Improving maintenance efficiency and controlling costs in healthcare institutions through advanced analytical method.

This study aims to address the shortage of manpower and resources in the medical engineering departments of healthcare institutions while efficiently executing medical equipment maintenance and achieving controllable maintenance costs. In the absence of historical maintenance data, this research uses multi-parameter monitors as a case study. The methodology integrates Fault Tree Analysis (FTA) with the Fuzzy Analytic Hierarchy Process (FAHP) to combine expert judgments and address uncertainties in failure data. The results of this integration are then converted into a Bayesian Network (BN) for probabilistic reasoning and failure analysis. This comprehensive approach enables both qualitative and quantitative analysis of monitor failures across different usage stages (early, mid-term, and late). The analysis encompasses determining the failure probability at each stage, identifying high-risk components, examining the transition of failure modes, gaining insights into the aging characteristics of components, and developing preventive maintenance strategies. A cost-benefit analysis is conducted based on specific practical cases. This methodology successfully identified the failure probability of each component of the monitor at various stages, accurately pinpointed high-risk components, and provided a clear analysis of the transition of failure modes. Following one year of practical application, a significant reduction in costs was observed after implementing this method. The proposed approach effectively addresses the issue of low maintenance efficiency of medical equipment stemming from inadequate manpower and resources. It is particularly advantageous for healthcare institutions in developing countries and smaller medical facilities, significantly enhancing maintenance efficiency while controlling maintenance costs.