Human Factors Analysis And Classification System Research Articles

Modification of HFACS model for path identification of causal factors of collapse accidents in the construction industry

PurposeThis study aims to modify the human factors analysis and classification system (HFACS) to make it suitable for collapse accident analysis in construction. Based upon the modified HFACS, distribution patterns of causal factors across multiple levels were discerned among causal factors of various stakeholders at construction sites. It explored the correlations between two causal factors from different levels and further determined causation paths from two perspectives of level and stakeholder.Design/methodology/approachThe main research framework consisted of data collection, coding and analysis. Collapse accident reports were collected with adequate causation information. The modified HFACS was utilized for coding causal factors across all five levels in each case. A hybrid approach with two perspectives of level and stakeholder was proposed for frequency analysis, correlation analysis and path identification between causal factors.FindingsEight causal factors from external organizations at the fifth level were added to the original HFACS. Level-based correlation analyses and path identification provided safety managers with a holistic view of inter-connected causal factors across five levels. Stakeholder-based correlation analyses between causal factors from the fifth level and its non-adjacent levels were implemented based on client, government and third parties. These identified paths were useful for different stakeholders to develop specific safety plans for avoiding construction collapse accidents.Originality/valueThis paper fulfils an identified need to modify and utilize the HFACS model for correlation analysis and path identification of causal factors resulting in collapse accidents, which can provide opportunities for tailoring preventive and protective measures at construction sites.

Inadvertent administration of intravenous anaesthesia induction agents via the intracerebroventricular, neuraxial or peripheral nerve route – A narrative review

Intravenous (IV) medication administration error remains a major concern during the perioperative period. This review examines inadvertent IV anaesthesia induction agent administration via high-risk routes. Using Medline and Google Scholar, the author searched published reports of inadvertent administration via neuraxial (intrathecal, epidural), peripheral nerve or plexus or intracerebroventricular (ICV) route. The author applied the Human Factors Analysis and Classification System (HFACS) framework to identify systemic and human factors. Among 14 patients involved, thiopentone was administered via the epidural route in six patients. Four errors involved the routes of ICV (propofol and etomidate one each) or lumbar intrathecal (propofol infusion and etomidate bolus). Intrathecal thiopentone was associated with cauda equina syndrome in one patient. HFACS identified suboptimal handling of external ventricular and lumbar drains and deficiencies in the transition of care. Organisational policy to improve the handling of neuraxial devices, use of technological tools and improvements in identified deficiencies in preconditions before drug preparation and administration may minimise future risks of inadvertent IV induction agent administration.

Exploring human factors of major chemical accidents in China: Evidence from 160 accidents during 2011–2022

Major chemical accidents (MCAs) usually cause multiple casualties, property losses, and environmental damage. MCAs in hazardous chemical production enterprises (HCPEs) in China have been frequent in recent years. Understanding past accidents is essential for accident prevention and risk mitigation. However, no accurate statistical analysis and systematic human cause investigation of MCAs in China is available. Hence, this study systematically identified the main characteristics and human factors of MCAs in China during 2011–2022 using official accident investigation reports. First, the 160 MCAs were investigated by mathematical and statistical methods involving eight main characteristics: evolution trend, spatial locations, accident types, domino effects involved, occurrence links, time volatility, hazardous chemicals involved, and enterprise scales. Second, based on the characteristics of MCAs and existing research on chemical accidents, the Human Factors Analysis and Classification System (HFACS) for Chemical Accidents (HFACS-CA), which consists of 26 cause categories and is suitable for the analysis of chemical accidents, was proposed on the basis of HFACS and other extended HFACS. Then, the selected 146 representative MCAs were analyzed using the HFACS-CA framework; the human factors (comprising human and organizational factors (HOFs) and outside factors) of MCAs in China were systematically explored. Finally, according to the main characteristics and human factors of MCAs, comprehensive recommendations for the prevention of MCAs involving enterprises, governments, and interested parties were proposed. This work can provide a useful human factor analysis tool for investigating chemical accidents and valuable information for preventing MCAs in China and other countries.

