The current COVID-19 scenario has been our greatest inspiration. Our team members have considered the solution to the faced problems as the primary concern hence we can forward with this application which can help reduce the troubles being faced by the patients.

What it does

  • We have made a platform where the patients and the Doctors can register themselves. The patient can log in to his profile to make a request for an appointment with any registered Doctor. As soon as the Doctor Logs in, he will get the list of appointments on his Dashboard.
  • The Doctor can right away make a video call to the patient. We have made a status tag for every appointment. The status is yellow as long as the Doctor does not give the feedback (list of required medicines and checkups) and it becomes green once the Doctor's feedback is returned. The Feedback from the Doctor will be displayed on the patient's profile along with the appointment's history.
  • We have provided more features to assist and facilitate the Doctor as well as the patient. The Doctor will be given an option to send the list of checkups and medicines which patients need to get done. The Doctor will also be given an option to reappoint the patient after a given time.
  • The patient on the other hand will be given an option to upload the images of the reports of the test and scans which were asked by the Doctor. These reports will be displayed to the doctor along with the appointment request. We have added a Deep Learning model at an intermediate layer between the patient and the Doctor.
  • The model will be used to do an analysis of the reports/scans uploaded by the patients the model will predict the contingencies and in case of any severe disease, it will alert the doctor through the mail.

How I built it

  • The project has a tech stack consisting of HTML, CSS, Bootstrap & JavaScript at its front end. While the Backend Comprises of Django Framework.
  • Using an Google Cloud SQL database.

Challenges I ran into

  • We faced some severe challenges while deploying the project as initially, it was challenging to deploy full stack app on the Google cloud.
  • It was difficult to find the Data to train the Deep Learning model which we have hosted on Google cloud. GCP was new for our team so we took time to cope up with it. Uploading caused an error initially.
  • We came over each and every error by vigorous tries. We read and understood the documentation of Auth0 to overcome all errors arising due to it. For the Deep Learning model, we finally found data on Kaggle and implemented using Google Cloud Vision AI.

Accomplishments that I'm proud of

I and my team are proud to have made a well working project in very less time. We are proud to have dealt with a real medical issue which everyone is facing and we are glad to have coordinated with each other to give our best for the betterment of society.

What I learned

We have learned the importance of good management in a team. We have learned deployment on an entirely new platform. We have learned about the social causes and about the problem which patients face in day to day life.

What's next for Virtual Hospital

Now we look forward to do predictions with the help of data that we collect by this application. We want to involve Machine Learning predictive and Deep Learning Models to do predictions for the user, which can help doctors to give more care to a particular patient.

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