Karthik Raja

Queue Care

Clinics relying on paper or static token boards leave reception, doctors, and waiting patients out of sync, with no live view of queue status across rooms. Wait-time estimates are usually rough guesses rather than figures based on actual doctor consultation history, leaving patients frustrated and waiting rooms overcrowded. Under busy front-desk conditions, receptionists can accidentally double-register patients or assign clashing token numbers. Updates made at intake or in the consultation room often don't reach other screens until someone manually refreshes. Process I began by researching common challenges in hospital queue management, including long waiting times, lack of real-time updates, and inefficient patient tracking. I designed my application as a real-time queue management system using React, TypeScript, Node.js, Socket.IO, and MongoDB. Initially, I implemented an in-memory queue system, but data was lost on refresh, so I integrated MongoDB Atlas for persistent storage. I then added live synchronization between Receptionist, Doctor, and Patient dashboards using web, Multiple iterations were made to improve wait-time prediction, priority handling, error validation, and system reliability. Finally, I deployed the application, connected cloud database storage, and tested real-time queue operations across all user roles. Results QueueCare 26 successfully provides a real-time hospital queue management solution with live synchronization across Receptionist, Doctor, Waiting Room, and Patient Tracker interfaces. The integration of Socket.IO enabled instant queue updates without page refreshes, while MongoDB Atlas ensured persistent data storage across sessions. The system reduced manual queue handling, improved patient visibility, supported priority-based treatment workflows, and delivered accurate wait-time predictions. Through testing and deployment, the platform demonstrated reliable performance, scalability. Reflection If given more time, I would conduct testing with actual clinic staff and patients to gather real-world feedback. I would also implement SMS/WhatsApp notifications, appointment scheduling, AI-powered wait-time prediction, and role-based authentication for enhanced security. Additionally, I would optimize the system for larger hospitals with multiple departments and integrate it with existing Electronic Health Record (EHR) systems for seamless healthcare operations.