Human Error Classification Research Articles

Infections in Inborn Errors of Immunity with Combined Immune Deficiency: A Review.

Enhanced susceptibility to microbes, often resulting in severe, intractable and frequent infections due to usually innocuous organisms at uncommon sites, is the most striking feature in individuals with an inborn error of immunity. In this narrative review, based on the International Union of Immunological Societies' 2022 (IUIS 2022) Update on phenotypic classification of human inborn errors of immunity, the focus is on commonly encountered Combined Immunodeficiency Disorders (CIDs) with susceptibility to infections. Combined immune deficiency disorders are usually commensurate with survival beyond infancy unlike Severe Combined Immune Deficiency (SCID) and are often associated with clinical features of a syndromic nature. Defective humoral and cellular immune responses result in susceptibility to a broad range of microbial infections. Although disease onset is usually in early childhood, mild defects may present in late childhood or even in adulthood. A precise diagnosis is imperative not only for determining management strategies, but also for providing accurate genetic counseling, including prenatal diagnosis, and also in deciding empiric treatment of infections upfront before investigation reports are available.

Research of maritime accidents based on HFACS framework

The paper deals with the identification and evaluation of factors affecting the origin of maritime accidents. Based on the analysis of investigation reports, human is the dominant causal factor. Authors provide comprehensive research of human failure in maritime transport; investigate and classify the errors, using the HFACS method. HFACS deals with the classification of human error based on human failure at various levels (organization, management, operation). At the end of the paper, measures to increase safety in critical areas are provided.

Interpretation of the classification of human inborn error of immunity (2022 edition)

J Clin Immunol于近期发布了最新版免疫出生错误分类，新分类包含485种免疫出生错误，较2019版新增55种基因缺陷及1种由自身抗体所致免疫出生错误拟表型。本文对2022版分类的调整进行解读，以促进临床医生对免疫出生错误的认识更新。.

Assessing naming errors using an automated machine learning approach.

After left hemisphere stroke, 20%-50% of people experience language deficits, including difficulties in naming. Naming errors that are semantically related to the intended target (e.g., producing "violin" for picture HARP) indicate a potential impairment in accessing knowledge of word forms and their meanings. Understanding the cause of naming impairments is crucial to better modeling of language production as well as for tailoring individualized rehabilitation. However, evaluation of naming errors is typically by subjective and laborious dichotomous classification. As a result, these evaluations do not capture the degree of semantic similarity and are susceptible to lower interrater reliability because of subjectivity. We investigated whether a computational linguistic measure using word2vec (Mikolov, Chen, et al., 2013) addressed these limitations by evaluating errors during object naming in a group of patients during the acute stage of a left-hemisphere stroke (N = 105). Pearson correlations demonstrated excellent convergent validity of word2vec's semantically related estimates of naming errors and independent tests of access to lexical-semantic knowledge (p < .0001). Further, multiple regression analysis showed word2vec's semantically related estimates were significantly better than human error classification at predicting performance on tests of lexical-semantic knowledge. Useful to both theorists and clinicians, our word2vec-based method provides an automated, continuous, and objective psychometric measure of access to lexical-semantic knowledge during naming. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Classification and Quantification of Human Error in Manufacturing: A Case Study in Complex Manual Assembly

Manual assembly operations are sensitive to human errors that can diminish the quality of final products. The paper shows an application of human reliability analysis in a realistic manufacturing context to identify where and why manual assembly errors occur. The techniques SHERPA and HEART were used to perform the analysis of human reliability. Three critical tasks were selected for analysis based on quality records: (1) installation of three types of brackets using fasteners, (2) fixation of a data cable to the assembly structure using cushioned loop clamps and (3) installation of cap covers to protect inlets. The identified error modes with SHERPA were: 36 action errors, nine selection errors, eight information retrieval errors and six checking errors. According to HEART, the highest human error probabilities were associated with assembly parts sensitive to geometry-related errors (brackets and cushioned loop clamps). The study showed that perceptually engaging assembly instructions seem to offer the highest potential for error reduction and performance improvement. Other identified areas of action were the improvement of the inspection process and workers’ provision with better tracking and better feedback. Implementation of assembly guidance systems could potentially benefit worker’s performance and decrease assembly errors.

