Study Set-up

Accelerate medical discoveries by building studies with ease, generating higher quality data with high accuracy 

I have omitted confidential information in this case study. All information in this case study is my own and does not reflect the views of Verily. 

Organization
Verily

Role
Lead UX designer

Team
Research Platform

Duration
2022 - present

Medium
Desktop web application

WHAT

Translate complex study plans into logic to be executed by the study team via zero to one redesign of the legacy product to fit study designers (clinical data managers) mental models

COMPLEXITY

I led UX strategy and execution for Builder: coordinating a 4 person UX team working across dependencies with 4 product teams spanning 6 product managers and 30+ engineers to deliver designs for zero to one product from 2022-present 

IMPACT

2024 launch has shown study set-up to be significantly easier, efficient, and accurate. 90%+ hours est* time saved per study set-up vs legacy product


PROBLEM

It’s hard to translate complex study plans into logic to be executed by the study team 


Research protocol

Schedule of activities

The research protocol is a document that describes the plan for a study: background, rationale, objective, design, method, etc.

Research activities


PROBLEM

Our legacy product was unusable: unsafe, error-prone, and slow

Too slow
Many weeks* from start to study-live, including business processes. Industry benchmark is 3-4 weeks

High engineering overhead
Many hours* spent per study by engineering, manually configuring visits & activities

Error prone
CAPAs* (compliance errors) in 2021 vs 0-1 expected for competitor average

Unscalable
Platform had years of growing UX and engineering debt from many* studies

For confidentiality reasons I have omitted the actual values for these metrics.  


SOLUTION

Zero-to-one redesign of the legacy product to fit user (clinical data managers) mental models 


SOLUTION

Navigating this problem led to many design artifacts exploring the mental model, system maps, and explorations for the design hypothesis. The following are some examples.


IMPACT

2024 internal launch has shown study set-up to be significantly faster, easier, and accurate

Efficient
95%+* reduced time to configure care use-case vs the old platform

Faster study config
90%+ est* engineering time saved per study set-up vs legacy product

UXR testing
20+ users strong positive qualitative data across all users tested (pre-release)

Low errors
No CAPAs* (compliance errors) thus far after launch

For confidentiality reasons I have omitted the actual values for these metrics.