BackgroundConsent for data research in acute and critical care is complex as patients become at least temporarily incapacitated or die. Existing guidelines and regulations in the European Union are of limited help and there is a lack of literature about the use of data from this vulnerable group. To aid the creation of a patient-centred framework for responsible data research in the acute setting, we explored views of patients and next-of-kin about the collection, storage, sharing (...) and use of genetic and health-related data for observational research.MethodsWe conducted qualitative interviews (n = 19) with Dutch sudden cardiac arrest survivors who donated clinical and socio-economic data and genetic samples to research. We also interviewed their next-of-kin. Topics were informed by ethics literature and we used scenario-sketches to aid discussion of complex issues.ResultsSudden cardiac arrest survivors displayed limited awareness of their involvement in health data research and of the content of their given consent. We found that preferences regarding disclosure of clinically actionable genetic findings could change over time. When data collection and use were limited to the medical realm, patients trusted researchers to handle data responsibly without concern for privacy or other risks. There was no consensus as to whether deferred consent should be explicitly asked from survivors. If consent is asked, this would ideally be done a few months after the event when cognitive capacities have been regained. Views were divided about the need to obtain proxy consent for research with deceased patients’ data. However, there was general support for the disclosure of potentially relevant post-mortem genetic findings to relatives.ConclusionsSudden cardiac arrest patients’ donation of data for research was grounded in trust in medicine overall, blurring the boundary between research and care. Our findings also highlight questions about the acceptability of a one-time consent and about responsibilities of patients, researchers and ethics committees. Finally, further normative investigation is needed regarding the (continued) use of participants’ data after death, which is of particular importance in this setting. Our findings are thought to be of relevance for other acute and life-threatening illnesses as well. (shrink)

The future of health research will be characterised by three continuing trends: rising demand for health data; increasing impracticability of obtaining specific consent for secondary research; and decreasing capacity to effectively anonymise data. In this context, governments, clinicians and the research community must demonstrate that they can be responsible stewards of health data. IRBs and RECs sit at heart of this process because in many jurisdictions they have the capacity to grant (...) consent waivers when research is judged to be of particular value. However, several different terms are used to refer to this value, indicating a lack of conceptual clarity regarding the appropriate test for access to health data for research without consent. In this paper we do three things. First we describe the current confusion and instability in terminology relating to public interest in the context of consent waivers. Second we argue for harmonisation of terminology on the grounds of clarity, transparency and consistency. Third we argue that the term ‘public interest’ best reflects the normative work required to justify consent waivers because it is the broadest of the competing terms. ‘Public interest’ contains within its scope positive and negative implications of a study, as well as welfare, justice and rights considerations. In making this argument, we explain the normative basis for consent waivers, and provide a starting place for further discussion about the precise conditions in which a given study can be said to advance the public interest. Ipsos MORI study found that: … the public would be broadly happy with administrative data linking for research projects provided Those projects have social value, broadly defined. Data are de-identified. Data are kept secure. Businesses are not able to access the data for profit. (shrink)

Before the EU General Data Protection Regulation entered into force in May 2018, we witnessed an intense struggle of actors associated with data-dependent fields of science, in particular health-related academia and biobanks striving for legal derogations for data reuse in research. These actors engaged in a similar line of argument and formed issue alliances to pool their collective power. Using descriptive coding followed by an interpretive analysis, this article investigates the argumentative repertoire of these actors (...) and embeds the analysis in ethical debates on data sharing and biobank-related data governance. We observe efforts to perform a paradigmatic shift of the discourse around the General Data Protection Regulation-implementation away from ‘protecting data’ as key concern to ‘protecting health’ of individuals and societies at large. Instead of data protection, the key risks stressed by health researchers became potential obstacles to research. In line, exchange of information with data subjects is not a key concern in the arguments of biobank-related actors and it is assumed that patients want ‘their’ data to be used. We interpret these narratives as a ‘reaction’ to potential restrictions for data reuse and in line with a broader trend towards Big Data science, as the very idea of biobanking is conceptualized around long-term use of readily prepared data. We conclude that a sustainable implementation of biobanks needs not only to comply with the General Data Protection Regulation, but must proactively re-imagine its relation to citizens and data subjects in order to account for the various ways that science gets entangled with society. (shrink)

The predominant view is that a study using health data is observational research and should require individual consent unless it can be shown that gaining consent is impractical. But recent arguments have been made that citizens have an ethical obligation to share their health information for research purposes. In our view, this obligation is sufficient ground to expand the circumstances where secondary use research with identifiable health information is permitted without explicit subject consent. (...) As such, for some studies the Institutional Review Board/Research Ethics Committee review process should not assess the practicality of gaining consent for data use. Instead the review process should focus on assessing the public good of the research, public engagement and transparency. (shrink)

