Redesigned OptionsPlay trading dashboard from 0-1 impacting 10K+ global users trading options
HealthTech
Conversational UX
AI native product
Shipped
Role
Solo UX Designer: Research, UX Designer, Interaction Design, Prompt Architecture
Duration
4 weeks (March 2026)
Stack
Figma, Claude Code, Lovable
Team
1 Product Designer
Collab w/Medical Board
Here’s a 1 min TL;DR version
THE PROBLEM
74% of medical students get no formal training in breaking bad news
They learn by doing it with a real family. The USMLE Step 2 CS exam, the only national assessment of clinical communication, was discontinued in 2021. No replacement exists.
RESEARCH
Students wanted to learn & practice. They just had no tool built for it.
8 interviews, 12 published studies, 1 ChatGPT prototype test. The same frustrations kept surfacing.
01
Training Gap
Many students receive little or no formal preparation for breaking bad news.
02
Scale Gap
Standardized patients are effective, but expensive, scheduled, and hard to scale across medical schools.
03
Feedback Gap
Students may leave practice sessions with vague feedback like “good effort” instead of knowing which conversational moment failed.
How might we help medical students repeatedly practice emotionally difficult family conversations while preserving the discomfort, uncertainty, and feedback quality of real simulation?
THE USERS
Who did I design for?
Medical Students/ residents
They need a safe place to practice difficult conversations, make mistakes, and improve before clinical rotations.
Medical Industry Users
They need scalable ways to assign practice, review progress, and identify skill gaps.
Faculty Members
They need to be able to include this in their curriculum easily and track the student progress,
Primary users
Secondary users
BEFORE
Before image
The barrier is a design decision, not a technical constraint. If the evaluation layer could talk to the LLM during simulation, the AI could self-correct toward a higher score. The barrier is what keeps feedback honest.
AFTER
After image
The barrier is a design decision, not a technical constraint. If the evaluation layer could talk to the LLM during simulation, the AI could self-correct toward a higher score. The barrier is what keeps feedback honest.
Play with the vibecoded prototype here: Options Trading Platform
Contact me for the full case study
Sreesha Suresh
