Application Of Human Reliability Analysis Research Articles

Taxonomy of performance shaping factors in manufacturing: A systematic literature review

AbstractHuman error in manufacturing can have substantial consequences, including loss of life, injuries, productivity, and financial losses. Human reliability analysis (HRA) methods can be used to evaluate the likelihood of human error in manufacturing tasks and identify potential sources of error. Performance shaping factors (PSFs) are internal and external factors that influence human performance and can affect the likelihood of human reliability estimates in HRA methods. Understanding the impact of PSFs on human performance in manufacturing is essential for developing effective strategies to minimize the likelihood of human error and improve the safety and efficiency of manufacturing processes. This systematic review scrutinizes the literature on PSFs within manufacturing, highlighting HRA applications. Using the PRISMA protocol, studies from 2000 to 2024 across engineering and psychology were examined, culminating in the analysis of 35 pertinent works. The review identifies and contrasts various PSF taxonomies from established HRA methods like SPAR‐H, HEART, CREAM, and THERP, revealing their diverse applications in different manufacturing settings. The review also uncovers a tendency to devise taxonomies through the lens of experts' domain knowledge, particularly tailored to discrete manufacturing contexts. A critical gap is observed in the lack of a uniform PSF framework, with the current literature reflecting a disparate understanding of PSFs' roles, definitions, and interrelations. This absence is further pronounced by the inadequate integration of human factors in the dialogue surrounding Industry 4.0. The analysis points to the necessity of harmonizing PSFs to better assess human reliability amid technological integration. The findings emphasize the need for an industry‐specific PSF framework that aligns with the intricacies of manufacturing operations, thus enabling more accurate HRA outcomes and informing strategies for error reduction and process optimization.

Phoenix–A model-based human reliability analysis methodology: Data sources and quantitative analysis procedure

Human Reliability Analysis (HRA) has continuously evolved to adopt more advanced human error probability (HEP) quantification approaches. Despite several efforts and enhancements of HRA methods, many still present limitations. Phoenix HRA Methodology aims to overcome known issues by incorporating strong elements of current HRA good practices, leveraging lessons learned from empirical studies and existing and emerging HRA methods' features. Phoenix models performance influencing factors (PIFs) and their impact on failure modes through Bayesian Networks (BBNs). Despite increased use in HRA applications, the large amount of data BBNs may require is still challenging. Thus, to populate Phoenix BBNs, several data sources were combined, including other HRA methods, expert judgment, and operating experience. The paper discusses Phoenix's quantitative analysis process, the data sources used to estimate the BBN parameters, and the applied data aggregation method. It further provides the quantitative analysis procedure guide with the significant steps and sub-steps an HRA analyst requires to implement this Phoenix methodology phase. Furthermore, the method for mapping the data to Phoenix BBN parameters and for aggregating the data considering non-homogeneous data sources can be further used for other HRA methods that apply BBNs or other applications.

The application of human reliability analysis to carpal tunnel decompression.

Many surgical procedures are prone to human error, particularly in the learning phase of skills acquisition. Task standardisation has been suggested as an approach to reducing errors, but it fails to account for the human factors associated with learning. Human reliability analysis (HRA) is a structured approach to assess human error during surgery. This study used HRA methodologies to examine skills acquisition associated with carpal tunnel decompression. The individual steps or subtasks required to complete a carpal tunnel decompression were identified using hierarchical task analysis (HTA). The systematic human error reduction and prediction approach (SHERPA) was carried out by consensus of subject matter experts. This identified the potential human errors at each subgoal, the level of risk associated with each task and how these potential errors could be prevented. Carpal tunnel decompression was broken down into 46 subtasks, of which 21 (45%) were medium risk and 25 (55%) were low risk. Of the 46 subtasks, 4 (9%) were assigned high probability and 18 (39%) were assigned medium probability. High probability errors (>1/50 cases) included selecting incorrect tourniquet size, failure to infiltrate local anaesthetic in a proximal-to-distal direction and completion of the World Health Organization (WHO) surgical sign-out. Three (6%) of the subtasks were assigned high criticality, which included failure to aspirate before anaesthetic injection, whereas 21 (45%) were assigned medium criticality. Remedial strategies for each potential error were devised. The use of HRA techniques provides surgeons with a platform to identify critical steps that are prone to error. This approach may improve surgical training and enhance patient safety.

