Data Management Process Research Articles

SLR ABOUT THE COSTS OF PRIVATE BLOCKCHAIN TECHNOLOGY FOR BUSINESSES

Since the appearance of Bitcoin (2009), blockchain technology has been used in many fields. Companies use it for financial services, goods tracking, data authentication and secure storage, among other things, in order to make their operations more transparent and efficient. This technology has revolutionized the data management and transaction processing, but the associated costs are also significant and relevant to understand. The costs of blockchains vary and depend on the type of blockchain and how it is used. In this article, we examine the costs of using blockchain technology based on international literature, studies and reports with systematic literature review. In the results the range of possibilities offered by technology is presented, as well as the technology related cost types, and expenditures what companies have to deal with.

Harnessing Big Data for Machine Learning (Strategies, Approaches, and Challenges)

This paper explores the dynamic relationship between big data and machine learning, highlighting the key strategies, methodologies, and challenges associated with their integration. The convergence of these technologies presents transformative opportunities across industries, but it also introduces complexities in terms of data management, infrastructure, and real-time processing. This study examines the role of big data in fueling machine learning models, discusses critical success factors, and identifies best practices for implementing machine learning at scale.

ERIMS assessment Tool Enhances Emergency Response in WHO Europe Region

Abstract Issue/problem The implementation of an Emergency Response Information Management System (ERIMS) assessment tool improves health emergency responses within the World Health Organization - European Region. The tool’s deployment aims to refine data management processes and bolster preparedness strategies. Managing health information systems (HIS) effectively during emergencies is a persistent challenge, as technologies keep innovating and potential challenges are constant. The ERIMS assessment tool addresses this by offering a structured approach, ensuring improved emergency responses and system resilience. Description of the problem Adopting a systems approach, the ERIMS tool was designed to be flexible, allowing adaptation to various HIS architectures and emergency coordination mechanisms. Its design and development process included consultations with digital health and emergency management experts and field testing across three WHO member states. Results Field applications of the ERIMS assessment tool demonstrated its capacity to evaluate, including, digital capability and knowledge management with focus on emergency scenarios. The tool identified systemic gaps, offered strategic guidance on enhancing data processes, and fortified decision-making protocols. These outcomes not only highlighted the tool’s utility in assessing HIS readiness but also its impact in improving emergency response mechanisms. Lessons The deployment of the ERIMS assessment tool enhanced HIS by providing an evaluation of emergency preparedness and response capabilities with a focus at the national perspective level. The tool’s application showcased its role in advancing public health emergency preparedness, illustrating how assessments can lead to substantial improvements in health information management and emergency response readiness. Key messages • ERIMS assessment implementation improves emergency data management. • Better preparedness relies on boosting digital capabilities and knowledge management.

4.B. Round table: Closing the loop in the European Health Data Space: relation between primary and secondary data use

Abstract Currently, data collection processes in healthcare institutions across Europe remain exclusively geared towards the needs of primary use. Neither medical professionals’ clinical workflows, nor the data infrastructures and data management processes established within different organisations allow for the kind of highly standardised and structured data collection, processing and segregation from operational systems that secondary use requires. The development of a European Health Data Space offers large possibilities for research and innovation. High quality or primary data and secure pathways are required for the secondary use of data. Closing the loop and making relevant information from secondary use of data available to clinicians and health care professionals is a crucial step to build trust and enable a cultural shift that will enable health innovations and targeted treatments reach all those who could benefit from them without delay. Closing the loop between primary and secondary use of data will accelerate the shift from treatment of illness to prediction and prevention in the way healthcare is delivered - a shift that will need to be accompanied by an overhaul of European health systems’ current funding and reimbursement models. Establishing trustworthy sources of information and guidance for the use of novel health technologies, as well as involving patients and their caregivers early in the development of concrete applications, could help to foster better understanding and build trust in this area. To ensure acceptance, it will be particularly important for these developments to be driven by identified problems and defined needs in healthcare. Efforts should be made to address and include all parts of the population, not just the highly engaged and usually highly educated patients who typically participate in collaborative initiatives. The workshop will provide a holistic understanding of the interconnectedness between primary data and secondary use of health data. High quality of data and secure pathways are required for the secondary use of data. The workshop will explore paths for the evaluation of data-driven innovation and its introduction into clinical pathways that need to be further developed and standardised across the EU and the role that different actors in the health data system must play to meet the needs for secure and high-quality of data for research and policy development. The importance of actors like healthcare professionals, patients and citizens as well as the role of industry, public-private partnerships and relevance of health data innovative solutions will be addressed by each of the panellists and collaboration pathways for closing the loop will be further explored. The discussion will use and build on the findings and solutions identified in the EIT Health Think Tank report ‘Implementing the EHDS across Europe’. Key messages • In order to move to a data sharing environment, a cultural shift is needed to foster an environment of equality and openness to share data. • Healthcare professionals, patients and citizens are active participants and public-private partnerships could be instrumental to foster novel solutions and trust in this cultural transformation. Speakers/Panelists Nienke Schutte Sciensano, Brussels, Belgium Milana Trucl European Patient Forum, Etterbeek, Belgium Sinead O’Connor Trinity College Dublin, Dublin, Ireland Aneta Tyszkiewicz EFPIA, Brussels, Belgium Elina Drakvik Sitra, Helsinki, Finland

