I Left an Industry I Loved to Disrupt It as a Founder — Here’s Why I Have No Regrets

My father was the first OB-GYN in a small town in South Georgia. He knew his patients personally, delivered their children and became a trusted part of their lives. He carried the full weight of being the only specialist in a community that depended on him — and ultimately paid the price, dying of a heart attack at 38.

That image shaped everything about how I think about building. I followed him into medicine, became an OB-GYN myself and spent years developing advanced skills in robotic surgery, pelvic reconstruction and operative obstetrics. I was trained to solve difficult clinical problems.

But over time, I realized the biggest problems in healthcare weren’t clinical. They were systemic. And solving systemic problems required stepping outside the system entirely — which is how I ended up leaving a profession I loved to become a founder.

It’s a path that has taught me principles about timing, distribution, trust and who you’re actually building for. Those principles apply well beyond healthcare to any founder trying to reshape a complex, entrenched industry.

Build in the gap between possibility and belief

In 2001, while the National Institutes of Health was sequencing the human genome, I founded NeoGenomics Laboratories, which later became NeoGenomics, Inc. Genomics wasn’t yet on most people’s radar as a business opportunity. The science was advancing, but the market hadn’t caught up.

That gap — between what’s scientifically possible and what the market believes — is where the most durable companies are built.

But being early and right is not enough. You have to be willing to endure the distance between vision and market readiness. Most founders underestimate how long real change takes in complex industries. Systems resist disruption not because the innovation is wrong, but because adoption requires behavior change at scale.

The founders who succeed are the ones who can outlast that gap.

Distribution first, product second

If I were starting over today, I would focus earlier on distribution and adoption — not just building the product.

In healthcare, even the best solutions fail if they don’t integrate into existing workflows or reach patients where they are. Founders often treat the technology as the finish line, when it’s really just the starting point.

What wins markets is access and engagement — a product that reaches and retains users at scale. In regulated, relationship-driven industries, trust and distribution matter more than technical elegance.

Build for the person, not the gatekeeper

The most common mistake I see is building for intermediaries instead of the end user.

In healthcare, that means designing for providers, administrators or payers because they control purchasing decisions. But that often adds complexity to an already complex system. The bigger opportunity — and the harder problem — is simplifying the experience for the person the system is meant to serve.

The core problem in healthcare isn’t a lack of medical knowledge. It’s a lack of coordination. Patients enter the system confused, overwhelmed and without a clear next step. They delay care, choose the wrong providers or enter too late — not because the care doesn’t exist, but because no one is guiding them to it.

Credibility compounds

Founders tend to focus on features and growth metrics. But in industries where people are making high-stakes decisions about their health, finances or legal standing, trust is the real product.

Credibility compounds. The founders who invest in trust early build moats that features can’t replicate.

My father understood this instinctively. Patients trusted him not just because of his clinical skill, but because of continuity — he was there year after year, for the same families, through the most important moments of their lives.

That’s the model I believe technology can now bring back at scale.

The next inflection point: AI and data

Just as genomics transformed how we understand disease, artificial intelligence represents the next major shift in healthcare.

AI has the potential to finally solve what healthcare has never done well: coordinate care at scale. It can guide patients before they enter the system, connect them to the right providers and reduce the administrative burden that drives cost and physician burnout.

That’s the problem I’m focused on solving at HealthLynked — creating a true front door to healthcare that helps patients understand what to do next before they ever step into the system.

But the opportunity goes far beyond coordination. Every patient generates data: medical history, outcomes and treatment responses. Today, that data is fragmented and underutilized. If individuals were empowered to control and share it securely and with consent, the pace of medical discovery could accelerate dramatically.

We could identify patterns earlier. Refine treatments faster. Shift from reactive care to predictive care. Discovery would no longer be confined to institutions — it would become a collective, population-scale effort.

The pattern is the same as it was in genomics: The science is ahead of the market, the infrastructure is being built and the founders who act before it becomes obvious will define the next decade.

Focus on the core problem at scale

When evaluating any major decision, I ask one question: Does this bring us closer to solving the core problem at scale? Not the adjacent problem. Not the easier one. The core one.

Short-term wins and incremental improvements can feel like progress without changing anything meaningful. The decisions that matter are the ones that fundamentally reshape how the system works — even if it takes years for that impact to become clear.

My father didn’t have AI or genomic sequencing. But he had continuity, trust and a deep understanding of his patients. What we have now is the opportunity to bring that model back — not for one small town, but for millions of people.

That’s not just an efficiency play. It’s a return to what medicine was always meant to be — only this time, at scale.