QueueCure Pro

Patients in hospitals often spend significant time waiting without knowing their queue status or estimated consultation time. Existing queue systems lack real-time visibility and intelligent predictions, leading to frustration and operational inefficiencies. Queue Cure solves this problem through AI-powered waiting-time prediction and real-time queue tracking. Process ### Our Process Our process began by identifying a common challenge faced by hospitals and patients: long waiting times and a lack of visibility into queue status. We researched existing queue management practices and analyzed the pain points experienced by both patients and healthcare providers. Based on these findings, we designed Queue Cure as an AI-powered queue management solution that provides real-time queue tracking and waiting-time predictions. We then planned the system architecture, including the patient interface, hospital dashboard, backend services, and AI components. Using Google AI Studio and modern web technologies, we developed the prototype and integrated real-time communication to ensure live queue updates. The system was tested under different queue scenarios to valid Results Queue Cure successfully demonstrates the potential of combining artificial intelligence with real-time queue management to improve the healthcare experience. The developed prototype enables patients to view their queue status, receive estimated waiting times, and stay informed through live updates, reducing uncertainty during the waiting process. For healthcare providers, the system offers better visibility into patient flow and supports more efficient queue management. Through the integration of AI-based wait-time prediction and real-time synchronization, the project d Reflection If given more time and access to real-world healthcare environments, I would focus on improving the accuracy, scalability, and practical adoption of Queue Cure. I would train the AI model using actual hospital queue data to provide more precise waiting-time predictions and integrate appointment scheduling to help patients plan their visits more effectively. I would also develop a dedicated mobile application with multilingual support to improve accessibility for a wider range of users. Additionally, I would introduce features such as emergency patient