Yukthasri Seelam

QueueCure AI – Smarter Queues, Better Care

Clinics often struggle with long and unpredictable patient waiting times, manual queue management, and difficulty handling emergency cases. Patients lack visibility into their position in the queue, while receptionists spend significant time coordinating appointments and managing walk-ins. This leads to inefficiencies, overcrowding, and reduced patient satisfaction. Process We identified key pain points in clinic queue management and designed a real-time solution focused on transparency and efficiency. We built a React-based dashboard for receptionists, a live waiting room display for patients, and a Node.js backend to manage queue operations. Emergency prioritization logic was added to ensure critical patients receive immediate attention. We then integrated AI-based wait-time prediction using queue length and consultation trends to provide accurate waiting estimates. Results QueueCure AI successfully provides real-time queue visibility, automatic token management, emergency patient prioritization, and AI-driven wait-time estimation. The system reduces uncertainty for patients, improves receptionist productivity, and creates a smoother clinic workflow. The prototype demonstrates how intelligent queue management can enhance healthcare service delivery. Reflection Given more time, we would integrate appointment scheduling, doctor availability tracking, SMS/WhatsApp notifications, patient history integration, and machine learning models trained on real hospital datasets to improve prediction accuracy. We would also deploy the platform to the cloud for multi-clinic scalability.