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Personal Information Management Systems (PIMS) are acquiring a prominent role in the data economy by promoting services that help individuals to have more control over the processing of their personal data, in line with the European data protection laws. One of the highlighted solutions in this area is Solid, a new protocol that is decentralizing the storage of data, through the usage of interoperable web standards and semantic vocabularies, to empower its users to have more control over the processing of data by agents and applications. However, to fulfill this vision and gather widespread adoption, Solid needs to be aligned with the law governing the processing of personal data in Europe, the main piece of legislation being the General Data Protection Regulation (GDPR). To assist with this process, we analyze the current efforts to introduce a policy layer in the Solid ecosystem, in particular, related to the challenge of obtaining consent for processing personal data, focusing on the GDPR. Furthermore, we investigate if, in the context of using personal data for biomedical research, consent can be expressed in advance, and discuss the conditions for valid consent and how it can be obtained in this decentralized setting, namely through the matching of privacy preferences, set by the user, with requests for data and whether this can signify informed consent. Finally, we discuss the technical challenges of an implementation that caters to the previously identified legal requirements. [ABSTRACT FROM AUTHOR]

We analysed the impact of the SARS-CoV-2 pandemic (COVID-19) on the quality of breast cancer care in certified EUSOMA (European Society of Breast Cancer Specialists) breast centres. The results of the EUSOMA quality indicators were compared, based on pseudonymised individual records, for the periods 1 March 2020 till 30 June 2020 (first COVID-19 peak in most countries in Europe) and 1 March 2019 till 30 June 2019. In addition, a questionnaire was sent to the participating Centres for investigating the impact of the COVID-19 pandemic on the organisation and the quality of breast cancer care. Forty-five centres provided data and 31 (67%) responded to the questionnaire. The total number of new cases dropped by 19% and there was a small significant higher tumour (p = 0.003) and lymph node (p = 0.011) stage at presentation. Comparing quality indicators (12,736 patients) by multivariable analysis showed mostly non-significant differences. Surgery could be performed in a COVID-free zone in 94% of the centres, COVID testing was performed before surgery in 96% of the centres, and surgical case load was reduced in 55% of the centres. Modifications of the indications for neoadjuvant endocrine therapy, chemotherapy, and targeted therapy were necessary in 23%, 23%, and 10% of the centres; changes in indications for adjuvant endocrine, chemo-, targeted, immune, and radiotherapy in 3%, 19%, 3%, 6%, and 10%, respectively. Quality of breast cancer care was well maintained in EUSOMA breast centres during the first wave of the COVID-19 pandemic. A small but significantly higher tumour and lymph node stage at presentation was observed. • Patients registered in eusomaDB dropped by 19% in the first wave of COVID-19. • We found higher tumour (p = 0.003) and lymph node (p = 0.011) stage at presentation. • Changes in locoregional and adjuvant treatment were seen in patients aged >70. • QIs multivariate analysis in two time periods shows no significant differences. • Quality was maintained in EUSOMA breast centres during the first wave of COVID-19. [ABSTRACT FROM AUTHOR]

Background and Objective: The increasing implementation and use of electronic health records over the last few decades has made a significant volume of clinical data being available. Over the past 20 years, hospitals have also adopted and implemented data warehouse technology to facilitate the reuse of administrative and clinical data for research. However, the implementation of clinical data warehouses encounters a set of barriers: ethical, legislative, technical, human and organizational. This paper proposes an overview of difficulties and barriers encountered during a clinical data warehouse (CDW) development and implementation project., Methods: We conducted a focus group at the 2023 Medical Informatics Europe Conference and invited professionals involved in the implementation of CDW. These experts described their CDW and the difficulties and barriers they encountered at each phase: (i) launching of the data warehouse project, (ii) implementing the data warehouse and (iii) using a data warehouse in routine operations. They were also asked to propose solutions they were able to implement to address the barriers previously reported., Results: After synthesis and consensus, a total of 26 barriers were identified, 10 pertained to tasks, 5 to tools and technologies, 4 to persons, 4 to organization, and 3 to the external environment. To address these challenges, a set of 15 practical recommendations was offered, covering essential aspects such as governance, stakeholder engagement, interdisciplinary collaboration, and external expertise utilization., Conclusions: These recommendations serve as a valuable resource for healthcare institutions seeking to establish and optimize CDWs, offering a roadmap for leveraging clinical data for research, quality enhancement, and improved patient care., Competing Interests: Declaration of competing interest The authors have no conflicts of interest to disclose., (Copyright © 2024 Elsevier B.V. All rights reserved.)

