# Patient Voice and Treatment Nonadherence in Cancer Care: A Scoping Review of Sentiment Analysis

**Authors:** Leon Wreyford, Raj Gururajan, Xujuan Zhou, Niall Higgins

PMC · DOI: 10.3390/nursrep16010018 · 2026-01-08

## TL;DR

This study uses sentiment analysis of patient-generated content to explore why cancer patients do not follow treatment recommendations and how communication can be improved.

## Contribution

The paper introduces a novel use of sentiment analysis to uncover emotional and communication factors linked to treatment nonadherence in cancer care.

## Key findings

- Sentiment analysis revealed unmet emotional needs and suboptimal communication as barriers to treatment adherence.
- Misinformation and perceived clinician bias were identified as key factors affecting patient concordance.
- The study suggests practical nursing interventions like distress screening and teach-back methods to improve adherence.

## Abstract

Background: Treatment nonadherence in oncology is common. Surveys often miss why patients do not follow recommendations. We synthesised Natural Language Processing (NLP) studies, mainly sentiment analysis, of patient-generated content (social media, forums, blogs, review sites, and survey free text) to identify communication and relationship factors linked to nonadherence and concordance. Methods: We conducted a scoping review (PRISMA-ScR). Searches of PubMed, CINAHL, and Scopus from 2013 to 15 June 2024 identified eligible studies. We included 25 studies. Data were charted by source, cancer type, NLP technique, and adherence/concordance indicators, then synthesised via discourse analysis and narrative synthesis. Results: Four themes emerged: (1) unmet emotional needs; (2) suboptimal information and communication; (3) unclear concordance within person-centred care; and (4) misinformation dynamics and perceived clinician bias. Sentiment analysis helped identify emotions and information gaps that surveys often miss. Conclusions: Patient-voice data suggest practical actions for nursing, including routine distress screening, teach-back, misinformation countermeasures, and explicit concordance checks to improve adherence and shared decision making. Registration: Not registered.

## Linked entities

- **Diseases:** cancer (MONDO:0004992)

## Full-text entities

- **Diseases:** Cancer (MESH:D009369), distress (MESH:D012128)
- **Species:** Homo sapiens (human, species) [taxon 9606]

## Figures

2 figures with captions in the complete paper: https://tomesphere.com/paper/PMC12844502/full.md

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Source: https://tomesphere.com/paper/PMC12844502