Designing the conditions for trust in public health AI

Context and stakes

Health811 is Ontario's front door for health navigation, a public service used by millions of residents. When AI symptom assessment entered the roadmap, the risk was treating it as a standalone intake tool bolted onto a service listing.

My role

I lead UX for Health811 and sit as the invited UX representative on the AI Governance Table, the group evaluating where and how AI belongs in a province wide public health service.

The question is not whether the tool can work. It is what conditions have to be in place for it to be trusted, understandable, and safe.

What I built or changed

I reframed evaluation away from model performance and intake efficiency and toward the surrounding experience: plain language safety messaging, clear escalation paths, human handoff points, and accessibility checks. That shifted the broader product conversation too, repositioning Health811 from a service listing model toward a health concern problem solver, where AI is one part of a safe path from describing a concern to reaching the right service.

Outcomes and open threads

This work is live and ongoing, so I am deliberately not claiming shipped outcomes here. The influence is the deliverable so far: the frame the table now uses to evaluate AI supported experiences.