Leading the clinical workstream inside a multi-team healthcare transformation

WebPT's transformation was organized as five parallel workstreams — I owned clinical.

Method partnered with WebPT and GlobalLogic to evolve an early "Gen3" concept into a real product. The work was split into five parallel workstreams — patient, front-office, back-office, clinical, and one more — each owned by a different Associate Director, with WebPT's own SMEs and client design system in the room from day one.

Clinical accuracy was non-negotiable — and it couldn't be solved in isolation.

WebPT's SMEs and therapists knew the clinical reality that had to be right. But clinical didn't exist on its own — patient, front-office, and back-office workstreams were moving in parallel, each with a different AD driving it, and a lot of decisions had dependencies that crossed those lines.

I owned my workstream end to end — and made sure it didn't ship in a vacuum.

My responsibility wasn't to own every decision across the program — it was to ensure the clinical workstream delivered real value while staying aligned with the broader product vision. Each Associate Director owned a different workstream, but a lot of decisions had dependencies that crossed those lines. A significant part of my role was bringing clarity to those discussions, aligning stakeholders, and making sure clinical integrated seamlessly with the rest of the experience.

Design reviews within my workstream, direct coordination with adjacent workstream leads on shared dependencies, and regular stakeholder presentations to keep clinical decisions visible across the program.

Before the clinical workstream had a direction, it had a question: could an AI scribe actually be trusted inside a clinical visit? I built the first working prototype myself to prove that out — the rest of the team used it as the base for their own workstreams from there.

What the three stages below don't show is how messy the path between them actually was. The client's scope shifted at nearly every review — this design went through five or six real pivots, not a clean three. AI-only note generation, a manual-only fallback, single-patient vs. multi-patient handling, live transcript capture — each got built, tested, and often reversed before we landed on the pattern that shipped. And even then, sign-off wasn't purely a design call: we'd already taken the split-screen concept to real therapists and they responded well to it, but the client's own team still needed to run their own round of validation before they'd commit. Sensing what users need and getting a client ready to validate and sign off on it aren't the same thing — you need both to actually ship. That's the reality of consulting work that a tidy case study usually leaves out.

clinician home · early proof of concept

Every visit starts with a clinician-facing home — today's schedule, what needs attention, notes awaiting signature.

live session · transcript feeding a structured SOAP note

The core mechanism, proved here first: a live transcript of the visit on one side, auto-populating a structured Subjective/Objective/Assessment/Plan note on the other, field by field, as the conversation happens.

AJ Clinical · AI assistant + structured note, side by side

The pattern matured into an AI assistant (EVA) running alongside the note — surfacing patient context, flagging gaps, suggesting exam items to check — while the clinician stayed in control of what actually got written.

dashboard · the day's patients, at a glance

The delivered dashboard — AI chat assistant on the left, today's patients, pending notes, and flagged items on the right.

live SOAP note · AI chat + structured note, split screen

This split-screen layout — AI assistant conversation on one side, the structured clinical note building in real time on the other — was a deliberate ask from the client, not a compromise. WebPT wanted their clinicians and therapists working exactly this way: able to question and direct the AI while watching the documentation take shape. As a consultant, that's the job — we delivered what the client needed their users to have. The pattern worked well enough that the other workstream pods (patient, front-office, back-office) adopted the same AI-chat-plus-structured-panel layout for their own workflows.