QueueCure AI: Real-Time Smart Clinic Queue & Patient Experience Management System
Most clinics still rely on paper tokens, manual queue management, and verbal announcements, causing patients to wait for long periods without visibility into their turn. Patients often experience uncertainty, frustration, and boredom while waiting, while receptionists manually manage queues and doctors lack real-time operational insights. Existing solutions primarily focus on token display and fail to address accurate wait-time prediction, emergency prioritization, doctor workload balancing, patient engagement, analytics, and communication. QueueCure AI was built to transform traditional clini
I started by analyzing the problems faced in traditional clinic queue systems, including long waiting times, lack of visibility, manual queue handling, and poor patient engagement. I designed a real-time architecture where every action instantly updates all connected users without requiring page refreshes.
Using Socket.IO, I implemented live synchronization between receptionist, patient, and doctor dashboards. I then created and structured historical datasets containing patient records, consultation durations, disease categories, doctor assignments, attendance patterns, and clinic traffic information.
To improve decision-making, I integrated machine learning models for wait-time prediction, emergency prioritization, no-show prediction, rush-hour forecasting, and doctor load balancing. I
I developed QueueCure AI, a fully real-time clinic management platform that transforms traditional token-based systems into an intelligent patient experience solution. The platform provides instant queue synchronization, AI-powered wait-time prediction, emergency triaging, doctor load balancing, live analytics, patient communication, family tracking, stress monitoring, community engagement, and interactive activities. All features operate using live database data and real-time updates, improving operational efficiency, transparency, and patient satisfaction.
With additional time, I would integrate WhatsApp Business notifications, multilingual voice assistants, appointment scheduling, telemedicine support, electronic health record integration, virtual waiting rooms, and larger healthcare datasets to further improve prediction accuracy and patient experience. These additions would make the platform even more scalable and suitable for deployment across clinics and hospitals.
real-time-queue--bansodesomnath0.replit.appView 