Human Error Analysis Research Articles

An analysis of adverse events and human error associated with the imaging of patients at a major trauma centre in South Africa.

There is growing realisation that human error contributes significantly to morbidity and mortality in modern healthcare. Anumber of taxonomies and classification systems have been developed in an attempt to categorise errors and quantify their impact. To record and identify adverse events and errors as they impacted on acute trauma patients undergoing a computed tomography (CT) scan, and then quantify the effect this had on the individual patients. It is hoped that these data will provide evidence to develop error prevention programmes designed to reduce the incidence of human error. The trauma database was interrogated for the period December 2012 - April 2017. All patients aged >18 years who underwent a CT scan for blunt trauma were included. All recorded morbidity for these patients was reviewed. During the period under review, a total of 1 566 patients required a CT scan at our institution following blunt trauma. Of these, 192 (12.3%, 134 male and 58 female) experienced an error related to the process of undergoing a CT scan. Of 755 patients who underwent a CT scan with intravenous contrast, detailed results were available for 312, and of these 46 (14.7%) had an acute deterioration in renal function. According to Chang's taxonomy, physical harm occurred as follows: grade I n=6, grade II n=62, grade III n=45, grade IV n=11, grade V n=27, grade VI n=21, grade VII n=15, grade VIII n=3 and grade IX n=2. Adverse events were performing an unnecessary scan (n=24), omitting an indicated scan (n=23), performing the scan incorrectly (n=8), scanning the wrong body part (n=7), equipment failure (n=18), omitting treatment following the scan (n=6), incorrect interpretation of the scan (n=65), deterioration during the scan (n=6) and others (n=35). The setting for the error was the ward (n=19), the radiology suite (n=126), the emergency department (n=45) and the operating theatre (n=2). The staff responsible for the adverse events were medical (n=155), nursing (n=4) and radiology staff (n=15). There were 67errors of commission and 125 errors of omission. The primary cause was a planning problem in 78 cases and an execution problem in 114. Errors and adverse events related to obtaining a CT scan following blunt polytrauma are not uncommon and may impact significantly on the patient. Communication is essential to eliminate errors related to performing the wrong type of scan. The commonest errors relate to misinterpretation of the scan.

Análisis cognitivos de carga mental e identificación del error humano para mejorar la experiencia de usuario

Las personas, en ocasiones, pueden sentirse abrumadas al tratar de interactuar con la tecnología moderna. Ciertos productos de uso cotidiano cuentan con diseños deficientes, por lo que pueden provocar una experiencia insatisfactoria y frustración, produciéndose con ellas una carga mental en el usuario que pueda inducir al error. El uso de técnicas para la identificación del error humano y de la evaluación de carga mental en el diseño y evaluación de productos, puede proporcionar información relevante y útil, para mejorar la experiencia del usuario. El objetivo de este trabajo fue proponer una metodología para integrar la evaluación de carga mental y el análisis del error humano en el diseño de productos. Se evaluó la carga mental mediante la técnica perfil de carga de trabajo, Workload Profile (WP), y se utilizó una metodología de identificación del error humano, Task Analysis For Error Identification (TAFEI), en la configuración de audífonos inalámbricos. Diez usuarios participaron voluntariamente en el estudio; sus experiencias en el uso de los audífonos fueron videograbadas. Se utilizaron formularios para evaluar carga mental, y se identificaron errores humanos durante la etapa de configuración de los audífonos con el teléfono móvil. El método utilizado en este trabajo ofrece una mayor sensibilidad en la evaluación de la carga mental y permite determinar los recursos de atención que más se utilizan en la realización de la tarea. Se detectaron dos oportunidades de rediseño, una relacionada con la señal lumínica, y la otra para la identificación de las puntas para cada oído. A partir de los resultados de la aplicación de la metodología propuesta en este estudio, los diseñadores pueden mejorar la interacción entre las personas y los productos.

