Human error has been identified as the primary contributing cause for up to 80% of the accidents in complex, high risk systems such as aviation, oil and gas, mining and healthcare. Many models have been proposed to analyze these incidents and identify their causes, focusing on the human factor. One such safety model is the Human Factors Analysis and Classification System (HFACS), a comprehensive accident investigation and analysis tool which focuses not only on the act of the individual preceding the accident but on other contributing factors in the system as well. Since its development, HFACS has received substantial research attention; however, the literature on its reliability is limited. This study adds to past research by investigating the overall intra-rater and inter-rater reliability of HFACS in addition to the intra-rater and inter-rater reliability for each tier and category. For this investigation, 125 coders with similar HFACS training coded 95 causal factors extracted from actual incident/accident reports from several sectors. The overall intra-rater reliability was evaluated using percent agreement, Krippendorff"s Alpha, and Cohen"s Kappa, while the inter-rater was analyzed using percent agreement, Krippendorff"s Alpha, and Fleiss" Kappa. Because of analytical limitations, only percent agreement and Krippendorff"s Alpha were used for the intra-rater evaluation at the individual tier and category level and Fleiss" Kappa and Krippendorff"s Alpha, for the corresponding inter-rater evaluation. The overall intra-rater and inter-rater results for the tier level and the individual HFACS tiers achieved acceptable reliability levels with respect to all agreement v ACKNOWLEDGMENTS First, I would like to express my sincerest thanks and gratitude to Allah, who is the source of my success for accomplishing this research. I acknowledge the insightful instruction and guidance of my advisors, Dr. Anand Gramopadhye and Dr. Scott Shappell, who have given me continuous support throughout this research. I also thank my committee members Dr. Kurz and Dr. Sharp, for their valuable suggestions for improving the quality of this work. I am especially grateful to Dr. Julia Sharp; her guidance, constructive feedback, and support significantly contributed to the accomplishment of this research. Special thanks also go to Barbara Ramirez, Director of the Class of 1941 Studio for Student Communication, for her technical help and support in editing this research. Much appreciation goes to my family: my parents, Omar and Mohra; my siblings, Sassia, Abdel-Hakim, Eman, Najmeddien, Wafa, and Housameddien; and my mother in law, Aisha. I am blessed that you are my family and am especially grateful for your prayers, thoughtfulness and emotional support. Special gratitude is also extended to my other family members, aunts, uncles, nieces, nephews, and cousins. Finally, I am very grateful to my husband, Ahmed; I could not have done any of this without you.

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SummaryThe objectives of this study were to identify the frequency and nature of flow disruptions in the operating room with respect to three cardiac surgical team members: anaesthetists; circulating nurses; and perfusionists. Data collected from 15 cases and coded using a human factors taxonomy identified 878 disruptions. Significant differences were identified in frequency relative to discipline type. Circulating nurses experienced more coordination disruptions (v 2 (2, N = 110) = 7.136, p < 0.028) and interruptions (v 2 (2, N = 427) = 29.743, p = 0.001) than anaesthetists and perfusionists, whereas anaesthetists and perfusionists experienced more layout issues than circulating nurses (v 2 (2, N = 153) = 48.558, p = 0.001). Time to resolve disruptions also varied among disciplines (k (12, 878) = 5.186, p = 0.000). Although most investigations take a one-size fits all approach in addressing disruptions to flow, this study demonstrates that targeted interventions must focus on differences with respect to individual role.

The Human Factors Analysis and Classification System for Healthcare (HFACS-Healthcare) was used to classify surgical near miss events reported via a hospital's event reporting system over the course of 1 year. Two trained analysts identified causal factors within each event narrative and subsequently categorized the events using HFACS-Healthcare. Of 910 original events, 592 could be analyzed further using HFACS-Healthcare, resulting in the identification of 726 causal factors. Most issues (n = 436, 60.00%) involved preconditions for unsafe acts, followed by unsafe acts (n = 257, 35.39%), organizational influences (n = 27, 3.72%), and supervisory factors (n = 6, 0.82%). These findings go beyond the traditional methods of trending incident data that typically focus on documenting the frequency of their occurrence. Analyzing near misses based on their underlying contributing human factors affords a greater opportunity to develop process improvements to reduce reoccurrence and better provide patient safety approaches.

Reliability levels were higher with increased training and sample sizes. Likewise, when deviations from the original framework were minimized, reliability levels increased. Future applications of the framework should consider these factors to ensure the reliability and utility of HFACS as an accident analysis and classification tool.