Human Factors Analysis And Classification System Research Articles

Accident cause analysis for lifting prefabricated components of assembled buildings based on hybrid simulation technology from human factor perspective

PurposeThis paper aims to explore the characteristics of lifting accidents and the significance of influencing factors and explain the causes from the perspective of human factors, thereby achieving a more accurate understanding of and prevention of lifting accidents.Design/methodology/approachA mixed simulation model for prefabricated component lifting is established by combining discrete event simulation (DES) with the system dynamics (SD) method. In addition, essential parameters and relationships within the system dynamics model are determined through survey questionnaires. Finally, the human factors analysis and classification system (HFACS) is used to analyze the cause of the accident.FindingsThe results show that workers falling from height and workers struck by objects are the most frequent types of lifting accidents. In 2072 experiments, these two types of accidents occurred three and five times, respectively. Besides, the links of “crane movement,” “component binding,” “component placement” and “component unhooking” are particularly prone to lifting accidents. In addition, the completeness of emergency plans, failure to observe the status of the tower crane and lack of safety education and training have emerged as primary influencing factors contributing to the occurrence of lifting accidents.Originality/valueThe findings of the study can serve as a reference basis for practitioners, enabling them to preemptively identify possible risk accidents and adopt corresponding measures to prevent them, ensuring the safety and property of practitioners. Additionally, targeted suggestions and innovative ideas are provided to enhance the safety guarantee of the lifting industry and promote its healthy and stable development through a more concrete theoretical foundation and practical guidance.

Risk Evaluation for Human Factors of Flight Dispatcher Based on the Hesitant Fuzzy TOPSIS-DEMATEL-ISM Approach: A Case Study in Sichuan Airlines

To effectively mitigate unsafe events and accident symptoms stemming from flight dispatchers’ human factors, this paper proposes a novel risk evaluation model to accurately identify and evaluate potential human risks associated with flight dispatchers. First, the HFACS (Human Factors Analysis and Classification System, HFACS) model is employed to construct a human risk assessment indicator system for flight dispatchers. Second, the hesitant fuzzy set is introduced to represent the uncertainty during experts’ evaluation, and the improved TOPSIS (Technique for Order Preference by Similarity to Ideal Solution, TOPSIS) method is applied within a hesitant fuzzy environment to obtain rankings of human factors. Third, the hesitant fuzzy DEMATEL (Decision-Making Trial and Evaluation Laboratory, DEMATEL)-ISM (Interpretive Structural Modeling, ISM) approach is constructed to analyze the correlation among human factors, leading to the establishment of a multi-level hierarchical structure model. Finally, a case study of risk assessment for human factors of flight dispatchers in Sichuan Airlines is conducted to demonstrate the effectiveness of the proposed method. The results revealed the flight dispatchers’ human factors associated with higher risks and identified the key factors with a larger impact on other factors in Sichuan Airlines. Subsequently, a multi-level hierarchical structure model comprising five layers is developed to investigate the internal correlations among human factors, facilitating the formulation of targeted improvement suggestions for the higher risk indicators and key influencing factors.

Incorporating human and organizational failures into the formation pattern for different Arctic maritime accidents using a data-driven Bayesian network

To gain a comprehensive understanding of formation patterns for various maritime accidents in Arctic waters, the present study integrates human and organizational failures (HOFs) with accident risk factors (AIFs) to examine the individual or collective impacts of different AIFs and HOFs on Arctic maritime accidents. Firstly, a novel database is established within the improved Human Factors Analysis and Classification System (HFACS), encompassing 14 AIFs with multiple states and 33 HOFs with binary states. Secondly, the Association rule mining (ARM) is employed to capture the causal relationships among HOFs, while Augmented Naive Bayes (ANB) is introduced to account for the interdependency of AIFs, thereby developing the data-driven BN model for analyzing and predicting Arctic maritime accidents. Finally, the criticality and sensitivity of AIFs and HOFs across different Arctic maritime accidents are identified and ranked by BN forward and backward inference. Furthermore, three scenarios are simulated to demonstrate effective management strategies for prevention and mitigation of Arctic maritime accidents. Additionally, a correlation graph depicting the relationship between HOFs and AIFs is generated for each accident scenario, facilitating crew onboard ships as well as shipping companies and maritime authorities in customizing targeted safety measures aimed at preventing Arctic maritime accidents.

