Human Error Rate Research Articles

Analysis of human errors in the operation of various treatment planning systems over a 10-year period.

The present study aimed to summarize and report data on errors related to treatment planning, which were collected by medical physicists. The following analyses were performed based on the 10-year error report data: (1) listing of high-risk errors that occurred and (2) the relationship between the number of treatments and error rates, (3) usefulness of the Automated Plan Checking System (APCS) with the Eclipse Scripting Application Programming Interface and (4) the relationship between human factors and error rates. Differences in error rates were observed before and after the use of APCS. APCS reduced the error rate by ~1% for high-risk errors and 3% for low-risk errors. The number of treatments was negatively correlated with error rates. Therefore, we examined the relationship between the workload of medical physicists and error occurrence and revealed that a very large workload may contribute to overlooking errors. Meanwhile, an increase in the number of medical physicists may lead to the detection of more errors. The number of errors was correlated with the number of physicians with less clinical experience; the error rates were higher when there were more physicians with less experience. This is likely due to the lack of training among clinically inexperienced physicians. An environment to provide adequate training is important, as inexperience in clinical practice can easily and directly lead to the occurrence of errors. In any environment, the need for additional plan checkers is an essential factor for eliminating errors.

Creating formative HRA dependency models using the HRA dependency idioms and SACADA data, Part II: Model quantification

Human reliability analysis (HRA) identifies the causal factors impacting the occurrence of human failure events and quantifies the human failure event probabilities based on those causal factors, and requires understanding the dependency structures that exist between failure events and causal factors. Many HRA methods incorporate the dependency framework established in the Technique for Human Error Rate Prediction (THERP), which uses simple multipliers on human error probabilities (HEPs) resulting from considering only a few factors. Accordingly, those HRA methods have a limited ability and accuracy when characterizing dependency. This paper presents a methodology for using HRA data to quantify Bayesian networks built from the HRA dependency idioms. The result is an objective and traceable human reliability model that enforces formative, rather than summative, dependency. The results show that baseline HEPs in this model are reasonable, and the data-informed dependency is a significant improvement for the field.

Application of the Multi-Threading Method and Python Script for the Automate of Network

In recent times, there has been a noticeable surge in the inclination towards the implementation of network automation solutions. This trend is driven by the objective of optimizing network availability within the context of hybrid data center networks, which are becoming increasingly prevalent. Reliability, performance, scalability, and minimal resource overhead are crucial solution design characteristics that significantly impact the decision- making process regarding adoption. Recent research has shown that 90% of network outages happen because of human factors and 69% of respondents manage their network with manual method. That high human error of up to 90%, occurs when configuring network devices using manual method. These problems had a negative impact on the functioning of the organization and were harmful to the user. In this research, we investigate solutions to reduce network outage that caused human error using network automation using the Python programming language and multi-threading method. This network automation will reduce configuration time, eliminate human error, and significantly increase efficiency. The aim of this research is to investigate the efficacy of network automation using Python and multi-threading to reduce network outages.The method used in this research is a parallel or multi-tread execution process method using GNS3 as network simulator. Based on experimental results, the multi- thread automation approach is significantly faster than both the serial automation method (67 seconds) and the manual method (248 seconds), this method requiring only 41 seconds to configure all Cisco router devices. If you're looking for speed, look no farther than the multi- thread automation approach, which is 6 times quicker than the manual method and 3.7 times quicker than the serial automation approach. The findings of this study have substantial and immediate implications for the ability of network engineers to speed up the configuration of networks and lower the rate of human error in doing so.

Quantitative Evaluation of the Pore and Window Sizes of Tissue Engineering Scaffolds on Scanning Electron Microscope Images Using Deep Learning.

