PVminerLLM: Structured Extraction of Patient Voice from Patient-Generated Text using Large Language Models

ArXi:2603.05776v1 Announce Type: cross Motivation: Patient-generated text contains critical information about patients' lived experiences, social circumstances, and engagement in care, including factors that strongly influence adherence, care coordination, and health equity. However, these patient voice signals are rarely available in structured form, limiting their use in patient-centered outcomes research and clinical quality improvement. Reliable extraction of such information is. therefore. essential for understanding and addressing non-clinical drivers of health outcomes at scale.