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    Learning healthcare systems and rapid learning in radiation oncology: Where are we and where are we going?

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    Authors
    Price, Gareth J
    Mackay, Ranald I
    Aznar, Marianne Camille
    McWilliam, Alan
    Johnson-Hart, Corinne
    van Herk, Marcel
    Faivre-Finn, Corinne
    Affiliation
    The University of Manchester, Manchester Academic Health Science Centre, The Christie NHS Foundation Trust, United Kingdom
    Issue Date
    2021
    
    Metadata
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    Abstract
    Learning health systems and rapid-learning are well developed at the conceptual level. The promise of rapidly generating and applying evidence where conventional clinical trials would not usually be practical is attractive in principle. The connectivity of modern digital healthcare information systems and the increasing volumes of data accrued through patients' care pathways offer an ideal platform for the concepts. This is particularly true in radiotherapy where modern treatment planning and image guidance offers a precise digital record of the treatment planned and delivered. The vision is of real-world data, accrued by patients during their routine care, being used to drive programmes of continuous clinical improvement as part of standard practice. This vision, however, is not yet a reality in radiotherapy departments. In this article we review the literature to explore why this is not the case, identify barriers to its implementation, and suggest how wider clinical application might be achieved.
    Citation
    Price G, Mackay R, Aznar M, McWilliam A, Johnson-Hart C, van Herk M, et al. Learning healthcare systems and rapid learning in radiation oncology: Where are we and where are we going?. Vol. 164, Radiotherapy and Oncology. Elsevier BV; 2021. p. 183�95.
    Journal
    Radiotherapy and Oncology
    URI
    http://hdl.handle.net/10541/624681
    DOI
    10.1016/j.radonc.2021.09.030
    PubMed ID
    34619237
    Additional Links
    https://dx.doi.org/10.1016/j.radonc.2021.09.030
    Type
    Article
    Language
    en
    ae974a485f413a2113503eed53cd6c53
    10.1016/j.radonc.2021.09.030
    Scopus Count
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