explicit consent
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Semantic Web ◽  
2022 ◽  
pp. 1-34
Author(s):  
Fajar J. Ekaputra ◽  
Andreas Ekelhart ◽  
Rudolf Mayer ◽  
Tomasz Miksa ◽  
Tanja Šarčević ◽  
...  

Small and medium-sized organisations face challenges in acquiring, storing and analysing personal data, particularly sensitive data (e.g., data of medical nature), due to data protection regulations, such as the GDPR in the EU, which stipulates high standards in data protection. Consequently, these organisations often refrain from collecting data centrally, which means losing the potential of data analytics and learning from aggregated user data. To enable organisations to leverage the full-potential of the collected personal data, two main technical challenges need to be addressed: (i) organisations must preserve the privacy of individual users and honour their consent, while (ii) being able to provide data and algorithmic governance, e.g., in the form of audit trails, to increase trust in the result and support reproducibility of the data analysis tasks performed on the collected data. Such an auditable, privacy-preserving data analysis is currently challenging to achieve, as existing methods and tools only offer partial solutions to this problem, e.g., data representation of audit trails and user consent, automatic checking of usage policies or data anonymisation. To the best of our knowledge, there exists no approach providing an integrated architecture for auditable, privacy-preserving data analysis. To address these gaps, as the main contribution of this paper, we propose the WellFort approach, a semantic-enabled architecture for auditable, privacy-preserving data analysis which provides secure storage for users’ sensitive data with explicit consent, and delivers a trusted, auditable analysis environment for executing data analytic processes in a privacy-preserving manner. Additional contributions include the adaptation of Semantic Web technologies as an integral part of the WellFort architecture, and the demonstration of the approach through a feasibility study with a prototype supporting use cases from the medical domain. Our evaluation shows that WellFort enables privacy preserving analysis of data, and collects sufficient information in an automated way to support its auditability at the same time.


2021 ◽  
pp. 147775092110704
Author(s):  
Chloe Bell ◽  
Nathan Emmerich

There have been many reports of medical students performing pelvic exams on anaesthetised patients without the necessary consent being provided or even sought. These cases have led to an ongoing discussion regarding the need to ensure informed consent has been secured and furthermore, how it might be best obtained. We consider the importance of informed consent, the potential harm to both the patient and medical student risked by the suboptimal consent process, as well as alternatives to teaching pelvic examinations within medical school. The subsequent discussion focuses on whether medical students should perform pelvic examinations on anaesthetised patients without personally ensuring that they have given their explicit consent. Whilst we question the need to conduct pelvic examinations on anaesthetised patients in any circumstance, we argue that medical students should not perform such exams without personally securing the patients informed consent.


2021 ◽  
Vol 13 (20) ◽  
pp. 11524
Author(s):  
Thashmee Karunaratne

Personalized learning is one of the main focuses in 21st-century education, and Learning Analytics (LA) has been recognized as a supportive tool for enhancing personalization. Meanwhile, the General Data Protection Regulations (GDPR), which concern the protection of personal data, came into effect in 2018. However, contemporary research lacks the essential knowledge of how and in which ways the presence of GDPR influence LA research and practices. Hence, this study intends to examine the requirements for sustaining LA under the light of GDPR. According to the study outcomes, the legal obligations for LA could be simplified to data anonymization with consequences of limitations to personalized interventions, one of the powers of LA. Explicit consent from the data subjects (students) prior to any data processing is mandatory under GDPR. The consent agreements must include the purpose, types of data, and how, when and where the data is processed. Moreover, transparency of the complete process of storing, retrieving, and analysing data as well as how the results are used should be explicitly documented in LA applications. The need for academic institutions to have specific regulations for supporting LA is emphasized. Regulations for sharing data with third parties is left as a further extension of this study.


Author(s):  
Matthew G. Davey ◽  
John P.M. O’Donnell ◽  
Elizabeth Maher ◽  
Cliona McMenamin ◽  
Peter F. McAnena ◽  
...  

