queue cure

Most neighbourhood clinics still rely on paper tokens and manual queue management, leading to long waiting times, confusion, and poor communication between receptionists, doctors, and patients. Patients have no visibility into their queue status or expected waiting time, while receptionists spend valuable time managing the queue manually. Queue Cure '26 addresses this by providing a real-time digital queue management system with live updates, smart token management, doctor selection, emergency prioritization, appointment booking, and accurate wait-time estimation to improve clinic efficiency a I began by studying how small clinics manage patient queues and identified key problems such as manual token handling, uncertain waiting times, and lack of real-time updates. Based on the hackathon requirements, I designed a two-screen solution for receptionists and patients. I built the frontend using React and the backend using Express.js with Socket.IO for instant synchronization. I implemented patient registration, automatic token generation, doctor selection, emergency priority, live queue updates, and dynamic wait-time calculation. I then added appointment booking, voice announcements, consultation history, search functionality, dashboard analytics, and queue export. Finally, I tested different queue scenarios to ensure accurate synchronization, smooth user experience, and reliable p The application successfully digitizes clinic queue management by enabling receptionists to register patients, assign doctors, prioritize emergency cases, book appointments, call the next patient, and complete consultations efficiently. Patients receive live queue updates, accurate waiting-time estimates, and voice announcements without refreshing the page. The dashboard displays patients served, waiting patients, emergency cases, consultation history, and real-time analytics, improving transparency, reducing manual effort, and enhancing the overall patient experience. If I continue developing this project, I would implement role-based authentication for receptionists, doctors, and administrators, add SMS/WhatsApp notifications to alert patients before their turn, support multiple doctors with independent queues, integrate online appointment scheduling, and deploy the application on the cloud with HTTPS. I would also enhance analytics with reports and predictive wait-time estimation using historical consultation data to make the system more scalable and suitable for real-world clinics.