VP / Head of Product · Chicago & Remote
Healthcare AI product leader and founder.
Clinical AI rarely stalls on capability. It stalls at the review where someone has to say who owns it when it's wrong. I've taken products through that review at Epic, Doximity, and Transcarent.
- Résumé →
- Founding product lead, Doximity Dialer — 300K+ calls a workday
- First product hire at an oncology venture — 30MM annual visitors
What I'm brought in for
I keep ending up wherever the problem moved after the obvious one was solved. At Epic the software was installed and the account was still failing, so I worked the issue list to root cause instead of triaging symptoms. At Doximity the obvious vendor route would never have survived hospital IT review, so I found one that would.
At CancerCompass more information wasn't solving the problem, so I changed how patients navigated it. At Transcarent more coordinators wouldn't scale, so I changed the operating architecture instead. At Andwise 1,200 physicians were engaged and the fundable paths would have changed who the product served, so I shut it down. Only the first was a technology problem, and only partly.
Companies bring me in at exactly that point — increasingly before it, while "will this work here" is still an open question rather than a stuck deployment. The work pays for itself where something already bought is stuck: a pilot that won't convert, a model that can't clear review, a contract nobody can turn on.
Selected work
Three of five, ordered by what each one demonstrates rather than when it happened.
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.
Read case study →
Scaling trusted financial guidance through automated review and accountable sign-off
Co-founded Andwise and built automated clause-level contract analysis with escalation-clock review routing — the infrastructure needed to deliver trusted, fiduciary-aligned financial guidance to physicians at scale.
Read case study →Current work
Working tools for the problem above — what an institution has to be able to see before it lets a system act.
- NextConsensus Reconstructs how a medical claim gained support, shed its qualifiers, and spread — so a team can see a position moving before an institution acts on it.
- Ambit Keeps a live map of what an AI agent can actually do — the tools, machines, accounts, and permissions it has, and how they fit together.
- Refract Tells you when a source changed and which claims or citations built on it may now be out of date.
- Fast Harm, Slow Repair Measures what happens after an AI system gives a wrong answer — how far the error spreads, how long repair takes, and what stays wrong.
Outcomes at scale
| 35K+ | physicians surveyed | Doximity — the largest national compensation survey to date; I directed the data-science team that turned it into a predictive salary model |
|---|---|---|
| 4 | specialty-care programs launched | Transcarent — Surgery, everyday 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 |
| 14 | cross-functional team led | CancerCompass — onshore and offshore, across three products |
Organizations I've worked with
Explore
- Current work Ambit, Refract, and the evaluation work around NextConsensus.
- Decisions Four calls, with the reasoning as I recorded it before the outcome was known.
- Framework Ethotechnics — the method for making consequential AI claims verifiable and correctable.
- Writing Essays on AI governance, institutional power, and cognitive scarcity.
- Build Practice The agent-driven toolchain this site is built and checked with.
- Research The open questions behind the method, and what would settle them.
Email me. For NextConsensus pilots, product leadership conversations, or advisory work.