Human Error Prevention Research Articles

New oscillatory features of electrodermal activity signals evaluated in automated mental workload monitoring

Oscillatory components are discriminative features of mental workload monitoring in electrodermal activity (EDA) signals. Currently, most feature extraction techniques mainly focus on time domain or linear techniques but rarely consider the non-stationary nature, individual differences, and recording noise. This study has proposed a new time–frequency feature extraction method based on Tunable Q-factor Wavelet Transform providing an effective quantification of oscillatory behavior of a single transient. The extracted features have been classified using a recurrent neural network with the advantage of dynamic mapping procedure. The proposed method has been evaluated using EDA data acquired during an arithmetic task of five workload levels with both subtle and distinct differences. In a comparative study, the effects of several decomposition parameters and cognitive factors have been explored. Experimental results have shown that it achieves an average accuracy rate of 98.52% for three workload levels discrimination by optimizing the number of features to 3 (features extracted from a low frequency sub-band) from 15 (features extracted from five frequency sub-bands), i.e., 80% feature reduction. The greatest contribution coming fromthe smallest frequency sub-bands has also been obtained. This method is superior to other existing methods in separating 3 to 5 workload levels with both distinct and subtle differences extracting effective oscillatory characteristics. Considering the merits of a simple feature extraction procedure and cost-effectiveness of a single lead EDA signal as well as high discriminability between several workload levels, it provides a trade-off between performance and computational complexity, thus demonstrating its potential for online human-error prevention systems.

Safety evaluation of human-caused errors in civil aviation based on analytic hierarchy process and fuzzy comprehensive evaluation method

PurposeAt present, the statistics of human error events in domestic civil aviation are limited, and the analysis indicators are difficult to quantify. The purpose of this study is to reduce the incidence of human error events and improve the safety of civil aviation.Design/methodology/approachIn this paper, a safety prevention evaluation method combining analytic hierarchy process (AHP) and fuzzy comprehensive evaluation (FCE) is proposed. The risk factors of civil aviation safety are identified through questionnaire survey and calculated by MATLAB software.FindingsThe results of the study are as follows: a safety risk evaluation index system including 4 first-level indicators and 16 second-level indicators is constructed; the AHP is used to calculate the weight of the influencing factors of human error and sort them; and the FCE method is used to quantitatively evaluate the safety prevention of civil aviation human error and put forward the countermeasures.Research limitations/implicationsThis study also has some limitations. While it provides an overall quantitative identification of civil aviation safety risk factors, the research methods chosen, such as the questionnaire survey method and the AHP, involve individual subjectivity. Consequently, the research results may have errors. In the preliminary preparation of the follow-up study, we should analyze a large number of civil aviation accident investigation reports, more accurately clarify the human error factors and completely adopt the quantitative analysis method in the research method.Practical implicationsThis study identifies the risk factors of civil aviation safety and conducts a reasonable analysis of human error factors. In the daily training of civil aviation, the training can be focused on previous man-made accidents; in view of the “important” influencing factors, the aviation management system is formulated to effectively improve the reliability of aviation staff; according to the evaluation criteria of human error in civil aviation, measures to prevent and control accidents can be better formulated.Social implicationsIn view of these four kinds of influencing factors, the corresponding countermeasures and preventive measures are taken according to the discussion, so as to provide the basis for the prevention of aviation human error analysis, management and decision-making, prevent the risk from brewing into safety accidents and improve the safety of aviation management.Originality/valueBased on the questionnaire survey, this study creatively applies the safety prevention evaluation method combining AHP and FCE to the study of civil aviation human error, integrates the advantages of qualitative and quantitative methods, flexibly designs qualitative problems, objectively quantifies research results and reduces subjective variables. Then, by discussing civil aviation safety management measures to avoid risk factors, reduce the incidence of human error events and improve the safety of civil aviation.

