Queue Cure – AI-Powered Real-Time Clinic Queue Management System

Neighborhood clinics still rely on paper tokens, manual patient calling, and static waiting areas. Patients often have no visibility into their queue position, estimated wait time, or when they will be called. Receptionists repeatedly answer the same questions ("How many people are ahead of me?"), reducing operational efficiency. Additionally, urgent cases can be buried in a first-come-first-served queue without any intelligent prioritization. We identified an opportunity to modernize clinic queue management by combining real-time synchronization, AI-assisted triage, and mobile-first patient Process We started by mapping the complete clinic workflow from patient registration to consultation completion. The primary goal was eliminating uncertainty for patients while reducing manual work for reception staff. Initially, we considered a polling-based architecture where patient screens would refresh every few seconds. However, this approach introduced unnecessary server load and delayed updates. We replaced it with an event-driven architecture using Supabase Realtime CDC and WebSockets, allowing every queue change to be pushed instantly to all connected devices. Next, we designed a queue state machine (WAITING → CALLED → COMPLETED / SKIPPED) to ensure predictable queue behavior and auditability. Results Queue Cure successfully transforms a traditional paper-token workflow into a real-time digital queue management system. Key outcomes achieved: • Real-time synchronization across receptionist and patient screens using WebSockets. • Sub-200ms queue update propagation through Supabase Realtime CDC. • Zero page refreshes required for patients to receive queue updates. • AI-assisted prioritization of ROUTINE, URGENT, and EMERGENCY cases. • QR-based access removes the need for app installation or patient accounts. • Multi-clinic architecture designed for future scalability. Reflection Given more time, I would conduct real-world testing with clinics, add a doctor dashboard and appointment scheduling, improve AI triage with clinician-validated models, and build advanced analytics for wait-time forecasting and operational insights. I would also perform large-scale load testing to prepare the system for multi-clinic deployment.