While Specific Informed Consent has been the established standard for obtaining consent for medical research for many years, it does not appear suitable for large-scale biobank and health data research. Thus, alternative forms of consent have been suggested, based on a variety of ethical background assumptions. This article identifies five main ethical perspectives at stake. Even though Tiered Consent, Dynamic Consent and Meta Consent are designed to the demands of the self-determination perspective as well as the perspective of research as (...) a public good, they are still also criticized from both perspectives. In addition, criticisms based on concerns of justice, participation and democratic deliberation, and relational concerns have been levelled at each of the models. As all of these perspectives have valid points to make, the task at hand lies in balancing these ethical perspectives. What constitutes an adequate balancing depends on contextual factors. These factors include digital infrastructure and digital literacy, data safety regulation, good scientific and clinical practice, transparent debates on ethically relevant features of research, social inequalities, anti-discrimination laws and practices, trust in health care institutions and recognition of patient preferences, and consensus on unethical research. We argue that the role of context in determining acceptable models of consent puts the ethical importance of models of consent into perspective. Since altering contextual factors can help to live up to the ethical concerns at stake in debates about models of consent, opting for such a shift of focus comes without ethical loss. (shrink)

Zusammenfassung Problemhintergrund Biomedizinische Forschung auf Grundlage von Big Data bietet immense Vorteile. Groß angelegte multizentrische Forschung, die große Mengen persönlicher Gesundheitsdaten einbezieht, v. a. genetische und genomische Daten, könnte zu einer stärker personalisierten Medizin beitragen. Dieser Typ Forschung erfordert den Transfer und die Speicherung hochsensibler Daten, wodurch sich die Frage ergibt, wie Datensubjekte vor „data harm“ geschützt werden können, etwa vor Verletzungen der Privatsphäre, Disempowerment, Verlust von Rechten und Ausbeutung. Hier ergibt sich das Dilemma, wie die Vorteile der auf Big (...) Data basierenden biomedizinischen Forschung zu nutzen sind und zugleich das Recht von Datensubjekten auf informationelle Selbstbestimmung zu schützen ist. Argumentation Blockchain-Technologien werden aufgrund ihrer Features häufig als technische Lösung des genannten Dilemmas diskutiert. Dazu gehören die Herkunftsbestimmung von Daten, Dezentralisierung, Unveränderbarkeit sowie Zugang und das Governance-System. Allerdings wirft die Implementierung von Blockchain-Technologien in der biomedizinischen Forschung auch Fragen auf, besonders hinsichtlich Consent, rechtlicher Rahmenbedingungen und Integration in den Workflow. Somit bedarf es begleitender Maßnahmen, die ich als „enabler“ bezeichne, um das Potenzial von Blockchain-Technologien aktualisieren zu können. Zu diesen gehören innovative Consent-Modelle, Modelle zum Datenbesitzrecht und regulatorische Modelle. Schlussfolgerung Blockchain-Technologien als bloß technische Lösung sind unzureichend hinsichtlich des beschriebenen Dilemmas. Die Kombination aus dieser technischen Lösung und den genannten „enablers“ könnte der richtige Weg sein, um auf Big Data fußende biomedizinische Forschung zu ermöglichen und zugleich die informationelle Selbstbestimmung von Datensubjekten zu schützen. (shrink)

Definition of the problem Biomedical research based on big data offers immense benefits. Large multisite research that integrates large amounts of personal health data, especially genomic and genetic data, might contribute to a more personalized medicine. This type of research requires the transfer and storage of highly sensitive data, which raises the question of how to protect data subjects against data harm, such as privacy breach, disempowerment, disenfranchisement, and exploitation. As a result, there is a trade-off between reaping the benefits (...) of big-data-based biomedical research and protecting data subjects’ right to informational privacy. Arguments Blockchain technologies are often discussed as a technical fix for the abovementioned trade-off due to their specific features, namely data provenance, decentralization, immutability, and access and governance system. However, implementing blockchain technologies in biomedical research also raises questions regarding consent, legal frameworks, and workflow integration. Hence, accompanying measures, which I call enablers, are necessary to unleash the potential of blockchain technologies. These enablers are innovative models of consent, data ownership models, and regulatory models. Conclusion Blockchain technologies as a technical fix alone is insufficient to resolve the aforementioned trade-off. Combining this technical fix with the enablers outlined above might be the best way to perform biomedical research based on big data and at the same time protect the informational privacy of data subjects. (shrink)