The morphological characteristics of tissue engineering scaffolds, such as pore and window diameters, are crucial, as they directly impact cell-material interactions, attachment, spreading, infiltration of the cells, degradation rate and the mechanical properties of the scaffolds. Scanning electron microscopy (SEM) is one of the most commonly used techniques for characterizing the microarchitecture of tissue engineering scaffolds due to its advantages, such as being easily accessible and having a short examination time. However, SEM images provide qualitative data that need to be manually measured using software such as ImageJ to quantify the morphological features of the scaffolds. As it is not practical to measure each pore/window in the SEM images as it requires extensive time and effort, only the number of pores/windows is measured and assumed to represent the whole sample, which may cause user bias. Additionally, depending on the number of samples and groups, a study may require measuring thousands of samples and the human error rate may increase. To overcome such problems, in this study, a deep learning model (Pore D2) was developed to quantify the morphological features (such as the pore size and window size) of the open-porous scaffolds automatically for the first time. The developed algorithm was tested on emulsion-templated scaffolds fabricated under different fabrication conditions, such as changing mixing speed, temperature, and surfactant concentration, which resulted in scaffolds with various morphologies. Along with the developed model, blind manual measurements were taken, and the results showed that the developed tool is capable of quantifying pore and window sizes with a high accuracy. Quantifying the morphological features of scaffolds fabricated under different circumstances and controlling these features enable us to engineer tissue engineering scaffolds precisely for specific applications. Pore D2, an open-source software, is available for everyone at the following link: https://github.com/ilaydakaraca/PoreD2.

Investigating the Effects of Music and Temperature Changes on Heart Function and Human Error

Introduction: Exposure to music and temperature variations can influence physiological state and impact human error. This study aims to investigate the combined and independent effects of music and temperature changes on heart rate (HR) and human error.
 Materials and Methods: This interventional study was conducted on 14 participants (7 men and 7 women) under controlled conditions in 2018. The subjects experienced four types of music (Rock, Rap, Pop, and Iranian traditional music) at the noise pressure level of 75 dB, along with temperatures of 20, 25, and 30 °C independently and in combination, within an acoustic room with controlled temperature conditions. HR and human error rate were measured using a HR meter and a researcher-made text with spelling mistakes, respectively.
 Results: Using SPSS 19, data analysis revealed that exposure to traditional Iranian and rock music in temperatures deviating by 5 °C from 25 °C was associated with increased errors in detecting spelling mistakes (p < 0.05). Interestingly, traditional and rock music did not significantly affect HR across temperatures of 20, 25, and 30 °C (p > 0.05).
 Conclusion: Listening to traditional Iranian and rock music in conditions with approximately 5 °C cooler or warmer than room temperature can have a positive and constructive effect on HR and reduce human error. The use of this type of music has potential implications for optimizing environmental conditions, especially in workplaces where such factors are critical.

PROBABILISTIC ANALYSIS OF A THREE-UNIT SERIES-PARALLEL SYSTEM WITH COMMON-CAUSE, HUMAN ERROR AND ENVIRONMENTAL STATE FAILURES

This paper presents a mathematical model for performing reliability and availability analyses of Series- Parallel system with constant human error and common-cause failure rates. The method of Integro-differential equation was used to develop equations for the model to obtain the general expression. The Laplace transform technique was used to obtain the system reliability with constant human error failure rate of the system

Using skeletal position to estimate human error rates in telemanipulator operators.

In current telerobotics and telemanipulator applications, operators must perform a wide variety of tasks, often with a high risk associated with failure. A system designed to generate data-based behavioural estimations using observed operator features could be used to reduce risks in industrial teleoperation. This paper describes a non-invasive bio-mechanical feature capture method for teleoperators used to trial novel human-error rate estimators which, in future work, are intended to improve operational safety by providing behavioural and postural feedback to the operator. Operator monitoring studies were conducted in situ using the MASCOT teleoperation system at UKAEA RACE; the operators were given controlled tasks to complete during observation. Building upon existing works for vehicle-driver intention estimation and robotic surgery operator analysis, we used 3D point-cloud data capture using a commercially available depth camera to estimate an operator's skeletal pose. A total of 14 operators were observed and recorded for a total of approximately 8 h, each completing a baseline task and a task designed to induce detectable but safe collisions. Skeletal pose was estimated, collision statistics were recorded, and questionnaire-based psychological assessments were made, providing a database of qualitative and quantitative data. We then trialled data-driven analysis by using statistical and machine learning regression techniques (SVR) to estimate collision rates. We further perform and present an input variable sensitivity analysis for our selected features.

Leveraging AI and Machine Learning in Six-Sigma Documentation for Pharmaceutical Quality Assurance.