Analysis of factors associated with needlestick injuries of clinical nurses by applying a human factor analysis and classification system: A nationwide cross-sectional survey.

This study aims to investigate the current situation of needlestick injuries (NSIs) of clinical nurses and identify associated factors by using the theoretical framework of the human factors analysis and classification system (HFACS). A nationwide cross-sectional survey was conducted. Multi-stage sampling was used to investigate 3336 nurses in 14 Chinese hospitals. Descriptive statistics and univariate and multivariate logistic regression were employed to reveal the rate of NSIs and their associated factors. A total of 970 nurses (29.1%) reported having experienced at least one NSI in the past year. The multivariate logistic regression analysis showed that good hospital safety climate and clinical nurses in intensive care unit (ICU) and emergency department had protective effects against NSIs compared with nurses in internal medicine department. The nurse, senior nurse, and nurse in charge have significantly increased the risk for NSIs compared with the associate chief nurse or above. Patients with poor vision but wearing glasses and poor vision but not wearing glasses were more prone to have NSIs. Working in the operating room compared with internal medicine, average weekly working time of >45 h compared with ≤40 h and poor general health led to increased risk of NSIs. The rate of NSIs in clinical nurses was high in China. Individual factors including professional title, department, visual acuity and general mental health and organisational factors including weekly working hours and hospital safety atmosphere were significantly correlated with the occurrence of NSIs. Nursing managers should focused on physical and psychological conditions of clinical nurses, and organisational support is required to enhance the hospital safety atmosphere. Contributions from patients or the public are irrelevant because this study aims to explore current situation and factors associated with NSIs in clinical nurses.

A Natural Language Processing System for Text Classification Corpus Based on Machine Learning

A classification system for hazardous materials in air traffic control was investigated using the Human Factors Analysis and Classification System (HFACS) framework and natural language processing to prevent hazardous situations in air traffic control. Based on the development of the HFACS standard, an air traffic control hazard classification system will be created. The dangerous data of the aviation safety management system is selected by dead bodies, classified and marked in 5 levels. TFIDF TextRank text classification method based on key content extraction and text classification model based on CNN and BERT model were used in the experiment to solve the problem of small samples, many labels and random samples in hazardous environment of air pollution control. The results show that the total cost of model training time and classification accuracy is the highest when the keywords are around 8. As the number of points increases, the time spent in dimensioning decreases and affects accuracy. When the number of points reaches about 93, the time spent in determining the size increases, but the accuracy of the allocation remains close to 0.7, but the increase in the value of time leads to a decrease in the total cost. It has been proven that extracting key content can solve text classification problems for small companies and contribute to further research in the development of security systems.

Identification of critical causes of construction accidents in China using a hybrid HFACS-CN model

Construction safety is of significance since construction accidents can result in loss of property and large numbers of casualties. This research aims to identify the critical causes of construction accidents by introducing a hybrid approach. The hybrid approach is developed to identify the critical causes of construction accidents by combining the human factors analysis and classification system (HFACS) model with complex network (CN) theory. A total of 863 construction accident cases were collected, and 46 causal factors were identified. Subsequently, the accident causal network was established, and six critical causal factors were extracted. The hybrid analysis approach is demonstrated with a real construction accident case, and the results demonstrate that the hybrid approach could better identify the critical causal factors. Consequently, this research enables the enhancement of understanding the HFACS framework and CN theory, as well as a contribution to safety management in the construction industry at different levels.

Dynamic risk analysis for oil and gas exploration operations based on HFACS model and Bayesian network

Oil and gas exploration (OGE) is an important part of the petroleum industry. How to predict and prevent risks of OGE accidents in a targeted way is still a challenge, while few researchers have focused on this issue because of the lack of data from the worksite. Therefore, the accident cases for decades of years in a globally operated OGE company are collected as a basis, a dynamic risk analysis method combining the human factors analysis and classification system (HFACS) and the Bayesian network (BN) has been proposed in this article. First, identify the accident cases, analysis, and divided the risk factors into four levels and 27 classification categories, and setup a HFACS-OGE model. Then, map the factors to the BN and identify the correlation of the secondary level factors. Finally, setup a BN model with the Genie software. Through posterior probability analysis and sensitivity analysis, we conclude the major and sensitive factors leading to OGE accidents. According to the conclusion, administrators could decide on corresponding management and control methods. This method can hopefully provide an effective quantitative basis for the identification and prediction of the factors that may lead to OGE accidents.

