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

1. Introduction

Treatment nonadherence in oncology remains a persistent challenge, especially with the growing use of oral anticancer medications (OAMs) administered outside the clinic, which shifts daily responsibility to patients and elevates adherence risk [1]. Although adherence is widely recognized as a multi-factorial construct involving patient and health system dimensions [2,3], the field has relied heavily on structured questionnaires and numeric scores that often fail to illuminate why patients deviate from recommended regimens.

Conventional patient-reported measures present several limitations. Many studies do not distinguish intentional from unintentional nonadherence, this has been described as a distinction that matters for the design of targeted interventions [4]. Survey instruments can be affected by recall bias and acquiescence, and closed-question formats compress experience into fixed categories, obscuring the nuanced reasons behind patient dissatisfaction or discordance with care [5,6,7]. In addition, small or selective samples reduce generalizability, while conceptualizations of “satisfaction” vary, and timelines for acting on feedback can be protracted [9,48]. These constraints limit visibility into the emotional, informational, and relational factors that shape concordance and adherence [10,11,12].

Natural Language Processing (NLP), particularly sentiment analysis of patient-generated narratives, offers a complementary lens on the lived experience of cancer care. Analyses of social media, online support groups, blogs, public review sites, and free-text responses can bring to the surface what have otherwise been “invisible” patient-reported concerns at scale, providing a crowd-validated window into what matters most to patients [13,14]. Compared with structured surveys, sentiment and related text analytics interrogate the tone, polarity, and emotion of language, capturing subtle affective states and informational gaps [15,16]. Prior work has leveraged these approaches to characterize longitudinal emotion dynamics and the role of emotions in adherence behaviors. Examples include hormonal therapy for breast cancer [17] and extraction of patient perspectives across platforms and conditions [18,19,20,21]. Recurrent emotional constructs (e.g., fear, sadness, disgust, surprise) and perceptions of clinician bias or conflicting information have been identified as salient correlates of dissatisfaction, discordance, or withdrawal [22,23,24]. At the same time, patient empowerment in online communities underscores substantial informational needs that may exceed generic emotional support, pointing toward specific communication targets for teams and services [25].

Placing these insights within patient/person-centred care (PCC) highlights the importance of shared decision-making, goal alignment, and relationship quality [26,27,28,29]. If emotions, information needs, and perceptions of communication are key determinants of whether recommendations are accepted, acted upon, and sustained, then synthesizing the “patient voice” from unstructured text should help reveal actionable levers for nursing practice, education, and service design.

Aim and research questions. This scoping review synthesizes evidence from NLP studies of patient-generated narratives (with an emphasis on sentiment analysis) to illuminate how emotions, information needs, and perceptions of communication relate to treatment nonadherence and concordance. Guided by discourse analysis, we address:

• How can secondary data from NLP studies of patient narratives reveal factors influencing cancer-patient nonadherence that are missed by traditional surveys [13,14]?
• Which patient-side themes (emotional, informational, relational) consistently emerge that can inform nursing strategies to improve communication, concordance, and adherence [22,24,25]?

2. Method

2.1. Design and Reporting

We conducted a scoping review to map and synthesize evidence on patient generated oncology narratives analyzed using Natural Language Processing (NLP), with emphasis on sentiment analysis. Reporting follows the PRISMA-ScR guideline [30]. Given the anticipated heterogeneity of data sources, analytic pipelines, and outcomes, we complemented PRISMA-ScR with the SWiM guidance for transparent narrative synthesis (nine items). The review questions and eligibility criteria were defined a priori and aligned to the review aim (communication, relationship, and information related determinants of adherence/concordance detectable in the patient voice).

2.2. Eligibility Criteria

Inclusion. Studies were eligible if they: (i) analyzed patient generated text related to cancer using NLP or sentiment analysis (e.g., social media posts, online support communities, blogs, public review platforms, survey free text responses); (ii) reported findings relevant to treatment adherence, nonadherence, or treatment concordance/discordance (definitions below); (iii) were peer reviewed journal articles or full peer reviewed conference papers; and (iv) were published in English between 2013 and early 2024.