Health research ethics training is highly variable, with some researchers receiving little to none, which is why ethical frameworks represent critical tools for ethical deliberation and guiding responsible practice. However, these documents' voluntary and abstract nature can leave health researchers seeking more operationalised guidance, such as in the form of checklists, even though this approach does not support reflection on the meaning of principles nor their implications. In search of more reflective and participatory practices in a pandemic (...) context with distance learning, this study explored whether co-design could support engagement and reflection on ethical principles amongst early-stage health researchers. In a pilot test using the Swiss Personalized Health Network's ethical framework for Responsible Usage of Personal Data in Health Research as a case study, we engaged health researchers to design visuals of four ethical principles. In two online workshops, participants (N = 10, N = 8) completed activities such as individual reflection, collaborative ideation, sketching, prototyping, discussion, and feedback. Our analysis shows that the co-design process helped foster in-depth engagement and reflection on the meaning and relevance of the SPHN ethical principles. Participants reported enjoying the experience, and most felt that visuals could motivate engagement with ethical frameworks. This study demonstrates the feasibility of a participatory, design-oriented approach to promote engagement with research ethics among early-career health researchers and highlights key challenges and lessons learned. In doing so, it lays the foundation for future research to investigate the impact of design-oriented, participatory learning to foster reflection and deliberation in ethics education. (shrink)

In their response to ‘Public interest in health data research: laying out the conceptual groundwork’, Grewal and Newson critique us for inattention to the law and putting forward an impracticably broad conceptual understanding of public interest. While we agree more work is needed to generate a workable framework for Institutional Review Boards/Research Ethics Committees, we would contend that this should be grounded on a broad conception of public interest. This broadness facilitates regulatory agility, and is already (...) reflected by some current frameworks such as that found in the guidelines approved under Australia’s Privacy Act. It remains unclear which elements of our broad account Grewal and Newson would reject, or indeed where the substantive disagreement with our position lies. (shrink)

The law often calls on the concept of public interest for assistance. Privacy law makes use of this concept in several ways, including to justify consent waivers for secondary research on health information. Because the law sees information privacy as a means for individuals to control their personal information, consent can only be set aside in special circumstances. Ballantyne and Schaefer argue that only public interest, and only a broad conception of public interest, can do the special ‘normative (...) justificatory work’ to override consent requirements. Other, similar-sounding concepts, such as public benefit, public good and social value, also provide useful services. But none more so than public interest. In fact, they argue, public interest is the superior concept precisely because it can capture those concepts as well as a range of other interests. Our response focuses on this claim. We argue their strategy is not as promising as it might first seem. Ballantyne and Schaefer construe the important role that public interest plays in this context as their endpoint. They claim that unless the concept is open and content-rich, it will lose some of its importance. But by refusing to place limits around it, their inquiry leads us back to a catch-all concept that lacks clear focus or meaning. In reply, we argue that, for practically minded theorists, a narrow conception of public interest is more useful. Further, the narrowing of public interest in this context can be achieved by first analysing it in its legal, rather than ethical, sense. (shrink)

In this article, we use the theory of Information Ethics to argue that deceased people have a prima facie moral right to privacy in the context of health data research, and that this should be reflected in regulation and guidelines. After death, people are no longer biological subjects but continue to exist as informational entities which can still be harmed/damaged. We find that while the instrumental value of recognising post-mortem privacy lies in the preservation of the social (...) contract for health research, its intrinsic value is grounded in respect for the dignity of the post-mortem informational entity. However, existing guidance on post-mortem data protection is available only in the context of genetic studies. In comparing the characteristics of genetic data and other health-related data, we identify two features of DNA often given as arguments for this genetic exceptionalism: relationality and embodiment. We use these concepts to show that at the appropriate Level of Abstraction, there is no morally relevant distinction between posthumous genetic and other health data. Thus, genetic data should not automatically receive special moral status after death. Instead we make a plea for ‘contextual exceptionalism’. Our analysis concludes by reflecting on a real-world case and providing suggestions for contextual factors that researchers and oversight bodies should take into account when designing and evaluating research projects with health data from deceased subjects. (shrink)

Developments in medical big data analytics may bring societal benefits but are also challenging privacy and other ethical values. At the same time, an overly restrictive data protection regime can form a serious threat to valuable observational studies. Discussions about whether data privacy or data solidarity should be the foundational value of research policies, have remained unresolved. We add to this debate with an empirically informed ethical analysis. First, experiences with the implementation of the General (...) Data Protection Regulation (GDPR) within a European research consortium demonstrate a gap between the aims of the regulation and its effects in practice. Namely, strictly formalised data protection requirements may cause routinisation among researchers instead of substantive ethical reflection, and may crowd out trust between actors in the health data research ecosystem; while harmonisation across Europe and data sharing between countries is hampered by different interpretations of the law, which partly stem from different views about ethical values. Then, building on these observations, we use theory to argue that the concept of trust provides an escape from the privacy-solidarity debate. Lastly, the paper details three aspects of trust that can help to create a responsible research environment and to mitigate the encountered challenges: trust as multi-agent concept; trust as a rational and democratic value; and trust as method for priority setting. Mutual cooperation in research—among researchers and with data subjects—is grounded in trust, which should be more explicitly recognised in the governance of health data research. (shrink)

We welcome Ballantyne & Schaefer’s discussion of the issues concerning consent and use of health data for research. In response to their acknowledgement of the need for public debate and discussion, we provide evidence from our own public consultation on this topic.

