Human Error Identification Techniques Research Articles

Human error identification for laparoscopic surgery: Development of a motion economy perspective

This study postulates that traditional human error identification techniques fail to consider motion economy principles and, accordingly, their applicability in operating theatres may be limited. This study addresses this gap in the literature with a dual aim. First, it identifies the principles of motion economy that suit the operative environment and second, it develops a new error mode taxonomy for human error identification techniques which recognises motion economy deficiencies affecting the performance of surgeons and predisposing them to errors. A total of 30 principles of motion economy were developed and categorised into five areas. A hierarchical task analysis was used to break down main tasks of a urological laparoscopic surgery (hand-assisted laparoscopic nephrectomy) to their elements and the new taxonomy was used to identify errors and their root causes resulting from violation of motion economy principles. The approach was prospectively tested in 12 observed laparoscopic surgeries performed by 5 experienced surgeons. A total of 86 errors were identified and linked to the motion economy deficiencies. Results indicate the developed methodology is promising. Our methodology allows error prevention in surgery and the developed set of motion economy principles could be useful for training surgeons on motion economy principles.

Applications of integrated human error identification techniques on the chemical cylinder change task

This paper outlines the human error identification (HEI) techniques that currently exist to assess latent human errors. Many formal error identification techniques have existed for years, but few have been validated to cover latent human error analysis in different domains. This study considers many possible error modes and influential factors, including external error modes, internal error modes, psychological error mechanisms, and performance shaping factors, and integrates several execution procedures and frameworks of HEI techniques. The case study in this research was the operational process of changing chemical cylinders in a factory. In addition, the integrated HEI method was used to assess the operational processes and the system's reliability. It was concluded that the integrated method is a valuable aid to develop much safer operational processes and can be used to predict human error rates on critical tasks in the plant.

Application of Integrated Human Error Identification Techniques for Chemicals Cylinder Change Task

This paper outlines the human error identification techniques currently existent to assess and analyze the risk and human error. Even if many formal error identification techniques have existed for years, none could be validated for use to every different domain. This study considers lots of possible error modes that include external error modes, internal error modes, performance shaping factors and psychological error mechanisms, and integrates a number of execution procedures of human error identification techniques, including task analysis, criticality analysis and reliability analysis, to assess the latent human error and hazard risk in a case plant. It is concluded that the integrated method is a valuable aid to develop much safer operational processes and can be used to predict human error rates of critical tasks in the plant.

Predicting pilot error: Testing a new methodology and a multi-methods and analysts approach

The Human Error Template (HET) is a recently developed methodology for predicting design-induced pilot error. This article describes a validation study undertaken to compare the performance of HET against three contemporary Human Error Identification (HEI) approaches when used to predict pilot errors for an approach and landing task and also to compare analyst error predictions to an approach to enhancing error prediction sensitivity: the multiple analysts and methods approach, whereby multiple analyst predictions using a range of HEI techniques are pooled. The findings indicate that, of the four methodologies used in isolation, analysts using the HET methodology offered the most accurate error predictions, and also that the multiple analysts and methods approach was more successful overall in terms of error prediction sensitivity than the three other methods but not the HET approach. The results suggest that when predicting design-induced error, it is appropriate to use a toolkit of different HEI approaches and multiple analysts in order to heighten error prediction sensitivity.

Classification of errors contributing to rail incidents and accidents: A comparison of two human error identification techniques

Identifying the errors that frequently result in the occurrence of rail incidents and accidents can lead to the development of appropriate prevention and/or mitigation strategies. Nineteen rail safety investigation reports were reviewed and two error identification tools, the Human factors analysis and classification system (HFACS) and the Technique for the retrospective and predictive analysis of cognitive errors (TRACEr-rail version), used as the means of identifying and classifying train driver errors associated with rail accidents/incidents in Australia. We aimed to identify the similarities and differences between the techniques in their capacity to identify and classify errors and also to determine how consistently the tools are applied. The HFACS analysis indicated that slips of attention (i.e. ‘skilled based errors’) were the most common ‘unsafe acts’ committed by drivers. The TRACEr-rail analysis indicated that most ‘train driving errors’ were ‘violations’ while most ‘train stopping errors’ were ‘errors of perception’. Both tools identified the underlying factors with the largest impact on driver error to be decreased alertness and incorrect driver expectations/assumptions about upcoming information. Overall, both tools proved useful in categorising driver errors from existing investigation reports, however, each tool appeared to neglect some important and different factors associated with error occurrence. Both tools were found to possess only moderate inter-rater reliability. It is thus recommended that the tools be modified, or a new tool be developed, for complete and consistent error classification.

