Despite significant progress in patient safety, human error continues to occur at high rates in surgical settings. The Human Factors Analysis and Classification System (HFACS) offers a proactive lens to understand how and where errors emerge. This study examines HFACS's utility and reliability in categorizing and comparing human error in cardiovascular, orthopedic, trauma care, and neurosurgery. Observational data from cardiovascular, orthopedic, trauma, and neurosurgery cases were coded using HFACS by trained analysts applying unanimous, majority, and reconciled consensus strategies to assess interrater reliability. Across specialties, 98.25% of disruptions occurred at the "preconditions for unsafe acts," indicating latent failures. In cardiovascular surgery, 49.20% were linked to adverse mental states (e.g., cognitive overload, stress), 26.95% to physical environment issues, and 12.69% to crew resource management. Orthopedic surgery showed 68.75% of crew resource management failures, 19.47% personal readiness issues, and 5.87% environment stressors. Trauma care involved 61.38% crew resource management, 26.71% adverse mental states, and 10.33% team availability. Neurosurgery disruptions stemmed 59.42% from technological environment/layout and 35.92% from communication, coordination, and planning. HFACS is a reliable tool for categorizing human factors in diverse surgical environments. Findings highlight distinct latent failure profiles across specialties and underscore the importance of data driven specialty-specific safety interventions.

Background Efficient and safe perioperative care is critical to optimizing surgical outcomes and reducing preventable errors. Orthopedic procedures, ranging from minimally invasive techniques to complex surgeries, place significant cognitive and physical demands on surgical teams. Disruptions in workflow can compromise efficiency, coordination, and patient safety. This study aimed to systematically identify and categorize surgical flow disruptions to inform quality improvement efforts. Method Forty orthopedic surgeries were observed. A human factors taxonomy was used to classify disruptions, and descriptive statistical analysis was applied. Result Of the 2343 total disruptions observed, Interruptions (46.39 %) were the most frequent, followed by communication failures (33.25 %), coordination challenges (13.19 %), layout inefficiencies (5.25 %), equipment issues (1.20 %), and usability concerns (0.73 %). This translated into one disruption every 3.7 min for the 40 surgeries. Conclusions Addressing surgical flow disruptions proactively can enhance perioperative efficiency, safety, and team coordination. This study presents system vulnerabilities, enabling the possibility of shifting the focus from reactive error analysis to proactive mitigation strategies.

Human factors significantly influence medical quality, especially in complex environments like orthopedic surgery, where latent failures can compromise patient safety. A total of 3168 intraoperative events were observed across 40 orthopedic procedures and classified using the Human Factors Analysis and Classification System (HFACS). Three trained coders independently applied HFACS across 4 tiers and 19 causal categories. Interrater reliability was measured through percent agreement and Fleiss’ Kappa using unanimous, majority, and reconciled coding conditions. Nearly all observed disruptions (98.97%) were classified as preconditions to unsafe acts, most (68.75%) stemmed from crew resource management failures, distractions from personal electronic devices, poor communication, and sales representative presence. A total of 19.47% of disruptions were due to personal readiness, due to the sales representation supporting role in ensuring technologies. An additional 5.87% were due to physical environment issues like equipment noise. Conclusions: The HFACS framework demonstrated strong reliability in identifying systemic weaknesses within orthopedic surgical workflows. These findings emphasize the urgent need for structured interventions that reduce distractions, improve team communication, and regulate vendor interactions in the operating room, all essential steps toward advancing safety and enhancing overall patient care quality.

Introduction:Minimally invasive innovations enhance procedural technology. However, healthcare quality demands addressing mental and physical workloads. This study analyzes flow disruptions uncovering specialty-specific patterns and systemic weaknesses, to enhance quality, processes, and patient safety.Methods:Twenty-five cardiovascular, 40 orthopedic, 65 trauma care, and 30 neurosurgical cases were examined. The data were categorized using human factor taxonomy, and descriptive statistics were applied.Results:Comparing the four specialties using RIPCORD TWA taxonomy, cardiovascular and trauma care were translated into one disruption every 2.2 minutes, followed by neurosurgery with one disruption every 2.7 minutes and orthopedics with one disruption every 3.7 minutes. Interruptions were the highest percentage for cardiovascular and orthopedics, with 54% and 46% of flow disruptions. Trauma care was heavily affected by communication flow disruption with 33%. Layout and coordination issues accumulate 26% of flow disruptions in neurosurgery.Conclusions:Cardiovascular, orthopedics, trauma care, and neurosurgery each feature distinct workflows, risks, and teamwork dynamics, demanding tailored process improvements. By analyzing flow disruptions and systemic weaknesses, this study highlights patterns unique to each specialty, advocating for tailored interventions to enhance communication, coordination, layout optimization, and equipment usability for improved surgical safety and quality.

