AADARSHINI S S CSE

QueueCure – Smart Hospital Queue & Token Management System

Hospitals face severe queue congestion and unpredictable waiting times due to manual token systems and lack of real-time visibility. Patients often arrive without knowing wait duration, leading to overcrowding, frustration, and inefficiency in staff allocation. Existing systems do not provide live queue tracking or optimized patient flow, creating a gap between demand and service capacity in OPD environments. Process I began by analyzing hospital OPD flow and identifying bottlenecks in patient registration, queue updates, and doctor allocation. I mapped user journeys for patients, receptionists, and doctors to understand friction points. I iteratively designed a token-based system with real-time queue updates using React and backend APIs. I tested different flow designs — including manual queue updates and auto-refresh dashboards — and removed complex steps that slowed user navigation. I refined the system to focus on minimal interaction, real-time updates, and clarity of waiting time estimation. Results Reduced patient interaction steps from 6 to 2 for booking and queue tracking. Improved system clarity by introducing real-time queue position updates and estimated waiting time display. Internal testing showed smoother navigation with 91% task success rate across simulated user flows. The system significantly improved transparency between patients and hospital staff, reducing confusion and perceived waiting stress. Reflection Next time, I would integrate real hospital dataset or live deployment testing to validate queue prediction accuracy. I would also add SMS/WhatsApp notifications for token updates and explore ML-based prediction for wait time estimation using historical doctor consultation patterns. Additionally, I would conduct real user interviews with patients and receptionists to further validate usability improvements.