QueueCure AI — Replacing Paper Tokens with Live Queue Intelligence

Neighborhood clinics across India still rely on paper token slips and manual patient calling, leaving patients waiting 2–3 hours with no visibility while receptionists manage queues from memory and doctors lack a live view of upcoming consultations. This fragmented workflow creates delays, repeated inquiries, and operational inefficiency. QueueCure AI solves this by introducing a real-time digital queue that registers patients in under 10 seconds, synchronizes every stakeholder instantly, and predicts waiting time from live consultation data instead of hardcoded estimates. Process We analyzed healthcare queue management research, patient prioritization frameworks, and queueing theory to understand bottlenecks in neighborhood clinics. Mapping receptionist, doctor, and patient journeys revealed that fragmented communication was the core problem. Our first prototype used periodic API polling, but it caused synchronization delays and unnecessary network requests. We pivoted to a Socket.IO event-driven architecture with a centralized backend, enabling instant multi-screen updates. We then replaced fixed wait times with an adaptive ETA engine based on actual consultation durations, simplified receptionist interactions, and integrated multilingual voice announcements to create a transparent and efficient clinic workflow. Results Delivered a real-time queue ecosystem where a single receptionist action synchronizes 5 connected interfaces without page refreshes using Socket.IO. Reduced patient registration to under 10 seconds through a streamlined workflow and replaced static wait times with adaptive ETA prediction based on live consultation durations. End-to-end testing validated instant multi-screen updates, multilingual voice announcements, and consistent queue state, improving transparency for patients and reducing manual coordination for clinic staff. Reflection Given more time, I would evolve QueueCure AI into an intelligent clinic operating platform by incorporating AI-driven patient prioritization, historical consultation data for more accurate ETA prediction, and cloud-backed persistent storage for multi-clinic deployment. I would also conduct usability studies with receptionists, doctors, and patients to validate workflows, measure cognitive load, and refine interactions using real-world feedback instead of development assumptions.