RPD313 - CommSense: Improving Patient-Clinician Communication with Artificial Intelligence and Wearable Technology

Key Message : Quality patient-clinician communication is especially critical in palliative care where clinicians must learn skills to engage in high-stakes conversations related to goals of care and prognosis. Artificial Intelligence (AI) powered technologies offer unique opportunities to provide clinicians with real-time feedback about their communication performance and track their progress over time.

Abstract:

Background: Quality communication is foundational to palliative care, but methods to monitor and track communication performance in clinical settings are limited. CommSense is an AI-powered system created by our team and deployed on wearable devices to securely capture and process clinical conversations and provide actionable feedback to clinicians (1,2).

Objective: To deploy CommSense and assess clinical and technical acceptability and feasibility.

Methods: CommSense was deployed within a palliative care clinic and audio-recorded conversations between clinicians and patients (≥18 years, any diagnosis). Brief surveys were completed by patients and clinicians after each recorded conversation to assess perception of the quality of the conversation. Conversation transcripts were cleaned, de-identified and annotated by clinical experts to provide ground truth data for CommSense model comparison. Large Language Models (LLMs) evaluated task-specific language analytics, specifically assessing balanced accuracy and precision of detecting understanding, empathy, emotion, clarity, and presence (3). Internal (patient and clinician users of CommSense) and external stakeholders (policy makers, communication scientists, administrators, and industry experts) participated in semi-structured interviews to explore their perceptions of CommSense.

Results: Fifty-one (n=51) conversations were collected by CommSense from 36 (n=36) patients and 7 (n=7) palliative care clinicians between May – August 2025. External stakeholder participants (n=14) discussed system-level implementation considerations, including: importance of integrating communication technologies within existing health system infrastructure; engagement of champions throughout the implementation process; attention to data security and privacy; and intentionality with the design and delivery of communication performance feedback. Participants also emphasized the benefit of CommSense to support trainees.

Conclusion: AI-powered technology has tremendous potential to support healthcare communication, particularly by offering trainees a way to objectively assess communication skills and longitudinally track their progress in implementing communication skills in the actual (non-simulation lab) setting. Future work should focus on optimal ways to provide contextualized communication feedback to clinicians.

References: 1. LeBaron, V., Boukhechba, M., Edwards, J., et al. (2022). Exploring the use of wearable sensors and natural language processing technology to improve patient-clinician communication: Protocol for a feasibility study. JMIR Research Protocols, 11(5):e37975.

2. LeBaron, V., Flickinger, T., Ling, D., et al. (2023). Feasibility and acceptability testing of CommSense: A novel communication technology to enhance health equity in clinician-patient interactions. Digital Health; 9

3. Wang, Z., Yuan, F., LeBaron, V., Flickinger, T. & Barnes, L.(2024).
PALLM: Evaluating and Enhancing Palliative Care Conversations with Large Language Models, ACM Transactions on Computing for Healthcare. Special Issue on Large Language Models, Conversational Systems, and Generative AI in Health. https://arxiv.org/abs/2409.15188.