An Integrated FMEA Method for Medical Human Reliability Analysis Based on DEMATEL and TOPSIS in Interval-Valued Intuitionistic Fuzzy Environment.

Abstract

Failure mode and effect analysis (FMEA) is a prospective method for medical human reliability analysis that evaluates the risks of potential medical failure modes. To better address the complexities of medical environments characterized by uncertainty and limited information, this study employs an interval-valued intuitionistic fuzzy set (IVIFS) to represent and analyze such environments within the FMEA framework. To tackle the challenges posed by the subjective ambiguity and hesitation in expert decision-making during the risk assessment of medical failure modes, this paper proposes an integrated approach based on the decision-making trial and evaluation laboratory (DEMATEL) methodology and the technique for order preference by similarity to an ideal solution (TOPSIS) within an interval-valued intuitionistic fuzzy framework. To overcome the limitations of traditional FMEA, which neglects expert weight and risk factor weight, this paper introduces an enhanced methodology. First, a dual-goal programming model is developed, incorporating both individual uncertainty and group consensus among experts to determine expert weight. Second, a comprehensive weighting method that combines expert-driven weighting with information-based weighting derived from fuzzy entropy calculations applied to expert data is applied to calculate the weights of risk factors. The proposed FMEA model presented in this study provides a systematic method to identify and evaluate high-risk failure modes in medical systems proactively. By doing so, it seeks to minimize the occurrence of human medical errors and adverse events while enhancing the safety and reliability of medical service delivery processes.