The dashboard screen, easy access, beautiful colors, simple to use.
The information screen for our Urgent Care features.
This is the screen that leads to the calling UI, it is very simple and explains the process.
The inspiration for Medscribe came from our parents’ experience as immigrants. For many immigrants, English is not their native language and they have trouble finding their way in a country that speaks English, especially with complicated medical terminology. Our aim is to put language barriers in the past and move towards a world where anybody can communicate, no matter what language they speak.
Over half a million immigrants come to the United States every year. These immigrants come from all walks of life, meaning that they likely have varying levels of health based upon their home country. As a result, local hospitals have to work towards accommodating immigrants that may or may not be familiar with the United States health system. Immigrants that are unfamiliar with the English language can have a particularly difficult time navigating the pitfalls of the United States' healthcare system. This can put an extremely heavy burden upon regional health systems.
What It Does
Medscribe offers a host of features that will assist immigrants in understanding America’s complicated medical system. Chief among these features is the live translation system, which uses speaker recognition in concert with Google's translation API in order to translate any language to English in real-time during phone calls with medical professionals or emergency services.
How We Built It
We built Medscribe using Swift, Python, and Node.js. We used Sketch to design the app and logo.
Challenges we ran into
One major challenge we ran into was searching for an easily usable API that gives access to information on nearby doctors and health insurance. Many APIs involving such data are private and it can often be challenging or impossible to get a key. The live translation feature also led to many roadblocks. For example, we first attempted to use Twillio but realized after about an hour that it wouldn't be possible to use it for our desired implementation.
Accomplishments that we're proud of
Our group is very proud of the live translation feature of Medscribe, as well as the design of the app.
What we learned
We learned how to use text-to-speech engines, speech-to-text engines, and web-sockets while implementing live translation.
What's Next For Team Panda
We want to expand Medscribe beyond a live translation app for medical services to an all encompassing health hub for immigrants. We think that a blockchain based Medical ID that links with Apple's Medical ID would be a good way to transition immigrants to the United States' healthcare system in a way that is secure and technologically sound.