Error-producing Conditions Research Articles

Maritime accident analysis and reduction technique for analysing maritime collision accidents

ABSTRACT The Human Error and Reduction Technique (HEART) is a robust method consisted of both qualitative and quantitative steps. The HEART method has been utilized to analysed the human reliability in various industries. In maritime safety research, understanding human error's role in accidents is crucial. This study introduces a novel methodology integrating the HEART method with the 4M framework and TOPSIS analysis to comprehensively assess human error's interplay with other accident factors. By categorizing generic tasks and error-producing conditions (EPC) into man, machine, media, and management domains, the methodology reveals intricate relationships among accident factors. Additionally, TOPSIS analysis facilitates quantitative evaluation, enhancing decision-making by assigning weights to identified factors. The methodology offers a systematic approach to identify critical areas for intervention, such as improving lookout tasks and communication. Application of this methodology can enhance maritime safety practices by providing actionable insights into accident prevention strategies. Through its holistic and quantitative approach, this study contributes to advancing maritime safety research and fostering a safer operational environment.

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.

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.

Analysis of Human Error Potential as a Cause of Work Accident using Sherpa and Heart Method in The Cement Industry

This study was conducted to analyze the potential for human error that can cause workplace accidents in the cement industry of PT.X. The study was conducted by conducting observations, interviews, and distributing questionnaires in the mining area and production area. Questionnaires were distributed to 96 people who were mining heavy equipment operators and production operators. The research method used through Human Reliability Analysis with the Hierarchial Task Analysis (HTA) method, knowing the types of errors that occur using the SHERPA method, then calculating the probability of human errors that occur with the HEART method. Based on the results of research using the SHERPA method, the most common types of errors in mining areas and production areas are action errors (60,25%). The type of error that occurs is due to negligence in using personal protective equipment and the work process is not completed properly. Based on research using the HEART method, the greatest opportunity for human error in the mining department is cleaning with dozer with a value of 1,056 and in the production department is in the area of cleaning work with a value of 1,89. Based on the error producing conditions questionnaire (EPCs) for workers in the mining department the most common cause of error is poor equipment / instruments while for workers in the production department is a mismatch between the imagined risk and the actual risk. The recommendations given to companies are to improve work equipment, improve visual displays and provide an appropriate assessment checklist.

Estimation of human error probabilities in marine safety services: The case of lifeboat and davits inspection

Shipboard Operation Human Reliability Analysis (SOHRA) method is recognized as a practical tool to predict human error probability (HEP) of operators engaging marine operations. Identifying the generic task types (GTTs) and marine specific error producing conditions (m-EPCs), the tool successfully derives HEP value distribution of critical operations in maritime environment. However, the real-time applications of SOHRA to prioritize and implement the suitable recovery actions have still open for development due to the limited time and expertise at pre-operation stage. This paper adapts SOHRA into lifeboat and davits inspection process as a critical marine safety service. Considering the tasks conducted by marine safety service engineers in routine and remote support modes, the GTTs and m-EPCs are assigned to estimate HEP values. In this context, a remote assistance system, is alternatively extended to involve standardization, camera tracking, and advisory support to enhance human reliability through inspection stages. The findings spotlight the deviation in HEP between routine ( 8.26E + 00) and remote support (4.04E-01) modes. A set of recovery actions (i.e. instructional materials) to remedy the HEP values in routine mode are suggested while it is not required at the remote mode assistance. The application illustrates that remote supporting to the marine service might reasonably reduce service engineers’ error rates. Consequently, the study is expected to enhance SOHRA applications in inspection period, particularly added value to marine service engineers in duty. The further studies on the proposed remote assistance concept as new generation solution will contribute to the service quality of marine safety companies.

