FlowCare: Real-Time Clinic Queue Platform
Many clinics still rely on paper token slips and manual queue management, leaving patients uncertain about wait times and queue status. Receptionists must handle registrations, token assignment, and patient flow manually, while doctors have limited visibility into clinic operations. This results in long waiting times, overcrowding, and poor patient experience. FlowCare solves this by providing a real-time digital queue system where patients can track their position and estimated wait time on their phones, while clinic staff manage queues efficiently through a live synchronized dashboard.
Process
I began by analyzing how clinics currently manage patient queues and identified key pain points: manual token systems, lack of wait-time visibility, and inefficient communication between receptionists, doctors, and patients. I designed FlowCare around three core goals: fast patient registration, real-time queue synchronization, and accurate wait-time estimation. The system uses a receptionist dashboard for token management, a patient-facing live queue view, and WebSocket-based updates to synchronize all screens instantly. Wait times are calculated dynamically using queue position and consultation duration data, ensuring patients receive meaningful and up-to-date information.
Results
FlowCare successfully transformed a manual paper-token workflow into a real-time digital queue management system for clinics. The platform enables receptionists to register patients and generate tokens in under 10 seconds while providing patients with live visibility into their queue position and estimated waiting time. Real-time synchronization ensures that queue updates are instantly reflected across all connected screens without requiring page refreshes. The solution improves operational efficiency, reduces patient uncertainty, enhances transparency in clinic workflows, and provides a scala
Reflection
If I had more time, I would enhance FlowCare by incorporating appointment scheduling, doctor dashboards, and predictive wait-time estimation based on historical consultation data rather than relying solely on average consultation duration. I would also add offline support for network disruptions, advanced analytics for clinic administrators, multilingual support for broader accessibility, and integration with patient records, lab reports, pharmacy, and billing systems. Additionally, I would conduct usability testing with real clinic staff and patients to further streamline workflows and improv
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