In a world full of AI hype, Dr. Severence MacLaughlin is laser-focused on results. As the founder and CEO of DeLorean AI, he’s spent the last six and a half years proving that artificial intelligence can do more than make bold claims, it can save lives. From bootstrapping the company with his own savings to achieving biologically validated predictions in chronic disease care, MacLaughlin has built DeLorean AI into a leader in predictive healthcare.

In this conversation with CityBiz, he shares the moments that proved the company’s potential, what real ROI looks like in healthcare AI, and how he sees DeLorean becoming the gold standard for trustworthy, mission-critical technology.

What was the tipping point when you realized DeLorean AI wasn’t just a concept, but a scalable company with market demand?

This happened in three phases. I originally designed the company to develop great AI technologies that I thought I would patent and sell. However, I quickly learned that you can’t just sell a technology, it needs a revenue stream.

The first technology we developed was a Customer Relationship Management AI. Essentially, the CRM AI sat on top of any CRM system, Salesforce, Microsoft Dynamics, etc., and generated better insights and next-best actions to drive increased sales.

The realization that DAI wasn’t just a concept came in late 2019, when we applied our tech to Salesforce CRM and outperformed Salesforce’s own AI 90% of the time. I remember turning to a colleague and saying, “We cracked the code.”

As we began to focus more on our Medical AI product, another breakthrough came when we were able to identify Chronic Kidney Disease patients five years earlier and accurately predict their disease progression from one stage to the next. This work was done at UnitedHealthcare. What we had envisioned two years earlier had now become a reality.

The third and most important moment for our Medical AI was when independent organizations, including UnitedHealthcare, Innovative Renal Care, and Constellation Kidney Group, were able to independently validate our technology’s predictions and forecasts. Using electronic medical records, claims data, hospitalization data, and more, they confirmed that our predictions were accurate with a biological confidence level of 95 to 99%.

These three experiences confirmed that we were the first in the world to do what we were doing: applicable and scalable artificial intelligence. We were the first AI in the world to be biologically validated.

You’ve emphasized real ROI over hype. How do you quantify value for enterprise partners, and what does a successful deployment look like from their end?

This is a big question and one that deserves a thoughtful answer. Everyone in the industry starts by asking, What’s your accuracy?

Accuracy can be measured in multiple ways. Our biological confidence ranges from 95% to 99%. Our accuracy, measured by the area under the curve (AUC), falls between 85% and 89%, depending on the disease state. But simply stating these success parameters isn’t enough.

When it comes to applying AI in healthcare, the rubber really meets the road: clients, patients, and physicians need to see that it works. This became especially clear to me during a 2023 meeting with an SVP at Anthem. He asked, “What happens when the doctor or nurse actually does what your AI recommends?” At the time, I didn’t yet have enough real-world evidence to give a scientific answer.

Fast forward a year, now we do.

Here’s the practical, outcome-based answer: If you use our technology, you will reduce hospitalizations by 50% within 90 days. If you follow the AI’s recommended action, 99.987% of patients will avoid hospitalization. If you don’t take the recommended next-best action, 100% of those patients end up hospitalized.

These are the kinds of tested, validated results that patients, customers, and physicians want to see.

Translating this into financial terms, each hospitalization costs at least $8,500. By our calculations, we’ve saved nearly $1 billion for insurers and the Centers for Medicare & Medicaid Services.

Successful deployment means physicians and nurses are using our technology to practice at the top of their license, with our AI integrated so seamlessly that it requires minimal effort on their part. We know we’ve achieved that when we start seeing a measurable drop in hospitalizations or other positive health outcomes.

How do you approach conversations with institutional healthcare players who are traditionally slow to adopt new tech?

This is part of my daily routine. Healthcare organizations are typically resistant to change or risk. If the organization is publicly traded or operates within a complex matrix structure, it becomes even harder, decision-makers are numerous, and no one wants to jeopardize their job. In many cases, it’s easier to do nothing than to be an early adopter. This is especially true for healthcare payers, where we tend to see less innovation aimed at helping patients.

We approach these more traditional organizations through their business and medical leaders and show them actual real world data of improved health outcomes and insights. It’s hard to argue with our numbers and scientific outcomes. Still, even that is often not enough. Competing priorities, such as internal attempts to develop similar technologies or ongoing debates around buy-versus-build strategies, frequently get in the way.

What we’ve found is that smaller, mid-market companies, particularly physician-owned or physician-focused organizations, are more open to being early adopters. They can see both the health and financial benefits and are willing to take the risk of a new technology to drive more hugs for their patients.

