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Putting FAIR Evidence into Practice

  • Tiffany I. LeungEmail author
  • Michel Dumontier
Letter to the Editor

To the Editors:

In the commentary by Guise et al., the authors describe a learning cycle for learning health systems in which evidence is rapidly generated, integrated into practice, and further evidence can be generated for further medical and clinical insights.1 The novel aspect suggested is the archiving of data, whether from clinical trials, systematic reviews and meta-analyses, and other study types, in such a manner that enables rapid reproducibility and continuous updating of medical evidence. In the clinical world, this is indeed a novel approach: the realities of healthcare delivery—the fragmented systems of care, misaligned values and incentives, slow adoption of new effective technologies, or the recognizable black box in which clinicians enter burdensome amounts of data without the feedback of intelligent insights—could all significantly benefit from the findability, accessibility, interoperability, and reusability of data in all of its forms.

These principles form the FAIR guiding principles for digital objects, such as algorithms, workflows, as well as data2—identical to the digital knowledge objects that the authors describe, which include recommendations, guidelines, and other forms of evidence. Originating from a workshop of diverse stakeholders who convened in The Netherlands in 2014, the FAIR guiding principles were developed to address thorny problems of data discovery and reuse. For example, the principles stipulate that digital objects should have unique identifiers, high-quality metadata, unambiguous licensing, adhere to data standards, and follow community expectations.3 Since then, the principles have been further delineated as a result of debates about each principle to ensure meaningful implementation.4 Also, metrics for measuring FAIRness of digital objects have been developed.5 Furthermore, FAIR principles have already been adopted across global communities, including governments, governing bodies, publishers, and funding bodies.

The FAIR guiding principles may be a complementary and already widely accepted framework for the construction of digital infrastructure needed to empower a learning health system. Ensuring that all digital objects are readable by human and machine is no small feat. But it seems to us that in order to “mind the gap” between evidence generation and practice, and back again, also means that it is time to be FAIR.

Notes

References

  1. 1.
    Guise J-M, Savitz LA, Friedman CP. Mind the gap: putting evidence into practice in the era of learning health systems. J Gen Intern Med 2018;  https://doi.org/10.1007/s11606-018-4633-1
  2. 2.
    Wilkinson MD, Dumontier M, Aalbersberg IJJ, et al. The FAIR guiding principles for scientific data management and stewardship. Sci Data 2016;3:160018.CrossRefGoogle Scholar
  3. 3.
    Leung TI, Dumontier M. The role of FAIR in the integration of clinical practice guidelines for the learning health system. In: MEDINFO 2017: Health and Wellbeing E-Networks for All - Proceedings of the 17th World Congress on Medical and Health Informatics, Lyon, France, 25-30 August 2019. Studies in Health Technology and Informatics; accepted for publication.Google Scholar
  4. 4.
    Mons B, Neylon C, Velterop J, Dumontier M, da Silva Santos LOB, Wilkinson MD. Cloudy, increasingly FAIR; revisiting the FAIR Data guiding principles for the European Open Science Cloud. ISU 2017;37(1):49–56.CrossRefGoogle Scholar
  5. 5.
    Wilkinson MD, Sansone S-A, Schultes E, Doorn P, Bonino da Silva Santos LO, Dumontier M. A design framework and exemplar metrics for FAIRness. Sci Data 2018;5:180118.CrossRefGoogle Scholar

Copyright information

© Society of General Internal Medicine 2019

Authors and Affiliations

  1. 1.Faculty of Health, Medicine and Life Sciences  Maastricht UniversityMaastrichtThe Netherlands
  2. 2.Institute of Data ScienceMaastricht UniversityMaastrichtThe Netherlands

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