Learning Safety Research Articles

The Influence of Hospital Physician Integration on Culture of Patient Safety.

Medical errors are responsible for a large number of deaths every year in the Unites States. Hospitals use various strategies including leadership, staffing, and structural changes to deal with this concerning issue. Hospital physician integration is a structural strategy to possibly improve patient safety. Using the conceptual lens of Donabedian's Structure Process Outcome model, this study aims to investigate how hospital physician integration affects organizational, management, and communication attributes of patient safety culture. A pooled cross sectional study design using the Hospital Survey on Patient Safety Culture, the American Hospital Association data, and Area Health Resource File was used to analyze the relationship between hospital physician integration and organizational and management patient safety culture attributes. The dataset comprised of hospital level data from 2021 and 2022 for patient safety culture and hospital characteristics and contained a final sample of 205 observations. The independent variable was levels of integration across five levels. Three attributes of safety culture were chosen-positive perceptions of communication openness, organizational learning, and hospital management support for patient safety. The control variables were organizational characteristics. Multivariable linear regression was used as the analytic method. Findings demonstrated a statistically significant correlation between higher level of hospital physician integration and positive perceptions of hospital management support for patient safety. There are 0.063 higher positive perceptions of hospital management support for patient safety for higher levels of integration compared with lower levels of integration (P < 0.05). Perceptions of communication openness and organizational learning did not demonstrate a statistically significant correlation with any level of hospital physician integration. Hospital physician integration and hospital management support for patient safety were moderately but significantly related. Hospital physician integration allows resources to become available for physician. As physicians take on management roles management of resources and eventually performance can improve, there is a need for future research in this area to examine if integration is a step in the right direction to overcome the challenges of patient safety and if investments in resources and training can be beneficial to safety culture.

Optimizing machine learning for water safety: A comparative analysis with dimensionality reduction and classifier performance in potability prediction

Optimizing machine learning for water safety: A comparative analysis with dimensionality reduction and classifier performance in potability prediction

Machine Learning for Enhancing Public Safety in Modern Cities

Machine Learning for Enhancing Public Safety in Modern Cities

Machine Learning for Women's Safety Based on Analysis of Tweets

This research paper extensively examines the multifaceted role played by social media platforms, specifically Twitter, Facebook, and Instagram, in advocating for the safety and security of women within Indian cities. It delves into the intricate ways in which these digital platforms are leveraged to amplify voices, share narratives, and mobilize collective action against the prevalent issue of violence and harassment faced by women and girls in public spaces. Through the dissemination of hashtags, campaigns, and personal anecdotes, social media serves as a powerful tool for raising awareness, fostering dialogue, and catalyzing societal change. Moreover, the paper investigates the profound impact of social media in nurturing a culture of responsibility and accountability within Indian society towards the safety and protection of women. By providing a platform for individuals to share their experiences, express concerns, and advocate for policy reforms, social media facilitates meaningful engagement and empowers communities to address systemic challenges. Through online activism, advocacy campaigns, and community initiatives, social media platforms enable individuals to connect, collaborate and collectively work toward creating safer and more inclusive urban environments for women and girls.

The study of the transformation education policy and regulation during the COVID-19 pandemic

The research delves into the repercussions of the COVID-19 pandemic on Indonesia’s education sector, notably the transition from conventional face-to-face teaching to distance learning policies (Haqien &amp; Rahman, 2020). It centres on identifying challenges and appraising government policies responding to the crisis, focusing on effectively implementing distance learning, especially in remote regions grappling with network constraints and inadequate facilities. Employing a qualitative case study methodology rooted in Dunn’s (2003) analytical theory, the research scrutinises the outcomes of Indonesian government policies during the pandemic, emphasising their impact on the shift to distance learning. The findings underscore the rapid governmental responses to the pandemic, particularly in education, necessitating a shift to distance learning for safety. However, challenges persist, notably in remote areas with connectivity and infrastructure limitations. The study advises reevaluating zoning policy’s impact on learning modes. Research highlights challenges, urging policy reassessment and increased support in Indonesia. The paper is crucial for policymakers, educators, and stakeholders, providing vital insights to enhance the resilience and adaptability of the education system in unforeseen crises.