Improving the Process of Accident Analysis Using Human Factor Analysis and Classification System (HFACS) Framework in Construction

Accidents in the construction industry remain a significant concern, demanding a comprehensive and systematic approach to their analysis and prevention. This study proposes an innovative method to improve the process of accident analysis in construction by using the Human Factor Analysis and Classification System (HFACS) framework. HFACS, originally developed in aviation, has proven effective in identifying and categorizing human errors contributing to accidents. This research adapts HFACS to the construction context, aiming to provide a complete understanding of the human factors influencing accidents on construction sites. The adapted HFACS framework will serve as a structured tool for analyzing these factors, encompassing organizational, supervisor, and individual levels. The findings are expected to contribute valuable insights into the root causes of accidents, allowing for the development of targeted mediations and preventive measures. The significance of this study lies in the potential to enhance safety practices and minimize accidents in the construction industry. By applying the HFACS framework, construction companies and stakeholders can gain a deeper understanding of the underlying causes of accidents and take proactive steps to mitigate risks. By using the HFACS framework, accident analysis in construction can be improved by systematically identifying and categorizing human errors that contribute to accidents. This systematic approach will help in the development of effective strategies to prevent similar accidents from occurring in the future. This literature review aims to explore previous studies that have utilized the HFACS framework in various industries, such as aviation, maritime, and rail, to improve safety and reduce human errors. Key Words: Accident Analysis, HFACS, HFACS Framework, Safety Management, Human Factors.

Human Error Analysis and Fatality Prediction in Maritime Accidents

The main objective of this paper is to underscore the significance of human error as a dominant cause of maritime accidents. The research is based on a comprehensive analysis of 247 maritime accidents, with the aim being to identify human failures occurring during onboard and port activities, as well as during the supervision process. The first step of the analysis was facilitating the Human Factor Analysis and Classification System (HFACS) as an advanced analytical tool for the identification and categorisation of human factors. Based on coding process, the most critical areas of human error are identified, based on the process of risk evaluation and assessment. Furthermore, a prediction model was developed for predicting the probability of fatality in a maritime accident. This model was constructed using logistic regression, considering the predominant causal factors and their interplay. Lastly, a set of preventive measures aimed at enhancing the efficiency and safety of maritime transport is provided.

Patient safety incidents in endoscopy: a human factors analysis of nonprocedural significant harm incidents from the National Reporting and Learning System (NRLS).

Despite advances in understanding and reducing the risk of endoscopic procedures, there is little consideration of the safety of the wider endoscopy service. Patient safety incidents (PSIs) still occur. We sought to identify nonprocedural PSIs (nPSIs) and their causative factors from a human factors perspective and generate ideas for safety improvement. Endoscopy-specific PSI reports were extracted from the National Reporting and Learning System (NRLS). A retrospective, cross-sectional human factors analysis of data was performed. Two independent researchers coded data using a hybrid thematic analysis approach. The Human Factors Analysis and Classification System (HFACS) was used to code contributory factors. Analysis informed creation of driver diagrams and key recommendations for safety improvement in endoscopy. From 2017 to 2019, 1181 endoscopy-specific PSIs of significant harm were reported across England and Wales, with 539 (45.6%) being nPSIs. Five categories accounted for over 80% of all incidents, with "follow-up and surveillance" being the largest (23.4% of all nPSIs). From the free-text incident reports, 487 human factors codes were identified. Decision-based errors were the most common act prior to PSI occurrence. Other frequent preconditions to incidents were focused on environmental factors, particularly overwhelmed resources, patient factors, and ineffective team communication. Lack of staffing, standard operating procedures, effective systems, and clinical pathways were also contributory. Seven key recommendations for improving safety have been made in response to our findings. This was the first national-level human factors analysis of endoscopy-specific PSIs. This work will inform safety improvement strategies and should empower individual services to review their approach to safety.