(1) Background: Cloud storage is often required for successful operation of novel smart applications, relying on data produced by the Internet of Things (IoT) devices. Big Data processing tasks and management operations for such applications require high Quality of Service (QoS) guarantees, requiring an Edge/Fog computing approach. Additionally, users often require specific guarantees in the form of Service Level Agreements (SLAs) for storage services. To address these problems, we propose QoS-enabled Fog Storage Services, implemented as containerised storage services, orchestrated across the Things-to-Cloud computing continuum. (2) Method: The placement of containerised data storage services in the Things-to-Cloud continuum is dynamically decided using a novel Pareto-based decision-making process based on high availability, high throughput, and other QoS demands of the user. The proposed concept is first confirmed via simulation and then tested in a real-world environment. (3) Results: The decision-making mechanism and a novel SLA specification have been successfully implemented and integrated in the DECENTER Fog and Brokerage Platform to complement the orchestration services for storage containers, thus presenting their applicable value. Simulation results as well as practical experimentation in a Europe-wide testbed have shown that the proposed decision-making method can deliver a set of optimal storage nodes, thus meeting the SLA requirements. (4) Conclusion: It is possible to provide new smart applications with the expected SLA guarantees and high QoS for our proposed Fog Storage Services. [ABSTRACT FROM AUTHOR]

Objective: To assess organization of care in several bone sarcoma centers in Europe affiliated with the European Musculoskeletal Oncology Society (EMSOS) for comparison and to identify potential improvements in organization of care. Methods: Data for this observational cross‐sectional study was obtained through healthcare professionals affiliated to EMSOS. The authors formulated 10 questions regarding organization of care. The questions were focused on guidance, multidisciplinary decision‐making, and data storage. A digital questionnaire was synthesized and included quality control. The digital questionnaire was sent to 54 representative members of EMSOS. We did not receive responses from 29 representative countries (53.7%) after one digital invitation and two digital reminders. Results: We received data from 25 representatives of bone sarcoma centers from 17 countries across Europe (46.3%). Authorization to perform oncological care in a bone sarcoma center was government issued in 41.2% of cases and based on expertise without governmental influence in 52.9% of cases. In 64.7% of the countries, a national bone tumor guideline regarding for diagnosis and treatment is used in oncological care. A national bone tumor board for extensive case evaluation including classification and advice for treatment is available for 47.1% of the countries. All participating bone sarcoma centers have a mandatory local multidisciplinary meeting before the start of treatment; in 84.0% this meeting takes place once a week. During this multidisciplinary meeting a median of 15 cases (range, 4–40 cases) are discussed. In terms of storage of oncological data, a local registry is used in eight countries (47.1%). A national registry is used in eight countries (47.1%). Conclusions: A national bone tumor board gives bone sarcoma centers with little adherence the opportunity to gain knowledge from a more experienced team. Centralization of care in a bone sarcoma center is important to lower incidences. The optimal size for a bone sarcoma center in terms of patient adherence is not known at present. [ABSTRACT FROM AUTHOR]

Monoclonal antibodies (MAs) are increasingly used in the therapeutic arsenal. Clinical Data Warehouses (CDWs) offer unprecedented opportunities for research on real-word data. The objective of this work is to develop a knowledge organization system on MAs for therapeutic use (MATUs) applicable in Europe to query CDWs from a multi-terminology server (HeTOP). After expert consensus, three main health thesauri were selected: the MeSH thesaurus, the National Cancer Institute thesaurus (NCIt) and the SNOMED CT. These thesauri contain 1,723 MAs concepts, but only 99 (5.7 %) are identified as MATUs. The knowledge organisation system proposed in this article is a six-level hierarchical system according to their main therapeutic target. It includes 193 different concepts organised in a cross lingual terminology server, which will allow the inclusion of semantic extensions. Ninety nine (51.3 %) MATUs concepts and 94 (48.7 %) hierarchical concepts composed the knowledge organisation system. Two separates groups (an expert group and a validation group) carried out the selection, creation and validation processes. Queries identify, for unstructured data, 83 out of 99 (83.8 %) MATUs corresponding to 45,262 patients, 347,035 hospital stays and 427,544 health documents, and for structured data, 61 out of 99 (61.6 %) MATUs corresponding to 9,218 patients, 59,643 hospital stays and 104,737 hospital prescriptions. The volume of data in the CDW demonstrated the potential for using these data in clinical research, although not all MATUs are present in the CDW (16 missing for unstructured data and 38 for structured data). The knowledge organisation system proposed here improves the understanding of MATUs, the quality of queries and helps clinical researchers retrieve relevant medical information. The use of this model in CDW allows for the rapid identification of a large number of patients and health documents, either directly by a MATU of interest (e.g. Rituximab) but also by searching for parent concepts (e.g. Anti-CD20 Monoclonal Antibody)., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier Inc. All rights reserved.)