The pharmaceutical industry must maintain stringent quality assurance standards to ensure product safety and regulatory compliance. A key component of the well-known Six Sigma methodology for process improvement and quality control is precise and comprehensive documentation. However, there are a number of significant issues with traditional documentation procedures, including as slowness, human error, and difficulties with regulatory standards. This review research looks at innovative ways to employ machine learning (ML) and artificial intelligence (AI) to enhance Six Sigma documentation processes in the pharmaceutical sector. AI and ML provide cutting-edge technologies that have the potential to drastically alter documentation processes by automating data entry, collection, and analysis. Natural language processing (NLP) and computer vision technologies have the potential to significantly reduce human error rates and increase the efficacy of documentation processes. By applying machine learning algorithms to support real-time data analysis, predictive analytics, and proactive quality management, pharmaceutical organizations may be able to identify potential quality issues early on and take proactive efforts to address them. Combining AI and ML improves documentation accuracy and reliability while also strengthening compliance with stringent regulatory criteria. The primary barriers and limitations to the current state of Six Sigma documentation in the pharmaceutical industry are identified in this study. It examines the fundamentals of AI and ML with an emphasis on their specific applications in quality assurance and potential benefits for Six Sigma processes. The report includes extensive case studies that highlight notable developments and explain how AI/ML enhanced documentation is used in the real world.

INTEGRATING ARTIFICIAL INTELLIGENCE IN DISEASE DIAGNOSIS, TREATMENT, AND FORMULATION DEVELOPMENT: A REVIEW

Artificial intelligence (AI) is rapidly advancing and significantly impacting clinical care and treatment. Machine learning and deep learning, as core digital AI technologies, are being extensively applied to support diagnosis and treatment. With the progress of digital health-care technologies such as AI, bioprinting, robotics, and nanotechnology, the health-care landscape is transforming. Digitization in health-care offers various opportunities, including reducing human error rates, improving clinical outcomes, and monitoring longitudinal data. AI techniques, ranging from learning algorithms to deep learning, play a critical role in several health-care domains, such as the development of new health-care systems, improvement of patient information and records, and treatment of various ailments. AI has emerged as a powerful scientific tool, capable of processing and analyzing vast amounts of data to support decision-making. Numerous studies have demonstrated that AI can perform on par with or outperform humans in crucial medical tasks, including disease detection. However, despite its potential to revolutionize health care, ethical considerations must be carefully addressed before implementing AI systems and making informed decisions about their usage. Researchers have utilized various AI-based approaches, including deep and machine learning models, to identify diseases that require early diagnosis, such as skin, liver, heart, and Alzheimer’s diseases. Consequently, related work presents different methods for disease diagnosis along with their respective levels of accuracy, including the Boltzmann machine, K nearest neighbor, support vector machine, decision tree, logistic regression, fuzzy logic, and artificial neural network. While AI holds immense promise, it is likely to take decades before it completely replaces humans in various medical operations.

OCTess: AN OPTICAL CHARACTER RECOGNITION ALGORITHM FOR AUTOMATED DATA EXTRACTION OF SPECTRAL DOMAIN OPTICAL COHERENCE TOMOGRAPHY REPORTS.

Manual extraction of spectral domain optical coherence tomography (SD-OCT) reports is time and resource intensive. This study aimed to develop an optical character recognition (OCR) algorithm for automated data extraction from Cirrus SD-OCT macular cube reports. SD-OCT monocular macular cube reports (n = 675) were randomly selected from a single-center database of patients from 2020 to 2023. Image processing and bounding box operations were performed, and Tesseract (an OCR library) was used to develop the algorithm, OCTess. The algorithm was validated using a separate test data set. The long short-term memory deep learning version of Tesseract achieved the best performance. After reverifying all discrepancies between human and algorithmic data extractions, OCTess achieved accuracies of 100.00% and 99.98% in the training (n = 125) and testing (n = 550) datasets, while the human error rate was 1.11% (98.89% accuracy) and 0.49% (99.51% accuracy) in each, respectively. OCTess extracted data in 3.1 seconds, compared with 94.3 seconds per report for human evaluators. We developed an OCR and machine learning algorithm that extracted SD-OCT data with near-perfect accuracy, outperforming humans in both accuracy and efficiency. This algorithm can be used for efficient construction of large-scale SD-OCT data sets for researchers and clinicians.

