Commercially offered · public forecast registry and review methodology; no resolved forecasts or paid engagements yet
NextConsensus
Estimates how likely a guideline, label, or coverage decision is to change by a set date.
I’ve taken healthcare products from demo through hospital review into daily use
Now I build AI products the way I built the healthcare ones: find the decision everyone has taken for granted, then build the version that removes the workaround. At Doximity, it was what phone number a patient sees.
AI product leader, engineer, and researcher · Chicago
- calls on an average workday
- 300,000+
- later, in daily use
- 9 years
Doximity Dialer, which I originated as founding product lead. Doximity’s own figures, February 2026.
Selected case studies
The patient sees the office number, not the doctor’s cell
Adoption may come before official approval only while the risk stays with the person choosing. When the risk is clinical, approval comes first.
- The situation Physicians called patients from personal phones, giving the number away for good or showing “Unknown Caller.”
- My choice I proved caller ID over a weekend and launched on a vendor that did not require proof of number control.
- What followed Hospital authorization followed adoption, and the route was unchanged when I left.
Scaling oncology navigation for 30 million annual visitors
A frightened person is not short of information. The scarce resource is attention and trust, so the product’s job is the next step and a person to call.
- The situation A family facing a new cancer diagnosis needed the next step and a number to call. The site gave them encyclopedia entries.
- My choice I relaunched around plain-language care pathways and nursing triage, with product and nursing reviewing real questions together.
- What followed Chat conversions rose 267% and bounce rate fell 25% across 30 million annual visitors.
Four care programs, one system, and an owner for every case
One system can carry many programs once every decision point has a named owner.
- The situation High-complexity cases in surgery, oncology, and behavioral health were stalling in exception queues.
- My choice I built one shared routing system instead of four custom ones, and stated the trade: slower to build, faster to scale.
- What followed Four programs launched on shared infrastructure, and routing delays stopped being the primary bottleneck.
What I’m building
The record above is why these exist: an estimate that resolves on a date, a permission that can be withdrawn, a source change that reaches the claims citing it.
Experimental · open source, with a live demo and a published roadmap
Ambit
Maps what an agent setup can already do, what one more step would unlock, and which of those tools actually work.
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Live · open source, with a scheduled observation bot and a 15,926-event public test set
Refract Replays source revisions to catch a change before the citations that rely on it go stale.
Writing and standards
Two things came out of the product work: essays about who covers for a system’s failures, and standards that turn that into requirements a team can check.
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Research and essays
From a first-author ACS Symposium Series chapter on RNA thermodynamics to 230+ essays at The Crumple Zone on how institutions distribute burden, absorb failure, and decide who can exist inside them. Selected writing →
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Proposed AI standards
I wrote Ethotechnics’ framework for recording approval, challenges, and corrections. ethotechnics.org
What I want to test next
Whether a record can carry a real dispute end to end: an agent acts, a person challenges the action, and the correction reaches every decision that depended on it. NextConsensus, Refract, and Ambit each take up one part of that question, and the record format is published as an open standard so others can check it.
One essay that makes it concrete Staff Fix the Errors Your Dashboard Doesn’t Show An automation can report 94% straight-through processing while staff fix mismatched codes in shadow spreadsheets.
I take advisory and fractional product leadership roles for healthcare and AI ventures, partner with commercial co-founders on new builds, and research how institutions correct their failures. If you are building something that needs to survive review, or run a system where people are covering for its defects, I would like to hear from you. kanav@kanav.net
Revised