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EULAR points to consider for the use of big data in rheumatic and musculoskeletal diseases
  1. Laure Gossec1,2,
  2. Joanna Kedra1,2,
  3. Hervé Servy3,
  4. Aridaman Pandit4,
  5. Simon Stones5,
  6. Francis Berenbaum6,
  7. Axel Finckh7,
  8. Xenofon Baraliakos8,9,
  9. Tanja A Stamm10,
  10. David Gomez-Cabrero11,
  11. Christian Pristipino12,
  12. Remy Choquet13,
  13. Gerd R Burmester14,
  14. Timothy R D J Radstake4
  1. 1Institut Pierre Louis d'Epidémiologie et de Santé Publique, INSERM, Sorbonne Universite, Paris, France
  2. 2APHP, Rheumatology Department, Pitie Salpetriere Hospital, Paris, France
  3. 3Sanoïa, e-Health services, Gardanne, France
  4. 4Dept of Rheumatology, Clinical Immunology and Laboratory of Translational Immunology, Universitair Medisch Centrum Utrecht, Utrecht, The Netherlands
  5. 5School of Healthcare, University of Leeds, Leeds, UK
  6. 6Rheumatology, St Antoine Hospital, Sorbonne Université, INSERM, Paris, France
  7. 7Division of Rheumatology, University of Geneva, Geneva, Switzerland
  8. 8Rheumazentrum Ruhrgebiet Sankt Josefs-Krankenhaus, Herne, Germany
  9. 9Ruhr-Universitat Bochum, Bochum, Germany
  10. 10Section for Outcomes Research, Center for Medical Statistics, Informatics, and Intelligent Systems, Medical University of Vienna, Vienna, Austria
  11. 11Translational Bioinformatics Unit, Navarra Biomed, Departamento de Salud-Universidad Públicade Navarra, Pamplona, Navarra, Spain
  12. 12Ospedale San Filippo Neri, Rome, Italy
  13. 13Orange Healthcare, INSERM U1142, Paris, France
  14. 14Rheumatology and Clinical Immunology, Charité University Hospital, Berlin, Germany
  1. Correspondence to Professor Laure Gossec, Institut Pierre Louis d'Epidémiologie et de Santé Publique (iPLESP), UMR S 1136, Sorbonne Universite, Paris 75013, France; laure.gossec{at}gmail.com

Abstract

Background Tremendous opportunities for health research have been unlocked by the recent expansion of big data and artificial intelligence. However, this is an emergent area where recommendations for optimal use and implementation are needed. The objective of these European League Against Rheumatism (EULAR) points to consider is to guide the collection, analysis and use of big data in rheumatic and musculoskeletal disorders (RMDs).

Methods A multidisciplinary task force of 14 international experts was assembled with expertise from a range of disciplines including computer science and artificial intelligence. Based on a literature review of the current status of big data in RMDs and in other fields of medicine, points to consider were formulated. Levels of evidence and strengths of recommendations were allocated and mean levels of agreement of the task force members were calculated.

Results Three overarching principles and 10 points to consider were formulated. The overarching principles address ethical and general principles for dealing with big data in RMDs. The points to consider cover aspects of data sources and data collection, privacy by design, data platforms, data sharing and data analyses, in particular through artificial intelligence and machine learning. Furthermore, the points to consider state that big data is a moving field in need of adequate reporting of methods and benchmarking, careful data interpretation and implementation in clinical practice.

Conclusion These EULAR points to consider discuss essential issues and provide a framework for the use of big data in RMDs.

  • epidemiology
  • health services research
  • outcomes research
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Footnotes

  • LG and JK contributed equally.

  • Handling editor Professor Josef S Smolen

  • Correction notice This article has been corrected since it published Online First. The equal contribution statement has been added.

  • Contributors All authors have contributed to this work and have approved the final version.

  • Funding The authors have not declared a specific grant for this research from any funding agency in the public, commercial or not-for-profit sectors.

  • Competing interests LG has published a study for which Orange IMT (telecommunications company) performed machine-learning analyses, without charge to the author. HS is an employee of Sanoïa, Digital CRO providing clinical research services including data science. RC is an employee of Orange Healthcare. There are no competing interests for the other authors.

  • Provenance and peer review Not commissioned; externally peer reviewed.

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