Dependency Idioms for Quantitative Human Reliability Analysis

Human reliability analysis (HRA) is approaching nearly 60 years of reliance on key aspects of the original HRA method Technique for Human Error Rate Prediction (THERP), including its process for analyzing dependency. Despite advances in computational abilities and HRA-relevant techniques, the conceptualization, modeling, and quantification of dependency have remained largely unchanged since the introduction of THERP. As a result, current HRA methods do not consider dependency in a realistic manner, and there remain foundational gaps related to the definition, lack of causality, and quantification for HRA dependency. In this paper, we review the current conceptualization of dependency and demonstrate that current research in dependency is not addressing all of the technical gaps. To address the outstanding technical gaps in HRA dependency, we propose a set of fundamental dependency structures (HRA dependency idioms) that capture the spectrum of relationships possible between HRA variables. The idioms provide a robust logical structure for HRA dependency that emphasizes causality and is based on a causal Bayesian network modeling architecture. The idioms conceptualize and model HRA dependency in an objective, traceable, and causally informed manner that facilitates data-based quantification of HRA dependency.

Wireless standard-compliant e-health solution for elderly people with multiuser identification

ObjectiveOne of the main problems in e-health environments for developing an accurate multiuser identification is in the large number of patients, especially with itinerant medical devices and elderly people. This paper aims to contribute with two approaches to be included in ISO/IEEE 11073 family of standards as a standardized procedure for multiuser identification that can be used for a large variety of medical devices, regardless of their brand or model. From this contribution and to validate it, this work proposes a standardized e-health solution, including multiuser identification, implementing it in real health environments for elderly people, and evaluating their usability, interoperability, and adoption in daily living. Materials and methodsThis works implements the internationally recommended Personal Health Devices ISO/IEEE 11073 standards as a multiplatform environment (Windows/Linux/Mac OS) and fulfils the paradigms of scalability, modularity, portability, maintainability, and robustness. The standard e-health solution has been implemented in 26 health environments in several cities in Spain (Madrid, Barcelona, Sevilla, Zaragoza, etc.), consisting of 118 health professionals, 319 senior patients, and 18 technical professionals. ResultsThe proposed multiuser identification reduces the human error rate (from 13.3% to less than 5%) with positive evaluation: almost 70% of users are satisfied, with usability and time savings that are more than 50% in all the groups (nursing, medicine, and caregiving) and environments (residences, health centres, and hospitals). DiscussionThe use of e-health solutions within multiuser identification, as it is proposed through two standard-compliant approaches, permits advanced services and data analysis for a large variety of medical devices, regardless of their brand or model. ConclusionThis paper contributes an open interoperable e-health solution as alternative to the closed and commercial solutions and enables third-party developers to work collaboratively and extend the already implemented features, owing to the design based on plugins, value-added services, and multiple transport technologies and protocols.

Quantification of human behavior levels by extending Rasmussen's SRK model and the effects of time pressure and training on the levels switching

Human factor-related accidents account for an increasing portion of the total accidents through the advancing level of system automation. Human reliability becomes the key issue in human-machine systems especially for safety-relevant tasks and operations. Rasmussen's SRK (skill-rule-knowledge) model is well known in the field of human factors. Likewise, it is well known that skill-based behaviors have the highest human reliability, while knowledge-based behaviors are associated with the lowest reliability scores. Although numerous studies exist on human error probability (HEP), correspondingly typically attributed directly or indirectly to these three levels of behavior, a coherent, consistent representation, especially using data sources, has not been available. In this contribution, the quantification of human behavior levels with Rasmussen's SRK model is given based on three databases for the first time. Effects of time pressure and training on human reliability switching are also analyzed based on related publications. To determine the HEP of these three levels, three databases, technique for human error rate prediction (THERP), Savannah river site human reliability analysis (SRS-HRA) and nuclear action reliability assessment (NARA), from human reliability analysis (HRA) methods are used. The procedure contains identifying the tasks including the operator involved and the assumptions the analysts made and classifying the tasks into suitable cognitive behavior mode (CBM). In this case, the relationship between SRK levels and HEP is mapped. The effects of the two in automation context very relevant performance shaping factors (PSFs), time pressure and training/knowledge degradation, on human behavior levels switching are analyzed and the explanations of the SRK switching are presented. In this case, a more general structure is established to illustrate the dynamic behavior of levels switching with six directions under different conditions. From the results we conclude that skill, rule, and knowledge behavior levels are continuous in terms of HEP and therefore allow a new inside into this key aspect of human factor quantification. Based on this analysis the consequences of daily automation in the context of autonomous transport systems in combination with human qualification and reliability degrading are from this specific and in the current automation discussion very intensively discussed. The presented discussion linking SRK levels and HEP gives a new perspective on the foreseeable consequences of further automation in application areas with increasing automation of everyday tasks (like using a highly automated vehicle).

