LeBaron Virginia, Flickinger Tabor, Ling David, Lee Hansung, Edwards James, Tewari Anant, Wang Zhiyuan, Barnes Laura E
University of Virginia School of Nursing, Charlottesville, VA.
University of Virginia School of Medicine, Charlottesville, VA.
Digit Health. 2023 Jul 11;9:20552076231184991. doi: 10.1177/20552076231184991. eCollection 2023 Jan-Dec.
Quality patient-clinician communication is paramount to achieving safe and compassionate healthcare, but evaluating communication performance during real clinical encounters is challenging. Technology offers novel opportunities to provide clinicians with actionable feedback to enhance their communication skills.
This pilot study evaluated the acceptability and feasibility of CommSense, a novel natural language processing (NLP) application designed to record and extract key metrics of communication performance and provide real-time feedback to clinicians. Metrics of communication performance were established from a review of the literature and technical feasibility verified. CommSense was deployed on a wearable (smartwatch), and participants were recruited from an academic medical center to test the technology. Participants completed a survey about their experience; results were exported to SPSS (v.28.0) for descriptive analysis.
Forty ( = 40) healthcare participants (nursing students, medical students, nurses, and physicians) pilot tested CommSense. Over 90% of participants "strongly agreed" or "agreed" that CommSense could improve compassionate communication ( = 38, 95%) and help healthcare organizations deliver high-quality care ( = 39, 97.5%). Most participants ( = 37, 92.5%) "strongly agreed" or "agreed" they would be willing to use CommSense in the future; 100% ( = 40) "strongly agreed" or "agreed" they were interested in seeing information analyzed by CommSense about their communication performance. Metrics of most interest were medical jargon, interruptions, and speech dominance.
Participants perceived significant benefits of CommSense to track and improve communication skills. Future work will deploy CommSense in the clinical setting with a more diverse group of participants, validate data fidelity, and explore optimal ways to share data analyzed by CommSense with end-users.
优质的医患沟通对于实现安全且富有同情心的医疗保健至关重要，但在实际临床诊疗过程中评估沟通表现具有挑战性。技术提供了新的机会，可为临床医生提供可采取行动的反馈，以提高他们的沟通技巧。
这项试点研究评估了CommSense的可接受性和可行性，CommSense是一种新型自然语言处理（NLP）应用程序，旨在记录和提取沟通表现的关键指标，并向临床医生提供实时反馈。通过文献综述确定沟通表现指标，并验证技术可行性。CommSense部署在可穿戴设备（智能手表）上，从一家学术医疗中心招募参与者来测试该技术。参与者完成了关于他们体验的调查；结果导出到SPSS（v.28.0）进行描述性分析。
四十名（n = 40）医疗保健参与者（护理专业学生、医学专业学生、护士和医生）对CommSense进行了试点测试。超过90%的参与者“强烈同意”或“同意”CommSense可以改善富有同情心的沟通（n = 38，95%），并帮助医疗保健机构提供高质量的护理（n = 39，97.5%）。大多数参与者（n = 37，92.5%）“强烈同意”或“同意”他们未来愿意使用CommSense；100%（n = 40）“强烈同意”或“同意”他们有兴趣查看CommSense分析的关于他们沟通表现的信息。最受关注的指标是医学术语、打断和言语主导。
参与者认为CommSense在跟踪和提高沟通技巧方面有显著益处。未来的工作将在临床环境中与更多样化的参与者群体一起部署CommSense，验证数据保真度，并探索与最终用户共享CommSense分析数据的最佳方式。
