Human Factors Analysis And Classification System Research Articles

Effect of HFACS and Non-HFACS-Related Factors on Fatalities in General Aviation Accidents Using Neural Networks

This study applied a backpropagation artificial neural network approach to investigate both the Human Factors Analysis and Classification System (HFACS)-related unsafe act tiers of factors and other non-HFACS factors in an attempt to recognize patterns for general aviation accident fatalities. Data were obtained from the HFACS database and extracted from the National Transportation Safety Board database from 1990 to 2002. Multiple neural network models were created and the best fit model was selected based on a sequence of criteria. A sensitivity analysis was performed on the validated model to rank the factors that lead to general aviation fatalities. Results are discussed and practical implications are given.

Requirements Elicitation Through Legacy Mishap Analysis

The archives of mishap investigation reports provide a large database of documented system failures. We present a method that studies human error in these mishaps and quantitatively predicts areas of focus for new system development. Even very novel systems have similarities, either in structure or operation, to legacy systems. Knowing what design induced errors have plagued past human-machine interactions can guide new system requirements generation. This method is demonstrated through its application in a next generation military unmanned aerial system (UAS). For this, the last five years of US Air Force aircraft mishaps are studied using the Human Factors Analysis and Classification System (HFACS) and mapped to the domains of human systems integration (HSI).

An Empirical Research of Correlation Analysis on Engineering Construction Safety Influencing Factors

The correlation analysis of the independence between the behavioral influencing factors has been performed in order to find the non-independent factors. Among the Human Factors Analysis and Classification System (HFACS), the impact of the high-level factors on the bottom level is determined. This study uses empirical method, namely Structural Equation Modeling (SEM) to build the relationship model. After finishing the framework of construction safety influencing factors, we design the questionnaire survey, ultimately draw SEM model by AMOS software. The results by AMOS show that the model can effectively describe the relationship between the influence factors of which affecting construction safety and provide strong evidence for improving the engineering and construction management.

Commentary on ‘Design and Validation of an Error Capture Tool for Quality Evaluation in the Vascular and Endovascular Surgical Theatre’

The study by Mason et al. presenting the Imperial College Error CAPture (ICECAP) tool aims to provide vascular surgeons with a valid and reproducible tool for identification and categorization of common errors during vascular and endovascular operations. Despite some methodological limitations that have been properly addressed, six primary categories (communication, equipment, procedure independent pressures, technical, safety awareness and patient related) and 20 sub-categories were determined as the most frequent and important vascular procedural errors. “To err is human”. This famous adage by Alexander Pope was used in a report issued in November 1999 by the U.S. Institute of Medicine in order to increase awareness towards preventable medical errors in hospitals that exceeded deaths by motor-vehicle accidents, breast cancer and AIDS. Among the report’s main conclusions was that the majority of medical errors were not a “bad apple” problem. They did not result from individual recklessness or the actions of a particular group, but mainly by faulty systems, processes and conditions that lead to errors or fail to prevent them. But are errors during surgical operations as complex and multidisciplinary as plane crash errors? Interestingly, ElBardissi et al. successfully adapted and applied in the OR the Human Factors Analysis and Classification System (HFACS); a universally accepted human error framework that was originally used by the US air force to investigate and analyze human factors and errors in aviation. This model illustrated that the organizational influence (climate, resource management and policies) impact supervisory processes (scheduling, training, and oversight), which in turn establish the preconditions (technological and teamwork related) that produce errors.

Identifying Training Deficiencies in Military Pilots by Applying the Human Factors Analysis and Classification System

Without accurate analysis, it is difficult to identify training needs and develop the content of training programs required for preventing aviation accidents. The human factors analysis and classification system (HFACS) is based on Reason’s system-wide model of human error. In this study, 523 accidents from the Republic of China Air Force were analyzed in which 1762 human errors were categorized. The results of the analysis showed that errors of judgment and poor decision-making were commonly reported amongst pilots. As a result, it was concluded that there was a need for military pilots to be trained specifically in making decisions in tactical environments. However, application of HFACS also allowed the identification of systemic training deficiencies within the organization further contributing to the accidents observed.

