Nearly two decades inside the decisions that determine whether healthcare technology scales.
I am a healthcare technology operator and advisor. Through Vahana Labs I work at the intersection of healthcare operating leverage, product commercialization, and operating model design.
I help healthcare CEOs, boards, investors, and operating partners determine where growth, technology, and operating-model change should create value, what has to change for that value to reach the P&L, and which decisions leadership must make to get there.
I have been on both sides of enterprise scrutiny: the vendor trying to clear the review, and the executive whose signature authorized the technology’s use.
Building healthcare technology
Shipped across AI, diagnostics, wearables, and clinical trials
Led product, engineering, regulatory, quality, security, and privacy
Value attributed by a client to one accelerated launch
The work between the functions
The hardest healthcare technology problems rarely belong to one department.
A product decision changes the regulatory claim. The claim changes the evidence requirement. The evidence changes the buyer. The buyer changes the workflow, reimbursement path, integration burden, and economics.
Each function may be doing its job correctly while the company as a whole makes the wrong decision.
That is the work I have spent my career doing: seeing the complete system, identifying the assumption that will not survive contact with the market, and creating a practical path through it.
Sometimes the answer is to invest.
Sometimes it is to change the operating model, narrow the market, remove human review, build missing evidence, or redesign the commercial motion.
Sometimes the answer is to stop.
My role is not to defend the AI story a company already wants to tell. It is to determine which parts of the story are real enough to fund, operate, sell, and stand behind.
I have made these calls from the inside.
Four companies, one pattern: the technical work was the smaller half of the decision.
Korio’s COO, who had previously been my CEO at Endpoint Clinical, recruited me to help build the regulated clinical-trial technology company from the ground up.
One recurring client process took six weeks before delivery could begin. AI agents brought the initial documentation cycle down to a day and a half. The important decision came afterward: reducing what humans still had to review, so the technology created capacity rather than merely producing faster drafts.
I helped build the product-development and testing systems behind one of the first commercially approved CAR-T therapies in the United States.
Risk-based testing and automation reduced a regulated release cycle from roughly four months to three weeks. The customer attributed $81 million in value to the earlier launch.
I worked across regulatory strategy, product development, market entry, privacy, and enterprise adoption for an FDA-regulated cardiac platform, including opening pathways into the NHS and the U.S. Department of Veterans Affairs, and supporting the integration of regulated cardiac technology into the Apple Watch.
I carried compliance, privacy, and information-security accountability through rapid growth and a Fortune 500 acquisition. I have rebuilt these functions from the ground up, defended MHRA scrutiny, remediated enterprise controls, and worked through the reviews that determine whether a promising product ever reaches production.
The model is rarely the entire problem.
Value is usually trapped in the workflow, review burden, evidence, decision rights, commercial assumptions, or operating structure surrounding it.
Two related value-creation problems
Both are Vahana Labs.
Healthcare operating leverage
Through Vahana Labs, I work with healthcare CEOs, boards, operating partners, and deal teams to determine where growth, integration, shared services, technology, or AI should improve capacity, margin, cash, or quality.
The work includes pressure-testing value-creation assumptions, identifying operating constraints, defining decision rights, and determining what should be funded, fixed, tested, deferred, or stopped.
Engagements range from a fixed-scope Multi-Site Performance Read or Operating Leverage Diagnostic to fractional executive responsibility for a defined set of decisions.
Explore healthcare operating leverageRegulated product commercialization
Through Vahana Labs, I work with healthtech, medtech, diagnostics, SaMD, and digital-therapeutics companies to align claims, evidence, payment, workflow, implementation, governance, and enterprise adoption.
The objective is paid, routine, repeatable use and a commercial model that becomes easier to execute with each customer, indication, and market.
Sequencing is the central question: which market can buy, what evidence it requires, what claims the company can make, and what must happen first. US market entry is one use case.
Explore product commercializationIn both cases, the problem is conversion.
Healthcare operators must convert scale and capability into capacity, margin, cash, and quality. Healthtech companies must convert product performance and buyer interest into paid, routine, repeatable use.
Writing, teaching, and the broader field
I write Operating in HealthTech, where I examine how healthcare products survive the distance between technical possibility and enterprise reality.
I am the co-author, with Kimberly Bloomston, of Built to Survive: Building Trusted AI Products and Organizations — on the infrastructure, decisions, and operating disciplines required to build AI products that buyers can adopt and organizations can responsibly support.
I teach and advise healthcare leaders, founders, and emerging companies through:
- Carnegie Mellon’s Chief Data and AI Officer program
- MedTech Innovator · BioTools Innovator
- Alchemist Accelerator · Plug and Play
- Redesign Health, and other industry organizations
In front of boards
I serve on the board of Red Dot Ranch and previously served as secretary and Audit Committee member for ASQ Biomedical. I have presented product, AI, cybersecurity, privacy, regulatory, and enterprise risk directly to boards, translating technical complexity into decisions about investment, growth, launch readiness, and risk.
At the University of Michigan’s Accelerate Blue program, I recommended a no-go decision after identifying structural weaknesses in the venture economics, preserving capital and leadership attention.
For healthcare organizations
Where healthcare scale should create leverage, and which interventions are worth the capital.
Operating leverage →For healthtech companies
Commercialization sequencing for SaMD, diagnostics, and AI-enabled clinical products.
Regulated commercialization →Bring me the decision your team keeps circling.
The useful starting point is usually not “What can we do with AI?” It is a specific decision the organization has been unable to make.
Tell me the decision in front of you. I will tell you candidly whether it is one I can help you make.
Book a call →