Using Vocal-Based Emotions as a Human Error Prevention System with Convolutional Neural Networks

Human error is a mark assigned to an event that has negative effects or does not produce a desired result, with emotions playing an important role in how humans think and behave. If we detect feelings early, it may decrease human error. The human voice is one of the most powerful tools that can be used for emotion recognition. This study aims to reduce human error by building a system that detects positive or negative emotions of a user like (happy, sad, fear, and anger) through the analysis of the proposed vocal emotion component using Convolutional Neural Networks. By applying the proposed method to an emotional voice database (RAVDESS) using Librosa for voice processing and PyTorch, with the emotion classification of (happy/angry), the results show a better accuracy (98%) in comparison to the literature with regard to making a decision to deny or allow a user to access sensitive operations or send a warning to the system administrator prior to accessing system resources.

Exploring medical and nursing students’ perceptions about a patient safety course: a qualitative study

BackgroundEducating health professionals on patient safety can potentially reduce healthcare-associated harm. Patient safety courses have been incorporated into medical and nursing curricula in many high-income countries and their impact has been demonstrated in the literature through objective assessments. This study aimed to explore student perceptions about a patient safety course to assess its influence on aspiring health professionals at a personal level as well as to explore differences in areas of focus between medical and nursing students.MethodsA dedicated patient safety course was introduced for year III medical and year II and IV nursing students at the Aga Khan University (2021–2022). As part of a post-course assessment, 577 participating students (184 medical and 393 nursing) wrote reflections on the course, detailing its influence on them. These free-text responses were thematically analyzed using NVivo.ResultsThe findings revealed five major themes: acquired skills (clinical, interpersonal), understanding of medical errors (increased awareness, prevention and reduction, responding to errors), personal experiences with patient safety issues, impact of course (changed perceptions, professional integrity, need for similar sessions, importance of the topic) and course feedback (format, preparation for clinical years, suggestions). Students reported a lack of baseline awareness regarding the frequency and consequences of medical errors. After the course, medical students reported a perceptional shift in favor of systems thinking regarding error causality, and nursing students focused on human factors and error prevention. The interactive course format involving scenario-based learning was deemed beneficial in terms of increasing awareness, imparting relevant clinical and interpersonal skills, and changing perspectives on patient safety.ConclusionsStudent perspectives illustrate the benefits of an early introduction of dedicated courses in imparting patient safety education to aspiring health professionals. Students reported a lack of baseline awareness of essential patient safety concepts, highlighting gaps in the existing curricula. This study can help provide an impetus for incorporating patient safety as a core component in medical and nursing curricula nationally and across the region. Additionally, patient safety courses can be tailored to emphasize areas identified as gaps among each professional group, and interprofessional education can be employed for shared learning. The authors further recommend conducting longitudinal studies to assess the long-term impact of such courses.

Human reliability assessment in hydrogen refuelling stations: A system dynamic approach

This study develops a methodology using a system dynamics approach to analyse human error probability (HEP) within the context of hydrogen fuelling station (HFS) maintenance, mainly focusing on the dynamic nature of performance shaping factors (PSFs) over time. The developed model offers a comprehensive understanding of how diverse human factors dynamically influence task performance, yielding critical insights for safety management strategies to study 8-hour day shifts in maintenance activity. The study explores the intricate relationship between time-dependent PSFs and human error probability, highlighting that as the number of maintenance tasks rises, so does the potential for increased fatigue levels, subsequently elevating HEP. Introducing breaks during work emerges as a promising intervention to mitigate task-related fatigue, reducing HEP. Careful break implementation is necessary to prevent shifts from extending, which could inadvertently raise HEP. This research has implications extending beyond HFS, benefiting industries where operational safety and efficiency are paramount. Future studies can build upon these findings, exploring additional interventions and diverse work scenarios to advance our understanding of human performance and error prevention strategies, ultimately fostering safer and more productive work environments. The integration of system dynamics and the insights gained contribute significantly to hydrogen safety and offer a robust foundation for future investigations in this field.

