Laal Fereydoon, Hanifi Saber Moradi, Madvari Rohollah Fallah, Khoshakhlagh Amir Hossein, Arefi Maryam Feiz
Determinants of Health Research Center, Department of Occupational Health Engineering, Birjand University of Medical Sciences, Birjand, Iran.
Department of Occupational Health Engineering, School of Public Health, Iran University of Medical Sciences, Tehran, Iran.
Heliyon. 2023 Jul 27;9(8):e18736. doi: 10.1016/j.heliyon.2023.e18736. eCollection 2023 Aug.
The central oxygen unit of hospitals is considered a high-risk unit, requiring high safety standards to maintain the integrity of the system during the COVID-19 pandemic. The linear reasoning assumption of conventional risk analysis methods cannot adequately describe these modern systems, which are characterized by tight connections and complex interactions between technical, human, and organizational aspects. Therefore, this study presents a new and comprehensive approach to oxygen tanks in hospitals during the COVID-19 pandemic. In this study, trapezoidal fuzzy numbers were used to calculate failure rates. After determining the probability of basic events (BEs), intermediate events (IE), and top event (TE) with fuzzy logic and transferring it into Bayesian Network (BN), deductive and inductive reasoning, and sensitivity analysis were performed using RoV in GeNIe software. The results of the case study showed that the IE of "Human Error" had the highest probability of fuzzy fault tree (FFT) and the probability of oxygen leakage was lower using FBN than FFT. According to the results, BE16 (failure to use standard and updated instructions) and BE12 (defects in the inspection and testing program of tank devices) had the highest posterior probability, while based on the FFT results, BE4 (defects in the external coating system of the tank) and, BE3 (Corrosive environment (acidity state)) had the least probability. According to the sensitivity analysis, basic events 10, 11, and 16 were the most important in the oxygen leakage event with a very small difference, which was almost in line with the results of posterior FBN (FBN). Updating the existing guidelines, fixing defects in the inspection of all types of tank gauges, and testing related equipment can greatly help the reliability of these tanks. Root cause analysis of these events provides opportunities for prevention and emergency response in critical situations, such as the COVID-19 pandemic.
医院的中央供氧单元被视为高风险单元，在新冠疫情期间需要高标准的安全性以维持系统的完整性。传统风险分析方法的线性推理假设无法充分描述这些现代系统，其特点是技术、人员和组织层面之间联系紧密且相互作用复杂。因此，本研究提出了一种针对新冠疫情期间医院氧气罐的全新综合方法。在本研究中，使用梯形模糊数来计算故障率。在用模糊逻辑确定基本事件（BEs）、中间事件（IE）和顶事件（TE）的概率并将其转换为贝叶斯网络（BN）后，在GeNIe软件中使用风险值（RoV）进行演绎和归纳推理以及敏感性分析。案例研究结果表明，“人为错误”这一中间事件在模糊故障树（FFT）中的概率最高，且使用模糊贝叶斯网络（FBN）时氧气泄漏的概率低于FFT。根据结果，BE16（未使用标准和更新后的说明）和BE12（罐体设备检查和测试程序中的缺陷）的后验概率最高，而基于FFT结果，BE4（罐体外部涂层系统中的缺陷）和BE3（腐蚀环境（酸性状态））的概率最低。根据敏感性分析，基本事件10、11和16在氧气泄漏事件中最为重要，差异非常小，这几乎与后验FBN（模糊贝叶斯网络）的结果一致。更新现有指南、修复各类罐体仪表检查中的缺陷以及测试相关设备，可极大地提高这些罐体设备的可靠性。对这些事件进行根本原因分析，为诸如新冠疫情等关键情况下的预防和应急响应提供了机会。
