Srigirisudhan Srigirisudhan

Queue Cure 26: AI-Powered Real-Time Clinic Queue Management System

Many clinics still depend on paper-based token systems, creating long waiting times, poor queue visibility, and operational inefficiencies. Patients often have no idea how many people are ahead of them or when they will be called, while receptionists face challenges such as duplicate token allocation, manual errors, and constant patient inquiries. Additionally, traditional systems lack real-time synchronization, resulting in delays and the need for manual refreshes. To address these challenges, we designed Queue Cure 26 using a modern event-driven architecture. The system combines a Next.js 15 frontend, an Express and Socket.IO backend, and a PostgreSQL database managed through Prisma ORM. A shared TypeScript package was used to maintain type safety and validation across the application. This architecture ensured real-time communication, reliable data management, and a seamless user experience for both patients and clinic staff. Achieved real-time queue synchronization across all connected devices. Provided accurate wait-time predictions using actual consultation data. Eliminated duplicate token generation through robust concurrency controls. Reduced patient uncertainty by offering live queue visibility and status updates. Simplified patient onboarding through QR-based access. Improved receptionist productivity by minimizing manual interventions and repetitive tasks. 1. Horizontal Scaling Integrate Redis Pub/Sub with Socket.IO to support multiple backend instances and larger healthcare networks. 2. Multi-Tenant Architecture Extend the platform to support multiple clinics through clinic-specific data isolation and WebSocket namespaces. 3. Intelligent Queue Prioritization Introduce priority-based scheduling for emergency cases and pre-booked appointments while maintaining queue fairness. 4. Offline Resilience Implement Service Worker caching to ensure a smoother experience during temporary network interruptions.