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  1. Openness in Big Data and Data Repositories: The Application of an Ethics Framework for Big Data in Health and Research.Vicki Xafis & Markus K. Labude - 2019 - Asian Bioethics Review 11 (3):255-273.
    There is a growing expectation, or even requirement, for researchers to deposit a variety of research data in data repositories as a condition of funding or publication. This expectation recognizes the enormous benefits of data collected and created for research purposes being made available for secondary uses, as open science gains increasing support. This is particularly so in the context of big data, especially where health data is involved. There are, however, also challenges relating to the collection, storage, and re-use (...)
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  • The Social Licence for Data-Intensive Health Research: Towards Co-Creation, Public Value and Trust.Johannes J. M. van Delden, Menno Mostert, Ghislaine J. M. W. van Thiel, Shona Kalkman & Sam H. A. Muller - 2021 - BMC Medical Ethics 22 (1):1-9.
    BackgroundThe rise of Big Data-driven health research challenges the assumed contribution of medical research to the public good, raising questions about whether the status of such research as a common good should be taken for granted, and how public trust can be preserved. Scandals arising out of sharing data during medical research have pointed out that going beyond the requirements of law may be necessary for sustaining trust in data-intensive health research. We propose building upon the use of a social (...)
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  • Emerging Paradigms for Ethical Review of Research Using Artificial Intelligence.James Shaw - 2022 - American Journal of Bioethics 22 (5):42-44.
    The ethical review of research using methods of artificial intelligence and machine learning in health care contexts has become an important challenge for Research Ethics Boards (also refer...
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  • Precision Medicine and Big Data: The Application of an Ethics Framework for Big Data in Health and Research.G. Owen Schaefer, E. Shyong Tai & Shirley Sun - 2019 - Asian Bioethics Review 11 (3):275-288.
    As opposed to a ‘one size fits all’ approach, precision medicine uses relevant biological, medical, behavioural and environmental information about a person to further personalize their healthcare. This could mean better prediction of someone’s disease risk and more effective diagnosis and treatment if they have a condition. Big data allows for far more precision and tailoring than was ever before possible by linking together diverse datasets to reveal hitherto-unknown correlations and causal pathways. But it also raises ethical issues relating to (...)
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  • Individual Benefits and Collective Challenges: Experts’ Views on Data-Driven Approaches in Medical Research and Healthcare in the German Context.Silke Schicktanz & Lorina Buhr - 2022 - Big Data and Society 9 (1).
    Healthcare provision, like many other sectors of society, is undergoing major changes due to the increased use of data-driven methods and technologies. This increased reliance on big data in medicine can lead to shifts in the norms that guide healthcare providers and patients. Continuous critical normative reflection is called for to track such potential changes. This article presents the results of an interview-based study with 20 German and Swiss experts from the fields of medicine, life science research, informatics and humanities (...)
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  • Reimagining Research Ethics to Include Environmental Sustainability: A Principled Approach, Including a Case Study of Data-Driven Health Research.Gabrielle Samuel & Cristina Richie - forthcoming - Journal of Medical Ethics.
    In this paper we argue the need to reimagine research ethics frameworks to include notions of environmental sustainability. While there have long been calls for healthcare ethics frameworks and decision-making to include aspects of sustainability, less attention has focused on how research ethics frameworks could address this. To do this, we first describe the traditional approach to research ethics, which often relies on individualised notions of risk. We argue that we need to broaden this notion of individual risk to consider (...)
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  • Digital Contact Tracing and Exposure Notification: Ethical Guidance for Trustworthy Pandemic Management.Robert Ranisch, Niels Nijsingh, Angela Ballantyne, Anne van Bergen, Alena Buyx, Orsolya Friedrich, Tereza Hendl, Georg Marckmann, Christian Munthe & Verina Wild - 2020 - Ethics and Information Technology 23 (3):285-294.
    There is growing interest in contact tracing apps for pandemic management. It is crucial to consider ethical requirements before, while, and after implementing such apps. In this paper, we illustrate the complexity and multiplicity of the ethical considerations by presenting an ethical framework for a responsible design and implementation of CT apps. Using this framework as a starting point, we briefly highlight the interconnection of social and political contexts, available measures of pandemic management, and a multi-layer assessment of CT apps. (...)
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  • Perceptions of ‘Precision’ and ‘Personalised’ Medicine in Singapore and Associated Ethical Issues.Serene Ong, Jeffrey Ling, Angela Ballantyne, Tamra Lysaght & Vicki Xafis - 2021 - Asian Bioethics Review 13 (2):179-194.
