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Big data and data processing in rheumatology: bioethical perspectives

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Abstract

Big data analytics and processing through artificial intelligence (AI) are increasingly being used in the health sector. This includes both clinical and research settings, and newly in specialties like rheumatology. It is, however, important to consider how these new methodologies are used, and particularly the sensitivities associated with personal information. Based on current applications in rheumatology, this article provides a narrative review of the bioethical perspectives of big data. It presents examples of databases, data analytic methods, and AI in this specialty to address four main ethical issues: privacy and confidentiality, informed consent, the impact on the medical profession, and justice. The use of big data and AI processing in healthcare has great potential to improve the quality of clinical care, including through better diagnosis, treatment, and prognosis. They may also increase patient and societal participation and engagement in healthcare and research. Developing these methodologies and using the information generated from them in line with ethical standards could positively affect the design of global health policies and introduce a new phase in the democratization of health.

Key Points

• Current applications of big data, data analytics, and AI in rheumatology—including registries, machine learning algorithms, and consumer-facing platforms—raise issues in four main bioethical areas: privacy and confidentiality, informed consent, the impact on the medical profession, and justice.

• Bioethical concerns about rheumatology registries require careful consideration of privacy provisions, set within the context of local, national, and regional law.

• Machine learning and big data aid diagnosis, treatment, and prognosis, but the final decision about the use of information from algorithms should be left to rheumatology specialists to maintain the promise of fiduciary obligations in the physician–patient relationship.

• International collaboration in big data projects and increased patient engagement could be ways to counteract health inequalities in the practice of rheumatology, even on a global scale.

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Acknowledgments

We would like to thank Dr. Federico Lefranc Weegan and Dr. Claudia Infante Castañeda for comments which greatly improved this manuscript, although any errors are our own. We thank Melissa Leffler, MBA, from Edanz Group (www.edanzediting.com/ac), for editing a draft of this manuscript. Finally, we thank the reviewers who gave very helpful comments and advice to improve our work.

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Authors and Affiliations

  1. Bioethics, Health and Law Diploma Program, Instituto de Investigaciones Jurídicas, Universidad Nacional Autónoma de México, Mexico City, Mexico

    Amaranta Manrique de Lara

  2. Rheumatology Unit, Hospital General de México “Dr. Eduardo Liceaga”, Mexico City, Mexico

    Ingris Peláez-Ballestas

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  1. Amaranta Manrique de Lara
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Correspondence to Amaranta Manrique de Lara.

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Manrique de Lara, A., Peláez-Ballestas, I. Big data and data processing in rheumatology: bioethical perspectives. Clin Rheumatol 39, 1007–1014 (2020). https://doi.org/10.1007/s10067-020-04969-w

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