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.

Interpretation of the classification of human inborn errors of immunity (2019 edition)

J Clin lmmunol近期更新了"原发性免疫缺陷病分类" 。2019版分类共纳入430种原发性免疫缺陷病，含64种新增基因缺陷。本文对新增疾病和分类调整进行描述和解读，以促进临床医生对儿童先天性免疫疾病表型及潜在分子免疫学机制的理解和认识。.

Can machines classify picture naming errors? Evidence from Dell’s model

Can machines classify picture naming errors? Evidence from Dell’s model

Human Factors and Errors in Security Aviation: An Ergonomic Perspective

In a previous study we have identified the incidence of human factors and errors in the security screening process, concerning the Brazilian civil aviation. This finding led to the analysis of labor activity in the security checkpoint from an ergonomic perspective. The objective of this study was to evaluate the various labor conditions in the security checkpoints of Brazilian airports, in order to promote safer and higher performance of the AVSEC professionals, as well as the security equipment. For this purpose, we analyzed 14 of 60 questions of a self-observation questionnaire based on the theory of Generic Error–Modelling System (GEMS) and the four themes about human factors recommended by the International Civil Aviation Organization (ICAO). The questionnaire was responded to by 602 AVSEC professionals who worked in the security checkpoints of 18 Brazilian airports. Our analysis focused on the preponderant indexes of each question, taking into account the sequential arrangement in which the questions were displaced in the questionnaire and the classification of human factors and errors.

Methods for evaluating the human factors influence on the safety in transport

Safety is one of the main conditions for the functioning of the transport. The most attention is paid to the technical aspects of transport safety. However, accidents in transport are caused by many different factors and these primarily are: human factors, organizational factors, technical factors and environmental factors. Statistical data indicate that the main cause of accidents and disasters in transport are human errors. Thus, the elimination or reduction of their number could significantly improve the safety in transport. In the article the issues of human reliability in the context of ensuring safety are discussed. Additionally, the classification of human errors was given and an analysis of the causes of these errors was conducted. The main emphasis was put to present and evaluate of the selected methods of qualitative and quantitative Human Reliability Analysis (HRA), such as: THERP, ASEP, HEART, SPAR-H, ATHEANA, CREAM.

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.

A new organization-oriented technique of human error analysis in digital NPPs: Model and classification framework

In nuclear power plants (NPPs), man-machine interfaces (MMIs) are being transformed from conventional panels to computer workstations. Therefore, operators’ behavior and performance shaping factors (PSFs) in modern main control rooms (MCRs) differs from what they do in a conventional one. New human error, error mechanism and PSFs etc. are introduced into digital control systems (DCS). The conventional human error analysis (HEA) methods cannot meet new requirements. So it is important to establish a new technique of HEA for investigating human failure events in digital NPPs. In order to investigate and analyze human errors and to trace organizational root causes in digital NPPs, an Organization-oriented Technique of Human Error Analysis (OTHEA) was established. At this first paper, an organization-oriented conceptual model of HEA was established. Furthermore, the classification framework of HEA was developed based on the established conceptual model of HEA, including classification of human error, PSFs, psychological error mechanisms (PEMs), error recovery failures (ERFs) and safety barriers. It provides a theoretical guidance and practical support for the investigation of human failure events in digital NPPs.

