Validating computer-generated measures of linguistic style matching and accommodation in patient-clinician communication

Objective: To explore the validity of computer-analyzed linguistic style matching (LSM) in patient-clinician communication. Methods: Using 330 transcribed HIV patient encounters, we quantified word use with Linguistic Inquiry and Word Count (LIWC), a dictionary-based text analysis software. We measured LSM by calculating the degree to which clinicians matched patients in the use of LIWC “function words” (e.g., articles, pronouns). We tested associations of different LSM metrics with patients’ perceptions that their clinicians spoke similiarly to them. Results: We developed 3 measures of LSM: 1) at the whole-visit level; (2) at the turn-by-turn level; and (3) using a “rolling-window” approach, measuring matching between clusters of 8 turns per conversant. None of these measures was associated with patient-rated speech similarity. However, we found that increasing trajectories of LSM, from beginning to end of the visit, were associated with higher patient-rated speech similarity (β 0.35, CI 0.06, 0.64), compared to unchanging trajectories. Conclusions: Our findings point to the potential value of clinicians’ adapting their communication style to match their patients, over the course of the visit. Practice implications: With further validation, computer-based linguistic analyses may prove an efficient tool for generating data on communication patterns and providing feedback to clinicians in real time.