Adaptation of the standardized plant analysis–risk human reliability analysis technique for the surgical setting: expert judgment approach

Objectives. Application of human reliability analysis (HRA) techniques originally developed for industrial settings to the healthcare sector may be controversial in terms of reliability and methodological level. The aim of the present study was to adapt a standardized plant analysis risk–human reliability analysis (SPAR-H) technique for application in surgical settings through suggesting more context-specific definitions for performance shaping factors (PSFs), designing precise levels and elicitation of multipliers through a domain expert judgment approach. Methods. A ratio magnitude estimation approach was used for carrying out domain expert judgment for multiplier elicitation. Experts from four teaching hospitals participated in the present study. Intra-class correlation was used in order to examine the inter-rater reliability of the estimated multipliers for each level of diagnosis and action task type. Results. Available time, threat stress, task complexity, experience/training, procedures, working conditions, human–machine interface, fatigue and teamwork were the nine suggested PSFs for the adapted SPAR-H technique. Conclusion. Context-specific definitions of the PSFs can enhance the reliability of human error probability assessments. Eventually, it could be concluded that multiplier elicitation through domain expert judgment is an efficient approach for adaptation of the HRA techniques for application in specific contexts.

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.

The application of human reliability analysis to three critical care procedures

BackgroundProcedures carried out in the intensive care unit are prone to human error. Standardisation has been suggested as an approach for reducing errors. This study used human reliability analysis methodologies to examine commonly performed critical care procedures: endotracheal suctioning; ultrasound-guided right internal jugular vein cannulation; and rapid-sequence intubation. MethodsThe subgoals, or individual steps, required to complete the three procedures were identified using hierarchical task analysis. The systematic human error reduction and prediction approach was then used to identify potential human errors at each subgoal, the level of risk and how these potential errors could be prevented. ResultsEndotracheal suctioning procedure was broken down into 129 subgoals, of which 49 (38.0%) were high-risk. Ultrasound-guided right internal jugular venous cannulation was divided into 224 subgoals, of which 131 (58.4%) were medium-risk, and 20 (8.9%) were identified as high-risk. Rapid sequence intubation was divided into 167 subgoals. A total of 73 (43.7%) of these subgoals were judged to be high-risk. ConclusionsThe use of human reliability analysis techniques can support healthcare professionals to gain an in-depth understanding of how particular procedures are carried out in order to reduce the risk of, and improve training in, how to perform these procedures.

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.

Application of human reliability analysis in the deterministic safety analysis for nuclear power plants

Licensing of nuclear power plants relies on safety analyses showing compliance of the design and operation with safety requirements. The safety analyses comprise of both deterministic analyses, where the compliance with deterministic rules shall be shown, and probabilistic analyses where the compliance with risk criteria shall be shown. Human reliability analysis (HRA) is a vital part of probabilistic safety assessment (PSA). There are a variety of methods and analysis principles that can be applied in the PSA context. On the other hand, there has been very little discussion and guidance on how human interactions should be assessed in the context of deterministic safety analyses (DSA). Since human interactions can be relevant contributors for many accident scenarios, there is clearly a need to develop rules on the treatment of human interactions in DSA. This paper discusses the consideration of human interactions in DSA, such as linking human interactions into DSA scenario categorisation, principles for crediting and not crediting human interactions and implications to the safety demonstration. The aim with the proposed approach is to improve the formalism in the treatment of human interactions context of DSA. Hypothetical but realistic assessment cases are presented as examples on how the principles can be applied.

Human Reliability Analysis (HRA) in surgery: Identification and assessment of Influencing Factors

Several authors encouraged the application of Human Reliability Analysis (HRA) to patient care, recognising the benefits it brought to high-risk industries in terms of human errors reduction, thanks to its anticipative approach. However, the literature on HRA in healthcare is still scanty, and shows significant gaps at methodological level. Only a very limited number of HRA studies implemented Influencing Factors (IFs) analysis in the quantification of the Human Error Probability, despite it represents a peculiar component and a well-established step in several HRA techniques. Furthermore, the need for a deep adaptation and translation of existing HRA techniques to the healthcare domain is emphasised by several authors. This paper aims at designing a taxonomy of Influencing Factors for HRA studies in surgery, and assessing their influence on surgeons’ technical performance by means of structured elicitation of experts’ judgement. An ad hoc taxonomy of 21 IFs has been validated through focus group and individual interviews, in Denmark and Italy; 215 questionnaires from expert surgeons in open and mini-invasive surgeries were then used for the quantification of the impact of each IF on surgeons’ performance. An investigation on the possible influence of different surgical techniques revealed that only two out of the twenty-one IFs show different probability density functions (pdf) when considered in open or mini-invasive surgeries. The study offers an original and relevant contribution to the development of domain-specific knowledge and, as such, to fostering the diffusion of HRA studies in healthcare, and in surgery in particular.

