Clinical Trial

Matching Platform

CX Project Overview

TrialBridge is a healthcare customer experience (CX) design project aimed at simplifying how cancer patients discover, understand, and enroll in clinical trials. The project was conducted as part of a structured design collaboration under guidance from Mayo Clinic physicians, ensuring real-world clinical relevance and feasibility.

Despite breakthroughs in cancer research, fewer than 5% of adult cancer patients participate in clinical trials. Our work focused on transforming this problem from a medical challenge into a solvable user experience problem.

Challenge 1

Challenge:

Understanding the platform requirements was difficult because most of the input came from physicians and was filled with clinical terminology and domain-specific knowledge.

Solution:

We conducted in-depth research on clinical trials, reviewed existing healthcare platforms, and held multiple sessions with physicians to translate medical jargon into clear product requirements that could be implemented effectively.

Problem Statement

Cancer clinical trials represent hope and innovation—but for patients, the journey to finding one is overwhelming.

Patients face:

Confusing eligibility criteria
Fragmented trial registries
Emotionally draining decision-making
Lack of caregiver support
Logistical barriers like travel and scheduling

Existing platforms provide long technical lists instead of clear guidance. As a result, patients like “Sarah”- a recently diagnosed mother - struggle to identify trials that truly fit their needs.

Core Challenge

How might we simplify clinical trial discovery so that cancer patients can easily find, understand, and confidently participate in trials aligned with their medical needs and personal circumstances?

Research

Literature Review

The literature revealed key insights:

Trial participation remains critically low despite advancements (Unger et al., 2019).
Patients struggle with technical medical language and complex criteria.
Current tools are fragmented and lack personalization.
Caregivers play a crucial role but are rarely supported by platforms.
AI-powered matching tools exist but lack transparency and trust.

Major gaps identified:

Poor readability and usability
Lack of caregiver collaboration tools
Minimal explanation of algorithmic decisions
Limited integration with Electronic Health Records

These findings shaped our belief that the issue was not access to trials - but access to understanding them.

Survey Insights

User research confirmed what the literature suggested:

Patients feel overwhelmed by complex medical jargon.
Discovery is fragmented across platforms like ClinicalTrials.gov, Antidote, and Trialjectory.
Trust in AI requires transparency and institutional backing.
Users need emotional reassurance, not just data.

Key takeaways:

Patients want clear, simple information
They need personalized guidance, not long lists
Emotional support is just as important as logistics

Summary of Issues

Our research synthesized into four major barriers:

Cognitive overload: Too much technical complexity
Lack of trust: Opaque AI recommendations
Fragmentation: Disconnected tools and systems
Limited support: Minimal caregiver involvement

These became the foundation for our design direction.

Concepts Formation

Three Initial Concepts

We generated three distinct solution directions:

Concept A – Guided Journey Platform

Goal:
Make trial discovery feel simple, structured, and supportive.

Key Ideas:

Step-by-step guided experience for patients
Clear, plain-language trial information
Personalized dashboard for exploration
Ability to compare trials side-by-side
Decision-support tools to reduce confusion

Focus:
Reducing cognitive overload and helping patients make confident, informed choices.

Concept B – EHR Integrated Decision Hub

Goal:
Embed trial matching directly into clinical workflows.

Key Ideas:

Integration with Electronic Health Records (EHR)
Automated eligibility screening
Alerts for relevant trials
Physician collaboration tools
Streamlined referrals and coordination

Focus:
Clinical accuracy, efficiency, and seamless doctor–patient coordination.

Concept C – Community Powered Platform

Goal:
Provide emotional and social support throughout the trial journey.

Key Ideas:

Peer-to-peer connection with similar patients
Shared experiences and mentorship
Community groups and messaging
Real-world stories to build trust

Focus:
Reducing anxiety and isolation while strengthening confidence in trial participation.

Merge into a Unified Vision

Through design discussions and direct collaboration with Mayo Clinic physicians (Dr. Umar, Dr. Riaz, and Dr. Kumar), we merged the strongest elements of all three concepts into a single platform:

AI-powered matching
Transparent explanations
Caregiver collaboration
Educational support
Physician-aligned workflows

This collaboration ensured the platform reflected real clinical processes rather than theoretical ideas.

Challenge 2

Challenge:

The initial concept diagrams were too complex and overwhelming for physicians to understand.

Solution:

We used Lovable to convert abstract concepts into visual mockup screens, making it easier for physicians to see how their ideas translated into real product experiences.

Low-fidelity

Low-fidelity prototypes were created to map the full journey:

Onboarding
Dashboard
Trial exploration
Care Circle
Education hub
Appointments
Profile management

These early wireframes validated information architecture and exposed usability gaps before visual design began.

Iterations

Each iteration was driven by think-aloud testing and reviews with faculty and Mayo Clinic physicians, focusing on reducing cognitive load, improving clinical realism, and preserving patient autonomy.

