As vital as healthcare is in our society, it is sometimes difficult to access medical resources within a short period of time during emergency; Maybe you live in the suburbs and it takes 1 hr to get to the nearest hospital, or maybe your health insurance hasn't kicked in yet, or maybe it's the middle of the night and your kid is having a terrible fever. These requirements could easily be met with online healthcare systems, inspired by this, we created a matching service system which provides people an alternative solution to their medical needs. On MatchDoc, patients are matched and recommended, according to their symptoms, location, and personal profile, with doctors or medical students and received professional advice online. If partnered with pharmacies, prescriptions could even be sent to the pharmacy which is nearest to the patient. This system benefits not only the people but doctors and medical students in practicing their skills and gain necessarily experience in consulting patients.

What it does

We gather the necessarily information (e.g., gender, age, symptoms) from patients with our website, and send it to our backend. Our backend system comprised of patient/doctor database and a search-based algorithm. Our system will predict patient's disease to find in state or best score doctors that can give professional advice to the patient users. Our backend will send the recommended doctors back to the user, and they can get best advice from the best doctors!

How we built it

We use Python to build our system. We also use MongoDB to save patients' symptoms.

Challenges we ran into

Integrate the frontend and backend system.

Accomplishments that we're proud of

  • We create a algorithm to recommend best doctors to the users.
  • We learn new frontend building tools and use in our project.

What we learned

  • New frontend building tools.
  • Build a new system in 24 hours.
  • Collaboration with other teammates.
  • Inspire our potential.

What's next for Match Doc

Expand our doctor dataset with real doctors' data, and embed video stream between patient and doctor or medical student.

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