BackgroundThe aim of the present survey was to investigate newly discharged hospital patients’ opinions on secondary use of their hospital data and biospecimens within the context of health research in general and, more specifically, on genetic research, data sharing across borders and cooperation with the health industry.MethodsA paper questionnaire was sent to 1049 consecutive newly discharged hospital patients.ResultsThe vast majority of the respondents preferred to be informed or to receive no notification at all for secondary research on their health data (...) and biospecimens. The rest wanted to be asked for active consent. The same trend applied for the other aspects also. 81% of respondents were positive towards genetic research without active consent. 95% were positive towards cooperating with the health industry, and 90% were positive towards data sharing.ConclusionsThese results suggest that hospital patients generally are very positive to secondary research and support the concept of opting out rather than opting in. (shrink)

Increasing adoption of electronic health records in hospitals provides new opportunities for patient data to support public health advances. Such learning healthcare models have generated ethical debate in high-income countries, including on the role of patient and public consent and engagement. Increasing use of electronic health records in low-middle income countries offers important potential to fast-track healthcare improvements in these settings, where a disproportionate burden of global morbidity occurs. Core ethical issues have been raised around the role and form of (...) information sharing processes for learning healthcare systems, including individual consent and individual and public general notification processes, but little research has focused on this perspective in low-middle income countries. We conducted a qualitative study on the role of information sharing and governance processes for inpatient data re-use, using in-depth interviews with 34 health stakeholders at two public hospitals on the Kenyan coast, including health managers, providers and researchers. Data were collected between March and July 2016 and analysed using a framework approach, with Nvivo 10 software to support data management. Most forms of clinical data re-use were seen as an important public health good. Individual consent and general notification processes were often argued as important, but contingent on interrelated influences of the type of data, use and secondary user. Underlying concerns were linked to issues of patient privacy and autonomy; perceived risks to trust in health systems; and fairness in how data would be used, particularly for non-public sector re-users. Support for engagement often turned on the anticipated outcomes of information-sharing processes, as building or undermining trust in healthcare systems. As reported in high income countries, learning healthcare systems in low-middle counties may generate a core ethical tension between supporting a public good and respecting patient autonomy and privacy, with the maintenance of public trust acting as a core requirement. While more evidence is needed on patient and public perspectives on learning healthcare activities, greater collaboration between public health and research governance systems is likely to support the development of efficient and locally responsive learning healthcare activities in LMICs. (shrink)

Background We aimed to examine the ethical concerns Singaporeans have about sharing health-data for precision medicine and identify suggestions for governance strategies. Just as Asian genomes are under-represented in PM, the views of Asian populations about the risks and benefits of data sharing are under-represented in prior attitudinal research. Methods We conducted seven focus groups with 62 participants in Singapore from May to July 2019. They were conducted in three languages and analysed with qualitative content and thematic analysis. Results Four (...) key themes emerged: nuanced understandings of data security and data sensitivity; trade-offs between data protection and research benefits; trust in the public and private sectors; and governance and control options. Participants were aware of the inherent risks associated with data sharing for research. Participants expressed conditional support for data sharing, including genomic sequence data and information contained within electronic medical records. This support included sharing data with researchers from universities and healthcare institutions, both in Singapore and overseas. Support was conditional on the perceived social value of the research and appropriate de-identification and data security processes. Participants suggested that a data sharing oversight body would help strengthen public trust and comfort in data research for PM in Singapore. Conclusion Maintenance of public trust in data security systems and governance regimes can enhance participation in PM and data sharing for research. Contrary to themes in much prior research, participants demonstrated a sophisticated understanding of the inherent risks of data sharing, analysed trade-offs between risks and potential benefits of PM, and often adopted an international perspective. (shrink)