Artificial intelligence applications in aviation accident classification: A preliminary exploratory study

Artificial intelligence (AI) applications are already integrated and expected to expand within the aviation sociotechnical ecosystem. Machine Learning (ML) models are capable and could excel on accident report classification tasks within the aviation context, assuming adequate and proper training. Human subject matter experts (SMEs) and the open access AI ChatGPT reanalyzed the TransAsia Airways flight GE235, Air France AF447, and Helios Airways Flight HCY552 accidents using the Human Factors Analysis and Classification System (HFACS) to investigate whether the AI agent is capable of conducting aviation accident analysis and comparing raters’ results, of verifying AI’s learning abilities, and, finally, investigating whether AI analysis could expand in other systems-based models. The results suggested that: (a) AI/ML applications could be an efficient tool for learning organizations’ continuous learning, growth, and improvement by creating new knowledge from previous failures, (b) the proper prompts and well-established frameworks could optimize the agreement between human SMEs and AI in accident analysis, (c) the ChatGPT performed better on linear models (i.e., Bowtie) compared to complex systemic accident causation models (e.g., HFACS), (d) the ChatGPT could not conduct high- and low-level analysis simultaneously, and, (e) a multidisciplinary team including human factors, IT, and accident investigators experts is essential in developing and overseeing the W-shaped learning assurance process.

The hybrid systems method integrating STAMP and HFACS for the causal analysis of the road traffic accident

The role of traditional analysis methods in improving complex socio-technical system safety has reached a ceiling, and thus systems theory has been utilised to support the investigations and countermeasures for road traffic accidents. As two widely applied systems accident analysis models, STAMP (systems theoretic accident model and process) and HFACS (human factors analysis and classification system) have their own advantages in accident analysis and safety improvement. Therefore, this study develops a new hybrid systems method integrating STAMP and HFACS for road traffic accident (SH-RTA), which can adopt HFACS to enhance the identification and analysis ability of STAMP for human factors and employ control concepts and elements of STAMP to cement the characteristic of HFACS. To illustrate the applicability of the hybrid method, a case study of ‘9·22’ major road traffic accident in China is thoroughly analysed. Finally, preventive countermeasures and suggestions are presented. Practitioner Summary: This paper proposes a new hybrid systems method integrating STAMP and HFACS for road traffic accident. The new method reveals dysfunctional interactions within the parallel level and across levels, and identifies additional human and organisational factors. The recommendations for preventing road traffic accident are provided from higher levels of system.

Analysis on coupling dynamic effect of human errors in aviation safety

Human factors have increasingly been the leading cause of aircraft accidents. In most cases, human factors are not working alone, instead they are coupled with complex environment, mechanical factors, physiological and psychological factors of pilots, and organizational management, all of which form a complex aviation safety system. It is vital to investigate the coupling impact of human errors to avoid the occurrence of aviation accidents. In view that the Human Factors Analysis and Classification System (HFACS) provides a hierarchical classification principle of human errors in aviation accidents, and the System Dynamics (SD) approach is helpful to describe the risk evolution process, this paper establishes a hybrid HFACS-SD model by employing the HFACS and the SD approach to reveal the aviation human factors risk evolution mechanism, in which the HFACS is first used to capture the causal factors of human errors risk, and a coupling SD model is then built to describe the evolution of aviation human factors risk supported by historical data. The eigenvalue elasticity analysis is taken to identify critical loops and parameters that have a substantial impact on the system structural behavior, and the influence of parameters and loops is assessed. Simulation results show that the evolution trend of the accident rate can be replicated by the proposed HFACS-SD model, and the structural dominance analysis can efficiently identify critical loops and parameters. Simulation results further show that, with the recommended safety enhancement measures, the stability of the aviation system is increased, and thus lowering the overall accident rate.

