QUEUE CURE'26-KNOW YOUR TURN BEFORE YOUR TURN

In India, nearly 76% of the 1.5 million clinics still rely on paper tokens and manual queue management. Patients often wait 2–3 hours without knowing when their turn will come, while receptionists manually maintain queues and doctors have limited visibility into patient flow. This results in long waiting times, poor communication, and operational inefficiency. Our goal was to build a real-time digital queue management system that improves transparency, reduces confusion, and streamlines clinic operations. Process We started by analyzing the hackathon problem statement and identifying the pain points of patients, receptionists, and doctors. We designed two synchronized interfaces: a receptionist dashboard for patient registration, token management, and consultation time updates, and a patient dashboard displaying the current token, queue position, and estimated waiting time. We selected Socket.IO to enable real-time synchronization between both screens without requiring page refreshes. React was used for building responsive interfaces, while Node.js, Express.js, and MongoDB handled backend logic and data storage. We continuously tested live updates, queue accuracy, and edge cases such as adding new patients or updating consultation durations to ensure reliability. Results The solution successfully digitizes clinic queue management by replacing paper-based token systems with a live, synchronized platform. Receptionists can register patients, update consultation times, and call the next token with a single click. Patients receive instant queue updates, know their position, and can view estimated waiting times without repeatedly asking staff. The application delivers real-time synchronization with minimal latency, improves clinic efficiency, reduces manual errors, and creates a more transparent and organized patient experience. Reflection With additional development time, I would implement online appointment booking, SMS and WhatsApp notifications for upcoming turns, doctor-specific queues, patient authentication, analytics dashboards, multilingual support, and role-based access control. I would also optimize the waiting-time prediction algorithm using historical consultation data and deploy the application on a scalable cloud infrastructure with comprehensive automated testing to improve reliability for large clinics.