Nirupama Ravichandra

Queue Cure '26 – Smart Clinic Queue Management System

Many clinics still manage patient queues manually, leading to long waiting times, inefficient prioritization, and poor visibility for both patients and reception staff. Handling emergency cases and senior citizens while maintaining a fair queue is difficult. Patients also have no easy way to track their position or estimated wait time. A modern, real-time solution is needed to streamline operations, improve transparency, and enhance the overall patient experience. Process I began by identifying the key pain points in manual clinic queue systems and defined the core workflow from patient registration to consultation completion. I designed the frontend in React with reusable components and built the backend using FastAPI and SQLite. A priority-based queue engine was implemented to handle emergency, senior citizen, and normal patients fairly. WebSockets were added for instant synchronization across the dashboard, live queue, analytics, history, and TV display. I continuously tested the application, refined the UI, and optimized the queue logic to provide a smooth real-time experience. Results Queue Cure '26 successfully demonstrates a real-time clinic queue management system with intelligent patient prioritization and instant synchronization across multiple screens. The application provides live queue tracking, estimated waiting times, analytics, digital TV display, and QR-based access. The result is a scalable prototype that improves operational efficiency, reduces manual effort, and delivers a better patient experience. Reflection With more development time, I would integrate SMS and WhatsApp notifications, implement secure role-based authentication, support multiple clinic branches, and add appointment scheduling. I would also deploy the application to the cloud, integrate a production-grade database such as PostgreSQL, and use real clinic feedback to further improve the user experience and queue prediction accuracy.