A Qualitative Analysis of Human Error During the DIBH Procedure

IntroductionThis quality assurance study analyzed human errors that occurred during the radiation treatment delivery of the deep-inspiration breath hold (DIBH) technique at a tertiary cancer centre. The intention is to recommend solutions and system changes that have the potential to decrease the frequency of errors based on human factors principles. MethodsEighty-two incident reports from January 2012 to July 2017 were retrieved and analysed to determine theme bins of performance-influencing factors contributing to the error. Performance-influencing factors were generated from the incident reports and from focus group discussions with volunteer radiation therapists in the department. Potential solutions to mitigate the error were sought from incident reports, focus groups, literature search, and an interview with a human factors specialist. The solutions were ranked based on the hierarchy of effectiveness, and recommendations were classified using a priority matrix. ResultsEighty-nine percent of the errors captured in the incident reports were defined as a slip or lapse error type, and 11% of the remaining errors were defined as a mistake error type. Treatment-related problem solving and distractions/interruptions were the highest frequency causative factors that contributed to the observed error. Potential solutions that were suggested across sources included implementing a forcing function, such as the real-time position management system, adding reminders, such as a console sign-off, and updating the current task checklist. DiscussionThe potential solutions generated were summarized into four recommendations that have varying degrees of association with known causative factors. The four recommendations include investing in (1) a forcing function, (2) updating/reinforcing the procedure, (3) managing workload, and (4) updating the checklist. A priority matrix was used to assess both potential effectiveness and cost/effort of each recommendation. Ideally, recommendation 1 would be implemented; however, it is understood that there would be an associated cost. It is therefore suggested that recommendations 2, 3, and 4 are implemented together to increase the effectiveness of the intervention until recommendation 1 can be achieved. ConclusionThis qualitative study introduced a method that analyzed human factors in a specialized procedure used in the treatment of a specific population of patients with cancer. Recommendations were formulated and proposed to the radiation therapy department in hopes of potentially decreasing the frequency of this specific error in the future.

Treatment of human and organizational factors for multi-unit HRA: Application of SPAR-H method

Human reliability analysis (HRA) refers to a method for evaluating human errors and providing human error probabilities for application in probabilistic safety assessments (PSAs). Since the Fukushima accident, HRA has been considered an important issue in multi-unit PSAs. However, existing HRA methods generally focus on the analysis of human errors in single-unit PSAs, while HRA methods for the multi-unit PSA have not been well established. If an accident occurs that influences more than one unit, the human and organizational factors due to the formation of accident management organizations, the use of shared or mobile equipment, and the influence of a severe accident on another unit may have a crucial impact on the plant safety and the result of the PSA. Therefore, issues have been raised regarding the analysis and evaluation of the human and organizational factors in a multi-unit HRA.This study aims to develop an approach to treat the human and organizational factors for a multi-unit HRA on the basis of Standardized Plant Analysis Risk HRA (SPAR-H) method. This approach is applicable to level 1, 2, and 3 PSA. Firstly, six multi-unit task types were considered based on a previous study that identified and categorized human and organizational factors for multi-unit HRA. Secondly, a task analysis considering HRA event tree analysis, suggested by Technique for Human Error Rate Prediction (THERP), and a timeline analysis were performed for six multi-unit task types. Thirdly, to identify the challenges of the existing SPAR-H method for multi-unit task types, an evaluation of the applicability of the existing SPAR-H method was conducted by qualitative and quantitative analyses. In addition, the dependence assessment approach suggested in the existing SPAR-H was also evaluated in terms of multi-unit situations. Lastly, by treating the multi-unit challenges identified in the previous section, methods to 1) analyze six multi-unit task types and 2) evaluate the dependence between the operator actions were suggested with some examples. The study is expected to contribute to the estimation and calculation of human error probabilities for applications in multi-unit PSAs.

Human error analysis on production process of door products with SHERPA and HEART method

Company engaged in manufacturing that produces door leaf. This company is a factory whose work is done semi-automatic that man has a role in sustainability of production process. A problem in the production process of the door where there is a defect which is the main cause factor is human (human error). This problem can be solved by performing human error analysis with SHERPA and HEART methods. Systematic Human Error Reduction and Prediction Approach (SHERPA) analyzed occurrence of human error by using a hierarchical task input basic level while Human Error Assessment and Reduction Technique (HEART) part the calculation reliability is defined as how much operator made a mistake in the task that should be done. From the research found the results obtained SHERPA methods work items that have a critical level that is 2.2 (too long), 8.2 ( too long), and 11.2 (the flat part of the door yet). Based on the research with HEART method obtained the total value of HEP is 0.4986 where the value is influenced by EPCs factor. Based on the data obtained then possible troubleshooting for item 2.2, 8.2, and 11.2 issues is by improving operator skill.

FMARI Application in Innovation and IT for Accidents' Investigation

FMARI Database Application is designed to help safety auditor in investigating work accidents and as routine monitoring in regular companies reports to safety board organization in Indonesia (BPJS for Employee) with the main goal is to reach zero accident. FMARI stands for Faktor Manusia dan Analisis Resiko Industri (application: Human Factors and Industrial Risk Analysis) is following HFACS method and make a modification from HFIX in Indonesian terms. The Human Factors Analysis and Classification System (HFACS) is a system-safety model that effectively bridges the gap between human error theory and applied human error analysis. The Human Factors Intervention matriX (HFIX) is an innovative tool for mapping intervention strategies onto the specific forms of human error identified in the HFACS.