Error by design: methods for predicting device usability

This paper introduces the idea of predicting ‘designer error’ by evaluating devices using Human Error Identification (HEI) techniques. This is demonstrated using Systematic Human Error Reduction and Prediction Approach (SHERPA) and Task Analysis For Error Identification (TAFEI) to evaluate a vending machine. Appraisal criteria which rely upon user opinion, face validity and utilisation are questioned. Instead a quantitative approach, based upon signal detection theory, is recommended. The performance of people using SHERPA and TAFEI are compared with heuristic judgement and each other. The results of these studies show that both SHERPA and TAFEI are better at predicting errors than the heuristic technique. The performance of SHERPA and TAFEI are comparable, giving some confidence in the use of these approaches. It is suggested that using HEI techniques as part of the design and evaluation process could help to make devices easier to use.

Learning to predict human error: issues of acceptability, reliability and validity

Human Error Identification (HEI) techniques have been used to predict human error in high risk environments for the past two decades. Despite the lack of supportive evidence for their efficacy, their popularity remains unabated. The application of these approaches is ever-increasing, to include product assessment. The authors feel that it is necessary to prove that the predictions are both reliable and valid before the approaches can be recommended with any confidence. This paper provides evidence to suggest that human error identification techniques in general, and SHERPA in particular, may be acquired with relative ease and can provide reasonable error predictions.

Human error identification techniques for risk assessment of high risk systems—Part 2: towards a framework approach

This is the second paper in a series of two, reviewing human error identification approaches for risk assessment of high risk socio-technical systems. The previous paper identified and reviewed thirtyeight techniques. One of the closing comments was that no single technique sufficed for all the practitioner’s needs, and that a potential way forward was to utilise a number of techniques in a toolkit fashion, or to develop a new framework-based or toolkit-based approach. This paper therefore describes in detail a framework approach developed for the UK nuclear power and reprocessing industry, showing the practical implementation of such a system. It then considers some of the advantages and disadvantages of such framework approaches. The paper also discusses some of the relationships between error identification and Ergonomics.

Human error identification techniques for risk assessment of high risk systems—Part 1: review and evaluation of techniques

This is the first in a two-part series of papers dealing with the area of assessing human errors in high risk complex systems. This first paper outlines thirty-eight approaches of error identification, categorising them into types of error identification approach. The paper then reviews these techniques with respect to a broad range of criteria. Viable and non-viable techniques are identified. Trends and research needs are also noted. The second paper proposes a framework or tool-kit approach to Human Error Identification, and presents a prototype methodology to show what such a framework approach would look like in practice, for the nuclear power domain.

Human error identification techniques applied to public technology: predictions compared with observed use

In this paper, we consider the use of human error identification (HEI) techniques as a possible alternative to observation studies for product evaluation. The HEI techniques used were Task Analysis for Error Identification (TAFEI) and Predictive Human Error Analysis (PHEA). The comparison was undertaken in connection with the prediction of errors in the use of a ticket vending machine. Two main findings emerged from the study. First, predictions derived from the HEI techniques compared favourably with errors observed in actual machine use. Second, the HEI techniques took far less time than direct observation to reach comparable levels of performance. Such rates suggest that these techniques can be usefully applied to the study of consumer products.

Human error identification in human reliability assessment. Part 2: Detailed comparison of techniques

This is the second part of a two-part review of human error identification (HEI) approaches in human reliability assessment (HRA). Part 1 reviewed the probabilistic risk assessment (PRA) context in which HRA occurs, and then detailed 12 HEI techniques which have evolved in the field of HRA. Part 2 attempts to compare the way these techniques perform against a range of criteria relevant to HEI theoretical and empirical validity, and practical usefulness in applied HRA. It is hoped that these comparisons will help assessors in the selection of techniques for practical applications. The comparisons also point to research and development needs in the area of applied HEI.