Projet Name
Clinspire
Duration
4 months
Team
My Role
Research & usability testing
Wireframe & prototype
Projet Name
Clinspire
Duration
4 months
Team
My Role
Research & usability testing
Wireframe & prototype
Clinical trials are key to advancing cancer care, but over 85% fail to enroll enough participants on time (ACRP, 2024). This isn't just a clinical issue, it’s also a user experience problem. Our project redesigns the trial-matching experience with a patient-centered focus to reduce friction, build trust, and support users throughout their journey.
We began by reviewing academic literature and analyzed competitive platforms like ClinicalTrials.gov, TrialJectory, and Antidote to understand why cancer trials struggle with recruitment.
While these platforms aim to match patients with trials, they all fall short in some key areas.
We gathered real-world insights through a online survey of 35 participants and conducted in-depth interviews with three cancer patients.
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We organized research findings into themes to reframe the problem from the user’s perspective and identify key pain points to address.
From the research, we defined 4 key problem statements and mapped them to actionable design opportunities.
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We mapped the patient journey to visualize interactions, ensuring each step met users’ emotional and informational needs.
With support from Mayo Clinic clinicians, we tested prototypes with patients, caregivers, and providers through interviews and co-creation sessions. Their feedback directly informed key iterations, which are detailed in the following section.
❌ Patients reported feeling confused and anxious when reviewing trial details or navigating complex information.
✅ In response, we integrated an AI assistant across onboarding, landing, and results pages. This assistant offers real-time explanations, emotional reassurance, and task-specific guidance—making the experience feel more supportive and less overwhelming.
❌ Users shared that the original filter layout felt cluttered and hard to use.
✅ To address this, we simplified the interface using color-coded categories and progressive disclosure. This allowed users to focus on relevant criteria without being overwhelmed by too many options at once.
❌ Several participants said they weren’t sure how well they fit a given trial.
✅ To clarify this, we introduced a visible match score, helping users quickly assess their eligibility and build confidence in their choices.
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What Worked?
✅ Empathetic AI Guidance — Helped users feel supported and less overwhelmed during onboarding and trial review.
✅ Clear Visual Cues — Simplified complex medical info with color-coded markers and progress indicators.
✅ Personalized Pathways — Allowed users to explore trials via AI-assisted or manual entry, accommodating different preferences.
✅ Stakeholder-Driven Iteration — Feedback from patients, caregivers, and clinicians led to emotionally resonant and functionally robust solutions.
What Needed Improvement?
⚠️ Accessibility — Multilingual support, voice input, and screen reader compatibility still need to be implemented.
⚠️ Public Perception — Users lacked context around trial reputation or social sentiment.
This project was deeply grounded in real user feedback. Thanks to the full participation of Mayo Clinic clinicians, we had the rare opportunity to closely observe the real-world struggles patients and providers face when navigating clinical trials.It allowed us to see firsthand how thoughtful design and AI can work together to make complex healthcare systems feel more accessible, supportive, and human.
