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The Ethics of Biomedical Big Data pp 339–365Cite as

Reporting and Transparency in Big Data: The Nexus of Ethics and Methodology

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Part of the book series: Law, Governance and Technology Series ((LGTS,volume 29))

Abstract

Examples of biomedical big data are routinely-collected health data. These may include information collected in electronic health records (EHRS), disease registries, or health administrative datasets. The ability to use this information for research has raised important questions regarding security and confidentiality. However, we suggest that a neglected area of discussion pertains to post-analytic aspects of research using biomedical big data. Specifically, there has been a lack of attention paid to the ethical obligation of transparent and complete reporting of studies using large-scale health-related datasets.

In this chapter we argue that improving the transparency and quality of reporting is ethically important for a number of practical as well as principled reasons. From a practical perspective the accurate reporting of methods allows for appropriate peer review and critical evaluation of studies; facilitates reproduction and replication of research findings; may help to reduce waste, and avoid redundancy and unnecessary repetition; and may facilitate public trust in scientific research. We may also have principled reasons to improve the reporting of studies using routinely-collected health data; reasons that may relate to researcher integrity and population benefits such as the fair use of resources, minimising risk of harms, and maximising benefits.

We conclude this chapter by presenting the recommendations from the RECORD (REporting of studies Conducted using Observational Routinely-collected Data) Statement (http://record-statement.org/), an international collaboration involving stakeholders using routinely-collected health data, together with a reflection on the way in which reporting guidelines improve the quality of reporting and where there is still more to do.

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Notes

  1. 1.

    Our focus here is limited to steps to improve the transparency and completeness of reporting as part of an effort to improve standards of reporting. As such, we do not engage in discussion of deliberate acts of research misconduct, such as the falsification or fabrication of research results. In the subsequent sections we limit our discussion to the former, but acknowledge that biomedical big data also potentially presents important issues pertaining to the latter – particularly with respect to the ease with which data may be manipulated – that require further consideration.

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Authors and Affiliations

  1. Children’s Hospital of Eastern Ontario (CHEO) Research Institute, Research Building #1, 401 Smyth Road, Ottawa, ON, K1H 8L1, Canada

    Stuart G. Nicholls & Eric I. Benchimol

  2. School of Epidemiology, Public Health and Preventive Medicine, Faculty of Medicine, University of Ottawa, Ottawa, ON, Canada

    Stuart G. Nicholls & Eric I. Benchimol

  3. London School of Hygiene and Tropical Medicine, London, UK

    Sinéad M. Langan

  4. Institute for Clinical Evaluative Sciences, Toronto, ON, Canada

    Eric I. Benchimol

  5. Department of Pediatrics, University of Ottawa, Ottawa, ON, Canada

    Eric I. Benchimol

  6. Division of Gastroenterology, Hepatology and Nutrition, Children’s Hospital of Eastern Ontario, 401 Smyth Road, Ottawa, ON, K1H 8L1, Canada

    Eric I. Benchimol

Authors
  1. Stuart G. Nicholls
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  2. Sinéad M. Langan
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  3. Eric I. Benchimol
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Correspondence to Stuart G. Nicholls .

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Editors and Affiliations

  1. Oxford Internet Institute, University of Oxford, Oxford, United Kingdom

    Brent Daniel Mittelstadt

  2. Oxford Internet Institute, University of Oxford, Oxford, United Kingdom

    Luciano Floridi

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Nicholls, S.G., Langan, S.M., Benchimol, E.I. (2016). Reporting and Transparency in Big Data: The Nexus of Ethics and Methodology. In: Mittelstadt, B., Floridi, L. (eds) The Ethics of Biomedical Big Data. Law, Governance and Technology Series, vol 29. Springer, Cham. https://doi.org/10.1007/978-3-319-33525-4_15

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