As aircraft have become more reliable, humans have played a progressively more important causal role in aviation accidents. Consequently, a growing number of aviation organizations are tasking their ...safety personnel with developing accident investigation and other safety programs to address the highly complex and often nebulous issue of human error. Yet, many safety professionals are illequipped to perform these new duties.
The purpose of the present book is to remedy this situation by presenting a comprehensive, userfriendly framework to assist practitioners in effectively investigating and analyzing human error in aviation. Coined the Human Factors Analysis and Classification System (HFACS), its framework is based on James Reason's (1990) well-known "Swiss cheese" model of accident causation. In essence, HFACS bridges the gap between theory and practice in a way that helps improve both the quantity and quality of information gathered in aviation accidents and incidents.
The HFACS framework was originally developed for, and subsequently adopted by, the U.S. Navy/Marine Corps as an accident investigation and data analysis tool. The U.S. Army, Air Force, and Coast Guard, as well as other military and civilian aviation organizations around the world are also currently using HFACS to supplement their preexisting accident investigation systems. In addition, HFACS has been taught to literally thousands of students and safety professionals through workshops and courses offered at professional meetings and universities. Indeed, HFACS is now relatively well known within many sectors of aviation and an increasing number of organizations worldwide are interested in exploring its usage. Consequently, the authors currently receive numerous requests for more information about the system on what often seems to be a daily basis.
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.
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.
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.
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 revealed that the majority of these papers reported acceptable levels of interrater and intrarater reliability.
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.
Coding Human Factors Observations in Surgery Cohen, Tara N; Wiegmann, Douglas A; Reeves, Scott T ...
American journal of medical quality,
2017 Sep/Oct, Letnik:
32, Številka:
5
Journal Article
Recenzirano
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.
Root Cause Analysis and Action (RCA
) 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 RCA
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 RCA
to create a robust human factors RCA process called HFACS-RCA
. We also present qualitative results from an 18-month implementation study within a large academic health center. Results demonstrate how HFACS-RCA
can foster a more comprehensive, human factors analysis of serious patient harm events and the identification of broader system interventions. Following HFACS-RCA
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.
Abstract Background The aim of this investigation is to place surgical disruptions in a different light. Rather than viewing these disruptions as isolated events which may affect the surgical team, ...we represent them as an aggregated space which serves to disconnect the team from the task at hand. Furthermore, we make the case that by understanding this error space, one can begin to target interventions that reduce the boundaries of this space and as a consequence reduce the opportunity for errors to develop. Methods Trained doctoral-level human factors students observed 24 cardiac procedures for flow disruptions impacting the surgical team and recorded the frequency as well as time needed to resolve these events. Observations were later coded using a human factors taxonomy and descriptive statistics were applied. Results A total of 693 workflow disruptions were experienced by the surgical team where interruption issues accounted for the greatest frequency of events (32.61%). Of 139.06 hours of total observation time, 10.14 hours was needed to resolve the 693 disruptions identified. On average, each disruption took 61.99 seconds to resolve. Conclusion While there is value in identifying the frequency of flow disruptions, this only addresses part of the problem. What is missing from analyses of this sort is the time that the healthcare professional is separated from their central task; in this case the surgeon performing thoracic surgery. This paper provides a conceptual and quantitative metric that allows for the practical application of proactive methods for identifying systemic threats.
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