Queue Cure '26 – AI-Powered Smart Clinic Queue Management System with Real-Time Tracking

Most small and medium clinics in India still rely on paper tokens, manual registers, and verbal announcements to manage patient queues. Patients often wait for hours without knowing their queue position, estimated consultation time, or when their turn will arrive. This creates frustration, overcrowded waiting areas, and inefficient clinic operations. The problem becomes more severe in rural and multilingual regions where patients may not understand announcements made in a single language. Additionally, internet connectivity issues can completely disrupt digital systems, forcing clinics to rev Process The project began by analyzing common pain points experienced in clinics, including long waiting times, lack of queue visibility, manual token handling, and communication barriers. We identified three primary stakeholders: patients, receptionists, and doctors. After defining user requirements, we designed a workflow where the receptionist manages registrations and token generation, doctors handle consultations digitally, and patients receive real-time updates through QR codes and notifications. To ensure reliability, an Offline-First architecture was selected. IndexedDB was used to store queue actions locally when internet connectivity is unavailable. Once connectivity is restored, a synchronization engine automatically updates the central database. For real-time communication, Socket.i Results Queue Cure '26 successfully transformed a traditionally manual clinic process into a digital, transparent, and scalable workflow. Key outcomes achieved include: • Real-time queue visibility across reception, doctor rooms, patient devices, and display screens. • Reduction in patient uncertainty through live token tracking and estimated wait-time calculations. • Support for 5+ regional languages, making the system accessible to diverse patient groups. • Continuous clinic operations during internet outages through offline synchronization. • Instant queue updates delivered in less than one s Reflection If given additional time, I would expand the system with AI-powered capabilities such as intelligent wait-time prediction based on historical consultation patterns, automated patient triage, and smart queue optimization. I would also integrate WhatsApp Business APIs and SMS gateways to support patients who may not have access to smartphones or push notifications. Another improvement would be introducing advanced analytics dashboards for clinic administrators to monitor peak hours, doctor utilization, and patient flow trends. To further improve accessibility, I would add voice-command support