Operational definitions. Patient generated text refers to content authored by adult patients themselves (i.e., individuals self-identifying as living with cancer) in public or semi-public online environments or in survey free text fields. Content authored by caregivers, family members, clinicians, journalists, or institutions was excluded unless the text was explicitly written from the patient’s first person perspective and attributable as such. Relevance to adherence/concordance was established through a two step procedure: (a) keyword screening for concepts such as adherence, nonadherence, concordance, compliance, persistence, discontinuation, withdrawal, refusal, skipping doses, dose reduction, stopping treatment, shared decision making (SDM), goals/values alignment, and (b) contextual/thematic confirmation that the text or study findings linked these concepts to treatment taking behavior, decision making, or participation in recommended care.

Exclusion. We excluded studies that: (i) had no cancer focus; (ii) analyzed content produced primarily by carers/children/adolescents rather than adult patients (unless written from the patient’s perspective as defined above); (iii) were economic evaluations, editorials, protocols, or non empirical commentaries; (iv) were non English; or (v) constituted grey literature, with the exception of policy/ethics statements (e.g., WHO, ACSQHC) used for definitional clarity [2,31].

2.3. Information Sources and Search Strategy

We searched PubMed/MEDLINE, CINAHL, and Scopus for records from 2013 to mid 2024. The last search was performed on [15 June 2024]. To maximize sensitivity for studies where titles/abstracts may not co-list all constructs, we built concept blocks around (1) cancer/neoplasms, (2) NLP/sentiment/text mining, (3) patient generated narratives/platforms, and (4) adherence/concordance/SDM constructs, and combined them with Boolean operators. Database specific syntax and field tags were adapted accordingly. Full database strings are provided in Supplementary File S1, including example blocks such as:

• Concept A (cancer):(cancer* OR neoplasm* OR oncolog*)
• Concept B (NLP/SA):("natural language processing" OR "text mining" OR sentiment OR emotion* OR classifier* OR lexicon* OR transformer*)
• Concept C (patient voice/platforms):("social media" OR Twitter OR Reddit OR forum* OR blog* OR "patient review" OR "free text")
• Concept D (adherence/concordance):(adheren* OR nonadheren* OR concordan* OR complian* OR persistence OR discontinu* OR "shared decision*")

To identify additional applications or cross disciplinary work, we ran supplementary searches in Google Scholar and screened reference lists of included records (backward citation chasing) and citing articles where relevant (forward citation chasing). Search results were exported to a reference manager/spreadsheet, and duplicates were removed using automated matching and manual verification.

Automation sensitivity.

Because some platforms are susceptible to automated/bot content and because bot-detection performance varies by method and language, we recorded where source platforms might be affected and treated such findings cautiously, consistent with prior corpus work in oncology narratives [13,15]. When included studies reported bot-filtering or authenticity checks, we noted their approach in the characteristics table.

2.4. Study Selection

Two reviewers independently screened titles and abstracts against the eligibility criteria. We conducted a calibration exercise on an initial sample to standardize the application of inclusion/exclusion rules. Potentially eligible records underwent full text assessment by the same reviewers. Disagreements were resolved through discussion; a third reviewer was available for arbitration if consensus could not be reached. Reasons for exclusion at the full text stage were documented. The study selection process is depicted in the PRISMA-ScR flow diagram (Figure 1) [30].

2.5. Data Charting

We developed and piloted a standardized charting form aligned to the review questions. Extracted items included: author/year/country; cancer type/domain; data source and platform (e.g., Twitter, Reddit, forums, blogs, survey free text); sampling frame/size; NLP/sentiment methods (e.g., lexicon/rule based, supervised models, transformer architectures; emotion vs. polarity outputs); key analytic parameters (e.g., lexicons used, model transparency); reported adherence/concordance indicators and how they were operationalized; and main findings relevant to emotions, information, and relationships. The form was iteratively refined after piloting. Charted data informed a characteristics table and thematic synthesis of patient side constructs.

2.6. Critical Appraisal

Given the heterogeneity of study designs (observational NLP analyses; mixed methods; qualitative components), we used the Critical Appraisal Toolkit (CAT) to describe methodological rigor across sources [32]. Appraisal focused on clarity of research question, appropriateness and transparency of data sources and NLP pipelines, sampling limitations and platform biases, validity of adherence/concordance inferences, and ethical handling of online content. Ratings (e.g., low/medium/high) and brief justifications were recorded to inform interpretation rather than to exclude studies, consistent with scoping review practice [33,34].

