What the Patient Voice Reveals About Treatment Nonadherence in Oncology: A Scoping Review of Sentiment Analysis and Online Narratives

Background: Treatment nonadherence in oncology is prevalent and often assessed by surveys that miss the qualitative reasons underpinning nonadherence or treatment discordance. We aimed to synthesize evidence from Natural Language Processing (NLP) studies, primarily sentiment analysis of patient generated content (social media, forums, blogs, review platforms, and survey free text), to identify communication-related and relational factors linked to nonadherence/concordance. Methods: We conducted a scoping review following PRISMA-ScR. Searches of PubMed, CINAHL, and Scopus (2013 early 2024) targeted NLP studies of the cancer patient experience; conventional reports were included where they clarified communication/adherence constructs. Data were charted against source, cancer type, NLP technique, and inferred adherence/concordance factors, then synthesized using discourse analysis and narrative synthesis. Results: Four patient side themes consistently emerged: (1) unmet emotional needs; (2) suboptimal information and communication; (3) unclear treatment concordance within patient/person-centred care; and (4) online misinformation dynamics (and perceptions of clinician bias). Sentiment analysis detected fine grained emotions (fear, disgust, sadness, surprise), informational gaps and trust issues that are often less visible in structured surveys. Conclusions: Patient voice data offer actionable insights for nursing practice: routine distress screening, teach back strategies, misinformation countermeasures, and explicit concordance checks. Integrating these into person-centred workflows may improve adherence and shared decision making. Registration: Not registered.