Generative artificial intelligence (GenAI) promises to revolutionize data-driven fields (Milmo 2023). Building on decades of large language modeling (LLM) (Toner 2023), GenAI can collect, harmonize...

The article discusses the impact different concepts of solidarity can have on debates on models of consent for non-interventional research. It introduces three concepts of solidarity that have been referenced in bioethical debates: a purely descriptive concept, a concept that claims some derivative value for most but not all practices of solidarity, as well as a clearly normative concept where solidarity is tied to justice and taken to ground moral duties. It shows that regarding the rivalling models of study-specific (...) consent, tiered consent and broad consent, the first two concepts can be taken to favour tiered consent while only normative solidarity supports a model of broad consent—or an argument to allow non-interventional research without requiring consent at all. As normative solidarity is tied to considerations of justice, however, the argument appears less straightforward than one might expect: It presupposes that the research contributes to overcoming existing social injustices. (shrink)

This paper contends that a research ethics approach to the regulation of health data research is unhelpful in the era of population‐level research and big data because it results in a primary focus on consent (meta‐, broad, dynamic and/or specific consent). Two recent guidelines – the 2016 WMA Declaration of Taipei on ethical considerations regarding health databases and biobanks and the revised CIOMS International ethical guidelines for health‐related research involving humans – (...) both focus on the growing reliance on health data for research. But as research ethics documents, they remain (to varying degrees) focused on consent and individual control of data use. Many current and future uses of health data make individual consent impractical, if not impossible. Many of the risks of secondary data use apply to communities and stakeholders rather than individual data subjects. Shifting from a research ethics perspective to a public health lens brings a different set of issues into view: how are the benefits and burdens of data use distributed, how can data research empower communities, who has legitimate decision‐making capacity? I propose that a public health ethics framework – based on public benefit, proportionality, equity, trust and accountability – provides more appropriate tools for assessing the ethical uses of health data. The main advantage of a public health approach for data research is that it is more likely to foster debate about power, justice and equity and to highlight the complexity of deciding when data use is in the public interest. (shrink)

Background Improving the ways in which routinely-collected mental health data are shared could facilitate substantial advances in research and treatment. However, this process should only be undertaken in partnership with those who provide such data. Despite relatively widespread investigation of public perspectives on health data sharing more generally, there is a lack of research on the views of people with mental illness. Methods Twelve people with lived experience of mental illness took part in (...) semi-structured interviews via online video software. Participants had experience of a broad range of mental health conditions including anxiety, depression, schizophrenia, eating disorders and addiction. Interview questions sought to establish how participants felt about the use of routinely-collected health data for research purposes, covering different types of health data, what health data should be used for, and any concerns around its use. Results Thematic analysis identified four overarching themes: benefits of sharing mental health data, concerns about sharing mental health data, safeguards, and data types. Participants were clear that health data sharing should facilitate improved scientific knowledge and better treatments for mental illness. There were concerns that data misuse could become another way in which individuals and society discriminate against people with mental illness, for example through insurance premiums or employment decisions. Despite this there was a generally positive attitude to sharing mental health data as long as appropriate safeguards were in place. Conclusions There was notable strength of feeling across participants that more should be done to reduce the suffering caused by mental illness, and that this could be partly facilitated by well-managed sharing of health data. The mental health research community could build on this generally positive attitude to mental health data sharing by following rigorous best practice tailored to the specific concerns of people with mental illness. (shrink)

IntroductionInternational sharing of health data opens the door to the study of the so-called ‘Big Data’, which holds great promise for improving patient-centred care. Failure of recent data sharing initiatives indicates an urgent need to invest in societal trust in researchers and institutions. Key to an informed understanding of such a ‘social license’ is identifying the views patients and the public may hold with regard to data sharing for health research.MethodsWe performed a narrative (...) review of the empirical evidence addressing patients’ and public views and attitudes towards the use of health data for research purposes. The literature databases PubMed, Embase, Scopus and Google Scholar were searched in April 2019 to identify relevant publications. Patients’ and public attitudes were extracted from selected references and thematically categorised.ResultsTwenty-seven papers were included for review, including both qualitative and quantitative studies and systematic reviews. Results suggest widespread—though conditional—support among patients and the public for data sharing for health research. Despite the fact that participants recognise actual or potential benefits of data research, they expressed concerns about breaches of confidentiality and potential abuses of the data. Studies showed agreement on the following conditions: value, privacy, risk minimisation, data security, transparency, control, information, trust, responsibility and accountability.ConclusionsOur results indicate that a social license for data-intensive health research cannot simply be presumed. To strengthen the social license, identified conditions ought to be operationalised in a governance framework that incorporates the diverse patient and public values, needs and interests. (shrink)

Ethical decision-making frameworks assist in identifying the issues at stake in a particular setting and thinking through, in a methodical manner, the ethical issues that require consideration as well as the values that need to be considered and promoted. Decisions made about the use, sharing, and re-use of big data are complex and laden with values. This paper sets out an Ethics Framework for Big Data in Health and Research developed by a working group convened by (...) the Science, Health and Policy-relevant Ethics in Singapore Initiative. It presents the aim and rationale for this framework supported by the underlying ethical concerns that relate to all health and research contexts. It also describes a set of substantive and procedural values that can be weighed up in addressing these concerns, and a step-by-step process for identifying, considering, and resolving the ethical issues arising from big data uses in health and research. This Framework is subsequently applied in the papers published in this Special Issue. These papers each address one of six domains where big data is currently employed: openness in big data and data repositories, precision medicine and big data, real-world data to generate evidence about healthcare interventions, AI-assisted decision-making in healthcare, public-private partnerships in healthcare and research, and cross-sectoral big data. (shrink)

Volume 19, Issue 9, September 2019, Page 50-52.

