Over the course of two 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.Live Site
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.
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.
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.
The research led me to define the following problem statements:
Taking the learnings from the survey and the usability tests, I began to iterate on the design of the platform. Take a scroll in the section below to see all the changes the new platform underwent.
1. Changed platform from mdbrain platform to mediaire platform, allowing all product lines (incl. mdbrain) to be shown here.
2. Improved visual design by making the platform in dark mode. As radiologists stare at black screens all day, this colour scheme is easier on their eyes.
3.Changed organization of the platform so that it is patient-centric which means the issue of several entries for one patient no longer exists, and all analyses for all products (organs) can be viewed from one analysis screen.
4. Cleaned up the entries in the “completed” tab, with less important data shown upon expanding an entry list.
5. Added options to set patient consent and clinical finding before going to the analysis screen (user requests).
6. Created a third “rejected” tab in the first level of the platform to show more elaborate reasons for erroneously processed studies.
7. Made the different products available via side tabs in the analysis screen.
8. Added option to copy whole report as an image file to attach in medical report.
9. Changed the prime action on the analysis screen from “Send to PACS” to “Approve” as not all users wish to send their reports to their PACS and approval is necessary for algorithm improvement.
10. Created more options to copy data from the reports and paste into users’ medical reports in word processing software.
Once the new platform design was implemented, we waited to see what our customers thought. Here is some of the feedback we got:
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