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A cloud based web application that connects multiple centers around the world, captures massive amounts of data points, and makes sense of it all.

Emory University was conducting a medical trial which required them to connect participating hospitals in different cities in India, to the trial’s headquarters in Atlanta Georgia, USA. The centers would have users from many different roles interacting with the system in various ways, at many different stages of the trial. We built a tailor-made platform to address all the minor nuances that would otherwise be left behind.

About The App

Built as a very user friendly and intuitive app for end-users who might not be the most technically savvy, the front-end system allows for caregivers, doctors, hospital admin staff or others to capture information about the patient’s condition at many different stages of the trial. The back-end of the system allows physicians to generate and view reports, review treatment prescribed and even get recommendations from the system itself based on the symptoms. This is thanks to a custom built algorithm in the back-end which required a lot of cross collaboration between Emory and Cygnis in order to build.

Design considerations were made to ensure that the application is usable on tablet computers as well, which allows doctors, nurses, physicians and others using the system to be able to do so in a mobile manner. All dashboards and other interactive elements were created keeping cross platform compatibility in mind.


  • Surveys

    Gather critical information to monitor progress and make adjustments as needed.

  • Big Data Collection

    Create a system that scales seamlessly in the back-end while optimized for report generation.

  • Subject selection

    Create a subject selection algorithm that follows industry standards and requirements.

  • Administer Instruments

    A tiered structure of user roles and privileges to ensure a workflow that everyone adheres to.

  • Process & Analyze data

    ift through large data sets to find patterns that fall above or below certain thresholds in order to generate alerts.

  • I based suggestion system

    A custom designed algorithm that mimics early stage AI bots to analyze symptoms given and suggest a treatment.

  • Reporting and conclusion finding

    Run customized queries on massive data sets to generate meaningful information presented in easy to absorb and visually stunning graphs

The Results

The project and trials are due to run for 2 years, so we will not be certain of its results until then. However, it has seen remarkable activity and progress over the months since release and all involved with the project are satisfied and optimistic for the future, as we continue to maintain and iterate on this application.

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