Digital Queue Management System for Hospitals using QR Codes

Abstract

The traditional queue management systems used in hospitals often result in long waiting times, overcrowding, and inefficient service delivery. Patients are required to stand in physical queues without real-time information about their turn. To addressthesechallenges,thispaperproposesaDigital Queue Management System using QR codes. The proposed system allows users to generate digital tokens through QR codes and track their queue status inrealtime. Astaff panelisprovidedtomanagetoken flowusingFIFO(FirstInFirstOut)logic,whilealive display board shows the currently serving and upcoming tokens. Additionally, the system collects and analyzes queue data such as waiting time and peak hours to improve service efficiency. The system is developed using Python (Django) for backend and HTML, CSS, Bootstrap, and JavaScript for frontend. The proposed solution enhances user experience, reduces waiting time, and provides a scalable and efficient queue management system.

Introduction

QueueSync is a web-based intelligent queue management system designed to replace traditional manual queueing in healthcare and service environments. Conventional systems often cause long waiting times, overcrowding, and lack of transparency due to the absence of real-time tracking.

The proposed system introduces a QR-based digital token mechanism that allows users (patients) to generate tokens, track their queue position in real time, and receive service in an orderly FIFO sequence. Staff manage queues through a dashboard where they call, serve, or skip tokens, while administrators monitor system performance and analyze metrics such as waiting time and peak hours.

The system follows a modular client–server architecture with layers for user interface, authentication, application logic, database management (SQLite), queue processing, live display updates, analytics, and security. The backend is implemented using Python (Django), with a web frontend built using HTML, CSS, Bootstrap, and JavaScript. Real-time updates ensure smooth synchronization between users, staff, and system data.

Key features include QR-based token generation, live queue tracking, FIFO-based queue control, and an analytics dashboard. The system is tested for functionality, integration, performance, and security, showing improved efficiency and reduced waiting times.

Conclusion

• The proposed digital queue management system provides an efficient solution to overcome the limitations of traditional queue systems. By integrating QR-based token generation, real-time tracking, and analytics, the system improves service efficiency and user experience. • It reduces waiting time, avoids overcrowding, and ensures a smooth and transparent queue management process. The system is scalable and can be implemented in various service environments.

References

[1] N. Joshi and S. Patil, “Digital Token-Based Queue Management System with Automated Display,” International Journal ofComputer Applications,2022. [2] M. A. Al-Tahat, A. Al-Dweik, and R. Raza, “Smart Queue Management System Using QR Code and Real-Time Monitoring,” IEEE Access, 2020. [3] K. Sharma and P. Verma, “Web-Based Queue Management System for Public Services,” International Journal of Engineering Research, 2021. [4] R. Jain, S. Paul, and A. Gupta, “Data Analyticsfor Service Time Optimization in Queue-Based Systems,” IEEE Conference, 2019. [5] S. Kumar and R. Singh, “Real-Time Queue MonitoringandManagementSystem,”International Journalof ComputerScience,2018. [6] N. Kumar and S. Bansal, “Web-Based Queue Management System Using Django Framework,” IEEE Software Engineering Conference, 2021. [7] S. Karthik and M. Priya, “Smart Hospital Queue Management Using IoT and Web Technologies,” IEEE Healthcare Conference, 2022. [8] V. Patel and D. Shah, “Performance Analysis of Service Systems Using Data Analytics,” IEEE International Conference on Data Science, 2020. [9] J. Lee and H. Kim, “Data-Driven Service Optimization in Queue Management Systems,” IEEE Access, 2022. [10] A. Verma and N. Sharma, “Online Queue Management System Using Web Technologies,” International Journal of Computer Applications, 2021. [11] P. Gupta and R. Mehta, “Smart Queue Management System Using Cloud Computing,” International Journal of Advanced Research in Computer Science, 2020. [12] T. Nguyen and H. Tran, “Real-Time Service OptimizationinQueueSystemsUsingMachineLearning,” IEEE Access, 2021. [13] D. Roy and S. Banerjee, “Automated Queue Management System with Digital Display,” International Journal of Engineering and Technology, 2019. [14] L. Chen and Y. Wang, “Intelligent Queue Prediction Using Data Analytics,” IEEE International Conference on Artificial Intelligence, 2022. [15] R. Singh and P. Kumar, “QR Code Based Smart Queue Management System for Public Services,” International Journal of Innovative Technology and Exploring Engineering, 2021.

Copyright

Copyright © 2026 Mr. R. Kavitha , S Haja Sharief, S Mohamed Siddiq , T Abdul Kafoor, I Mohamed Pazil . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.