HEALTHCARE AI

Smart Notes: AI-powered patient summaries for oncologists

Oncologists at Mayo Clinic spent 15+ minutes per patient reading dense clinical notes before each consultation. Our two-person team rebuilt that summary into an AI-assisted Smart Note and a longitudinal timeline. In a formative study with practicing oncologists, review time roughly halved.

Role

UX Designer

Team

2 designers

Timeline

Jan - Apr 2025

Tools

FIgma, FigJam

PROBLEM STATEMENT

Oncologists spend 15+ minutes reading dense patient history before every consult

Oncologists spend 15+ minutes reading dense patient history before every consult

Oncologists spend 15+ minutes reading dense patient history before every consult

Cost 01

Time away from patients

"I would love to use that time with my patients"

Cost 02

Missed Details

A year of history held in your head invites error.

Cost 03

Eye-strain

Hours of scrolling dense PDFs, every clinic day.

1

RESEARCH

RESEARCH

Designed with oncologists, not just for them

Three methods, two oncologists, three patient scenarios — new diagnosis, follow-up, and treatment change.

Method 01

Day-long shadowing

Contextual inquiry in clinic, watching how oncologists parsed current notes between consults — where they paused, scrolled back, re-read.

OUTPUT

Workflow map + 10 friction points

Method 02

Co-ideation sessions

Sketched directions alongside the oncologists, pressure-testing what would actually work in the flow of a consult before we committed to prototyping.

OUTPUT

Clear insights to avoid design rework during final stages

Method 03

Usability testing

Tested the final prototype with the same oncologists across the same 3 scenarios, measuring SUS and time-to-key-insight.

OUTPUT

SUS 52 → 80 (+28)

2

IMPACT

Turning insights into impact

Turning insights into impact

Turning insights into impact

Three observed frictions, three design responses, measured.

Three observed frictions, three design responses, measured.

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