What’s in a Name? Exploring definitions and implications of the term ‘Human Error’

Dear Editor-in-ChiefNurses spend much more time dealing with patients compared with other healthcare personnel. This group has a significant role in identification of threatening complexities of patients, removing medical errors and in keeping patients safe, on the other hand, a considerable amount of human errors is committed by nurses (1). Therefore, nurses have to face with many occupational risks compared with other jobs and may suits relating to occupational negligence (1). Human errors associated with nursing duties; in particular, medication error has been identified as a central concern for the nursing profession (1). As the result of such events, patient is hurt unwantedly and staff suffers a great deal of damage. These events often result from the change of systemic specifications in duties, group, work environment, and organization (3).The present study was a cross sectional research aimed at comparing two human error identification and assessment methods, SPAR-H and CREAM, in nursing practice at a hospital in Iran. We investigated 31 duties according to job description announced by Iran Nursing Organization and professors of nursing.In the first step, we analyzed occupational duties using Hierarchical Task Analysis (HTA) method (4). Next, different tasks are executed for each sub task. After completing CREAM and SPAR-H worksheets, we first grouped the worksheets for each duty and analyzed total error probability and error risk using SPSS-16 (Chicago, IL, USA). In order to compare the two techniques based on multivariable decision making, we used analytical hierarchy process (5).The findings of demonstrated that, the estimated human error probability by SPAR-H was higher than those estimated by CREAM, but the most critical task, taking initial measures for cardiopulmonary resuscitation until the arrival of resuscitation team, was the same in both techniques. Moreover, it was determined that both techniques have similar results in identifying and assessing critical tasks with high probability of human error (Table 1). Furthermore, there was more difference between these two techniques in ranking potential errors with low probabilities; for instance, preparing bed before arrival of patient have the rank of 27 when we used SPAR-H technique 27, while its rank using CREAM technique was determined at 10 (Table 1).Based on previous studies in this field and also eliciting domain expert knowledge, seven criteria were selected for comparing two techniques with each other (6). …

In this paper machine learning and human learning are applied jointly to optimize the training of linear regression. Human learning is exploited to label extra training data so as to resolve problems such as insufficient training and over-fitting. Considering the inevitable human errors in labeling, two machine learning algorithms are developed which optimize the selection of the extra training data and detect human errors during linear regression. The first algorithm assumes sparse human errors and implements a sparse optimization within a sequential active learning procedure. The second algorithm deals with non-sparse human errors. By exploiting the IRT (item response theory) to model the distribution of human errors, it reconstructs the training data set so that the human labeling errors become sparse. Simulations are conducted to show that the two algorithms are effective in resolving the insufficient training and human labeling error problems.

In a robot teleoperation system, humans operate and respond by relying on the content displayed on the video screen. However, unavoidable delays, such as network transmission, cause asynchronous communication between humans and robot and further result in human operation errors such as redundant or duplicate operations or even wrong commands. Users of consumer electronics in daily life are different from users in large-scale industry as they may not be experts or even trained to use the robots leading to new challenges in developing home-used robots. In this study, a module-based remote control system is designed to illustrate the possible human operation errors caused by transmission delay of transmission control protocol/Internet protocol–based network and to provide a user-friendly framework for simulating solutions to improve human errors. This is followed by proposing two target prediction methods, namely the target area predictor and stop position predictor, to decrease the human errors. The experimental results show that target area predictor and stop position predictor effectively improve the performance by reducing task completion time and object trajectory errors by 60% and 36%, respectively.

In this paper, we propose a joint machine learning and human learning design approach to make the training data labeling task in linear regression problems more efficient and robust to noise, modeling mismatch, and human labeling errors. Considering a sequential active learning scheme which relies on human learning to enlarge training data set, we integrate it with sparse outlier detection algorithms to mitigate the inevitable human errors during training data labeling. First, we assume sparse human errors and formulate the outlier detection as a sparse optimization problem within the sequential active learning procedure. Then, for non-sparse human errors, with the IRT (item response theory) to model the distribution of human errors, appropriate data are selected to reconstruct a training data set with sparse human errors. Simulations are conducted to verify the desirable performance of the proposed approach.

