Objective: Automating conversation analysis in the natural clinical setting is essential to scale serious illness communication research to samples that are large enough for traditional epidemiological studies. Our objective is to automate the identification of pauses in conversations because these are important linguistic targets for evaluating dynamics of speaker involvement and turn-taking, listening and human connection, or distraction and disengagement.
Design: We used 354 audio recordings of serious illness conversations from the multisite Palliative Care Communication Research Initiative cohort study.
Setting/subjects: Hospitalized people with advanced cancer seen by the palliative care team.
Measurements: We developed a Random Forest machine learning (ML) algorithm to detect Conversational Pauses of two seconds or longer. We triple-coded 261 minutes of audio with human coders to establish a gold standard for evaluating ML performance characteristics.
Results: ML automatically identified Conversational Pauses with a sensitivity of 90.5 and a specificity of 94.5.
Conclusions: ML is a valid method for automatically identifying Conversational Pauses in the natural acoustic setting of inpatient serious illness conversations.
Keywords: communication; conversational pauses; machine learning; palliative care; silence.