SWETHA SRI B CSE

queuecure

Most clinics and small hospitals still rely on paper tokens and manual queue management systems. Patients often: Wait for long periods without knowing when their turn will come. Miss their tokens due to uncertainty. Experience frustration because of long waiting times. Receptionists: Manage queues manually. Handle multiple patients simultaneously. Have no digital tracking system. Doctors: Lose consultation time due to absent patients. Cannot prioritize emergency cases effectively. This results in poor patient experience and inefficient healthcare service. Project Process 1.Identified the problem of long waiting times and manual token systems in clinics. 2.Designed two separate dashboards for the receptionist and patients. 3.Developed a token management system to handle patient queues. 4.Added estimated waiting time and remaining patient count features. 5.Implemented Emergency Risk Score to prioritize critical patients. 6.Added a No-Show Predictor to improve queue management. 7.Tested the application and improved the user interface. The Queue Cure AI project successfully digitizes the traditional clinic token system by providing real-time queue management, estimated waiting times, and patient tracking. The system improves the patient waiting experience by reducing uncertainty and providing live queue updates. The Emergency Risk Score helps identify critical patients and prioritize them for faster treatment, while the digital queue system reduces the receptionist's workload and improves overall clinic efficiency. As a result, the project enhances patient satisfaction, streamlines healthcare operations, and provides a smart 1.Emergency alert 2.Patient's history 3.Over all analytics