Human Error Assessment Research Articles

A Comparative Risk Assessment of Human Error in Firefighting Using SLIM and Fuzzy SLIM Method

Introduction: The accurate evaluation of error probability and risk is important. Accordingly, this Comparative study was conducted to evaluate the risk of human error in emergency situations using SLIM and Fuzzy SLIM techniques in fierfighting tasks. Material and Methods: This cross-sectional and descriptive-analytical study was conducted among 12, using Fuzzy SLIM and SLIM techniques. 39 sub-tasks were studied in 4 phases (Awareness, Evaluation, Egress and Recovery). Considering the advantages of the Fuzzy SLIM method, fuzzy logic was used in weighting of performance shaping factors (PSF). Excel software was used to calculate the probability of error. Also, correlation and kappa statistical tests were used for data analysis in SPSS software. Results: The mean and standard deviation of human error probability in different sub-tasks of firefighting in SLIM and Fuzzy SLIM methods were 0.095357 ± 0.026193 and 0.06490 ± 0.051748, respectivly. In 48.7 percent of the sub-tasks, the probability category of human error and the assessed risk were the same; however, in 89.7 percent of the sub-tasks, the estimated level of risk was the same in both methods. Correlation test showed that the correlation coefficient of error probability values between the two methods was 0.32, which indicated a moderate correlation in this regard. Additionally, the results of kappa statistical test for the estimated level of risk showed that there is a high agreement between Fuzzy SLIM and SLIM (P value <0.05). Conclusion: The results of the study indicated meaningful agreement and a moderate correlation between Fuzzy SLIM and SLIM. Therefore, due to the relatively high accuracy of Fuzzy logic methods, and also the long steps of implementing the SLIM method, the Fuzzy SLIM method can be a good alternative to this method.

A large-scale group SLIM considering expert credibility under social network to estimate human error probabilities in the railway driving process

The Large-scale Group Success Likelihood Index Method (LG-SLIM) can eliminate bias caused by a single expert in human error assessment. The traditional LG-SLIM uses trust degrees to cluster and reach a consensus. However, the existing clustering algorithms do not consider the trust degrees between a given pair of experts to be multiple and vary according to different evaluated tasks. Besides, the existing consensus models do not consider various combinations of the evaluated tasks and trusted experts’ professions when managing trust degrees and self-confidence. Therefore, the similarity-trust-based clustering algorithm is improved using the comprehensive trust degree integrated from diverse trust degrees concerning all evaluated tasks. Moreover, expert credibility is proposed to reflect the quality of the expert's evaluation results, determined by self-confidence and trust degree simultaneously according to various combinations of the expert profession and target task. Accordingly, under the social network derived from expert credibility, the incompatible outliers change their opinions by referring to the views of those with the highest expert credibility. Finally, the sensitivity experiment and comparative analysis verify the effectiveness of the proposed model. The proposed LG-SLIM model is useful for human error assessment when critical operations need many experts to obtain reliable and accurate results.

Methods for Human Reliability Analysis in Dentistry.

Human error (HE) is one of the main causes of accidents in different organizations and industries. Dentistry is a medical branch with a high risk of error since it involves complex manual tasks that must be performed with a high degree of accuracy. To understand the various aspects of HE in dentistry, which is crucial for developing strategies to mitigate its impact on patients' safety, it is necessary to perform a human reliability analysis (HRA). However, there is scarce data on the use of HRA in dentistry. In this paper, we give a brief description of the main phases of HRA with an emphasis on HRA methods that could be used in dentistry. Since HRA methods have been designed for diverse industrial applications, we discuss their possible application in dentistry. Among the discussed methods, the Systematic Human Error Reduction and Prediction Approach (SHERPA) and the Human Error Assessment and Reduction Technique were identified as the best candidates for performing HRA in dentistry. This is of great importance since understanding and addressing HEs is crucial for improving patient safety and the overall quality of dental care.

Sensitivity analysis of human error in the steel industry: exploring the effects of psychosocial and mental health risk factors and burnout using Bayesian networks.