Augmented reality, deep learning and vision-language query system for construction worker safety

Low situational awareness contributes to safety incidents in construction. Existing Deep Learning (DL)-based applications lack the capability to provide context-specific and interactive feedback that is essential for workers to fully understand their surrounding environments. This paper proposes the Visual Construction Safety Query (VCSQ) system. The system encompasses real-time Image Captioning (IC), safety-centric Visual Question Answering (VQA), and keyword-based Image-Text Retrieval (ITR), integrated with head-mounted Augmented Reality (AR) devices. System validation includes benchmarks and real-world images. The ITR module posted high recall rates of 0.801 and 0.835 for Recall@5 and @10. The VQA module achieved an 89.7% accuracy rate, and the IC module had a SPICE score of 0.449. Feasibility tests and surveys confirmed the system's practical advantages in different construction scenarios. This study establishes an integration roadmap adaptable to future advancements in interactive DL and immersive AR.

Joint IoT/ML Platforms for Smart Societies and Environments: A Review on Multimodal Information-Based Learning for Safety and Security

The application of the Internet of Things (IoT) is highly expected to have comprehensive economic, business, and societal implications for our smart lives; indeed, IoT technologies play an essential role in creating a variety of smart applications that improve the nature and well-being of life in the real world. Consequently, the interconnected nature of IoT systems and the variety of components of their implementation have given rise to new security concerns. Cyber-attacks and threats in the IoT ecosystem significantly impact the development of new intelligent applications. Moreover, the IoT ecosystem suffers from inheriting vulnerabilities that make its devices inoperable to benefit from instigating security techniques such as authentication, access control, encryption, and network security. Recently, great advances have been achieved in the field of Machine Intelligence (MI), Deep Learning (DL), and Machine Learning (ML), which have been applied to many important applications. ML and DL are regarded as efficient data exploration techniques for discovering “normal” and “abnormal” IoT component and device behavior inside the IoT ecosystem. Therefore, ML/DL approaches are required to convert the security of IoT systems from providing safe Device-to-Device (D2D) communication to providing security-based intelligence systems. The proposed work examines ML/DL technologies that may be utilized to provide superior security solutions for IoT devices. The potential security risks associated with the IoT are discussed, including pre-existing and newly emerging threats. Furthermore, the benefits and challenges of DL and ML techniques are examined to enhance IoT security.

Unmanned Aerial Systems and Deep Learning for Safety and Health Activity Monitoring on Construction Sites.

Construction is a highly hazardous industry typified by several complex features in dynamic work environments that have the possibility of causing harm or ill health to construction workers. The constant monitoring of workers' unsafe behaviors and work conditions is considered not only a proactive but also an active method of removing safety and health hazards and preventing potential accidents on construction sites. The integration of sensor technologies and artificial intelligence for computer vision can be used to create a robust management strategy and enhance the analysis of safety and health data needed to generate insights and take action to protect workers on construction sites. This study presents the development and validation of a framework that implements the use of unmanned aerial systems (UASs) and deep learning (DL) for the collection and analysis of safety activity metrics for improving construction safety performance. The developed framework was validated using a pilot case study. Digital images of construction safety activities were collected on active construction sites using a UAS, and the performance of two different object detection deep-learning algorithms/models (Faster R-CNN and YOLOv3) for safety hardhat detection were compared. The dataset included 7041 preprocessed and augmented images with a 75/25 training and testing split. From the case study results, Faster R-CNN showed a higher precision of 93.1% than YOLOv3 (89.8%). The findings of this study show the impact and potential benefits of using UASs and DL in computer vision applications for managing safety and health on construction sites.

A Theoretical Study on Implementing Machine Learning for Safety and Security of Oil and Gas Pipelines

A Theoretical Study on Implementing Machine Learning for Safety and Security of Oil and Gas Pipelines

The construction and practice path of safety education mechanism in colleges and universities integrating the psychological characteristics of students in the new era.

With the rapid development of higher education in China, the scale of colleges and universities is expanding, and the phenomenon of campus socialization is becoming more and more obvious. In particular, the campus and its surrounding environment are becoming more and more complex, which brings many hidden dangers in university life. In order to improve the effectiveness of safety education in colleges and universities and maintain the long-term effectiveness of college students' safety awareness, the paper proposes the construction and practice path of college safety education mechanism that integrates the psychological characteristics of students in the new era. Security issues facing universities at home, this track identifies the relationship between campus security incidents and security education and advocacy. Eight solutions to prevent and reduce incidents in schools. The paper proposes to give importance to the study of the security of college students, to create an awareness of security questions in the bank based on the recommendation algorithm, and to create to have online learning and testing for safety awareness. The passing rate of 10 majors such as humanities, composition and theory of composition technology was 100%, accounting for 12% of the 83 enrolled majors, and the passing rate of 54 majors such as clinical medicine was over 90%. The safety online learning and testing system of college students' safety education is lively in form and highly accepted by students. The development of college students' safety education starts from the time of receiving the university admission notice, making full use of the "golden time," so as to effectively prevent and reduce the occurrence of campus safety accidents.

