scholarly journals Public engagement with health data governance: the role of visuality

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Joanna Sleigh ◽  
Effy Vayena
Keyword(s):  
Data Sharing ◽  
Information And Communication Technologies ◽  
Public Engagement ◽  
Public Support ◽  
Personal Data ◽  
Analytical Framework ◽  
Health Data ◽  
Policy Debate ◽  
Data Governance ◽  
Communicative Practices

AbstractOver the last years, public engagement has become a topic of scholarly and policy debate particularly in biomedicine, a field that increasingly centres around collecting, sharing and analysing personal data. However, the use of big data in biomedicine poses specific challenges related to gaining public support for health data usage in research and clinical settings. The improvement of public engagement practices in health data governance is widely recognised as critical to address this issue. Based on OECD guidance, public engagement serves to enhance transparency and accountability, and enable citizens to actively participate in shaping what affects their lives. For health research initiatives, this provides a way to cultivate cooperation and build public trust. Today, the exact formats of public engagement have evolved to include approaches (such as social media, events and websites) that exploit visualisation mediated by emerging information and communication technologies. Much scholarship acknowledges the advantages of visuality for public engagement, particularly in information-dense and digital contexts. However, little research has examined how health data governance actors utilise visuality to promote clarity, understandability and audience participation. Beyond simply acknowledging the diversity of possible formats, attention must also be paid to visualisations’ rhetorical capacity to convey arguments and ideas and motivate particular audiences in specific situations. This paper seeks to address this gap by analysing both the approaches and methods of argumentation used in two visual public engagement campaigns. Based on Gottweis’ analytical framework of argumentative performativity, this paper explores how two European public engagement facilitators construct contending narratives in efforts to make sense of and grapple with the challenges of health data sharing. Specifically, we analyse how their campaigns employ the three rhetorical elements logos, ethos and pathos, proposed by Gottweis to assess communicative practices, intermediated and embedded in symbolically rich social and cultural contexts. In doing so, we highlight how visual techniques of argumentation seek to bolster engagement but vary with rhetorical purposes, as while one points to health data sharing risks, the other focuses on benefits. Moreover, drawing on digital and visual anthropology, we reflect on how the digitalisation of communicative practices impacts visual power.

scholarly journals Emerging models of data governance in the age of datafication

2020 ◽  
Vol 7 (2) ◽  
pp. 205395172094808
Author(s):  
Marina Micheli ◽  
Marisa Ponti ◽  
Max Craglia ◽  
Anna Berti Suman
Keyword(s):  
Social Interactions ◽  
Small Businesses ◽  
Personal Data ◽  
Policy Debate ◽  
Data Governance ◽  
Data Infrastructure ◽  
Civic Society ◽  
Public Data ◽  
Major Attention ◽  
Public Bodies

The article examines four models of data governance emerging in the current platform society. While major attention is currently given to the dominant model of corporate platforms collecting and economically exploiting massive amounts of personal data, other actors, such as small businesses, public bodies and civic society, take also part in data governance. The article sheds light on four models emerging from the practices of these actors: data sharing pools, data cooperatives, public data trusts and personal data sovereignty. We propose a social science-informed conceptualisation of data governance. Drawing from the notion of data infrastructure we identify the models as a function of the stakeholders’ roles, their interrelationships, articulations of value, and governance principles. Addressing the politics of data, we considered the actors’ competitive struggles for governing data. This conceptualisation brings to the forefront the power relations and multifaceted economic and social interactions within data governance models emerging in an environment mainly dominated by corporate actors. These models highlight that civic society and public bodies are key actors for democratising data governance and redistributing value produced through data. Through the discussion of the models, their underpinning principles and limitations, the article wishes to inform future investigations of socio-technical imaginaries for the governance of data, particularly now that the policy debate around data governance is very active in Europe.

scholarly journals The French Health Data Hub and the German Medical Informatics Initiatives: Two National Projects to Promote Data Sharing in Healthcare

2019 ◽  
Vol 28 (01) ◽  
pp. 195-202 ◽  
Author(s):  
Marc Cuggia ◽  
Stéphanie Combes
Keyword(s):  
Data Sharing ◽  
Medical Informatics ◽  
Data Privacy ◽  
Data Use ◽  
Health Data ◽  
The European Union ◽  
Global Approach ◽  
Data Governance ◽  
Academic Hospitals ◽  
Access Data