Abstract Background Europe’s General Data Protection Regulation, or GDPR, is a set of data protection rules on the acquisition, storage, use, and access of personal data. GDPR came into effect in May 2018 when it was introduced across all 27 European Union (EU) member states and the European Economic Area (EEA). Maintaining compliance with this legislation has presented significant new challenges for ongoing clinical research. Aims To evaluate the knowledge and expectations of patients and doctors regarding GDPR and implications for future clinical research. Methods An anonymous 12-item questionnaire was circulated to patients and doctors at a University Teaching Hospital. Data analysis included descriptive statistics. Results Five hundred nine participants were included: 261 females (51.3%) and 248 males (48.7%). Three hundred fifty were patients (68.8%) and 159 were doctors (31.2%). Three hundred thirty-four participants were aware of GDPR (65.7%): 116 doctors (73.0%) and 218 patients (62.3%, P = 0.018). 71.1% of doctors were willing to allow their personal data to be processed anonymously as part of a clinical research project compared to 43.4% of patients (P < 0.001). 80.2% of patients believed explicit consent is needed before using personal data in clinical research in comparison to 60.4% of doctors (P < 0.001). Level of education impacted awareness of GDPR (P < 0.001); a higher level of education among patients increased GDPR familiarity (P < 0.001), however failed to impact doctor familiarity (P = 0.117). Conclusion GDPR has introduced complexity to the processing and sharing of personal data among researchers. This study has identified differences in the perception of GDPR and willingness to consent to data being used in clinical research between doctors and patients. Measures to adequately inform prospective research participants on data processing and the evolving landscape of data protection regulation should be prioritised.


2021 ◽  
Vol 14 (1) ◽  
Author(s):  
Eva Rens ◽  
Joris Michielsen ◽  
Geert Dom ◽  
Roy Remmen ◽  
Kris Van den Broeck

Abstract Objective The study of care trajectories of psychiatric patients across hospitals was previously not possible in Belgium as each hospital stores its data autonomously, and government-related registrations do not contain a unique identifier or are incomplete. A new longitudinal database called iPSYcare (Improved Psychiatric Care and Research) was therefore constructed in 2021, and links the electronic medical records of patients in psychiatric units of eight hospitals in the Antwerp Province, Belgium. The database provides a wide range of information on patients, care trajectories and delivered care in the region. In a first phase, the database will only contain information about adult patients who were admitted to a hospital or treated by an outreach team and who gave explicit consent. In the future, the database may be expanded to other regions and additional data on outpatient care may be added. Results IPSYcare is a close collaboration between the University of Antwerp and hospitals in the province of Antwerp. This paper describes the development of the database, how privacy and ethical issues will be handled, and how the governance of the database will be organized.


2021 ◽  
Author(s):  
Jiahong Chen ◽  
Edward S. Dove ◽  
Himani Bhakuni

In this chapter, we discuss the boundaries between explicit consent and other available legitimizing mechanisms in EU data protection law for health research, focusing on sensitive data. This begins with an overview of the normative values and limitations of consent, highlighting the partially overlapping but not entirely identical roles of consent in health research and data protection, and the possibility of relying on alternative safeguards when public interests are involved. Such normative debates, including the differences between research participant consent and data subject consent, are then put into a legal context, with an analysis of the interplay between explicit consent and other exemptions under Article 9(2) GDPR, such as the scientific research exemption. Grounded in this baseline framework, all 30 jurisdictions currently subject to the GDPR (as well as the UK) are compared in terms of how national laws have treated explicit consent and the scientific research exemption differently. Our analysis shows a divergent regulatory landscape when it comes to the rules governing the use of sensitive data for health research.


Author(s):  
Holger Lüthen ◽  
Carsten Schröder ◽  
Markus M. Grabka ◽  
Jan Goebel ◽  
Tatjana Mika ◽  
...  