Background: This Research Category Full Paper presents the results of authors' efforts to develop and evaluate learning materials for introducing Software Engineering (SE) students to the Cognitive Psychology concept of human errors (specifically to those human errors that occur during software development). During the last few years, the authors have developed, through a rigorous literature review and empirical investigation, human error intervention instrumentation and supporting training/teaching material. The intervention instrument consists of a corpus of human errors and a tool to support human error based software requirements inspections. The primary aim of developing this instrumentation and training material is to impart SE/CS students with the knowledge about the most frequently committed human errors during the software development process. Goal and Method: First, a study was conducted with Industry Practitioners with the goal of examining if the practitioners believed that human errors and human error training are useful and relevant to the software development process. Next, based on feedback from the practitioners, a study was conducted in an undergraduate Software Engineering course where students were trained using the human error instrument and were asked to perform error based requirements inspections. The high-level goal of this paper is to evaluate whether requirements inspections supported by human errors can be used to deliver knowledge about software engineering human errors as well as knowledge about requirements inspections (a key industry skill) to students. Results: Results showed that industry practitioners found the human error instrumentation and training useful. Based on their feedback, when the training was administered to students, it helped students understand those human errors that are the frequently committed during the software development process.

In the civil aviation sector, human factors is the primary cause of many safety incidents. Aircraft flying, maintenance, and operations are the major tasks that are heavily dependent on professionals; thus, they are subject to human error probability. Human reliability analysis (HRA), which can evaluate human state and managing risk, has been developed over the years to identify, predict, and reduce human failures throughout aircraft operating procedures. Different generations of HRA tools have been developed to quantify the risks that are associated with safety accidents, including such as the Human Error Assessment and Reduction Technique, Technique for Human Error Prediction, Standardized Plant Analysis Risk Human Reliability Analysis, Cognitive Reliability and Error Analysis Methods, and Bayesian Network (BN). However, little is known about how these approaches are applied in aviation safety. This review aimed to systematically examine the current status of research on HRA in aviation accidents. A total of 13 studies were included and encompassed the studies of the first, second, and third generalizations of HRA alone or in combination with other methods (e.g., Improved Analytics Hierarchy Process, Functional Resonance Analysis Methods, Human Factor Analysis and Classification System, and Fault Tree Analysis). Results revealed that the third-generation HRA with BN was frequently used, showing great application potential for flight safety risk prevention and reduction. In the future, testing other third-generation HRA models driven by data in the field of airworthiness is necessary.

Human operator error is one of the most insidious sources of failure and data loss in today’s IT environments. In early 2001, Microsoft suffered a nearly 24-hour outage in its Web properties as a result of a human error made while configuring a name resolution system. Later that year, an hour of trading on the Nasdaq stock exchange was disrupted because of a technicians mistake while testing a development system. More recently, human error has been blamed for outages in instant messaging networks, for security and privacy breaches, and for banking system failures.

Development of a formal model for control and analysis of manufacturing systems in which a human operator is incorporated is difficult, nevertheless the human is a key component. It requires building a model of human functional specifications for executing various tasks. In this paper, a human parts handler is considered and the human task performing process is described in terms of human tasks and errors. For this purpose, human tasks and errors are classified into two sets, respectively. Based on the number of locations where an operator is required to move to complete a task, tasks are divided into an on-the-spot task set and an around-the-system task set. For human errors associated with each of task set, a location error set and an orientation error set are defined, respectively. As the initial step toward building a controller for Human-involved Computer Integrated Manufacturing Systems (HiCIM), assessment of the impact of human involvement on the control of a system, much of which is controlled by a computerized execution system, is presented. The complexity of systems from the control perspective is modeled and analyzed with human tasks and errors. The human task performing process is described using a flow chart format and a colored Petri net model is presented, and the complexity of process is analyzed in terms of human errors. A shop floor control example where the human operator performs material handling tasks is provided to illustrate the proposed methodology.

