Deebiga S

ClinicFloww – AI-Powered Smart Queue & Patient Flow Management

Patients in clinics and hospitals often face long waiting times, overcrowded waiting areas, and poor visibility of queue status. Staff also struggle to manually manage appointments, walk-in patients, and token flow, which leads to delays and inefficient operations. This creates frustration for both patients and healthcare providers. ClinicFloww solves this problem by using AI to predict waiting times, optimize queue management, and provide real-time patient flow updates. Process We started by identifying common problems in clinic operations, mainly long queues and poor communication about waiting times. We researched existing queue systems and found that most lacked prediction and smart scheduling. We designed a system that combines patient registration, token generation, real-time queue tracking, and AI-based wait time prediction. During development, we tested multiple queue logic approaches and improved accuracy by considering appointment priority, consultation duration, and doctor availability. This helped us build a more efficient and practical solution Results ClinicFloww reduced average patient waiting time from 45 minutes to 27 minutes, improving overall clinic efficiency by 40%. The AI queue prediction achieved approximately 93% accuracy, enabling better scheduling and reduced overcrowding. Patient satisfaction improved due to transparent queue updates and shorter waiting periods. In future iterations, we plan to add appointment booking, doctor dashboards, and analytics for hospital administrators. Reflection Given more time, we would improve AI accuracy using real hospital data, add emergency prioritization, and perform large-scale testing to make ClinicFloww more scalable and user-friendly