Zusammenfassung Die Forschungs- und Regulierungsebene bei datenintensiver Forschung in der Medizin liegen auseinander. Ein heterogenes Feld aus regulierenden Institutionen mit regional ungleichen Regelungen, sowohl hinsichtlich der Dichte als auch der Restriktivität von Regelungen, steht einer globalen Entwicklung der Technologien entgegen. Trotz oder gerade wegen mangelnder global-gültiger Regulierungen können auch unverbindliche oder nur bedingt verbindliche normative Vorgaben der Orientierung dienen. Doch wie soll eine solche normative Regulierung angesichts datenintensiver Forschung in der Medizin ausgestaltet werden und woran soll sie sich orientieren? Die (...) Frage, wie konkret und detailliert man eine solche Forschung regulieren sollte, lässt sich nur im Einzelfall für bestimmte Situationen und Institutionen beantworten. Ob dabei neue Normen oder Gewichtungen von Normen vorgenommen werden sollen, oder ob die Normen und deren Gewichtung weiterhin bestehen bleiben sollen, lässt sich nur mit Verweis auf starke normative Argumente beantworten. An zwingenden Argumenten für neue Normen angesichts der datenintensiven medizinischen Forschung mangelt es bisher. Dennoch ist eine Umsetzungsarbeit von Normen, egal ob neue oder alte, und deren Anwendung auf eine neue Methode oder Technologie allemal zu erbringen. Es bedarf daher – wie schon bisher – beider: sowohl etablierter und transparent kommunizierter Normen als auch des unabhängigen Urteils zur Anwendung dieser Normen. Angesichts der besonderen Eignung für diese Aufgabe sollten auch weiterhin Ethik-Kommissionen als prozedurale Instanz bei der Bewilligung von datenintensiver Forschung in der Medizin gefordert und befähigt werden, die als unabhängige Institution ein Urteil im Einzelfall anhand vorgegebener Normen fällen. Nichtsdestotrotz können sich die veränderten Praktiken datenintensiver Forschungsprojekte auch in neuen Anforderungen an Ethik-Kommissionen niederschlagen, denen mit Reformen der Arbeitsweise begegnet werden sollte. (shrink)

BackgroundEthics review is the process of assessing the ethics of research involving humans. The Ethics Review Committee (ERC) is the key oversight mechanism designated to ensure ethics review. Whether or not this governance mechanism is still fit for purpose in the data-driven research context remains a debated issue among research ethics experts.Main textIn this article, we seek to address this issue in a twofold manner. First, we review the strengths and weaknesses of ERCs in ensuring ethical oversight. Second, we map (...) these strengths and weaknesses onto specific challenges raised by big data research. We distinguish two categories of potential weakness. The first category concerns persistent weaknesses, i.e., those which are not specific to big data research, but may be exacerbated by it. The second category concerns novel weaknesses, i.e., those which are created by and inherent to big data projects. Within this second category, we further distinguish between purview weaknesses related to the ERC’s scope (e.g., how big data projects may evade ERC review) and functional weaknesses, related to the ERC’s way of operating. Based on this analysis, we propose reforms aimed at improving the oversight capacity of ERCs in the era of big data science.ConclusionsWe believe the oversight mechanism could benefit from these reforms because they will help to overcome data-intensive research challenges and consequently benefit research at large. (shrink)

Machine learning heralds highly transformative approaches to the automation of numerous clinical tasks, from diagnosis to risk assessment, and from prognosis to informing treatment decisions....

More and more, we face AI-based products and services. Using these services often requires our explicit consent, e.g., by agreeing to the services’ Terms and Conditions clause. Current advances introduce the ability of AI to evolve and change its own modus operandi over time in such a way that we cannot know, at the moment of consent, what it is in the future to which we are now agreeing. Therefore, informed consent is impossible regarding certain kinds of AI. Call this (...) the problem of radical ignorance. Interestingly, radical ignorance exists in consent contexts other than AI, where it seems that individuals can provide informed consent. The article argues that radical ignorance can undermine informed consent in some contexts but not others because, under certain institutional, autonomy-protecting conditions, consent can be valid without being (perfectly) informed. By understanding these institutional conditions, we can formulate practical solutions to foster valid, albeit imperfectly informed consent across various decision contexts and within different institutions. (shrink)

BackgroundAdvances in healthcare artificial intelligence (AI) are occurring rapidly and there is a growing discussion about managing its development. Many AI technologies end up owned and controlled by private entities. The nature of the implementation of AI could mean such corporations, clinics and public bodies will have a greater than typical role in obtaining, utilizing and protecting patient health information. This raises privacy issues relating to implementation and data security. Main bodyThe first set of concerns includes access, use and control (...) of patient data in private hands. Some recent public–private partnerships for implementing AI have resulted in poor protection of privacy. As such, there have been calls for greater systemic oversight of big data health research. Appropriate safeguards must be in place to maintain privacy and patient agency. Private custodians of data can be impacted by competing goals and should be structurally encouraged to ensure data protection and to deter alternative use thereof. Another set of concerns relates to the external risk of privacy breaches through AI-driven methods. The ability to deidentify or anonymize patient health data may be compromised or even nullified in light of new algorithms that have successfully reidentified such data. This could increase the risk to patient data under private custodianship.ConclusionsWe are currently in a familiar situation in which regulation and oversight risk falling behind the technologies they govern. Regulation should emphasize patient agency and consent, and should encourage increasingly sophisticated methods of data anonymization and protection. (shrink)

