Inspiration

Our inspiration came from misinformation regarding which medical facilities treat certain issues. Often, people will rush to the ER for insignificant problems while others, who may be suffering from stroke symptoms, need immediate attention. No matter the person however, there is a need for an app to suggest actions for people to take instead of simply going to the ER for anything.

What it does

By asking specific questions, the user will give information to HealtHere's algorithms to determine the best place for them to go. The options available so far are: no action, schedule hospital visit, go to ER, and call 9-1-1. In the hopes of assisting the user, we also recommend the hospital with the lowest risk factor at the closest distance.

How we built it

We used Python's Pandas library for hospital recommendations, the Flask framework for hosting the web app, AJAX to request information from the server, and the web development suite for giving the app appearance and basic functionality. In addition, we used Google Maps APIs for displaying locations of the medical facilities for the user.

To run this app yourself, type

git clone https://github.com/spencerchurchill/HealtHere.git
cd HealtHere/
chmod +x run.sh
./run.sh

Then go to http://127.0.0.1:5000/home

Challenges we ran into

A major challenge we encountered while making this app was accurate data analysis. Often times, data scientists will misinterpret medical data and we were fortunate enough to have a MD on our team to help us use a good dataset to enact positive change.

Accomplishments that we're proud of

We are proud to put together an app which can help people today. By recommending the safest and closest medical facilities based on symptoms, we can hopefully catch stokes before the hemorrhage or mitigate many long-term issues.

What's next for HealtHere

The next goal for HealtHere is to promote this app and encourage Insurance Companies to give benefits to users as less trips to the ER and more accurate facility usage will vastly decrease costs of return visits and misclassifications.

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