BackgroundThe past 10 years have witnessed a significant growth in sharing of health data for secondary uses. Alongside this there has been growing interest in the public acceptability of data sharing and data linkage practices. Public acceptance is recognised as crucial for ensuring the legitimacy of current practices and systems of governance. Given the growing international interest in this area this systematic review and thematic synthesis represents a timely review of current evidence. It highlights the key (...) factors influencing public responses as well as important areas for further research.MethodsThis paper reports a systematic review and thematic synthesis of qualitative studies examining public attitudes towards the sharing or linkage of health data for research purposes. Twenty-five studies were included in the review. The included studies were conducted primarily in the UK and North America, with one study set in Japan, another in Sweden and one in multiple countries. The included studies were conducted between 1999 and 2013. The qualitative methods represented in the studies included focus groups, interviews, deliberative events, dialogue workshops and asynchronous online interviews.ResultsKey themes identified across the corpus of studies related to the conditions necessary for public support/acceptability, areas of public concern and implications for future research. The results identify a growing body of evidence pointing towards widespread general—though conditional—support for data linkage and data sharing for research purposes. Whilst a variety of concerns were raised in cases where participants perceived there to be actual or potential public benefits from research and had trust in the individuals or organisations conducting and/or overseeing data linkage/sharing, they were generally supportive. The studies also find current low levels of awareness about existing practices and uses of data.ConclusionsWhilst the results indicate widespread public support for data sharing and linkage for research purposes, a range of concerns exist. In order to ensure public support for future research uses of data greater awareness raising combined with opportunities for public engagement and deliberation are needed. This will be essential for ensuring the legitimacy of future health informatics research and avoiding further public controversy. (shrink)

The increased use of information technology in every day health care creates vast amounts of stored health data that can be used for research. The secondary research use of routinely collected data raises questions about appropriate consent mechanisms for such use. One option is meta consent where individuals state their own consent preferences in relation to future use of their data, e.g. whether they want the data to be accessible to researchers under (...) conditions of specific consent, broad consent, blanket consent or not at all. This study investigates whether meta consent preferences can be successfully elicited by a smartphone application in the adult Danish population. A smartphone app was developed for the elicitation of meta consent preferences. An invitation to use the app was distributed to a stratified, representative sample of the Danish adult population. The meta consent choices, the use of the app, user experience data, and demographic data were logged and analysed statistically using IBM SPSS version 20. Of 1000 potential respondents 221 used the app. One hundred eighty-eight of the respondents were female and 103 male. The age range was 19 to 79 years with an average of 51 years. Most users indicate 1) that they find the choices they are asked to make easy to understand, 2) that the application is easy to use, and 3) that this kind of choice should be offered to people. It is possible to collect meta consent preferences in the general, adult population using a smartphone app. (shrink)

As opposed to a ‘one size fits all’ approach, precision medicine uses relevant biological, medical, behavioural and environmental information about a person to further personalize their healthcare. This could mean better prediction of someone’s disease risk and more effective diagnosis and treatment if they have a condition. Big data allows for far more precision and tailoring than was ever before possible by linking together diverse datasets to reveal hitherto-unknown correlations and causal pathways. But it also raises ethical issues relating (...) to the balancing of interests, viability of anonymization, familial and group implications, as well as genetic discrimination. This article analyses these issues in light of the values of public benefit, justice, harm minimization, transparency, engagement and reflexivity and applies the deliberative balancing approach found in theEthical Framework for Big Data in Health and Research to a case study on clinical genomic data sharing. Please refer to that article for an explanation of how this framework is to be used, including a full explanation of the key values involved and the balancing approach used in the case study at the end. Our discussion is meant to be of use to those involved in the practice as well as governance and oversight of precision medicine to address ethical concerns that arise in a coherent and systematic manner. (shrink)

This paper conducts a comparative analysis of data governance mechanisms concerning the secondary use of health data in Taiwan and the European Union (EU). Both regions have adopted distinctive approaches and regulations for utilizing health data beyond primary care, encompassing areas such as medical research and healthcare system enhancement. Through an examination of these models, this study seeks to elucidate the strategies, frameworks, and legal structures employed by Taiwan and the EU to strike a (...) delicate balance between the imperative of data-driven healthcare innovation and the safeguarding of individual privacy rights. This paper examines and compares several key aspects of the secondary use of health data in Taiwan and the EU. These aspects include data governance frameworks, legal and regulatory frameworks, data access and sharing mechanisms, and privacy and security considerations. This comparative exploration offers invaluable insights into the evolving global landscape of health data governance. It provides a deeper understanding of the strategies implemented by these regions to harness the potential of health data while upholding the ethical and legal considerations surrounding its secondary use. The findings aim to inform best practices for responsible and effective health data utilization, particularly in the context of medical AI applications. (shrink)

