<![CDATA[This study examines the role of sentiment analysis in EHR narratives for enhancing patient-centred care, focusing on methodological approaches, application domains, and implementation challenges in clinical settings. A systematic literature review (SLR) was conducted in accordance with PRISMA guidelines. Relevant studies were retrieved from Scopus, Web of Science, IEEE Xplore, and PubMed. The search, conducted in September 2025, included peer-reviewed articles published between 2021 and September 2025. The findings reveal a clear shift from rule-based and traditional machine learning approaches to transformer-based models. Sentiment analysis is increasingly applied in areas such as mental health, oncology, and patient experience monitoring. However, most implementations remain domain-specific and are not fully integrated into routine clinical workflows. This study provides a structured synthesis of sentiment analysis in EHRs and identifies key gaps between methodological advancements and real-world implementation. It advances a socio-technical perspective that integrates analytical performance, clinical applicability, and governance considerations, offering a consolidated lens for understanding sentiment-aware healthcare systems. Despite rapid methodological progress, the impact of sentiment analysis in EHRs remains constrained by limited scalability and insufficient integration into clinical practice.]]>
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