Human error analysis of commercial aviation accidents: application of the Human Factors Analysis and Classification system (HFACS)

Towards enhanced performance: an integrated framework of emotional valence, arousal, and task demand

Extensive evaluations exist concerning the linkage between objective task demands and subsequent effects on user performance. However, the human user also experiences a range of emotions related to external task demands. Problematically, little is known about the associations between emotional valence, and arousal associated with the task demand-performance axis. In this paper, we advance a theoretical model concerning such interactive influences using three dimensions: (1) emotional valence, (2) arousal, and (3) task demand. The model evaluates the impact of these dimensions on user performance. It also identifies critical emotional user states, particularly those resulting in negative performance effects, as well as non-critical emotional states that can positively impact performance. Finally, we discuss the implications for affect-adaptive systems that can mitigate the impact of critical emotional states while leveraging the benefits of non-critical ones.

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

Objective

This research investigates the role of human factors of all hierarchical levels in radiotherapy safety incidents and examines their interconnections.

Methods

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.

Results

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.

Conclusion

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.

Safety in radiation oncology (SAFRON): Learning about incident causes and safety barriers in external beam radiotherapy

PURPOSE

Safety in Radiation Oncology (SAFRON) is a reporting and learning system on radiotherapy and radionuclide therapy incidents and near misses. The primary aim of this paper is to examine whether any discernible patterns exist in the causes of reported incidents and safety barriers within the SAFRON system concerning external beam radiotherapy.

METHODS AND MATERIALS

This study focuses on external beam radiotherapy incidents, reviewing 1685 reports since the inception of SAFRON until December 2021. Reports that did not identify causes of incidents and safety barriers were excluded from the final study population.

RESULTS

Simple two-dimensional radiotherapy or electron beam therapy were represented by 97 reports, three-dimensional conformal radiotherapy by 39 reports, modulated arc therapy by 12 reports, intensity modulated radiation therapy by 11 reports, stereotactic radiosurgery by 4 reports, and radiotherapy with protons or other particles by 1 report, while for 92 of them, no information on treatment method had been provided. Most of the reported incidents were minor incidents and were discovered by the radiation therapist. Inadequate direction/information in staff communication was the most frequently reported cause of incident, and regular independent chart check was the most common safety barrier.

CONCLUSIONS

The results indicate that the majority of incidents were reported by radiation therapists, and the majority of these incidents were classified as minor. Communication problems and failure to follow standards/procedures/practices were the most frequent causes of incidents. Furthermore, regular independent chart checking was the most frequently identified safety barrier.

A content-aware corpus-based model for analysis of marine accidents

In the past decades, marine accidents brought the serious loss of life and property and environmental contamination. With the accumulation of marine accident data, especially accident investigation reports, compared with subjective reasoning based on expert experience, data-driven methods for analysis and accident prevention are more comprehensive and objective. This paper aims to develop a content-aware corpus-based model for the analysis of marine accidents to mine the accident semantic features. The general research framework is established to combine accident data, expert prior knowledge, and semi-automated natural language processing (NLP) technology. The NLP models are optimized, fused, and applied to the case study of ship collision accidents. The results show that the proposed model can accurately and quickly extract hazards, accident causes, and scenarios from the accident reports, and perform semantic analysis for the latent relationships between them to extend the accident causation theory. This study can provide a powerful and innovative analysis tool for marine accidents for maritime traffic safety management departments and relevant research institutions.

The Influence of Emotion Induced by Accidents and Incidents on Pilots' Situation Awareness

(1) Background: This study examines the differences in emotions induced by accidents and incidents as emotional stimuli and the effects on pilot situation awareness (SA) after induction. (2) Method: Forty-five jet pilots were randomly divided into three groups after which their emotions were induced using the pictures of accident, incident, and neutral stimulus, respectively. (3) Results: The conditions of accidents and incidents both induced changes in the pilots' happiness and sadness and the changes in the emotion were regulated by the emotional intelligence of pilots in the high SA group. The emotion induction, which caused a direct change in pilot's happiness and fear, resulted in conditions that indirectly affected level 1 of SA in pilots. (4) Conclusions: The research elucidates the difference between accident and incident in inducing pilot emotions, and reminds us that SA level exerts the regulating effects on the same emotional induction conditions.

Risk Assessment of Unsafe Acts in Coal Mine Gas Explosion Accidents Based on HFACS-GE and Bayesian Networks

Even in the context of smart mines, unsafe human acts are still an important cause of coal mine gas explosion accidents, but there are few models to analyze unsafe human acts in coal mine gas explosion accidents. This study tries to solve this problem through a risk assessment method of unsafe acts in coal mine gas explosion accidents based on Human Factor Analysis and Classification system (HFACS-GE) and Bayesian networks (BN). After verifying the reliability of HFACS-GE framework, a BN model of risk factors of unsafe acts was established with the Chi-square test and odds ratios analysis. After reasoning analysis, risk paths and key risk factors of unsafe acts were obtained, and preventive measures were granted. Based on the analysis of 100 coal mine gas explosion cases, the maximum probability of five kinds of unsafe acts of employees is 38%. Among the 22 risk factors, the mental state of employees has the greatest influence on the habitual violation of regulations, and the sensitivity value is 12.7%. This study can provide technical assistance for the risk management of unsafe acts in coal mine gas explosions.

