QureBoard

Many small clinics still rely on paper token slips and manual queue management, leading to long waiting times, poor visibility for patients, and increased workload for receptionists. Patients often have no idea how many people are ahead of them or how long they must wait. Through discussions with clinic staff and observing existing workflows, we identified the need for a simple, low-cost digital solution that could provide real-time queue updates without requiring expensive infrastructure or third-party services. Process We began by studying the workflow followed in small outpatient clinics and identified key pain points for both receptionists and patients. We mapped the user journey and prioritized features that would solve the most common frustrations. Initial concepts included SMS notifications and QR-based check-ins, but these added complexity and external dependencies. Instead, we focused on a lightweight browser-based system. We designed separate interfaces for receptionists and patients. Multiple iterations were made to simplify token generation, token calling, and waiting-time estimation. Real consultation durations provided by receptionists were used to calculate average consultation time and generate more accurate wait estimates. To ensure instant synchronization across screens without requiring Results QureBoard successfully digitized the clinic queue process and eliminated dependency on paper tokens. User testing with 14 participants showed that tasks were completed with an average of 4.1 taps, while achieving a task success rate of 92.9% (13 out of 14 successful completions). Participants appreciated the real-time token updates and estimated waiting times, which improved transparency and reduced uncertainty. The system provided synchronized updates across browser tabs without external services, making it suitable for small clinics with limited resources. Reflection Given more time, I would conduct larger usability studies involving actual clinic staff and patients to validate assumptions in real-world settings. I would also improve the waiting-time prediction model using historical consultation data instead of relying solely on average durations. Additional features such as appointment scheduling, SMS notifications, multilingual support, and analytics dashboards would further enhance the experience. Finally, deploying the system in a live clinic environment would provide valuable insights for scalability and long-term adoption.