Queue Cure '26 – Real Time Clinic Queue Management System

Most small and medium clinics still rely on paper tokens and manual queue management. Patients often wait 2–3 hours without knowing when they will be called, leading to frustration and repeated inquiries at the reception desk. Receptionists manage queues manually, making it difficult to track patient flow, estimate waiting times, or handle peak-hour congestion. There is also no real-time communication between the reception desk and waiting patients. Queue Cure '26 addresses these challenges by providing live queue visibility, intelligent wait-time estimation, and synchronized updates across re Process I started by identifying the core pain points in traditional clinic queue systems: lack of transparency, receptionist overload, and inaccurate wait-time estimates. The solution was designed around two user roles: receptionists and patients. A receptionist dashboard was created to manage patient tokens, advance the queue, and monitor doctors on duty. A patient waiting room interface was designed to provide real-time queue status, estimated waiting time, and queue position visibility. Socket.IO was selected to enable instant synchronization between both screens without page refresh. To improve wait-time accuracy, we implemented an AI-inspired prediction system that calculates ETA using consultation history and rolling averages from recent appointments. Concurrency handling was also consid Results Queue Cure '26 provides real-time synchronization between receptionist and patient interfaces, eliminating the need for page refreshes. The system improves queue transparency by showing current token status, queue position, and estimated wait times. Dynamic ETA prediction uses consultation history rather than hardcoded values. The solution also addresses concurrency challenges through server-side queue control and supports multiple operational edge cases for a reliable clinic workflow. Reflection While the current solution successfully provides real-time queue synchronization and intelligent wait-time estimation, I would further improve it by conducting user testing with receptionists and patients in real clinic environments. Future iterations would include WhatsApp notifications, appointment booking, multi-clinic support, predictive staffing insights, and enhanced AI models trained on larger consultation datasets. I would also perform load testing to validate performance under high patient volumes and multiple concurrent operators.