Pradeep Mohan

cliniQ

Small and medium-sized clinics often rely on paper tokens, manual announcements, and crowded waiting areas to manage patient queues. Patients have no visibility into their position in line, resulting in uncertainty, repeated inquiries at reception, and frustration during long waiting periods. Receptionists spend significant time answering queue-related questions instead of focusing on patient care. We identified an opportunity to create a digital queue management system that provides real-time updates, improves transparency, and reduces administrative workload while enhancing the overall patie Process We began by analyzing the workflow of neighborhood clinics and identifying common pain points for patients and staff. The primary issues were lack of queue visibility, inefficient manual management, and frequent interruptions at reception desks. The solution was designed around two user groups: receptionists and patients. We created user flows for both interfaces and mapped the complete patient journey from registration to consultation. Early wireframes focused on minimizing receptionist interactions while maximizing visibility for patients. The backend was developed using Node.js, Express, MongoDB Atlas, and Socket.IO to enable real-time queue synchronization. The frontend was designed with a mobile-first approach to ensure accessibility on smartphones and tablets. Multiple iterations w Results ClinicQ successfully transformed a manual queue management process into a digital real-time experience. The system reduced the number of steps required for patients to check queue status from six interactions to two. Real-time updates improved queue transparency and reduced dependence on reception staff for status inquiries. Key outcomes included: • Real-time token and queue updates across devices • Faster patient access to queue information • Reduced reception desk interruptions • Mobile-friendly patient tracking interface • Scalable architecture suitable for clinics of varying sizes The so Reflection If given additional time, I would integrate AI-powered wait time prediction, multilingual voice notifications, WhatsApp/SMS updates through Twilio, and analytics dashboards for clinic administrators. I would also conduct usability testing with real clinic staff and patients to gather quantitative feedback and further refine the user experience. Additionally, deploying the platform as a multi-clinic SaaS product with role-based access control and appointment scheduling would significantly expand its real-world applicability.