Automation in pharmacovigilance: artificial intelligence and machine learning for patient safety

Automation promises to be a game- change for pharmacovigilance decreasing the cost of case reporting and improving data quality to truly add value, including signal detection in drug safety. Pharmacovigilance analytic and benefit – risk assessment.Technology advances are playing a major role in pharmaceutical PV strategy updates. For example more companies are looking towards cloud- based solutions, mobile applications, robotic automation, artificial intelligence and big data analytics as a vital part of clinical safety and regulatory operations in the pharmaceutical industry. Applying innovative technology automation tools and processes to PV strategies is now a critical requirement for managing the safety of pharmaceutical products. The role of artificial intelligence and machine learning in pharmacovigilance can enhance the productivity in identification, detection, management and reporting of ADRs. The main objective ofArtificial intelligence is meant to challenges to implementing intelligent automatic solutions include finding / having appropriate training data for machine learning models and the need for harmonised regulatory guidance. AI can analyse and interpret data at lightning speed, never gets tried or sick and can simply work by 24/7. Thousands of adverse effects are processed every month by ICSR in PV that incudes native automation and standalone technologies like AI and ML that reduce the manual effort.

PI3K signalling at the intersection of cardio-oncology networks: cardiac safety in the era of AI.

Class I phosphoinositide 3-kinases (PI3Ks) are a family of lipid kinases. They are super elevated in many human cancer types and exert their main cellular functions by activating Akt to trigger an array of distinct responses, affecting metabolism and cell polarity. The signal equally plays important roles in cardiovascular pathophysiology. PI3K is required for cardiogenesis and regulation of cardiac structure and function. Overexpression of PI3K governs the development of cardiac pressure overload adaptation and compensatory hypertrophy. Therefore, inhibition of PI3K shortens life span, enhances cardiac dysfunction and pathological hypertrophy. The inverse inhibition effect, however, desirably destroys many cancer cells by blocking several aspects of the tumorigenesis phenotype. Given the contrasting effects in cardio-oncology; the best therapeutic strategy to target PI3K in cancer, while maintaining or rather increasing cardiac safety is under intense investigational scrutiny. To improve our molecular understanding towards identifying cardiac safety signalling of PI3K and/or better therapeutic strategy for cancer treatment, this article reviews PI3K signalling in cardio-oncology. PI3K signalling at the interface of metabolism, inflammation and immunity, and autonomic innervation networks were examined. Examples were then given of cardiovascular drugs that target the networks, being repurposed for cancer treatment. This was followed by an intersection scheme of the networks that can be functionalised with machine learning for safety and risk prediction, diagnoses, and defining new novel encouraging leads and targets for clinical translation. This will hopefully overcome the challenges of the one-signalling-one-health-outcome alliance, and expand our knowledge of the totality of PI3K signalling in cardio-oncology.

