A Metrics-Driven Approach to Develop a Hybrid Model of Staffing and Workload Balance in the NGHA Hospitals

Clinical Engineering Departments (CEDs) face growing challenges in managing rapidly evolving medical technologies and increasing equipment inventories under constrained budgets and limited human resources. These pressures often result in strained staffing capacity and imbalanced workload distribution. This study aimed to develop and validate a metrics-driven hybrid staffing model to optimize workforce allocation and improve workload efficiency across National Guard Health Affairs (NGHA) hospitals in Saudi Arabia. Five years of maintenance data were extracted from the Computerized Maintenance Management System (CMMS) and Oracle E-Business Suite. These data were analyzed to construct a hybrid staffing model that combined quantitative workload metrics with qualitative input from clinical engineering staff across 11 NGHA hospitals. Model validation included a detailed case study at King Abdullah Specialized Children's Hospital (KASCH), with comparisons to existing staffing models, including the Ottawa Hospital approach. The case study revealed that the current staffing of 14 full-time equivalents (FTEs) at KASCH was insufficient, with the model projecting a requirement of 17 FTEs, indicating a 7.8% shortfall. Workload analysis showed highly uneven staff utilization rates, ranging from 20.8% to 71.5%. High-maintenance equipment, such as MRI machines, required up to 42.1 hours per device annually. The proposed hybrid model achieved more balanced staffing, predictive maintenance scheduling, and dynamic task assignments. Compared to traditional models, it demonstrated an estimated 25% cost savings, equipment uptime exceeding 95%, and improved workload distribution. The hybrid staffing model provides a data-driven framework that integrates preventive and corrective maintenance requirements with staff input to support risk-based decisions. While validated within the NGHA system, the model is adaptable for healthcare facilities with different device profiles, regulatory pressures, and financial constraints. Successful implementation depends on strong institutional leadership, continuous data collection, and comprehensive staff training to ensure long-term sustainability and scalability.