You chose to bootstrap and grow before seeking major funding. How has that shaped your company’s discipline and strategic decisions?

I honestly did not think anyone would invest in us until we had a fully built product. I sold everything I had, house, assets, and retirement, and put it into the company to fund the R&D. No company, not even a Fortune 500, would dedicate a team of data scientists to sit in a room for three to four years doing the necessary research and development, especially without a clear ROI. It would’ve just shown up as a red number on their ledger. I knew I had to do this myself if I wanted to see the technological change I wanted and envisioned. It turned out I was right, but it was a big risk. It is a disruption and it takes an independent mind and/or organization to do disruption without legacy influence.

This is why I chose to bootstrap. I knew exactly what I wanted to build and didn’t want to be influenced by outside variables. Once my team and I had built it, I felt more comfortable bringing in outside capital from like-minded partners.

This has shaped us in other ways. We’re extremely frugal and operate as a small, tight-knit team of dedicated professionals united by one vision: there is no Plan B, and failure is not an option. We’re on a mission.

What lessons from the past five years would you share with founders trying to commercialize deep tech in regulated industries like healthcare?

There are so many:

• Just do it. If you have a vision and you believe in yourself, just go for it. There will be so many people that will tell you “NO” or that you can’t do it. Don’t listen to them, just keep going.
• Start ups are HARD, it’s not easy. Be prepared for it.
• It is the best decision you will ever make.
• I question if I would do it over again, it’s tough. I used to have all brown hair in 2019, now it’s mostly grey. But I wake up every morning getting to do what I want to do, which is making more hugs for patients.
• Be careful with your first hires, they are the foundation of your company.
• When looking at capital, go at your own speed, do not let anyone pressure you. Be comfortable with the partner you choose.

How do you balance innovation with compliance when your product touches so many layers of the healthcare system?

It’s not a balance. Regulatory compliance is a must and is part of every decision we make and every key stroke we do. We do it right the first time and if that takes more time and money, then so be it. We are dealing with patient’s lives and we are proud of the accomplishments we have made, but we do it the right way, the compliant way. Our AI products must be trusted.

Compliance does not mean you cannot be innovative. We are scientists, we have been trained in the scientific process and this aids in how we develop our technologies.

What role do strategic partnerships play in your current growth strategy and what do you look for in an ideal partner?

When I first started the company, we became “partners” with Microsoft, AWS, Google, etc. My thought process was that without a large sales force, these relationships would help with initial sales. I was wrong. We are fully interoperable because of these agreements, which is a blessing. It’s good that we are interoperable and we did the work early on, but the partnerships have done little to help us. Partners are usually larger companies and they are not really interested in supporting your or your sales unless you’ve brought ten deals to the table.

You relocated your company to Florida. Beyond tax and regulatory perks, what strategic advantages does Florida offer for healthtech growth?

We love the great state of Florida and deeply appreciate Governor DeSantis and his administration. Florida supports business success, and that mindset is reflected in nearly every decision the state government makes. Additionally, Florida is quickly emerging as a tech hub. When you combine that with its growing healthcare systems, it’s clear that Florida is becoming a natural nexus for innovation.

Florida’s commitment to collegiate and post graduate education systems is impressive, we have no issues finding good talent in Florida.

Finally, people smile in Florida, they’re optimistic and happy. That’s not always the case in the Northeast.

How do you evaluate talent in AI when technical ability alone isn’t enough, especially for mission-critical work like healthcare?

Technical ability is a major criterion. You can’t just go to “school” and get an “AI” degree, that’s not what we’re looking for. We look for specific skill sets that include programming, mathematics, and a demonstrated track record of applying those skills in real-world scenarios. We also value domain knowledge.

What’s your long-term vision for DeLorean AI and where do you think predictive medicine will be in five to ten years?

I want DeLorean AI to be the Intel of the AI revolution, with a little sticker on anything AI that says “Powered by DeLorean AI.” I want DAI to become the gold standard for quality in AI, starting with healthcare and extending to all our other products. It’s been a fun 6.5 years, and I want to ride this wave as long as I can.

In five years, I believe success will be measured by the number of years we’ve helped extend our patients’ lives and the number of hugs they’ve shared because of our technologies. By then, I think we’ll be a household name.

The post Q&A with Dr. Severence MacLaughlin: DeLorean AI’s CEO on Predictive Medicine, Bootstrapping, and Building the Intel of Healthcare AI appeared first on citybiz.

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