Dawood Marva R

QueueCure AI – Smart Clinic Queue & Patient Flow Management

Most clinics still rely on manual token systems, leaving patients uncertain about waiting times and receptionists overwhelmed with managing queues. Doctors often experience uneven patient distribution, while emergency cases require manual prioritization. Existing systems rarely provide real-time updates, multilingual announcements or intelligent patient routing. QueueCure AI addresses these challenges by introducing an AI-powered queue management platform that improves patient flow, reduces waiting uncertainty and synchronizes reception, doctors and waiting room displays in real time. I started by analyzing how clinics currently manage patient queues and identified common pain points such as long waiting times, no visibility into queue status, manual token management and inefficient doctor allocation. I designed the solution around two interfaces: Reception Command Center and a Patient Waiting Room that stay synchronized in real time using Socket.IO. I integrated AI-based doctor assignment, emergency prioritization, wait time prediction and multilingual voice announcements. During development, I iterated multiple times on queue logic, doctor reassignment and real-time synchronization. One challenge was deploying Socket.IO across Vercel and Render, which required debugging API routing, environment variables and production deployment before achieving a stable solution. QueueCare AI successfully transforms a manual clinic queue into a real-time intelligent workflow. The system automatically assigns doctors, prioritizes emergency patients, predicts waiting times, synchronizes reception and waiting room displays and supports multilingual voice announcements. Automated tests were added for queue logic and priority handling to improve reliability. The solution demonstrates a scalable architecture that can be extended to multiple clinics, appointment booking, patient notifications and analytics dashboards in future versions. With additional time, I would integrate a cloud database for persistent patient records, implement appointment scheduling, support QR-based patient check-in, add SMS and WhatsApp notifications, improve AI waiting time prediction using historical consultation data, and develop a mobile application for patients and doctors. Future versions would also support multiple hospital branches and Electronic Health Record (EHR) integration.