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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., Journal of Business Ethics, ISSN:0167-4544, ISSN:1573-0697

Spurred by recent advances in machine learning and electronic hardware, digital health promises to profoundly transform medicine. At the same time, however, it raises conspicuous ethical and regulatory issues. This has led to a growing number of calls for responsible digital health. Based on stakeholder engagement methods, this paper sets out to identify core impediments hindering responsible digital health in Switzerland. We developed a participatory research methodology to access stakeholders' fragmented knowledge of digital health, engaging 46 digital health stakeholders over a period of five months (December 2020–April 2021). We identified ineffective stakeholder collaboration, lack of ethical awareness among digital health innovators, and lack of relevant regulation as core impediments to responsible digital health. The stakeholders' accounts indicate that ethical concerns may considerably slow the pace of digital health innovation – implying that responsible innovation is a core catalyst for the progress of digital health overall., Frontiers in Digital Health, 5, ISSN:2673-253X

Background Increasingly, 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. Methods To 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. Results For 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. Conclusion Our findings can help guide the deployment of these technologies, as well as determine ethics and legal requirements for such technologies. ISSN:1472-6939

Background: Advances in machine learning and robotics have allowed the development of increasingly autonomous robotic systems which are able to make decisions and learn from experience. This distribution of decision-making away from human supervision poses a legal challenge for determining liability. Methods: The iRobotSurgeon survey aimed to explore public opinion towards the issue of liability with robotic surgical systems. The survey included five hypothetical scenarios where a patient comes to harm and the respondent needs to determine who they believe is most responsible: the surgeon, the robot manufacturer, the hospital, or another party. Results: A total of 2,191 completed surveys were gathered evaluating 10,955 individual scenario responses from 78 countries spanning 6 continents. The survey demonstrated a pattern in which participants were sensitive to shifts from fully surgeon-controlled scenarios to scenarios in which robotic systems played a larger role in decision-making such that surgeons were blamed less. However, there was a limit to this shift with human surgeons still being ascribed blame in scenarios of autonomous robotic systems where humans had no role in decision-making. Importantly, there was no clear consensus among respondents where to allocate blame in the case of harm occurring from a fully autonomous system. Conclusions: The iRobotSurgeon Survey demonstrated a dilemma among respondents on who to blame when harm is caused by a fully autonomous surgical robotic system. Importantly, it also showed that the surgeon is ascribed blame even when they have had no role in decision-making which adds weight to concerns that human operators could act as “moral crumple zones” and bear the brunt of legal responsibility when a complex autonomous system causes harm., Frontiers in Surgery, 9, ISSN:2296-875X

Over the last years, public engagement has become a topic of scholarly and policy debate particularly in biomedicine, a field that increasingly centres around collecting, sharing and analysing personal data. However, the use of big data in biomedicine poses specific challenges related to gaining public support for health data usage in research and clinical settings. The improvement of public engagement practices in health data governance is widely recognised as critical to address this issue. Based on OECD guidance, public engagement serves to enhance transparency and accountability, and enable citizens to actively participate in shaping what affects their lives. For health research initiatives, this provides a way to cultivate cooperation and build public trust. Today, the exact formats of public engagement have evolved to include approaches (such as social media, events and websites) that exploit visualisation mediated by emerging information and communication technologies. Much scholarship acknowledges the advantages of visuality for public engagement, particularly in information-dense and digital contexts. However, little research has examined how health data governance actors utilise visuality to promote clarity, understandability and audience participation. Beyond simply acknowledging the diversity of possible formats, attention must also be paid to visualisations’ rhetorical capacity to convey arguments and ideas and motivate particular audiences in specific situations. This paper seeks to address this gap by analysing both the approaches and methods of argumentation used in two visual public engagement campaigns. Based on Gottweis’ analytical framework of argumentative performativity, this paper explores how two European public engagement facilitators construct contending narratives in efforts to make sense of and grapple with the challenges of health data sharing. Specifically, we analyse how their campaigns employ the three rhetorical elements logos, ethos and pathos, proposed by Gottweis to assess communicative practices, intermediated and embedded in symbolically rich social and cultural contexts. In doing so, we highlight how visual techniques of argumentation seek to bolster engagement but vary with rhetorical purposes, as while one points to health data sharing risks, the other focuses on benefits. Moreover, drawing on digital and visual anthropology, we reflect on how the digitalisation of communicative practices impacts visual power., Humanities and Social Sciences Communications, 8 (1), ISSN:2662-9992