As donor trust legitimises research, trust is vital for research in the fields of biomedicine, genetics, translational medicine and personalised medicine. For parts of the donor community, the consent signature is a sign of trust in research. Many consent processes in biomedical research ask donors to provide their data for an unspecified future use, which introduces uncertainty of the unknown. This uncertainty can jeopardise donor trust or demand blind trust. But which donor wants to trust blindly? To reduce this uncertainty, (...) we explore first, which future-proof actors donors could trust when signing a consent form. Second, we discuss the question Can we know if donor trust expires? and what prevents donor trust from expiring. Finally, we present possible measures that can help to nurture trust in the far future. In this article, we draw on our previous research on trust in biomedical research, on trust in the broader healthcare system and Niklas Luhmann’s and Anthony Giddens’ trust theories. Our findings suggest that, in the far future, researchers will need to consider donor autonomy, as well as societal norms and values of the time period in which the data were donated. They will need to find mechanisms where possible to publicly announce the use of old data sets. However, foremost researchers will need to treat the data respectfully. It remains vital that professionals and the society continue to elaborate on the norms and values that shape the common understanding of what is morally right and wrong when researching data. (shrink)

Background The opioid epidemic has enabled rapid and unsurpassed use of big data on people with opioid use disorder to design initiatives to battle the public health crisis, generally without adequate input from impacted communities. Efforts informed by big data are saving lives, yielding significant benefits. Uses of big data may also undermine public trust in government and cause other unintended harms. Objectives We aimed to identify concerns and recommendations regarding how to use big data on opioid use in ethical (...) ways. Methods We conducted focus groups and interviews in 2019 with 39 big data stakeholders who had interest in or knowledge of the Public Health Data Warehouse maintained by the Massachusetts Department of Public Health. Results Concerns regarding big data on opioid use are rooted in potential privacy infringements due to linkage of previously distinct data systems, increased profiling and surveillance capabilities, limitless lifespan, and lack of explicit informed consent. Also problematic is the inability of affected groups to control how big data are used, the potential of big data to increase stigmatization and discrimination of those affected despite data anonymization, and uses that ignore or perpetuate biases. Participants support big data processes that protect and respect patients and society, ensure justice, and foster patient and public trust in public institutions. Recommendations for ethical big data governance offer ways to narrow the big data divide, enact shared data governance, cultivate public trust and earn social license for big data uses, and refocus ethical approaches. Conclusions Using big data to address the opioid epidemic poses ethical concerns which, if unaddressed, may undermine its benefits. Findings can inform guidelines on how to conduct ethical big data governance and in ways that protect and respect patients and society, ensure justice, and foster patient and public trust in public institutions. (shrink)

In today’s online data-driven world, people constantly shed data and deposit digital footprints. When individuals access health services, governments and health providers collect and store large volumes of health information about people that can later be retrieved, linked and analysed for research purposes. This can lead to new discoveries in medicine and healthcare. In addition, when securely stored and de-identified, the privacy risks are minimal and manageable. In many jurisdictions, ethics committees routinely waive the requirement for researchers to obtain consent (...) from data subjects before using and linking these datasets in an effort to balance respect for individuals with research efficiency. In this paper, I explore the ethical justification for using routinely collected health data for research without consent. I conclude that, not only is this morally justified but also that data subjects have a moral obligation to contribute their data to such research, which would obviate the need for ethics committees to consider consent waivers. In justifying this argument, I look to the duty of easy rescue, distributive justice and draw analogies with vaccination ethics. (shrink)

Precision medicine is an emerging approach to treatment and disease prevention that relies on linkages between very large datasets of health information that is shared amongst researchers and health professionals. While studies suggest broad support for sharing precision medicine data with researchers at publicly funded institutions, there is reluctance to share health information with private industry for research and development. As the private sector is likely to play an important role in generating public benefits from precision medicine initiatives, it is (...) important to understand what the concerns are and how they might be mitigated. This study reports outcomes of a deliberative method of citizen engagement in Singapore that asked whether sharing precision medicine data with private industry would be permissible, and if so, under what circumstances. Findings from this citizens’ jury suggest sharing with industry would be permissible under certain conditions that are set out in nine recommendations. Implications of the recommendations and their underlying assumptions for policy decision makers are discussed. This study aligns with prior international studies which found conditional acceptance for data sharing with private industry, a public benefit requirement, specific reluctance to share with insurance companies and an emphasis on accountability and transparency to demonstrate trustworthiness. However, our results differ from prior studies in that opt-in consent did not dominate the deliberations as jurors were able to set it aside as an assumed prerequisite for participation in a precision medicine programme. (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)