Clinicians and scientists study for years to be able to understand and correctly interpret information on genetic variants. Our goal was to make this information accessible to a wider audience while maintaining the integrity of the data.
The first step in the development of EZ. Clinvar was figuring out what doesn't work. Our research quickly showed us that many of these databases are information dense, packed with medical jargon, and organized in a way that might be difficult for patients to understand. This shaped how we approach the design of our product. The colors used in our front-end are high-contrast, easy on the eyes, and standard for medical fields. Patients do not understand the terminology used in datasets that are catered to clinicians, but it was equally important that patients were informed of what they were reading. Instead of replacing the original terms, we kept the original names of the information in the data but included a "hover for more information" feature into our front-end design so that patients could understand the meaning of the terms. The search boxes allow the user to filter by multiple terms at once, easing the search for specific information.
They dataset we were given for this prompt was very large -- over 8 million entries -- so to combat slow process speeds, we condensed our data to 1000 entries for the sake of testing and the demo.
After brainstorming multiple directions that we could take the project in, we decided that an interactive dictionary format best suited our needs. In the future, we hope to further advanced filtering on fields, such as: Origin (somatic vs germline), NumberSubmitters, and ReviewStatus. We want to build an advanced view toggle that would allow patients to view a dashboard with more complex information, similar to the original dataset. Lastly we hope to integrate ClinVar data with other databases like GeneCards to provide additional information including gene description and clinical guidelines.
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