Department of Mathematics, Ibn Tofail University, Kenitra, Morocco; National Institute of Oncology, Ibn Sina University Hospital Center, Rabat, Morocco.
Department of Mathematics, Ibn Tofail University, Kenitra, Morocco.
Int J Med Inform. 2024 Jun;186:105442. doi: 10.1016/j.ijmedinf.2024.105442. Epub 2024 Mar 30.
The nature of activities practiced in healthcare organizations makes risk management the most crucial issue for decision-makers, especially in developing countries. New technologies provide effective solutions to support engineers in managing risks.
This study aims to develop a Decision Support System (DSS) adapted to the healthcare constraints of developing countries that enables the provision of decisions about risk tolerance classes and prioritizations of risk treatment.
Failure Modes and Effects Analysis (FMEA) is a popular method for risk assessment and quality improvement. Fuzzy logic theory is combined with this method to provide a robust tool for risk evaluation. The fuzzy FMEA provides fuzzy Risk Priority Number (RPN) values. The artificial neural network is a powerful algorithm used in this study to classify identified risk tolerances. The risk treatment process is taken into consideration in this study by improving FMEA. A new factor is added to evaluate the feasibility of correcting the intolerable risks, named the control factor, to prioritize these risks and start with the easiest. The new factor is combined with the fuzzy RPN to obtain intolerable risk prioritization. This prioritization is classified using the support vector machine.
Results prove that our DSS is effective according to these reasons: (1) The fuzzy-FMEA surmounts classical FMEA drawbacks. (2) The accuracy of the risk tolerance classification is higher than 98%. (3) The second fuzzy inference system developed (the control factor for intolerable risks with the fuzzy RPN) is useful because of the imprecise situation. (4) The accuracy of the fuzzy-priority results is 74% (mean of testing and training data).
Despite the advantages, our DSS also has limitations: There is a need to generalize this support to other healthcare departments rather than one case study (the sterilization unit) in order to confirm its applicability and efficiency in developing countries.
医疗机构所开展的活动性质使得风险管理成为决策者最关键的问题，尤其是在发展中国家。新技术为支持工程师管理风险提供了有效的解决方案。
本研究旨在开发一种适应发展中国家医疗保健限制的决策支持系统 (DSS)，以便能够提供关于风险容忍度类别和风险处理优先级的决策。
失效模式与影响分析 (FMEA) 是一种流行的风险评估和质量改进方法。模糊逻辑理论与该方法相结合，为风险评估提供了一个强大的工具。模糊 FMEA 提供模糊风险优先数 (RPN) 值。本研究中使用人工神经网络对识别的风险容忍度进行分类。通过改进 FMEA，考虑风险处理过程。添加了一个新的因素来评估纠正不可容忍风险的可行性，称为控制因素，以便对这些风险进行优先级排序，并从最简单的风险开始。将新因素与模糊 RPN 相结合，以获得不可容忍风险的优先级排序。使用支持向量机对这种优先级排序进行分类。
结果证明，我们的 DSS 是有效的，原因如下：(1) 模糊-FMEA 克服了经典 FMEA 的缺点。(2) 风险容忍度分类的准确性高于 98%。(3) 开发的第二个模糊推理系统（带有模糊 RPN 的不可容忍风险的控制因素）很有用，因为存在不精确的情况。(4) 模糊优先级结果的准确性为 74%（测试和训练数据的平均值）。
尽管存在优势，但我们的 DSS 也有局限性：需要将这种支持推广到其他医疗保健部门，而不仅仅是一个案例研究（灭菌单元），以确认其在发展中国家的适用性和效率。
