A Metrics-Driven Approach to Develop a Hybrid Model of Staffing and Workload Balance in the NGHA Hospitals
Bibliographic record
Abstract
Introduction: 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. Methods: 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. Results: 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. Discussion: 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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".