Objectives This article aims to measure the willingness of the Swiss public to participate in personalised health research, and their preferences regarding data management and governance. Setting Results are presented from a nationwide survey of members of the Swiss public. Participants 15 106 randomly selected Swiss residents received the survey in September 2019. The response rate was 34.1% (n=5156). Respondent age ranged from 18 to 79 years, with fairly uniform spread across sex and age categories between 25 and 64 years. Primary and secondary outcome measures Willingness to participate in personalised health research and opinions regarding data management and governance. Results Most respondents preferred to be contacted and reconsented for each new project using their data (39%, 95% CI: 37.4% to 40.7%), or stated that their preference depends on the project type (29.4%, 95% CI: 27.9% to 31%). Additionally, a majority (52%, 95% CI: 50.3% to 53.8%) preferred their data or samples be stored anonymously or in coded form (43.4%, 95% CI: 41.7% to 45.1%). Of those who preferred that their data be anonymised, most also indicated a wish to be recontacted for each new project (36.8%, 95% CI: 34.5% to 39.2%); however, these preferences are in conflict. Most respondents desired to personally own their data. Finally, most Swiss respondents trust their doctors, along with researchers at universities, to protect their data. Conclusion Insight into public preference can enable Swiss biobanks and research institutions to create management and governance strategies that match the expectations and preferences of potential participants. Models allowing participants to choose how to interact with the process, while more complex, may increase individual willingness to provide data to biobanks., BMJ Open, 12 (8), ISSN:2044-6055

Background Increasingly, 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. Methods To 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. Results For 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. Conclusion Our findings can help guide the deployment of these technologies, as well as determine ethics and legal requirements for such technologies., BMC Medical Ethics, 23 (1), ISSN:1472-6939

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See: https://www.who.int/publications/i/item/9789240029200: The WHO guidance on Ethics & Governance of Artificial Intelligence for Health is the product of eighteen months of deliberation amongst leading experts in ethics, digital technology, law, human rights, as well as experts from Ministries of Health. While new technologies that use artificial intelligence hold great promise to improve diagnosis, treatment, health research and drug development and to support governments carrying out public health functions, including surveillance and outbreak response, such technologies, according to the report, must put ethics and human rights at the heart of its design, deployment, and use.The report identifies the ethical challenges and risks with the use of artificial intelligence of health, six consensus principles to ensure AI works to the public benefit of all countries. It also contains a set of recommendations that can ensure the governance of artificial intelligence for health maximizes the promise of the technology and holds all stakeholders – in the public and private sector – accountable and responsive to the healthcare workers who will rely on these technologies and the communities and individuals whose health will be affected by its use.

The SARS-CoV-2 pandemic is a public health challenge of unprecedented scale. In the midst of the first wave of the pandemic, governments worldwide introduced digital contact tracing systems as part of a strategy to contain the spread of the virus. In Europe, after intense discussion about privacy-related risks involving policymakers, technology experts, information technology companies, and—albeit to a limited extent—the public at large, technical protocols were created to support the development of privacy-compatible proximity tracing apps. However, as the second wave of SARS-CoV-2 sweeps the continent, digital contact tracing in Europe is evolving in terms of both technological and governance features. To enable policymakers to harness the full potential of digital health tools against SARS-CoV-2, this paper examines the evolution of digital contact tracing in eight European countries. Our study highlights that while privacy and data protection are at the core of contact tracing apps in Europe, countries differ in their technical protocols, and in their capacity to utilize collected data beyond proximity tracing alone. In particular, the most recently released apps tend to offer users more granular information about risk in specific locations, and to collect data about user whereabouts, in order to enhance retrospective contact tracing capacity. These developments signal a shift from a strict interpretation of data minimization and purpose limitation toward a more expansive approach to digital contact tracing in Europe, calling for careful scrutiny and appropriate oversight., Frontiers in Digital Health, 3, ISSN:2673-253X

In recent times, biomedical research has begun to tap into larger-than-ever collections of different data types. As a consequence, the notion of health data – data that are of relevance for health-related research or for clinical purposes – is expanding to include a variety of non-clinical data, as well as data provided by research participants themselves through commercially available products such as smartphones and fitness bands. To date, most scholarship and policy on these issues has focused on privacy and data protection. Less attention has been paid to addressing other aspects of the wicked challenges posed by big data health research and even less work has been geared towards the development of novel governance frameworks. In this chapter, we make the case for adaptive and principle-based governance of big data research. We outline six principles of adaptive governance for big data research and propose key factors for their implementation into effective governance structures and processes., Cambridge Law Handbooks, The Cambridge Handbook of Health Research Regulation, ISBN:978-1-108-47597-6, ISBN:978-1-108-62002-4

