KANTHASAMY K N IT-2023

Queue Cure '26 | Real-Time Clinic Queue Management System with Live Patient Tracking

Many small clinics still rely on paper tokens and verbal announcements, causing patients to wait for long periods without knowing when they will be called. Receptionists manually manage the queue, making it difficult to track the current patient and communicate wait times. Patients have no visibility into the queue, and any delay requires repeated enquiries to the reception desk. The goal was to create a simple real-time queue management system that provides visibility to both the receptionist and the waiting room while automatically estimating waiting times and synchron Process I started by identifying the two main users of the system: the receptionist and the patients in the waiting room. I divided the application into two screens, one for managing the queue and another for displaying live queue information. I first implemented the backend using Node.js and Socket.io because real-time synchronization was the highest priority. An in-memory queue was used to maintain patient information and token numbers. React was used to create separate interfaces for the receptionist and waiting room. Initially, I considered manually refreshing data between screens, but this approach would not provide a real-time experience. I switched to Socket.io so that every queue update is broadcast instantly to all connected clients. Dynamic waiting time was calculated Results Successfully developed a real-time clinic queue management system with live synchronization between receptionist and waiting room screens. The system eliminates manual token tracking and provides patients with queue visibility and estimated waiting times. Socket.io ensures updates are reflected instantly without requiring page refreshes. Input validation and edge-case handling improve reliability and usability. The final solution demonstrates a simple and scalable approach that can be adopted by small clinics with minimal training. Reflection If given more time, I would replace the in-memory queue with a database to preserve data across server restarts. I would also introduce doctor dashboards, priority queues for emergency patients, appointment scheduling, SMS or WhatsApp notifications, and analytics for consultation durations. Additionally, I would enhance the UI and add authentication to support multiple receptionists and doctors in larger clinics.