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 five levels. Time Frame Return Frequency TextRank text classification method based on key content extraction and text classification model based on Convolutional Neural Network and Bidirectional Encoder Representations from Transforms models 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.

Review of Hydrogen-Related Accidents: Root Causes, Mitigation Strategies, and Recommendations for secure utilization.

Abstract Hydrogen, acknowledged as a versatile and sustainable energy carrier, is gaining significant attention across industries, becoming a focal point in the pursuit of cleaner energy solutions. However, Ensuring the safe utilization of hydrogen presents inherent challenges, giving rise to a spectrum of related accidents. This study delves into the intricate landscape of hydrogen utilization, acknowledging both its significance and inherent challenges associated with its safety. This comprehensive review examines hydrogen-related accidents from the Hydrogen Incident and Accident Database (HIAD) within a defined timeframe, analyzing causes and evaluating the effectiveness of current mitigation strategies through categorization and assessment of the root causes of hydrogen-related incidents, this review aspired to provide an in-depth understanding of safety issues and also reviewed the effectiveness of current mitigating strategies, employing risk assessment models. A thorough analysis of these incidents uncovered crucial insights, The findings of the review paper illustrate that human factor and management factors were possibly greater causes of hydrogen accidents than other factors, and management factors to be more hazardous than other factors. Through comprehensive literature review, the study aims to offer practical recommendations for refining safety protocols in hydrogen utilization like the implementation of Human Error Assessment and Reduction Technique (HEART) and Human Factor Analysis and Classification System (HFACS) for human factor induced incidents and process safety management (PSM) program for management factor induced incidents. Additionally, specific recommendation for reoccurring incidents based on severity are made to enhance safety protocols, thereby fostering its secure application across diverse industries.

Over a decade of UAV incidents: A human factors analysis of causal factors

This analysis examined systemic causes of Uncrewed Air Vehicle (UAV) accidents identifying operator, environmental, supervisory, and organisational factors through the use of the Human Factors Analysis and Classification System (HFACS). HFACS is a system-based analysis method for investigating the causal factors associated with accidents and incidents and has previously been used to reliably and systematically identify active and latent failures associated with both military and general aviation accidents. Whilst HFACS has previously been applied to UAV accidents, the last known application was conducted in 2014. Using reports retrieved from nine accident investigation organisations’ databases, causal factors were coded against unsafe acts, preconditions, and failures at the supervisory, organisational, and environmental levels. Causal factors were assessed on 77 medium or large UAV mishaps/accidents that occurred over a 12-year period up to 2024. 42 mishap reports were deemed to involve a human factor as a causal factor. A large proportion of the mishaps contained factors attributed to Decision Errors at level 1 (Unsafe Acts) which was found to be associated with both the Technological Environment and Adverse Mental State at level 2 (Pre-conditions). Causal factors were identified at each of the other 3 levels (Supervisory, Organisational and External) with a number of emergent associations between causal factors. These data provide support for the identification and development of interventions aimed at improving the safety of organisations and advice of regulators for Uncrewed Air Systems.

Analysis of Factors Influencing Work Accidents Among Workers at Exploitation Facilities and Offshore Oil and Gas Production at PT X from 2018 – 2023

Background: The workplace risks faced at PT X oil and gas company, located offshore Natuna, are relatively high evidenced by fluctuations in accidents from 2018 – 2022. In 2018, recordable injury attributable to accidents was recorded, then in 2019-2020, there were no accidents, and the prevalence increased in 2021-2022. The company analyzed the main causes responsible for work accidents during this period and carried out intervention measures in Occupational Health and Safety (OHS) program to reverse the trend in 2023. By implementing OHS program, the number of accidents in recordable injury category fell from 4 (2021- 2022) to 1 (2023). This shows that the program has been successful in managing work accidents. Considering that the investigation and analysis identified unsafe acts as the direct causes, Human Factor Analysis and Classification System (HFACS) method was used to analyze other factors associated with accidents. Objective: This study aimed to investigate OHS program actions at PT X in responding to increased work accidents in 2021-2022, and compare with recommendations for improvements from HFACS method. Method: A qualitative design was used with secondary data, comprising 10 work accidents records and investigation reports for recordable injury cases from 2018 – 2022 available in PT X information system. Subsequently, the secondary data were then classified according to four failure stages in HFACS, namely unsafe acts, the precondition of unsafe acts, unsafe supervision, and organizational influence. This classification produced recommendations for improvement, which were then compared with the prevention intervention program for accidents at PT X. Results: The results showed that OHS program intervention to reduce the number of accidents in 2023 was in accordance with the recommendations of HFACS analysis. Conclusion: The application of HFACS in analyzing accidents records and investigation reports produced relatively comprehensive recommendations by focusing on the component stages. Several additional recommendations from the analysis of latent conditions will further improve OHS performance at PT X.