Over the last years, public engagement has become a topic of scholarly and policy debate particularly in biomedicine, a field that increasingly centres around collecting, sharing and analysing personal data. However, the use of big data in biomedicine poses specific challenges related to gaining public support for health data usage in research and clinical settings. The improvement of public engagement practices in health data governance is widely recognised as critical to address this issue. Based on OECD guidance, public engagement serves to enhance transparency and accountability, and enable citizens to actively participate in shaping what affects their lives. For health research initiatives, this provides a way to cultivate cooperation and build public trust. Today, the exact formats of public engagement have evolved to include approaches (such as social media, events and websites) that exploit visualisation mediated by emerging information and communication technologies. Much scholarship acknowledges the advantages of visuality for public engagement, particularly in information-dense and digital contexts. However, little research has examined how health data governance actors utilise visuality to promote clarity, understandability and audience participation. Beyond simply acknowledging the diversity of possible formats, attention must also be paid to visualisations’ rhetorical capacity to convey arguments and ideas and motivate particular audiences in specific situations. This paper seeks to address this gap by analysing both the approaches and methods of argumentation used in two visual public engagement campaigns. Based on Gottweis’ analytical framework of argumentative performativity, this paper explores how two European public engagement facilitators construct contending narratives in efforts to make sense of and grapple with the challenges of health data sharing. Specifically, we analyse how their campaigns employ the three rhetorical elements logos, ethos and pathos, proposed by Gottweis to assess communicative practices, intermediated and embedded in symbolically rich social and cultural contexts. In doing so, we highlight how visual techniques of argumentation seek to bolster engagement but vary with rhetorical purposes, as while one points to health data sharing risks, the other focuses on benefits. Moreover, drawing on digital and visual anthropology, we reflect on how the digitalisation of communicative practices impacts visual power. ISSN:2662-9992

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.

Supplemental material, sj-docx-1-jre-10.1177_15562646211053538 for The Challenges of Big Data for Research Ethics Committees: A Qualitative Swiss Study by Agata Ferretti, Marcello Ienca, Minerva Rivas Velarde, Samia Hurst and Effy Vayena in Journal of Empirical Research on Human Research Ethics

Background: The Covid-19 pandemic is characterized by uncertainty and constant change, forcing governments and health authorities to ramp up risk communication efforts. Consequently, visuality and social media platforms like Twitter have come to play a vital role in disseminating prevention messages widely. Yet to date, only little is known about what characterizes visual risk communication during the Covid-19 pandemic. To address this gap in the literature, this study’s objective was to determine how visual risk communication was used on Twitter to promote the World Health Organisations (WHO) recommended preventative behaviours and how this communication changed over time. Methods: We sourced Twitter’s 500 most retweeted Covid-19 messages for each month from January–October 2020 using Crowdbreaks. For inclusion, tweets had to have visuals, be in English, come from verified accounts, and contain one of the keywords ‘covid19’, ‘coronavirus’, ‘corona’, or ‘covid’. Following a retrospective approach, we then performed a qualitative content analysis of the 616 tweets meeting inclusion criteria. Results: Our results show communication dynamics changed over the course of the pandemic. At the start, most retweeted preventative messages came from the media and health and government institutions, but overall, personal accounts with many followers (51.3%) predominated, and their tweets had the highest spread (10.0%, i.e., retweet count divided by followers). Messages used mostly photographs and images were found to be rich with information. 78.1% of Tweets contained 1–2 preventative messages, whereby ‘stay home’ and ‘wear a mask’ frequented most. Although more tweets used health loss framing, health gain messages spread more. Conclusion: Our findings can inform the didactics of future crisis communication. The results underscore the value of engaging individuals, particularly influencers, as advocates to spread health risk messages and promote solidarity. Further, our findings on the visual characteristic of the most retweeted tweets highlight factors that health and government organisations should consider when creating visual health messages for Twitter. However, that more tweets used the emotive medium of photographs often combined with health loss framing raises concerns about persuasive tactics. More research is needed to understand the implications of framing and its impact on public perceptions and behaviours., BMC Public Health, 21 (1), ISSN:1471-2458

Background: The Covid-19 pandemic is characterized by uncertainty and constant change, forcing governments and health authorities to ramp up risk communication efforts. Consequently, visuality and social media platforms like Twitter have come to play a vital role in disseminating prevention messages widely. Yet to date, only little is known about what characterizes visual risk communication during the Covid-19 pandemic. To address this gap in the literature, this study’s objective was to determine how visual risk communication was used on Twitter to promote the World Health Organisations (WHO) recommended preventative behaviours and how this communication changed over time. Methods: We sourced Twitter’s 500 most retweeted Covid-19 messages for each month from January–October 2020 using Crowdbreaks. For inclusion, tweets had to have visuals, be in English, come from verified accounts, and contain one of the keywords ‘covid19’, ‘coronavirus’, ‘corona’, or ‘covid’. Following a retrospective approach, we then performed a qualitative content analysis of the 616 tweets meeting inclusion criteria. Results: Our results show communication dynamics changed over the course of the pandemic. At the start, most retweeted preventative messages came from the media and health and government institutions, but overall, personal accounts with many followers (51.3%) predominated, and their tweets had the highest spread (10.0%, i.e., retweet count divided by followers). Messages used mostly photographs and images were found to be rich with information. 78.1% of Tweets contained 1–2 preventative messages, whereby ‘stay home’ and ‘wear a mask’ frequented most. Although more tweets used health loss framing, health gain messages spread more. Conclusion: Our findings can inform the didactics of future crisis communication. The results underscore the value of engaging individuals, particularly influencers, as advocates to spread health risk messages and promote solidarity. Further, our findings on the visual characteristic of the most retweeted tweets highlight factors that health and government organisations should consider when creating visual health messages for Twitter. However, that more tweets used the emotive medium of photographs often combined with health loss framing raises concerns about persuasive tactics. More research is needed to understand the implications of framing and its impact on public perceptions and behaviours. ISSN:1471-2458

