AutoDVT

— Memo

CASE STUDY

I led UX research and design of AutoDVT – an Android app that enables non-specialists to detect deep vein thrombosis (DVT).

PROJECT KEY GOALS

Design and launch an MVP app to carry out clinical trials in the UK to obtain medical device approval.

TIMELINE & TEAM

Five months start to finish working closely with founders and dev team — remote project spread across four different time zones.

RESPONSIBILITIES

• UX research
• Interaction design
• Prototyping and usability testing
• Design library and developer handoff

RESULTS

Preliminary results indicate AutoDVT to be 30x faster than the existing clinical pathway without sacrificing accuracy.

WHAT IS DVT

Deep vein thrombosis (DVT) is a blood clot that forms in the deep veins of the legs. If parts of the clot break off, it could travel to the lungs and cause a potentially deadly disease called pulmonary embolism (PE).

900k

People are affected each year

100k

People die each year

$10+B

cost to treat DVT each year

THINKSONO OVERVIEW

ThinkSono is a startup that makes ultrasound software intelligent.

AutoDVT is the company’s first product currently in clinical trials.

Founded: 2016

Core Area: MedTech

Location: London, UK

Stage: Seed

CONTEXT

After a few years of backend work, I was tasked to design the company’s first MVP to carry out clinical trials.

My job was to analyze the existing research and design to continue building the UX with additional research and IxD best practices.

THE TEAM

Co-founder & CEO

LONDON, UK

Co-founder & CTO

POTSDAM, GERMANY

Developers

GERMANY & INDIA

UX Designer (Me)

BAY AREA, CA

AUTODVT OVERVIEW

AutoDVT enables a non-specialist to detect DVT with the same accuracy as a Radiologist but is cheaper and faster.

The product for clinical trials is twofold: a non-specialist Android app and a radiologist cloud dashboard.

MOBILE APP

A non-specialist will use a small handheld ultrasound machine and the AutoDVT app to conduct compression ultrasound scans assisted by AI guidance for a provisional diagnosis.

CLOUD DASHBOARD

The provisional diagnosis is sent to the cloud dashboard for review, where a radiologist can log in remotely and make a final diagnosis.

So, what’s the problem?

Medical professionals without formal ultrasound training need a way to diagnose DVT. Currently, a radiologist is required, but it’s expensive, time-consuming, and not always possible.

OUR SOLUTION

AutoDVT will provide an ultrasound software solution for non-specialists to detect DVT by harnessing AI-based guidance and diagnostics. We’ll know we’re right if our solution takes less than 15 minutes, has less than 1% false negatives, and 10% false positives.

THE PROCESS

I aimed to open up the design process, create lightweight deliverables, frequent design reviews, and a shared understanding within the team.

Conducted 2 stakeholder interviews with founders and reviewed 10+ sources about DVT, diagnosis, and treatments.

Collectively created a problem statement, use cases, executive summary, and design principles to create a shared vision and understanding.

Designed a high-level flow chart with user actions, user decisions, and main screens.

Mapped out mid-fidelity wireflows with all the necessary screens to complete an ultrasound exam.

Created a hi-fi interactive prototype to conduct remote usability testing with 5 users to test satisfaction and performance.

I created a design library to keep the design consistent between the Android mobile app and the dashboard web app and ensure a seamless design handoff.

The design library was built in Sketch and was handed off to developers using Zeplin.