QueueCure – Real-Time Digital Clinic Queue Management System

Many small clinics still manage queues using paper tokens and manual announcements. Patients often have no visibility into their queue position or expected waiting time, while receptionists must handle registrations, token tracking, and status updates manually. QueueCure was built to replace this process with a real time digital queue system that provides live updates, priority based patient management, and wait time estimates calculated from actual consultation data rather than hardcoded assumptions. Process I began by analyzing the clinic workflow and identifying three core requirements: fast patient registration, real-time queue updates, and accurate wait-time estimation. I first built a local prototype using React to validate queue operations, token generation, and patient prioritization. After confirming the workflow, I integrated Express.js, MongoDB Atlas, and Socket.IO to enable persistent storage and live synchronization across receptionist and patient screens. Wait times were calculated using completed consultation durations instead of fixed values. Multiple iterations were used to handle edge cases like emergency prioritization, empty queues, duplicate entries, concurrent updates, and database failures. The final solution focuses on simplicity, reliability, and real world usability. Results Receptionists can register patients and issue tokens in under 10 seconds. Queue updates appear instantly across all connected screens without page refreshes using Socket.IO. Estimated wait times are generated from actual completed consultation durations, improving accuracy over static estimates. Emergency and urgent patients are prioritized automatically. The system maintains live synchronization between reception, doctor, and patient views while providing searchable patient records and visit history. Reflection With more time, I would add SMS/WhatsApp notifications for token calls, doctor-specific consultation analytics, appointment scheduling, multi-doctor support, and role-based authentication. I would also improve the wait-time algorithm using machine learning and historical trends instead of simple averages. Additional features such as offline first support, receptionist audit logs, and clinic level reporting dashboards would make the platform production ready for larger healthcare centers.