Background Ethics 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 text In 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. Conclusions We 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., BMC Medical Ethics, 22 (1), ISSN:1472-6939

Multisite medical data sharing is critical in modern clinical practice and medical research. The challenge is to conduct data sharing that preserves individual privacy and data utility. The shortcomings of traditional privacy-enhancing technologies mean that institutions rely upon bespoke data sharing contracts. The lengthy process and administration induced by these contracts increases the inefficiency of data sharing and may disincentivize important clinical treatment and medical research. This paper provides a synthesis between 2 novel advanced privacy-enhancing technologies—homomorphic encryption and secure multiparty computation (defined together as multiparty homomorphic encryption). These privacy-enhancing technologies provide a mathematical guarantee of privacy, with multiparty homomorphic encryption providing a performance advantage over separately using homomorphic encryption or secure multiparty computation. We argue multiparty homomorphic encryption fulfills legal requirements for medical data sharing under the European Union’s General Data Protection Regulation which has set a global benchmark for data protection. Specifically, the data processed and shared using multiparty homomorphic encryption can be considered anonymized data. We explain how multiparty homomorphic encryption can reduce the reliance upon customized contractual measures between institutions. The proposed approach can accelerate the pace of medical research while offering additional incentives for health care and research institutes to employ common data interoperability standards., Journal of Medical Internet Research, 23 (2), ISSN:1438-8871

Supplemental material, sj-docx-1-jre-10.1177_15562646211053538 for The Challenges of Big Data for Research Ethics Committees: A Qualitative Swiss Study by Agata Ferretti, Marcello Ienca, Minerva Rivas Velarde, Samia Hurst and Effy Vayena in Journal of Empirical Research on Human Research Ethics

Frontiers in Genetics, 11, ISSN:1664-8021

Background Explainability is one of the most heavily debated topics when it comes to the application of artificial intelligence (AI) in healthcare. Even though AI-driven systems have been shown to outperform humans in certain analytical tasks, the lack of explainability continues to spark criticism. Yet, explainability is not a purely technological issue, instead it invokes a host of medical, legal, ethical, and societal questions that require thorough exploration. This paper provides a comprehensive assessment of the role of explainability in medical AI and makes an ethical evaluation of what explainability means for the adoption of AI-driven tools into clinical practice. Methods Taking AI-based clinical decision support systems as a case in point, we adopted a multidisciplinary approach to analyze the relevance of explainability for medical AI from the technological, legal, medical, and patient perspectives. Drawing on the findings of this conceptual analysis, we then conducted an ethical assessment using the “Principles of Biomedical Ethics” by Beauchamp and Childress (autonomy, beneficence, nonmaleficence, and justice) as an analytical framework to determine the need for explainability in medical AI. Results Each of the domains highlights a different set of core considerations and values that are relevant for understanding the role of explainability in clinical practice. From the technological point of view, explainability has to be considered both in terms how it can be achieved and what is beneficial from a development perspective. When looking at the legal perspective we identified informed consent, certification and approval as medical devices, and liability as core touchpoints for explainability. Both the medical and patient perspectives emphasize the importance of considering the interplay between human actors and medical AI. We conclude that omitting explainability in clinical decision support systems poses a threat to core ethical values in medicine and may have detrimental consequences for individual and public health. Conclusions To ensure that medical AI lives up to its promises, there is a need to sensitize developers, healthcare professionals, and legislators to the challenges and limitations of opaque algorithms in medical AI and to foster multidisciplinary collaboration moving forward., BMC Medical Informatics and Decision Making, 20, ISSN:1472-6947

Governments around the globe have started to develop and deploy digital contact tracing apps to gain control over the spread of the novel coronavirus (Covid-19). The appropriateness and usefulness of these technologies as a containment measure have since sparked political and academic discussions globally. The present paper contributes to this debate through an exploration of how the national daily newspapers in Germany, Austria, and Switzerland reported on the development and adoption of digital contact-tracing apps during early and after stages of the lockdown. These countries were among the first in Europe to develop apps and were critical voices in the debate of decentralized vs. centralized data processing. We conducted thematic analysis on news coverage published between January and May 2020 in high-circulation national daily newspapers (print) from Germany, Austria, and Switzerland. A total of 148 newspaper articles were included in the final analysis. From our analysis emerged six core themes of the development and adoption of digital contact tracing apps: 1) data governance; 2) role of IT giants; 3) scientific rigor; 4) voluntariness; 5) functional efficacy; 6) role of the app. These results shed light on the different facets of discussion regarding digital contact tracing as portrayed in German-speaking media. As news coverage can serve as a proxy for public perception, this study complements emerging survey data on public perceptions of digital contact tracing apps by identifying potential issues of public concern., medRxiv

The aim of this report is to support the Competence Center Citizen Science Zurich (CCCS). Starting out from the point of research oversight in the Swiss context, it gives an overview of existing ethics guidelines in citizen science as promoted by the Citizen Science Association (CSA), the European Citizen Science Association (ECSA) and similar organizations. It highlights the relative lack of attention to ethical governance and processes in current citizen science guidelines and recommends to set up an intra-institutional process to deal with ambiguities in the future.