A Framework for Systematic Assessment of Human Error in Construction Sites – A Sustainable Approach

The construction industry is one of the most accident-susceptible sectors of the national economy and is characterized by a high rate of accidentality. The Human Error Assessment and Reduction Technique (HEART) is a generic method to identify human error. This technique uses generic task types and error producing conditions to calculate the probable human error. It is known that unsafe acts in the activity will also lead to unplanned events. Therefore, in this research, in addition to the existing factors, the probability of unsafe acts is also integrated. From the results, it is known that excavation (0.957), reinforcement erection for footing & column (0.631) and crane operation (0.269) are the tasks with a higher probability of human error. This can be minimized by frequent safety trainings to the workers and providing suitable personnel protective equipment (PPE) by the management. This proposed method may be applicable for all the workplaces, as it has a generic method to quantify human error with the task and error producing conditions. Knowledge of the circumstances of accidents will enable the formulation or modification of the labour law to be properly formulated, as well as the appropriate orientation of preventive measures and trainings in the field of occupational safety. All participants in the investment process: workers, construction site managers and supervisors, should be the recipients of these activities, who are also exposed to hazards and may suffer from accidents while performing their activities at a construction site. Parameters and the probable human error described by the authors allow for a comprehensive assessment of hazards and the probability of accident occurrences.

Choking injuries: Associated factors and error-producing conditions among acute hospital patients in Japan.

Choking can lead to mortality and residual impairments. This study aimed to determine the factors associated with choking among acute hospital patients and examine error-producing conditions to suggest choking-prevention policies. Among 36,364 cases reported by hospital staff at an acute university hospital from 2012 to 2018 were examined using a retrospective study, 35,440 were analysis as the number of cases analysed for the study. We used descriptive statistics to present patient characteristics and conducted univariable and multivariable logistic regression analyses to identify factors associated with choking. Additionally, we conducted content analysis (root cause analysis) to examine error-producing conditions and prevention policies. Sixty-eight cases were related to choking injuries; of these, 43 patients (63.2%) were male, and 38 (55.9%) were aged 65 years and older. Choking cases had a high percent of adverse outcomes involving residual impairment or death (n = 23, 33.8%). Mental illness (adjusted odds ratio [95% confidence interval]: 3.14 [1.39−7.08]), and hospitalisation in the general wards (adjusted odds ratio [95% confidence interval]: 3.13 [1.70−5.76]) were associated with an increased probability of choking. Error production was caused by food (n = 25, 36.8%) and medical devices or supplies (n = 13, 19.1%). Almost all contributory factors were associated with inadequate checking (n = 66, 97.1%) and misperception of risk (n = 65, 95.6%). Choking poses a highly significant burden on patients, and hospital administrators should minimise the risk of choking to prevent related injuries. Hospital administrators should provide training and education to their staff and develop adequate protocols and procedures to prevent choking.

A Cognitive Autopsy Approach Towards Explaining Diagnostic Failure

Diagnostic failure has emerged as one of the most significant threats to patient safety. It is important to understand the antecedents of such failures both for clinicians in practice as well is those in training. A consensus has developed in the literature that the majority of failures are due to individual or system factors or some combination of the two. A major source of variance in individual clinical performance is cognitive and affective biases; however, their role in clinical decision making has been difficult to assess partly because they are difficult to investigate experimentally. A significant drawback has been that experimental manipulations appear to confound the assessment of the context surrounding the diagnostic process itself. We conducted an exercise on selected actual cases of diagnostic errors to explore the effect of biases in the ‘real world’ emergency medicine (EM) context.Thirty anonymized EM cases were analysed in depth through a process of root cause analysis that included an assessment of error-producing conditions (EPCs), knowledge-based errors, and how clinicians were thinking and deciding during each case. A prominent feature of the exercise was the identification of the occurrence of and interaction between specific cognitive and affective biases, through a process called cognitive autopsy. The cases covered a broad range of diagnoses across a wide variety of disciplines. A total of 24 discrete cognitive and affective biases that contributed to misdiagnosis were identified and their incidence recorded. Five to six biases were detected per case, and observed on 168 occasions across the 30 cases. Thirteen EPCs were identified. Knowledge-based errors were rare, occurring in only five definite instances. The ordinal position in which biases appeared in the diagnostic process was recorded. This experiment provides a baseline for investigating and understanding the critical role that biases play in clinical decision making as well as providing a credible explanation for why diagnoses fail.

