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

Most clinics still rely on paper token slips and verbal announcements to manage patient queues. Patients often wait for hours without knowing when they will be called, while receptionists manually track tokens and consultation progress. This results in confusion, long wait times, and operational inefficiencies Process Identified the core problem: lack of visibility and inefficient queue management in clinics. Defined two primary users: Receptionist: needs a fast and mistake-proof way to manage patient queues. Patient: needs real-time information about their position in the queue and expected wait time. Designed two synchronized screens: Receptionist Dashboard for adding patients and calling the next token. Patient Waiting Room displaying the current token, tokens ahead, and estimated wait time. Implemented real-time communication using Socket.io so that both screens update instantly when the receptionist performs an action. Calculated estimated wait time dynamically using the average consultation time and the number of patients ahead in the queue. Considered edge cases such as: No patients in queue Results Successfully built a real-time clinic queue management system with synchronized receptionist and patient views. Achieved instant queue updates across both screens using Socket.io without requiring page refresh. Reduced queue status checking effort from multiple manual interactions to a simple live dashboard view. Enabled patients to see the current token, tokens ahead, and estimated waiting time in real time. Improved receptionist efficiency through a simple interface for adding patients and calling the next token. Implemented dynamic wait-time calculation based on average consultation time Reflection If given more time, I would enhance Queue Cure by adding appointment scheduling, SMS/WhatsApp notifications for patients, and multi-clinic support. I would also improve the wait-time prediction system using historical consultation data instead of relying only on average consultation time. Additionally, I would implement user authentication, queue analytics for doctors, and offline data persistence to make the solution production-ready and more scalable.