Patient-reported symptom monitoring: using (big) data to improve supportive care at the macro-, meso-, and micro-levels

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

Metadata

Item Type: Article
Authors/Creators:
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:
  • Published: 22 February 2024
  • Published (online): 22 February 2024
  • Accepted: 11 February 2024
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:
  • Sustainable Development Goals: Goal 3: Good Health and Well-Being
Open Archives Initiative ID (OAI ID):

Export

Statistics