Hospitals worldwide face critical challenges in optimising workforce allocation while maintaining high standards of patient care.This study presents the development and real-world evaluation of an AI and ICT-enabled hospital human resource management decision support system (HRM-DSS) designed to enhance staffing efficiency, improve staff satisfaction, and support patient care outcomes.Utilising a hybrid deep learning and predictive analytics framework, the system was piloted in a major hospital and demonstrated a substantial reduction in scheduling conflicts, overtime hours, and staffing costs.Significantly, staff satisfaction and perceived fairness of scheduling increased, while patient wait times modestly improved without compromising care quality.A mixed-methods analysis revealed that the system's effectiveness was strongly linked to user trust and the transparency of its implementation.These findings provide robust evidence that integrating AI into HRM can sustainably transform hospital operations, supporting both workforce well-being and patient-centred care.The study offers a scalable blueprint for healthcare leaders seeking data and ICT-enabled solutions to complex HRM challenges.The integration of AI with ICT infrastructures demonstrates how advanced information and communication technologies can transform hospital HRM.
Jiayue Zhang (Thu,) studied this question.