DocDiscovery

Taking care of your health can be a burden for many. Don't worry; save yourself the headache! Use DocDiscovery’s simple UI to find the best healthcare provider for you in a matter of seconds.

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Inspiration

As young adults, our group members have felt the stress of becoming independent and not being so reliant on our parents. We have found ourselves many times searching hundreds of postings online to find the right doctor for our needs and felt a project such as this where it is a one stop shop saves the headache and makes this process much easier.

What it does

A user enters the type of doctor they need, their personal zip code, insurance type, and specific symptoms. The program then returns a collection of the best doctors for them. These cards have a tinder like matching mechanism where user selects whether to favorite a doctor or remove them from the collection. Ultimately, the user is presented with all their favorited cards and can select their best fit doctor. Once a doctor is selected, it removes all other doctors and simply displays all information of your selected doctor.

How we built it

We wanted to use a flashier tech stack, but due to our limited knowledge on certain functionalities in a technology like Next.js we opted to go very plain and old school route with html5, cc3, and vanilla javascript -- this way we were all able to actively contribute. Ultimately, the program requires a user to fill in 4 required fields which are doctor type, zip code, insurance type, and any specific symptoms. These values get stored in variables that become an object and passed onto our server side. On the server side the script iterates throughout all active rows in the database and finds rows that match doctor type and insurance type exactly and searches for the closest match in zip code and doctors who specialize in the users inputted symptoms. All the rows that are best fit for the user are stored into an array that get passed back to the client side code and that data is then used to be display.

Challenges we ran into

We had difficulty finding the proper API that contained all the required criteria we needed for a search result thus we had to merge 2 APIs into one database. We also didn't have prior experience with the more popular databases such as MongoDB so we opted for a more unique approach and used Google Sheets as our database and used Google App Script to pull data from 2 APIs, populate our spreadsheet, then connected our local javascript file to API deployment of the App Script.

Accomplishments that we're proud of

We are all first time hackers and we are very proud of how managed our time, took a planned approach, and collaborated effectively to be able to successfully have a finished product.

What we learned

We learned how to navigate a hackathon more than anything with the completion of this project. We now understand that we must come in with a much better game plan to build bigger and better projects in this restricted time frame. This includes us learning technologies such as Next.js, MongoDB, and web scrapping to collect more of our own intended data. We learned how to work as a team and communicate in a fashion that all group members are occupied and contributing to the project.

What's next for DocDiscovery

What we currently have for DocDiscovery is very much an minimum viable product. If we wanted to enhance this project to a greater level that requires us having much more data in an efficient manner which could mean learning how to store the fetched data from APIs into a better database such as MongoDB or Firebase to handle a much more production ready data set.

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