Human error and the high rates of fatalities and other occupational accidents in the steel industry are of significant global relevance. The aim of this study was to investigate the effect of psychosocial, mental health, and burnout risk factors on human error probabilities in an industrial environment using Bayesian networks. This cross-sectional study was conducted in 2023. The participants were 252 employees of a steel company. Error probabilities related to the tasks of participants were estimated using the Human Error Assessment and Reduction Technique (HEART). Other data was collected using a survey that consisted of demographic information, the Maslach Burnout Inventory, Depression Anxiety Stress Scales, and a short version of the Copenhagen Psychosocial Questionnaire. A theoretical model was drawn in GeNIe academic software (version 2.3). The results showed that all the studied variables were able to significantly affect the distribution of human error probabilities. Considering a distribution of 100% for the high state of these variables, the results showed that the greatest increases in error probability were related to two burnout dimensions: emotional exhaustion (29%) and depersonalization (28%). All the variables, with a probability of 100%, increased the probability of high human error probabilities by 46%. The most important variables in terms of their effect on human error probabilities were burnout dimensions, and these variables also had a mediation effect on the psychosocial and mental health variables. Therefore, preventive measures to control human error should first focus on managing the risks of burnout in workers. This, in turn, can also reduce the effect of psychosocial risk factors and mental health problems on human error in the workplace.

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.

Quantitative assessment of human error of emergency behavior for hazardous chemical spills in chemical parks

In-depth research on human error is of great significance to improve the human reliability in the emergency rescue as well as to avoid the occurrence of secondary accidents. Therefore, the standardized process and key emergency behaviors in the emergency rescue process of hazardous chemical leakage accidents are first analyzed. Based on the Cognitive Reliability and Error Analysis Method, the human error mode was sorted out, and the human error probability estimation model was established. Combining the improved Analytic Hierarchy Process and entropy weight method, a model for estimating the severity of human error was established, realizing the quantitative analysis of the human reliability of the emergency behavior. The study showed that the 21 emergency behaviors were clustered into 3 categories, with clustering 1 including 2 emergency behaviors, which have the highest level of severity, clustering 2 including 11 emergency behaviors, and clustering 3 including 8 emergency behaviors. Finally, countermeasures were proposed in four areas, including increasing the level of Common Performance Conditions and the ability to transmit information on accidents, etc. It indicates that the method can widely guide the improvement of emergency response capability in chemical parks.

Cognitive Factors Leading to Human Error: A Major Contributing Factor for Quality Deviations in Pharmaceutical Industry

The pharmaceutical sector is one of the top research-based sectors in the world which continuously produces new medications that improve and save human lives. Patients, as well as healthcare professionals depend on pharmaceutical companies for the treatment of ailments, so it is very important to develop a good quality product following Good manufacturing practices guidelines that ensure the quality, safety, and efficacy of the product. Human cognitive errors are a major cause of quality deviations. These quality deviations result in product recalls, a ban on products by regulators where the company’s reputation will be tarnished which can result in loss of sales and revenue. Human errors can be identified and mitigated by various techniques. In this paper, we discuss the factors contributing to cognitive errors, the impact of cognitive errors on the pharmaceutical industry, the identification of the cognitive errors causing current good manufacturing practices (cGMP) deviations, and challenges in the mitigation of cognitive errors. A survey questionnaire and the form human error assessment and reduction technique (HEART) tool were also developed and discussed in the manuscript.

Analysis of Human Error Probability at Shipyard Using Human Error Assessment and Reduction Technique (HEART)

Analysis of Human Error Probability at Shipyard Using Human Error Assessment and Reduction Technique (HEART)

Analysis of human errors in human-autonomy collaboration in autonomous ships operations through shore control experimental data

Human-autonomy collaboration plays a pivotal role in the development of Maritime autonomous surface ships (MASS), as Shore control center (SCC) operators may engage in the control loop by directly operating the MASS, or, in the supervisory loop, monitoring the MASS and taking over control when needed. Thus, effective human performance during takeover control and operation is crucial for the safety of MASS operations. However, since the MASS is still in the early phase of development, the mechanism of human errors is unknown, and the data on human-autonomy collaborative operation is scarce. Human reliability analysis (HRA) aims to assess human errors qualitatively and quantitatively, and is widely used in various complex systems to help safety analysis. This study is dedicated to incorporating advanced HRA methods elements to identify and quantify human errors during taking over control and operation of a MASS in collision avoidance scenarios. It presents virtual experimental results, combined with theoretical human error identification and assessment methods. At first, we apply the Human-System Interaction in Autonomy (H-SIA) method to identify potential human errors; secondly, we identify relevant Performance Shaping Factors (PSFs) including Experience, Boredom, Task complexity, Available time and Pre-warning, and performance measures of the human errors, and implement them in the virtual experiment based on a full-scale autonomous ferry research vessel called milliAmpere2. Finally, we build a Bayesian Network (BN) to present causal and probabilistic relationships between PSFs and human errors through experimental data. The results show that available time has the highest impact on takeover performance of operators, followed by task complexity and pre-warning. Boredom does not present a significant sole impact unless combined with available time. Experience does not show a significant impact on human performance. In addition to the relevance of the human errors analysis to the safe development and operational design of MASS, the developed method benefits other human-autonomy collaborative systems. The developed BN model shows adaptability to assess human error probabilities, and the practical significance of integrating experimental data into the existing HRA methodologies for complex systems.

