Wang, Y., Allsop, M. orcid.org/0000-0002-7399-0194, Epstein, J.B. et al. (8 more authors) (2024) Patient-reported symptom monitoring: using (big) data to improve supportive care at the macro-, meso-, and micro-levels. Supportive Care in Cancer, 32. 182. ISSN 0941-4355
Abstract
Purpose This paper aims to provide a comprehensive understanding of the need for continued development of symptom monitoring (SM) implementation, utilization, and data usage at the macro-, meso-, and micro-levels.
Methods Discussions from a patient-reported SM workshop at the MASCC/ISSO 2022 annual meeting were analyzed using a macro-meso-micro analytical framework of cancer care delivery. The workshop categories “initiation and implementation, barriers to adoption and utilization, and data usage” were integrated for each level.
Results At the macro-level, policy development could encourage data sharing and international collaboration, including the exchange of SM methods, supportive care models, and self-management modules. At the meso-level, institutions should adjust clinical workflow and service delivery and promote a thorough technical and clinical integration of SM. At the micro-level, SM should be individualized, with timely feedback for patients, and should foster trust and understanding of AI decision support tools amongst clinicians to improve supportive care.
Conclusions The workshop reached a consensus among international experts on providing guidance on SM implementation, utilization, and (big) data usage pathways in cancer survivors across the cancer continuum and on macro-meso-micro levels.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024. This version of the article has been accepted for publication, after peer review (when applicable) and is subject to Springer Nature’s AM terms of use (https://www.springernature.com/gp/open-research/policies/accepted-manuscript-terms), but is not the Version of Record and does not reflect post-acceptance improvements, or any corrections. The Version of Record is available online at: https://doi.org/10.1007/s00520-024-08373-x. |
Keywords: | Symptom monitoring, Real-world data, Supportive care |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Medicine and Health (Leeds) > School of Medicine (Leeds) > Leeds Institute of Health Sciences (Leeds) > Academic Unit of Primary Care (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 01 Jul 2024 14:17 |
Last Modified: | 22 Feb 2025 01:13 |
Status: | Published |
Publisher: | Springer |
Identification Number: | 10.1007/s00520-024-08373-x |
Related URLs: | |
Sustainable Development Goals: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:213966 |