QueueCure: Smart Healthcare Queue & Appointment Management System

Many clinics still rely on manual appointment booking and token-based queue systems, leaving patients uncertain about their consultation status and expected waiting times. Patients often spend hours waiting without visibility into queue progress, while receptionists manually manage appointments, approvals, and walk-ins. The lack of real-time communication leads to overcrowding, scheduling conflicts, and inefficient clinic operations. QueueCure addresses this challenge by providing a centralized platform where receptionists can manage appointments and queues efficiently while patients receive l Process I started by analyzing the workflow of a typical clinic, identifying two primary stakeholders: receptionists and patients. The system was designed around role-based access, separating operational tasks from patient-facing features. I first created the appointment request workflow, allowing patients to select doctors, preferred dates, and reasons for visits. Next, I implemented an approval system where receptionists could review requests, schedule appointments, and provide visit instructions. To satisfy the hackathon's real-time requirements, I integrated WebSocket-based synchronization so that queue updates, appointment approvals, and token calls are reflected instantly across both dashboards without page refreshes. Dynamic wait-time estimation was added using queue length and consultatio Results QueueCure successfully transformed a traditional clinic queue system into a real-time digital experience. The platform enables patients to book appointments, track queue positions, receive appointment confirmations, and view estimated waiting times through a dedicated dashboard. Receptionists can manage appointments, approve requests, handle walk-ins, and control consultation queues efficiently. Real-time synchronization ensures that queue movements, appointment updates, and token calls are reflected instantly across both portals without requiring page refreshes. The final solution addresses k Reflection If I were to continue developing QueueCure, I would focus on making it production-ready for real healthcare environments. This would include integrating SMS and email notifications, QR-based patient check-ins, AI-powered wait-time prediction, and multi-clinic support. I would also conduct user testing sessions with receptionists and patients to validate workflows and identify usability improvements. Additionally, I would implement analytics dashboards for appointment trends, no-show rates, and consultation efficiency, along with stronger security measures and healthcare compliance features to