Technical Research Priorities for Big Data

To drive innovation and competitiveness, organisations need to foster the development and broad adoption of data technologies, value-adding use cases and sustainable business models. Enabling an effective data ecosystem requires overcoming several technical challenges associated with the cost and complexity of management, processing, analysis and utilisation of data. This chapter details a community-driven initiative to identify and characterise the key technical research priorities for research and development in data technologies. The chapter examines the systemic and structured methodology used to gather inputs from over 200 stakeholder organisations. The result of the process identified five key technical research priorities in the areas of data management, data processing, data analytics, data visualisation and user interactions, and data protection, together with 28 sub-level challenges. The process also highlighted the important role of data standardisation, data engineering and DevOps for Big Data

System Architecture of A European Platform for Health Policy Decision Making: MIDAS

Background: Healthcare data is a rich yet underutilized resource due to its disconnected, heterogeneous nature. A means of connecting healthcare data and integrating it with additional open and social data in a secure way can support the monumental challenge policy-makers face in safely accessing all relevant data to assist in managing the health and wellbeing of all. The goal of this study was to develop a novel health data platform within the MIDAS (Meaningful Integration of Data Analytics and Services) project, that harnesses the potential of latent healthcare data in combination with open and social data to support evidence-based health policy decision-making in a privacy-preserving manner. Methods: The MIDAS platform was developed in an iterative and collaborative way with close involvement of academia, industry, healthcare staff and policy-makers, to solve tasks including data storage, data harmonization, data analytics and visualizations, and open and social data analytics. The platform has been piloted and tested by health departments in four European countries, each focusing on different region-specific health challenges and related data sources. Results: A novel health data platform solving the needs of Public Health decision-makers was successfully implemented within the four pilot regions connecting heterogeneous healthcare datasets and open datasets and turning large amounts of previously isolated data into actionable information allowing for evidence-based health policy-making and risk stratification through the application and visualization of advanced analytics. Conclusions: The MIDAS platform delivers a secure, effective and integrated solution to deal with health data, providing support for health policy decision-making, planning of public health activities and the implementation of the Health in All Policies approach. The platform has proven transferable, sustainable and scalable across policies, data and regions

Addendum to Informatics for Health 2017: Advancing both science and practice

This article presents presentation and poster abstracts that were mistakenly omitted from the original publication

The Elements of Big Data Value

This open access book presents the foundations of the Big Data research and innovation ecosystem and the associated enablers that facilitate delivering value from data for business and society. It provides insights into the key elements for research and innovation, technical architectures, business models, skills, and best practices to support the creation of data-driven solutions and organizations. The book is a compilation of selected high-quality chapters covering best practices, technologies, experiences, and practical recommendations on research and innovation for big data. The contributions are grouped into four parts: · Part I: Ecosystem Elements of Big Data Value focuses on establishing the big data value ecosystem using a holistic approach to make it attractive and valuable to all stakeholders. · Part II: Research and Innovation Elements of Big Data Value details the key technical and capability challenges to be addressed for delivering big data value. · Part III: Business, Policy, and Societal Elements of Big Data Value investigates the need to make more efficient use of big data and understanding that data is an asset that has significant potential for the economy and society. · Part IV: Emerging Elements of Big Data Value explores the critical elements to maximizing the future potential of big data value. Overall, readers are provided with insights which can support them in creating data-driven solutions, organizations, and productive data ecosystems. The material represents the results of a collective effort undertaken by the European data community as part of the Big Data Value Public-Private Partnership (PPP) between the European Commission and the Big Data Value Association (BDVA) to boost data-driven digital transformation

Mobile Technology Deployment Strategies for Improving the Quality of Healthcare

Ineffective deployment of mobile technology jeopardizes healthcare quality, cost control, and access, resulting in healthcare organizations losing customers and revenue. A multiple case study was conducted to explore the strategies that chief information officers (CIOs) used for the effective deployment of mobile technology in healthcare organizations. The study population consisted of 3 healthcare CIOs and 2 healthcare information technology consultants who have experience in deploying mobile technology in a healthcare organization in the United States. The conceptual framework that grounded the study was Wallace and Iyer\u27s health information technology value hierarchy. Data were collected using semistructured interviews and document reviews, followed by within-case and cross-case analyses for triangulation and data saturation. Key themes that emerged from data analysis included the application of disruptive technology in healthcare, ownership and management of mobile health equipment, and cybersecurity. The healthcare CIOs and consultants emphasized their concern about the lack of cybersecurity in mobile technology. CIOs were reluctant to deploy the bring-your-own-device strategy in their organizations. The implications of this study for positive social change include the potential for healthcare CIOs to emphasize the business practice of supporting healthcare providers in using secure mobile equipment deployment strategies to provide enhanced care, safety, peace of mind, convenience, and ease of access to patients while controlling costs

Natural Language Processing in Electronic Health Records in Relation to Healthcare Decision-making: A Systematic Review

Background: Natural Language Processing (NLP) is widely used to extract clinical insights from Electronic Health Records (EHRs). However, the lack of annotated data, automated tools, and other challenges hinder the full utilisation of NLP for EHRs. Various Machine Learning (ML), Deep Learning (DL) and NLP techniques are studied and compared to understand the limitations and opportunities in this space comprehensively. Methodology: After screening 261 articles from 11 databases, we included 127 papers for full-text review covering seven categories of articles: 1) medical note classification, 2) clinical entity recognition, 3) text summarisation, 4) deep learning (DL) and transfer learning architecture, 5) information extraction, 6) Medical language translation and 7) other NLP applications. This study follows the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Result and Discussion: EHR was the most commonly used data type among the selected articles, and the datasets were primarily unstructured. Various ML and DL methods were used, with prediction or classification being the most common application of ML or DL. The most common use cases were: the International Classification of Diseases, Ninth Revision (ICD-9) classification, clinical note analysis, and named entity recognition (NER) for clinical descriptions and research on psychiatric disorders. Conclusion: We find that the adopted ML models were not adequately assessed. In addition, the data imbalance problem is quite important, yet we must find techniques to address this underlining problem. Future studies should address key limitations in studies, primarily identifying Lupus Nephritis, Suicide Attempts, perinatal self-harmed and ICD-9 classification

Data Spaces

This open access book aims to educate data space designers to understand what is required to create a successful data space. It explores cutting-edge theory, technologies, methodologies, and best practices for data spaces for both industrial and personal data and provides the reader with a basis for understanding the design, deployment, and future directions of data spaces. The book captures the early lessons and experience in creating data spaces. It arranges these contributions into three parts covering design, deployment, and future directions respectively. The first part explores the design space of data spaces. The single chapters detail the organisational design for data spaces, data platforms, data governance federated learning, personal data sharing, data marketplaces, and hybrid artificial intelligence for data spaces. The second part describes the use of data spaces within real-world deployments. Its chapters are co-authored with industry experts and include case studies of data spaces in sectors including industry 4.0, food safety, FinTech, health care, and energy. The third and final part details future directions for data spaces, including challenges and opportunities for common European data spaces and privacy-preserving techniques for trustworthy data sharing. The book is of interest to two primary audiences: first, researchers interested in data management and data sharing, and second, practitioners and industry experts engaged in data-driven systems where the sharing and exchange of data within an ecosystem are critical