Avanish Shukla

QueueCure – Real-Time Clinic Queue Management System

Most neighborhood clinics still rely on paper tokens, verbal announcements, and manual queue tracking. Patients often wait for hours without knowing their position in the queue or estimated waiting time. Receptionists spend significant time managing patient flow, while doctors lack visibility into queue status. The challenge was to create a real-time digital queue system that provides transparency for patients, reduces receptionist workload, and updates instantly across all screens without requiring page refreshes. Process I started by analyzing the clinic workflow and identifying the main pain points: lack of visibility, manual token management, and unpredictable waiting times. I designed two separate interfaces—a Receptionist Dashboard for queue management and a Patient Waiting Room for live tracking. For the backend, I chose Express.js with MongoDB Atlas to store queue data and Socket.IO to enable real-time synchronization. I implemented automatic token generation, queue state management, and dynamic wait-time calculations based on average consultation duration. Several UI iterations were created before finalizing a modern, high-contrast interface that works well in clinic environments. The final system ensures that whenever the receptionist adds a patient or calls the next token, every connected screen Results QueueCure successfully replaces manual clinic queue management with a live digital system. Receptionists can register patients and issue tokens in under 10 seconds, while patients receive real-time updates without refreshing the page. Queue status, current token, patients ahead, and estimated waiting time remain synchronized across all connected screens. The solution improves transparency, reduces confusion in waiting areas, and demonstrates how modern real-time web technologies can streamline everyday healthcare operations. Reflection If given more time, I would expand QueueCure beyond a single-clinic queue system into a multi-clinic platform with role-based authentication, appointment booking, SMS/WhatsApp notifications, and analytics dashboards for clinic owners. I would also improve the wait-time prediction model by using historical consultation data and machine learning instead of a fixed average consultation duration. From a technical perspective, I would add automated testing, offline support, and stronger security controls for production deployment. These improvements would make the system more scalable, accurate, an