Abstract The aim of the project SOEP-RV is to link data from participants in the German Socio-Economic Panel (SOEP) survey to their individual Deutsche Rentenversicherung (German Pension Insurance) records. For all SOEP respondents who give explicit consent to record linkage, SOEP-RV creates a linked dataset that combines the comprehensive multi-topic SOEP data with detailed cross-sectional and longitudinal data on social security pension records covering the individual’s entire insurance history. This article provides an overview of the record linkage project, highlights potentials for analysis of the linked data, compares key SOEP and pension insurance variables, and suggests a re-weighting procedure that corrects for selectivity. It concludes with details on the process of obtaining the data for scientific use.


2021 ◽  
Author(s):  
Geetha R ◽  
T. Padmavathy ◽  
G.Umarani Srika

Abstract In a decentralized network every user makes use of personal identity details at different places for various services and these details are shared with third-parties without their consent and stored at an unknown location. Organizations like government, banks and social platforms are considered to be the weakest point in the current identity management system as they are vulnerable which leads to compromising billions of user identity data. Block chain based User Identity Management is a solution which provides a decentralized environment that manages the user identity data and their related Know-Your-Customer (KYC) documents in a distributed ledger. All the transactions of the network are stored in the block which is a type of a data structure and these blocks are validated using the powerful consensus algorithms and linked to form a block chain. Smart contracts will act as an interface between the client and the block chain network. User’s information cannot be provided to any third party vendors without the explicit consent of the user. This paper proposes a framework for User Identity Management using Block chain technology in a decentralized Network. The proposed framework ensures a high level privacy and security for the personal identity details and the documents. In addition to that the performance analysis of the framework is presented in terms of Transaction, Mining Resource and Difficulty Variation.


2021 ◽  
Author(s):  
Linda A. Jones ◽  
Jenny R. Nelder ◽  
Joseph M. Fryer ◽  
Philip H. Alsop ◽  
Michael R. Geary ◽  
...  

BACKGROUND. In the UK, National Health Service (NHS/HSC) data is variably shared between healthcare organizations for direct care, and increasingly used in de-identified forms for research. Few large-scale studies have examined public opinion on sharing, including the treatment of mental health (MH) versus physical health (PH) data. METHODS. Pre-registered anonymous online survey open to all UK residents, recruiting Feb-Sep 2020. Participants were randomized to one of three framing statements regarding MH versus PH data. FINDINGS. Participants numbered 29275; 40% had experienced a MH condition. A majority supported identifiable data sharing for direct clinical care without explicit consent, but 20% opposed this. Preference for clinical/identifiable sharing decreased with distance and was slightly less for MH than PH data, with a small framing effect. Preference for research/de-identified data sharing without explicit consent showed the same small PH/MH and framing effects, plus greater preference for sharing structured data than de-identified free text. There was net support for research sharing to the NHS, academic institutions, and national research charities, net ambivalence about sharing to profit-making companies researching treatments, and net opposition to sharing to other companies (similar to sharing publicly). De-identified linkage to non-health data was generally supported, except to data held by private companies. We report demographic influences on preference. A clear majority supported a single NHS mechanism to choose uses of their data. Support for data sharing increased during the pandemic. INTERPRETATION. Support for healthcare data sharing for direct care without explicit consent is broad but not universal. There is net support for the sharing of de-identified data for research to the NHS, academia, and the charitable sector, but not the commercial sector. A single national NHS-hosted system for patients to control the use of their NHS data for clinical purposes and for research would have broad public support. FUNDING. MRC.


Author(s):  
Francisca Nordfalk

Public health research depends on access to population data. This article is a study of the practices and the work enabling data collection for public health research. In Denmark, a blood sample is taken from practically every single newborn baby through a national screening programme. These samples can be combined with other health data and used for research purposes without explicit consent from those giving the samples. With an ethnographic approach, I study the practices, the work and the workers of the Danish NDBS samples, and explore how newborn babies come to serve as an important national research resource. From these studies, I argue that the making of national research resources in this way is ‘mutual enablement’ of research data and care. The work of both health professionals and researchers mutually enables professional care and opportunities for collection of samples and data for research. It is through this mutual enablement of research data and care that newborn babies become a national research population.


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