LGU: AI-ENABLED QUEUE MANAGEMENT FOR ENHANCED PUBLIC HEALTH SERVICES

Abstract

Technology plays a vital role in improving healthcare services, particularly in enhancing patient flow within healthcare systems. Efficient queue management is essential to delivering timely and effective care. At Holy Spirit Health Center, traditional manual systems have resulted in numerous operational inefficiencies, including disorganized appointment booking, prolonged waiting times, low patient volume, and poor resource allocation. This study introduces an AI-enabled Queue Management System aimed at streamlining patient flow, improving cost-effectiveness, enhancing patient satisfaction, and reducing overall wait times in public health facilities. The study follows a system development process that includes defining the system architecture, selecting appropriate technologies, and planning the system's implementation at Holy Spirit Health Center. The system utilizes AI capabilities to manage appointment scheduling, monitor real-time queue status, and allocate resources effectively. An Agile Scrum methodology guides the development process, allowing iterative feedback and continuous system refinement. Evaluation metrics focus on patient waiting time, user satisfaction, staff workload, and resource utilization to assess system effectiveness after deployment. Preliminary expectations indicate that the implementation of the Public Health Center Management System (PHCMS) will lead to a significant reduction in patient wait times and improved scheduling efficiency. The system’s real-time monitoring and appointment features are anticipated to enhance patient satisfaction and allow healthcare providers to allocate more focused time to care delivery. Additionally, the data gathered from system usage may serve as a foundation for future enhancements involving AI-based decision-making and predictive analytics in healthcare operations. The deployment of the AI-enabled queue management system demonstrates the potential of digital innovation in addressing persistent inefficiencies in public health services. By integrating Agile development practices with AI technologies, the system not only optimized patient flow but also laid the groundwork for future scalable improvements. Literature on existing queue management frameworks and agile methodologies supports the system’s design and implementation strategy. The study emphasizes the importance of modern queue management tools in improving both patient experience and healthcare service delivery in local government units.