t until COVID shifted healthcare’s mindset around data that hospitals finally caught up. “Only at that moment, the hospitals were ready to catch up with budgets and with people waiting to use our tools,” Ignacio says. “And that’s how we came back somehow to the original idea.”
The Product They Had to Kill
But getting back to hospitals meant confronting a harsh reality about their original product. Savana Consulta was built on what seemed like a solid premise: if doctors could see what their colleagues were doing in real clinical practice, they could make better decisions based on collective wisdom.
“We thought that was a good idea. We were thinking about the wisdom of the crowds, the idea that if many people are thinking about that, it means something,” Ignacio explains. They were trying to digitize the clinical sessions where doctors discuss cases and learn from each other.
The rejection was swift and brutal. “It was incredibly rejected by my colleagues,” Ignacio admits. “And the reason was very simple. The fact that the majority is doing something doesn’t mean it’s the right thing to do. And that’s quite opposite to what Evidence means. Evidence means this is the right thing, no matter if few people or a lot of people are doing it.”
Savana had accidentally positioned itself against the fundamental principle of evidence-based medicine. “In a way, were trying to go against the status quo, which is using the right evidence instead of using the majority, the wisdom of the majority,” Ignacio says. They killed the tool before it was too late and rebuilt using the same NLP capabilities for a completely different purpose: creating reports on disease progression and patient behavior that could support—not replace—evidence-based decision making.
Why Healthcare Founders Need Different Assumptions
After a decade in healthcare tech, Ignacio has advice that cuts against conventional startup wisdom. The biggest differentiator in healthcare? “A bigger amount of decisions that your customers will make will not be based on the rational laws of the market,” he says.
He’s seen too many founders from other sectors assume they’ll crack healthcare in a year, only to discover that financial logic doesn’t always apply. “A typical case would be you optimize something with which you need, I don’t know, 10 nurses less. And it doesn’t really matter because politically it’s against everything not to count on 10 nurses,” Ignacio explains. The innovation might work perfectly, but you end up with double expenses—the software and the staff—because laying off healthcare workers creates political and social backlash that trumps pure efficiency.
“So you come with an innovation that apparently is useful, but then at the end of the day, you have double expense,” he notes. “You now have the software and you have the people, and that keeps replicating and replicating in the sector.”
His advice for healthcare entrepreneurs? “Considering that not because something makes sense is going to be accepted by healthcare providers, I think is good advice.”
The Fundraising Reality
Savana has raised $44 million, but Ignacio is candid about the tension between healthcare timelines and VC expectations. “The pace is lower so it’s very regulated. Cultural change needs time. And even if your tool is great, it’s going to need time,” he says.
The comparison to drug development is instructive. “We have incredible new drugs that would save lives. And even when that’s the case and it’s lives of people, what is at stake, it takes 10 years to validate them through the proper workflow circuitry,” Ignacio points out.
Traditional VCs typically need returns in three to four years. “For us and for any company in healthcare, it’s very difficult to give returns. And in that amount of time you normally, you need longer,” he explains. “So that’s something that you may want to think about before fundraising.”
The AI Phone Revolution
Ignacio’s vision for the next three to five years is bold: AI in your phone will become the primary gatekeeper for healthcare, not hospitals. He sees two forces converging to make this possible.
First, the machine learning algorithms being developed today will finally have robust clinical validation. “The machine learning AI algorithms that we’ve been creating for five, 10 years, five years from now, will be finally validated with clinical trials and everything,” Ignacio predicts. “Something that we don’t have today, but we’ll have it in five years.”
Second, generative AI will make these complex predictive tools accessible to regular people. “Generative AI doesn’t really add new knowledge, but it improves access to already existing knowledge,” he explains. “So generative AI will help these customers, these patients, navigate those complex algorithms in an easy way.”
The result? People will upload their ECGs, medical records, and health data to apps that run predictive algorithms with precision high enough that “people are going to want to go there for advice and for medical care,” Ignacio says. Physical hospitals will remain, but they won’t be where healthcare begins. “The gatekeeper is going to be a sequence of personalized algorithms on digital that people are going to use on a normal basis.”
It’s a future where the skeptical colleagues in that hospital cafeteria might finally understand what Ignacio saw a decade ago: that machine learning wasn’t a hundred years away from transforming medicine. It was just getting started.
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