Electronic commerce as a tool for implementation of the constitutional right to business activity

The scientific article examines the essence and development of e-commerce as a key element of the modern economy, which significantly changes traditional business models and creates new opportunities for entrepreneurs and consumers. The impact of information and communication technologies on economic development is analyzed and their decisive role in the implementation of electronic commerce is indicated. Several key innovations that have fueled the rapid growth of e-commerce are explored, namely: AI-powered personalization, omni-channel commerce across platforms, and the integration of secure electronic payments using emerging technologies such as blockchain. In addition to technological aspects, social and economic factors that contributed to the development of e-commerce are considered, including the COVID-19 pandemic, changing consumer habits and the growth of logistics companies. The article also covers the issue of legal regulation of electronic commerce, analyzing international legal acts. It is concluded that e-commerce is a complex phenomenon that includes not only trade transactions but also related business processes such as marketing, data processing and supply chain management. The need for further development of the legal framework for its effective functioning in Ukraine and the world is indicated. E-commerce is proposed to be understood as the process of buying, selling or exchanging goods, services and information using electronic means, mainly through Internet platforms, covering a variety of business processes, such as: online trade, electronic payments, digital marketing, supply chain management, etc. . It was established that the relationship between the right to entrepreneurial activity and e-commerce is that e-commerce is one of the forms of entrepreneurial activity that is adapted to the modern conditions of the digital economy and the development of information technologies, in particular, this relationship is embodied through the prism the following legal relations: the right to entrepreneurial activity as a basis. The constitutional right to entrepreneurial activity provides an opportunity to freely choose and carry out economic activity for the purpose of obtaining profit. This right covers the freedom to choose the form of doing business, dispose of profits and protect one’s interests in the process of carrying out economic activities; digitalization as a form of entrepreneurship. Electronic commerce (e-commerce) expands the traditional possibilities of conducting business, offering new ways for entrepreneurs through the Internet. It covers not only the purchase and sale of goods and services, but also various business processes related to the exchange of digital information, conducting financial transactions and managing logistics; digital capabilities. Thanks to electronic commerce, the right to do business is expanded by accessing global markets and simplifying interaction with customers, which allows doing business regardless of geographical boundaries and time. Entrepreneurs can quickly implement new technologies, automate business processes and provide an individual approach to consumers through digital platforms; protection, security and settlement. Thus, the state, realizing the constitutional right to entrepreneurial activity, must ensure legal regulation of electronic commerce. This includes the protection of consumer rights, control over the quality of services, regulation of digital signatures, protection of personal data and support for the security of online payments; innovation and competition. E-commerce stimulates the development of entrepreneurship, increasing competition through opportunities for innovation. Entrepreneurs working in e-commerce gain competitive advantages due to the efficiency of digital technologies and global opportunities.

