Human Error Reduction Research Articles

Utilization of Non-verbal Communication in the Engine Room Training – Basic Research on Hand Signals among Monocultural Crew on Board

Accidents are likely to occur in an environment where work is done with teamwork by multiple crew members due to lack of communication, especially in a multicultural environment. Therefore, if there are internationally common maritime hand signals, it would contribute to reduction of human error among multicultural crews on board. As basic research on utilization of hand signals among multicultural crew on board, we invented several common hand signals. Furthermore, as a first step in an experiment to verify the effectiveness of hand signals during teamwork operation in engine room, we conducted the experiment in a mono-cultural environment through onboard training on a training ship. Our target task was practical onboard training for main engine start-up procedure in engine room because it required a lot of multitasking and teamwork. We studied the effectiveness and improvement of use of hand signals through questionnaires and interviews conducted with cadets and onboard instructors.

An analysis of human error and reliability in the operation of fixed CO2 systems on cargo ships using HEART Dempster-Shafer evidence theory approach

CO2 fixed fire extinguishing system activation accidents may occur as a result of unintentional system activation or human error. Crew members present in the area could suffer serious consequences, including death, if CO2 gas is released into a protected compartment such as the engine room, CO2 room, or cargo area. It is therefore crucial to investigate and address the causes of such accidents. The use of Dempster-Shafer (D-S) theory and the Human Error Assessment and Reduction Technique (HEART) to the Human Reliability Analysis (HRA) of CO2 fixed fire extinguishing system activation accidents is discussed in this study. On the one hand, D-S theory allows for uncertainty-based reasoning and can be extremely advantageous, especially when analysing complex systems where there are many potential challenges. On the other hand, HEART is an organised method to examine and reduce human error in high-risk systems. Together, these techniques provide a powerful tool for identifying and addressing the underlying causes of CO2 fixed fire extinguishing system activation accidents. The study revealed that the total human error probability (HEP) for the CO2 fixed fire extinguishing system was 3.10E-01 and the reliability of human performance for the given process was 6.90E-01. The results of this paper highlight many factors such as equipment failure, inadequate training, and poor maintenance practices, as well as factors contributing to the risk of accidental system activation. By addressing these factors, the probability of CO2-fixed fire suppression system activation accidents can be significantly reduced.

Assessment and Reduction of Human Error using SHERPA Technique in Chemotherapy Department of a Large Military Hospital.

Medical errors are numerous in medical activities. Considering the sensitivity and importance of the medical group's professions, the emergence of an apparently simple error can cause the death of an individual or even a group of individuals. The present study aims the evaluation and reduction of human error using a system human error reduction and prediction approach System Human Error Reduction and Prediction Approach (SHERPA) in the nurses of Baqiyatallah hospital's chemotherapy ward in 2019. A cross-sectional study was conducted in the chemotherapy ward using the SHERPA technique. Then, the duties were determined in detail using Hierarchical Task Analysis (HTA). The errors were identified using the SHERPA checklist, and the risk outcomes and intensities were finally evaluated. Based on the study findings, there are 109 possible errors for 48 sub-duties. The most frequent errors fall in the functional area (54%) and the least frequent errors pertain to the area of selection (3%). In order to reduce the errors and increase the quality of the services and safety of the patients, errors can be identified by using the SHERPA technique; after identifying these errors, using this technique, it is possible to prevent the recurrence of the identified errors by careful planning. Considering the fact that the most frequent error was found in the functional domain, modern protocols can be codified in this area, and standards can be observed for putting the problems of this section atop of the priority list and reducing the errors and increasing safety of the patients.

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.

The roles of cognitive ergonomics in reducing human error and burnout among emergency room nurses during the Covid-19 pandemic

Background: Mental health, particularly burnout syndrome, is one of the concerns that can influence the productivity of health workers during the pandemic. This is because health professionals are under intense pressure, but there is no guideline or procedure in place to safeguard them in terms of mental health. The objective of this study is to discover burnout and human error, as well as to provide advice, by analyzing the ergonomic factors. Method: Human Error Assessment and Reduction Technique was the identifying approach employed (HEART) in this study. The sample consisted of ten emergency room personnel from one of the hospitals in Yogyakarta. Results: The activity with the greatest HEP was the delivery of first aids, which had a value of 0.068. This activity requires more difficult and complex labor abilities, making human unreliability more significant and increasing the likelihood of human error. Conclusion: Recommendations are made to assist nurses in dealing with heavy and varied workloads so that the reliability of nurses at work can be increased and burnout can be reduced, including the suppression of environmental factors to be as small as possible to reduce the workload of nurses, urging nurses to begin monitoring the situation and taking anticipatory steps so that the burnout does not worsen, and holding regular sharing sessions among nurses, upgrading knowledge and fostering a sense of togetherness.