Applicability of drug-related problem (DRP) classification system for classifying severe medication errors

BackgroundSeveral classification systems for medication errors (MEs) have been established over time, but none of them apply optimally for classifying severe MEs. In severe MEs, recognizing the causes of the error is essential for error prevention and risk management. Therefore, this study focuses on exploring the applicability of a cause-based DRP classification system for classifying severe MEs and their causes.MethodsThis was a retrospective document analysis study on medication-related complaints and authoritative statements investigated by the Finnish National Supervisory Authority for Welfare and Health (Valvira) in 2013–2017. The data was classified by applying a previously developed aggregated DRP classification system by Basger et al. Error setting and harm to the patient were identified using qualitative content analysis to describe the characteristics of the MEs in the data. The systems approach to human error, error prevention, and risk management was used as a theoretical framework.ResultsFifty-eight of the complaints and authoritative statements concerned MEs, which had occurred in a wide range of social and healthcare settings. More than half of the ME cases (52%, n = 30) had caused the patient’s death or severe harm. In total, 100 MEs were identified from the ME case reports. In 53% (n = 31) of the cases, more than one ME was identified, and the mean number of MEs identified was 1.7 per case. It was possible to classify all MEs according to aggregated DRP system, and only a small proportion (8%, n = 8) were classified in the category “Other,” indicating that the cause of the ME could not be classified to specific cause-based category. MEs in the “Other” category included dispensing errors, documenting errors, prescribing error, and a near miss.ConclusionsOur study provides promising preliminary results for using DRP classification system for classifying and analyzing especially severe MEs. With Basger et al.’s aggregated DRP classification system, we were able to categorize both the ME and its cause. More research is encouraged with other ME incident data from different reporting systems to confirm our results.

Human reliability assessment of intelligent coal mine hoist system based on Bayesian network

The human reliability of intelligent coal mine hoist operation system is affected by many factors, in order to reduce the occurrence of human error in the hoist system and improve the reliability of the system. The characteristics of phased-mission task operation of hoists is combined, the phase dependence of human cognitive errors is considered and, a new human reliability evaluation method is proposed with the help of Bayesian network (BN) model in this paper. Firstly, the phase dependence of human cognitive errors was analyzed based on the cognitive behavior model. Then the human error analysis in the hoist system was carried out, and several main performance shaping factors are selected. Secondly, BN was used to build the human reliability model of the hoist system at each stage. Finally, it is found that the phase dependence of cognitive errors has a negative impact on the human reliability of the hoist system through the case analysis. At the same time, several main performance shaping factors (PSFs)were quantitatively analyzed by using the reverse reasoning ability of BN, which proves the effectiveness of the proposed method, and provides a scientific and reasonable theoretical basis for the development of effective human error prevention measures for the operation of intelligent coal mine hoists.

Integration of the HEART and SHERPA Approach to Evaluating Human Errors in the Refinery Salt Production

Human error can be prevented by measuring human reliability through the Human Reliability Assessment approach. PT A is a national scale salt producer that produces salt. The quality division identifies human error problems made by operators in the salt refining process. This study aims to identify, calculate probability values, and provide recommendations for improvement to reduce human error in salt refining production operators. HEART and SHERPA are research approaches used because they are considered the most suitable for this study. The results showed that the most visible human error for the error mode category was C2, incomplete checking, and 42 error modes because the operator infrequently did a complete check. The process with the highest Human Error Probability value is the drying process 1, with a value of 7.49. In contrast, the process with the most minor Human Error Probability value score of 0.085 is the bagging process 2. Human error prevention efforts are carried out for each process based on the highest score on the Human Error Probability Index, including Personal Protective Equipment, Standard Operating Procedures, and training to improve operator skills.

