{"ID":2860171,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2510.03997","arxiv_id":"2510.03997","title":"Mapping Patient-Perceived Physician Traits from Nationwide Online Reviews with LLMs","abstract":"Understanding how patients perceive their physicians is essential to improving trust, communication, and satisfaction. We present a large language model (LLM)-based pipeline that infers Big Five personality traits and five patient-oriented subjective judgments. The analysis encompasses 4.1 million patient reviews of 226,999 U.S. physicians from an initial pool of one million. We validate the method through multi-model comparison and human expert benchmarking, achieving strong agreement between human and LLM assessments (correlation coefficients 0.72-0.89) and external validity through correlations with patient satisfaction (r = 0.41-0.81, all p\u003c0.001). National-scale analysis reveals systematic patterns: male physicians receive higher ratings across all traits, with largest disparities in clinical competence perceptions; empathy-related traits predominate in pediatrics and psychiatry; and all traits positively predict overall satisfaction. Cluster analysis identifies four distinct physician archetypes, from \"Well-Rounded Excellent\" (33.8%, uniformly high traits) to \"Underperforming\" (22.6%, consistently low). These findings demonstrate that automated trait extraction from patient narratives can provide interpretable, validated metrics for understanding physician-patient relationships at scale, with implications for quality measurement, bias detection, and workforce development in healthcare.","short_abstract":"Understanding how patients perceive their physicians is essential to improving trust, communication, and satisfaction. We present a large language model (LLM)-based pipeline that infers Big Five personality traits and five patient-oriented subjective judgments. The analysis encompasses 4.1 million patient reviews of 22...","url_abs":"https://arxiv.org/abs/2510.03997","url_pdf":"https://arxiv.org/pdf/2510.03997v1","authors":"[\"Junjie Luo\",\"Rui Han\",\"Arshana Welivita\",\"Zeleikun Di\",\"Jingfu Wu\",\"Xuzhe Zhi\",\"Ritu Agarwal\",\"Gordon Gao\"]","published":"2025-10-05T02:16:35Z","proceeding":"cs.CL","tasks":"[\"cs.CL\"]","methods":"[\"Large Language Model\",\"Language Model\"]","has_code":false}