Healthcare AI governance advisory
I help healthcare teams govern AI and algorithmic systems so decisions stay explainable, challengeable, and correctable.
Explainable · Challengeable · Correctable
Advisory work on the workflows, records, and escalation paths that make automated decisions safe to rely on and easy to fix.
Doximity Dialer product lead · Epic implementation · Transcarent and CancerCompass director-level product · Andwise co-founder
Current advisory focus Generative AI in clinical workflow — Review paths, confidence signaling, and escalation for models that clinicians need to trust or override.
Open to advisory work and project reviews.
Case studies
Verified caller ID for HIPAA-conscious telehealth
Patients ignored calls from unknown numbers, stalling telehealth. Dialer showed the doctor's office number — verified and HIPAA-safe.
Care navigation that cut avoidable escalations
Led product work across value-based specialty-care and care-navigation programs (Surgery, Urgent Care, Behavioral Health, Oncology Care).
Andwise: fiduciary planning & physician support
Co-founded Andwise; public pages corroborate founder and advisory-board context, while growth and funding metrics are founder-reported.
Who I work best with
Teams where a wrong AI decision changes patient care.
Health systems & payers
Evaluating AI pilots, drafting governance charters, or designing escalation paths for algorithm-assisted decisions.
Clinical AI vendors
Making model behavior, uncertainty, and override paths legible to clinicians, compliance, and operations.
Product & operations leads
Mapping who owns what when a recommendation changes, and how to recover when it is wrong.
Legal & risk reviewers
Building records that make oversight easier to actually do.
How I approach AI governance
I check whether an automated decision is explainable, challengeable, and correctable before it reaches a patient. Read the framework →
Organizations I've worked with
U.S. healthcare has spent 15 years moving decisions into software. My work is about making sure responsibility moves with them.
Current work
Methods
Current methods and research practices, distinct from shipped-product proof, for products where teams need to know what evidence supports a decision, who owns it, and how to correct it when facts change.
Product method
Ethotechnics
A product method for designing workflows where people know what changed, who owns the next step, and how to recover when something goes wrong.
Explore method →
Policy proposal
Proof-Based Governance for Algorithmic and Automated Systems (v1.2)
A proposal for making automated decisions easier to review, challenge, pause, and update in production.
Review practice context →
If you're building healthcare AI that has to survive real review and real mistakes, I'd like to hear about it.
Want the decision-readiness checklist I use with teams? Send me a note and I'll reply with it.