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.

翻译：优质的医患沟通对于改善临床护理和患者预后至关重要。尽管临床医生的沟通技能培训已取得进展，但在如何最佳监测、测量和评估沟通技能在实际临床环境中的实施方面仍存在显著差距。普适计算技术与自然语言处理的进步使得对临床交互进行更客观、实时的评估成为可能，从而为临床医生提供关于其沟通有效性的更及时反馈。本文提出CommSense，一种结合智能手表音频、转录文本与自然语言处理方法的计算感知框架，用于测量在安宁疗护交互场景中通过可穿戴设备捕获的选定"最佳实践"沟通指标，包括理解、共情、在场、情感和清晰度。我们开展了一项涉及N=40名临床医生参与者的试点研究，在模拟临床环境中测试CommSense的技术可行性与可接受性。研究结果表明，CommSense能有效捕获大部分沟通指标，并受到执业临床医生与学生学员的广泛认可。本研究同时凸显了数字技术在增强医疗从业者及学生沟通技能培训方面的潜力，最终有望推动更公平的医疗服务提供，并通过可及性更高、成本更低的培训工具改善患者预后。