    Governments are investing in precision medicine with the aim of improving healthcare through the use of genomic analyses and data analytics to develop tailored treatment approaches for individual patients. The success of PM is contingent upon clear public communications that engender trust and secure the social licence to collect and share large population-wide data sets because specific consent for each data re-use is impractical. Variation in the terminology used by different programmes used to describe PM may hinder clear communication and (...)
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  • AI-Assisted Decision-Making in Healthcare: The Application of an Ethics Framework for Big Data in Health and Research.Tamra Lysaght, Hannah Yeefen Lim, Vicki Xafis & Kee Yuan Ngiam - 2019 - Asian Bioethics Review 11 (3):299-314.
    Artificial intelligence is set to transform healthcare. Key ethical issues to emerge with this transformation encompass the accountability and transparency of the decisions made by AI-based systems, the potential for group harms arising from algorithmic bias and the professional roles and integrity of clinicians. These concerns must be balanced against the imperatives of generating public benefit with more efficient healthcare systems from the vastly higher and accurate computational power of AI. In weighing up these issues, this paper applies the deliberative (...)
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  • Real-World Data to Generate Evidence About Healthcare Interventions: The Application of an Ethics Framework for Big Data in Health and Research.Wendy Lipworth - 2019 - Asian Bioethics Review 11 (3):289-298.
    It is increasingly recognised that evidence generated using “real-world data” is crucial for assessing the safety and effectiveness of health-related interventions. This, however, raises a number of issues, including those related to the quality of RWD, and of the scientific methods used to generate evidence from it, and the potential for those gathering and using RWD be driven by commercial, political, professional or personal self-interest. This article is an application of the framework presented in this issue of ABR. Please refer (...)
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  • Delivering a Practical Framework for Ethical Decision-Making Involving Big Data in Health and Research.Graeme T. Laurie & E. Shyong Tai - 2019 - Asian Bioethics Review 11 (3):223-225.
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  • Cross-Sectoral Big Data: The Application of an Ethics Framework for Big Data in Health and Research.Graeme T. Laurie - 2019 - Asian Bioethics Review 11 (3):327-339.
    Discussion of uses of biomedical data often proceeds on the assumption that the data are generated and shared solely or largely within the health sector. However, this assumption must be challenged because increasingly large amounts of health and well-being data are being gathered and deployed in cross-sectoral contexts such as social media and through the internet of things and wearable devices. Cross-sectoral sharing of data thus refers to the generation, use and linkage of biomedical data beyond the health sector. This (...)
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  • A Qualitative Study of Big Data and the Opioid Epidemic: Recommendations for Data Governance.Elizabeth A. Evans, Elizabeth Delorme, Karl Cyr & Daniel M. Goldstein - 2020 - BMC Medical Ethics 21 (1):1-13.
    Background The opioid epidemic has enabled rapid and unsurpassed use of big data on people with opioid use disorder to design initiatives to battle the public health crisis, generally without adequate input from impacted communities. Efforts informed by big data are saving lives, yielding significant benefits. Uses of big data may also undermine public trust in government and cause other unintended harms. Objectives We aimed to identify concerns and recommendations regarding how to use big data on opioid use in ethical (...)
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  • Advancing a Data Justice Framework for Public Health Surveillance.Mara Buchbinder, Eric Juengst, Stuart Rennie, Colleen Blue & David L. Rosen - 2022 - AJOB Empirical Bioethics 13 (3):205-213.
    Background Bioethical debates about privacy, big data, and public health surveillance have not sufficiently engaged the perspectives of those being surveilled. The data justice framework suggests that big data applications have the potential to create disproportionate harm for socially marginalized groups. Using examples from our research on HIV surveillance for individuals incarcerated in jails, we analyze ethical issues in deploying big data in public health surveillance. -/- Methods We conducted qualitative, semi-structured interviews with 24 people living with HIV who had (...)
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  • Big Data and Public-Private Partnerships in Healthcare and Research: The Application of an Ethics Framework for Big Data in Health and Research.Angela Ballantyne & Cameron Stewart - 2019 - Asian Bioethics Review 11 (3):315-326.
    Public-private partnerships are established to specifically harness the potential of Big Data in healthcare and can include partners working across the data chain—producing health data, analysing data, using research results or creating value from data. This domain paper will illustrate the challenges that arise when partners from the public and private sector collaborate to share, analyse and use biomedical Big Data. We discuss three specific challenges for PPPs: working within the social licence, public antipathy to the commercialisation of public sector (...)
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