Data collection and processing via digital public health technologies are being promoted worldwide by governments and private companies as strategic remedies for mitigating the COVID-19 pandemic and loosening lockdown measures. However, the ethical and legal boundaries of deploying digital tools for disease surveillance and control purposes are unclear, and a rapidly evolving debate has emerged globally around the promises and risks of mobilizing digital tools for public health. To help scientists and policymakers navigate technological and ethical uncertainty, we present a typology of the primary digital public health applications currently in use. Namely: proximity and contact tracing, symptom monitoring, quarantine control, and flow modeling. For each, we discuss context-specific risks, cross-sectional issues, and ethical concerns. Finally, in recognition of the need for practical guidance, we propose a navigation aid for policymakers made up of ten steps for the ethical use of digital public health tools., 14 pages, 3 figures

With increased complexity in various global health challenges comes a need for increased precision and the adoption of more tailored health interventions. Building on precision public health, we propose precision global health (PGH), an approach that leverages life sciences, social sciences, and data sciences, augmented with artificial intelligence (AI), in order to identify transnational problems and deliver targeted and impactful interventions through integrated and participatory approaches. With more than four billion Internet users across the globe and the accelerating power of AI, PGH taps on our current augmented capacity to collect, integrate, analyse and visualise large volumes of data, both non-specific and specific to health. With the support of governments and donors, and together with international and non-governmental organisations, universities and research institutions can generate innovative solutions to improve health and wellbeing of the most vulnerable populations around the world. In line with the Sustainable Development Goals, we propose here a road map for the development and implementation of PGH.

Multisite medical data sharing is critical in modern clinical practice and medical research. The challenge is to conduct data sharing that preserves individual privacy and data usability. The shortcomings of traditional privacy-enhancing technologies mean that institutions rely on bespoke data sharing contracts. These contracts increase the inefficiency of data sharing and may disincentivize important clinical treatment and medical research. This paper provides a synthesis between two novel advanced privacy enhancing technologies (PETs): Homomorphic Encryption and Secure Multiparty Computation (defined together as Multiparty Homomorphic Encryption or MHE). These PETs provide a mathematical guarantee of privacy, with MHE providing a performance advantage over separately using HE or SMC. We argue MHE fulfills legal requirements for medical data sharing under the General Data Protection Regulation (GDPR) which has set a global benchmark for data protection. Specifically, the data processed and shared using MHE can be considered anonymized data. We explain how MHE can reduce the reliance on customized contractual measures between institutions. The proposed approach can accelerate the pace of medical research whilst offering additional incentives for healthcare and research institutes to employ common data interoperability standards., Comment: 19 pages, 2 figures, 1 table

PLoS ONE, 13 (10), ISSN:1932-6203

The implementation of the General Data Protection Regulation (GDPR) raises a series of challenges for scientific research, in particular for research that is dependent on data. This study comprehensively investigates the promises and challenges associated with the implementation of the GDPR in the scientific domain, with a special focus on the impact of the new rights and obligations enshrined in the GDPR on the design and conduct of scientific research. Furthermore, the study examines the adequacy of the GDPR's derogations for scientific research in terms of safeguarding scientific freedom and technological progress. The study also provides policy options that delineate a pathway towards enhancing rather than stifling research, and facilitating privacy-preserving data-driven research under the provisions of the GDPR.

In the last five years, private companies, research institutions as well as public sector organisations have issued principles and guidelines for ethical AI, yet there is debate about both what constitutes "ethical AI" and which ethical requirements, technical standards and best practices are needed for its realization. To investigate whether a global agreement on these questions is emerging, we mapped and analyzed the current corpus of principles and guidelines on ethical AI. Our results reveal a global convergence emerging around five ethical principles (transparency, justice and fairness, non-maleficence, responsibility and privacy), with substantive divergence in relation to how these principles are interpreted; why they are deemed important; what issue, domain or actors they pertain to; and how they should be implemented. Our findings highlight the importance of integrating guideline-development efforts with substantive ethical analysis and adequate implementation strategies., 42 pages (incl. figures and supplementary information)

