PDF(Pubmed)
Abstract：
Patient-provider communication influences patient health outcomes, and analyzing such communication could help providers identify opportunities for improvement, leading to better care. Interpersonal communication can be assessed through \"social-signals\" expressed in non-verbal, vocal behaviors like interruptions, turn-taking, and pitch. To automate this assessment, we introduce a machine-learning pipeline that ingests audio-streams of conversations and tracks the magnitude of four social-signals: dominance, interactivity, engagement, and warmth. This pipeline is embedded into ConverSense, a web-application for providers to visualize their communication patterns, both within and across visits. Our user study with 5 clinicians and 10 patient visits demonstrates ConverSense\'s potential to provide feedback on communication challenges, as well as the need for this feedback to be contextualized within the specific underlying visit and patient interaction. Through this novel approach that uses data-driven self-reflection, ConverSense can help providers improve their communication with patients to deliver improved quality of care.
摘要：
患者与提供者的沟通会影响患者的健康结果，分析这种沟通可以帮助提供者确定改进的机会，导致更好的护理。人际沟通可以通过非语言表达的“社会信号”来评估，像打断这样的声音行为，转身，和音高。要自动执行此评估，我们引入了一个机器学习管道，该管道摄取对话的音频流并跟踪四个社交信号的大小：优势，交互性,订婚,和温暖。此管道嵌入到ConverSense中，提供者可视化其通信模式的Web应用程序，内部和跨访问。我们对5名临床医生和10名患者进行的用户研究表明，ConverSense有可能提供有关沟通挑战的反馈。以及这种反馈需要在特定的基础访问和患者互动中进行背景化。通过这种使用数据驱动的自我反思的新颖方法，ConverSense可以帮助提供商改善与患者的沟通，以提高护理质量。
