Patient voice at scale: artificial intelligence-assisted qualitative analysis of patient-clinician rapport in public...

Clinician-patient rapport is linked to safety, satisfaction, and staff wellbeing, yet large-scale, real-time listening across the National Health Service (NHS) is limited. We examined how public discourse reflects rapport experiences in UK healthcare and assessed the utility of an artificial intelligence-assisted qualitative workflow.

We conducted an observational qualitative study using reflexive thematic analysis of 5011 publicly available post submissions from the Reddit community r/NHS (1^st January - 31^st December 2024). After cleaning, deduplication, and lexical screening for rapport-related language, a large language model (LLM) supported clustering suggestions and provisional summaries; human researchers led interpretation and theme development. Trustworthiness techniques included analyst triangulation, an audit trail, negative case analysis, and stability checks. Data were non-identifiable and public; research ethics committee review was not required. This study is reported in accordance with the Standards for Reporting Qualitative Research (SRQR).

Five overarching themes were identified: 1) access and delays that erode feelings of being heard; 2) first-contact experiences and gatekeeping at reception/telephone interfaces; 3) professionalism and empathy during clinical encounters; 4) emotional reciprocity and staff wellbeing shaping relational tone; and 5) service variation and perceived inequity across settings. Posts more often described administrative/communication breakdowns than clinical competence issues. Positive narratives highlighted brief empathetic acts that buffered system pressures. Cross-cutting, perceived relational communication moderated how operational strain was experienced. Paraphrased, de-identified exemplars underpin each theme.

Public social-media listening can surface scalable signals about clinician-patient rapport across the NHS. An AI-assisted (LLM-supported) qualitative workflow is feasible and enhances, rather than replaces, human interpretation. Findings suggest targeting first-contact communication and access processes, while aligning patient-facing empathy with staff support.