Project Overview
Clinical trials are essential for advancing cancer treatment and precision medicine. Our AI-powered clinical trial matching platform is designed with a patient-first approach, featuring an intuitive, conversational interface that guides users clearly and empathetically through the process.
The Challenges
Over 85% of clinical trials struggle with timely patient recruitment, delaying access to critical therapies.
Complex matching of eligibility criteria creates confusion and barriers for both patients and providers.
Low digital literacy limits patient engagement with online trial-matching tools.
Dense medical jargon and complex terminology on trial platforms confuse patients and contribute to high drop-off rates.
Distrust in AI tools reduces willingness to use digital matching solutions.
User Needs
Business Goals
Research Insights
We began with affinity mapping to identify key user pain points, followed by competitive analysis to benchmark existing solutions. Targeted interviews with clinicians and caregivers then validated these findings and guided our design decisions.
Confusing Medical Jargon Drove Users Away
Lack of Personalized Matching and Poor Navigation
Overwhelming Eligibility Criteria
Step back
Once done talking to doctors, we realized that focusing on both doctor and patient flows was diverting us from our main goal, so we shifted our attention to the patient flow.
Ideation
After gathering research insights, we mapped the patient user flow to pinpoint exactly where users encountered challenges. Addressing these pain points enabled us to design a more streamlined and effective experience.
Initial Wireframes
Given the current complexities in clinical trial matching, we developed wireframes grounded in user flows and research insights to address key challenges such as confusing medical jargon, unclear eligibility, and accessibility barriers. This approach enabled us to create a more intuitive and user-friendly experience for patients.
Final Designs
We started by designing the platform for both doctors and patients, but realized that creating a separate flow for doctors was diverting us from our main goal, improving trial matching for patients. Based on this, and feedback from clinicians, we took a step back and shifted our focus to a patient-centric approach.
We organized eligibility criteria into clear sections Met, Unmet, and Need Review, so users could easily understand their status and progress, effectively reducing confusion.
By simplifying medical jargon with AI and presenting clear eligibility criteria, we make information accessible to users with varying health literacy, helping them quickly understand their status.
Final Prototype
Outcomes
Clinspire’s design is expected to drive the following positive outcomes, making clinical trial participation more human-centered and equitable.
My Learnings
Empathy and flexibility are essential when designing for vulnerable populations.
Continuous engagement with stakeholders, especially clinicians and caregivers, leads to more inclusive and effective solutions.
Visual clarity and transparent AI explanations greatly improve user trust and comprehension.
Iterative prototyping and real-world feedback are key to refining digital health tools.
Future Enhancement: Due to confidentiality and medical restrictions, we were unable to interview real patients directly. In future iterations, collaborating with patients will be a priority to gain deeper, firsthand insights and further validate our design decisions.