SISTEM INFORMASI PERPUSTAKAAN MENGGUNAKAN QR CODE PADA SMP NEGERI 6 KEMPAS BERBASIS WEB

Information technology has developed very rapidly nowadays, this has had a significant influence on human activities, such as making various fields of human work easier. One of these technological developments is the Library Information System. The Library of SMP Negeri 6 Kempas currently still uses media in the form of master books as storage media to manage library data. Storage that uses master books has the potential to experience many problems such as data loss because master books are easily torn, faded or lost, inaccuracies in reports, the length of the book management process and many other problems. The aim of this research is to produce a website-based library information system using QR Codes that can be used to handle the service process and book data management at the SMP Negeri 6 Kempas library and overcome the problems and weaknesses that occur when using the old system that is currently in use. The result of this research is to produce a Web-based QR Code Library Information System and has passed system Functionality testing with a score of 100% and Usability with a score of 81.6%.

Small Ruminant Parturition Detection Based on Inertial Sensors—A Review

The birth process in animals, much like in humans, can encounter complications that pose significant risks to both offspring and mothers. Monitoring these events can provide essential nursing support, but human monitoring is expensive. Although there are commercial monitoring systems for large ruminants, there are no effective solutions for small ruminants, despite various attempts documented in the literature. Inertial sensors are very convenient given their low cost, low impact on animal life, and their flexibility for monitoring animal behavior. This study offers a systematic review of the literature on detecting parturition in small ruminants using inertial sensors. The review analyzed the specifics of published research, including data management and monitoring processes, behaviors indicative of parturition, processing techniques, detection algorithms, and the main results achieved in each study. The results indicated that some methods for detecting birth concentrate on classifying unique animal behaviors, employing diverse processing techniques, and developing detection algorithms. Furthermore, this study emphasized that employing techniques that include analyzing animal activity peaks, specifically recurrent lying down and getting up occurrences, could result in improved detection precision. Although none of the studies provided a completely valid detection algorithm, most results were promising, showing significant behavioral changes in the hours preceding delivery.

Managing Adverse Events in Clinical Trials with R and Shiny

Adverse events (AEs) are critical occurrences that can significantly impact patient safety and the overall success of clinical trials. Effective management of AEs is essential for ensuring regulatory compliance and safeguarding participants' well-being. This paper explores the integration of R and Shiny as innovative tools for the real-time monitoring, visualization, and analysis of AEs in clinical trials. By leveraging R’s robust statistical capabilities and Shiny’s interactive interface, researchers can enhance data management processes, facilitate informed decision- making, and foster collaboration among stakeholders. A case study is presented to illustrate the practical application of R Shiny in tracking AEs, demonstrating its effectiveness in improving trial outcomes and ensuring patient safety. The findings emphasize the need for adopting dynamic data analysis tools to optimize adverse event management in the evolving landscape of clinical research. KEYWORDS Adverse Events, Clinical Trials, R, Shiny, Data Management, Visualization, Real-Time Monitoring, Patient Safety.

Economic aspects of error identification in semi-structured publications in the state language

Due to the rapid growth of information on the Internet and social networks, research in the field of computational linguistics has become very relevant. The volume of information that people and machines create in natural language needs to be processed, analyzed and verified. Information retrieval systems, dialog systems, and machine translation tools are used for this. The range of automatic text processing systems is very wide, it covers various tasks. Finding errors in texts and words, identifying and correcting incorrect words is one of the most important tasks of natural language processing (NLP). The article provides an overview of semi-structured data, methods and technologies for identifying incorrect words in natural languages. The paper gives an overview of semi-structured data, methods and techniques for detecting incorrect words in natural languages. The aim of the research is to develop an effective approach for detecting and correcting errors occurring in Kazakh-language texts, especially in the context of limited resources and unstructured data. The research includes the use of machine learning techniques as well as economic analysis of the costs of developing and implementing such solutions. The proposed approach facilitates the automation of text verification, which can significantly reduce the cost of manual data processing and improve the quality of information in various spheres, including business and public administration.

