An AI-native clinical workflow clinicians can trust

The work was less about making AI visible and more about making clinical responsibility visible.

ATTUNE is Tebra's ground-up, AI-native practice platform for solo mental health providers — the clinicians who run their own therapy and counseling practices. Rather than bolt AI onto a legacy EHR, the team reframed the entire product around the provider's day in six jobs: book and manage appointments, prep for the day, deliver care, get paid, the cross-cutting capabilities that connect them, and decide to buy.

I led product design direction across a cross-functional team spanning product, research, engineering, and PM, and directed an AI-augmented design workflow. My remit was strategy, design judgment, design-system stewardship, executive alignment, and review discipline — turning open-ended AI enthusiasm into jobs, requirements, gates, and artifacts that the whole team could inspect and stand behind.

Healthcare AI outruns accountability when teams optimize for generated output before the clinical workflow is clear. ATTUNE had to support real provider work — and earn trust — without ever implying that a model owned the record. Day-in-the-life research with solo providers surfaced the same pressures again and again:

• Documentation is a burden, not a tool — notes spill into nights and weekends, and providers keep a second, informal system just to remember the patient.
• No single platform covers the day; providers stitch together three or four tools and quietly use general-purpose AI as a shadow clinical assistant.
• Preparation is a gap, not a habit — many walk into sessions cold, which providers themselves named as a source of stress.
• The calendar is the command center — any product that treats it as a secondary screen loses.
• And AI assistance only works if it stays inspectable, source-aware, and explicitly approved by the clinician.

I shaped a decision model that connected provider research signals to PRD requirements, journey maps, design decisions, prototype evidence, and release gates — a continuous chain from "what we heard" to "what we ship." The goal was not to slow the team down. It was to make each move inspectable enough that speed never created hidden risk.

The decision that defined the product was a constraint, not a feature: AI could draft, synthesize, and recommend, but it could never own the record. I made that boundary the spine of the design — provenance, review, correction, and explicit clinician approval as first-class states — and tied every requirement back to provider evidence through a signals → requirements → decisions chain, so the team could tell a validated bet from an untested one. I led design direction across a cross-functional team of product, research, engineering, and PM, and directed an AI-augmented design workflow that let a small team cover more research synthesis, requirements, and review than its headcount implied.

ATTUNE is heading into a focused first release with a small cohort of solo practices, so the honest proof here is pre-launch: a de-risked bet, a validated experience, and an operating model — not yet shipped revenue.