The problem Compact HealthCare solves

In today's busy world, where long queues and waiting times are synonymous with hospital visits, people lack time and patience to wait in these queues. In hospitals, queuing is often managed manually by administrative staff, which can lead to a chaotic environment.To ensure safety, it can be exhausting for hospital staff to monitor if each patient is wearing a mask. Also, the patient has to find nearby hospitals with specialist doctors according to their symptoms which can be a tedious work specially when the person is in a new city.

After analyzing all these problems and the continuous need to improve and optimize healthcare systems, we have developed the ‘Compact Healthcare’ system. It is a computerized recommendation system designed to help patients with doctor appointments and recommend them to hospitals based on their respective needs.

Our application ‘Compact Healthcare’ will save patient’s time and manual efforts. After logging in, patients can search for their symptoms in a search bar. Then with the help of machine learning algorithms we will predict the disease and accordingly suggest the doctor and hospitals with respect to the geographic location and medical requirements. After clicking on the hospital, the patient can book an appointment. After booking the appointment, our system displays the approximate waiting time with the help of our scheduling algorithm. This algorithm will divide the doctor’s available time in x slots (x- the average time required to treat each patient by doctor), and this will help us to predict the patient’s number in the appointment list. Before entering the hospital, patients will have to verify their identity using a QR code which is assigned to them while profile creation. Then our system uses a CNN algorithm for face mask detection where it will check if a patient has a mask on or not. Patients will be updated about all the details of the appointment through Whatsapp notifications.

Challenges we ran into

Technical Challenges

Integration of Whatsapp notifications with our system as contextual integration can be critical.

Integration of ML model

Developing an efficient Scheduling Algorithm.
Developing a Recommendation System of diseases and doctors where fetching data for matching symptoms to categories of diseases and then type of doctors was a bit challenging.