Li Kun, Su Lei, Cheng Jing, Sun Yinyan, Ma Xinghua
3201 Hospital of Xi'an Jiaotong University Health Science Center, Hanzhong, Shaanxi, 723000, China.
Sci Rep. 2025 May 26;15(1):18377. doi: 10.1038/s41598-025-02176-8.
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.
本研究旨在解决医疗机构医学工程部门人力和资源短缺的问题，同时高效地执行医疗设备维护并实现可控的维护成本。在缺乏历史维护数据的情况下，本研究以多参数监护仪为例进行研究。该方法将故障树分析（FTA）与模糊层次分析法（FAHP）相结合，以整合专家判断并解决故障数据中的不确定性。然后，将这种整合的结果转换为贝叶斯网络（BN），用于概率推理和故障分析。这种综合方法能够对监护仪在不同使用阶段（早期、中期和后期）的故障进行定性和定量分析。分析内容包括确定每个阶段的故障概率、识别高风险组件、检查故障模式的转变、深入了解组件的老化特性以及制定预防性维护策略。基于具体实际案例进行了成本效益分析。该方法成功确定了监护仪各组件在不同阶段的故障概率，准确找出了高风险组件，并清晰地分析了故障模式的转变。经过一年的实际应用，实施该方法后成本显著降低。所提出的方法有效地解决了因人力和资源不足导致的医疗设备维护效率低下的问题。它对发展中国家的医疗机构和小型医疗设施特别有利，在控制维护成本的同时显著提高了维护效率。