The implementation of the General Data Protection Regulation (GDPR) raises a series of challenges for scientific research, in particular for research that is dependent on data. This study comprehensively investigates the promises and challenges associated with the implementation of the GDPR in the scientific domain, with a special focus on the impact of the new rights and obligations enshrined in the GDPR on the design and conduct of scientific research. Furthermore, the study examines the adequacy of the GDPR's derogations for scientific research in terms of safeguarding scientific freedom and technological progress. The study also provides policy options that delineate a pathway towards enhancing rather than stifling research, and facilitating privacy-preserving data-driven research under the provisions of the GDPR., ISBN:978-92-846-5045-3

L’exposition Les nouveaux états d’être / The New States of Being, 1er volet du projet interdisciplinaire AIship, est présentée au Centre d’exposition de l’Université de Montréal du 27 septembre au 14 décembre 2019., Textes, Jean-Christophe Bélisle-Pipon, Cansu Canca, Laurence Devillers, Pascale Lehoux, Aseman Sabet, Robert Truog, Effy Vayena; commissaire, Aseman Sabet; coordination, Karine Larocque

A growing number of individuals who are not professional scientists are working with professional scientists to contribute to scientific research through a broad spectrum of volunteer activities and roles. To guide the regulatory oversight of scientists carrying out citizen science projects, we draw distinctions among five categories of projects in which volunteer activities and roles vary based on the combination of setting (institutional or not), subject matter (human or not), and the norms and expectations of handling volunteers’ personally identifiable information. Each category has potentially different ethical considerations and forms of institutional (or regulatory) oversight. We identify and assign numeric labels to these categories rather than names to avoid confusion and value-laden connotations regarding terminology. We hope the absence of terminology will initiate conversations and encourage rapid evolution of necessary vocabulary in this area. We focus on Type 4 research, projects led in academia and in which volunteers are not the subject of the research, but provide personally identifiable information with expectations of non-confidentiality. Our preliminary data show that current Type 4 projects generally lack informed consent, and most do not provide details about their handling of personally identifiable data. We identify areas where federal guidelines, as well as existing institutional ethics review protocols for protection of human subjects in research, might be applied to some forms of citizen science in ways that could either support, or inadvertently undermine, the Common Rule (the US regulation regarding protection of human subjects). We illustrate these areas with examples of projects from Cooper’s lab. By highlighting the complex and distinct challenges of responsible conduct with each project type, we urge professional scientists, citizen scientists, regulators, and other stakeholders to jointly determine the type of institutional oversight that will best mitigate risks without stymying innovation and benefits. We hope that this essay will spark a lively discussion and refinement of concepts, research, and improved practices., Citizen Science: Theory and Practice, 4 (1)

A recent United Kingdom survey reports that 63% of the adult population is uncomfortable with allowing personal data to be used to improve healthcare and is unfavorable to artificial intelligence (AI) systems replacing doctors and nurses in tasks they usually perform [1]. Another study, conducted in Germany, found that medical students—the doctors of tomorrow—overwhelmingly buy into the promise of AI to improve medicine (83%) but are more skeptical that it will establish conclusive diagnoses in, for instance, imaging exams (56% disagree) [2]. When asked about the prospects of AI, United States decision-makers at healthcare organizations are confident that it will improve medicine, but roughly half of them think it will produce fatal errors, will not work properly, and will not meet currently hyped expectations [3]. These survey data resonate to the ethical and regulatory challenges that surround AI in healthcare, particularly privacy, data fairness, accountability, transparency, and liability. Successfully addressing these will foster the future of machine learning in medicine (MLm) and its positive impact on healthcare. Ethical and regulatory concerns about MLm can be grouped into three broad categories according to how and where they emerge, namely, around the sources of data needed for MLm and the approaches used in the development and deployment of MLm in clinical practice (for a hypothetical case study, see Fig 1). ISSN:1549-1277 ISSN:1549-1676

Precision or personalised medicine/health aims to prevent, diagnose and treat diseases by taking into account individual variability of genes, environment and lifestyle for each person. In order to tap the full potential of the increasing amount of molecular, biological and clinical health data, new infrastructures are required that permit collection, storage and analysis of high quality data and adequately link biobank samples to clinical data. Many countries have started to build interconnected “precision/personalised medicine ecosystems”, but the federalist and heterogeneous healthcare system has so far prevented nationwide coordinated activities in Switzerland. Therefore, the Swiss Personalized Health Network (SPHN) initiative has been launched. Its mission is to lay the foundations for personalised health on a national level by the development of a nationally coordinated interoperable data infrastructure to enable nationwide accessibility and exchange of health-related data. This paper describes the goals of SPHN, its current procedures and funding regulations, its organisation, its ethical and legal framework for responsible data processing, its data management infrastructure and information security and its relevant partnerships with other organisations and institutions. Although the SPHN initiative starts with the institutions of higher education such as university hospitals, universities and the ETH domain, other hospitals, public health institutions and medical practitioners, as well as private institutions such as industry and health insurers, will be included into the initiative at a later time point. Ultimately, the SPHN initiative shall lead to the development of an effective Swiss personalised health ecosystem, which is required to advance effective individual prevention, diagnosis and treatment of disease states and to push Switzerland to the international forefront of personalised health-related research and health care., Swiss Medical Weekly, ISSN:1424-7860, ISSN:1424-3997

