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@ARTICLE{Wang:288539,
      author       = {Y. Wang and M. J. Allsop and J. B. Epstein and D. Howell
                      and B. L. Rapoport and P. Schofield and Y. Van Sebille and
                      M. Thong$^*$ and I. Walraven and J. Ryan Wolf and C. J. G.
                      van den Hurk},
      title        = {{P}atient-reported symptom monitoring: using (big) data to
                      improve supportive care at the macro-, meso-, and
                      micro-levels.},
      journal      = {Supportive care in cancer},
      volume       = {32},
      number       = {3},
      issn         = {0941-4355},
      address      = {New York,NY},
      publisher    = {Springer},
      reportid     = {DKFZ-2024-00403},
      pages        = {182},
      year         = {2024},
      abstract     = {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.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.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.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.},
      keywords     = {Humans / Cancer Survivors / Cognition / Consensus /
                      Information Dissemination / Patient Reported Outcome
                      Measures / Real-world data (Other) / Supportive care (Other)
                      / Symptom monitoring (Other)},
      cin          = {C071},
      ddc          = {610},
      cid          = {I:(DE-He78)C071-20160331},
      pnm          = {313 - Krebsrisikofaktoren und Prävention (POF4-313)},
      pid          = {G:(DE-HGF)POF4-313},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:38386101},
      doi          = {10.1007/s00520-024-08373-x},
      url          = {https://inrepo02.dkfz.de/record/288539},
}