Waste Reduction on Personality Action System of Daily/Piece-rated Process

PT. X is a multinational company that works in the consumer goods sector. PT. X has many departments and one of them is the Human Resources (HR) Department whose role is to maintain all of the employees under PT. X. The HR Department also has some subfunctions. One of them is HR Services. Employees that work with PT. X are divided into two categories: monthly employees and daily/piece-rated employees. One of the processes that HR Services provides for the daily/piece-rated employees is ePAF daily/piece-rated, which can be monitored by a verification process. If an ePAF is not verified, then it is possible for the ePAF process to be denied and that is a waste that can cause over-processing for HR Services. There were 750 ePAF denials in 2016 with an average processing time of 1.78 hours needed to resubmit each process. An analysis was done using a Pareto Chart and Fishbone Diagram to reduce the time wasted and to look for countermeasures for each of the root causes. The Pareto Chart shows three major problems occurring. These are: errors in the closing date, errors in the transaction date, and errors in the supporting document. The root causes of the problem come from human error, method errors, and system classification errors. The solutions proposed are expected to reduce the numbers of ePAF denials by around 51.2%.

IUPAC/CITAC Guide: Classification, modeling and quantification of human errors in a chemical analytical laboratory (IUPAC Technical Report)

Abstract The classification, modeling, and quantification of human errors in routine chemical analysis are described. Classifications include commission errors (mistakes and violations) and omission errors (lapses and slips) in different scenarios at different steps of the chemical analysis. A Swiss cheese model is used to characterize error interaction with a laboratory quality system. The quantification of human errors in chemical analysis, based on expert judgments, i.e. on the expert(s) knowledge and experience, is applied. A Monte Carlo simulation of the expert judgments was used to determine the distributions of the error quantification scores (scores of likelihood and severity, and scores of effectiveness of a laboratory quality system against the errors). Residual risk of human error after the error reduction by the laboratory quality system and consequences of this risk for quality and measurement uncertainty of chemical analytical results are discussed. Examples are provided using expert judgments on human errors in pH measurement of groundwater, multi-residue analysis of pesticides in fruits and vegetables, and elemental analysis of geological samples by inductively coupled plasma mass spectrometry.

Nursing Errors in Intensive Care Unit by Human Error Identification in Systems Tool: A Case Study

Background: Although health services are designed and implemented to improve human health, the errors in health services are a very common phenomenon and even sometimes fatal in this field. Medical errors and their cost are global issues with serious consequences for the patients’ community that are preventable and require serious attention. Objectives: The current study aimed to identify possible nursing errors applying human error identification in systems tool (HEIST) in the intensive care units (ICUs) of hospitals. Patients and Methods: This descriptive research was conducted in the intensive care unit of a hospital in Khuzestan province in 2013. Data were collected through observation and interview by nine nurses in this section in a period of four months. Human error classification was based on Rose and Rose and Swain and Guttmann models. According to HEIST work sheets the guide questions were answered and error causes were identified after the determination of the type of errors. Results: In total 527 errors were detected. The performing operation on the wrong path had the highest frequency which was 150, and the second rate with a frequency of 136 was doing the tasks later than the deadline. Management causes with a frequency of 451 were the first rank among identified errors. Errors mostly occurred in the system observation stage and among the performance shaping factors (PSFs), time was the most influencing factor in occurrence of human errors. Conclusions: Finally, in order to prevent the occurrence and reduce the consequences of identified errors the following suggestions were proposed : appropriate training courses, applying work guidelines and monitoring their implementation, increasing the number of work shifts, hiring professional workforce, equipping work space with appropriate facilities and equipment.

The Error Analysis Approach for the Assessment of Automatic Translation

The Error Analysis Approach has been widely used to improve the learning of foreign languages and to evaluate human translation, but Error Analysis can also be applied to automatic translation, as shown by different scholars (see M. Koponen 2010). This article reports on a European project, called Organic.Lingua, which tries to demonstrate the potential of a multilingual web portal for Sustainable Agricultural & Environmental Education by using a machine translator in order to make available all materials in different languages. The selected texts are taken from the corpus compiled for this project. The source texts are translated into various target languages using computer translation tools. We have chosen some of these texts in English and their translations into Spanish, and have applied a classification of human errors to check whether the errors generated by machines could be avoided if we can ‘teach’ the machines or to what extent they are specific to computer tools or human beings. Some metric systems, such as BLEU (Bilingual Evaluation Understudy), are being employed to evaluate the quality of the target translation. However, our role as philologers is to concentrate on more specific linguistic features. By defining the different types of errors and by trying to establish a scale of gravity, we intend to determine the quality of the automatic translations. After analysing the data, we will propose linguistic measures for improvement that would be implemented by computer experts working on the project.