A hybrid HFACS model using DEMATEL-ORESTE method with linguistic Z-number for risk analysis of human error factors in the healthcare system

The human error factor is one of the leading causes of medical errors. Among risk analysis techniques for human error factors, HFACS (Human Factor Analysis and Classification System) method has been regarded as one of the most valuable approaches due to its advantage in potential failure classification. However, the conventional HFACS method is insufficient to handle the risk analysis problem for human error factors considering the interactive relationship among these factors. Moreover, the current fuzzy HFACS frameworks cannot address quantitative risk analysis issues, including imprecision and reliability of information. Thus, this paper constructs an integrated linguistic Z-number-based HFACS framework for analyzing the risk of human error factors. This framework can capture the uncertainty and reliability of the risk evaluation information and the interactive relationships among factors. First, the linguistic Z-number-DEMATEL (Decision Making Trial and Evaluation Laboratory) method is used to generate the comprehensive risk matrix of human error factors identified by the HFACS method. Then, an extended linguistic Z-number-ORESTE method based on the score function is presented to prioritize the risk of human error factors, which can show the preference, indifference, and incomparability relationship among human error factors. Finally, a case study of a healthcare system is conducted to illustrate the reliability of the proposed method. The result of this case indicates that inadequate resources are the most severe risk. Sensitivity analysis and comparative analysis indicate the necessity of considering the effect of the semantics of language terms and the interrelationships between human error factors on risk analysis results. These results show that the proposed hybrid framework is a reliable means to analyze the risk of human error factors within the HFACS method.

Human Error Analysis and Modeling of Medication-Related Adverse Events in Taiwan Using the Human Factors Analysis and Classification System and Logistic Regression.

Medical institutions worldwide strive to avoid adverse medical events, including adverse medication-related events. However, studies on the comprehensive analysis of medication-related adverse events are limited. Therefore, we aimed to identify the error factors contributing to medication-related adverse events using the Human Factors Analysis and Classification System (HFACS) and to develop error models through logistic regression. These models calculate the probability of a medication-related adverse event when a healthcare system defect occurs. Seven experts with at least 12 years of work experience (four nurses and three pharmacists) were recruited to analyze thirty-seven medication-related adverse events. The findings indicate that decision errors, physical/mental limitations, failure to correct problems, and organizational processes were the four factors that most frequently contributed to errors at the four levels of the HFACS. Seven error models of two types (error occurrence and error analysis pathways) were established using logistic regression models, and the relative probabilities of failure factor occurrences were calculated. Based on our results, medical staff can use the error models as a new analytical approach to improve and prevent adverse medication events, thereby improving patient safety.

Research on the classification and control of human factor characteristics of coal mine accidents based on K-Means clustering analysis

The occurrence of coal mine accidents is closely related to human factors. Six-hundred eighty five coal mine accident reports were analyzed to identify risk factors for coal mine accidents. A total of 29 human factors were classified from three levels of supervision-management-production using the improved HFACS (Human Factors Analysis and Classification System) model, and the degree of clustering of human factors was verified by complex networks. Then, the 29 human factors were classified into four categories by K-Means clustering analysis, namely subjective corrective accident human factors, perceived corrective accident human factors, associated corrective accident human factors, and critical corrective accident human factors, and control suggestions were proposed for different categories. Finally, the interface of the supervision-management-production-based coal mine employee control system was designed in order to provide a solid theoretical basis for the subsequent system development and its application in specific coal mines. This study can provide new ideas for the study of human factors of coal mine accidents and help coal mine enterprises to strengthen the control of human factors.

Qualitative Analysis of General Aviation Pilots’ Aviation Safety Reporting System Incident Narratives Using the Human Factors Analysis and Classification System

ABSTRACT NASA's Aviation Safety Reporting System (ASRS) gathers data from voluntary aviation safety reports for nonfatal accidents and incidents. These reports are a valuable resource in identifying unsafe occurrences and hazardous situations in the aviation industry. A tool known as the Human Factors Analysis and Classification System (HFACS), initially developed for military use, has proven useful for root cause analysis of human causes in aviation accidents and incidents, including in commercial contexts. This research study utilizes HFACS to classify the ASRS safety reports to identify key indicators in General Aviation (GA) non-fatal accidents and incidents. Qualitative data analysis reveals relationships between incidents and causal factors, indicating 53% of incidents to be perceptual errors, 32% decision errors, and 26% skill-based errors. Prevalent preconditions for these errors include crew resource management (53%), physical environment (32%), and technological environment (16%). The study aligns with previous research findings, suggesting that crew resource management is a common factor in both GA incidents and commercial and military aviation. While GA pilots may make different types of errors, such discrepancies are not prevalent in the voluntarily reported data.