A Method for Identifying the Key Performance Shaping Factors to Prevent Human Errors during Oil Tanker Offloading Work

Oil tanker offloading is a human-related and high-risk task. A small operational error may trigger catastrophic accidents such as fire and explosion. It is recognised that more than 70% of industrial accidents are blamed for human errors, so preventing them is crucial. As human error is associated with a variety of Performance Shaping Factors (PSFs), it is meaningful to identify key PSFs for safe operations during oil tanker offloading process. However, some issues are obstacles to finding the crucial PSFs. The recording data of most PSFs are always incomplete and imperfect. Moreover, the standard for ranking PSFs should be rational. In addition, the performance of each PSF at the different stages is oil offloading is usually unstable and may change with time. As a result, this study aims to conduct a method that mainly relies on Grey Relational Analysis (GRA), the definition of “Risk” (combination of likelihood and impact), and Hierarchical Task Analysis (HTA) to find several significant PSFs to prevent human errors. GRA deals with the incomplete and imperfect data; the definition of “Risk” provides a rational basis for ranking PSFs; and HTA gives support for considering the PSFs’ changes at different stages of a task. The proposed approach is tested on a real engineering case of oil tanker offloading work at offshore terminal. The result indicates that the method can be applied to identify key PSFs, which in turn provides recommendations for human error prevention to ensure the safety both on board and at terminal.

Methodology for dynamic reliability assessment of team situation awareness of digital nuclear power plants

Team situation awareness (TSA) reliability is an important factor for team reliability. Moreover, situation awareness (SA) is a prominent problem in digital nuclear power plants (NPPs). Currently, since there is no suitable method to dynamically assess TSA reliability, we constructed a dynamical assessment method of TSA reliability based on a dynamic Bayesian network (DBN) to evaluate TSA reliability. First of all, a TSA causal concept model through qualitative analysis, expert group discussion and sample data analysis. On this basis, the quantitative assessment method of TSA reliability was constructed based on DBN and obtained probability distribution of variables. A standardized method was established to obtain the probability distribution of variables. Furthermore, we evaluated TSA dynamic reliability in a steam generator tube rupture (SGTR) accident. The results showed that the error probability of TSA decreased, and the level of TSA reliability continuously increased in SGTR. TSA reliability can be dynamically predicted by causal reasoning, the most important cause of TSA error could be identified by diagnostic reasoning, which provided theoretical support for the targeted prevention of human error. Finally, this established method was proved to be reasonable through sensitivity analysis.

Improvement of human error prediction accuracy in single-trial analysis of electroencephalogram.

The prevention of human error is an important task that has already been researched. Previous studies have shown that EEG signals can predict the occurrence of human errors. However, high accuracy has not yet been achieved in a single-trial analysis. This study is aimed to improve the accuracy of single-trial analysis, and propose a method for anomaly detection with auto encoder(AE). In the experiment, we conducted "Press the button(Go)" or "Do nothing(No-Go)" according to the visual stimulus and analyzed the EEG signal from -1000 ms to 0 ms when the stimulus was displayed. We prepared two types of inputs, time series data and frequency spectrum, and an AE was trained to reconstruct the inputs. We then calculated the difference between the reconstructed data and input data and predicted human error by its largeness. In the prediction using Support Vector Machine (SVM) based on the frequency spectrum, some over-fitting occurred and the average accuracy was 43 %. In the prediction using anomaly detection with frequency spectrum was 53 % and could not be classified. The time series data was 63 % which improved the accuracy. A previous study has shown frequency-dependent features such as -band activity and rhythm, as precursors of human error. However, in single-trial analysis, we obtained a higher accuracy by time series data than when by using the frequency spectrum. However, there was no noticeable difference between SVM and anomaly detection methods other than over-fitting. Therefore, in this case, the improvement in accuracy by the anomaly detection method could not be confirmed. However, the result suggests that it is more effective to use the frequency spectrum than the time series data in the single-trial analysis in the future.

