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

Area of Science:

• Medical Human Reliability Analysis
• Decision Science
• Fuzzy Set Theory

Background:

• Traditional Failure Mode and Effect Analysis (FMEA) struggles with uncertainty in medical environments.
• Existing methods often neglect crucial expert and risk factor weightings.
• Subjective ambiguity and hesitation in expert decision-making complicate risk assessment.

Purpose of the Study:

• To develop an advanced FMEA framework for medical risk assessment.
• To integrate Decision-Making Trial and Evaluation Laboratory (DEMATEL) and Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) within an interval-valued intuitionistic fuzzy set (IVIFS) framework.
• To address limitations of traditional FMEA by incorporating expert and risk factor weights.

Main Methods:

• Utilized interval-valued intuitionistic fuzzy sets (IVIFS) to model uncertainty and ambiguity.
• Employed a dual-goal programming model to determine expert weights, considering individual uncertainty and group consensus.
• Developed a comprehensive weighting method for risk factors, combining expert input and fuzzy entropy calculations.

Main Results:

• Proposed a systematic FMEA model for proactive identification and evaluation of high-risk medical failure modes.
• Successfully integrated DEMATEL and TOPSIS within the IVIFS framework for robust risk analysis.
• Introduced novel methods for calculating expert and risk factor weights, enhancing FMEA accuracy.

Conclusions:

• The enhanced FMEA model offers a systematic approach to mitigate human medical errors.
• The methodology improves the safety and reliability of medical service delivery.
• This study provides a robust framework for risk assessment in complex, uncertain medical settings.