Queue Cure AI – Smart Clinic Queue Management System

76% of India's 1.5 million clinics still rely on paper token systems and manual queue management. Patients often wait 2–3 hours without knowing when they will be called, causing frustration and overcrowding. Receptionists manage queues manually, while doctors lack visibility into patient flow and waiting times. This results in poor patient experience, inefficient clinic operations, and increased administrative burden. Queue Cure AI addresses this problem by providing real-time queue tracking, doctor dashboards, and AI-powered waiting time prediction to improve transparency and efficiency. Process I started by analyzing the challenges faced in small and medium-sized clinics where queue management is mostly manual. I identified three key stakeholders: patients, doctors, and receptionists. Based on their pain points, I designed a digital queue management system with separate dashboards for each user. First, I developed a reception dashboard to register patients and generate tokens automatically. Next, I created a doctor dashboard that displays the current patient, waiting count, and queue status. Then, I designed a patient view that provides real-time token tracking and estimated waiting time. To make the system smarter, I implemented an AI-based waiting time prediction model that calculates estimated wait time based on queue length and consultation duration. Finally, I built an ana Results Queue Cure AI successfully demonstrated a digital solution for clinic queue management. The system provides real-time token generation, live queue tracking, doctor dashboards, patient visibility, and AI-based waiting time prediction. Key outcomes achieved: * Reduced waiting uncertainty from 2–3 hours to real-time visibility. * Improved patient transparency through live queue updates. * Automated token management, reducing receptionist workload. * Enabled doctors to track patient flow efficiently. * Predicted waiting times using AI-based queue analysis. Reflection If given more time, I would enhance Queue Cure AI by integrating a cloud database and real-time communication using WebSockets for live updates across multiple devices. Additional improvements would include: * Appointment booking before clinic visits. * QR code-based patient check-in. * SMS and WhatsApp notifications for upcoming turns. * Machine learning models trained on historical clinic data for more accurate waiting time prediction. * Emergency patient prioritization. * Multi-doctor and multi-branch clinic support. * Mobile application for patients and doctors. * Advanced analytics for