Healthcare AI product & systems leader · Chicago, open to relocation
I build the product systems that let AI move through healthcare at full speed — and stay answerable when it's wrong.
14 years building automated decision systems in clinical settings — decision rules at Epic, an ML salary model at Doximity, triage and routing at Transcarent, contract analysis at Andwise. The hard part was never the model — it was whether the institution could stand behind it when it was wrong. Now I build the infrastructure that makes that possible.
Organizations I've worked with
Selected work
Scaling specialty-care through unified clinical workflows and automated ownership
Led product work across value-based specialty-care and care-navigation programs (Surgery, everyday urgent care, Behavioral Health, and Oncology Care).
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 product and advisory infrastructure required to deliver trusted, fiduciary-aligned financial guidance to physicians at scale.
Read case study → Scaling clinician communication through verified identity and workflow integration
Built the identity and verification layer for Doximity Dialer, solving the 'unknown caller' bottleneck that prevented mass telehealth adoption.
Read case study →Latest writing
The Official Record Is Late
A structure can be correct on paper and dangerous in practice — here is where that observation came from, and what I'm building now
Don't Let Reassurance Do Engineering's Job
Why 'we care' substitutes for obligation — and how delay gets disguised as kindness.
If Every User Is a Potential Threat
People are not becoming dishonest. They are becoming game-theoretically optimal for the environment they have been placed in.
Current work
Same problems, generalized — who owns the decision, what happens when it's wrong, how you prove it was safe. One flagship: NextConsensus. Supporting it: Refract (verification), Ethotechnics (governance), Crumple Zone (analysis).
Powers NextConsensus — change detection
Refract
Reports what changed and when; deciding whether the change matters is the caller's job. Under the hood: an open-source engine (AGPL-3.0) that replays a source's revision history into deterministic, byte-reproducible change events with full provenance — the observation layer that AI citation and compliance systems need but haven't built yet.
Published standards
Ethotechnics
Open standards for AI decision accountability — how to contest an automated decision, how to get recourse when it's wrong, and how to prove a system is safe before it ships. Published while most institutions still treated AI governance as a future problem.
Writing on institutional accountability
The Crumple Zone
Writing on how institutions absorb automated decisions — where accountability thins, and what keeps systems answerable.
Operating principle
Every AI system that influences a clinical decision makes a claim — this drug is safe, this guideline is current, this patient should be prioritized. I make those claims answerable: what evidence backs them, who can challenge them, what happens when they're wrong. Those questions go into the product itself, not into downstream compliance. Read the method →
The deeper question is one level above the method: what makes a system inhabitable for the people inside it →
Email me. I'm looking for product roles where the constraint is getting AI through institutional adoption — security review, compliance, and regulatory approval. Product management at AI labs building for regulated industries, or senior product leadership at healthcare AI companies.