A hybrid HFACS model using DEMATEL-ORESTE method with linguistic Z-number for risk analysis of human error factors in the healthcare system

The human error factor is one of the leading causes of medical errors. Among risk analysis techniques for human error factors, HFACS (Human Factor Analysis and Classification System) method has been regarded as one of the most valuable approaches due to its advantage in potential failure classification. However, the conventional HFACS method is insufficient to handle the risk analysis problem for human error factors considering the interactive relationship among these factors. Moreover, the current fuzzy HFACS frameworks cannot address quantitative risk analysis issues, including imprecision and reliability of information. Thus, this paper constructs an integrated linguistic Z-number-based HFACS framework for analyzing the risk of human error factors. This framework can capture the uncertainty and reliability of the risk evaluation information and the interactive relationships among factors. First, the linguistic Z-number-DEMATEL (Decision Making Trial and Evaluation Laboratory) method is used to generate the comprehensive risk matrix of human error factors identified by the HFACS method. Then, an extended linguistic Z-number-ORESTE method based on the score function is presented to prioritize the risk of human error factors, which can show the preference, indifference, and incomparability relationship among human error factors. Finally, a case study of a healthcare system is conducted to illustrate the reliability of the proposed method. The result of this case indicates that inadequate resources are the most severe risk. Sensitivity analysis and comparative analysis indicate the necessity of considering the effect of the semantics of language terms and the interrelationships between human error factors on risk analysis results. These results show that the proposed hybrid framework is a reliable means to analyze the risk of human error factors within the HFACS method.