Understanding the "Swiss Cheese Model" and Its Application to Patient Safety.

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.

Implementing a human factors approach to RCA2 : Tools, processes and strategies.

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.

Using Broken Windows Theory as the Backdrop for a Proactive Approach to Threat Identification in Health Care.

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.

A Data-Driven Approach to Team Training for Nurses in a Level II Trauma Center.

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.

Using HFACS-Healthcare to Identify Systemic Vulnerabilities During Surgery.

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.

Proactive Safety Management in Trauma Care: Applying the Human Factors Analysis and Classification System.

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.

Coding Human Factors Observations in Surgery.

The reliability of the Human Factors Analysis and Classification System (HFACS) for classifying retrospective observational human factors data in the cardiovascular operating room is examined. Three trained analysts independently used HFACS to categorize observational human factors data collected at a teaching and nonteaching hospital system. Results revealed that the framework was substantially reliable overall (Study I: k = 0.635; Study II: k = 0.642). Reliability increased when only preconditions for unsafe acts were investigated (Study I: k =0.660; Study II: k = 0.726). Preconditions for unsafe acts were the most commonly identified issues, with HFACS categories being similarly populated across both hospitals. HFACS is a reliable tool for systematically categorizing observational data of human factors issues in the operating room. Findings have implications for the development of a HFACS tool for proactively collecting observational human factors data, eliminating the necessity for classification post hoc.

Evaluating the Reliability of the Human Factors Analysis and Classification System.

INTRODUCTION: This paper examines the reliability of the Human Factors Analysis and Classification System (HFACS) as tool for coding human error and contributing factors associated with accidents and incidents. METHODS: A systematic review of articles published across a 13-yr period between 2001 and 2014 revealed a total of 14 peer-reviewed manuscripts that reported data concerning the reliability of HFACS. RESULTS: Results revealed that the majority of these papers reported acceptable levels of interrater and intrarater reliability. CONCLUSION: 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.

Operator error and system deficiencies: analysis of 508 mining incidents and accidents from Queensland, Australia using HFACS.

Historically, mining has been viewed as an inherently high-risk industry. Nevertheless, the introduction of new technology and a heightened concern for safety has yielded marked reductions in accident and injury rates over the last several decades. In an effort to further reduce these rates, the human factors associated with incidents/accidents needs to be addressed. A modified version of the Human Factors Analysis and Classification System was used to analyze incident and accident cases from across the state of Queensland to identify human factor trends and system deficiencies within mining. An analysis of the data revealed that skill-based errors were the most common unsafe act and showed no significant differences across mine types. However, decision errors did vary across mine types. Findings for unsafe acts were consistent across the time period examined. By illuminating human causal factors in a systematic fashion, this study has provided mine safety professionals the information necessary to reduce mine incidents/accidents further.