Mapping Patient-Perceived Physician Traits from Nationwide Online Reviews with LLMs

Interpersonal and professional qualities of physicians profoundly shape patient trust, communication, adherence, and health outcomes [1, 2]. Understanding these qualities from the patient's perspective is essential to advancing patient-centered care, yet current measurement tools--such as standardized surveys or aggregate star ratings--capture only a narrow view of the physician-patient relationship. In parallel, millions of online physician reviews now provide an abundant, patient-generated record of real-world experiences, offering an unprecedented opportunity to examine how physicians are perceived in everyday practice [3, 4, 5, 6]. Extracting clinically meaningful information from such narrative data remains challenging. Prior studies have typically relied on sentiment analysis or topic modeling, approaches that overlook the multidimensional nature of patient perceptions. Well-established frameworks from psychology, such as the Big Five personality traits [7], offer interpretable constructs for describing interpersonal style, but have rarely been operationalized at scale in healthcare settings [8]. Similarly, healthcare-specific qualities--communication effectiveness, perceived competence, attentiveness to outcomes, and trustworthiness--are widely recognized as central to care quality but are difficult to measure systematically. Manual coding of these traits is costly, inconsistent, and infeasible for national datasets. Recent advances in large language models (LLMs) enable a new approach [9].