Mapping Patient-Perceived Physician Traits from Nationwide Online Reviews with LLMs
This work addresses the need for scalable, interpretable metrics to improve physician-patient relationships in healthcare, though it is incremental as it applies existing LLM methods to a new domain with large-scale data.
The researchers tackled the problem of understanding patient perceptions of physicians by developing an LLM-based pipeline to extract personality traits and subjective judgments from 4.1 million online reviews, achieving strong agreement with human assessments (correlation coefficients 0.72-0.89) and revealing systematic patterns such as male physicians receiving higher ratings and traits predicting satisfaction.
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<0.001). 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.