Patient voice at scale: artificial intelligence-assisted qualitative analysis of patient-clinician rapport in public social media
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.
Authors
- Rayyan BARAKAT
- Mohsin F. Butt (ORCID: https://orcid.org/0000-0003-0859-6470)
- Zafar Ahmad (ORCID: https://orcid.org/0009-0007-3371-0040)
- Muhammad A. GHOUS
Institutions
- Nottingham University Hospitals NHS Trust (GB)
- University of Nottingham (GB)
- Royal Stoke University Hospital (GB)
- East Surrey Hospital (GB)
- NIHR Nottingham Digestive Diseases Biomedical Research Unit (GB)
- Nottingham Biomedical Research Centre (GB)
- Surrey and Sussex Healthcare NHS Trust (GB)
Publication Details
- Journal
- Panminerva Medica
- Published
- 2025-12-09
- DOI
- https://doi.org/10.23736/s0031-0808.25.05418-7
- Citations
- 1
- Primary Topic
- Patient-Provider Communication in Healthcare
- Type
- article
- Field-Weighted Citation Impact
- 2.82