The integrated prospect theory with consensus model for risk analysis of human error factors in the clinical use of medical devices

The risk analysis of human error factors is one of the most significant procedures for preventing and reducing risk in the clinical use of medical devices. Human Factor Analysis and Classification System (HFACS) framework, a systematic human error analysis tool, is widely used for human error factors risk analysis. However, the traditional HFACS framework is insufficient to deal with the scenario with complex and uncertain risk information and conflicting opinions among experts caused by their heterogeneous risk preferences and diverse knowledge. To address these limitations, this paper integrated the prospect theory with the consensus model under Interval Type-2 Fuzzy Sets (IT2FSs) environment for addressing the HFACS-based human error factors risk analysis problem. This integrated method enabled the HFACS to yield highly acceptable risk analysis results considering experts' heterogeneous risk preferences. Specifically, the IT2FSs are utilized to represent highly complex and uncertain risk assessment information of human error factors. Secondly, the prospect theory is applied to model the heterogeneous risk preferences of experts and eliminate their impact on risk evaluation results. After obtaining the risk evaluation matrix by prospect theory, the consensual risk evaluation matrix is yielded by a consensus model that can balance the group aim of reaching a consensus and the individual aim of keeping original risk evaluation information as much as possible. Then, the risk ranking of human error factors is determined based on the distance of IT2FSs. Finally, a case study of the clinical use of ventilators, including sensitivity analysis and comparative analysis, is presented to illustrate the efficiency of the proposed method.

Automated summary

This paper proposes an integrated method that combines prospect theory with a consensus model to address the problem of HFACS-based human error factors risk analysis. The integrated method can yield highly acceptable risk analysis results by considering experts' heterogeneous risk preferences.