Human Factors Analysis and Classification System (HFACS) on Accidents Due to Tilting Ship

The transportation industry is the main point of the distribution of goods and passengers. Data shows the number of shipwrecks is very high. The accident was caused by many factors, one of which was the tilting of the ship. The research starts from categorizing the causes of ship accidents through existing secondary data, FGD with expert judgments to find the root of the problem and also looking for what mitigations can be done so that similar incidents do not repeat. And the analysis is made in the Human Factors Analysis and Classification System (HFACS) method. The results of this study indicate that there are human factors in recurring events at all stages of HFACS such asunsafe acts, the preconditions for unsafe acts, unsafe supervision and organisational influences. For the stages of unsafe acts are Skill based Errors, decision errors, Perceptual Errors, and Exceptional. For the stage of the preconditions for unsafe acts are technological environment, physical environment, and adverse mental state; and for the stage of unsafe supervision is the lack of supervision; for organizational influences there are resource management and organizational climate.

Identification of risk key factors and prevention strategies for collision accidents between merchant and fishing vessels in China waters based on complex network

In recent years, Collision Accidents between Merchant and Fishing Vessels (CAMF) in China waters have been increasing, resulting in severe casualties and property losses. To effectively prevent such frequent collision accidents and ensure the safety of waterways and navigation routes, this study aims to identify the key risk factors associated with CAMF. Firstly, the Human Factors Analysis and Classification System (HFACS) model is employed as a comprehensive framework to extract the risk factors contributing to CAMF. By introducing edge length as the weight of network edges, a directed weighted network of CAMF is constructed. Secondly, the overall topological characteristics of the network are analyzed, and the network's robustness is studied based on the degree centrality, weighted closeness centrality, and weighted betweenness centrality of the risk factors in the network, and the key factors contributing to the accidents are identified. The research findings indicate that the network exhibits evident scale-free characteristics and small-world network properties. Deliberate attacks on approximately top 15% of the risk factors lead to a 55% degradation in the global efficiency of the accident network. Ultimately, accident prevention and control strategies and recommendations are proposed from the perspectives of both merchant and fishing vessels.

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.

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.

Causal analysis of radiotherapy safety incidents based on a hybrid model of HFACS and Bayesian network.

This research investigates the role of human factors of all hierarchical levels in radiotherapy safety incidents and examines their interconnections. Utilizing the human factor analysis and classification system (HFACS) and Bayesian network (BN) methodologies, we created a BN-HFACS model to comprehensively analyze human factors, integrating the hierarchical structure. We examined 81 radiotherapy incidents from the radiation oncology incident learning system (RO-ILS), conducting a qualitative analysis using HFACS. Subsequently, parametric learning was applied to the derived data, and the prior probabilities of human factors were calculated at each BN-HFACS model level. Finally, a sensitivity analysis was conducted to identify the human factors with the greatest influence on unsafe acts. The majority of safety incidents reported on RO-ILS were traced back to the treatment planning phase, with skill errors and habitual violations being the primary unsafe acts causing these incidents. The sensitivity analysis highlighted that the condition of the operators, personnel factors, and environmental factors significantly influenced the occurrence of incidents. Additionally, it underscored the importance of organizational climate and organizational process in triggering unsafe acts. Our findings suggest a strong association between upper-level human factors and unsafe acts among radiotherapy incidents in RO-ILS. To enhance radiation therapy safety and reduce incidents, interventions targeting these key factors are recommended.

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.