MD with deep clinical training, not a practicing physician. 20+ years in orthopedic technology and health-system partnerships. Running a device or clinical-AI program? The readiness check tells you in four questions which seam is open.
Two jobs, one skill. I take health-technology companies into the U.S. market — commercialization, health-system partnerships, reimbursement. And I embed with AI and deep-tech teams as the clinical translator who turns lab science and models into signed, reimbursed, adopted care.
I've spent 20+ years building relationships between technology companies and health systems. I know how deals get done, where they stall, and why. From pilot programs to system-wide adoption, I speak the language on both sides.
The proof: seven years at Pristine →I've been in the room when healthcare companies get acquired. InVivoLink (HCA Healthcare), PumpOne (Anytime Fitness), Disior (Paragon 28) — I've helped navigate the diligence process, validate market positioning, and close deals.
The proof: the InVivoLink chapter →I built BrainLAB's orthopedic navigation vertical from zero by getting surgeons to adopt planning and navigation in the OR. For a device company between first-in-human and pivotal, I bring the two things a Series A data room asks for that an engineering team can't write alone: the U.S. reimbursement path (coverage, coding, site of service) and the surgeon-adoption plan.
The proof: the BrainLAB chapter →Right now I'm looking to go deep with a small number of teams — a health-technology company taking on the U.S. market, or an AI / deep-tech group that needs a clinical translator in the room, not on a call. Fractional, embedded, or full-time. If that sounds like your team, let's talk.
Start a conversation →Healthcare doesn't have an app shortage — it has a systems shortage. I don't build another interface. I architect the whole chain, where every layer serves a clinical purpose and a human owns every decision that carries liability. Three I've built:
The system: the technology that tells a surgeon where to place the instrument — imaging, planning, sensors, and the OR workflow, integrated into one trusted loop. What it proves: I've architected clinical technology in the highest-stakes room there is, and gotten surgeons to trust it. Grew that vertical from zero across two continents; five patents in the method.
The patents, primary source →The system: assessment → care plan → delivery → billing for home-based care, mapping soft home visits into hospital-grade structured data (Omaha System → FHIR). What it proves: I ship, now — a deployed platform, not a deck. Every layer earns its place, clinically and financially.
See it running: co-op.care →The system: an on-device model for orientation, sensors for signal, and a physician for every decision that carries liability — the machine never becomes the source of a medical fact. What it proves: I design at the current frontier, and I put human accountability at the exact seam where it belongs. Proven end-to-end on Apple's on-device model.
The architecture, published →Connect biology, sensors, AI, clinicians, and reimbursement into one chain — and keep a person accountable where it counts.The pattern never changes. That’s the job. If you’re building a system like this, let’s build one →
Twenty years at the boundary between surgeons and technology — one continuous thread, not a list of jobs. Tap any stop to see what it taught me about getting innovation adopted.
Every role below is the same job in a different costume: translate between the people who hold the scalpel and the people who build the technology. Tap a stop to follow the thread.
Live sites, each with something you can use today.
A care cooperative where the caregiver is an owner. Families can see what help they may qualify for.
AI drafts coding and prior-authorization paperwork from the note; the surgeon signs or strikes every line.
An AI output goes to a licensed, specialty-matched physician, who signs or declines it.
It lives on your computer, answers there, and remembers what you teach it.
Rule checks and a physician sign-off step around any model. Sandbox and demonstration today.
A sweat calculator, no account: tell it your workout, get your sodium, potassium and magnesium target.
On the post-AI transition in healthcare — accountability, reimbursement, and what it takes to make the science actually get adopted. Owned, not rented: every essay lives here first, in full. LinkedIn is where I hand it to my network.
The tutorials give the AI what you’d give a new hire — never what you’d give a licensed one. The missing layer, a named human who signs the output and owns the liability, is the whole game for regulated work.
Built from Tyler Cowen’s thesis: the bottleneck isn’t the models, it’s whether you’ve changed what you actually do. Most people only freelance with AI — where do you sit?
Every essay lands here first, in full — then I hand it to my network on LinkedIn. No Substack, no algorithm, no email wall.
Read all writing →Clinical training, business foundation, and deep domain expertise in orthopedic technology.
Fractional strategy, advisory board, healthcare M&A consulting, or just a real conversation about AI in care. I read everything.
Book an intro call →