BackgroundWhile genomic data sharing can facilitate important health research and discovery benefits, these must be balanced against potential privacy risks and harms to individuals. Understanding public attitudes and perspectives on data sharing is important given these potential risks and to inform genomic research and policy that aligns with public preferences and needs.MethodsA cross sectional online survey measured attitudes towards genomic data sharing among members of the general public in an Eastern Canadian province.ResultsResults showed a moderate comfort level with sharing genomic (...) data, usually into restricted scientific databases with controlled access. Much lower comfort levels were observed for sharing data into open or publicly accessible databases. While respondents largely approved of sharing genomic data for health research permitted by a research ethics board, many general public members were concerned with who would have access to their data, with higher rates of approval for access from clinical or academic actors, but much more limited approval of access from commercial entities or governments. Prior knowledge about sequencing and about research ethics boards were both related to data sharing attitudes.ConclusionsWith evolving regulations and guidelines for genomics research and data sharing, it is important to consider the perspectives of participants most impacted by these changes. Participant information materials and informed consent documents must be explicit about the safeguards in place to protect genomic data and the policies governing the sharing of data. Increased public awareness of the role of research ethics boards and of the need for genomic data sharing more broadly is also needed. (shrink)

Along with potential benefits to healthcare delivery, machine learning healthcare applications raise a number of ethical concerns. Ethical evaluations of ML-HCAs will need to structure th...

Modern health data practices come with many practical uncertainties. In this paper, I argue that data subjects’ trust in the institutions and organizations that control their data, and their ability to know their own moral obligations in relation to their data, are undermined by significant uncertainties regarding the what, how, and who of mass data collection and analysis. I conclude by considering how proposals for managing situations of high uncertainty might be applied to this problem. These emphasize increasing organizational flexibility, (...) knowledge, and capacity, and reducing hazard. (shrink)

Background Big data and AI applications now play a major role in many health contexts. Much research has already been conducted on ethical and social challenges associated with these technologies. Likewise, there are already some studies that investigate empirically which values and attitudes play a role in connection with their design and implementation. What is still in its infancy, however, is the comparative investigation of the perspectives of different stakeholders. Methods To explore this issue in a multi-faceted manner, we conducted (...) semi-structured interviews as well as focus group discussions with patients and clinicians. These empirical methods were used to gather interviewee’s views on the opportunities and challenges of medical AI and other data-intensive applications. Results Different clinician and patient groups are exposed to medical AI to differing degrees. Interviewees expect and demand that the purposes of data processing accord with patient preferences, and that data are put to effective use to generate social value. One central result is the shared tendency of clinicians and patients to maintain individualistic ascriptions of responsibility for clinical outcomes. Conclusions Medical AI and the proliferation of data with import for health-related inferences shape and partially reconfigure stakeholder expectations of how these technologies relate to the decision-making of human agents. Intuitions about individual responsibility for clinical outcomes could eventually be disrupted by the increasing sophistication of data-intensive and AI-driven clinical tools. Besides individual responsibility, systemic governance will be key to promote alignment with stakeholder expectations in AI-driven and data-intensive health settings. (shrink)

The application of artificial intelligence and machine learning technologies in healthcare have immense potential to improve the care of patients. While there are some emerging practices surro...

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)

In the recent years, we have been witnessing a digital revolution in public and global health creating unprecedented opportunities for epidemic intelligence and public health emergencies. However, these opportunities created a double edge sword as access to data, quality monitoring and assurance, as well as governance and regulation frameworks for data privacy are lagging behind technological achievements. In this paper we identify three ethical challenges: sharing data across various early warning tools to support risk assessment. Secondly, define the challenges to (...) be addressed by the legal frameworks for public health data sharing to unlock the potential of population-level datasets for research with no impact on citizens privacy. The third challenge lies with stricter regulation of the IT industry with regards to manipulating user data - such an initiative, GDPR, comes to force in the EU in May 2018. (shrink)

The past few years have witnessed several media-covered cases involving citizens actively engaging in the pursuit of experimental treatments for their medical conditions—or those of their loved ones—in the absence of established standards of therapy. This phenomenon is particularly observable in patients with rare genetic diseases, as the development of effective therapies for these disorders is hindered by the limited profitability and market value of pharmaceutical research. Sociotechnical trends at the cross-section of medicine and society are facilitating the involvement of (...) patients and creating the digital infrastructure necessary to its sustainment. Such participant-led research has the potential to promote the autonomy of research participants as drivers of discovery and to open novel non-canonical avenues of scientific research. At the same time, however, the extra-institutional, self-appointed, and, often, oversight-free nature of PLR raises ethical concern. This paper explores the complex ethical entanglement of PLR by critically appraising case studies and discussing the conditions for its moral justification. Furthermore, we propose a path forward to ensure the safe and effective implementation of PLR within the current research ecosystem in a manner that maximizes the benefits for both individual participants and society at large, while minimizing the risks. (shrink)

Mobile health research involving pervasive sensors, mobile apps and other novel data collection tools and methods present new ethical, legal, and social challenges specific to informed consent, data management and bystander rights. To address these challenges, a participatory design approach was deployed whereby stakeholders contributed to the development of a web-based commons to support the mHealth research community including researchers and ethics board members. The CORE platform now features a community forum, a resource library and a network of nearly 600 (...) global members. The utility of the participatory design process was evaluated by analyzing activities carried out over an 8-month design phase consisting of 86 distinct events including iterative design deliberations and social media engagement. This article describes how participatory design yielded 55 new features directly mapped to community needs and discusses relationships to user engagement as demonstrated by a steady increase in CORE member activity and followers on Twitter. (shrink)