DOCEST-fast and accurate estimator of human NGS sequencing depth and error rate.

Accurate estimation of next-generation sequencing depth of coverage is needed for detecting the copy number of repeated elements in the human genome. The common methods for estimating sequencing depth are based on counting the number of reads mapped to the genome or subgenomic regions. Such methods are sensitive to the mapping quality. The presence of contamination or the large deviance of an individual genome from the reference may introduce bias in depth estimation. Here, we present an algorithm and implementation for estimating both the sequencing depth and error rate from unmapped reads using a uniquely filtered k-mer set. On simulated reads with 20× coverage, the margin of error was less than 0.01%. At 0.01× coverage and the presence of 10-fold contamination, the precision was within 2% for depth and within 10% for error rate. DOCEST program and database can be downloaded from https://bioinfo.ut.ee/docest/. Supplementary data are available at Bioinformatics Advances online.

Comparative Analysis of Ensemble Learning Methods for Enhancing Fetal Health Prediction Using Cardiotocography

Nearly half of the world’s stillbirths occur during labour and delivery. Early detection of any fetal distress can prompt the doctors to take appropriate measures. Cardiotocography (CTG) is one such technique that continuously records the fetal heart rate and uterine contractions during childbirth. Along with indicating signs of fetal hypoxia, CTG can also be interpreted to detect fetal abnormalities. Using the cardiotocography dataset from the UCI Machine Learning Repository, our paper displays a comparative analysis of different classifiers and ensemble learning methods such as max voting, weighted average, blending, bagging and boosting to enhance the fetal state prediction. Of all the ensemble methods used in our analysis, it was found that the Light Gradient Boosting Machine (LightGBM) gave the highest accuracy of 95.90%, which exceeded similar existing models. This increase in accuracy can prove to be potentially life saving, aid doctors in a more accurate detection of fetal abnormalities, reduce human error rates and increase infant mortality rates.

An Explainable and Lightweight Deep Convolutional Neural Network for Quality Detection of Green Coffee Beans

In recent years, the demand for coffee has increased tremendously. During the production process, green coffee beans are traditionally screened manually for defective beans before they are packed into coffee bean packages; however, this method is not only time-consuming but also increases the rate of human error due to fatigue. Therefore, this paper proposed a lightweight deep convolutional neural network (LDCNN) for a quality detection system of green coffee beans, which combined depthwise separable convolution (DSC), squeeze-and-excite block (SE block), skip block, and other frameworks. To avoid the influence of low parameters of the lightweight model caused by the model training process, rectified Adam (RA), lookahead (LA), and gradient centralization (GC) were included to improve efficiency; the model was also put into the embedded system. Finally, the local interpretable model-agnostic explanations (LIME) model was employed to explain the predictions of the model. The experimental results indicated that the accuracy rate of the model could reach up to 98.38% and the F1 score could be as high as 98.24% when detecting the quality of green coffee beans. Hence, it can obtain higher accuracy, lower computing time, and lower parameters. Moreover, the interpretable model verified that the lightweight model in this work was reliable, providing the basis for screening personnel to understand the judgment through its interpretability, thereby improving the classification and prediction of the model.