Path Analysis of Causal Factors Influencing Marine Traffic Accident via Structural Equation Numerical Modeling

Many causal factors to marine traffic accidents (MTAs) influence each other and have associated effects. It is necessary to quantify the correlation path mode of these factors to improve accident prevention measures and their effects. In the application of human factors to accident mechanisms, the complex structural chains on causes to MTA systems were analyzed by combining the human failure analysis and classification system (HFACS) with theoretical structural equation modeling (SEM). First, the accident causation model was established as a human error analysis classification in sight of a MTA, and the constituent elements of the causes of the accident were conducted. Second, a hypothetical model of human factors classification was proposed by applying the practice of the structural model. Third, with the data resources from ship accident cases, this hypothetical model was discussed and simulated, and as a result, the relationship path dependency mode between the latent independent variable of the accident was quantitatively analyzed based on the observed dependent variable of human behavior. Application examples show that relationships in the HFACS are verified and in line with the path developing mode, and resource management factors have a pronounced influence and a strong relevance to the causal chain of the accidents. Appropriate algorithms for the theoretical model can be used to numerically understand the safety performance of marine traffic systems under different parameters through mathematical analysis. Hierarchical assumptions in the HFACS model are quantitatively verified.

Evaluation of human error contribution to oil tanker collision using fault tree analysis and modified fuzzy Bayesian Network based CREAM

Human error is a significant factor triggering oil tanker collisions. Drawbacks jeopardizing the effectiveness of human error analysis from traditional studies are spotted. This study proposes a risk assessment of human error contribution to oil tanker collision. The research evaluates the collision probability of oil tanker using a Fault Tree Analysis (FTA) structure under which a modified Fuzzy Bayesian Network based Cognitive Reliability Error Analysis Method (CREAM) is developed to conduct human error assessment. 39 specialists with substantial seagoing experience are invited to provide expert judgement for oil tanker navigation particularly in vicinity of the Taiwanese waters. Different from traditional studies, the proposed methodology provides a higher degree of result distinguishability meanwhile contemplating the weights and quantitative effects caused by the ambient elements without the loss of information from the experts. It is concluded that lack of Bridge Resource Management Communication, lack of Communication between Ships, Fatigue and Collision Regulation Violations are the elements with higher occurrence rates and would also have great potential contributing to oil tanker collision once the likelihoods deteriorated. In addition, Number of Simultaneous Goals in the bridge is the Common Performance Condition (CPC) that significantly causes the occurrence of all the human error basic events.

A taxonomy of performance shaping factors for human reliability analysis in industrial maintenance

Purpose: Human factors play an inevitable role in maintenance activities, and the occurrence of Human Errors (HEs) affects system reliability and safety, equipment performance and economic results. The high HE rate increased researchers’ attention towards Human Reliability Analysis (HRA) and HE assessment approaches. In these approaches, various environmental and individual factors influence the performance of maintenance operators affecting Human Error Probability (HEP) with a consequent variability in the success of intervention. However, a deep analysis of such factors in the maintenance field, often called Performance Shaping Factors (PSFs), is still missing. This has led the authors to systematically evaluate the literature on Human Error in Maintenance (HEM) and on the PSFs, in order to provide a shared PSF taxonomy.Design/methodology/approach: A Systematic Literature Review (SLR) was conducted to identify and select peer-reviewed papers that provided evidence on the relationship between maintenance activities and human performance. The obtained results provided a wide overview in the field of interest, shedding light on three main research areas of investigation: methodologies for human error analysis in maintenance, performance shaping factors and maintenance error consequences. In particular, papers belonging to the area of PSFs were analysed in-depth in order to identify and classify the PSFs, with the aim of achieving the PSF taxonomy for maintenance activities. The effects of each PSF on human reliability were defined and detailed.Findings: A total of 63 studies were selected and then analysed through a systematic methodology. 46% of these studies presented a qualitative/quantitative assessment of PSFs through application in different maintenance activities. Starting from the findings of the aforementioned papers, a PSF taxonomy specific for maintenance activities was proposed. This taxonomy represents an important contribution for researchers and practitioners towards the improvement of HRA methods and their applications in industrial maintenance.Originality/value: The analysis outlines the relevance of considering HEM because different error types occur during the maintenance process with non-negligible effects on the system. Despite a growing interest in HE assessment in maintenance, a deep analysis of PSFs in this field and a shared PSF taxonomy are missing. This paper fills the gap in the literature with the creation of a PSF taxonomy in industrial maintenance. The proposed taxonomy is a valuable contribution for growing the awareness of researchers and practitioners about factors influencing maintainers’ performance.