Challenges of Machine Learning for eVTOL Reliability and Safety

The increasing number of requests for type certification received by the European Union Safety Agency on Vertical Takeoff and Landing (VTOL) aircraft attests to the expansion of frontiers in Urban Air Mobility (UAM). In addition, it has revealed the interest of traditional airplane and helicopter manufacturers in this new technology, all the while highlighting the emergence of new players developing their respective versions of electric-powered VTOLs (eVTOL). The perspective of eVTOLs going into service in the coming years for the transport of passengers raises new safety concerns. Indeed, it is necessary to ensure the reliability and safety aspects of those aircraft systems that will be flying under new operational missions, differing from current fixed wing (airplanes) and rotorcraft (helicopters) aircraft. At the same time, the evolution of aircraft systems monitoring technology is making it possible to acquire increasing amounts of data. The high complexity of new systems, combined with the huge amount of data provided, can make the decision-making process more difficult for pilots. Machine learning makes it possible to evaluate this data and improve reliability and safety.&#x0D; Even as the number of aeronautical accidents has decreased over the last years, 60–80% of those accidents are the result of human failure. In the initial implementation and operation stages of eVTOLs, machine learning (ML) can support pilots by using aircraft data to predict system failures and contribute to improve reliability and safety. Then, at an advanced stage of eVTOL operation, ML may help reduce human interaction with the aircraft, paving the way toward fully autonomous aircraft. The association of ML with technologies such as Digital Twins and 6G networks has the potential to enable safe and reliable autonomous flight. However, the introduction of eVTOLs will also increase air traffic in highly populated areas and thus needs to be studied to support the incorporation of the future autonomous aircraft. This paper addresses the main challenges for the incorporation of ML in the upcoming eVTOL fleet and its potential contribution to aircraft systems reliability and safety. It also explores the need for the use of ML techniques in a more autonomous air traffic management systems the face of increased air traffic.

Establishing a Prediction Model by Machine Learning for Accident-Related Patient Safety

Patient safety has always been an important issue when improving the quality of medical care. The first step when preventing accidents is to screen for the high-risk groups that are prone to accidents. A patient safety reporting system is one of the best tools for such screening. We used machine learning techniques to analyze events involving falling and establish a risk prediction model. The results are then fed back to medical organizations with the aim of raising their quality of medical care. Bayesian network, artificial neural network, logistic regression, and decision tree-based chi-square automatic interaction detection were applied to analyze a database covering a 14-month period from November 2012 to December 2013, and the area under the ROC curve (AUC) values were 0.940, 0.979, 0.981, and 0.971, respectively. Next, data from January to February 2013 was verified by the model showing the highest discrimination ability, namely, logistic regression, and the AUC and F -measure values were 0.944 and 0.871, respectively. Our results demonstrate that a high level of performance was obtained when using a machine learning model for fall prediction among newly admitted patients. Machine learning can thus be applied to provide the most appropriate decision-aided prediction model when screening high-risk groups that are prone to accidents within hospitals.

Developing professional expertise for safety: a learning design framework

Continuing professional development recognises that changes in the contemporary world demand that engineering professionals continuously learn. Today’s professional landscape requires the provision for ongoing learning relevant to evolving workplace requirements. This is particularly the case for engineers working in hazardous industries who make decisions every day with significant consequences. Despite this, the safety literature has paid little attention to best practice in professional learning. There is a large literature regarding lessons to be learned from accidents. Other published studies focus on training methods such as simulation. Educator-focused approaches such as these separate learning from real day-to-day workplace contexts and the learning needs of professionals. It is increasingly recognised that professionals learn, in a way that shapes their practice, from a diverse range of activities. Learning must therefore be active, social, and situated within the sphere of professional responsibilities, contexts, and groups. This paper presents a learner-centred framework that can be used to develop professional learning for safety that is grounded in day-to-day work practices and professional context needs. The framework aims to move away from the limitations that have been found with the current professional development approaches to enhance learner-centred professional learning. The framework was developed and used in the context of engineering practices regarding safety, but, because the framework encourages learning to be designed based on workplace contexts, it is applicable across a range of training needs and professions.

Artificial intelligence and machine learning for process safety: Points to ponder

Process Safety ProgressVolume 40, Issue 4 p. 189-190 EDITORIAL Artificial intelligence and machine learning for process safety: Points to ponder Salim Ahmed, Corresponding Author Salim Ahmed sahmed@mun.ca orcid.org/0000-0002-2279-1603 Centre for Risk, Integrity, and Safety Engineering (C-RISE), Memorial University, St. John's, Newfoundland and Labrador, Canada Correspondence Salim Ahmed, Centre for Risk, Integrity, and Safety Engineering (C-RISE), Memorial University, St. John's, Newfoundland and Labrador, Canada. Email: sahmed@mun.ca Contribution: Conceptualization (equal), Writing - original draft (lead), Writing - review & editing (lead)Search for more papers by this author Salim Ahmed, Corresponding Author Salim Ahmed sahmed@mun.ca orcid.org/0000-0002-2279-1603 Centre for Risk, Integrity, and Safety Engineering (C-RISE), Memorial University, St. John's, Newfoundland and Labrador, Canada Correspondence Salim Ahmed, Centre for Risk, Integrity, and Safety Engineering (C-RISE), Memorial University, St. John's, Newfoundland and Labrador, Canada. Email: sahmed@mun.ca Contribution: Conceptualization (equal), Writing - original draft (lead), Writing - review & editing (lead)Search for more papers by this author First published: 29 October 2021 https://doi.org/10.1002/prs.12321 Funding information: Natural Sciences and Engineering Research Council of Canada Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinked InRedditWechat No abstract is available for this article. Volume40, Issue4December 2021Pages 189-190 RelatedInformation

