SELVAPRIYA S CSE

Queue Cure '26 – Real-Time Smart Clinic Queue Management System

Most neighborhood clinics still rely on paper token slips and manual queue management. Patients often wait 2–3 hours without knowing when they will be called, causing frustration and overcrowding. Receptionists manage queues from memory, while doctors lack visibility into patient flow. Queue Cure was built to replace this process with a real-time digital queue system that provides instant updates, dynamic wait-time prediction, and better operational visibility. Process • Analyzed clinic pain points and identified three priorities: fast check-in, live queue updates, and accurate wait-time estimation. • Designed separate dashboards for Receptionists, Patients, and Analytics. • Built the system using React, Node.js, Socket.IO, and MongoDB Atlas. • Implemented real-time synchronization without page refresh. • Added database persistence to prevent data loss after refresh or restart. • Improved reliability with Skip, Recall, Undo, and edge-case handling. • Focused on simplicity, responsiveness, and usability for real clinic workflows. Results The final system enables patient check-in in less than five seconds and provides instant queue synchronization across all screens. Patients can track their position and estimated waiting time without refreshing the page. MongoDB persistence ensures that queue information survives application restarts, while analytics provide visibility into consultation activity and patient flow. The project successfully transforms a manual paper-token workflow into a real-time digital experience. In future versions, I would integrate doctor-specific dashboards, appointment scheduling, and notification systems Reflection Given more time, I would implement role-based authentication, SMS or WhatsApp notifications, appointment booking, and historical reporting dashboards. I would also improve the wait-time prediction algorithm using consultation history and machine learning techniques. Additional testing with real clinic staff would help optimize the user experience and ensure the system scales effectively for larger clinics.