Comprehensive Evaluation of Deep Coal Miners' Unsafe Behavior Based on HFACS-CM-SEM-SD

The unsafe behavior of miners seriously affects the safety of deep mining. A comprehensive evaluation of miners' unsafe behavior in deep coal mines can prevent coal mine accidents. This study combines HFACS-CM, SEM, and SD models to evaluate miners' unsafe behaviors in deep coal mining. First, the HFACS-CM model identifies the risk factors affecting miners' unsafe behavior in deep coal mines. Second, SEM was used to analyze the interaction between risk factors and miners' unsafe behavior. Finally, the SD model was used to simulate the sensitivity of each risk factor to miners' unsafe behavior to explore the best prevention and control strategies for unsafe behavior. The results showed that (1) environmental factors, organizational influence, unsafe supervision, and unsafe state of miners are the four main risk factors affecting the unsafe behavior of miners in deep coal mines. Among them, the unsafe state of miners is the most critical risk factor. (2) Environmental factors, organizational influence, unsafe supervision, and the unsafe state of miners have both direct and indirect impacts on unsafe behaviors, and their immediate effects are far more significant than their indirect influence. (3) Environmental factors, organizational influence, and unsafe supervision positively impact miners' unsafe behavior through the mediating effect of miners' unsafe states. (4) Mental state, physiological state, business abilities, resource management, and organizational climate were the top five risk factors affecting miners' unsafe behaviors. Taking measures to improve the adverse environmental factors, strengthening the organization's supervision and management, and improving the unsafe state of miners can effectively reduce the risk of miners' unsafe behavior in deep coal mines. This study provides a new idea and method for preventing and controlling the unsafe behavior of miners in deep coal mines.

Cause Analysis of Unsafe Acts of Pilots in General Aviation Accidents in China with a Focus on Management and Organisational Factors

Objectives. General aviation (GA) safety has become a key issue worldwide and pilot errors have grown to be the primary cause of GA accidents. However, fewer empirical studies have examined the contribution of management and organizational factors for these unsafe acts. Flawed decisions at the organizational level have played key roles in the performance of pilots. This study provides an in-depth understanding of the management and organizational factors involved in GA accident reports. Methods. A total of 109 GA accidents in China between 1996 and 2021 were analysed. Among these reports, pilot-related accidents were analysed using the human factors analysis and classification system (HFACS) framework. Results. The significant effects of managerial and organizational factors and the failure pathways on GA accidents have been identified. Furthermore, unlike traditional HFACS-based analyses, the statistically significant relationships between failures at the organizational level and the sub-standard acts of the pilots in GA accidents were revealed. Conclusions. Such findings support that the GA accident prevention strategy that attempts to reduce the number of unsafe acts of pilots should be directed to the crucial causal categories at HFACS organizational levels: resource management, organizational process, failure to correct a known problem, inadequate supervision and supervisory violations.

Risk Factors Identification of Unsafe Acts in Deep Coal Mine Workers Based on Grounded Theory and HFACS

The risk factors affecting workers' unsafe acts were comprehensively identified by Human Factors Analysis and Classification System (HFACS) and grounded theory based on interview data and accident reports from deep coal mines. Firstly, we collected accident case and field interview data from deep coal mines issued by authoritative institutions. Then, the data were coded according to grounded theory to obtain relevant concepts and types. The HFACS model was used to classify the concepts and categories. Finally, the relationship between core and secondary categories was sorted out by applying a story plot. The results show that risk factors of unsafe acts of deep coal mine workers include environmental factors, organizational influence, unsafe supervision and unsafe state of miners, and the main manifestations of unsafe acts are errors and violations. Among them, the unsafe state of miners is the intermediate variable, and other factors indirectly affect risky actions of coal miners through unsafe sates. Resource management, organizational processes and failure to correct problems are the top three risk factors that occur more frequently in unsafe acts. The three most common types of unsafe act are unreasonable labor organization, failure to enforce rules, and inadequate technical specifications. By combining grounded theory and the HFACS framework to analyze data, risk factors for deep coal miners can be quickly identified, and more precise and comprehensive conceptual models of risk factors in unsafe acts of deep coal miners can be obtained.

Airline Safety measures to prevent the COVID-19 pandemic that affect the confidence of passenger’s decision making to travel with domestic low-cost airlines during the pandemic

The airline business is one of the businesses which have heavily been impacted from the COVID-19 pandemic. International travel restriction due to border closing policy in some countries, restricted mobility, and social distancing have significantly ceased the aviation industry. Departments concerned have issued both short-term and long-term safety measures for the prevention and control the spread of the infection. Several preventive measures have been proposed.

