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基于DEMATEL和TOPSIS在间隔估值的直觉模糊环境中的医疗人类可靠性分析的综合FMEA方法
Qinglian Lin1, Xue Pei1, Jianfa Zhuang2
1School of Management, Xiamen University, Xiamen, PR China.
Risk analysis : an official publication of the Society for Risk Analysis
|September 23, 2025
概括
本研究引入了一种改进的故障模式和效应分析 (FMEA),使用间隔值的直观模糊集. 它通过考虑专家和风险因素权重来改善医疗风险评估,以最大限度地减少人为错误并提高患者安全.
科学领域:
- 医疗人类可靠性分析分析
- 决策科学 决策科学 决策科学
- 模糊的集合理论 模糊的集合理论
背景情况:
- 传统的故障模式和效果分析 (FMEA) 在医疗环境中面临不确定性.
- 现有的方法往往忽略了关键的专家和风险因素权重.
- 专家决策中的主观模两可和犹使风险评估复杂化.
研究的目的:
- 开发一个先进的FMEA医疗风险评估框架.
- 将决策试验和评估实验室 (DEMATEL) 和通过类似于理想解决方案的订单偏好技术 (TOPSIS) 集成到一个区间值直观模糊集 (IVIFS) 框架中.
- 通过结合专家和风险因素权重来解决传统FMEA的局限性.
主要方法:
- 利用区间值的直观模糊集合 (IVIFS) 来建模不确定性和模糊性.
- 采用双重目标编程模型来确定专家权重,考虑个人不确定性和团体共识.
- 开发了风险因素的综合权重方法,将专家输入和模糊计算结合起来.
主要成果:
- 提出了一个系统的FMEA模型,用于积极识别和评估高风险的医疗失败模式.
- 成功地将DEMATEL和TOPSIS集成到IVIFS框架中,以进行可靠的风险分析.
- 引入了计算专家和风险因素权重的新方法,提高了FMEA的准确性.
结论:
- 增强的FMEA模型提供了一种系统的方法来减轻人类医疗错误.
- 该方法提高了医疗服务提供的安全性和可靠性.
- 这项研究为复杂,不确定的医疗环境中的风险评估提供了强大的框架.
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