Objective: The diversity and volume of health data have been rapidly increasing in recent years. While such big data hold significant promise for accelerating discovery, data use entails many challenges including the need for adequate computational infrastructure and secure processes for data sharing and access. In Europe, two nationwide projects have been launched recently to support these objectives. This paper compares the French Health Data Hub initiative (HDH) to the German Medical Informatics Initiatives (MII). Method: We analysed the projects according to the following criteria: (i) Global approach and ambitions, (ii) Use cases, (iii) Governance and organization, (iv) Technical aspects and interoperability, and (v) Data privacy access/data governance. Results: The French and German projects share the same objectives but are different in terms of methodologies. The HDH project is based on a top-down approach and focuses on a shared computational infrastructure, providing tools and services to speed projects between data producers and data users. The MII project is based on a bottom-up approach and relies on four consortia including academic hospitals, universities, and private partners. Conclusion: Both projects could benefit from each other. A Franco-German cooperation, extended to other countries of the European Union with similar initiatives, should allow sharing and strengthening efforts in a strategic area where competition from other countries has increased.

scholarly journals Public Preferences regarding Data Linkage for Health Research: A Discrete Choice Experiment

2018 ◽  
Vol 3 (1) ◽  
Author(s):  
Mhairi Aitken ◽  
Gareth McAteer ◽  
Sara Davidson ◽  
Clive Frostick ◽  
Sarah Cunningham-Burley
Keyword(s):  
Data Sharing ◽  
Discrete Choice Experiment ◽  
Discrete Choice ◽  
Public Engagement ◽  
Health Research ◽  
Choice Experiment ◽  
Data Linkage ◽  
Public Support ◽  
Influential Factors ◽  
Relative Importance

The potential for data collected in the public and private sector to be linked and used in research has led to increasing interest in public acceptability of data sharing and data linkage. The literature has identified a range of factors that are important for shaping public responses and in particular has noted that public support for research conducted through data linkage or data sharing is contingent on a number of conditions being met. In order to examine the relative importance of these conditions a Discrete Choice Experiment (DCE) was conducted via an online questionnaire among members of Ipsos MORI’s online panel in Scotland. The survey was completed by 1,004 respondents. Overall the two most influential factors shaping respondents’ preferences are: the type of data being linked; and, how profits are managed and shared. The type of data being linked is roughly twice as important as who the researchers are. There were slight differences across age groups and between genders and slight differences when comparing respondents with and without long term health conditions. The most notable differences between respondents were found when comparing respondents according to employment and working sector. This study provides much needed evidence regarding the relative importance of various conditions which may be essential for securing and sustaining public support for data-linkage in health research. This may be useful for indicating which factors to focus on in future public engagement and has important implications for the design and delivery of research and public engagement activities. The continuously evolving nature of the field means it will be necessary to revisit the key conditions for public support on an ongoing basis and to examine the contexts and circumstances in which these might change. .

scholarly journals The “A” of FAIR – As Open as Possible, as Closed as Necessary

2020 ◽  
Vol 2 (1-2) ◽  
pp. 47-55 ◽  
Author(s):  
Annalisa Landi ◽  
Mark Thompson ◽  
Viviana Giannuzzi ◽  
Fedele Bonifazi ◽  
Ignasi Labastida ◽  
...  
Keyword(s):  
Data Sharing ◽  
Data Protection ◽  
Personal Data ◽  
Health Data ◽  
Privacy Rights ◽  
Regulatory Requirements ◽  
Data Set ◽  
General Data Protection Regulation ◽  
Digital Era ◽  
General Data