Digital Solutions for Efficient Posyandu Services

Abstract: The advancement of digital technology emphasizes the importance of management information systems in enhancing efficiency in health care. General Background: Integrated Health Posts (Posyandu) are crucial facilities at the village level, focusing on preventive health efforts. Specific Background: In Ganggang Panjang Village, queuing issues and manual data processing hinder Posyandu cadres, leading to inefficiencies. Knowledge Gap: Existing literature lacks tailored solutions for the unique challenges faced by Posyandu in rural areas. Aims: This study aims to design a digital-based Posyandu service system to streamline data collection, recording, and reporting. Results: Using Agile Web Development, the proposed system is browser-accessible, significantly improving operational efficiency through a functional website developed in PHP and JavaScript. Novelty: This research introduces a comprehensive digital solution tailored to rural health providers’ needs. Implications: The findings highlight the potential for digital solutions to enhance health service delivery in rural communities, contributing to improved health outcomes. Highlights: Efficiency Improvement: The digital system streamlines data collection and reporting for Posyandu, reducing manual processing time. Accessibility: The system is designed to be accessible via various browsers, enhancing usability for cadre members. Tailored Solutions: This research addresses the specific challenges faced by rural health providers, promoting effective health service delivery. Keywords: Posyandu, Health Care, Agile Development, Community Health, Information System

Radar forward modeling and intelligent identification of shallow subgrade defects

Geological radar is the primary nondestructive testing method for evaluating shallow subgrade defects. However, the radar atlas contains a large amount of information, and the efficiency of manual data interpretation and processing is low. In this study, the characteristics of radar maps of different defects were analyzed via forward simulation using finite-difference time-domain technology. The instantaneous characteristic information of different defect maps was integrated using map post-processing technology to improve recognition and translation accuracy. Finally, the convolution neural network algorithm was used to conduct data recognition to achieve the intelligent recognition of subgrade defects with an average detection accuracy of 73.93 % based on the radar subgrade defect atlas dataset, and the results were practically verified. The results show that the developed approach can accurately distinguish subgrade shallow defect information in the radar atlas. This approach is useful for accurate and efficient identification of latent highway defects.

Classification of the L-, H-mode, and plasma-free state: Convolutional neural networks and variational autoencoders on the edge reflectometer for KSTAR.

Classifying and monitoring the L-, H-mode, and plasma-free state are essential for the stable operational control of tokamaks. Edge reflectometry measures plasma density profiles, but the large volume of data and complexity in reconstruction pose significant challenges. There is a need for efficient methods to analyze complex reflectometer data in real-time, which can be addressed using advanced computational techniques. Here, we show that machine learning (ML) techniques can classify discharge states using raw signal data from an edge reflectometer installed on the Korea Superconducting Tokamak Advanced Research. The deep convolutional neural network models achieved classification accuracy of up to 99% when using 2D spectrogram inputs, demonstrating a significant improvement over 1D raw signal inputs. Additionally, the variational autoencoder model effectively clustered the discharge states in the latent space without any label information, further validating the model's capability to classify discharge states. These results suggest that the ML model can effectively handle the complexity of reflectometer data and accurately classify plasma discharge states. This approach not only facilitates real-time diagnosis but also reduces the need for manual data processing.

Adaptive Middleware to Analyze and Confirm Data Centric Parameters for Data Aggregation in Federated Cloud Environment

In the last decade rapid data evolution has contributed to tremendous data movement. The efficient data movement from origin to cloud storage and vice versa leads to the significant performance of application. A software layer situated between user and cloud storage called middleware plays major role in handling data to full fill user goals and requirements. In this paper, proposed a development of middleware frameworks from generic to context aware framework. Explicit importance is given to the adaptation feature of the middleware. A preliminary background is presented to list out importance and details of adaptability of the middleware and curious differentiation in static and self-adaptive adaptation features of the middleware. The literature started with the description of many generic middleware which can be used for building applications in variety of domains and further the limitations imposed are exposed. The issues like multidisciplinary data, vendor lock-in, disparity of service and interoperability related to federated cloud services serving to the fast-developing technology are highlighted. Along with that the use of context aware data management techniques for efficient handling of federated cloud system are defined. An extensive and comprehensive analysis of context-aware middleware is presented. For skilful data management processes need of context aware application with self-adaptation capability is presented especially in response to dynamic situations in federated cloud environment. At the end three techniques to design and develop context-aware middleware are presented. Alongside, four different perspectives of realizing adaptive middleware are also detailed. The presented work concludes with the highlighted need of context aware middleware framework to enhance the performance of federated cloud system.

Advancing Science to Prevent Firearm Violence in Communities: A Process for Harmonizing Studies to Develop Research Infrastructure.