2.7. Ethical Considerations for Online Patient Generated Data

We followed the Association of Internet Researchers’ ethics principles (contextual integrity, harm minimization) and relevant clinical communications guidance [31,34]. We restricted attention to public or clearly semi-public spaces; private groups or closed forums requiring membership approval were not targeted as primary sources. Where studies quoted user content, we favored paraphrased reporting in this review to reduce traceability via search engines. No attempts were made to identify or re-identify individuals. Platform terms of service and community guidelines cited by included studies were noted where available. The review used only published, aggregated findings from included sources and did not involve intervention or contact with users; institutional ethical review was therefore not required for this synthesis.

2.8. Synthesis Approach

We employed a two stage synthesis. First, we mapped included studies by platform, cancer domain, and NLP approach to describe the evidence landscape. Second, we undertook narrative synthesis, integrating discourse analysis to link sentiment derived constructs (emotion, information clarity, relationship/trust/SDM) to adherence and concordance phenomena [22,23]. To enhance transparency (SWiM), we pre-specified groupings by platform (Twitter/X, Reddit, forums/blogs, survey free text), analytic family (lexicon/rule based vs. supervised/transformer), and cancer domain; we summarized outcomes using standardized polarity/emotion metrics where available and otherwise used structured textual summaries. We explored heterogeneity by platform and method and prioritized results by analytic transparency and sample size. Visual synthesis is provided via a conceptual map (Figure 2), indicating whether links were directly observed in sentiment outputs or inferred through discourse analysis.

2.9. Synthesis Without Meta Analysis (SWiM)

In line with SWiM, we: (i) specified the synthesis objective (identify communication, information, and relationship related determinants of adherence/concordance emergent from patient narratives); (ii) defined grouping variables (platform, cancer domain, NLP family); (iii) stated outcome handling (polarity/emotion classes, concordance/adherence indicators); (iv) described the synthesis method (structured narrative plus discourse analysis); (v) addressed limitations of combining diverse metrics (no quantitative pooling; emphasis on direction and consistency of effects/signals); (vi) examined heterogeneity qualitatively (by platform/method and appraisal ratings); (vii) considered study appraisal in interpretation (down weighting weakly reported inferences); (viii) used visual displays (PRISMA diagram; conceptual map); and (ix) documented decisions and deviations (in Supplementary File S4). Reporting complements PRISMA-ScR to support reproducible narrative synthesis [30].

3. Results

3.1. Study Selection and Overview

From an initial corpus spanning PubMed, CINAHL, and Scopus (2013–early 2024), 25 studies met the inclusion criteria for analysis of patient-generated cancer narratives using NLP or sentiment analysis. Reasons for exclusion included non-cancer focus, non-English language, content authored by carers or children, and grey literature outside defined exceptions. The study selection process is illustrated in the PRISMA flow diagram (Figure 1).

Figure 1. PRISMA Flow Diagram [30].

Figure 1. PRISMA Flow Diagram [30].

3.2. Characteristics of Included Studies

The included studies varied in cancer type, data source (e.g., Twitter, forums, blogs, survey free text), and NLP technique (e.g., rule-based filtering, transformer models, emotion classification). Table 1, Table 2, Table 3 and Table 4 summarizes these characteristics, highlighting the adherence or concordance indicators most relevant to nursing practice.

3.3. Emergent Patient-Side Themes

Narrative synthesis and discourse analysis revealed four recurrent patient-side themes:

• Unmet Emotional Needs: Emotions such as fear, sadness, disgust, and surprise peaked around treatment milestones and adverse effects, often correlating with withdrawal or intentional nonadherence.
• Suboptimal Information and Communication: Conflicting or incomplete information, including misleading framings (e.g., “good cancer”), contributed to confusion and reduced trust in care teams.
• Unclear Concordance within Person-Centred Care (PCC): Patient narratives often lacked explicit indicators of shared decision-making (SDM) or goal alignment, suggesting missed opportunities for concordance checks.
• Online Misinformation Dynamics and Perceived Clinician Bias: Online narratives amplified confusion and sometimes reflected perceived bias, further eroding trust and complicating adherence.

These themes are visualized in the conceptual map (Figure 2), which links sentiment-derived constructs (emotion, information, relationship) to adherence outcomes via direct and inferred indicators.

Figure 2. Conceptual links between sentiment-derived constructs (emotion, information, relationship) and adherence/concordance phenomena, grouped into patient-side themes by narrative synthesis ( [15,17,24,25]; discourse-analysis inference: [22,23]).

