Lisa Loud of Deph Labs: How Prioritizing Usability Over Complexity Wins Over Overwhelmed Healthcare Buyers

As a part of this series, we had the pleasure to interview Lisa Loud.

Lisa Loud is CEO of DEPH Labs, a precision health company using DNA, epigenetics, and AI to turn complex biological data into personalized, actionable health insights. With a career spanning leadership roles at Apple, PayPal, BitMEX, ShapeShift, and Secret Network, she brings extensive experience in emerging technologies, data privacy, and digital innovation. She is passionate about making personalized healthcare more accessible while ensuring individuals retain direct control over their biological data through privacy-first technology and granular, programmable consent.

Thank you so much for joining us in this interview series. Before we dive in, our readers would love to learn a bit more about you. Can you tell us a bit about your childhood backstory and how you grew up?

Growing up, I was always fascinated by the human body, particularly how people sometimes recovered from illnesses holistically. That curiosity sparked an early interest in medical science and alternative approaches to health. I actually came very close to studying biomedical engineering before choosing mathematics and computer science. At the time, I thought I was moving away from healthcare, but looking back, I was developing an ideal skillset to later be deployed in the health sector.

Can you share the most interesting story that happened to you since you began your career?

One experience that really changed my thinking happened during my time in blockchain. A co-founder and I needed to recover cryptocurrency that had been sent to the wrong address, which required me to share a wallet’s private key. The recovery worked, but sometime later, approximately $20,000 disappeared from that wallet. I never established exactly what happened, and that’s what made the experience so interesting. I trusted the person involved, but trust alone couldn’t guarantee that sensitive information remained protected. That lesson has stayed with me, particularly in healthcare. If someone gains access to your financial credentials, you can replace them. But your DNA is different. You can’t simply generate a new genome. It’s why I believe people should have direct ownership of their biological information, supported by granular, programmable consent that allows them to change permissions whenever they choose.

You are a successful business leader. Which three character traits do you think were most instrumental to your success? Can you please share a story or example for each?

I’d probably choose adaptability, transparency, and conviction. Adaptability has been essential throughout my career. Moving from established companies like Apple and PayPal into blockchain meant entering an industry where there wasn’t always a playbook. You had to get comfortable with uncertainty, make decisions quickly, and learn as you went. Healthcare technology is going through something similar today. Transparency is another big one. Working in financial technology taught me that people can forgive mistakes much more easily than they can forgive being kept in the dark. When you’re handling someone’s money or biological information, being open about risks and limitations is absolutely essential.

Finally, conviction. At Secret Network, I pushed back against the idea that strong privacy protections necessarily make technology difficult to use. I’ve always believed privacy and a great user experience can coexist. That same principle guides my work at DEPH Labs, where we’re trying to make sophisticated health technology accessible without compromising people’s control over their data.

Can you tell us the backstory about what inspired you to enter the health-tech industry and develop this particular solution?

I’ve been interested in health science since I was a teenager, but it took a career in technology to recognize where I could make the biggest contribution. One thing that became increasingly clear to me was how much valuable health information remains inaccessible to ordinary people. We have incredible advances in genetics, epigenetics, and longevity research, but many of these technologies have traditionally been available primarily to wealthy individuals. At DEPH Labs, we’re trying to change that. DNA provides an incredible biological foundation, but it doesn’t tell you everything about where you are in your health journey. Epigenetics helps us understand how biological patterns change over time and potentially respond to lifestyle choices. Combining that information with AI creates opportunities to give people more personalized, actionable insights. But just as importantly, I wanted to apply what I’d learned about privacy and data ownership. People should understand their biology without surrendering control of their most personal information.

Can you share a story about a time a healthcare buyer told you, or showed you, that what they actually wanted was very different from what was being pitched? What did you learn from that moment?

One thing we’ve learned through user experience research around our health applications is that people aren’t necessarily interested in receiving more biological data. They’re interested in understanding what that data actually means for their lives. It’s easy for a technology company to get excited about sophisticated testing, detailed measurements, and impressive scientific capabilities. But someone using the product might simply want to know why they’re feeling exhausted, whether their health is improving, or what changes they should make. A complicated report might demonstrate scientific sophistication, but if someone can’t understand it or act on it, its practical value is limited. The lesson is to start with the problem people are trying to solve, rather than the technology you’re excited to show them. That’s equally relevant when selling to healthcare organizations, where clinicians already have more information competing for their attention than they can reasonably process.

