Tackling IBM's challenge to design trust and transparency for AI-powered healthcare
Timeline
Nov. 2024 - May. 2025 (8 months)
My role
Team lead, UX Designer
Team
2 UX Researcher
3 UX Designer
Context
Mitigating bias in AI healthcare is one of the biggest task faced worldwide
During the 2025 Student service design challenge, IBM challenged teams to design equitable services that help human navigate bias. After secondary research, we chose to tackle the challenge in the complex space of AI healthcare, exploring and paving the way toward equitable and unbiased future.
$4.9 trillion
spend in U.S. healthcare, creating pressure for more efficient, tech-enabled solutions
> 40%
underdiagnose rate by AI diagnostic tools are experienced by underserved populations
Among 11 high-income countries…
U.S. ranked last in health equity, reflecting deep disparities in care
Key achievements
Led design of Echo that came in second place in the IBM design challenge amongst 200+ international teams!
The design is featured on the official website!
As team lead, I managed project planning and timelines, balanced critical decisions, and guided research and design efforts to keep the team aligned and collaborative, successfully meeting each milestone throughout the 8-month design challenge
Collaborated across disciplines by engaging clinicians, researchers, and AI experts, I facilitated cross-disciplinary conversations to surface pain points and align diverse perspectives toward equitable AI solutions
Proposed scalable design solutions by leading workshops with patients, clinicians, and AI engineers to balance stakeholder needs with technical feasibility
Summary
ECHO, a digital platform that fosters transparent communication between speech-language pathologists (SLPs) and families around the use of AI, addressing critical gaps in AI trust, cultural responsiveness, and explainability within pediatric speech therapy.
Problem
Families and SLP clinicians lacked a clear way to communicate about how AI was being used in pediatric speech therapy. This created mistrust, misdiagnoses, and anxiety, especially for immigrant families.
Outcome
An AI powered platform that captures family context and makes AI use transparent, fostering trust and culturally responsive care.
The application is streamlined online and includes dedicated section about AI usage information.
Problem framing
From clinical research to Speech-language pathologist
To deeply understand the problem space, I spoke with diverse range of professionals working in the healthcare system:
Seattle Clinical researchers focused on recruitment and trial design
UW Health equity experts studying access & bias
Speech-Language Pathologists (SLPs) working in various settings (hospitals, schools, private clinics)
AI/ML engineers building healthcare tools
Patients and caregivers with lived experience in speech therapy
1
72% Patients demanded transparency in how healthcare AI makes decisions.
2
80% Patients were comfortable with AI in care, but only if clinicians understood and explained it
3
48.4% Clinicians agreed utilization of AI tools can increase the risk to patient privacy
I mapped out the system map of stakeholder relations in the clinical research context to understand the interactions and values exchange in between
Problem statement
How might we create a single source of transparency that helps patients communicate clearly, and support clinicians in using AI ethically and efficiently despite time constraints, cultural gaps, and limited system guidance?
Design objectives
Setting clear compass that guides design direction
Transparency
Make AI’s role explainable and visible in care decisions
Voice
Ensure families can share nuanced cultural and linguistic context
Reflection
Support clinicians in reviewing, adapting, and communicating AI outputs
Ideation & Workshops
I make ideas spark through complexity
I initiated brainstorming sessions where we explored human-AI collaboration models, balancing automation with clinician oversight. While we had many great ideas, I made sure to prioritize concepts that would best bring out our design objectives of transparency, voice, and reflection.
Patient side features
AI Explanation Layer
Simple visuals and language showing what the AI analyzed and why
Voice of the Patient
A channel for patients to share feedback on AI-driven results
Research opportunity
Voluntary option for patients to contribute data to strengthen AI models
wireframe of patient side phone screen
Clinician side features
Bias Flags
Alerts for potential dataset mismatches (e.g., bilingual cases)
Feedback Loop
Patient feedback integrated into clinicians’ workflows for continuous improvement
Insight community
A collaborative space for clinicians to access research insights from the community to facilitate diagnosis
Wireframe of clinician side screen
Service blueprint of the service
Co-Design & Iteration
Validate designs with real-world use case
I conducted a co-design session with a speech-language pathologist, who confirmed the value of contextual intake and AI explanation features. I iterated based on their feedback, simplifying workflows and clarifying client-facing language.
1
Adding summary on critical information of community resources helps clinicians utilize it quickly and effectively
2
Showing personalized bias considerations of patient's overall file is more useful than listing out general bias considerations of AI tools
3
Adding features like communication log and action guide facilitates clinicians navigate medical communication customized to different levels of patient AI concern level
Final design and Key features
Future healthcare is where trust and AI power "echos"
-
More transparent and equitable experience for patients/caregivers.
1
Context -rich intake form
2
AI-usage preference
Detailed patient information and AI integration guidance that makes clinicians feel confident.
1
Context-rich intake view
2
Clinician network board
Community research resources that boost engagement and continuous improvements in the healthcare system.
1
Research board
Impact metrics
How I would evaluate impact
Increased satisfaction of application process
Parent users submit enriched intake form that enables them to add contextual and cultural details and compare the perceived usability or satisfaction score with previous form.
Reduction rate in clinician prep time
Time spent preparing for sessions, by using Echo’s AI-supported summaries and context views which flag potential biases.
Improved percieved transparency of AI use
Parents’ understanding on how AI is (or isn’t) used in their child’s care, as measured by a post-visit survey & clinicians’s perceived transparency after using Echo Insights for guidance.
Learnings
0 to 1 is a process of deconstruct and rebuilding the mindset
Although this was not my first time working in the healthcare domain, leading a team to explore an unfamiliar space pushed me to be even more proactive. A key challenge I faced was the need to quickly adjust our project direction based on emerging research. To keep the team aligned, I organized debriefing sessions after each interview and reflection meetings after every round, which were effective approaches that fostered shared understanding and agility.
I’m proud that through hard work and the courage to step outside our comfort zones, we achieved a well-deserved accomplishment!
The amazing Healthcare and Humanity team! What a journey we've been through together!!