  Quality patient-provider communication is critical to improve clinical care
and patient outcomes. While progress has been made with communication skills
training for clinicians, significant gaps exist in how to best monitor,
measure, and evaluate the implementation of communication skills in the actual
clinical setting. Advancements in ubiquitous technology and natural language
processing make it possible to realize more objective, real-time assessment of
clinical interactions and in turn provide more timely feedback to clinicians
about their communication effectiveness. In this paper, we propose CommSense, a
computational sensing framework that combines smartwatch audio and transcripts
with natural language processing methods to measure selected ``best-practice''
communication metrics captured by wearable devices in the context of palliative
care interactions, including understanding, empathy, presence, emotion, and
clarity. We conducted a pilot study involving N=40 clinician participants, to
test the technical feasibility and acceptability of CommSense in a simulated
clinical setting. Our findings demonstrate that CommSense effectively captures
most communication metrics and is well-received by both practicing clinicians
and student trainees. Our study also highlights the potential for digital
technology to enhance communication skills training for healthcare providers
and students, ultimately resulting in more equitable delivery of healthcare and
accessible, lower cost tools for training with the potential to improve patient
outcomes.