Sharing research data has great potential to benefit science and society. However, data sharing is still not common practice. Since public research funding agencies have a particular impact on research and researchers, the question arises: Are public funding agencies morally obligated to promote data sharing? We argue from a research ethics perspective that public funding agencies have several pro tanto obligations requiring them to promote data sharing. However, there are also pro tanto obligations that speak against promoting data sharing in (...) general as well as with regard to particular instruments of such promotion. We examine and weigh these obligations and conclude that all things considered funders ought to promote the sharing of data. Even the instrument of mandatory data sharing policies can be justified under certain conditions. (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...

Social media data hold considerable potential for predicting health-related conditions. Recent studies suggest that machine-learning models may accurately predict depression and other mental health-related conditions based on Instagram photos and Tweets. In this article, it is argued that individuals should have a sui generis right not to be subjected to AI profiling based on publicly available data without their explicit informed consent. The article (1) develops three basic arguments for a right to protection of personal data trading on the notions (...) of social control and stigmatization, (2) argues that a number of features of AI profiling make individuals more exposed to social control and stigmatization than other types of data processing (the exceptionalism of AI profiling), (3) considers a series of other reasons for and against protecting individuals against AI profiling based on publicly available data, and finally (4) argues that the EU General Data Protection Regulation does not ensure that individuals have a right not to be AI profiled based on publicly available data. (shrink)

Research question The secondary use of clinical data for research and learning activities has the potential to significantly improve medical knowledge and clinical care. To realize this potential, an ethical and legal basis for data use is needed, preferably in the form of patient consent. This raises the question: Do patients have a moral duty to provide their clinical data for research and learning activities? Methods On the basis of an ethical approach that we call “caring liberalism,” we evaluate plausibility (...) and moral weight of the following arguments in favor of a duty on patients to provide their clinical data for research and learning activities: the general duty to help; solidarity; the duty to act in the public interest; the free-rider argument; transgenerational justice; the principle of giving back; the do-not-harm principle; freedom of research and the value of science. Results The general duty to help and the duty to act in the public interest are strong reasons for a moral duty of patients to provide their clinical data for research and learning activities. Transgenerational justice and the principle of giving back are ethically weak reasons for such a duty but might be motivational factors. The other reasons are not appropriate to justify a duty. The results are relevant in several respects: for patients who are asked to consent to the secondary use of their clinical data; for the ethical discussion of the question whether and to what extent deviations from the classical specific consent are ethically justifiable under certain conditions; for the legal discussion of the conditions for a proportionate secondary use of clinical data for research and learning activities. (shrink)