Innovation, price, and clinical data get most of the attention. What are buyers quietly weighing that medtech companies rarely talk about? Can you give an example of one of these deciding a purchase?

I think companies underestimate how much buyers care about the work involved in actually using their technology. A product might look fantastic in a demonstration, but if it requires additional training, complicated integrations, or creates more administrative work, that becomes a serious obstacle. Healthcare workers are already stretched, and many aren’t technology specialists. We see sophisticated cyberattacks successfully target employees at major technology companies, so it’s unrealistic to expect busy healthcare professionals to manage complicated security procedures perfectly. That’s why usability and privacy need to be built into the experience rather than treated as separate features. At Secret Network, I became convinced that privacy shouldn’t come at the expense of convenience. The same applies to healthcare. If two products offer similar clinical benefits, but one is considerably easier to use and protects sensitive information without additional complexity, I think that can make all the difference.

Based on your experience, what are the “5 Things Healthcare Buyers Really Want From Medical Technology Companies”? Please share a story or an example for each.

1. Solutions to real problems, not impressive technology looking for a purpose.

Healthcare buyers don’t necessarily care how sophisticated your underlying technology is. They care whether it addresses something that genuinely affects patients or clinicians. At BitMEX, we developed financial products by focusing on problems traders were actually experiencing. That approach has stayed with me. At DEPH Labs, people aren’t necessarily looking for an epigenetic test. They’re looking for answers about their health, aging, and the changes they can make to improve their lives. The technology needs to serve those goals.

2. Technology that makes their lives easier.

Healthcare organizations are dealing with enormous operational pressures, and introducing another complicated system isn’t particularly appealing. We’ve learned through our health app research that people engage more consistently when information is presented clearly and accompanied by practical recommendations. I think the same principle applies to clinicians. AI should help them interpret complex biological information more efficiently, not overwhelm them with additional reports or administrative responsibilities.

3. Strong privacy protections without unnecessary complications.

Privacy has been central to my career, particularly during my time at Secret Network. One thing I’ve always believed is that privacy and usability shouldn’t be mutually exclusive. Healthcare buyers need confidence that patient information is protected, but patients should also have meaningful control over how their information is used. That’s where granular control and programmable consent become important. Rather than agreeing to a privacy policy once and granting indefinite access, individuals should be able to adjust or revoke permissions whenever they want.

4. Evidence that the technology actually works.

Healthcare has heard plenty of ambitious promises, particularly around AI. Buyers increasingly want practical evidence rather than another presentation about how technology will transform medicine. One interesting aspect of epigenetic testing is the ability to assess changes over time. Someone can establish a baseline, make lifestyle adjustments, and retest several months later to observe changes in measured biological markers. Those results still require careful interpretation, but they create opportunities to evaluate progress rather than relying entirely on predictions.

5. Human judgment and accountability.

AI can process enormous amounts of information, but it shouldn’t be treated as an unquestionable authority. AI systems learn from historical information and can gravitate toward familiar, widely repeated conclusions. Those conclusions aren’t necessarily the most appropriate for an individual patient. That’s why keeping a human in the loop is so important. Healthcare buyers need technology that supports clinicians’ expertise rather than trying to replace it. Ultimately, someone needs to understand the patient’s circumstances, question the recommendations, and take responsibility for important decisions.

In your experience, who are the key decision-makers involved when a health system considers purchasing new technology? How can health-tech companies successfully navigate a buying process that involves so many different stakeholders?

One of the challenges with healthcare is that you’re rarely selling to just one person. Clinicians, IT departments, security teams, procurement, finance, and senior executives can all influence the decision, and they’re often evaluating completely different things. A clinician might be excited about improving patient outcomes, while the IT department is concerned about integration and cybersecurity. Meanwhile, finance wants to know whether the investment is justified. I think successful companies recognize these differences early and adapt their conversations accordingly. You can’t expect the same technical presentation to resonate with everyone. It’s also important to find someone within the organization who genuinely understands the problem you’re solving. That person can help explain the value internally, identify potential obstacles, and make sure the technology fits how the organization actually operates.