On the application of Human Reliability Analysis in healthcare: Opportunities and challenges

Safety in healthcare is a relatively recent field, but has received considerable attention over the past 15 years. Healthcare organisations have been encouraged to learn from safety management practices in other industries. In this paper we analyse opportunities and challenges for the application of Human Reliability Analysis (HRA) in healthcare. We consider the poor levels of reliability of many healthcare processes, performance variability, the absence of regulatory frameworks that incentivise proactive risk management, and the unique role of the patient. We conclude that HRA could provide a useful framework for the analysis and reduction of risk in healthcare, but techniques might have to be adapted and applied with due consideration of the specifics of the cultural and regulatory context of this domain. This includes clinical engagement with and ownership of the HRA process, greater focus on rigorous evaluation of cost-effectiveness of HRA techniques, and active involvement of patients.

Retrospective Application of Human Reliability Analysis for Oil and Gas Incidents: A Case Study Using the Petro-HRA Method

Human reliability analysis (HRA) may be performed prospectively for a newly designed system or retrospectively for an as-built system, typically in response to a safety incident. The SPAR-H HRA method was originally developed for retrospective analysis in the U.S. nuclear industry. As HRA has found homes in new safety critical areas, HRA methods developed predominantly for nuclear power applications are being used in novel ways. The Petro-HRA method represents a significant adaptation of the SPAR-H method for petroleum applications. Current guidance on Petro-HRA considers only prospective applications of the method, such as for review of new systems to be installed at offshore installations. In this paper, we review retrospective applications of Petro-HRA and analyze the Macando Oil Well-Deepwater Horizon accident as a case study.

Methods for building Conditional Probability Tables of Bayesian Belief Networks from limited judgment: An evaluation for Human Reliability Application

The present paper evaluates five methods for building Conditional Probability Tables (CPTs) of Bayesian Belief Networks (BBNs) from partial expert information: functional interpolation, the Elicitation BBN, the Cain calculator, Fenton et al. and Røed et al. methods. The evaluation considers application to a specific field of risk analysis, Human Reliability Analysis (HRA). The five methods are particularly suited for HRA models calculating the human error probability as a function of influencing factor assessments. The performance of the methods is evaluated on two simple examples, designed to test aspects relevant for HRA (but not exclusively): the representation of strong factor influences and interactions, the representation of uncertainty on the BBN relationships, and the method requirements as the BBN size increases. The evaluation underscores modelling limitations related to the treatment of multi-factor interdependencies and of different degrees of uncertainty in the factor relationships. The functional interpolation method is the least susceptible to these limitations; however, its elicitation requirements grow exponentially with the model size. Besides expert judgment, HRA applications of BBNs include the use of empirical data, combination of data and judgment, information from existing HRA methods: the building of the CPTs in these applications is outside the scope of the evaluation.

Towards a Taxonomy of Influencing Factors for Human Reliability Analysis (HRA) Applications in Surgery

Despite the growing interest in HRA applications in healthcare and in particular in surgery, in literature there is a limited number of HRA studies that use the so called Performance Shaping Factors or Influencing Factors to describe the working context. These factors (IFs) are those human, environmental, organizational or task specific factors that positively or negatively affect surgeons’ performance and the Error Probability. This study aims at developing an ad hoc taxonomy of Influencing Factors for surgery and it is meant to represent a first contribution towards the application of IF-based HRA in healthcare. The study methodology is twofold: firstly, literature review was used to identify and select personal and organizational factors that shape surgical performance; secondly, a field study was carried out to validate the preliminary list of IFs. Minimally Invasive Surgery (MIS) has been chosen as the field study domain. Ten factors have been extracted from the literature and classified according to the SHEL model. All the IFs in the taxonomy were observed throughout observation sessions. Furthermore, the observations prompted the addition of one more factor, namely “distractions”. In order to arrive at a stronger validation of the taxonomy, further research is needed to develop extensive validation of the conceptualization, clustering and assessment of IFs, by eliciting surgeons’ perceptions through multiple qualitative and quantitative methods.