The primary goal of genomic medicine is to make use of an individual’s genomic information to support the clinical decision-making process. At present, several international organizations and research consortia exist, which aim to support the translation of genomic research into clinical practice so that genomic medicine can ultimately be used to benefit the global community. The Genomic Medicine Alliance (http://www.genomicmedicinealliance.org/) is a global academic research network that seeks to establish and strengthen collaborative ties between different genomic medicine stakeholders. Its focus lies firmly with the translation of scientific research findings into clinical practice. To this end, it brings together experts from various disciplines including genome informatics, pharmacogenomics, and public health genomics, as well as experts on ethical, legal, and social issues in the sphere of genomics/health economics. These multidisciplinary activities are supervised by a 15-member International Scientific Advisory Committee. Its official journal, Public Health Genomics, offers members a highly respected publication forum for reviews, original research findings, and policy in this field. In the short-to-medium term, the Genomic Medicine Alliance aims to promote research collaborations between developed and developing countries and to organize educational activities in the field of genomic medicine. In the longer term, it aspires to become a focal point for global collaboration in the field of genomic medicine while helping to pave the way for a smoother transition from genomics research to genomic medicine.

The primary goal of genomic medicine is to make use of an individual’s genomic information to support the clinical decision-making process. At present, several international organizations and research consortia exist, which aim to support the translation of genomic research into clinical practice so that genomic medicine can ultimately be used to benefit the global community. The Genomic Medicine Alliance (http://www.genomicmedicinealliance.org/) is a global academic research network that seeks to establish and strengthen collaborative ties between different genomic medicine stakeholders. Its focus lies firmly with the translation of scientific research findings into clinical practice. To this end, it brings together experts from various disciplines including genome informatics, pharmacogenomics, and public health genomics, as well as experts on ethical, legal, and social issues in the sphere of genomics/health economics. These multidisciplinary activities are supervised by a 15-member International Scientific Advisory Committee. Its official journal, Public Health Genomics, offers members a highly respected publication forum for reviews, original research findings, and policy in this field. In the short-to-medium term, the Genomic Medicine Alliance aims to promote research collaborations between developed and developing countries and to organize educational activities in the field of genomic medicine. In the longer term, it aspires to become a focal point for global collaboration in the field of genomic medicine while helping to pave the way for a smoother transition from genomics research to genomic medicine.

Trust is a ubiquitous term used in emerging technology (e.g., Big Data, precision medicine), innovation policy, and governance literatures in particular. But what exactly is trust? Even though trust is considered a critical requirement for the successful deployment of precision medicine initiatives, nonetheless, there is a need for further conceptualization with regard to what qualifies as trust, and what factors might establish and sustain trust in precision medicine, predictive analytics, and large-scale biology. These new fields of 21st century medicine and health often deal with the “futures” and hence, trust gains a temporal and ever-present quality for both the present and the futures anticipated by new technologies and predictive analytics. We address these conceptual gaps that have important practical implications in the way we govern risk and unknowns associated with emerging technologies in biology, medicine, and health broadly. We provide an in-depth conceptual analysis and an operative definition of trust dynamics in precision medicine. In addition, we identify three main types of “trust facilitators”: (1) technical, (2) ethical, and (3) institutional. This three-dimensional framework on trust is necessary to building and maintaining trust in 21st century knowledge-based innovations that governments and publics invest for progressive societal change, development, and sustainable prosperity. Importantly, we analyze, identify, and deliberate on the dimensions of precision medicine and large-scale biology that have carved out trust as a pertinent tool to its success. Moving forward, we propose a “points to consider” on how best to enhance trust in precision medicine and predictive analytics. ISSN:1536-2310 ISSN:1557-8100

Emerging trends in pervasive computing and medical informatics are creating the possibility for large-scale collection, sharing, aggregation and analysis of unprecedented volumes of data, a phenomenon commonly known as big data. In this contribution, we review the existing scientific literature on big data approaches to dementia, as well as commercially available mobile-based applications in this domain. Our analysis suggests that big data approaches to dementia research and care hold promise for improving current preventive and predictive models, casting light on the etiology of the disease, enabling earlier diagnosis, optimizing resource allocation, and delivering more tailored treatments to patients with specific disease trajectories. Such promissory outlook, however, has not materialized yet, and raises a number of technical, scientific, ethical, and regulatory challenges. This paper provides an assessment of these challenges and charts the route ahead for research, ethics, and policy., Frontiers in Medicine, 5, ISSN:2296-858X

This opionion paper is published on Elephant in the lab – a blog journal on science policy. It is part of a series of articles analysing the meaning and future of scientific impact. Follow the blog: http://www.elephantinthelab.org/ – Twitter: @EITL_blog

