Queue Cure

Many clinics still use manual token systems, resulting in long waiting times, overcrowding, and poor visibility into queue status. Patients often do not know when they will be called, while receptionists must manage queues manually. Queue Cure addresses this problem by providing a real-time digital queue management system with live token tracking and wait-time prediction. I started by identifying the key pain points faced by patients and receptionists in clinic environments. I designed separate interfaces for both user groups and mapped the patient journey from registration to consultation. The system was built using a full-stack architecture with real-time communication through Socket.IO. Multiple iterations were made to simplify queue management, improve wait-time calculations, and handle edge cases such as emergency patients, empty queues, and simultaneous updates. An initial polling-based approach was replaced with WebSockets to achieve instant synchronization. Queue Cure successfully digitizes clinic queue management through real-time synchronization and automated wait-time prediction. Patients can view their queue position, tokens ahead, and estimated waiting time instantly. Receptionists can efficiently manage queues with minimal effort. The system supports priority-based handling for emergency and senior citizen cases while maintaining transparency and reducing uncertainty for patients. With more time, I would integrate SMS and WhatsApp notifications so patients can receive queue updates remotely. I would also implement AI-powered wait-time prediction using historical consultation data, add appointment scheduling, support multiple clinics, and conduct real-world user testing to further improve usability and system performance.