Queue Cure '26 – Intelligent Real-Time Patient Flow System

Healthcare environments face critical operational inefficiencies due to manual, unoptimized token generation and rigid patient queuing. This lack of transparency causes high waiting-room friction, erratic delay estimations, and fragmented data states that fail to update dynamically across clinical hubs. Process Designed and built a cross-platform architecture separating concerns into a modular full-stack layout. Developed a responsive frontend to expose immediate user controls, paired with an isolated Node.js/Express backend service engine. Integrated a centralized state architecture hosted in the cloud to manage state synchronizations under load variations without manual database polling intervals. Results Successfully deployed a live, operational full-stack application capable of handling multi-client sessions seamlessly. The system achieves a real-time UI data sync latency of 0 ms, ensuring instant queue status updates across active dashboards while maintaining a resilient 100% data sync accuracy under repetitive state flushes. Reflection I implemented an intentional 2-second debouncing delay on the frontend click handler for the "Call Next Token" button. This prevents users from spam-clicking the button, which protects the cloud backend from race conditions, avoids accidental token skips, and ensures stable state transitions under heavy load.Given more time, I would implement robust client side caching strategies to guarantee seamless offline persistence, and integrate local automated state synchronization fallbacks to preserve critical queue metrics if a network disconnect occurs mid-session.