Aims: This study reports on the attitudes of 179 Italian Medical Students to direct-to-consumer genetic test and to participation in research practices. Methods: Data were collected using a self-completion online questionnaire sent to 380 medical students at the faculty of Medicine of the Università Cattolica del Sacro Cuore in Rome, Italy. Questions pertained issues related to awareness and attitudes towards genetic testing, reactions to hypothetical results, and views about contributing to scientific research. Results: The response rate was 47.1%. Less than 50% of students were aware of DTC genetic test. Seventy-four percent of the sample were interested in undergoing DTC genetic test, and the main reason was being aware on genetic predisposition to diseases. Among those who were not willing to undergo a genetic test, the main reason was the lack of confidence in the results. In the hypothetical situations of an increased disease risk after undergoing DTC genetic testing, respondents would take actions to reduce that risk, while in the opposite scenario they would feel unaffected because of the probabilistic nature of the test. Conclusions: We reported a good level of awareness about DTC genetic test and a high interest in undergoing DTC genetic test in our sample. Nevertheless, opinions and reactions are strongly dependent by the hypothetical good or bad result that the test could provide and by the context whereby a genetic test could be performed. Respondents seem to be exposed to the risk of psychological harms, and a strong regulation regarding their use is required.

In the wake of the pandemic of coronavirus disease 2019 (COVID-19), contact tracing has become a key element of strategies to control the spread of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2). Given the rapid and intense spread of SARS-CoV-2, digital contact tracing has emerged as a potential complementary tool to support containment and mitigation efforts. Early modelling studies highlighted the potential of digital contact tracing to break transmission chains, and Google and Apple subsequently developed the Exposure Notification (EN) framework, making it available to the vast majority of smartphones. A growing number of governments have launched or announced EN-based contact tracing apps, but their effectiveness remains unknown. Here, we report early findings of the digital contact tracing app deployment in Switzerland. We demonstrate proof-of-principle that digital contact tracing reaches exposed contacts, who then test positive for SARS-CoV-2. This indicates that digital contact tracing is an effective complementary tool for controlling the spread of SARS-CoV-2. Continued technical improvement and international compatibility can further increase the efficacy, particularly also across country borders.

Precision medicine promises to develop diagnoses and treatments that take individual variability into account. According to most specialists, turning this promise into reality will require adapting the established framework of clinical research ethics, and paying more attention to participants’ attitudes towards sharing genotypic, phenotypic, lifestyle data and health records, and ultimately to their desire to be engaged as active partners in medical research. Notions such as participation, engagement and partnership have been introduced in bioethics debates concerning genetics and large-scale biobanking to broaden the focus of discussion beyond individual choice and individuals’ moral interests. The uptake of those concepts in precision medicine is to be welcomed. However, as data and medical information from research participants in precision medicine cohorts will be collected on an individual basis, translating a participatory approach in this emerging area may prove cumbersome. Therefore, drawing on Joseph Raz’s perfectionism, we propose a principle of respect for autonomous agents that, we reckon, can address many of the concerns driving recent scholarship on partnership and public participation, while avoiding some of the limitations these concept have in the context of precision medicine. Our approach offers a normative clarification to how becoming partners in precision is compatible with retaining autonomy. Realigning the value of autonomy with ideals of direct engagement, we show, can provide adequate normative orientation to precision medicine; it can do justice to the idea of moral pluralism by stressing the value of moral self-determination: and, finally, it can reconcile the notion of autonomy with other more communitarian values such as participation and solidarity., BMC Medical Ethics, 17 (1), ISSN:1472-6939

Trust is a ubiquitous term used in emerging technology (e.g., Big Data, precision medicine), innovation policy, and governance literatures in particular. But what exactly is trust? Even though trust is considered a critical requirement for the successful deployment of precision medicine initiatives, nonetheless, there is a need for further conceptualization with regard to what qualifies as trust, and what factors might establish and sustain trust in precision medicine, predictive analytics, and large-scale biology. These new fields of 21st century medicine and health often deal with the “futures” and hence, trust gains a temporal and ever-present quality for both the present and the futures anticipated by new technologies and predictive analytics. We address these conceptual gaps that have important practical implications in the way we govern risk and unknowns associated with emerging technologies in biology, medicine, and health broadly. We provide an in-depth conceptual analysis and an operative definition of trust dynamics in precision medicine. In addition, we identify three main types of “trust facilitators”: (1) technical, (2) ethical, and (3) institutional. This three-dimensional framework on trust is necessary to building and maintaining trust in 21st century knowledge-based innovations that governments and publics invest for progressive societal change, development, and sustainable prosperity. Importantly, we analyze, identify, and deliberate on the dimensions of precision medicine and large-scale biology that have carved out trust as a pertinent tool to its success. Moving forward, we propose a “points to consider” on how best to enhance trust in precision medicine and predictive analytics., Omics, 21 (12), ISSN:1536-2310, ISSN:1557-8100