A human reliability analysis methodology based on an extended Phoenix method for severe accidents in nuclear power plants: Qualitative analysis framework

The mitigation of the Fukushima accident exposed the detrimental action of human errors once again. However, deficiencies in existing human reliability analysis (HRA) methods prevent the effective prediction and analysis of potential human errors during the mitigation of severe accidents following core damage. To address this problem, this study presents a qualitative analysis framework for emergency response personnel (ERP) reliability analysis under severe accident conditions. This framework is developed using the Phoenix HRA method in conjunction with analyzing the ERP response characteristics and human-related issues from previous accidents, literature surveys, drill observations and interviews, as well as an assessment of the applicability of existing HRA methods. The analysis framework includes the establishment of an ERP response tree and a human failure fault tree (FT), as well as the identification and compilation of ERP cognitive functions, human error modes (HEMs), performance-influencing factors (PIFs), and the construction of Bayesian belief networks, to provide methodological guidance for the prevention of human errors under severe accident conditions.

Ontology generation for flight safety messages in air traffic management

The analysis of various data is a challenging issue in the field of air traffic flow management (ATFM). Managing a large volume of data and their correct interpretation plays a key role in the prevention of air accidents, human errors, and flight interactions. In general, pilots and flight navigators deal with massive and variety information, including flight safety messages, meteorological messages, aviation notes for air man (NOTAMs) and etc. Therefore relationship between Heterogeneous big data is problem. Recently, the semantic web has been recognized as a novel foundation of knowledge management that relies on ontology (concept modeling language). The present study aimed to propose an efficient infrastructure for ATMs by defining the concepts, rules, and relationships between the concepts related to the scope of flight information. To this end, we designed and implemented the ontology of flight safety messages, which included an important part of flight operation information. In this research, the internet protocol suite (IPS)-based aviation networks and methontology methodology were exploited for the engineering of the ontology of flight messages. In addition, the implementation process was carried out using the Protégé software. The analysis of the system was performed using the actual data of Mashhad Airport, Iran. Finally, the implementation of the ontology of flight messages was demonstrated after a case study of several flight examples. The developed ontology could be incorporated into the foundation of an aeronautical telecommunication network (ATN) aviation network as an application.

Performance Shapping Factors in Coal Mining

Health and Safety Executive statistics, states that 90% of accidents are caused by human error. Coal mining is an industry that has a high risk of work accidents. If the mining process is not in accordance with procedures, the worker's life will be at stake. Most accidents that occur in coal mining are caused by human error, therefore an assessment of the probability of human error or what is called the Human Error Probability (HEP) is very important to do. Human Reliability Analysis (HRA) is part of the risk assessment process which functions to analyze and predict HEP. HRA has been used in many studies to assess the risks involved in large, complex, and dangerous systems. SLIM (Success Likelihood Index Method) is a method used in the HRA field. Prevention and control of human error needs to be done at every stage of work. SLIM can analyze HEP at each stage of the work. SLIM aims to obtain a HEP value. To get the HEP value, we must first find the SLI (Success Likelihood Index) value. Finding the SLI value comes from a weighting questionnaire and PSF (Performance Shapping Factor) assessment that has been filled in by an expert judgment. After the HEP value is obtained, it can be seen which jobs fall into the safe risk level and which the risk cannot be accepted. Furthermore, risk reduction is carried out by making a task analysis of jobs that have a high risk of danger. From the results of the research conducted, it is known what factors cause the error, namely unsafe conditions, unsafe actions, personal factors, and job factors.

An AIS Data-Driven Approach to Analyze the Pattern of Ship Trajectories in Ports Using the DBSCAN Algorithm

As the maritime industry enters the era of maritime autonomous surface ships, research into artificial intelligence based on maritime data is being actively conducted, and the advantages of profitability and the prevention of human error are being emphasized. However, although many studies have been conducted relating to oceanic operations by ships, few have addressed maneuvering in ports. Therefore, in an effort to resolve this issue, this study explores ship trajectories derived from automatic identification systems’ data collected from ships arriving in and departing from the Busan New Port in South Korea. The collected data were analyzed by dividing them into port arrival and departure categories. To analyze ship trajectory patterns, the density-based spatial clustering of applications with noise (DBSCAN) algorithm, a machine learning clustering method, was employed. As a result, in the case of arrival, seven clusters, including the leg and turning section, were derived, and departure was classified into six clusters. The clusters were then divided into four phases and a pattern analysis was conducted for speed over ground, course over ground, and ship position. The results of this study could be used to develop new port maneuvering guidelines for ships and represent a significant contribution to the maneuvering practices of autonomous ships in port.