Nurse-Task Matching Decision Support System Based on FSPC-HEART Method to Prevent Human Errors for Sustainable Healthcare

To increase the levels of sustainability of service quality as well as to ensure satisfaction and assurance of patients in the health sector, minimizing the probability of making mistakes nurses is of great importance. The extent of this probability is considerably affected by task types, physical conditions of the working environment, workload, and working conditions. Moreover, the physical and mental characteristics of nurses also have a colossal influence on this probability. It is also possible to increase the sustainability of health services by matching nurses appropriately to a specific task according to related risk levels, and by balancing their workload accordingly. This study proposes FSPC-HEART method in that purpose, as a new type of human error reduction and assessment technique (HEART) application based on fuzzy step-wise weight assessment ratio analysis and principal component analysis methods. Unlike the methods in the literature, this new method offers a person-specific proactive error prevention approach. With FSPC-HEART, the probability of each nurse to make a mistake, that is, the human error probability (HEP) values are calculated separately for each task. Also, the combined effect of physical and mental workload factors for each employee was taken into account. In the proposed method, the effect of the subjective judgments of the decision-makers on the objectively obtained HEP values was tried to be reduced. The developed nurse-task matching decision support system enables the FSPC-HEART method to be easily used by decision-makers, and to assign employees to tasks with low error probabilities.

Abstract 4698: Reproducibility in spatial biology: reducing variables to improve the reliability of insight generation

Abstract There is an ongoing crisis in reproducibility in scientific studies, and studies in spatial biology are no exception. Cell DIVETM Multiplex imaging solution (Leica Microsystems) provides reliable workflow solutions to minimize variability from study to study. Cell DIVE allows probing and imaging of dozens of biomarkers on a whole tissue section with an iterative staining and dye inactivation workflow. At its core, Cell DIVE is designed to provide methods reproducibility, from tissue preparation, antigen retrieval, and sample imaging and slide storage. Cell DIVE is designed to work with directly conjugated primary antibodies, another source of variability. However, conjugated antibodies from Cell Signaling Technology (CST) are rigorously validated using stringent acceptance criteria to reduce variability. In addition, the use of recombinant antibodies, the consistent conjugate brightness and antibody degree-of-labeling reduce lot to lot variability, ensuring reliable conjugated antibodies for spatial biology studies. High resolution imaging results are obtained by consistent round to round imaging, consistent calibration and corrections, and reduction of human error using the BAB 200 liquid handling solution (Advanced Solutions Life Sciences). We present here, an iterative biomarker study using adjacent tissue sections probed with distinct lots of CST antibody panels, imaged in temporally separated batches using a Cell DIVE imager fitted with a BAB200 liquid handler. Following robust analysis (segmentation, phenotyping and statistical analysis), we report the reproducibility findings across parameters. Methods and results reproducibility in spatial biology is essential for reliable insight generation, giving confidence in the quality of future studies aimed at improving patient outcomes. Citation Format: Lisa Arvidson, Reginaldo Prioli, Samuel Jensen, James B. Hoying, Michael W. Golway, Michael J. Smith, Katie O. White, Richard A. Heil-Chapdelaine, Chi-Chou Huang, Tuan H. Phan, Hideki Sasaki, Melinda Angus-Hill. Reproducibility in spatial biology: reducing variables to improve the reliability of insight generation. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 4698.

Concerns, perceived impact, practices, preventive measures, and stress among healthcare workers during COVID-19 pandemic in Malaysia.

Healthcare workers (HCWs) have been continually exposed to patients with COVID-19 and are at higher risk of contracting the disease. Their psychological health is important for overall wellbeing and productivity, which could lead to a reduction in human errors during the pandemic crisis. This study aimed to measure the level of concerns, work practices, adequacy of preventive measures among HCWs, and the impacts on their life and work, including mental health status during the second wave of the COVID-19 pandemic in Malaysia. An online questionnaire was distributed randomly to 1,050 HCWs from the Ministry of Health facilities in the Klang Valley who were involved directly in managing or screening COVID-19 cases from May to August 2020. The questionnaire was divided into five domains, which were concerns, impact on life and work, practice, perceived adequacy of preventive measures, and Revised Impact of Event Scale (IES-R). Logistic regression was used to identify sociodemographic predictors of the five domains. A total of 907 respondents (86.4%) participated in this survey. Approximately half of the respondents had a low concern (50.5%), most of them had a good practice (85.1%), with 67.5% perceiving there were adequate preventive measures, and they perceived the outbreak had a low impact (92%) on their life and work. From the IES-R domain, 18.6% of respondents potentially suffered from post-traumatic stress disorder (PTSD). During the second wave of the COVID-19 outbreak in Malaysia, HCWs practiced high levels of precautions and preventive measures because they were aware of the risk of infection as an occupational hazard. With the adequate implementation of policy and control measures, the psychological wellbeing of the majority HCWs remained well and adequately supported.

