Medicine is a pillar of our society, and yet going to the doctor can be incredibly difficult. In conversation with a few medical professionals, “patients often have to wait so long only to be told to visit another hospital, where there were even more forms”. In fact, patients spent an average of 16.3 hours waiting in emergency rooms, and this number has escalated with the COVID-19 Pandemic.

There isn't a platform that digitizes medical interactions. The same boring forms, the same wait lines and the same decade-old strategies.

What it does

This problem is a realistic and common problem impacting many, which is why we created SwiftMed. The universal platform for centralized data transportation in medicine. Our guiding exploration was to focus on transparency, medical transportation and data privacy. We tackled the three-pronged problem of (1) Do I need to go to the hospital? (2) Which hospital is actually available to take me? (3) How does the hospital know who I am? SwiftMed makes medical transportation with data and travels easy, with the exploration of high-impact frameworks and high-efficiency algorithms.

How we built it

Frontend - Figma, React, Javascript; Backend - Redux, ExpressJS, NodeJS, GraphQL; ML Model - Python, Anaconda, Tensorflow; Python Transportation Algorithm - Python, Anaconda, 3+ Libraries

Challenges we ran into

We faced two large challenges. Firstly, the frontend was planned to be built with NextJS with Typescript for performance, but we wanted to prioritize our design and features. We utilized Figma, which is powered by Typescript, and alternatively built two APIs to go with the rest of the backend (including our algorithm, and ml model). Furthermore, working virtually was incredibly difficult. However, we had music blasting on our server and focused on collaborating from our desks.

Accomplishments & Learning

Rajan - Learned how to create multiple APIs, the importance of medical innovation and how to create large-scale applications. Daksh - This was my first hackathon, but I'm super excited to create SwiftMed alongside my team Jaival - Learned how to implement new algorithms with Python and location tracking and to auto-generate HTML code. Tirth - I've barely touched code in my past, but creating a full ML model with 99% accuracy from scratch was definitely an accomplishment I will cherish forever.

What's next for SwiftMed

  • Full implementation (Frontend, Backend, ML and Algorithm combined into one)
  • Marketing/Market Research
  • Privacy
  • Identification of Differential Diagnosis

Exploration, Data Centralization and Medical Transportation. That’s SwiftMed.

Built With

Share this project: