PALANISAMY R

Queue Cure – Real-Time Clinic Queue Management System

Many clinics still rely on paper tokens and manual queue management, creating long waiting times, confusion, and poor communication between receptionists and patients. Patients often do not know their position in the queue or expected consultation time. Receptionists must manually manage records and announcements, leading to inefficiencies. Queue Cure solves this by providing a real-time digital queue management system with live updates, automated token handling, voice announcements, waiting time prediction, and queue history tracking. I started by analyzing the common problems faced in clinic waiting rooms, including manual token management, lack of queue visibility, and inefficient patient communication. I designed a system with two interfaces: a receptionist dashboard and a patient display dashboard. The receptionist dashboard manages patient registration, token generation, queue operations, waiting time settings, and consultation history. The patient dashboard displays the current token, queue status, expected waiting time, and voice announcements. I implemented real-time communication using WebSockets so both dashboards stay synchronized instantly. After development, I tested patient flow scenarios, queue updates, waiting time calculations, voice notifications, and history management to ensure smooth operation. Queue Cure successfully digitizes clinic queue management by replacing paper tokens with a real-time monitoring system. Receptionists can efficiently manage patients, call the next token, track consultation history, and update waiting time settings. Patients receive live queue information, estimated waiting times, and voice announcements. The solution improves transparency, reduces confusion in waiting areas, and provides a better overall clinic experience through synchronized real-time updates. If given more time, I would add SMS and WhatsApp notifications, online appointment booking, doctor-specific queues, analytics dashboards for clinics, and cloud deployment for wider accessibility. I would also improve waiting time prediction using historical consultation data and add multilingual voice announcements for better patient accessibility.