A pre-exercise screening of helicopter underwater escape training using a modified human reliability analysis with rule-based mechanism

This study conducts a human reliability analysis to develop a pre-exercise screening tool for helicopter underwater escape training (HUET) to proactively improve training facilities and to contribute attendees' performance. In this context, a modified human error assessment and reduction technique (HEART) was adapted. The technique investigates error producing conditions (EPC) and general task type (GTT) variables considering attendees profile, training infrastructure conditions, and 16 sub-steps of HUET exercises. Since the identification of EPC and GTT is still a key limitation of traditional HEART, this study extends a basic rule-based mechanism based on if-then-else comparison matrix structure to enhance accuracy in practice. The case study underscores the feasibility of individual, provider, and procedural interventions, enhancing training success and practical application of skills in real incidents. The application of a rule-based selection algorithm for GTT determination enhances objectivity, contributing to a more controlled identification and minimisation of errors. The case study, tailored specifically for practical training, delineates the feasibility of individual-based, training provider-based, and procedural-based interventions. Additionally, mitigating factors like ‘emotional stress’ and ‘unfamiliarity’ through advanced safety measures can boost attendees' self-confidence, leading to improved training success and practical skill application in real-world scenarios.

A hybrid SNN-STLSTM method for human error assessment in the high-speed railway system

Over the years, many state-of-the-art technologies have reinforced safety in the high-speed railway driving process. However, the accident rate has not dropped significantly with the advanced technology onboard. The leading cause of this phenomenon is adverse human performance. The crucial aspects contributing to human performance are performance shaping factors (PSFs). The existing expert judgment techniques evaluate human error probabilities depending on the descriptions of PSFs’ influences on human performance. However, they cannot perform dynamic human error assessment with the real-time fluctuations of PSF interventions. To overcome this deficiency, we propose a deep learning-based method considering the instantaneous effect and time-dependent influence of PSF interventions on human performance and matching different neural network characteristics to human cognitive processes. The proposed SNN-STLSTM method combines the spiking neural network (SNN) auto-encoder and the sparse-temporal long short-term memory (STLSTM) network. The unsupervised SNN, a brain-like computational model, simulates the human brain’s response when PSF interventions occur instantaneously. Meanwhile, an auto-encoder architecture integrating the unsupervised SNN makes the representations learned by SNN more meaningful. In addition, for the irregularity of PSF interventions, the standard LSTM is extended to the architecture STLSTM to predict the long-term effects of PSFs on human performance. Finally, a case study of the Wuhan–Guangzhou (WH–GZ) high-speed railway (HSR) shows how Monte Carlo simulation based on Fault Tree Analysis is used to assess and collect human error data and then how PSF and human error data are used to train the whole framework. The comparative experiment and ablation study indicate that the proposed SNN-STLSTM method outperforms other artificial intelligence approaches. This study provides insightful findings that help to understand how and what aspects affect human performance in HSR operations. The proposed method can dynamically predict the human error probabilities of the drivers, which facilitates taking timely prevention measures and reduces the accident rate.

Estimating the impact of label design on reducing the risk of medication errors by applying HEART in drug administration

Medication errors are one of the biggest problems in healthcare. The medicines’ poor labelling design (i.e. look-alike labels) is a well-recognised risk for potential confusion, wrong administration, and patient damage. Human factors and ergonomics (HFE) encourages the human-centred design of system elements, which might reduce medication errors and improve people’s well-being and system performance.ObjectiveThe aim of the present study is twofold: (i) to use a human reliability analysis technique to evaluate a medication administration task within a simulated scenario of a neonatal intensive care unit (NICU) and (ii) to estimate the impact of a human-centred design (HCD) label in medication administration compared to a look-alike (LA) label.MethodThis paper used a modified version of the human error assessment and reduction technique (HEART) to analyse a medication administration task in a simulated NICU scenario. The modified technique involved expert nurses quantifying the likelihood of unreliability of a task and rating the conditions, including medicine labels, which most affect the successful completion of the task.ResultsFindings suggest that error producing conditions (EPCs), such as a shortage of time available for error detection and correction, no independent checking of output, and distractions, might increase human error probability (HEP) in administering medications. Results also showed that the assessed HEP and the relative percentage of contribution to unreliability reduced by more than 40% when the HCD label was evaluated compared to the LA label.ConclusionIncluding labelling design based on HFE might help increase human reliability when administering medications under critical conditions.