Public-private partnerships are established to specifically harness the potential of Big Data in healthcare and can include partners working across the data chain—producing health data, analysing data, using research results or creating value from data. This domain paper will illustrate the challenges that arise when partners from the public and private sector collaborate to share, analyse and use biomedical Big Data. We discuss three specific challenges for PPPs: working within the social licence, (...) public antipathy to the commercialisation of public sector health data, and questions of ownership, both of the data and any resulting intellectual property or products. As a specific example we consider the case of the UK National Health Service providing patient data to Google’s DeepMind AI program to develop a diagnostic app for kidney disease. This article is an application of the framework presented in this issue of ABR. Please refer to that article for more information on how this framework is to be used, including a full explanation of the key values involved and the balancing approach used in the case study at the end. We use four specific values to help analysis these issues: public benefit, stewardship, transparency and engagement. We demonstrate how the Deliberative Framework can support ethical governance of PPPs involving biomedical big data. (shrink)

Artificial intelligence is set to transform healthcare. Key ethical issues to emerge with this transformation encompass the accountability and transparency of the decisions made by AI-based systems, the potential for group harms arising from algorithmic bias and the professional roles and integrity of clinicians. These concerns must be balanced against the imperatives of generating public benefit with more efficient healthcare systems from the vastly higher and accurate computational power of AI. In weighing up these issues, this paper applies the deliberative (...) balancing approach of the Ethics Framework for Big Data in Health and Research. The analysis applies relevant values identified from the framework to demonstrate how decision-makers can draw on them to develop and implement AI-assisted support systems into healthcare and clinical practice ethically and responsibly. Please refer to Xafis et al. in this special issue of the Asian Bioethics Review for more information on how this framework is to be used, including a full explanation of the key values involved and the balancing approach used in the case study at the end of this paper. (shrink)

Background Several jurisdictions, including Singapore, Australia, New Zealand and most recently Ireland, have a public interest or public good criterion for granting waivers of consent in biomedical research using secondary health data or tissue. However, the concept of the public interest is not well defined in this context, which creates difficulties for institutions, institutional review boards and regulators trying to implement the criterion. Main text This paper clarifies how the public interest criterion can be defensibly deployed. We (...) first explain the ethical basis for requiring waivers to only be granted to studies meeting the public interest criterion, then explore how further criteria may be set to determine the extent to which a given study can legitimately claim to be in the public interest. We propose an approach that does not attempt to measure magnitude of benefit directly, but rather takes into account metrics that are more straightforward to apply. To ensure consistent and justifiable interpretation, research institutions and IRBs should also incorporate procedural features such as transparency and public engagement in determining which studies satisfy the public interest requirement. Conclusion The requirement of public interest for consent waivers in secondary biomedical research should be guided by well-defined criteria for systematic evaluation. Such a criteria and its application need to be periodically subject to intra-committee and intra-institution review, reflection, deliberation and amendment. (shrink)

Discussion of uses of biomedical data often proceeds on the assumption that the data are generated and shared solely or largely within the health sector. However, this assumption must be challenged because increasingly large amounts of health and well-being data are being gathered and deployed in cross-sectoral contexts such as social media and through the internet of things and wearable devices. Cross-sectoral sharing of data thus refers to the generation, use and linkage of biomedical (...) data beyond the health sector. This paper considers the challenges that arise from this phenomenon. If we are to benefit fully, it is important to consider which ethical values are at stake and to reflect on ways to resolve emerging ethical issues across ecosystems where values, laws and cultures might be quite distinct. In considering such issues, this paper applies the deliberative balancing approach of the Ethics Framework for Big Data in Health and Research to the domain of cross-sectoral big data. Please refer to that article for more information on how this framework is to be used, including a full explanation of the key values involved and the balancing approach used in the case study at the end. (shrink)

There is a growing expectation, or even requirement, for researchers to deposit a variety of research data in data repositories as a condition of funding or publication. This expectation recognizes the enormous benefits of data collected and created for research purposes being made available for secondary uses, as open science gains increasing support. This is particularly so in the context of big data, especially where health data is involved. There are, however, also (...) challenges relating to the collection, storage, and re-use of research data. This paper gives a brief overview of the landscape of data sharing via data repositories and discusses some of the key ethical issues raised by the sharing of health-related research data, including expectations of privacy and confidentiality, the transparency of repository governance structures, access restrictions, as well as data ownership and the fair attribution of credit. To consider these issues and the values that are pertinent, the paper applies the deliberative balancing approach articulated in the Ethics Framework for Big Data in Health and Research to the domain of Openness in Big Data and Data Repositories. Please refer to that article for more information on how this framework is to be used, including a full explanation of the key values involved and the balancing approach used in the case study at the end. (shrink)