Hybrid Human Error Assessment Approach for Critical Aircraft Maintenance Practice in the Training Aircraft

ABSTRACT Objective In this study, a hybrid human error assessment (HHEA) approach is proposed to determine the probability of aircraft maintenance technician (AMT) error occurring during aircraft repair. Background The human error assessment and reduction technique (HEART) is the first-generation empirical human reliability analysis (HRA) approach. Although this technique is used in several industries, AMTs’ errors have not been assessed with HEART and interactions of error-producing conditions (EPCs) have not been considered in calculating the probability of AMTs’ errors. Method HHEA is proposed to reduce the subjectivity of experts’ judgments by calculating assessed proportion of affect of each EPC on human error probability (HEP) integrating HEART and the analytic network process (ANP) method. The proposed model has been applied in the maintenance of the elevator, which is one of the most important primary control surfaces for the Cessna 172 Series aircraft type. Results The HEP values of 3 subtasks are found to be higher than the average HEP value. These subtasks have the highest probability of human error due to time pressure, poor environmental conditions, and being a repetitive task. Conclusion This study could guide other aircraft maintenance organizations using this integration of human reliability assessment approach for critical maintenance practices to enhance flight safety and minimize human error.

Why do medication errors occur in veterinary practice?

Medication errors are common in veterinary practice. While many of these do not result in harm to a patient, those that do can have serious consequences for the patient and also affect the people involved. Patient safety event reporting provides insight into how and why medication errors occur in practice. Although human error plays a part, there are many contributing factors that make mistakes more likely to happen. This article discusses the types of errors that are reported, common error-producing conditions and specific high-risk situations where errors can have the most serious consequences.

A Human Reliability Assessment of Marine Engineering Students through Engine Room Simulator Technology

Background It is widely accepted that the simulators are important technological instruments which can be utilized as an effective assessment tool in various domains Developing technologies allow the functionality levels of simulators to increase behavioural realism. For this reason, students in higher educations are involved in various useful practices using simulators. Purpose In this respect, simulators can also provide great opportunities to conduct analysis through human error on which this study conceptualized. Model In this context, this study proposes a human error evaluation approach through simulator technology whilst taking advantage of the SOHRA (Shipboard Operation Human Reliability Analysis) method. As a case study, the proposed approach was applied to a simulator environment with the involvement of marine engineering students. Throughout this case, the students were challenged with various error producing conditions (EPCs) while their performances were observed. Results The attendees were achieved good practice when confronted with EPC23 (unreliable instruments), EPC17 (inadequate checking), and EPC5 (spatial & functional incompatibility). However, the points open for improvement are found on EPC2 (time shortage), EPC24 (absolute judgments required), EPC18 (objectives conflict) and EPC9 (technical unlearning). Conclusion This framework can be utilized in simulator-based training activities to increase operational awareness of marine engineering students. The recent developments in simulator technology can boost the effectiveness of the proposed framework.

A hybrid evaluation method for human error probability by using extended DEMATEL with Z-numbers: A case of cargo loading operation

Human error probability (HEP) evaluation and prediction is one of the most significant tasks to enhance human reliability in marine industry. Among various kinds of HEP evaluation techniques, the Human Error Assessment and Reduction Technique (HEART) technique is regarded as an effective and empirical tool that has been widely adopted in various fields. However, current HEART techniques are insufficient to address HEP evaluation problem in which the self-assurance of expert's judgment and inter-dependencies between Error-producing conditions (EPCs) are considered. Therefore, the purpose of this paper is to develop a hybrid HEART framework (H-HEART-F) to address this problem by integrating Z-numbers and the decision-making trial and evaluation laboratory (DEMATEL) method. First, the Z-numbers are introduced to model the uncertainty and self-assurance of evaluation information from various experts. Then, the Z-number power weighted average (Z-PWA) operator is proposed to aggregate the individual evaluation information into a group direct influenced matrix. Next, an extended DEMATEL method based on possibility degree measure is constructed to determine the proportion of effect of each EPC by considering the self-assurance of expert's judgment and inter-dependencies between EPCs. Finally, the HEP estimation for the cargo loading operation in oil or chemical tanker ship is presented to demonstrate the availability and feasibility of the H-HEART-F. After that, the sensitivity analysis and comparison study are conducted to further illustrate the reasonableness and effectiveness of the proposed method.

