Designing a Conversational AI for Clinical Communication Simulation
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.
Design challenge: Can a conversational AI simulate a grieving family with enough emotional fidelity that a student feels real discomfort — and learns from it?
SOLUTION OVERVIEW
A tool for students to practice
IMPACT
Currently being used by Indiana University School of Medicine students & faculty
Let’s start from the beginning
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
HOW I USED AI?
How did I design the Conversational UX using AI?
The biggest UX risk was role drift. If the AI started coaching mid-conversation, the student would no longer be practicing with a family member. To prevent this, I separated the system into two states.
The prompt is the design artifact
In conversational AI, the system prompt holds the same weight as a wireframe. Here’s an annotated excerpt.
FRAMEWORK USED
I grounded the entire design in the SPIKES clinical framework
I used SPIKES as the product’s learning backbone. It gave the simulator a way to evaluate observable communication behaviors instead of making vague judgments about whether a student was “empathetic.”
S
SETTING
Prepare the space. Privacy, seating.
P
PERCEPTION
Ask what they already know.
I
INVITATION
Ask how much they want to hear.
K
KNOWLEDGE
Deliver clearly. No euphesims.
E
EMPATHY
Acknowledge emotion. Silence is a tool.
S
STRATEGY
Summarize next steps.
SPIKES became both the conversation map and the evaluation rubric.
SOLUTION
Prepare → Practice → Review
PHASE 1: PREPARE - The Pre-Encounter Door Note
Students review a clinical brief before entering, exactly how real clinicians prepare. No more “interviewing the AI” for basic background.
PHASE 2: PRACTICE - The Live Encounter
The AI stays in character throughout. A real-time SPIKES tracker runs silently underneath.
PHASE 3: REVIEW - Evaluation + Branch
Students get a SPIKES-mapped score, an annotated transcript, and the ability to replay from any flagged moment.
ANNOTATED TRANSCRIPT - Branch from any flagged moment
DASHBOARD
Per-skill SPIKES trends across every session. Weak areas surface recommended scenarios.
WHAT COMES NEXT
What I learned
WHAT COMES NEXT
01
Voice Mode
Real BBN conversations are vocal. Prosody analysis could detect rushing.
02
Cultural Variants
Same SPIKES framework, entirely different expression across cultures.
03
Multi-Party Dynamics
Two family members with conflicting goals. Two AI personas.
Sreesha Suresh