Figure 2. Conceptual links between sentiment-derived constructs (emotion, information, relationship) and adherence/concordance phenomena, grouped into patient-side themes by narrative synthesis ( [15,17,24,25]; discourse-analysis inference: [22,23]).

3.4. Theme Prevalence Across Studies

Table 5 reports the prevalence of each theme across the included studies. Suboptimal communication and unclear concordance were the most frequently observed, followed by emotional distress and misinformation dynamics.

3.5. Operational Definitions of Adherence and Concordance

To clarify key constructs:

These definitions are mapped to the Three Talk SDM model (Team, Option, Decision talk) and inform the interpretation of patient voice data.

3.6. Emotion–Behavior–Response Framework

Table 6 presents a practical framework linking emotion signals to likely patient behaviors and concordance-oriented nursing responses. For example, fear may lead to dose skipping, which can be addressed through distress screening and values clarification.

3.7. Narrative Summary

Across platforms, emotional distress was a consistent signal of nonadherence risk. Informational gaps and conflicting messages undermined understanding and trust, while sparse evidence of SDM pointed to missed opportunities for concordance. Online misinformation and perceived bias further complicated the communication landscape. These findings suggest that integrating emotion detection, teach-back strategies, and concordance checks into nursing workflows may improve adherence and patient engagement.

4. Discussion

This scoping review synthesizes findings from 25 studies that used primarily NLP-based sentiment analysis to explore cancer patient treatment nonadherence and discordance. The review identifies four key themes: unmet emotional needs, suboptimal communication and information, unclear concordance in Patient Centred Care (PCC), and EHR barriers and compliance design.

4.1. Linking Findings to Review Questions

The review addressed two questions: (1) how secondary data from NLP studies can reveal insights hidden from traditional surveys, and (2) how oncologist narratives in EHRs reflect communication factors linked to nonadherence. The findings suggest that NLP techniques, particularly sentiment analysis, can uncover emotional and relational dimensions of the patient experience that are often overlooked in conventional research [13,14].

4.2. Conceptual Contributions

This review contributes to the understanding of treatment nonadherence by showing that discordance is more frequently inferred than directly measured. Emotional constructs such as fear, sadness, and spiritual pain were detected in patient authored content but rarely documented in EHRs [23,36]. The review also highlights the potential of NLP to detect fine grained emotional states and communication breakdowns that influence adherence.

4.3. Platform and Methodological Insights

Sentiment analysis of social media and online support groups revealed richer emotional content than text mining of EHRs, which often lacked adherence related notes. Studies suggest oncologists may avoid documenting adverse effects or emotional distress due to time constraints or EHR design limitations [52,53]. This discrepancy underscores the need for improved EHR systems that support narrative input and patient centred documentation.

4.4. Appraisal Informed Interpretation

Using the Critical Appraisal Toolkit (CAT) [32], studies were rated for methodological rigor. High quality studies provided clearer links between patient sentiment and adherence, while lower quality studies often lacked transparency in NLP pipelines or adherence definitions. This informed the weighting of evidence in the synthesis.

4.5. Implications for Practice

Findings suggest that oncologist communication, emotional support, and shared decision making are central to treatment concordance. NLP tools could be used to monitor patient sentiment and flag potential nonadherence risks. However, ethical considerations and interpretive caution are essential, especially when analyzing public online data [4,34].

4.6. Limitations

The review is limited by the interpretive nature of discourse analysis and the paucity of NLP studies directly addressing nonadherence. Economic factors were excluded, and the focus was primarily on oral anti cancer medication. Bias may arise from inferring nonadherence from related concepts, though this was mitigated by referencing prior work [4].

5. Conclusions

Despite limited direct evidence, this scoping review demonstrates that NLP studies of the cancer patient experience can infer treatment nonadherence and discordance through discourse analysis. Emotional needs may surpass informational needs, and oncologist attitudes and communication styles significantly influence concordance.

Most NLP studies focus on technical extraction rather than clinical application. However, sentiment analysis reveals unmet emotional needs and dissatisfaction that may impact adherence. EHR text mining shows that oncologist notes often lack detail on adverse effects, pain, or emotional distress, potentially due to time constraints or compliance oriented design [52,53].

Improved oncologist communication and shared narratives in EHRs could enhance health literacy and treatment concordance. Future research should develop lexicons and NLP models that detect oncologist patient communication barriers and treatment discordance. A combined qualitative and NLP approach may offer deeper insight into the patient experience and support the aims of Patient Centred Care [26,27].