Symptom Management in Cancer Care: An Evolutionary Concept Analysis for a Digitally-Enabled, Collaborative Framework

Abstract Objective An evolutionary concept analysis was conducted to clarify the core attributes, antecedents, consequences, and distinctions of "symptom management". Based on this analysis, an updated conceptual framework was developed to integrate digital health and collaborative care. Methods This study employed Rodgers' evolutionary concept analysis method. Through a systematic search of the literature related to cancer symptom management in databases including Web of Science, PubMed, Embase, the Cochrane Library, CINAHL and Chinese databases, a total of 23 studies that met the inclusion criteria were included in the conceptual analysis. Results Research has identified six core attributes of symptom management, namely dynamism, continuity, multidimensionality, multi-agent collaboration, diversity of intervention methods, and individual differences. Key antecedents (including patient self-management capacity, health literacy, professional assessment, and systemic support) and primary outcomes (such as improved quality of life, reduced symptom burden, as well as potential risks of overtreatment and inequity) have been clearly delineated. In addition, symptom management has been conceptually distinguished from related concepts such as "symptom control", "palliative care", and "self-management". Furthermore, a novel paradigm for symptom management has been proposed, characterized by digital empowerment and multi-stakeholder collaboration. Conclusions Cancer symptom management is a multidimensional, dynamic, complex process requiring multi-professional collaboration. This conceptual analysis provides a clear definition and theoretical framework for the field, informing future research, intervention development, and clinical practice. In particular, a conceptual foundation is established for advancing the integration of digital health technologies and collaborative care models. Future research should focus on cross-contextual validation, standardization of measurement instruments, and the balanced integration of technological solutions with human-centred care.