An Accidents Analysis Model to Identify the Correlation of Faulty Behavior Risk Factors in High-Risk Work System of Hydropower Construction

Work system safety is a function of many factors, besides it is dynamic and complex. There may be relations and dependencies among the safety factors. Therefore, work system safety should be analyzed in a holistic manner. In this study, the accidents data analysis is used to identify the links among the factors; the relationship analysis of the independence between the behavioral factors has been performed in order to find the non-independent factors. Among the Human Factors Analysis and Classification System (HFACS), the impact of the high-level factors on the bottom level is determined. After a period of monitoring and rectification, it implements a new round of safety monitoring and factors comparison and interaction analysis, so to achieve the goal of “the nature of safety” of hydropower projects construction.

Accident analysis model based on Bayesian Network and Evidential Reasoning approach

In this paper, an accident analysis model is proposed to develop the cost-efficient safety measures for preventing accidents. The model comprises two parts. In the first part, a quantitative accident analysis model is built by integrating Human Factors Analysis and Classification System (HFACS) with Bayesian Network (BN), which can be utilized to present the corresponding prevention measures. In the second part, the proposed prevention measures are ranked in a cost-effectiveness manner through Best-Fit method and Evidential Reasoning (ER) approach. A case study of vessel collision is analyzed as an illustration. The case study shows that the proposed model can be used to seek out accident causes and rank the derived safety measures from a cost-effectiveness perspective. The proposed model can provide accident investigators with a tool to generate cost-efficient safety intervention strategies.

Developing the understanding of underlying causes of construction fatal accidents

A study of a small sample of construction fatal accidents was used to pilot a potential method of analysis for the UK Health and Safety Executive (HSE) Construction Division. The work was conducted in the context of a Governmental Inquiry into construction fatalities in 2009, but has been developed further since. The sample of 26 accidents (28 fatalities) was drawn from the 211 fatal accidents in the years ending 2006–2008, to be broadly illustrative of the range of accident characteristics. The accidents were analysed on the basis of available inspectorate reports and structured interviews with the investigating inspectors. A standard method of classification on four levels was developed, based on the Human Factors Analysis and Classification System (HFACS) classification of errors and task level factors, with additional categories covering the organisational and regulatory/market levels of the system. The results showed a concentration of underlying factors associated with inadequacies in planning and risk assessment, competence assurance, hardware design, purchase and installation, and contracting strategy. These findings were partially validated by comparing them with another sample of 50 accidents analysed earlier by the HSE. This paper describes the development and testing of the investigation and classification method and how it is being further developed since the initial study. It also provides a summary of the findings relating to underlying causes.

Indonesian railway accidents – utilizing Human Factors Analysis and Classification System in determining potential contributing factors

The prevalence of Indonesian railway accidents has not been declining, with hundreds of fatalities reported in the past decade. As an effort to help the National Transportation Safety Committee (NTSC), this study was conducted that aimed at understanding factors that might have contributed to the accidents. Human Factors Analysis and Classification System (HFACS) was utilized for this purpose. A total of nine accident reports (provided by the Indonesian NTSC) involving fatalities were studied using the technique. Results of this study indicated 72 factors that were closely related to the accidents. Of these, roughly 22% were considered as operator acts while about 39% were related to preconditions for operator acts. Supervisory represented 14% of the factors, and the remaining (about 25%) were associated with organizational factors. It was concluded that, while train drivers indeed played an important role in the accidents, interventions solely directed toward train drivers may not be adequate. A more comprehensive approach in minimizing the accidents should be conducted that addresses all the four aspects of HFACS.

Evaluating The Utility of DoD Hfacs Using Lifted Probabilities

The Department of Defense continues to invest in the development and validation of several accident investigation and reporting methodologies geared toward the accurate analysis of human factors issues. Recently, a joint memorandum of agreement was signed by each of the military services to use the DoD Human Factors Analysis and Classification System (HFACS). While a great deal of research has been devoted to assessing the validity of HFACS as applied to DoD mishaps, less research has focused on the utility of the model as both an accident investigation and reporting tool. The primary aim of this research was to apply lifted rule probabilities at the nanocode level within HFACS to identify common linkages within the DoD version of HFACS.

Correlation Analysis of Behavioral Factors of Construction Safety Based on the Chi-Square Test

The construction safety supervision errors can lead to unsafe behavior; further affect the working conditions of workers and result in accidents at last. The probabilities of data are provided by statistical analysis between the different types of neighborhood-level factors by the independence of the chi-square test. The relationship analysis of the independence between the behavioral factors has been performed in order to find the non-independent factors. Among the Human Factors Analysis and Classification System (HFACS), the impact of the high-level factors on the bottom level is determined. Based on the data, the work path of various factors in the HFACS frame is confirmed and it can be a reference for the development of the preventive measures.