Intensified and extensive data production and data storage are characteristics of contemporary western societies. Health data sharing is increasing with the growth of Information and Communication Technology platforms devoted to the collection of personal health and genomic data. However, the sensitive and personal nature of health data poses ethical challenges when data is disclosed and shared even if for scientific research purposes. With this in mind, the Science and Values Working (...) Group of the COST Action CHIP ME ‘Citizen's Health through public-private Initiatives: Public health, Market and Ethical perspectives’ identified six core values they considered to be essential for the ethical sharing of health data using ICT platforms. We believe that using this ethical framework will promote respectful scientific practices in order to maintain individuals’ trust in research. We use these values to analyse five ICT platforms and explore how emerging data sharing platforms are reconfiguring the data sharing experience from a range of perspectives. We discuss which types of values, rights and responsibilities they entail and enshrine within their philosophy or outlook on what it means to share personal health information. Through this discussion we address issues of the design and the development process of personal health data and patient-oriented infrastructures, as well as new forms of technologically-mediated empowerment. (shrink)

Existing ethics guidelines, influential literature and policies on ethical research generally focus on real-time data collection from humans. They enforce individual rights and liberties, thereby lowering need for aggregate protections. Although dependable, emerging public health research paradigms like research using public health data raise new challenges to their application. Unlike traditional research, RUPD is population-based, aligned to public health activities, and often reliant on pre-collected longitudinal data. These characteristics, when considered (...) in relation to the generally lower protective ethico-legal frameworks of the Global South, including Africa, highlight ethical gaps. Health and demographic surveillance systems are examples of public health programs that accommodate RUPD in these contexts. We set out to explore the perspectives of professionals with a working knowledge of these systems to determine practical ways of appropriating the foundational principles of health research to advance the ever growing opportunities in RUPD. We present their perspectives and in relation to the literature and our ethical analysis, make context relevant recommendations. We further argue for the development of a framework founded on the discussions and recommendations as a minimum base for achieving optimal ethics for optimal RUPD in the Global South. (shrink)

Web scraping involves using computer programs for automated extraction and organization of data from the Web for the purpose of further data analysis and use. It is frequently used by commercial companies, but also has become a valuable tool in epidemiological research and public health planning. In this paper, we explore ethical issues in a project that “scrapes” public websites of U.S. county jails as part of an effort to develop a comprehensive database to enhance HIV (...) surveillance and improve continuity of care for incarcerated populations. We argue that the well-known framework of Emanuel et al. provides only partial ethical guidance for the activities we describe, which lie at a complex intersection of public health research and public health practice. We suggest some ethical considerations from the ethics of public health practice to help fill gaps in this relatively unexplored area. (shrink)

It is increasingly recognised that evidence generated using “real-world data” is crucial for assessing the safety and effectiveness of health-related interventions. This, however, raises a number of issues, including those related to the quality of RWD, and of the scientific methods used to generate evidence from it, and the potential for those gathering and using RWD be driven by commercial, political, professional or personal self-interest. This article is an application of the framework presented in this issue of ABR. (...) Please refer to that article for more information on how this framework is to be used, including a full explanation of the key values involved and the balancing approach used in the case study at the end to demonstrate how those generating or using RWD can make use of values when deliberating about ethical issues. (shrink)

For the first time, the revised Common Rule specifies that public health surveillance activities are not research. This article reviews the historical development of the public health surveillance exclusion and implications for other foundational public health practices.

BackgroundIncreasingly, hospitals and research institutes are developing technical solutions for sharing patient data in a privacy preserving manner. Two of these technical solutions are homomorphic encryption and distributed ledger technology. Homomorphic encryption allows computations to be performed on data without this data ever being decrypted. Therefore, homomorphic encryption represents a potential solution for conducting feasibility studies on cohorts of sensitive patient data stored in distributed locations. Distributed ledger technology provides a permanent record on all transfers (...) and processing of patient data, allowing data custodians to audit access. A significant portion of the current literature has examined how these technologies might comply with data protection and research ethics frameworks. In the Swiss context, these instruments include the Federal Act on Data Protection and the Human Research Act. There are also institutional frameworks that govern the processing of health related and genetic data at different universities and hospitals. Given Switzerland’s geographical proximity to European Union (EU) member states, the General Data Protection Regulation (GDPR) may impose additional obligations.MethodsTo conduct this assessment, we carried out a series of qualitative interviews with key stakeholders at Swiss hospitals and research institutions. These included legal and clinical data management staff, as well as clinical and research ethics experts. These interviews were carried out with two series of vignettes that focused on data discovery using homomorphic encryption and data erasure from a distributed ledger platform.ResultsFor our first set of vignettes, interviewees were prepared to allow data discovery requests if patients had provided general consent or ethics committee approval, depending on the types of data made available. Our interviewees highlighted the importance of protecting against the risk of reidentification given different types of data. For our second set, there was disagreement amongst interviewees on whether they would delete patient data locally, or delete data linked to a ledger with cryptographic hashes. Our interviewees were also willing to delete data locally or on the ledger, subject to local legislation.ConclusionOur findings can help guide the deployment of these technologies, as well as determine ethics and legal requirements for such technologies. (shrink)