OS-1 海難要因分析に応用する人間信頼性解析に関する調査研究(OS 機械安全の適用による産業・化学機械のイノベーション)

OS-1 海難要因分析に応用する人間信頼性解析に関する調査研究(OS 機械安全の適用による産業・化学機械のイノベーション)

Deriving causal Bayesian networks from human reliability analysis data: A methodology and example model

Within the probabilistic risk assessment community, there is a widely acknowledged need to improve the scientific basis of human reliability analysis (HRA). This has resulted in a number of independent research efforts to gather empirical data to validate HRA methods and a number of independent research efforts to improve theoretical models of human performance used in HRA. This paper introduces a methodology for carefully combining multiple sources of empirical data with validated theoretical models to enhance both qualitative and quantitative HRA applications. The methodology uses a comprehensive set of performance influencing factors to combine data from different sources. Further, the paper describes how to use data to gather insights into the relationships among performance influencing factors and to build a quantitative HRA causal model. To illustrate how the methodology is applied, we introduce the Bayesian network model that resulted from applying the methodology to two sources of human performance data from nuclear power plant operations. The proposed model is introduced to demonstrate how to develop causal insights from HRA data and how to incorporate these insights into a quantitative HRA model. The methodology in this paper provides a path forward for carefully incorporating emerging sources of human performance data into an improved HRA method. The proposed model is a starting point for the next generation of data-informed, theoretically-validated HRA methods.

Human reliability analysis: a new method to quantify errors in cataract surgery

To describe the application of human reliability analysis (HRA) as a tool to quantify errors that occur during small incision cataract surgery. Sixteen consecutive phacoemulsifications performed by one surgeon were assessed using HRA. Although there were no complications or adverse outcomes associated with any of the operations, 84 errors, which could potentially have caused a complication were noted. The commonest single error was difficulty in 'cracking' the nucleus. HRA attempts to recognise all errors that occur during a procedure rather than just the ones that result in a complication. As such it is a sensitive and prospective method of assessment of surgical performance. It would appear to be of value in the training and assessment of cataract surgery.

Application of Human Reliability Analysis to Nursing Errors in Hospitals

Adverse events in hospitals, such as in surgery, anesthesia, radiology, intensive care, internal medicine, and pharmacy, are of worldwide concern and it is important, therefore, to learn from such incidents. There are currently no appropriate tools based on state-of-the art models available for the analysis of large bodies of medical incident reports. In this study, a new model was developed to facilitate medical error analysis in combination with quantitative risk assessment. This model enables detection of the organizational factors that underlie medical errors, and the expedition of decision making in terms of necessary action. Furthermore, it determines medical tasks as module practices and uses a unique coding system to describe incidents. This coding system has seven vectors for error classification: patient category, working shift, module practice, linkage chain (error type, direct threat, and indirect threat), medication, severity, and potential hazard. Such mathematical formulation permitted us to derive two parameters: error rates for module practices and weights for the aforementioned seven elements. The error rate of each module practice was calculated by dividing the annual number of incident reports of each module practice by the annual number of the corresponding module practice. The weight of a given element was calculated by the summation of incident report error rates for an element of interest. This model was applied specifically to nursing practices in six hospitals over a year; 5,339 incident reports with a total of 63,294,144 module practices conducted were analyzed. Quality assurance (QA) of our model was introduced by checking the records of quantities of practices and reproducibility of analysis of medical incident reports. For both items, QA guaranteed legitimacy of our model. Error rates for all module practices were approximately of the order 10(-4) in all hospitals. Three major organizational factors were found to underlie medical errors: "violation of rules" with a weight of 826 x 10(-4), "failure of labor management" with a weight of 661 x 10(-4), and "defects in the standardization of nursing practices" with a weight of 495 x 10(-4).

Unsolved Problem: Acceptance of Human Reliability Analysis

The practice and application of human reliability analysis (HRA) have long been controversial among human factors' professionals. The controversy has centered on quantification, lack of data, and the proper view of the human in the system. Benefits of structured HRA techniques, such as increased understanding of tasks, and of the conditions that produce errors, have often been ignored. With the increased emphasis on risk-based management and regulation we see this as a problem that must be solved to further our discipline's contribution to society. The problem can be attacked by: establishing what can make HRA legitimate in the eyes of HFES members, reviewing HRA methodologies for appropriateness and developing new methods with current concerns in mind, and developing good HRA data to support HRA judgments. The problem is not only tractable, but easily solved with cooperation, understanding, learning, and resources.