Designing a Vendor Management Experience for Clinical Trials (AWS × Johnson & Johnson)
Manage Clinical Trials Studies and Vendor Selection
An AWS Project
#B2B #enterprise #datascience #SaaS
(Note: The branding of the design assets and some of its product features has been changed to protect the confidentiality.)
Project Scope: Enterprise Analytics & AI-Driven Insights
Role: Lead UX Designer
Timeline: Dec 2025 – Feb 2026 (3 Months)
Core Tools: Figma, Qiro (LLM Data Synthesis), Lovable, Jira
Key Impact: Accelerated stakeholder decision-making efficiency by 30%
Deliverables: End-to-End UX Architecture, Journey Maps, Interactive Prototypes, Design Systems (WCAG 2.1)
Project Context
This was not a traditional product build—it was a high-stakes, time-constrained proposal project.
The goal was to demonstrate how AWS could solve operational inefficiencies in clinical trial workflows through a strong UX vision and prototype.
Problem Statement
Data Acquisition Experts lacked an efficient way to identify, evaluate, and assign vendors to clinical studies.
The existing process was:
Manual and fragmented
Time-consuming
Prone to delays and inefficiencies
This directly impacted:
Clinical trial timelines
Operational costs
Decision-making speed
Goal
Design an experience that:
Streamlines vendor selection and management
Enables faster, insight-driven decision-making
Improves visibility into study data and anomalies
Reduces turnaround time for critical actions
Target Audience
Primary Users:
Data Acquisition Experts (DAEs) at Johnson & Johnson
These users are responsible for:
Managing clinical study data
Coordinating with vendors
Responding to urgent data issues
Constraints
This project required fast, high-impact decision-making under real-world limitations:
3-month timeline
No direct access to end users
Reliance on SME knowledge
Predefined technical ecosystem (AWS)
Complex clinical domain
SME-Driven Discovery
Due to time constraints, we relied heavily on Subject Matter Experts (SMEs).
Conducted collaborative discussions to understand workflows
Asked targeted questions to uncover edge cases and constraints
Continuously validated assumptions with stakeholders
Stock photo for Kick Off Meeting
Photo by Product School on Unsplash
Project Kickoff
During the initial AWS-led kickoff:
Cross-functional teams aligned on scope and expectations
On-site and remote collaboration (I joined remotely from the West Coast)
My contributions:
Asked clarifying questions around workflows and dependencies
Proposed creating user flow diagrams to simplify complex processes
Pushed for shared understanding early to reduce ambiguity later.
Journey Mapping for DAE
Journey Mapping
To better understand the ecosystem, we created a DAE journey map that outlined:
How DAEs interact with vendors
Key workflow stages in clinical trials
Pain points in vendor coordination
Opportunities to reduce friction and improve responsiveness
This exercise helped identify:
Bottlenecks in vendor assignment
Lack of centralized visibility
Delays caused by fragmented communication
Design & Prototyping
Design System
To move quickly and ensure scalability, we leveraged the
AWS Cloudscape Design System:
Ensured consistency and faster implementation
Reduced design decision overhead
Visual choices:
Font: Open Sans
Accent color: Red (aligned with Johnson & Johnson branding)
Wireframing
Created low-fidelity wireframes early in the process
Focused on layout, hierarchy, and workflow clarity
Shared frequently with stakeholders for early feedback
To ensure alignment:
Conducted weekly design reviews
Iterated based on feedback and evolving requirements
Interactive Prototyping
Developed high-fidelity interactive prototypes in Figma
Shared prototypes via email for stakeholder review
Provided clear instructions for commenting and feedback
This enabled:
Asynchronous collaboration
Faster iteration cycles
Stronger stakeholder engagement
Results & Impact
The prototype was very well received by stakeholders at Johnson & Johnson
It was perceived as a fully functional product, not just a concept
AWS gained confidence in the proposed solution
The project progressed into further development and testing phases
Challenges & Future Considerations
Challenges:
1. Domain Complexity
Clinical trial workflows are highly specialized, required rapid learning and continuous validation
2. Limited User Access
No direct interaction with end users, so relied on SMEs and secondary research
3. Scope & Scale
Multiple interconnected workflows and large number of screens and edge cases
Future Improvements:
If given more time, I would:
Conduct usability testing with real DAEs
Validate data prioritization and workflows
Explore edge cases (alerts, overload scenarios)
Introduce micro-interactions for better feedback and usability