Classification of human errors in grounding and collision accidents using the TRACEr taxonomy

This paper applies a Human Error Identification tool called Technique for the Retrospective and Predictive Analysis of Cognitive Errors to the analysis of ship accidents. Grounding and collision accidents investigation reports involving sixty-four vessels published by the UK’s Maritime Accident Investigation Branch, the Transportation Safety Board of Canada and the National Transportation Safety board of the United States of America are coded and analysed using the taxonomy of the Technique for the Retrospective and Predictive Analysis of Cognitive Errors. A total of two hundred and eighty-nine errors performed by the operators are coded. The results of the codification process are analysed with the objective of identifying the main task errors, cognitive domains and the technical equipment involved in grounding and collision accidents and the factors that affect the performance of the operators. This identification is a necessary step towards safety improvements resulting from dealing with the identified problems. A discussion on the use of the taxonomy of the Technique for the Retrospective and Predictive Analysis of Cognitive Errors is provided and it is proposed to combine it with some elements of the CASMET approach to accident investigation so as to improve the applicability of the methodology to the analysis of ship accidents.

Evaluation of the Human Error Probability in Cellular Manufacturing

Referring to Gutenberg there are three elementary production factors: material, equipment and human workforce. They all have a significant influence on the product quality. Nevertheless, in machining research they have been given differing attention, depending on the focused scope. This paper presents the results of an empirical study of the human error probability (HEP) in a Cellular Manufacturing environment. First, it is shown that the influence of human work on the resulting product quality in machining so far has only been given little attention. Therefore a content analysis according to Mayring has been conducted on publications in the domains of production technology and ergonomics. Second, various schemes for the classification of human errors are presented and evaluated in terms of their applicability to human tasks in machining. Finally the design and results of an empirical study which has been conducted at the Cellular Manufacturing reference line, consisting of two lathes and four milling machines, in the Center for industrial Productivity (CiP) at TU Darmstadt are presented. Overall 2700 human-machine interactions have been observed and evaluated in terms of their influence on product quality. Results show that there is a significant influence of the human worker as three percent of these interactions incorporated a spurious action.

The use of error analysis to assess resident performance

The aim of this study was to assess validity of a human factors error assessment method for evaluating resident performance during a simulated operative procedure. Seven postgraduate year 4-5 residents had 30 minutes to complete a simulated laparoscopic ventral hernia (LVH) repair on day 1 of a national, advanced laparoscopic course. Faculty provided immediate feedback on operative errors and residents participated in a final product analysis of their repairs. Residents then received didactic and hands-on training regarding several advanced laparoscopic procedures during a lecture session and animate lab. On day 2, residents performed a nonequivalent LVH repair using a simulator. Three investigators reviewed and coded videos of the repairs using previously developed human error classification systems. Residents committed 121 total errors on day 1 compared with 146 on day 2. One of 7 residents successfully completed the LVH repair on day 1 compared with all 7 residents on day 2 (P = .001). The majority of errors (85%) committed on day 2 were technical and occurred during the last 2 steps of the procedure. There were significant differences in error type (P ≤ .001) and level (P = .019) from day 1 to day 2. The proportion of omission errors decreased from day 1 (33%) to day 2 (14%). In addition, there were more technical and commission errors on day 2. The error assessment tool was successful in categorizing performance errors, supporting known-groups validity evidence. Evaluating resident performance through error classification has great potential in facilitating our understanding of operative readiness.

Analysis and classification of human error

Analysis and classification of human error