Abstract
Effective management of doctors and nurses is essential for hospital efficiency and quality patient care. However, many healthcare institutions, including Dr. Eduardo V. Roquero Memorial Hospital, still rely on manual or fragmented systems for scheduling, license monitoring, and performance evaluation. These practices often lead to scheduling conflicts, delayed compliance tracking, and inconsistent evaluations. While automated healthcare systems exist, there remains a gap in fully integrated platforms tailored to local hospital operations. This study aimed to design and develop a centralized, web-based Doctors and Nurses Management System with AI-assisted nurse performance evaluation. The system was developed using the Agile Scrum methodology with iterative sprint cycles. Data were gathered through interviews, workflow observation, and document analysis. The architecture followed TOGAF-based Enterprise Architecture principles. A cloud-based MySQL database was implemented to ensure secure, real-time data management. Modules included Scheduling, Registration and Compliance, and AI-based Performance Evaluation. The system successfully automated shift scheduling, reduced conflicts, centralized licensing records, and generated objective nurse evaluation reports. Integration of modules improved data accuracy, minimized redundancy, and enhanced administrative efficiency. Initial testing showed improved response time and better workforce monitoring. The findings demonstrate that a centralized and AI-integrated system enhances hospital staff management. Although implemented in one institution, the framework is scalable and adaptable. Future research may expand predictive scheduling and system interoperability.
