From words to action? A scoping review on automatic sentiment analysis of patient experience comments from online sources and surveys.

BACKGROUND: Automatic analysis of free-text patient comments enables the efficient processing of large feedback volumes, reducing reliance on manual review. A 2021 review examined natural language processing (NLP) and sentiment analysis (SA) in patient experience research; however, recent advances in deep learning and generative artificial intelligence (AI) call for an updated synthesis. OBJECTIVES: This scoping review aims to map and summarise recent studies applying SA to unstructured patient experience data related to healthcare services. METHODS: Following Joanna Briggs Institute methodology and PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) guidelines, we conducted a comprehensive search across Medline, CINAHL, Web of Science, Cochrane, Embase and APA PsycINFO. We included studies published from January 2020 to March 2024 in English or a Scandinavian language. Eligible studies analysed patient feedback using NLP techniques and described the development or validation of SA models. Two reviewers independently screened the studies and extracted data, which were presented in tables both tabular and narrative forms. RESULTS: 30 studies were included, primarily from the USA, Europe and Asia. Patient comments were mostly sourced from online platforms such as social media. Feedback largely concerned hospital care. 18 studies employed rule-based SA approaches, while 12 applied supervised machine learning (ML) and only 4 studies used deep learning models. Few addressed the visualisation or practical applications in healthcare. CONCLUSION: Despite significant progress, modern methods like deep learning and generative AI remain underused in SA of patient-experience data. Limited focus on implementation restricts SA's role in quality improvement. Future research should assess advanced methods and their cost-effectiveness versus traditional ML.