Die Sekundärnutzung klinischer Daten für Forschungs- und Lernaktivitäten hat das Potenzial, medizinisches Wissen und klinische Versorgung erheblich zu verbessern. Zur Realisierung dieses Potenzials bedarf es einer ethischen und rechtlichen Grundlage für die Datennutzung, vorzugsweise in Form der Einwilligung von Patient*innen. Damit stellt sich die grundsätzliche Frage: Haben Patient*innen eine moralische Pflicht, ihre klinischen Daten für Forschungs- und Lernaktivitäten zur Verfügung zu stellen?Auf Basis eines ethischen Ansatzes, der als „sorgender Liberalismus“ bezeichnet werden kann, werden folgende Argumente zur Begründung einer Pflicht von (...) Patient*innen zur Bereitstellung ihrer klinischen Daten für Forschungs- und Lernaktivitäten auf Plausibilität und moralisches Gewicht untersucht: die allgemeine Hilfspflicht; Solidarität; die Pflicht zu gemeinwohlförderlichem Handeln; das Trittbrettfahrerargument; transgenerationale Gerechtigkeit; das Prinzip des Zurückgebens; das Prinzip des Nicht-Schädigens; die Forschungsfreiheit und der Wert der Wissenschaft.Die allgemeine Hilfspflicht und die Pflicht zu gemeinwohlförderlichem Handeln sind gewichtige Gründe für eine moralische Pflicht von Patient*innen zur Bereitstellung ihrer klinischen Daten für Forschungs- und Lernaktivitäten. Das Argument der transgenerationalen Gerechtigkeit und das Prinzip des Zurückgebens sind ethisch schwache Gründe für eine solche Pflicht, können jedoch eine motivationale Rolle spielen. Die anderen Gründe sind nicht geeignet, eine Pflicht zu begründen. Das Ergebnis ist in mehrfacher Hinsicht relevant: für Patient*innen, die um die Einwilligung in die Sekundärnutzung ihrer klinischen Daten gebeten werden; für die ethische Diskussion der Frage, ob und inwieweit Abstriche von der klassischen spezifischen Einwilligung unter bestimmten Bedingungen ethisch akzeptabel sind; für die rechtwissenschaftliche Diskussion der Bedingungen für eine juristisch verhältnismäßige Sekundärnutzung klinischer Daten für Forschungs- und Lernaktivitäten. (shrink)

Zusammenfassung Die informierte Einwilligung von Teilnehmer:innen gilt in vielen Fällen als Voraussetzung auch für die rein datenbasierte medizinische Forschung. In diesem Kontext wird ein Modell der breiten Einwilligung (_Broad Consent_) diskutiert. In Deutschland hat die Medizininformatik-Initiative einen konkreten Vorschlag für deutsche Kliniken ausgearbeitet, der eine Gültigkeit der Einwilligung für einen Zeitraum von 30 Jahren vorsieht. Der vorliegende Artikel diskutiert vor diesem Hintergrund die Frage, wie der Anspruch der Informiertheit in dieser zeitlichen Perspektive einzuordnen ist. Die Praxis der Einwilligung wird dabei (...) so verstanden, dass sie auf die Verwirklichung von Wohlergehen, persönlicher Souveränität und Vertrauen ausgerichtet ist. Eine anzunehmende Informationsasymmetrie zwischen Forschungsteilnehmenden, Forschenden, und datenspeichernden Institutionen wird dabei als spezifisches Kontextmerkmal herausgestellt. Um dieser Informationsasymmetrie in angemessener Weise zu begegnen, so die These, ist eine kontinuierliche Weitergabe von verständlich aufbereiteten Informationen an Forschungsteilnehmende notwendig, um die Wahrnehmung persönlicher Souveränität zu ermöglichen. (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)

Medical artificial intelligence (AI) is considered to be one of the most important assets for the future of innovative individual and public health care. To develop innovative medical AI, it is necessary to repurpose data that are primarily generated in and for the health care context. Usually, health data can only be put to a secondary use if data subjects provide their informed consent (IC). This regulation, however, is believed to slow down or even prevent vital medical research, including AI (...) development. For this reason, a number of scholars advocate a moral civic duty to share electronic health records (EHRs) that overrides IC requirements in certain contexts. In the medical AI context, the common arguments for such a duty have not been subjected to a comprehensive challenge. This article sheds light on the correlation between two normative discourses concerning informed consent for secondary health record use and the development and use of medical AI. There are three main arguments in favour of a civic duty to support certain developments in medical AI by sharing EHRs: the ‘rule to rescue argument’, the ‘low risks, high benefits argument’, and the ‘property rights argument’. This article critiques all three arguments because they either derive a civic duty from premises that do not apply to the medical AI context, or they rely on inappropriate analogies, or they ignore significant risks entailed by the EHR sharing process and the use of medical AI. Given this result, the article proposes an alternative civic responsibility approach that can attribute different responsibilities to different social groups and individuals and that can contextualise those responsibilities for the purpose of medical AI development. (shrink)