CommSense: A Wearable Sensing Computational Framework for Evaluating Patient-Clinician Interactions
<|reference_start|>CommSense: A Wearable Sensing Computational Framework for Evaluating Patient-Clinician Interactions: 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.<|reference_end|>
@article{wang2024commsense:,
    title={CommSense: A Wearable Sensing Computational Framework for Evaluating
  Patient-Clinician Interactions},
    author={Zhiyuan Wang, Nusayer Hassan, Virginia LeBaron, Tabor E. Flickinger,
  David Ling, James Edwards, Congyu Wu, Mehdi Boukhechba and Laura E. Barnes},
    journal={arXiv preprint arXiv:2407.08143},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.08143},
    primaryClass={cs.HC}
}