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可视化对话结构随时间的变化:对有效的医患咨询的见解。

Visualising conversation structure across time: insights into effective doctor-patient consultations.

作者信息

Angus Daniel, Watson Bernadette, Smith Andrew, Gallois Cindy, Wiles Janet

机构信息

The University of Queensland, St. Lucia, Queensland, Australia.

出版信息

PLoS One. 2012;7(6):e38014. doi: 10.1371/journal.pone.0038014. Epub 2012 Jun 5.

Abstract

Effective communication between healthcare professionals and patients is critical to patients' health outcomes. The doctor/patient dialogue has been extensively researched from different perspectives, with findings emphasising a range of behaviours that lead to effective communication. Much research involves self-reports, however, so that behavioural engagement cannot be disentangled from patients' ratings of effectiveness. In this study we used a highly efficient and time economic automated computer visualisation measurement technique called Discursis to analyse conversational behaviour in consultations. Discursis automatically builds an internal language model from a transcript, mines the transcript for its conceptual content, and generates an interactive visual account of the discourse. The resultant visual account of the whole consultation can be analysed for patterns of engagement between interactants. The findings from this study show that Discursis is effective at highlighting a range of consultation techniques, including communication accommodation, engagement and repetition.

摘要

医疗保健专业人员与患者之间的有效沟通对患者的健康结果至关重要。医生/患者对话已从不同角度进行了广泛研究,研究结果强调了一系列有助于有效沟通的行为。然而,许多研究涉及自我报告,因此行为参与度无法与患者对沟通效果的评分区分开来。在本研究中,我们使用了一种名为Discursis的高效且节省时间的自动计算机可视化测量技术来分析会诊中的对话行为。Discursis会根据文字记录自动构建内部语言模型,挖掘文字记录的概念内容,并生成对话的交互式可视化描述。可以分析整个会诊的可视化结果,以找出互动者之间的参与模式。本研究的结果表明,Discursis在突出一系列会诊技巧方面很有效,包括沟通调节、参与和重复。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4f35/3367908/d058715a70ee/pone.0038014.g001.jpg

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