In order to provide responsible access to health data by reconciling benefits of data sharing with privacy rights and ethical and regulatory requirements, Findable, Accessible, Interoperable and Reusable (FAIR) metadata should be developed. According to the H2020 Program Guidelines on FAIR Data, data should be “as open as possible and as closed as necessary”, “open” in order to foster the reusability and to accelerate research, but at the same time they should be “closed” to safeguard the privacy of the subjects. Additional provisions on the protection of natural persons with regard to the processing of personal data have been endorsed by the European General Data Protection Regulation (GDPR), Reg (EU) 2016/679, that came into force in May 2018. This work aims to solve accessibility problems related to the protection of personal data in the digital era and to achieve a responsible access to and responsible use of health data. We strongly suggest associating each data set with FAIR metadata describing both the type of data collected and the accessibility conditions by considering data protection obligations and ethical and regulatory requirements. Finally, an existing FAIR infrastructure component has been used as an example to explain how FAIR metadata could facilitate data sharing while ensuring protection of individuals.

scholarly journals Fostering trustworthy data sharing: Establishing data foundations in practice

Data & Policy ◽  
2021 ◽  
Vol 3 ◽  
Author(s):  
Sophie Stalla-Bourdillon ◽  
Laura Carmichael ◽  
Alexsis Wintour
Keyword(s):  
Data Sharing ◽  
Personal Data ◽  
Channel Islands ◽  
Independent Data ◽  
Data Governance ◽  
Good Data ◽  
Governance Model ◽  
Data Stewardship ◽  
The Guardian

Abstract Independent data stewardship remains a core component of good data governance practice. Yet, there is a need for more robust independent data stewardship models that are able to oversee data-driven, multi-party data sharing, usage and re-usage, which can better incorporate citizen representation, especially in relation to personal data. We propose that data foundations—inspired by Channel Islands’ foundations laws—provide a workable model for good data governance not only in the Channel Islands, but also elsewhere. A key advantage of this model—in addition to leveraging existing legislation and building on established precedent—is the statutory role of the guardian that is a unique requirement in the Channel Islands, and when interpreted in a data governance model provides the independent data steward. The principal purpose for this paper, therefore, is to demonstrate why data foundations are well suited to the needs of data sharing initiatives. We further examine how data foundations could be established in practice—and provide key design principles that should be used to guide the design and development of any data foundation.

scholarly journals Assessment of Nursing Students’ Attitudes Towards Recording and Protecting Patients’ Personal Health Data: A Descriptive Study

2021 ◽  
Vol 28 (3) ◽  
pp. E202133
Author(s):  
Sebahat Atalıkoğlu Başkan ◽  
Papatya Karakurt ◽  
Necla Kasımoğlu
Keyword(s):  
Nursing Students ◽  
Data Sharing ◽  
Sample Selection ◽  
Personal Data ◽  
Attitude Scale ◽  
Health Data ◽  
Personal Health ◽  
Academic Level ◽  
Significant Difference ◽  
Students Attitudes

Introduction. Since health information is considered as sensitive personal data and requires more careful protection, healthcare professionals need to be careful about this issue. The objective of this research was to determine nursing students’ attitudes towards recording and protecting patients’ personal health data. Materials and Methods. The population of this descriptive research consisted of 450 students who studied at the Department of Nursing, Faculty of Health Sciences, Erzincan Binali Yildirim University. Sample selection was not used, and the research was completed with 374 students who were continuing education and who were accepted to participate in the research. Descriptive Information template and Attitude Scale for Recording and Protecting Personal Health Data for nursing students were used as data-collection instruments. The numbers, percentage, mean, standard deviation, non-parametric tests (the Mann-Whitney U test and the Kruskal-Wallis test) were used in data analysis. Results. Among our research participants, 68.4% of the students were females; 28.1% of the students were freshmen; 69% of the students were graduates of Anatolian high schools. Approximately 72.5% and 52.9% of the participants stated that they were aware of the concept of “personal data” and “personal health data” , respectively. The mean score of nursing students on the Attitude Scale for Recording and Protecting Personal Health Data was 3.97±0.71. The means scores obtained from subscales were as follows: 3.91±0.72 for Personal Health Data Information, 4.15±0.80 for Legal Information, 4.05±0.94 for Legal Data Sharing, 3.90±0.80 for Personal Health Data Sharing, and 3.77±0.33 for Recording of Personal Health Data, respectively. A statistically significant difference was found between the total scale and subscale scores of the students regarding their academic level. Conclusions. Students were found to have a positive attitude towards recording and protecting personal data. Increasing the responsibilities and raising awareness of the students for the protection of personal health data during their study is suggested to be important.