Requirements volatility (RV) is one of the key risk sources in software development and maintenance projects because of the frequent changes made to the software. Human faults and errors are major factors contributing to requirement change in software development projects. As such, predicting requirements volatility is a challenge to risk management in the software area. Previous studies only focused on certain aspects of the human error in this area. This study specifically identifies and analyses the impact of human errors on requirements gathering and requirements volatility. It proposes a model based on responses to a survey questionnaire administered to 215 participants who have experience in software requirement gathering. Exploratory factor analysis (EFA) and structural equation modelling (SEM) were used to analyse the correlation of human errors and requirement volatility. The results of the analysis confirm the correlation between human errors and RV. The results show that human actions have a higher impact on RV compared to human perception. The study provides insights into software management to understand socio-technical aspects of requirements volatility in order to control risk management. Human actions and perceptions respectively are a root cause contributing to human errors that lead to RV.

Using system simulation to model the impact of human error in a maritime system

Review of “Just culture – Balancing safety and accountability” and “The field guide to understanding human error”

ObjectivesReducing human errors caused by daytime sleepiness among train drivers is important to prevent train accidents. Our purpose of the study was to investigate the association among sleep, workplace sleeping environments, and human errors.MethodsWe recruited 144 South Korean train drivers belongs to the Korean Railroad Corporation. This cross‐sectional data was analyzed to investigate the association of insomnia (insomnia severity index), sleep quality (Pittsburgh sleep quality index), obstructive sleep apnea (Berlin questionnaire), and daytime sleepiness (Epworth scale) with human error and near‐miss experiences. We examined whether human error and near‐miss events were associated with various sleeping environments at work and at home after adjusting for the sleep indices.ResultsThe experience of human errors was associated with insomnia and daytime sleepiness, and near‐miss events were associated with insomnia among South Korean drivers. Sleeping environments including cold temperature and odor were related to both human errors and near‐miss events among South Korean train drivers, after adjusted for age, working years, shiftwork, obesity, smoking, binge drinking, regular exercise, caffeine consumption, sleep quality, severity of insomnia, obstructive sleep apnea, and daytime sleepiness.ConclusionsThe train drivers’ workplace sleeping environment is significantly associated with human error events and near‐miss events after adjusting for sleep quality, insomnia, obstructive sleep apnea, and daytime sleepiness. To prevent train accidents caused by human errors, more attention is necessary for improving workplace sleeping environments.

As part of an overall effort to upgrade and streamline methodologies for safety analyses of nonreactor nuclear facilities at the Savannah River Site (SRS), a human error data base has been developed and is presented in this report. The data base fulfills several needs of risk analysts supporting safety analysis report (SAR) development. First, it provides a single source for probabilities or rates for a wide variety of human errors associated with the SRS nonreactor nuclear facilities. Second, it provides a documented basis for human error probabilities or rates. And finally, it provides actual SRS-specific human error data to support many of the error probabilities or rates. Use of a single, documented reference source for human errors, supported by SRS-specific human error data, will improve the consistency and accuracy of human error modeling by SRS risk analysts. It is envisioned that SRS risk analysts will use this report as both a guide to identifying the types of human errors that may need to be included in risk models such as fault and event trees, and as a source for human error probabilities or rates. For each human error in this report, ffime different mean probabilities or rates are presented to cover a wide range of conditions and influencing factors. The ask analysts must decide which mean value is most appropriate for each particular application. If other types of human errors are needed for the risk models, the analyst must use other sources. Finally, if human enors are dominant in the quantified risk models (based on the values obtained fmm this report), then it may be appropriate to perform detailed human reliability analyses (HRAS) for the dominant events. This document does not provide guidance for such refined HRAS; in such cases experienced human reliability analysts should be involved.

In most applications Human Error Analysis as a practice does not exist. When the result of a particular mishap investigation is identified as human error there is usually no further analysis. It appears that human errors are accepted to be an integral part of human-machine systems. The end result of this attitude is that human error has been blamed as causing a mishap far too often. I.E., mishap investigators, when confronted with complex situations, opt for human error as an easy way out, because they know no one will argue. Essentially to name human error as the cause of a mishap is tantamount to admitting that no other cause can satisfactorily be found. This is not an acceptable situation. A structured analysis approach is presented which will enable the mishap investigator to identify tangible causes which otherwise would have been termed human error. further a means of solving pure human error cases is suggested.

Chapter 9 - Human Factors and Human Error