A systems approach to accident causation in mining: An application of the HFACS method

This project aimed to provide a greater understanding of the systemic factors involved in mining accidents, and to examine those organisational and supervisory failures that are predictive of sub-standard performance at operator level. A sample of 263 significant mining incidents in Australia across 2007–2008 were analysed using the Human Factors Analysis and Classification System (HFACS). Two human factors specialists independently undertook the analysis. Incidents occurred more frequently in operations concerning the use of surface mobile equipment (38%) and working at heights (21%), however injury was more frequently associated with electrical operations and vehicles and machinery. Several HFACS categories appeared frequently: skill-based errors (64%) and violations (57%), issues with the physical environment (56%), and organisational processes (65%). Focussing on the overall system, several factors were found to predict the presence of failures in other parts of the system, including planned inappropriate operations and team resource management; inadequate supervision and team resource management; and organisational climate and inadequate supervision. It is recommended that these associations deserve greater attention in future attempts to develop accident countermeasures, although other significant associations should not be ignored. In accordance with findings from previous HFACS-based analyses of aviation and medical incidents, efforts to reduce the frequency of unsafe acts or operations should be directed to a few critical HFACS categories at the higher levels: organisational climate, planned inadequate operations, and inadequate supervision. While remedial strategies are proposed it is important that future efforts evaluate the utility of the measures proposed in studies of system safety.

Coding ATC incident data using HFACS: Inter-coder consensus

Reliability studies for coding contributing factors of incident reports in high hazard industries are rarely conducted and reported. Although the Human Factors Analysis and Classification System (HFACS) appears to have a larger number of such studies completed than most other systems doubt exists as the accuracy and comparability of results between studies due to aspects of methodology and reporting. This paper reports on a trial conducted on HFACS to determine its reliability in the context of military air traffic control (ATC). Two groups participated in the trial: one group comprised of specialists in the field of human factors and the other group comprised air traffic controllers. All participants were given standardised training via a self-paced workbook and then read 14 incident reports and coded the associated findings. The results show similarly low consensus for both groups of participants. Several reasons for the results are proposed associated with the HFACS model, the context within which incident reporting occurs in real organisations and the conduct of the studies.

Investigations of Human and Organizational Factors in hazardous vapor accidents

This paper presents a model to assess the contribution of Human and Organizational Factor (HOF) to accidents. The proposed model is made up of two phases. The first phase is the qualitative analysis of HOF responsible for accidents, which utilizes Human Factors Analysis and Classification System (HFACS) to seek out latent HOFs. The hierarchy of HOFs identified in the first phase provides inputs for the analysis in the second phase, which is a quantitative analysis using Bayesian Network (BN). BN enhances the ability of HFACS by allowing investigators or domain experts to measure the degree of relationships among the HOFs. In order to estimate the conditional probabilities of BN, fuzzy analytical hierarchy process and decomposition method are applied in the model. Case studies show that the model is capable of seeking out critical latent human and organizational errors and carrying out quantitative analysis of accidents. Thereafter, corresponding safety prevention measures are derived.

An extension of the Human Factors Analysis and Classification System for use in open systems

The Human Factors Analysis and Classification System (HFACS), based upon Reason's model of human error in an organisational context, is currently the most widely used human factors accident analysis framework. However, it has been criticised for merely categorising accident data rather than analysing it. Previous research has established statistical associations between the levels and categories within HFACS but has not specified a mechanism by which one category influences subsequent behaviour. This paper extends the approach in two ways. Using the categories of control flaws derived from Leveson's Systems–Theoretical Accident Model and Processes (STAMP) approach, it describes the mechanisms by which categories within HFACS are associated with other categories lower in the organisational hierarchy. It also provides a mechanism by which active failures can promulgate across organisations. The revised methodology HFACS-STAMP is illustrated using the case study of the Uberlingen mid-air collision on 1 July 2002.

Accident investigation reporting deficiencies related to organizational factors in machinery space fires and explosions

Careful accident investigation provides opportunities to review safety arrangements in socio-technical systems. There is consensus that human intervention is involved in the majority of accidents. Ever cautious of the consequences attributed to such a claim vis-à-vis the apportionment of blame, several authors have highlighted the importance of investigating organizational factors in this respect. Specific regulations to limit what were perceived as unsuitable organizational influences in shipping operations were adopted by the International Maritime Organization (IMO). Guidance is provided for the investigation of human and organizational factors involved in maritime accidents. This paper presents a review of 41 accident investigation reports related to machinery space fires and explosions. The objective was to find out if organizational factors are identified during maritime accident investigations. An adapted version of the Human Factor Analysis and Classification System (HFACS) with minor modifications related to machinery space features was used for this review. The results of the review show that organizational factors were not identified by maritime accident investigators to the extent expected had the IMO guidelines been observed. Instead, contributing factors at the lower end of organizational echelons are over-represented.