scholarly journals The Need for Machine-Processable Agreements in Health Data Management

Algorithms ◽  
2020 ◽  
Vol 13 (4) ◽  
pp. 87
Author(s):  
George Konstantinidis ◽  
Adriane Chapman ◽  
Mark J. Weal ◽  
Ahmed Alzubaidi ◽  
Lisa M. Ballard ◽  
...  
Keyword(s):  
Data Processing ◽  
Data Management ◽  
Data Sharing ◽  
Medical Information ◽  
Service Providers ◽  
Personal Data ◽  
Health Data ◽  
Coarse Grained ◽  
Semantic Web Technologies ◽  
Fine Grained

Data processing agreements in health data management are laid out by organisations in monolithic “Terms and Conditions” documents written in natural legal language. These top-down policies usually protect the interest of the service providers, rather than the data owners. They are coarse-grained and do not allow for more than a few opt-in or opt-out options for individuals to express their consent on personal data processing, and these options often do not transfer to software as they were intended to. In this paper, we study the problem of health data sharing and we advocate the need for individuals to describe their personal contract of data usage in a formal, machine-processable language. We develop an application for sharing patient genomic information and test results, and use interactions with patients and clinicians in order to identify the particular peculiarities a privacy/policy/consent language should offer in this complicated domain. We present how Semantic Web technologies can have a central role in this approach by providing the formal tools and features required in such a language. We present our ongoing approach to construct an ontology-based framework and a policy language that allows patients and clinicians to express fine-grained consent, preferences or suggestions on sharing medical information. Our language offers unique features such as multi-party ownership of data or data sharing dependencies. We evaluate the landscape of policy languages from different areas, and show how they are lacking major requirements needed in health data management. In addition to enabling patients, our approach helps organisations increase technological capabilities, abide by legal requirements, and save resources.

scholarly journals Artificial Intelligence in Biomedicine: A Legal Insight

BioTech ◽  
2021 ◽  
Vol 10 (3) ◽  
pp. 15
Author(s):  
Takis Vidalis
Keyword(s):  
Artificial Intelligence ◽  
Personal Data ◽  
Health Data ◽  
The Self ◽  
Data Handling ◽  
Direct Control ◽  
Eu Legislation ◽  
Attending Physicians ◽  
Self Learning ◽  
The Eu

The involvement of artificial intelligence in biomedicine promises better support for decision-making both in conventional and research medical practice. Yet two important issues emerge in relation to personal data handling, and the influence of AI on patient/doctor relationships. The development of AI algorithms presupposes extensive processing of big data in biobanks, for which procedures of compliance with data protection need to be ensured. This article addresses this problem in the framework of the EU legislation (GDPR) and explains the legal prerequisites pertinent to various categories of health data. Furthermore, the self-learning systems of AI may affect the fulfillment of medical duties, particularly if the attending physicians rely on unsupervised applications operating beyond their direct control. The article argues that the patient informed consent prerequisite plays a key role here, not only in conventional medical acts but also in clinical research procedures.

scholarly journals The funhouse mirror: the I in personalised healthcare

2021 ◽  
Vol 17 (1) ◽  
Author(s):  
Mira W. Vegter ◽  
Hub A. E. Zwart ◽  
Alain J. van Gool
Keyword(s):  
Precision Medicine ◽  
Personal Data ◽  
Health Data ◽  
Personal Health ◽  
Lifestyle Interventions ◽  
Phenotypic Data ◽  
Individual Health ◽  
Data Source ◽  
Tracking Devices ◽  
Personalised Healthcare

AbstractPrecision Medicine is driven by the idea that the rapidly increasing range of relatively cheap and efficient self-tracking devices make it feasible to collect multiple kinds of phenotypic data. Advocates of N = 1 research emphasize the countless opportunities personal data provide for optimizing individual health. At the same time, using biomarker data for lifestyle interventions has shown to entail complex challenges. In this paper, we argue that researchers in the field of precision medicine need to address the performative dimension of collecting data. We propose the fun-house mirror as a metaphor for the use of personal health data; each health data source yields a particular type of image that can be regarded as a ‘data mirror’ that is by definition specific and skewed. This requires competence on the part of individuals to adequately interpret the images thus provided.

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