The Community Firearm Violence Prevention Network (CFVP Network), funded by the National Institutes of Health (NIH), supports a network of research projects that develop and test interventions through collaborations with community partners to prevent firearm violence, injury, and mortality. The CFVP Network presents a unique opportunity to accelerate the science of preventing firearm injuries. The data harmonization workgroup of the CFVP Network led the process of aligning studies across the three unique inaugural network projects, with particular attention to how the CFVP Network could address current gaps in the science. The goal of the data harmonization workgroup was to align study measures, assessment timelines, and data management and archival processes across projects to enable robust cross-project analyses that accelerate the science of preventing firearm injuries. To accomplish this goal, the workgroup established the infrastructure to facilitate cross-project data collection, data sharing and archiving, and analyses. Among the three inaugural network projects, the workgroup's process resulted in harmonizing two assessment timepoints (baseline and one year post-implementation) and 60 constructs (with 31 identical standardized constructs). These harmonized products provide opportunities for novel analyses across the network projects. We expect that the harmonized study infrastructure developed through this process will catalyze future research focused on preventing firearm injury, including and extending beyond CFVP Network projects. The CFVP data harmonization workgroup's process can serve as a model for future networks that seek to build the science in a particular area.

Exploring patient death experiences among Iranian nursing students

Patient death can be a stressful experience for nursing students, leading to various undesirable psychological and emotional effects. However, research documenting these experiences remains limited. This study explores the experiences of Iranian nursing students when facing patient death. This qualitative study employed a purposive sample of 14 undergraduate nursing students. Data was collected through semi-structured interviews and analyzed using conventional content analysis. Lincoln and Guba’s (2004) criteria guided the data management process. Data analysis revealed three primary categories with six corresponding subcategories. These categories encompassed the significance of death, reactions to death, and the transformation of professional perspectives. Subcategories included positive and negative meanings attributed to death, emotional and behavioral responses, and the positive and negative effects on participants. The findings reveal that experiencing patient death significantly impacts nursing students psychologically and emotionally. Consequently, it is recommended that nursing curricula incorporate essential training modules addressing appropriate responses to death and effective communication with bereaved families. Further research should explore strategies to mitigate negative impacts and optimize positive outcomes related to this phenomenon.

Data linkage in public health research: a scoping review of ethical, legal and societal aspects surrounding administrative data

ObjectiveThe application of data linkage for scientific research purposes in the public health sphere requires using personal and sensitive data on an individual level while results are analyzed from a population perspective. Considering this, ethical, legal and social issues (ELSI) must be considered. In this sense, our study aims to better understand how ELSI surrounding administrative data linkage use in public health has been addressed in the scientific literature. ApproachOur scoping review is based on the Joann Briggs Institute Manual for Evidence Synthesis and will be reported according to the Preferred Report Items for Systematic Reviews and Meta-Analyses – Scoping Review Guideline. Two independent authors searched for articles published between January 2013 and December 2023 in the PubMed/MEDLINE, Web of Science, EMBASE and LILACS databases, as well as in Scielo and Google Scholar platforms. Keywords used were related to ‘data linkage’, ‘administrative data’, ‘ELSI’, “public policies’ and ‘public health’ terms. Articles were screened initially by title and abstract, then by full-text read. ResultsAfter duplicate removal and articles screening, two independent authors are performing data extraction considering ethical, legal and regulatory basis as well as procedures for data access; data management process including record linkage; type of administrative data linked; data protection strategies; and data subjects rights. ConsiderationsThis scoping is underway, we intend to have our final results by April to present at the conference, in September.