3D Animation Production and Generation Based on Human Body Base 3D Models

Abstract With the gradual increase in the demand for parametric human 3D models, the requirements for the accuracy of modeling and the quality of generation are getting higher and higher. In this paper, the human body structure model is constructed by 3D human posture estimation and spatio-temporal graph convolutional network, and after normalizing the data of the human body base, the 3D human body model based on the human body base is generated by combining the free deformation method, and the local light model is used to make the human body model more three-dimensional and real. In addition, the quality of 3D animation production is investigated by combining blind quality evaluation and validation analysis of the human body-based 3D model. The 3D human body modeling simulation error assessment has an error assessment root-means-square error that falls within 1.5%. In the validation analysis of model generation quality, the quality evaluation model proposed in this paper has a quality correlation coefficient and prediction accuracy of 0.9456 and 0.9454 on the dataset, respectively, and the performance of the ablation experiments and generalization experiments is stable. The mean value of the error between the animation production character generation effect and the actual testers is not more than 10mm.Combined with the research in this paper, it effectively improves the accuracy of human body modeling and provides an evaluation method for the quality of human 3D animation production and generation, which is of practical significance for its development.

Risk Assessment of Human Error in Orange-line Metro Depot Operation

Risk Assessment of Human Error in Orange-line Metro Depot Operation

SAPERO: a new tool for safety analyses in advanced radiotherapy

Improving safety standards in advanced radiotherapy technologies, where historical experience is not sufficient due to innovation aspects, raises the need to perform studies using new tools that follow a holistic view of the process chain. Such studies are useful to identify additional critical elements compared to safety analyses carried out by classic tools. In this field, SAPERO (La SicurezzA del PazientE: tecniche avanzate ed innovative per la valutazione del rischio di eventi indesiderati all'interno del percorso assistenziale nel settore RadioterapicO) is a new assessment tool that allows to use integrated of Hierarchical Task Analysis (HTA); Failure Mode Effects and Criticality Analysis (FMECA); Cognitive Task Analysis (CTA); Human Error Assessment Reduction Technique (HEART). The methodological approaches have been suitably modified to address their operating in the medical sector under study. Some improvements have concerned the application of the fuzzy theory in FMECA and the use of a linguistic approach in HEART. In this paper, SAPERO application on a case study related to treatment procedures in helical tomotherapy performed at the radiotherapy department of ARNAS Civico hospital, Italy, is described. The results have allowed to provide suggestions aimed to improve the examined process.

An analysis of human error and reliability in the operation of fixed CO2 systems on cargo ships using HEART Dempster-Shafer evidence theory approach

CO2 fixed fire extinguishing system activation accidents may occur as a result of unintentional system activation or human error. Crew members present in the area could suffer serious consequences, including death, if CO2 gas is released into a protected compartment such as the engine room, CO2 room, or cargo area. It is therefore crucial to investigate and address the causes of such accidents. The use of Dempster-Shafer (D-S) theory and the Human Error Assessment and Reduction Technique (HEART) to the Human Reliability Analysis (HRA) of CO2 fixed fire extinguishing system activation accidents is discussed in this study. On the one hand, D-S theory allows for uncertainty-based reasoning and can be extremely advantageous, especially when analysing complex systems where there are many potential challenges. On the other hand, HEART is an organised method to examine and reduce human error in high-risk systems. Together, these techniques provide a powerful tool for identifying and addressing the underlying causes of CO2 fixed fire extinguishing system activation accidents. The study revealed that the total human error probability (HEP) for the CO2 fixed fire extinguishing system was 3.10E-01 and the reliability of human performance for the given process was 6.90E-01. The results of this paper highlight many factors such as equipment failure, inadequate training, and poor maintenance practices, as well as factors contributing to the risk of accidental system activation. By addressing these factors, the probability of CO2-fixed fire suppression system activation accidents can be significantly reduced.