Human Factor Analysis of the Railway Traffic Operators

The human factor is an essential aspect of the operability and safety of many technical systems. This paper focuses on the analysis of human errors in the railway domain. The subject of human reliability analysis is the behavior of operators of station-signaling systems responsible for rail traffic management. We use a technique for human-error rate prediction as the 1st generation human reliability analysis to deal with task analyses, error identification and representation, and the quantification of human error probabilities. The paper contributes to the comparison of three technologically different railway traffic control systems, having different degrees of automation—from the manually operated (electro-mechanical), through semi-automated (relay-based) to almost fully automated (computer-based) station-signaling systems. We observe the frequency of individual operations performed in time intervals and calculate human error probability and human success probability values for each operation. Thus, we can analyze human reliability and compare the workload of operators working with control systems of different degrees of automation.

Gelişen ve değişen elektronik köprüüstü ekipmanları ve elektronik seyir haritalarının akıllı deniz ulaşım sistemlerine etkisi

It is seen that high technology and its products work to meet every stage and need of human life and navigational safety in the maritime sector. Electronic systems have been developed and are used today as a result of the search for alternatives to the well-known and approved classical methods used in safe navigation on the world's seas. Electronic systems are susceptible and more accurate than classical methods and significantly benefit maritime transport. Bridge electronic systems and infrastructures supporting its components also work with advanced technology products. In this context, electronic navigational charts - ENC, Electronic Chart Display Information systems - ECDIS and Automatic Information systems - AIS are undoubtedly the best examples. These and similar bridge and auxiliary devices ensure the safety of navigation on both national and international seas and oceans and cause a decrease in human error rates. This study mentions the positive effects of high-tech bridge navigation systems developed in the maritime sector on maritime transportation.

Semi‐Markov reliability model of manufacturing systems considering machine degradation, human errors, and their mutual facilitation effects

AbstractReliability plays a significant role in the overall performance of manufacturing systems, and accurate reliability assessments can guarantee their functionalities and efficiencies. This paper develops a reliability model of a manufacturing system considering machine degradation, human errors, and their mutual facilitation effects (MFE): machine degradation may increase fatigue rate and weaken learning process, thereby facilitating the occurrence of human errors, while human errors may, in turn, facilitate the machine degradation process. Although MFE may reduce system reliability, current reliability models ignore this effect. In this paper, to describe the MFE, a stochastic human error model under the effects of learning–forgetting and fatigue–recovery is proposed, where the human error rate depends on the current machine degradation state; next, based on their facilitation effects on machine degradation, human errors are divided into safety, damage, and fatal zones; finally, we incorporate the zoned human error model into the machine degradation model and develop a Semi‐Markov reliability model. A Monte Carlo method is implemented to calculate system reliability. The turret of a lathe operated by a worker is utilized to illustrate the effectiveness of the reliability model.

Hybrid fuzzy AHP-TOPSIS framework on human error factor analysis: Implications to developing optimal maintenance management system in the SMEs

The full performance and involvement of employees in the manufacturing industry can increase the productivity and profitability of that particular industry. Simultaneously the probability of human error presenting the entire production system of specific operations such as Human Error Rate (HER) directly affects the efficiency of the production plant. Because it leads to maximizing the maintenance cost, downtime of the machine, and the service activities in the industry. In the Small and Medium-sized Enterprises (SMEs) significance of service and maintenance management systems has been increased due to the product demand, and customer necessity in the recent world. The optimal Decision Support System (DSS) for maintenance management acts as a vital role in maintaining better product quality, safety requirements, maximum availability, and the effectiveness of the machines in the industry. This research mainly focuses on proposing a consistent, optimal decision support model for the best maintenance management systems by prioritizing the human error factors in the electronic switch and sensors manufacturing industry. The objective of this research is to identify the most critical factors and alternatives influencing the human error rate of SMEs. Through the application of hybrid Multi-Criteria Decision Analysis (MCDA) methods such as Fuzzy Analytic Hierarchy Process (AHP), and Techniques for Order Preference by Similarity to Ideal Solution (TOPSIS) approach. A real-time case study of SMEs demonstrates the effectiveness of this proposed decision-making framework. The outcome of this research unveiled that the alternative job process and working mode (organizational and environmental factors) was a maximum influence on the human error factors that need the utmost attention among all others. To organize the optimal maintenance strategy and maximum productivity in the sensor and electronic switch manufacturing plant of SMEs in the southern region of Tamil Nadu, India.