Determining the optimal human reliability analysis (HRA) method in healthcare systems using Fuzzy ANP and Fuzzy TOPSIS

Abstract

Purpose As one of the leading causes of deaths and injuries, medical errors constitute a veritable threat to patient safety. Despite this fact, no unique method has yet been established to identify and evaluate medical errors. This study was conducted to select an optimal human reliability analysis method compatible with healthcare systems from available methods. Design/methodology/approach: In order to select the optimal method for the identification and evaluation of medical errors, different criteria and sub-criteria were determined by reviewing the literature and based on experts’ opinions. Next, weights of criteria and sub-criteria were specified by using the fuzzy analytical network process (ANP). Finally, fuzzy technique for order preference by similarity to ideal solution method was used to prioritize the methods. Findings Six criteria and 21 sub-criteria for choosing the optimal method were determined. The utility, usability, and structure of a method had the highest influence with weights of 0.262, 0.191, and 0.187, respectively. Based on the results of fuzzy technique for order preference by similarity to ideal solution, the Human Error Assessment and Reduction Technique method with a closeness coefficient of 0.576 was selected as the optimal method for identifying medical errors. The Human Factors Analysis and Classification System and Systematic Human Error Reduction and Prediction Approach methods ranked second and third, respectively. Originality/value: To date, no studies have attempted to determine the optimal methods for identification and assessment of medical errors. This paper aimed to fill this gap by using fuzzy analytical network process and fuzzy technique for order preference by similarity to ideal solution techniques.