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April 3, 2026Iconic Research and Engineering Journals0 citations

Smart Queue and Appointment Orchestration Platform

RSR. SharveshDPDr. S. Parthasarathy

Key Points

  • The aim is to create a user-friendly platform for managing appointments and queues efficiently.
  • Developed front end using React.js and Tailwind CSS for responsive design.
  • Implemented back end with Node.js and Express for operational logic and user authentication.
  • Utilized MongoDB for organizing user profiles and appointment records.
  • Integrated WebSocket technology for real-time updates on queue statuses.
  • Incorporated a Chatbot for onboarding assistance and FAQ responses.
  • Improved user experience for clients and staff with a responsive interface.
  • Reduced physical wait times and facility congestion through dynamic scheduling.
  • Established a real-time communication channel that keeps users updated instantly.

Abstract

The front end of the Smart Queue and Appointment Orchestration Platform is developed using React.js and styled with Tailwind CSS, providing an intuitive, responsive, and seamless user experience for both clients and administrative staff. The back end is implemented using Node.js and the Express framework to handle core operational logic, secure user authentication through JSON Web Tokens, and dynamic scheduling algorithms. The system utilizes MongoDB as its primary database to effectively organize user profiles, service categories, and active appointment records. By integrating WebSocket technology (Socket.io), the platform establishes a real-time, bi-directional communication channel that updates active queue statuses instantly without requiring manual page refreshes. Furthermore, the system incorporates an intelligent Chatbot assistant designed to guide users through the onboarding process and automatically answer frequently asked questions. This solution serves as a highly scalable and foundational step toward modernizing enterprise queue coordination and out-patient healthcare scheduling, significantly reducing physical wait times and facility congestion. In the future, this system could be extended to include predictive data analytics for queue load forecasting, multi-branch facility administration, and multilingual chatbot support to serve a diverse, wider audience.

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Cite This Study

Sharvesh et al. (2026) studied this question.

synapsesocial.com/papers/69cf5f225a333a821460dfa4https://doi.org/10.64388/irev9i9-1715741
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