overview

Over the course of 2+ years, I worked with various stakeholders and developers to develop the product line for mdbrain. mdbrain is an AI-powered software that facilitates diagnosis of neurodegenerative diseases by analyzing MRIs and creating comprehensive reports for radiologists. I designed the full reports and the digital web platform used by radiologists.

role

Sole Designer

skills

Contextual Inquiry, Interviews, User Flows, User-Journey Mapping, Surveys, Prototyping, Visual Design

software

Sketch, InVision, SurveyMonkey

year

2019-2021

problem

Neuroradiologists have an immense workload that involves analyzing thousands of MRIs daily (there can be up to several hundreds of MRI images per patient) in order to identify and diagnose neurodegenerative disease(s). As routine work increases, so does human error. Fortunately, machine learning algorithms can automate many daily tasks for a radiologist which can provide more granular results, aiding diagnosis and reducing human error. But, without properly communicating the data, radiologists can’t benefit.

How might we ensure our neuroradiologist users get the most value from our software?

product strategy

The data is best viewed as a PDF report or on the webapp where users send images to be assessed. Since the field of radiology lacks large machine learning data sets, contributing to this data pool would make our business more valuable. Thus, the product must also allow users to mark our software’s analysis as correct or incorrect which would improve our algorithm while fulfilling our business requirements.

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SUCCESS CRITERIA:

Precise characterization of brain regions from the software as reported by our users;
Ability for doctors to discuss same-day results with patients;
And an increase in correctly-annotated data.

discover

The first step in answering the problem question was to understand our main user, a neuroradiologist, and the problems they face in their day-to-day life. I determined the best research method would be a contextual inquiry session where I would go to a radiologist’s office and watch the doctor work his usual tasks, asking him questions throughout the session.

I drew up an empathy map to summarize my findings from the session. This helped us to see where our software fits into the user flow and what the goals of the platform and reports should be based on the user’s actions, thoughts, pains and gains.

empathy map.png

define

The research led me to define the following problem statements:

A radiologist needs to see the automated calculations of measurements of suspicious areas from MRIs so that they don’t have to rely on their own measurements. This can lead to errors in diagnosis.
A radiologist needs correct data from the analysis that can be copied directly into their medical report for records/referrals to a neurologist.
A radiologist needs a quick solution as they are pressed for time.

ideate

How might we allow the radiologist to obtain the correct data needed for their reports in the fastest way?

reports

The user research showed us which regions are important to assess to diagnose the diseases Alzheimers/Dementia and Multiple Sclerosis. I prioritized the regions and data by what is most necessary for diagnosing these diseases from top to bottom.

Shown below is what the Volumetry and Lesion reports looked like before I joined the team and then the final iteration on the design after conducting research.