Evidence map›Paper›PMID 41623886›Full record

ArticleFrontiers in digital health2025

Using natural language processing to explore differences in healthcare professionals' language on Functional Neurological Disorder: a comparative topic and sentiment analysis study.

Md Shadab Mashuk, Yang Lu, Lana Y H Lai, Matthew Shardlow, Shumit Saha, Ashley Williams, Anna Lee, Sarah Lloyd, Rajiv Mohanraj, Daniela Di Basilio

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Article in Frontiers in digital health, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

10 authors.

Md Shadab MashukSchool of Science, Engineering & Environment, University of Salford, Salford, United Kingdom.
Yang LuDepartment of Computer Science, Loughborough University, London, United Kingdom.
Lana Y H LaiDivision of Informatics, Imaging & Data Sciences, University of Manchester, Manchester, United Kingdom.
Matthew ShardlowDepartment of Computing and Mathematics, Manchester Metropolitan University, Manchester, United Kingdom.
Shumit SahaDepartment of Biomedical Data Science, Meharry Medical College, Nashville, TN, United States.
Ashley WilliamsDepartment of Computing and Mathematics, Manchester Metropolitan University, Manchester, United Kingdom.
Anna LeeSalford Royal NHS Foundation Trust, Northern Care Alliance, Salford, United Kingdom.
Sarah LloydSalford Royal NHS Foundation Trust, Northern Care Alliance, Salford, United Kingdom.
Rajiv MohanrajSalford Royal NHS Foundation Trust, Northern Care Alliance, Salford, United Kingdom.
Daniela Di BasilioFaculty of Health and Medicine, Lancaster University, Lancaster, United Kingdom.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Effective communication is essential for delivering quality healthcare, particularly for individuals with Functional Neurological Disorders (FND), who are often subject to misdiagnosis and stigmatising language that implies symptom fabrication. Variability in communication styles among healthcare professionals may contribute to these challenges, affecting patient understanding and care outcomes. Methods: This study employed natural language processing (NLP) to analyse clinician-to-clinician and clinician-to-patient communication regarding FND. A total of 869 electronic health records (EHRs) were examined to assess differences in language use and emotional tone across various professionals-specifically, neurologists and psychologists-and different document types, such as discharge summaries and letters to general practitioners (GPs). Latent Dirichlet Allocation (LDA) topic modelling and two complementary sentiment models (VADER and Flair) were applied to the corpus. Sentiment analysis was also applied to evaluate the emotional tone of communications. Results: Findings revealed distinct communication patterns between neurologists and psychologists. Psychologists frequently used terms related to subjective experiences, such as "trauma" and "awareness," aiming to help patients understand their diagnosis. In contrast, neurologists focused on medicalised narratives, emphasising symptoms like "seizures" and clinical interventions, including assessment ("telemetry") and treatment ("medication"). Sentiment analysis indicated that psychologists tended to use more positive and proactive language, whereas neurologists generally adopted a neutral or cautious tone. Conclusions: These findings highlight differences in communication styles and emotional tones among professionals involved in FND care. The study underscores the importance of fostering integrated, multidisciplinary care pathways and developing standardised guidelines for clinical terminology in FND to improve communication and patient outcomes. Future research should explore how these communication patterns influence patient experiences and treatment adherence.

Indexed as

electronic health records (EHRs)Functional Neurological Disorder (FND)healthcare professionalshealthcare text analyticsmachine learningnatural language processing (NLP)sentiment analysistopic modelling

Identifiers

PMID41623886
PMCPMC12855439

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.