Abstract: Buildings account for almost 40% of the total energy usage and 30% of the total CO2 emissions in Europe. Environmental, legislative and economical drivers require more efficient and accurate energy management of buildings. Current building management systems do not have the capabilities of energy specific monitoring and management. In order to address these issues, in this paper, the Holistic Multi-Dimensional Information Management System will be described with its components: Data Warehouse Core, Extraction, Transformation and Loading (ETL) tool and Information Representation tools. The purpose of the developed system is to store, integrate, analyse complex data sets from multiple data and information sources such as wired/wireless sensing devices (e.g. sensor and meter readings) and Building Information Modelling (BIM) Tools (e.g. Autodesk Revit Architecture and MEP). The developed system is demonstrated and validated in the Environmental Research Institute (ERI) Building located on the campus of National University of Ireland-University College Cork. [Copyright &y& Elsevier]

Data availability is of vital importance for marine and oceanographic research but most of the European data are fragmented, not always validated and not easily accessible. In the countries bordering the European seas, more than 1000 scientific laboratories from governmental organisations and private industry collect data using various sensors on board of research vessels, submarines, fixed and drifting platforms, aeroplanes and satellites to measure physical, geophysical, geological, biological and chemical parameters, biological species and others. SeaDataNet is an Integrated Research Infrastructure Initiative (I3) (2006-2011) in the EU FP6 framework programme. It is developing an efficient distributed Pan-European marine data management infrastructure for managing these large and diverse data sets. It is interconnecting the existing professional data centres of 35 countries, active in data collection and providing integrated databases of standardised quality on-line. This article describes the architecture and the features of the SeaDataNet infrastructure. In particular it describes the way interoperability is achieved between all the contributing data centres. Finally it highlights the on-going developments and challenges. [ABSTRACT FROM AUTHOR]

The article provides information about Absa Bank, the leading bank in South Africa and which won the predictive analytics category in the Society for Information Management paper competition. The bank won the category for its business intelligence and data warehousing practice and has been recognized for its Information Management Group for its management and use of information. To further highlight Absa Bank's excellence in information management, its operations were compared with other business intelligence and data warehousing in North America and Europe.

The article focuses on data warehousing. A data warehouse is used to aid in decision making, knowledge creation and customer relationship management. Data warehousing originated in North America in the late 1980s and continues to be a leader in this field. Industries which are most advanced in data warehousing are airlines, banking, communications, retail and manufacturing. Areas of the world which are implementing this field are Western, Central and Northern Europe. Central and South America are less advanced in data warehousing. The reasons for the differences in data warehousing are explored.

Rare lung diseases affect 1.5-3 million people in Europe while causing bad prognosis or early deaths for patients. The European Reference Network for Respiratory Diseases (ERN-Lung) is a patient centric network, funded by the European Union (EU). The aims of ERN-LUNG is to increase healthcare and research regarding rare respiratory diseases. An initial need for cross-border healthcare and research is the use of registries and databases. A typical problem in registries for RDs is the data exchange, since the registries use different kind of data with different types or descriptions. Therefore, ERN-Lung decided to create a new Registry Data-Warehouse (RDW) where different existing registries are connected to enable cross-border healthcare within ERN-Lung. This work facilitates the aims, conception and implementation for the RDW, while considering a semantic interoperability approach. We created a common dataset (CDS) to have a common descriptions of respiratory diseases patients within the ERN registries. We further developed the RDW based on Open Source Registry System for Rare Diseases (OSSE), which includes a Metadata Repository with the Samply.MDR to unique describe data for the minimal dataset. Within the RDW, data from existing registries is not stored in a central database. The RDW uses the approach of the "Decentral Search" and can send requests to the connected registries, whereas only aggregated data is returned about how many patients with specific characteristics are available. However, further work is needed to connect the different existing registries to the RDW and to perform first studies.

The free movement of European citizens across member states of the European Union adds an important level of complexity to strategic efforts of health interoperability. The use of electronic health data has been marked as an important strategic activity and policy to improve healthcare in European countries. Cross-border healthcare depends on the ability to set up shared practices with respect to patient data exchange across the countries. Data flow must comply with demanding security, legal and interoperability requirements, as defined by the European Patients Smart Open Services project specifications. The aim of this article is to propose a novel design of healthcare data warehouse based on the restructured Extract-Transform-Load process. We describe a portal framework that offers a comprehensive set of interoperability services to enable national e-Health platforms to set up cross-border health information networks compliant with European Patients Smart Open Services. The presented approach incorporates the technical and organizational interoperability by interconnecting Health Level Seven standard and Open National Contact Points framework in order to provide a modular, scalable and inter-operating architecture.