The purposes of this research is to study the Airline Safety Measures to prevent the spread of the Coronavirus Disease 2019 (COVID-19) affecting the confidence of passengers in decision making to travel with domestic low-cost airlines during the pandemic. The quantitative and qualitative were conducted by online questionnaires. 400 Sample groups from passengers of 4 low-cost airlines which operated domestic flight in Thailand, 152 from Thai Air Asia, 86 from Thai Lion Air, 96 from Nok Air and 66 from Thai Viet jet. The descriptive statistical analysis included Pearson’s simple coefficient and multiple regression analysis were applied. The results found that the most significant factors of safety measures to prevent the spread of the Coronavirus Disease 2019 (COVID-19) affecting the confidence of passengers in decision making to travel domestic flight with low-cost airlines during the pandemic were cabin density control measures, passenger hygiene measures, passenger screening measures, pre-boarding measures, aircraft preparation measures and service personnel hygiene measures respectively. Touch less technology should be implemented in all activities related to the air transportation travel process for preventing the spread of the Coronavirus Disease 2019 (COVID-19).

Human factor associations with safety events in radiation therapy

BACKGROUND AND PURPOSE

Incident learning can reveal important opportunities for safety improvement, yet learning from error is challenged by a number of human factors. In this study, incident learning reports have been analyzed with the human factors analysis classification system (HFACS) to uncover predictive patterns of human contributing factors.

MATERIALS AND METHODS

Sixteen hundred reports from the Safety in Radiation Oncology incident learning system were filtered for inclusion ultimately yielding 141 reports. A radiotherapy-specific error type was assigned to each event as were all reported human contributing factors. An analysis of associations between human contributing factors and error types was performed.

RESULTS

Multiple associations between human factors were found. Relationships between leadership and risk were demonstrated with supervision failures. Skill-based errors (those done without much thought while performing familiar tasks) were found to pose a significant safety risk to the treatment planning process. Errors made during quality assurance (QA) activities were associated with decision-based errors which indicate lacking knowledge or skills.

CONCLUSION

An application of the HFACS to incident learning reports revealed relationships between human contributing factors and radiotherapy errors. Safety improvement efforts should include supervisory influences as they affect risk and error. An association between skill-based and treatment planning errors showed a need for more mindfulness in this increasingly automated process. An association between decision and QA errors revealed a need for improved education in this area. These and other findings can be used to strategically advance safety.

A Human Factors Approach to Surgical Patient Safety

This article explores the role of human factors engineering in patient safety in surgery. The authors discuss the history and evolution of human factors and the role of human factors in patient safety and provide a description of human factors methods used to study and improve patient safety.

Safety of Workers in Indian Mines: Study, Analysis, and Prediction

Background

The mining industry is known worldwide for its highly risky and hazardous working environment. Technological advancement in ore extraction techniques for proliferation of production levels has caused further concern for safety in this industry. Research so far in the area of safety has revealed that the majority of incidents in hazardous industry take place because of human error, the control of which would enhance safety levels in working sites to a considerable extent.

Methods

The present work focuses upon the analysis of human factors such as unsafe acts, preconditions for unsafe acts, unsafe leadership, and organizational influences. A modified human factor analysis and classification system (HFACS) was adopted and an accident predictive fuzzy reasoning approach (FRA)-based system was developed to predict the likelihood of accidents for manganese mines in India, using analysis of factors such as age, experience of worker, shift of work, etc.

Results

The outcome of the analysis indicated that skill-based errors are most critical and require immediate attention for mitigation. The FRA-based accident prediction system developed gives an outcome as an indicative risk score associated with the identified accident-prone situation, based upon which a suitable plan for mitigation can be developed.

Conclusion

Unsafe acts of the worker are the most critical human factors identified to be controlled on priority basis. A significant association of factors (namely age, experience of the worker, and shift of work) with unsafe acts performed by the operator is identified based upon which the FRA-based accident prediction model is proposed.

Application of a human error framework to conduct train accident/incident investigations

Accident/incident investigations are an important qualitative approach to understanding and managing transportation safety. To better understand potential safety implications of recently introduced remote control locomotive (RCL) operations in railroad yard switching, researchers investigated six railroad accidents/incidents. To conduct the investigations, researchers first modified the human factors analysis and classification system (HFACS) to optimize its applicability to the railroad industry (HFACS-RR) and then developed accident/incident data collection and analysis tools based on HFACS-RR. A total of 36 probable contributing factors were identified among the six accidents/incidents investigated. Each accident/incident was associated with multiple contributing factors, and, for each accident/incident, active failures and latent conditions were identified. The application of HFACS-RR and a theoretically driven approach to investigating accidents/incidents involving human error ensured that all levels of the system were considered during data collection and analysis phases of the investigation and that investigations were systematic and thorough. Future work is underway to develop a handheld software tool that incorporates these data collection and analysis tools.