Implementation of Heart and Dematel Integration to Steam Boiler Working Process

Human Error Assessment and Reduction Technique (HEART) is a practical and powerful approach to prioritize errors related to human actions, based on probabilities. HEART can determine error producing conditions (EPCs) which cause human errors for different processes including main duties (MDs) and sub-duties (SDs). HEART can be applied quickly for any process where human reliability is important. In this study, HEART and advanced version of Decision Making Trial and Evaluation Laboratory (AV-DEMATEL) integration proposed by Can and Delice in 2018 was performed for evaluating human related errors in steam boiler working process. In this way, the interactions between MDs, SDs and EPCs in a steam boiler working process were considered to compute process error probability (PEP). Additionally, the applicability of the proposed approach by Can and Delice (2018) was demonstrated again.

Integrated methods for analysing the causal factors in Australian maritime occupational accidents

This study employs the human error assessment and reduction technique (HEART) and its error producing conditions relating to the 4M (man, machine, media, and management) method to analyse the causes of occupational accidents in Australia. It seeks to find the causes of occupational accidents, apply the HEART-4M method to occupational accidents, and redefine generic tasks (GTs) in a maritime work environment. It acquires data from the 2008-2017 Australia Transport Safety Bureau report on merchant ships and analyses 30 occupational accident data reports through HEART-4M. The results consist of the onboard working situation and its relation to a GT, error producing conditions, and human error probability calculations. The most common GT in these accidents was a simple task performed rapidly, routine tasks involving a relatively low level of skill, and complex tasks. Furthermore, most accidents occurred during the loading and unloading of cargo.

Incorporating Human Reliability Analysis to enhance Maintenance Audits: The Case of Rail Bogie Maintenance

Human errors occurring during railway maintenance activities can significantly reduce the availability of equipment. Identification of potential human errors, their causes and prediction of the associated probabilities are important stages in order to manage such errors. This paper investigates the probability of human error during the maintenance of railway bogies. A case study examines technicians performing maintenance on the disc brake assembly unit, wheel set, and bogie frame under various error producing conditions in a railway maintenance workshop in Luleå, Sweden. The Human Error Assessment and Reduction Technique (HEART) is employed to determine the probability of human error occurring during each of the maintenance tasks, while fault tree analysis is used to define the potential errors throughout the maintenance process. The probability of a technician committing an error during the maintenance of the disc brake assembly, wheel set, and bogie frame is found to be 0.20, 0.039 and 0.021 respectively, with the human error probability (HEP) for the entire bogie 0.24. Time pressure, ability to detect and perceive problems, over-riding information, the need to make decisions and mismatches between the operator and designer’s model turn out to be major contributors to human error. These findings can help maintenance management personnel to better understand the error producing conditions that may lead to errors and in turn serve as an input to modify policies and guidelines for railway maintenance tasks.

Determining the error producing conditions in marine engineering maintenance and operations through HFACS-MMO

The error producing condition (EPC) values are decisive parameters of human error assessment and reduction technique (HEART). In order to conduct more effective and accurate safety analysis, the EPC values should be customized in accordance with the key aspects of the relevant disciplines. Since the maintenance and operational tasks in the ship engine room involve a vast number of particular actions, this study extends the current shipboard operations human reliability analysis (SOHRA) method by proposing marine engineering maintenance and operations specific error producing conditions (mmo-EPCs) via ship accident data. Moreover, the original human factor analysis and classification system for marine engineering operations (HFACS-MMO) is identified. By means of HFACS-MMO, marine maintenance and operations human reliability analysis (MMOHRA) is proposed. In the MMOHRA, the values of EPC5, EPC8, EPC12, EPC14, EPC16, EPC21, EPC32, EPC33 and EPC35 are substantially changed when comparing with similar approaches. A case study is also provided for benchmarking the proposed method. The findings of the case study show that the HEP results can change considerably with the MMOHRA. Since the mmo-EPCs are determined through obtained data from various ship accidents, MMOHRA can be utilized in safety practices regardless of ship types specifically in marine engineering field.