Background The ownership status of individual-level health data affects the manner in which it is used. In this paper we analyze two competing models of the ownership status of the data discussed in the literature recently: private ownership and public ownership. Main body In this paper we describe the limitations of these two models of data ownership with respect to individual-level health data, in particular in terms of ethical principles of justice and autonomy, risk (...) mitigation, as well as technological, economic, and conceptual issues. We argue that undifferentiated application of neither private ownership nor public ownership will allow us to resolve all the problems associated with effective, equitable, and ethical use of data. We suggest that, instead of focusing on data ownership, we should focus on the institutional and procedural aspects of data governance, such as using Data Access Committees (DACs) or equivalent managed access processes, which can balance the elements of these two ownership frameworks. Conclusion Undifferentiated application of the ownership concept (private or public) is not helpful in resolving problems associated with sharing individual-level health data. DACs or equivalent managed access processes should be an integral part of data governance. They can approve or disapprove data access requests after considering the potential benefits and harms to data subjects, their communities, primary researchers, and the wider society. (shrink)

One of the core problems of scientific research authorship is honorary authorship. It violates the ethical principle of clear and appropriate assignment of scientific research contributions. The prevalence of honorary authorship worldwide is alarmingly high across various research disciplines. As a result, many academic institutions and publishers were trying to explore ways to overcome this unethical research practice. The International Committee of Medical Journal Editors (ICMJE) recommended criteria for authorship as guidance for researchers submitting manuscripts to (...) biomedical Journals. However, despite the ICMJE guidelines, honorary authorship is still significantly present across various health research disciplines. The aim of this study was to explore the perceptions and knowledge of health care researchers towards honorary authorship according to the ICMJE guidelines across different health care fields in Jordan, which to our knowledge was never explored before. Data from an electronic survey that was distributed among researchers working in different healthcare fields across several major universities in Jordan, revealed that most of the respondents were assistant professors working mainly in the schools of Medicine and Pharmacy. The majority of the respondents (65.5%) were not aware of the ICMJE authorship guidelines. And, around 37% reported the inclusion of an honorary author, in which the most common non-authorship task reported by 73% of the respondents was reviewing the manuscript. Our findings emphasize the need for national academic and research institutions to address the issue of authorship in their educational programs and internal policies. (shrink)

Digitalisation of health and the use of health data in artificial intelligence, and machine learning (ML), including for applications that will then in turn be used in healthcare are major themes permeating current UK and other countries’ healthcare systems and policies. Obtaining rich and representative data is key for robust ML development, and UK health data sets are particularly attractive sources for this. However, ensuring that such research and development is in the public (...) interest, produces public benefit and preserves privacy are key challenges. Trusted research environments (TREs) are positioned as a way of balancing the diverging interests in healthcare data research with privacy and public benefit. Using TRE data to train ML models presents various challenges to the balance previously struck between these societal interests, which have hitherto not been discussed in the literature. These challenges include the possibility of personal data being disclosed in ML models, the dynamic nature of ML models and how public benefit may be (re)conceived in this context. For ML research to be facilitated using UK health data, TREs and others involved in the UK health data policy ecosystem need to be aware of these issues and work to address them in order to continue to ensure a ‘safe’ health and care data environment that truly serves the public. (shrink)

In this article we provide an in-depth description of a new model of informed consent called ‘meta consent’ and consider its practical implementation. We explore justifications for preferring meta consent over alternative models of consent as a solution to the problem of secondary use of health data for research. We finally argue that meta consent strikes an appropriate balance between enabling valuable research and protecting the individual.

Genomic and biobank research has undergone exponential growth in Africa. Traditionally this resulted in exploitative research practices in the form of so-called ‘parachute research’ with little or no consideration for capacity building. However there has been a recent growth of research and consortia where capacity building and equitable research have been a key objective of the research, and attention is now focused on the governance of this research. The importance of solidarity in genomic (...) biobank research in high income countries is well established and such an ethos reflects the communal based worldviews prevalent in many African communities. However, if the governance of the research is to foster equitable research practices, there must be some reciprocal benefit. This chapter will explore the impact that reciprocity will have on solidarity and how this may manifest in a governance framework in Africa. Particular focus will be given to capacity building and benefit sharing to enable the research to develop for the benefit of the local population, in a manner that promotes trust and fairness. This chapter will conclude with some recommendations on how to ensure reciprocity is enshrined in a governance framework for genomic biobank research in Africa. (shrink)

