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Human errors are an expected result of operations performed by individuals and frequently lead to accidents and other catastrophic events. The problem is that the current process used to investigate and mitigate human errors in the biopharmaceutical manufacturing industries is not effective, as it does not include the effects of human factors found to be effective in aviation and nuclear power organizations. The human factors and classification system (HFACS) was created for the investigations of accidents using the Swiss cheese model of accident causation as a theoretical framework. The purpose of this quantitative, inter-rater reliability study was to demonstrate the utility of the HFACS for human error investigations in the biopharmaceutical industry. The research questions focused on the level of agreement between independent raters using HFACS, as well as the difference in the level of agreement across different areas of biopharmaceutical manufacturing processes. In a fully crossed design, raters evaluated a stratified sample of 161 incident records further analyzed using Cohen's kappa, percentage agreement, and a 1-way analysis of variance test with Scheffe post hoc tests. Study results indicated the reliability of the modified HFACS taxonomy, which included no statistical difference (p < .05) with substantial Cohen's kappa values of .66. The social benefit of this study may stem from biopharmaceutical manufacturers using these findings to decrease human errors, improve the safety and reliability of their processes, decrease manufacturing costs, and support the development of drugs to address the unmet medical needs of society.
Cintron, Roberto, "Human Factors Analysis and Classification System Interrater Reliability for Biopharmaceutical Manufacturing Investigations" (2015). Walden Dissertations and Doctoral Studies. 194.