What is the biggest mistake you see health-tech companies making when pitching their products to hospitals and health systems? What should they be doing differently?

The biggest mistake is assuming that having advanced technology automatically makes you valuable to a hospital. We’re seeing this particularly with AI. Companies arrive promising automation, efficiency, and better outcomes, but sometimes struggle to explain exactly how their product improves a clinician’s working day. There’s also a tendency to overestimate what AI can reliably accomplish. These systems are trained on historical information, and they’re often very good at reproducing established patterns. That doesn’t mean they’re always capable of identifying the most accurate or creative solution. I’d encourage companies to be much more specific about what their technology does, where its limitations are, and how humans remain involved in important decisions. Healthcare buyers don’t need exaggerated promises. They need technology they can trust, understand, and realistically incorporate into their existing operations.

How important is demonstrating clinical outcomes, financial return on investment, and operational efficiency when selling to health systems? What is the most effective way for companies to prove their value?

They’re all essential, but I think the most convincing evidence comes from showing how technology performs in real situations rather than relying on theoretical benefits. Healthcare organizations need to understand whether a product improves patient outcomes, reduces unnecessary costs, or saves clinicians time. Ideally, companies should establish clear measurements before introducing their technology and then evaluate what actually changes. At DEPH Labs, we’re interested in the value of repeated biological measurements. Blood tests can provide useful information about someone’s current condition, while epigenetic testing offers another perspective on how their biology may be changing over time. That doesn’t mean every change in an epigenetic marker represents a proven clinical improvement. But combining measurements with appropriate medical interpretation can help build a clearer picture of someone’s health journey. The important thing is being transparent about what the evidence demonstrates and what still needs further research.

Clinicians, supply chain, value analysis committees, IT, and the C-suite all have a say in a purchase. How should a medtech company tailor its value story for each of them, and what does a truly great partner do that a typical vendor does not?

I’d start by recognizing that each group has a legitimate but different definition of value. Clinicians want to know whether the technology improves care and makes their work easier. Supply chain teams are concerned with reliability, costs, and standardization. Value analysis committees want credible evidence, while IT departments need confidence around integration, cybersecurity, and data governance. Executives are thinking about the organization’s broader priorities. The mistake is trying to convince everyone with the same message. A great partner goes beyond selling the product and takes responsibility for making it work within the organization. That means listening to feedback, being transparent when problems arise, and adapting to the needs of the people actually using it. I’ve always believed that trust comes from giving people meaningful control. In healthcare, that includes ensuring patients retain granular control over their biological data, rather than having decisions about its use made entirely on their behalf.

Looking ahead five years, how do you think the relationship between health-tech companies and health systems will change? What should innovators be doing today to prepare for that future?

I think healthcare will become much more personalized, with technology helping us move beyond treating everyone according to the same general assumptions. DNA gives us a foundation, but epigenetics offers additional information about how our biology changes throughout our lives. Combined with AI, that could help clinicians identify patterns and develop more individualized approaches to prevention and care. But I don’t believe the future is about replacing healthcare professionals with algorithms. AI is fundamentally learning from historical information, and while it can identify patterns incredibly quickly, it can also reproduce established assumptions and overlook important context. Keeping a human in the loop will become increasingly important as these systems influence more consequential decisions. I also expect patients to demand greater ownership of their biological information. Companies should be preparing for a world where granular, programmable consent becomes a normal expectation, allowing people to control who accesses their information and change those permissions at any time.

How can our readers further follow your work online?

The best place to learn more about what we’re doing is dephlabs.com where we’re working to make precision health more accessible through DNA, epigenetics, and AI. Readers can also follow my writing, speaking, and other projects at lisaloud.com. I’m particularly interested in conversations about longevity, biological data ownership, privacy, and the responsible use of AI in healthcare. There’s an enormous opportunity to give people a better understanding of their own health, but we need to make sure that progress doesn’t come at the expense of personal control or human judgment.

Thank you so much for the time, Lisa. This was very inspiring!

About The Interviewer: Saul Marquez started out selling spine implants for Stryker and left an executive role at Medtronic to build Outcomes Rocket, a healthcare strategy and full service marketing agency. Twenty years in healthcare and 2,300+ interviews later, his conviction is simple: success in healthcare isn’t luck, it’s algorithmic, and it’s still a relationship game. Learn more at outcomesrocket.com