Retracted] Coding ATC Incident Data Using HFACS: Intercoder Consensus

Reliability studies for coding contributing factors of incident reports in high hazard industries are rarely conducted and reported. Although the Human Factors Analysis and Classification System (HFACS) appears to have a larger number of such studies completed than most other systems doubt exists as the accuracy and comparability of results between studies due to aspects of methodology and reporting. This paper reports on a trial conducted on HFACS to determine its reliability in the context of military air traffic control (ATC). Two groups participated in the trial: one group comprised of specialists in the field of human factors, and the other group comprised air traffic controllers. All participants were given standardized training via a self‐paced workbook and then read 14 incident reports and coded the associated findings. The results show similarly low consensus for both groups of participants. Several reasons for the results are proposed associated with the HFACS model, the context within which incident reporting occurs in real organizations and the conduct of the studies.

Collection and Evaluation of Incidents in a Radiotherapy Department

to report on the use of an internal system for incident reporting. from October 2001 until June 2009, data on incidents were collected in the radiotherapy department (RT) by means of an incident reporting worksheet. The risk analysis was based on the US Navy method of mishap cause investigation, the Human Factors Analysis and Classification System (HFACS). 37 incidents over 5,635 treatments were collected. Of the incidents, 20 involved deviation of the dose to the patient; only 6 showed clinical evidence of overdosage, while 2 of them showed permanent evidence of overdosage. There were 24 incidents that were classified as near misses (NM). Incorrect data input and use of an incorrect treatment field were the most common causes of the registered incidents. Reactive risk analysis showed how skill-based errors were associated with attention failure at the unsafe act level. Dose prescription and dose calculation are the most critical phases of the entire process. Most of the errors were discovered in set-up/treatment and during treatment visit/follow-up phases. The highest number of correction procedures was necessary in the phases of dose prescription and dose calculation. collecting and analyzing internal incidents improves the operative procedures used in the department.

Carrier Air Wing Mishap Reduction Using a Human Factors Classification System and Risk Management

In 1998, the Navy's center of excellence for advanced air wing combat operations, namely the Naval Strike and Air Warfare Center (NSAWC), had a spike in Class A flight mishaps. The spike triggered an intense review of prior mishaps and current mishap-reduction practices using the Human Factors Analysis and Classification System (HFACS). The review resulted in NSAWC instituting a comprehensive multifactorial mishap reduction plan applying Operational Risk Management (ORM) precepts. This is a nonrandomized investigational study with use of a historical comparison population. The Class A mishap rate per flight hour covering 10 yr prior to the mishap reduction efforts was estimated and compared to the Class A mishap rate per flight hour for the 10 yr after implementation using Poisson regression. Combined Fleet and NSAWC data shows a 27% reduction in mishap rate, but the 21% reduction in the Fleet alone was not statistically significant. The mishap reduction at NSAWC was statistically significant with an 84% reduction. Fallon carrier air wing mishap rates post-ORM mishap reduction efforts are approaching those seen in the Fleet, but are still elevated overall (3.7 vs. 2.4). The incidence rate ratio was 80% lower at Fallon than the rest of the Fleet, indicating a significantly greater reduction in NSAWC air wing mishaps and suggests focused aviation mishap reduction efforts in similar circumstances could result in similar reductions.

9.2.1 RELAAy — A Tool to Guide HSI Requirements

AbstractArchives of Air Force mishap investigation reports provide a large data set of documented system failures. In this paper, we present a tool that analyzes human‐related errors in these mishaps and quantitatively predicts focus areas for new system development. Even new systems have similarities, either in structure or function, to legacy systems. Knowing what design induced errors have plagued past human‐machine interactions can guide new Human Systems Integration (HSI) requirements generation. This is accomplished using the Human Factors Analysis and Classification System (HFACS) as the underlying data. Our tool, with a novel method of using the entire mishap data set over the last five years is demonstrated through its application for a next generation unmanned aerial system (UAS). Lastly, the HFACS data is mapped to the domains of HSI for program office consumption.