Visual attention of experts and novices to a critical industrial maintenance task

Abstract: In the industrial maintenance field, critical tasks are activities that can endanger operating processes and cause serious accidents if not performed correctly. These tasks are typically performed by professionals with a considerable level of knowledge and rely on prescribed procedures that flag the task, drawing the attention of task performers. Given the importance of reviewing work procedures and monitoring tasks for human error prevention, this study analyzed similarities and differences in the visual attention of expert and novice task performers while following procedures with and without flagging of a critical task. The study included 48 maintenance professionals, divided into two groups of performers: experts and novices. For attention analysis, eye-tracking technology was used to monitor their observation during the work procedure, recording data on time to first fixation, total fixation time and number of visits. The results indicate that in the procedure without flagging of the critical task, the numbers of fixations on and visits to the stages of the task were higher in the novice than in the expert group. In the flagged procedure, the visual attention of experts and novices was similar when considering all stages of the task. In conclusion, the tasks must be revised between the two groups, and the flagged procedure can be used as a guide for work monitoring, helping experts or novices to more easily select relevant information.

Analysis on Cognitive Behaviors and Prevention of Human Errors of Coalmine Hoist Drivers

Human errors are commonplace among hoist drivers in the hoisting task of coalmines. To reduce these errors and prevent accidents, it is necessary to identify the features of cognitive behaviors and main cognitive errors of the hoist drivers. This paper analyzes the accident cases and operating flow of coalmine hoist, and establishes a cognitive process model of coalmine hoist drivers. Further, the cognitive behaviors and functions of the drivers were analyzed stage by stage, revealing the distributions of their main cognitive behaviors and functions. It is learned that most coalmine hoist accidents concentrate in two stages: lifting, and operation monitoring. The operating processes in the two stages were further deliberated. The specific operations were extracted as the influencing factors of human errors, and the importance of each index was calculated through analytic hierarchy process (AHP). The research results provide a theoretical reference for identifying the key factors affecting the human errors of the operations by coalmine hoist drivers, and shed new light on how to prevent such errors.

Why Accountability Sharing in Health Care Organizational Cultures Means Patients Are Probably Safer.

Because human errors should be regarded as expected events, health care organizations should routinize processes aimed at human error prevention, limit negative consequences when human errors do occur, and support and educate those who have erred. A just culture perspective suggests that responding punitively to those who err should be reserved for those who have willfully and irremediably caused harm, because punishment creates blame-based workplace cultures that deter error reporting, which makes patients less safe.

소형어선의 인적과실 방지에 관한 연구

소형어선의 인적과실 방지에 관한 연구

A Study on the Improvement of Methods for Human Error Prevention using a Smart MCC System

South Korea has seen remarkable growth in the world since its liberation. However, the incidence of industrial disasters has increased during its rapid industrial development, and South Korea is currently ranked as one of the Organisation for Economic Co-operation and Development (OECD) countries, with high rates of industrial accidents. In particular, the misuse of the Motor Control Center (MCC) panel switches leads to serious accidents caused by human error. Therefore, this study introduced and proposed the use of a smart MCC system for human error prevention to improve the inadequacies of the current Lock-Out, Tag-Out (LOTO) program. The present study compared and analyzed the existing and smart MCC systems. In addition, the durability and operation of the new system were verified by an authorized agency. Furthermore, a survey of users (operators and operating departments) reported good reliability, user convenience, and safety accident prevention of the new system. The on-site application of this system will contribute to the effective operation of electrical facilities and the prevention of the misuse (human error) of MCC switches and the development of accident prevention solutions combining ICT technology. Keywords: Human Error, System, Motor Control Center (MCC) Panel, Accident Prevention