Regulatory Compliance and Risk Management in Pharmaceuticals and Healthcare

Purpose: This study aims to examine the evolving regulatory landscape and its impact on compliance and risk management within the pharmaceutical and healthcare industries. It seeks to identify effective strategies for maintaining data integrity and traceability in accelerated pharmaceutical development (APD), and to understand how these strategies improve patient care and healthcare system responsiveness. Methodology: The research involves a comprehensive review of current regulations, emerging trends, and industry practices. It utilizes an analysis of regulatory updates, risk management frameworks, and case studies to illustrate successful compliance strategies and the challenges faced by organizations. Findings: The study highlights key regulatory requirements, identifies common risks and mitigation strategies, and provides insights into effective practices for ensuring data integrity and traceability. It demonstrates that robust compliance and risk management practices contribute to enhanced patient outcomes and improved healthcare system efficiency. Unique Contribution to Theory, Policy, and Practice: This research offers a detailed examination of the intersection between regulatory compliance and risk management, providing a valuable framework for understanding and addressing the complexities of the pharmaceutical and healthcare sectors. It contributes to theory by integrating recent regulatory changes with practical risk management approaches, informs policy by highlighting critical areas for regulatory focus, and offers actionable recommendations for industry practitioners to optimize compliance and data management processes.

The Medicine Revolution Through Artificial Intelligence: Ethical Challenges of Machine Learning Algorithms in Decision-Making.

The integration of artificial intelligence (AI) and its autonomous learning processes (or machine learning) in medicine has revolutionized the global health landscape, providing faster and more accurate diagnoses, personalization of medical treatment, and efficient management of clinical information. However, this transformation is not without ethical challenges, which require a comprehensive and responsible approach. There are many fields where AI and medicine intersect, such as health education, patient-doctor interface, data management, diagnosis, intervention, and decision-making processes. For some of these fields, there are some guidelines to regulate them. AI has numerous applications in medicine, including medical imaging analysis, diagnosis, predictive analytics for patient outcomes, drug discovery and development, virtual health assistants, and remote patient monitoring. It is also used in robotic surgery, clinical decision support systems, AI-powered chatbots for triage, administrative workflow automation, and treatment recommendations. Despite numerous applications, there are several problems related to the use of AI identified in the literature in general and in medicine in particular. These problems are data privacy and security, bias and discrimination, lack of transparency (Black Box Problem), integration with existing systems, cost and accessibility disparities, risk of overconfidence in AI, technical limitations, accountability for AI errors, algorithmic interpretability, data standardization issues, unemployment, and challenges in clinical validation. Of the various problems already identified, the most worrying are data bias, the black box phenomenon, questions about data privacy, responsibility for decision-making, security issues for the human species, and technological unemployment. There are still several ethical problems associated with the use of AI autonomous learning algorithms, namely epistemic, normative, and comprehensive ethical problems (overarching). Addressing all these issues is crucial to ensure that the use of AI in healthcare is implemented ethically and responsibly, providing benefits to populations without compromising fundamental values. Ongoing dialogue between healthcare providers and the industry, the establishment of ethical guidelines and regulations, and considering not only current ethical dilemmas but also future perspectives are fundamental points for the application of AI to medical practice. The purpose of this review is to discuss the ethical issues of AI algorithms used mainly in data management, diagnosis, intervention, and decision-making processes.

Toward Healthcare 4.0: Industry 4.0 Innovating Hospital Management

Healthcare 4.0 (H4.0) is revolutionizing the way hospitals are managed, bringing innovations to the health service. Therefore, this work proposes the use of drivers aimed at hospital management improvement through H4.0 grounded in a Content Analysis. It resulted in 36 booster elements for hospital management grouped into five drivers: Process management, Data management, Resource management, Patient management, and Assistance and Development of Healthcare Professionals. These drivers contribute to strengthen the link between the main needs of hospital settings and technologies capable of meeting them in an applied manner. The most significant scientific contribution of this work lies in deepening and extending the recent block of knowledge on integrating hospital management with H4.0 technologies. Future studies should investigate the application of H4.0 technologies to assist in managing environmental, social and economic sustainability aspects of hospital settings.

Handling Encryption and Data Loss prevention in the Cloud-Based Systems

The current study focused on assessing the handling encryption and data loss prevention in the cloud. Cloud-based systems also known as cloud computing, increase organisational efficiency for proper data management processes. Cloud-based data loss or leakage prevention (DLP) improve data visualisation; hence, the companies can understand the risks and issues through proper data management. This study highlighted the way handling encryption and data loss prevention in the cloud can be useful for the development of business processes and for managing all operations through proper data management. IoT-based and AI-based cloud computing assess root causes and provide a clear insight into business problems. Hence, they can improve their data loss risk management services and promote continuous improvement.