Assessment and Reduction of Human Error using SHERPA Technique in Chemotherapy Department of a Large Military Hospital.

Medical errors are numerous in medical activities. Considering the sensitivity and importance of the medical group's professions, the emergence of an apparently simple error can cause the death of an individual or even a group of individuals. The present study aims the evaluation and reduction of human error using a system human error reduction and prediction approach System Human Error Reduction and Prediction Approach (SHERPA) in the nurses of Baqiyatallah hospital's chemotherapy ward in 2019. A cross-sectional study was conducted in the chemotherapy ward using the SHERPA technique. Then, the duties were determined in detail using Hierarchical Task Analysis (HTA). The errors were identified using the SHERPA checklist, and the risk outcomes and intensities were finally evaluated. Based on the study findings, there are 109 possible errors for 48 sub-duties. The most frequent errors fall in the functional area (54%) and the least frequent errors pertain to the area of selection (3%). In order to reduce the errors and increase the quality of the services and safety of the patients, errors can be identified by using the SHERPA technique; after identifying these errors, using this technique, it is possible to prevent the recurrence of the identified errors by careful planning. Considering the fact that the most frequent error was found in the functional domain, modern protocols can be codified in this area, and standards can be observed for putting the problems of this section atop of the priority list and reducing the errors and increasing safety of the patients.

Attendance system based on facial recognition using multi-task convolutional neural network

Some businesses today do not keep track of their employees' attendance. Some of them are still using outdated techniques such as log-based manual inspections. These outdated methods of data collection require excessive paperwork, take time due to laborious recording procedures, and result in inaccurate data collection due to human error and inadequate assessment. The goal of this study is to create an attendance system that can make it simple and accurate for businesses to track their employees' attendance. The aim of this project is to create a system that will make it simple and automatic to record attendance using facial recognition. The result shows that the system is operational and possess a accurate and highly benefits the students and the faculty members.

The roles of cognitive ergonomics in reducing human error and burnout among emergency room nurses during the Covid-19 pandemic

Background: Mental health, particularly burnout syndrome, is one of the concerns that can influence the productivity of health workers during the pandemic. This is because health professionals are under intense pressure, but there is no guideline or procedure in place to safeguard them in terms of mental health. The objective of this study is to discover burnout and human error, as well as to provide advice, by analyzing the ergonomic factors. Method: Human Error Assessment and Reduction Technique was the identifying approach employed (HEART) in this study. The sample consisted of ten emergency room personnel from one of the hospitals in Yogyakarta. Results: The activity with the greatest HEP was the delivery of first aids, which had a value of 0.068. This activity requires more difficult and complex labor abilities, making human unreliability more significant and increasing the likelihood of human error. Conclusion: Recommendations are made to assist nurses in dealing with heavy and varied workloads so that the reliability of nurses at work can be increased and burnout can be reduced, including the suppression of environmental factors to be as small as possible to reduce the workload of nurses, urging nurses to begin monitoring the situation and taking anticipatory steps so that the burnout does not worsen, and holding regular sharing sessions among nurses, upgrading knowledge and fostering a sense of togetherness.

Nurse-Task Matching Decision Support System Based on FSPC-HEART Method to Prevent Human Errors for Sustainable Healthcare

To increase the levels of sustainability of service quality as well as to ensure satisfaction and assurance of patients in the health sector, minimizing the probability of making mistakes nurses is of great importance. The extent of this probability is considerably affected by task types, physical conditions of the working environment, workload, and working conditions. Moreover, the physical and mental characteristics of nurses also have a colossal influence on this probability. It is also possible to increase the sustainability of health services by matching nurses appropriately to a specific task according to related risk levels, and by balancing their workload accordingly. This study proposes FSPC-HEART method in that purpose, as a new type of human error reduction and assessment technique (HEART) application based on fuzzy step-wise weight assessment ratio analysis and principal component analysis methods. Unlike the methods in the literature, this new method offers a person-specific proactive error prevention approach. With FSPC-HEART, the probability of each nurse to make a mistake, that is, the human error probability (HEP) values are calculated separately for each task. Also, the combined effect of physical and mental workload factors for each employee was taken into account. In the proposed method, the effect of the subjective judgments of the decision-makers on the objectively obtained HEP values was tried to be reduced. The developed nurse-task matching decision support system enables the FSPC-HEART method to be easily used by decision-makers, and to assign employees to tasks with low error probabilities.