1. Assessment Screen – Travel & Logistics

Problem Identified (Before)

Travel preference question was unclear
Forced a single choice
Did not reflect real-world patient constraints

Design Change (After)

Introduced flexible travel distance options
Allowed multiple selections
Added contextual explanation for how travel impacts trial eligibility

Impact

Reduced confusion during early onboarding
Better alignment with logistical barriers identified by physicians
Enabled more accurate trial suggestions

Before

After

2. Care Circle – Task Ownership & Priority

Problem Identified (Before)

No relationship labels for caregivers
Tasks lacked urgency or priority cues
Hard to understand who was responsible for what

Design Change (After)

Added caregiver relationship tags (e.g., spouse, child, friend)
Introduced color-coded priority indicators
Improved task hierarchy and visibility

Impact

Clarified collaboration between patients and caregivers
Reduced coordination burden
Reflected real caregiver involvement in cancer care

Before

After

3. Community Hub – Privacy & Trust

Problem Identified (Before)

Limited privacy controls
No contextual information about community members
Hesitation around peer interaction

Design Change (After)

Added anonymity toggle
Included age, gender, and distance indicators
Introduced “Send Request” flow instead of open messaging
Integrated AI assistant for guidance

Impact

Increased sense of safety and control
Encouraged participation without over-exposure
Built trust through gradual engagement

Before

After

4. Dashboard – Information Clarity

Problem Identified (Before)

Inconsistent labels across sections
Redundant actions
Overwhelming visual density

Design Change (After)

Standardized headings and terminology
Removed redundant CTAs
Added trial phase indicators and match-percentage visuals

Impact

Faster comprehension of trial status
Reduced cognitive load
Supported informed comparison without overwhelming users

Before

After

5. Onboarding Flow – Cognitive Load Reduction

Problem Identified (Before)

Long, dense forms
Missing clinical nuance
Intimidating for newly diagnosed patients

Design Change (After)

Introduced “Not sure” options
Added comorbidities and key clinical inputs
Enabled medical file upload
Later iteration reduced initial questions and deferred depth to chatbot

Impact

More welcoming first-time experience
Improved data quality without forcing early overload
Better balance between clinical accuracy and emotional readiness

Before

After

Challenge 3

Challenge:

The onboarding flow was overwhelming because patients were required to answer too many medical questions manually.

Solution:

I suggested a one-click AI-based onboarding option by integrating the platform with EHR systems, allowing users to securely import their medical data and significantly simplify the onboarding process.

6. Trial Details – Privacy & Sharing Control

Problem Identified (Before)

Limited control over what data was shared
No flexibility in how information could be shared

Design Change (After)

Added show/hide personal health details
Enabled multi-platform sharing (caregivers, physicians)

Impact

Strengthened patient autonomy
Supported informed conversations with clinicians
Reflected physician feedback on consent realism

Before

After

Final Solution – TrialBridge

TrialBridge became a patient-centered clinical trial matching platform grounded in empathy and clinical accuracy.

Core Features:

Transparent AI Suggestions
Trials are shown as “Suggested Opportunities” with clear reasoning instead of black-box matches.
Progressive Onboarding
Gentle data collection that respects emotional burden.
Care Circle
Caregivers can collaborate with permission controls.
Educational Hub
Plain-language resources to demystify trials.
Logistics Support
Map-based centers, travel considerations, and appointment scheduling.
Community Support
Peer connections to reduce isolation.

Rather than promising instant enrollment, TrialBridge mirrors the real multi-step clinical process, aligning patient expectations with medical reality.

Challenge 4

Challenge:

Patient privacy was a critical concern, especially when sharing sensitive medical data with caregivers or third parties.

Solution:

We designed customizable privacy settings within user profiles, allowing patients to control what data is shared and with whom, ensuring compliance with HIPAA and building user trust.

Challenge 5

Challenge:

Patients were hesitant about clinical trials due to fear, lack of awareness, and too much complex information.

Solution:

We structured the educational content into clear categories - Basics, Rights, Safety, and Expectations - and incorporated videos to make the information easier to understand and more engaging.

Impact

Explored 3 concept directions and merged them into one unified platform focused on guided discovery, clinical workflow support, and community-based reassurance.
Addressed 4 major barriers in clinical trial discovery: cognitive overload, lack of trust, fragmented tools, and limited caregiver support.
Designed for 3 key user groups: patients, caregivers, and healthcare providers.
Documented 6 major design iterations across onboarding, trial matching, dashboard clarity, caregiver collaboration, community privacy, and data sharing controls.
Delivered 6 core solution areas: transparent AI suggestions, progressive onboarding, Care Circle, educational hub, logistics support, and community support.
Reduced conceptual complexity by translating physician-led clinical requirements into clear, patient-facing product flows.