Threat and error management in complex systems has historically centered around failures rather than successes. This has led to an unbalanced view of risk within organizations in that information regarding resiliency within the system (those factors which help to negate failure) are typically ignored. Currently, the National Aeronautics and Space Administration (NASA) employs the Human Factors Analysis and Classification System (HFACS) to both identify human error involved in mishaps and assess those events which prevented more serious consequences from occurring. This paper demonstrates a Red Light/Green Light analysis of a case study, EVA 23, to outline an incident in which an astronaut came dangerously close to drowning in space due to the collection of water in his helmet.

Abstract This article reviews several key aspects of the Theory of Active and Latent Failures, typically referred to as the Swiss cheese model of human error and accident causation. Although the Swiss cheese model has become well known in most safety circles, there are several aspects of its underlying theory that are often misunderstood. Some authors have dismissed the Swiss cheese model as an oversimplification of how accidents occur, whereas others have attempted to modify the model to make it better equipped to deal with the complexity of human error in health care. This narrative review aims to provide readers with a better understanding and greater appreciation of the Theory of Active and Latent Failures upon which the Swiss cheese model is based. The goal is to help patient safety professionals fully leverage the model and its associated tools when performing a root cause analysis as well as other patient safety activities.

Objectives Historically, health care has relied on error management techniques to measure and reduce the occurrence of adverse events. This study proposes an alternative approach for identifying and analyzing hazardous events. Whereas previous research has concentrated on investigating individual flow disruptions, we maintain the industry should focus on threat windows, or the accumulation of these disruptions. This methodology, driven by the broken windows theory, allows us to identify process inefficiencies before they manifest and open the door for the occurrence of errors and adverse events. Methods Medical human factors researchers observed disruptions during 34 trauma cases at a Level II trauma center. Data were collected during resuscitation and imaging and were classified using a human factors taxonomy: Realizing Improved Patient Care Through Human-Centered Operating Room Design for Threat Window Analysis (RIPCHORD-TWA). Results Of the 576 total disruptions observed, communication issues were the most prevalent (28%), followed by interruptions and coordination issues (24% each). Issues related to layout (16%), usability (5%), and equipment (2%) comprised the remainder of the observations. Disruptions involving communication issues were more prevalent during resuscitation, whereas coordination problems were observed more frequently during imaging. Conclusions Rather than solely investigating errors and adverse events, we propose conceptualizing the accumulation of disruptions in terms of threat windows as a means to analyze potential threats to the integrity of the trauma care system. This approach allows for the improved identification of system weaknesses or threats, affording us the ability to address these inefficiencies and intervene before errors and adverse events may occur.

Objectives This investigation explores flow disruptions observed during cardiothoracic surgery and how they serve to disconnect anesthesia providers from their primary task. We can improve our understanding of this disengagement by exploring what we call the error space or the accumulated time required to resolve disruptions. Methods Trained human factors students observed 10 cardiac procedures for disruptions impacting the anesthesia team and recorded the time required to resolve these events. Observations were classified using a human factors taxonomy. Results Of 301 disruptions observed, interruptions (e.g., those events related to alerts, distractions, searching activity, spilling/dropping, teaching moment, and task deviations) accounted for the greatest frequency of events (39.20%). The average amount of time needed for each disruption to be resolved was 48 seconds. Across 49.87 hours of observation, more than 4 hours were spent resolving disruptions to the anesthesia team's work flow. Conclusions By defining a calculable error space associated with these disruptions, this research provides a conceptual metric that can serve in the identification and design of targeted interventions. This method serves as a proactive approach for recognizing systemic threats, affording healthcare workers the opportunity to mitigate the development and incidence of preventable errors precedently.

