Latent Semantic Analysis: A new measure of patient-physician communication. (February 2018)

• Record Type: Journal Article
• Authors: Vrana, Scott R.; Vrana, Dylan T.; Penner, Louis A.; Eggly, Susan; Slatcher, Richard B.; Hagiwara, Nao
• Is Part Of: Social science & medicine. Volume 198(2018)
• Page Start: 22
• Page End: 26
• Publication Date: 2018-02
• Subjects: Latent Semantic Analysis -- Patient-physician communication -- Communication -- Text analysis -- Methodology; Social medicine -- Periodicals; Medical anthropology -- Periodicals; Public health -- Periodicals; Psychology -- Periodicals; Medicine -- Periodicals; Medicine -- Periodicals; Médecine sociale -- Périodiques; Anthropologie médicale -- Périodiques; Santé publique -- Périodiques; Psychologie -- Périodiques; Médecine -- Périodiques; Electronic journals; 362.105
• DOI: 10.1016/j.socscimed.2017.12.021
• Languages: English
• ISSNs: 0277-9536

Abstract

Abstract: Rationale: Patient–physician communication plays an essential role in a variety of patient outcomes; however, it is often difficult to operationalize positive patient-physician communication objectively, and the existing evaluation tools are generally time-consuming. Objective: This study proposes semantic similarity of the patient's and physician's language in a medical interaction as a measure of patient-physician communication. Latent semantic analysis (LSA), a mathematical method for modeling semantic meaning, was employed to assess similarity in language during clinical interactions between physicians and patients. Methods: Participants were 132 Black/African American patients (76% women, M age = 43.8, range = 18–82) who participated in clinical interactions with 17 physicians (53% women, M age = 27.1, range = 26–35) in a primary care clinic in a large city in the Midwestern United States. Results: LSA captured reliable information about patient-physician communication: The mean correlation indicating similarity between the transcripts of a physician and patient in a clinical interaction was 0.142, significantly greater than zero; the mean correlation between a patient's transcript and transcripts of their physician during interactions with other patients was not different from zero. Physicians differed significantly in the semantic similarity between their language and that of their patients, and these differences were related to physician ethnicity and gender. Female patients exhibited greater communication similarity with their physicians than did male patients. Finally, greater communication similarity was predicted by less patient trust in physicians prior to the interaction and greater patient trust after the interaction. Conclusion: LSA is a potentially important tool in patient-physician communication research. Methodological considerations in applying LSA to address research questions in patient-physician communication are discussed. Highlights: Latent semantic analysis (LSA) mathematically models semantic meaning in speech. LSA is proposed as a new tool to study patient-physician communication. LSA reveals reliable individual differences during medical interactions. Physician race and gender and patient gender are related to LSA results. Greater semantic similarity in medical interactions predicts higher patient trust.