A hybrid human and organisational analysis method for railway accidents based on STAMP-HFACS and human information processing

Safety is a constant priority for the railway industry and there are numerous hazards in and around the rail system which may result in damage to train and environment, human injury and fatalities. Low levels of human and organisational performance have been shown to be a prime cause of railway accidents and a number of accident models and methods have been developed in order to probe deeper into the role played by organisational factors in accident causation. The Systems–Theoretical Accident Modelling and Processes (STAMP) method for example, represents a promising systematic and systemic way of examining sociotechnical systems such as the railway. Another method, the Human Factors Analysis and Classification System (HFACS), based upon Reason's model of human error in an organisational context, has also proved popular as a human factors accident analysis framework. However, human factors elements are still somewhat limited and under-specified and these managerial and social issues within an organisation are simply regarded as sources of failure in the control constraints of STAMP. HFACS likewise, categorises accident data rather than analysing it in more depth. In this context, a hybrid human and organisational analysis method based on HFACS-STAMP (HFACS-STAMP method for railway accidents, HS-RAs) is proposed to identify and analyse human and organisational factors involved in railway accidents. Using the categories of human errors derived from HFACS and the structured systematic analysis process of STAMP, the HS-RAs method provides a mechanism by which active failures can promulgate across organisations and give a systemic analysis of human error in accidents. Combined with human information processing, the HS-RAs method gives a detailed causal analysis of human errors from receiving information to implement control actions. At last, the HS-RAs method is demonstrated using a case study of the 2011 Yong-Wen railway collision. A number of prominent accident causes of human factors are revealed and necessary countermeasures are proposed to avoid the recurrence of similar accidents. The HFACS-STAMP hybrid method has several advantages and can contribute to railway safety by providing a detailed analysis of the role of human error in railway accidents.

The investigation of human error analysis in adverse drug events in Taiwan—From the perspective of causality assessment

AbstractThe goal of this study is to analyze the adverse drug events, using the human factors analysis and classification system (HFACS), and to identify the causality between the error factors. Twenty‐five cases related to medication errors were identified. Seven experts were recruited to form an expert team for this study. The HFACS and root cause analysis were utilized in this study to identify the causal factors and the root causes of medication errors. The frequency of each error factor was recorded, and odds ratio was applied to measure the strength of the relevance of the error factors between adjacent levels of the HFACS. The results showed that 222 errors were identified and the main pathways and subpathways of medication errors from Level 4 to Level 1 of the HFACS were arranged in this study. According to the results, the deficiencies in organizations could be the main reasons causing adverse drug events in Taiwan.

Considerations for the Development of a Human Reliability Analysis (HRA) Model Oriented to the Maintenance Work Safety

Within the field of the Industrial Technology, maintenance of facilities and equipment is considered a key issue for the productive activity, although throughout history, engineering research efforts have been oriented mostly toward design than the maintenance of the facilities. From this point of view, an important effort has been made, especially in the last two decades, to improve safety aspects related to Industrial Maintenance, from two standpoints: on one hand, maintenance guarantees a safer operation activity, and in the other hand, the accident rate in maintenance activity had to be minimized.It is commonly accepted in specialized literature that Human Error may be at the origin of around 70% of industrial accidents, and this generates a special interest in the Human Error Analysis. Certainly Human Reliability Analysis (HRA) Techniques are not anything new, we can say that since early eighties of the 20th century, a lot of methods and models have been developed, many of them with a general character, but very focused on specific fields, such as nuclear or aeronautical industry, it is mainly due to the fact that accidents in these sectors could potentially have severe consequences.The main purpose of this paper is to establish basis for a farther development of a Human Reliability Analysis Model for industrial maintenance. This works has a cross-sectoral approach, including different fields and activities, whose object is to minimize accident risks and incidents in their earliest phase. For this, an exhaustive review of the available models of Human Reliability Analysis Models in the specialized literature from the two generations developed to date has been carried out, those models has been classified according to their characteristics and their usefulness, in order to detect which aspects are appropriate to the maintenance activity. The aim is to proceed towards a generalized Human Reliability Analysis model that can be used for the evaluation of maintenance work through a wide range of activities.