Root Cause Analysis and Action (RCA2 ) guidelines offer fundamental improvements to traditional RCA. Yet, these guidelines lack robust methods to support a human factors analysis of patient harm events and development of systems-level interventions. We recently integrated a complement of human factors tools into the RCA2 process to address this gap. These tools include the Human Factors Analysis and Classification System (HFACS), the Human Factors Intervention Matrix (HFIX), and a multiple-criterion decision tool called FACES, for selecting effective HFIX solutions. We describe each of these tools and illustrate how they can be integrated into RCA2 to create a robust human factors RCA process called HFACS-RCA2 . We also present qualitative results from an 18-month implementation study within a large academic health center. Results demonstrate how HFACS-RCA2 can foster a more comprehensive, human factors analysis of serious patient harm events and the identification of broader system interventions. Following HFACS-RCA2 implementation, RCA team members (risk managers and quality improvement advisors) also experienced greater satisfaction in their work, leadership gained more trust in RCA findings and recommendations, and the transparency of the RCA process increased. Effective strategies for overcoming implementation barriers, including changes in roles, responsibilities and workload will also be presented.

This prospective investigation describes the process of designing a targeted, data-driven team training aimed at reducing identified process inefficiencies or flow disruptions (FDs) that threaten the optimal delivery of trauma care. Trained researchers observed and classified FDs during 34 trauma cases in a Level II trauma center. Multidisciplinary trauma personnel generated interventions to identified issues using the human factors intervention matrix (HFIX). This article focuses on one intervention: a formal trauma nurse training program centered around leadership, teamwork, and communication. The training was well perceived and was found to have a significant impact on participant knowledge of course content; t (65) = -13.92, p ≤ .01. By using hospital-specific data to drive intervention development from multidisciplinary team members, it is possible to develop effective solutions aimed at addressing individual threats.

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.

The purpose of this paper is to identify personal electronic device (PED) use by cardiac team members during a series of cardiovascular surgeries. Authors make the case that these devices contribute to the cognitive disconnect between practitioners and their primary task of taking care of the surgical patient. This prospective observational study took place over four months of data collection. Twenty-five cardiovascular procedures (totaling 139.06 h) were observed for workflow disruptions and those related to the use of PEDs were further analyzed for frequency of occurrence and time spent attending to the PED. Data collection yielded 545 events for analysis; each requiring an average of 86.51 s of attention. Most PED use events took place during bypass (n = 233) followed by pre-bypass (n = 197) and post-bypass (n = 115). The results presented here indicate that mobile devices have infiltrated not just social interactions, but those situations that by their nature demand often times undivided attention to ensure safety and protection of others.

Introduction: This article examines the reliability of the Human Factors Analysis and Classification System (HFACS) for classifying observational human factors data collected prospectively in a trauma resuscitation center. Methods: Three trained human factors analysts individually categorized 1,137 workflow disruptions identified in a previously collected data set involving 65 observed trauma care cases using the HFACS framework. Results: Results revealed that the framework was substantially reliable overall (κ = 0.680); agreement increased when only the preconditions for unsafe acts were investigated (κ = 0.757). Findings of the analysis also revealed that the preconditions for unsafe acts category was most highly populated (91.95%), consisting mainly of failures involving communication, coordination, and planning. Conclusion: This study helps validate the use of HFACS as a tool for classifying observational data in a variety of medical domains. By identifying preconditions for unsafe acts, health care professionals may be able to construct a more robust safety management system that may provide a better understanding of the types of threats that can impact patient safety.

Human error has been implicated in 70 to 80% of all civil and military aviation accidents. Yet, most accident reporting systems are not designed around any theoretical framework of human error. As a result, most accident reporting systems are not conducive to a traditional human error analysis, making the identification of intervention strategies onerous. What is required is a general human error framework around which new investigative methods can be designed and existing accident databases restructured. This paper describes the development and theoretical underpinnings of a comprehensive human factors analysis and classification system in the hope that it will help safety professionals reduce the aviation accident rate through systematic, data driven investment strategies and objective evaluation of intervention programs.