Precision Medicine for Chronic Endometritis: Computer-Aided Diagnosis Using Deep Learning Model

Chronic endometritis (CE) is a localized mucosal infectious and inflammatory disorder marked by infiltration of CD138(+) endometrial stromal plasmacytes (ESPC). CE is drawing interest in the field of reproductive medicine because of its association with female infertility of unknown etiology, endometriosis, repeated implantation failure, recurrent pregnancy loss, and multiple maternal/newborn complications. The diagnosis of CE has long relied on somewhat painful endometrial biopsy and histopathologic examinations combined with immunohistochemistry for CD138 (IHC-CD138). With IHC-CD138 only, CE may be potentially over-diagnosed by misidentification of endometrial epithelial cells, which constitutively express CD138, as ESPCs. Fluid hysteroscopy is emerging as an alternative, less-invasive diagnostic tool that can visualize the whole uterine cavity in real-time and enables the detection of several unique mucosal findings associated with CE. The biases in the hysteroscopic diagnosis of CE; however, are the inter-observer and intra-observer disagreements on the interpretation of the endoscopic findings. Additionally, due to the variances in the study designs and adopted diagnostic criteria, there exists some dissociation in the histopathologic and hysteroscopic diagnosis of CE among researchers. To address these questions, novel dual immunohistochemistry for CD138 and another plasmacyte marker multiple myeloma oncogene 1 are currently being tested. Furthermore, computer-aided diagnosis using a deep learning model is being developed for more accurate detection of ESPCs. These approaches have the potential to contribute to the reduction in human errors and biases, the improvement of the diagnostic performance of CE, and the establishment of unified diagnostic criteria and standardized clinical guidelines for the disease.

Evaluation of human error using human error assessment and reduction technique (HEART) based on fuzzy logic (Case study: gas power plant

Evaluation of human error using human error assessment and reduction technique (HEART) based on fuzzy logic (Case study: gas power plant

How do medication errors occur in the nursing communication process? Investigating the relationship between error types and error factors.

Human error types and error factors are two important elements of error analysis. Understanding the relationship between them can contribute to new case analyses, the tendency of error occurrence statistics, error factor identification, and prevention of error recurrence. To provide evidence and guidance for the prevention and improvement of medication communication errors by quantitatively exploring the relationship between error types and error factors. Data were collected on self-reported errors in the medication administration process by nurses in all departments of three cooperative medical institutions, and an error sheet of specified style was adopted. Error types were determined by the systematic human error reduction and prediction approach method and human cognition processes. Error factors were extracted using the root cause analysis combined with Berlo's communication model, and the relationship between error types and error factors was quantitatively studied using the partial least-squares regression method. After a one-by-one analysis of 303 error cases, the communication errors occurring in the nursing medication process could be explained by six error types and 12 error factors. In addition, 20 correlation patterns between the error types and error factors were quantitatively obtained, and their path coefficient distributions ranged from 0.088 to 0.467. The results of this study may provide reference to understand errors and establish countermeasures from the statistics of error occurrence trends, extract error factors related to error types and determine key error factors.

A novel integrated methodology for human reliability assessment in hydrogen fuelling stations

Hydrogen fuel cell vehicles have proliferated in recent years, leading to a subsequent increase in hydrogen fuelling stations (HFSs). Operation and maintenance activities of an HFS prone to undesired events caused by human errors. Estimating and evaluating the risk associated with operators' performance can help to enhance the safety of such operations. The human error assessment and reduction technique (HEART) methodology has been widely used to assess the reliability of human performance. However, this technique is not entirely reliable since it depends on experts' experience and judgment, and the model's final outcome is subject to uncertainty. Besides, the HEART method ignores the dependencies of all the tasks required to assess human error probability (HEP). This paper presents a new integrated methodology for quantifying the impacts of different conditions on the reliability of operators' performances in HFSs. The proposed framework is constructed based on the Bayesian network (BN) for complimenting the application of HEART to deal with the lack of data and experts' judgment. Hence, it reduces the inaccuracy of HEP assessed by HEART by plotting it to a BN and considering the dependencies of all involved tasks. Subsequently, the best-worst method (BWM) is utilised to deal with the subjectivity of expert judgment. The integrated BN-BWM-HEART technique enhances the safety and reliability of the current operation and maintenance practices of an HFS. The results indicate that the integrated BN-BWM-HEART technique has the potential capacity to reduce the uncertainty associated with HEP estimation. So, the outcome of the developed methodology enhances the safety and reliability of operations and maintenance activities in an HFS.