Bayes’ rule shows how one might rationally change one’s beliefs in the light of evidence. It is the foundation of a statistical method called Bayesianism. In health care research, Bayesianism has its advocates but the dominant statistical method is frequentism. There are at least two important philosophical differences between these methods. First, Bayesianism takes a subjectivist view of probability (i.e. that probability scores are statements of subjective belief, not objective fact) whilst frequentism takes an objectivist view. Second, Bayesianism (...) is explicitly inductive (i.e. it shows how we may induce views about the world based on partial data from it) whereas frequentism is at least compatible with non-inductive views of scientific method, particularly the critical realism of Popper. Popper and others detail significant problems with induction. Frequentism’s apparent ability to avoid these, plus its ability to give a seemingly more scientific and objective take on probability, lies behind its philosophical appeal to health care researchers. However, there are also significant problems with frequentism, particularly its inability to assign probability scores to single events. Popper thus proposed an alternative objectivist view of probability, called propensity theory, which he allies to a theory of corroboration; but this too has significant problems, in particular, it may not successfully avoid induction. If this is so then Bayesianism might be philosophically the strongest of the statistical approaches. The article sets out a number of its philosophical and methodological attractions. Finally, it outlines a way in which critical realism and Bayesianism might work together. (shrink)

Data infrastructure includes the bureaucratic, technical, and social mechanisms that assist in actions like data management, analysis, storage, and sharing. While issues like data sharing have been addressed in depth in bioethical literature, data infrastructure presents its own ethical considerations, apart from the actions (such as data sharing and data analysis) that it enables. This essay outlines some of these considerations—namely, the ethics of efficiency, the visibility of infrastructure, the power of standards, and the (...) impact of new technologies—in order to invite the bioethics community to participate in conversations about infrastructure, as their expertise is both needed and welcomed. (shrink)

To address the ethical challenges in big data health research we propose the concept of systemic oversight. This approach is based on six defining features and aims at creating a common ground across the oversight pipeline of biomedical big data research. Current trends towards enhancing granularity of informed consent and specifying legal provisions to address informational privacy and discrimination concerns in data-driven health research are laudable. However, these solutions alone cannot have the (...) desired impact unless oversight activities by different stakeholders acquire a common substantive orientation. (shrink)

Data access and data sharing are vital to advance medicine. A growing number of public private partnerships are set up to facilitate data access and sharing, as private and public actors possess highly complementary health data sets and treatment development resources. However, the priorities and incentives of public and private organizations are frequently in conflict. This has complicated partnerships and sparked public concerns around ethical issues such as trust, justice or privacy—in turn raising an important (...) problem in business and data ethics: how can ethical theory inform the practice of public and private partners to mitigate misaligned incentives, and ensure that they can deliver societally beneficial innovation? In this paper, we report on the development of the Swiss Personalized Health Network’s ethical guidelines for health data sharing in public private partnerships. We describe the process of identifying ethical issues and engaging core stakeholders to incorporate their practical reality on these issues. Our report highlights core ethical issues in health data public private partnerships and provides strategies for how to overcome these in the Swiss health data context. By agreeing on and formalizing ethical principles and practices at the beginning of a partnership, partners and society can benefit from a relationship built around a mutual commitment to ethical principles. We present this summary in the hope that it will contribute to the global data sharing dialogue. (shrink)

Data platforms represent a new paradigm for carrying out health research. In the platform model, datasets are pooled for remote access and analysis, so novel insights for developing better stratified and/or personalised medicine approaches can be derived from their integration. If the integration of diverse datasets enables development of more accurate risk indicators, prognostic factors, or better treatments and interventions, this obviates the need for the sharing and reuse of data; and a platform-based approach is an (...) appropriate model for facilitating this. Platform-based approaches thus require new thinking about consent. Here we defend an approach to meeting this challenge within the data platform model, grounded in: the notion of ‘reasonable expectations’ for the reuse of data; Waldron’s account of ‘integrity’ as a heuristic for managing disagreement about the ethical permissibility of the approach; and the element of the social contract that emphasises the importance of public engagement in embedding new norms of research consistent with changing technological realities. While a social contract approach may sound appealing, however, it is incoherent in the context at hand. We defend a way forward guided by that part of the social contract which requires public approval for the proposal and argue that we have moral reasons to endorse a wider presumption of data reuse. However, we show that the relationship in question is not recognisably contractual and that the social contract approach is therefore misleading in this context. We conclude stating four requirements on which the legitimacy of our proposal rests. (shrink)

Large-scale linkage of international clinical datasets could lead to unique insights into disease aetiology and facilitate treatment evaluation and drug development. Hereto, multi-stakeholder consortia are currently designing several disease-specific translational research platforms to enable international health data sharing. Despite the recent adoption of the EU General Data Protection Regulation, the procedures for how to govern responsible data sharing in such projects are not at all spelled out yet. In search of a first, basic outline of (...) an ethical governance framework, we set out to explore relevant ethical principles and norms. We performed a systematic review of literature and ethical guidelines for principles and norms pertaining to data sharing for international health research. We observed an abundance of principles and norms with considerable convergence at the aggregate level of four overarching themes: societal benefits and value; distribution of risks, benefits and burdens; respect for individuals and groups; and public trust and engagement. However, at the level of principles and norms we identified substantial variation in the phrasing and level of detail, the number and content of norms considered necessary to protect a principle, and the contextual approaches in which principles and norms are used. While providing some helpful leads for further work on a coherent governance framework for data sharing, the current collection of principles and norms prompts important questions about how to streamline terminology regarding de-identification and how to harmonise the identified principles and norms into a coherent governance framework that promotes data sharing while securing public trust. (shrink)