Turnwell – AI-Powered Smart Queue Management for Clinics

Healthcare clinics often struggle with inefficient queue management, uncertain wait times, overcrowding, and uneven doctor workloads. Patients lack visibility into their position and expected consultation time, while reception staff must manually manage appointments, walk-ins, emergencies, and no-shows. Existing systems provide limited real-time insights and no accurate wait-time prediction. This creates frustration, operational inefficiencies, and poor patient experiences, highlighting the need for a smarter, data-driven queue management solution. Process We analyzed common challenges in outpatient clinics, including long wait times, poor queue visibility, emergency interruptions, and uneven doctor workloads. Based on these insights, we designed Turnwell as an AI-powered queue management platform. We built separate interfaces for reception staff, doctors, and administrators, integrating real-time queue tracking, smart ETA prediction, emergency prioritization, and multi-doctor monitoring. Historical and live queue factors such as consultation duration, queue length, no-shows, and doctor speed were used to generate wait-time estimates. We also developed analytics dashboards to help clinics identify bottlenecks, optimize resources, and improve patient experience. Results Turnwell provides real-time queue visibility, AI-powered wait-time prediction, multi-doctor workload monitoring, and operational analytics within a single platform. The system achieved a simulated ETA prediction accuracy of 94%, improves transparency for patients, helps reception teams manage queues more efficiently, and enables clinics to identify bottlenecks through actionable insights. By combining predictive intelligence with queue management, Turnwell reduces uncertainty and supports better healthcare operations. Reflection Given more time, I would integrate real clinic data to further validate prediction accuracy and conduct usability testing with patients, reception staff, and doctors. I would also add SMS/WhatsApp notifications, appointment rescheduling, and adaptive machine learning models that continuously improve ETA estimates based on live consultation patterns and historical outcomes. This would make the platform more scalable and production-ready.