Optimum Design of Stair-Climbing Robots Using Taguchi Method

Environmental issues like pollution are major threats to human health. Many systems are developed to reduce pollution. In this paper, an optimal mobile robot design to reduce pollution in Green supply chain management system. Green supply chain management involves as similating environmentally and economically feasible solutions into the supply chain life-cycle. Smartness, advanced technologies, and advanced networks are becoming pillars of a sustainable supply chain management system. At the same time, there is much change happening in the logistics industry. They are moving towards a new logistics model. In the new model, robotic logistics has a vital role. The reasons for this change are the rapid growth of the e-commerce business and the shortage of workers. The advantages of using robotic logistics are reduction in human errors, faster delivery speed, better customer satisfaction, more safety for workers, and high workforce adaptability. A robot with rocker-bogie suspension is a six-wheeled mobile platform that has a distinctive potential to keep all wheels on the ground continuously. It has been designed to traverse rough and uneven terrain by distributing the load over its wheels equally. However, there is a limitation to achieving high-speed mobility against vertical barriers. In this research, an optimal design of product delivery wheeled robots for a sustainable supply chain system is proposed to ensure higher adaptability and maximum stability during climbing staircases. The design parameters of the proposed robot are optimized using Taguchi Method. The aim is to get a smooth trajectory of the robot’s center-of-mass. The proposed approach realizes a robot with much-improved stability which can climb over heights more than the size of the wheel (i.e., 3 times the radius of wheels). The results reveal that the modified rocker-bogie system not only increases the stair-climbing capability but also thwarts instability due to overturning of a wheel of the robot.

Concept of a data-based approach for the prediction and reduction of human errors in manual assembly

Concept of a data-based approach for the prediction and reduction of human errors in manual assembly

IoT Based Menu Ordering System

Abstract: This project is to make the restaurant menu ordering system to make environment of the restaurant good. Typically, the restaurant ordering system is provided in the form of menu cards such that the customer selects order is then taking by the waiter. So am building a touch screen- grocery order system. It displays a customer's groceries on available devices such as the user's mobile phone or her tablet, and delivers orders directly with a touch. It display the data automatically, reception, transmission, data, and analysis. It has many advantages such as ease of use, time saving, portability, reduction of human error, flexibility and providing customer feedback. Remember the customer's dish placed on the abandoned table. The Intelligent Automated Restaurant aims to bring all the different touch screens together to provide connectivity, Information sharing. Acceleration methods and a personal experience. We use the RFID to transmit the data through electronic menu ordering system or the touch screen display.

A human reliability analysis for ship to ship LNG bunkering process under D-S evidence fusion HEART approach

LNG (Liquid Natural Gas) ship to ship bunkering process is quite a new concept for the maritime industry since the usage of LNG has been increasing worldwide. The LNG bunkering process poses a high risk due to human errors, while a minor error may be catastrophic. The expectation of the ship's crew is to carry out operations without any errors. Therefore, human reliability analysis (HRA) is paramount to improving operational safety during the ship to ship LNG bunkering process. In this context, this paper performs a systematic HRA under the D–S (Dempster-Shafer) evidence fusion-based HEART (human error assessment and reduction technique) approach. While the HEART quantifies human error for the tasks being performed, the extended D-S evidence fusion deals with the limitation of APOA (assessing the proportion of effect) calculation since it significantly relies on evaluating a single rater. The finding shows that human reliability for the ship to ship LNG bunkering process is 5.98E-01 and reasonable, but not at the desired level. The paper's outcomes will contribute to the utmost for LNG ship operators, safety inspectors, and ship owners to establish a safe and efficient ship to ship LNG bunkering process and minimise human error-based accidents.

