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More Than a Number, Moving Clinical Trial Matching from Percentages to Precision"

More Than a Number, Moving Clinical Trial Matching from Percentages to Precision"

Redesigning how patients discover and qualify for clinical trials, shifting from a simple percentage match to a smarter, multi-step eligibility experience that accounts for the edge cases a number alone can never catch.

Redesigning how patients discover and qualify for clinical trials, shifting from a simple percentage match to a smarter, multi-step eligibility experience that accounts for the edge cases a number alone can never catch.

Live Link

Role

UX designer & Researcher

Timeline

4 Months

Timeline

4 Months

Platform

Web

Tools

Figma, Claude, Vs Code, Notion

About the Product

Trial Nexus is a platform that connects patients with medical research studies (clinical trials), making it easier to discover promising new treatments and take an active role in their own care.

So it became clear the app had to evolve and we improved the entire experience.

PainPoints

So why did we need a fresh approach?

Most clinical trial platforms help patients find and connect with trials that match their health profile. But two things kept going wrong: patients were enrolling in trials they weren't actually eligible for, and patients who were a strong match for a specific trial were never finding it at all

Digging deeper, four core problems emerged

Wrong enrollments

Patients signing up for trials they

didn't qualify for

Problem #1

Missed matches

Eligible patients never finding the right trial


Problem #2

Language barrier

Medical jargon in trial listings with no plain-language translation

Problem #3

Misleading UI

Your profile matched 80%" shown to all users regardless of trial type

Problem #4

HMW Statement

How might we design a trial-matching experience that gives both non-clinical patients and medically informed users the right level of detail to accurately assess eligibility, without flattening that complexity into a single number?

Research

How I found it

Week 1

Field Observation

Visited a local clinic running an active trial after seeing a campus flyer

Week 2

Unstructured Interviews

Open conversations with a clinic coordinator and oncologists at Mayo Clinic, no script, following the thread of what they brought up naturally Chosen because I didn't yet know what questions to ask

Meet with an Oncologist at Mayo Clinic

Week 4

Competitive Analysis

Audited existing trial-matching platforms for UI patterns and failure modes

Want me to refine any of the wording here?

Competitive Visual Analysis of Clinical Trial Platforms: Antidote, MyTomorrows, Power, and ClinicalTrials.gov

Competitive Visual Analysis of Clinical Trial Platforms: Antidote, MyTomorrows, Power, and ClinicalTrials.gov

Affinity Diagram

Key Findings

Text

Patients searching for clinical trials were enrolling in the wrong trials or missing the right ones entirely, and this was happening to both ends of the spectrum. Non-clinical users could not parse medical language or interpret what a match score really meant. Clinical users (nurses, caregivers, patients with medical backgrounds) could read the criteria, but still could not trust a single percentage number to reflect the specificity a phase 3 or 4 trial actually demands. The interface was failing both audiences in different ways, for the same underlying reason: it made an imprecise signal feel authoritative.

Patients searching for clinical trials were enrolling in the wrong trials or missing the right ones entirely, and this was happening to both ends of the spectrum. Non-clinical users could not parse medical language or interpret what a match score really meant. Clinical users (nurses, caregivers, patients with medical backgrounds) could read the criteria, but still could not trust a single percentage number to reflect the specificity a phase 3 or 4 trial actually demands. The interface was failing both audiences in different ways, for the same underlying reason: it made an imprecise signal feel authoritative.

Has medical background

Has medical background

Could read the criteria but the UI gave them no way to cross-reference it meaningfully with their own profile. A single score flattened nuance that they, more than anyone, knew mattered.

Could read the criteria but the UI gave them no way to cross-reference it meaningfully with their own profile. A single score flattened nuance that they, more than anyone, knew mattered.

Problem #2

No medical background

No medical background

Could not understand eligibility criteria written in clinical language. Relied entirely on the match score, the only thing that felt readable, and trusted it to make decisions


Could not understand eligibility criteria written in clinical language. Relied entirely on the match score, the only thing that felt readable, and trusted it to make decisions


Problem #1

Design Solution

For User who Has medical background

Design Solution

How I found it

Patients searching for clinical trials on TrailNexus were enrolling in the wrong trials or missing the right ones entirely, and this was happening to both ends of the spectrum. Non-clinical users could not parse medical language or interpret what a match score really meant. Clinical users (nurses, caregivers, patients with medical backgrounds) could read the criteria, but still could not trust a single percentage number to reflect the specificity a phase 3 or 4 trial actually demands. The interface was failing both audiences in different ways, for the same underlying reason: it made an imprecise signal feel authoritative.

No medical background

Could not understand eligibility criteria written in clinical language. Relied entirely on the match score, the only thing that felt readable, and trusted it to make decisions


Problem #1

Has medical background

Could read the criteria but the UI gave them no way to cross-reference it meaningfully with their own profile. A single score flattened nuance that they, more than anyone, knew mattered.

Problem #2

Learnings

Throughout this project, several lessons stood out that shaped both our process and outcomes

01

Understanding our younger, tech-savvy users allowed us to tailor features specifically to their needs and behaviors.

02

Making key features, like the Vault, more accessible drove higher user engagement.

03

Testing multiple design iterations ensured we delivered a scalable and user-approved final solution.

0

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Hey

Thanks so much for checking out my work!

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kartikpd.work@gmail.com.

Open to Work