SEM-based study on the impact of safety culture on unsafe behaviors in Chinese nuclear power plants

This paper uses 135 Licensed Operator Event Reports (LOER) from Chinese nuclear plants to analyze how safety culture affects unsafe behaviors in nuclear power plants. On the basis of a modified human factors analysis and classification system (HFACS) framework, structural equation model (SEM) is used to explore the relationship between latent variables at various levels. Correlation tests such as chi-square test are used to analyze the path from safety culture to unsafe behaviors. The role of latent error is clarified. The results show that the ratio of latent errors to active errors is 3.4:1. The key path linking safety culture weaknesses to unsafe behaviors is Organizational Processes → Inadequate Supervision → Physical/Technical Environment → Skill-based Errors. The most influential factors on the latent variables at each level in the HFACS framework are Organizational Processes, Inadequate Supervision, Physical Environment, and Skill-based Errors.

Human Factors Analysis by Classifying Chemical Accidents into Operations

In the chemical industry, organizational and operational human factors significantly contribute to accidents. Chemical accidents occur in various operations of the industry due to a range of factors. Understanding the relationship between these factors and the accidents that happen is crucial in preventing similar accidents from happening repeatedly and promoting sustainability. Therefore, this study was divided into five operations: maintenance repair, process, loading unloading, storage, and shutdown startup of the chemical industry, to provide a more concrete, intuitive explanation of the interplay between causes and illustrate the routes to failure. The data were collected from 251 accident reports from various online data. The study was analyzed using the Human Factors Analysis and Classification System (HFACS) method as a conceptual framework. Each level’s frequency variables were obtained to define nominal and ordinal data. The chi-square test and Fisher’s exact test were used in the difference analysis of data in the model. The results show that the high-frequency accidents caused under the HFACS framework were organizational processes in the process (63.73%), in the storage (70.58%), and in the shutdown startup (91.66%), and skill-based errors in the maintenance repair (81.81%) and in the loading unloading (66.03%). Furthermore, resource management, technological environment, and personal readiness were significantly correlated with the operations. Human factors have differences in different operations in the chemical industry.

Development of effective human factors interventions for aviation safety management.

In the aviation industry, safety management has moved away from capturing frontline failures toward the management of systemic conditions through organizational safety management systems (SMS). However, subjective differences can influence the classification of active failures and their associated systemic precursors. With levels of professional experience known to influence safety attitudes, the present research examines whether experience levels among airline pilots had an impact on the classification of causal factors using the Human Factors Analysis and Classification System (HFACS). Differences in the paths of association between categories were evaluated in an open-system context. Pilots working in a large, international airline were categorized into high (≥10,000 total flight hours) and low (<10,000 h) experience groups and asked to classify aircraft accident causal factors using the HFACS framework. One-way ANOVA tests were carried out to determine experience effects on the utilization of the HFACS categories, and chi-squared analyses were used to assess the strength of association between different categories within the framework. Results from 144 valid responses revealed differences in the attribution of human factors conditions. The high experience group was more inclined to attribute deficiencies to high-level precursors and found fewer paths of associations between different categories. In contrast, the low experience group presented a greater number of associations and was comparatively more affected by stress and uncertainty conditions. The results confirm that the classification of safety factors can be influenced by professional experience, with hierarchical power distance impacting the attribution of failures to higher-level organizational faults. Different paths of association between the two groups also suggest that safety interventions can be targeted through different entry points. Where multiple latent conditions are associated, the selection of safety interventions should be made with consideration of the concerns, influences, and actions across the entire system. Higher-level anthropological interventions can change the interactive interfaces affecting concerns, influences, and actions across all levels, whereas frontline-level functional interventions are more efficient for failures linked to many precursor categories.