Democratizing business intelligence and machine learning for air traffic management safety

The ways in which Air Navigation Service Providers (ANSPs) monitor safety performance is strongly influenced by international regulations, standards, and agreements, although each State may also add its own local requirements. Particularly in the case of more mature ANSPs, the regulatory safety performance obligations are merely the tip of the iceberg in the undertaken safety performance activities. Much of the indicators, methods and tools are over and above what is required by regulations, either national or international. In modern settings, the usage of Business Intelligence and Machine Learning solutions can be enumerated under the continuous chasing of strategies to foster ANSPs’ safety intelligence capacities towards higher standards.This manuscript shows the development process of an integrated data-driven framework for self-service BI and ML on safety reporting data for the air traffic management system. The proposed framework firstly focuses on the development process of a BI architecture to extract meaningful knowledge from multiple data sources. Then, it progresses discussing how ML solutions may support gaining a deeper understanding of system’s performance and delineating specific safety recommendations. The explorative application of the proposed framework in multiple European ANSPs provides the basis for sharing lessons learned and outlining a possible path to start democratizing safety intelligence in aviation.

Deep learning for autonomous vehicle and pedestrian interaction safety

The present work aims to study how deep learning approaches solve the safety problems in the interaction between autonomous vehicles and pedestrians. A Vehicle-Pedestrian Detection (VPD) algorithm based on Convolutional Neural Network (CNN) is proposed regarding the massive amounts of parameters during feature extraction of traditional vehicle–pedestrian interaction algorithms. Furthermore, the Squeezenet algorithm is applied to extract traffic characteristics with fewer parameters. The performance of the proposed algorithm is analyzed through simulation experiments. Results demonstrate that when the successful transmission probability reaches 100% and the λ value is 0.01–0.05, the proposed algorithm can provide the result closest to the actual value, with the smallest data delay and the highest data transmission security. In different categories, the proposed algorithm can provide the highest accuracy as the number of iterations increases compared with other algorithms (AlexNet, DenseNet, VGGNet, IGCNet, and ResNet), which can accurately forecast traffic safety accidents. The proposed algorithm can provide an accuracy of 81.98%, an improvement of at least 1.94% over other advanced CNNs, and at least 3.3% over algorithms included in comparative simulations. Hence, it can recognize and identify safe interactions between autonomous vehicles and pedestrians. Through experiments, the constructed algorithm can significantly reduce the data transmission delay, improve the prediction accuracy of the safe interaction between autonomous vehicles and pedestrians, and increase the recognition accuracy remarkably, which can provide experimental references for the intelligent development of the transportation industry in the future.

Editorial: Machine learning for safety–critical applications in engineering

Editorial: Machine learning for safety–critical applications in engineering

An evaluation of Datix implementation for incident reporting at Johns Hopkins Aramco Healthcare

Background Incident-reporting systems are designed to obtain information about events and situations affecting patient safety. These incidents can be used to inform individual and organizational learning and improvement opportunities for quality and patient safety. Aim This study aims to evaluate incident reporting at the Johns Hopkins Aramco Healthcare (JHAH) since the implementation of Datix and the staff’s behavior towards incident reporting. Methods This is a prospective, mixed methods study. Incident-reporting system reports are used to evaluate the volume of incident reporting before and after implementing the new software. A questionnaire was developed to assess the attitude of hospital staff members to reporting incidents in general and the use of Datix in particular. Results Incident reporting increased in 2017 and 2018 by 51% and 57%, respectively, using Datix compared with the previously implemented software. JHAH has 3812 active employees, with nearly 60% of them using Datix. The study received 377 survey responses (response rate approximately 10%). We received a majority of positive responses about the workplace safety culture and the value of the system. Conclusion Implementation of the Datix reporting system resulted in an increased number of incident reports. An action plan was put in place that may improve incident reporting by further increasing awareness regarding the importance of reporting and supporting more training.