VP / Head of Product · Chicago & Remote
Healthcare AI product leader and founder.
I build consequential AI products and the systems around them—how they know, what they can do, what they're allowed to do, and what happens when they're wrong.
- Résumé →
- First product hire at two healthcare ventures
- Founding product lead of Doximity Dialer
Why me
Most healthcare AI fails at operationalization, not accuracy. I build products that survive clinical workflows and trust reviews.
Companies bring me in once the technology works but the product doesn't — to close the gap between a working model and a system clinicians actually use.
Selected work
The identity infrastructure behind trusted telehealth
Built the identity infrastructure that let the office number travel with the physician on their personal phone — dissolving the bind between hospital desk phones and exposed personal numbers. The architecture decision was to make trust a product surface, not a compliance footnote.
Read case study →
Scaling specialty-care product strategy across four clinical programs
Scaled product strategy across four specialty-care programs — Surgery, everyday urgent care, Behavioral Health, and Oncology — by building shared decision infrastructure instead of per-program customization, making routing, ownership, and escalation explicit at every decision point.
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What happens after the software is installed
Epic's software was installed and still failing at the accounts I inherited. Owning what happened after go-live meant root-cause analysis, re-implementation, and writing Caché plug-ins to close the gaps the base product left — then building the escalation path so regulatory changes and critical bugs stopped arriving as surprises.
Read case study →Where I create value
Clinical Intelligence
Turning complex clinical knowledge into products clinicians use.
Trusted AI
Building the evaluation, provenance, and human oversight systems that make AI deployable.
Product Leadership
Growing organizations, portfolios, and operating systems around those products.
Current work
My current projects explore a related question: what has to exist around an AI model for the overall system to stay reliable as information, goals, and operating conditions change?
- NextConsensus Healthcare transition intelligence — forecasts when accumulating medical evidence is likely to produce observable institutional change.
- Ambit Agent capability infrastructure — maps what an AI system can actually do across models, tools, credentials, and humans, and keeps capability separate from authorization.
- Refract Evidence and dependency infrastructure — tracks what claims depend on, so a change in evidence can trigger reconsideration downstream.
- Fast Harm, Slow Repair AI system recovery evaluation — measures what happens after a system is wrong: propagation, intervention, repair, and what stays broken.
Major product wins
| 35K+ | physicians surveyed | Doximity — I ran the largest national compensation survey to date |
|---|---|---|
| 4 | specialty-care programs launched | Transcarent — Surgery, urgent care, Behavioral Health, Oncology Care |
| 1,200+ | physician users | Andwise — I co-founded it |
| $5M | revenue | Doximity Talent Finder — I grew it 60% YoY |
Organizations I've worked with
Explore
- In progress What I'm building now — NextConsensus, Ambit, and the work around them.
- Principles What building consequential AI in healthcare taught me.
- Ethotechnics The method for making consequential AI claims verifiable and correctable.
- Writing Essays on AI governance, institutional power, and cognitive scarcity.
- Research The open questions behind the method, and what would settle them.
Email me. Open to executive product leadership roles, strategic advisory, and institutional pilots for NextConsensus.