Human reliability analysis in deep excavation projects using a fuzzy Bayesian HEART-5M integrated method: case of a residential tower in north Tehran

Objectives. Numerous labourers lose their lives or suffer from injuries and disabilities yearly due to the lack of safety enforcement in construction projects and accidents caused by excavation collapses. The identification and ranking of human errors have always been a central concern in civil engineering. Previous studies on excavation work and related risks have focused on retaining structure methods, while human errors may be a significant contributor to accidents and near misses. Methods. This study identified human errors in deep excavation projects using hierarchical task analysis (HTA) and a systematic human error reduction and prediction approach (SHERPA). Results. The fuzzy Bayesian human error assessment and reduction technique (HEART)-5M method was implemented to determine the human error probability (HEP) for all case-study tasks. Critical tasks were obtained as ‘drainage system execution’, ‘water and wastewater pipes’, ‘gas pipes’, ‘checking cracks in surrounding buildings’ and ‘checking soil slippage’ with probability levels of 0.46, 0.44, 0.44, 0.37 and 0.37, respectively. Finally, remedial measures were presented for crucial tasks. Conclusions. Six unbiased experts approved the model’s desirability. The suggested approach can serve as a valuable guide for all project stakeholders in identifying, evaluating and taking corrective actions in similar projects.

Totally endoscopic cartilage tympanoplasty: a hierarchical task analysis.

Totally endoscopic ear surgery is a novel method of conducting otological surgery. Hierarchical task analysis and the systematic human error reduction and prediction approach ('SHERPA') are valuable tools that can effectively deconstruct the technical and non-technical skills required to successfully complete a surgical procedure. Twenty-five endoscopic tragal cartilage tympanoplasties were observed, to identify the tasks and subtasks required for completion of totally endoscopic tragal cartilage tympanoplasty. The systematic human error reduction and prediction approach was used to identify the potential risks and methods, to reduce or remediate these risks. A hierarchical task analysis was performed, identifying 8 tasks and 50 subtasks for a safe approach to completing totally endoscopic tragal cartilage tympanoplasty. A risk score for each subtask was calculated to produce a systematic human error reduction and prediction approach and to highlight potential errors. This hierarchical task analysis allowed for quick reference to a correct method of endoscopic tympanoplasty. The systematic human error reduction and prediction approach was employed to reduce the risks associated with undergoing endoscopic tympanoplasty, to improve patient safety.

A Framework for Systematic Assessment of Human Error in Construction Sites – A Sustainable Approach

The construction industry is one of the most accident-susceptible sectors of the national economy and is characterized by a high rate of accidentality. The Human Error Assessment and Reduction Technique (HEART) is a generic method to identify human error. This technique uses generic task types and error producing conditions to calculate the probable human error. It is known that unsafe acts in the activity will also lead to unplanned events. Therefore, in this research, in addition to the existing factors, the probability of unsafe acts is also integrated. From the results, it is known that excavation (0.957), reinforcement erection for footing & column (0.631) and crane operation (0.269) are the tasks with a higher probability of human error. This can be minimized by frequent safety trainings to the workers and providing suitable personnel protective equipment (PPE) by the management. This proposed method may be applicable for all the workplaces, as it has a generic method to quantify human error with the task and error producing conditions. Knowledge of the circumstances of accidents will enable the formulation or modification of the labour law to be properly formulated, as well as the appropriate orientation of preventive measures and trainings in the field of occupational safety. All participants in the investment process: workers, construction site managers and supervisors, should be the recipients of these activities, who are also exposed to hazards and may suffer from accidents while performing their activities at a construction site. Parameters and the probable human error described by the authors allow for a comprehensive assessment of hazards and the probability of accident occurrences.

HEART Hybrid Methods for Assessing Human Reliability in Coal-Fired Thermal Power Plant Process

The assessment of human reliability is crucial in serious processes and operations, such as planned maintenance, unplanned maintenance, and troubleshooting in a coal-fired thermal power plant, as the nature of these processes poses significant threats. When the literature is examined, the evaluation of human reliability in any type of power plant, especially coal-fired thermal power plants, is limited. In order to fill this gap, we systematically assessed human reliability in an accident that occurred during a repair of a tube failure in a boiler in a coal-fired thermal power plant. The HEART (human error assessment and reduction technique) method was used in a hybrid way alongside the fuzzy AHP and SWARA (step weight assessment ratio analysis) methods. Although the HEART method is a practical, understandable, and easy-to-implement human reliability assessment method, the APOE (assessment of the proportion effect) value depends on the decision of a single decision maker. This study aimed to eliminate this deficiency and compare human error possibilities using HEART–fuzzy AHP and HEART–SWARA methods. This hybrid method can be used in the operation of all coal-fired thermal power plants and provides practical contributions to minimize human error.