Human Error Analysis to Customer Communication for Realtor Service Quality and The Implications to Personnel Training

The quality of customer service, especially in realtor service, is centered at customer contact and communication. Because of the inherent diversity in customer demands and the consequent service procedures, the analysis of the service errors or failures in customer communication from the human factors perspective may provide an effective alternative toward the quality improvement in customer services beside the SERVQUAL-based approaches. This study investigates the pattern of communication errors occurred in realtor service operations and the associated latent failures for the implications to personnel training for quality services. Customers with realtor service experiences were interviewed to provide detailed description regarding the communication error(s) encountered while dealt with realtors in the past. Among all types of communication errors in realtor services, it is found that the active errors on providing insufficient or incorrect information regarding the target real estates and the communication errors involved in the price negotiation processes are prominent. The analysis of latent failures further reveals the importance on process transparency and the role of the information systems. For modern C2C service settings like realtors, it is recommended that a double customer framework with the emphasis of support information system should be considered for communication error analysis. Implications and suggestions to personnel training for quality improvement in realtor customer service, in terms of system knowledge and process management, are discussed in details.

A novel qEEG measure of teamwork for human error analysis: An EEG hyperscanning study

Abstract In this paper, we propose a novel method to quantify the neural synchronization between subjects in the collaborative process through electroencephalogram (EEG) hyperscanning. We hypothesized that the neural synchronization in EEGs will increase when the communication of the operators is smooth and the teamwork is better. We quantified the EEG signal for multiple subjects using a representative EEG quantification method, and studied the changes in brain activity occurring during collaboration. The proposed method quantifies neural synchronization between subjects through bispectral analysis. We found that phase synchronization between EEGs of multi subjects increased significantly during the periods of collaborative work. Traditional methods for a human error analysis used a retrospective analysis, and most of them were analyzed for an unspecified majority. However, the proposed method is able to perform the real-time monitoring of human error and can directly analyze and evaluate specific groups.

인과순환지도(CLD) 작성을 통한 철도사고 인적 오류 분석

인과순환지도(CLD) 작성을 통한 철도사고 인적 오류 분석

Identification and Analysis of Human Errors in the Maintenance Activities of Molecular Sieve Replacement in the Dehydration Unit of Gas Refineries Using Predictive Human Error Analysis Method

Identification and Analysis of Human Errors in the Maintenance Activities of Molecular Sieve Replacement in the Dehydration Unit of Gas Refineries Using Predictive Human Error Analysis Method

Evaluation and analysis of human error in the use of equipment using PUEA technique and fuzzy logic

Evaluation and analysis of human error in the use of equipment using PUEA technique and fuzzy logic

New Approach of Assessing Human Errors in Railways

Abstract Inspired in particular by the works of Reason and Rasmussen and supported by application examples from the field of railway safety, the human error analysis approach proposed to improve the level of safety of rail transport systems involves three complementary levels. The first level of contextual analysis (before the accident) makes it possible to study the various factors favoring the production of the human error at the origin of the accident. The second cognitive level (during the accident) aims to identify the errors related to the human cognitive process involved in a given situation of insecurity. The third level of behavioral analysis (after the accident) focuses on evaluating the consequences of a wrong action in terms of damage to humans, the environment and the human-machine system. This article proposes an original methodological framework for the analysis, classification and evaluation of human errors involved in the safety of rail transport. The key factors taken into account in the analysis concern not only the inappropriate behavior of human operators involved in railway safety and risk management, but also the technical failures of the transport system and the operating and environmental conditions.

Implementation of deep neural networks to count dopamine neurons in substantia nigra.

Unbiased estimates of neuron numbers within substantia nigra are crucial for experimental Parkinson's disease models and gene‐function studies. Unbiased stereological counting techniques with optical fractionation are successfully implemented, but are extremely laborious and time‐consuming. The development of neural networks and deep learning has opened a new way to teach computers to count neurons. Implementation of a programming paradigm enables a computer to learn from the data and development of an automated cell counting method. The advantages of computerized counting are reproducibility, elimination of human error and fast high‐capacity analysis. We implemented whole‐slide digital imaging and deep convolutional neural networks (CNN) to count substantia nigra dopamine neurons. We compared the results of the developed method against independent manual counting by human observers and validated the CNN algorithm against previously published data in rats and mice, where tyrosine hydroxylase (TH)‐immunoreactive neurons were counted using unbiased stereology. The developed CNN algorithm and fully cloud‐embedded Aiforia™ platform provide robust and fast analysis of dopamine neurons in rat and mouse substantia nigra.

WITHDRAWN: A Hybrid Framework of Human Error Analysis for shale gas fracturing operation based on HAZOP and Fuzzy VIKOR

WITHDRAWN: A Hybrid Framework of Human Error Analysis for shale gas fracturing operation based on HAZOP and Fuzzy VIKOR