Classification and quantification of human error in air traffic control: a case study in an airport control tower

This study aims at exploring human error in an airport control tower through the technique for the retrospective and predictive analysis of cognitive error (TRACEr) and the controller action reliability assessment (CARA) method. Despite the presence of automated safety nets, air traffic control (ATC) is heavily dependent upon the capabilities of humans. A number of ATC-relevant accidents were characterized by human errors. The data related to error dimensions were collected through interview and direct observation. Then, human error probability and error-producing conditions were evaluated by the CARA method. The results showed that selection and quality, memory, distraction/preoccupation, and traffic and airspace have the highest percentage error rates. Furthermore, the results indicated that the highest probability of error was associated with emergency situation management. This study is the first research to classify and quantify human errors using the TRACEr and the CARA method to evaluate controller error in ATC.

Flight safety assessment based on an integrated human reliability quantification approach.

Human error is an important risk factor for flight safety. Although the human error assessment and reduction technique (HEART) is an available tool for human reliability derivation, it has not been applied in flight safety assessment. The traditional HEART suffers from imprecise calculation of the assessed proportion of affect (APOA) because it heavily depends on a single expert’s judgment. It also fails to provide remedial measures for flight safety problems. To overcome these defects of the HEART, this study proposes an integrated human error quantification approach that uses the improved analytic hierarchy process method to determine the APOA values. Then, these values are fused to the HEART method to derive the human error probability. A certain flight task is completed to assess human reliability. The results demonstrate that the proposed method is a reasonable and feasible tool for quantifying human error probability and assessing flight safety in the aircraft manipulation process. In addition, the critical error-producing conditions influencing flight safety are identified, and improvement measures for high-error-rate operations are provided. The proposed method is useful for reducing the possibility of human error and enhancing flight safety levels in aircraft operation processes.

An advanced human error assessment approach: HEART and AV‐DEMATEL

AbstractHuman error assessment and reduction technique (HEART) is one of the most commonly used human error assessment approaches which computes human error probability (HEP) to prioritize errors related to human actions. HEART is a powerful tool considering error producing conditions (EPCs) which increase the HEP for generalized task versions named as generic task types (GTTs). HEART can give a solution including prevention of human‐related errors (HREs) and reduction of the HREs’ impacts via implementing additional controls. However, it has many shortcomings for real‐life error assessments. In this context, this study aims to improve effective usage of HEART through an advanced version of decision‐making trial and evaluation laboratory (AV‐DEMATEL). The reason to perform AV‐DEMATEL is to show the complex effect relations between main tasks (MTs), subtasks (STs), and EPCs in a process. For this aim, an integrated effect relation matrix is proposed for DEMATEL and importance weights of MTs, STs, and EPCs are computed based on this matrix. In addition, not only HREs are considered but also machine‐related errors (MREs) are taken into account to make error assessment for the process. The proposed approach also provides flexibility to categorize STs in different GTTs. Finally, a new term “process error probability” including HREs’ probabilities and MREs’ probabilities is recommended to compute error probability in an integrated manner for the process. To utilize the proposed approach, an example of a steam boiler daily control process is given.

Application of human reliability analysis to repair & maintenance operations on-board ships: The case of HFO purifier overhauling

ABSTRACT Maintenance and repair at the operational level is a critical function to comply with the standard of competence for officers in charge of an engineering watch in a manned/unmanned ship engine room. The human factor in ship maintenance especially deals with fulfilling of the required competency about dismantling, inspecting, repairing and reassembling equipment in accordance with manuals and good practice stated in STCW Code, as amended in 2010. This study extends the shipboard operation human reliability analysis (SOHRA) through the marine auxiliary machinery maintenance operations. The approach involves the determined marine-specific error producing conditions (EPC) and generic task type parameters to quantify generic error probability (GEP) values along with the subtasks of the maintenance operation. To acquire the EPC and GEP values, the real case study on heavy fuel oil (HFO) purifier overhauling considers different operating conditions on-board selected five ships in a dry bulk fleet. The case study shows that human error probability (HEP) values of tasks in maintenance operation change in regard to ship operating conditions. Consequently, the study contributes to the marine engineering field in improving the human factor in ship repair and maintenance operations. This approach is further extended by incorporating maintenance 4.0 requirements into ship operating environment to conduct scenario-based task analysis.