Queue Cure AI – Real-Time Clinic Queue Management System

Traditional clinic queues rely on manual token management, verbal patient calling, and static waiting estimates. Patients often do not know their actual waiting time, while receptionists must repeatedly update and manage the queue manually. This leads to longer perceived wait times, communication gaps, and operational inefficiencies. The challenge was to build a real-time queue management system that keeps receptionists, patients, and display screens synchronized without requiring page refreshes while also providing intelligent wait-time predictions. I started by identifying the core workflow of a clinic: patient registration, queue management, patient calling, consultation completion, and live status tracking. I designed a Flask backend with SQLite for persistence and React for the frontend. To achieve instant synchronization across multiple screens, I implemented Socket.IO WebSockets for real-time communication. I built an AI-based wait-time estimation engine using historical consultation durations, doctor speed factors, and queue depth. I also added analytics dashboards, notifications, audit logs, and concurrency-safe queue operations. Multiple iterations were performed to ensure queue updates, patient calling, and wait-time recalculations happened automatically without requiring page refreshes. Successfully built a fully functional real-time clinic queue management platform with live queue synchronization across multiple screens. Patients receive updated queue information instantly without page refreshes. The system provides AI-powered wait-time estimation, live notifications, analytics dashboards, and automated patient calling workflows. The architecture demonstrates scalable event-driven communication using WebSockets and improves operational efficiency compared to manual queue management. Given more time, I would deploy the system using a cloud-based database and production-grade WebSocket infrastructure for larger-scale testing. I would also add SMS/WhatsApp patient notifications, role-based authentication, multi-clinic support, and a machine-learning model trained on larger historical datasets to improve wait-time prediction accuracy.