University of Vermont, Department of Family Medicine, Burlington, VT, USA.
University of Vermont, Department of Computer Science, Burlington, VT, USA.
Patient Educ Couns. 2021 Nov;104(11):2616-2621. doi: 10.1016/j.pec.2021.07.043. Epub 2021 Jul 29.
Understanding uncertainty in participatory decision-making requires scientific attention to interaction between what actually happens when patients, families and clinicians engage one another in conversation and the multi-level contexts in which these occur. Achieving this understanding will require conceptually grounded and scalable methods for use in large samples of people representing diversity in cultures, speaking and decision-making norms, and clinical situations.
Here, we focus on serious illness and describe Conversational Stories as a scalable and conceptually grounded framework for characterizing uncertainty expression in these clinical contexts. Using actual conversations from a large direct-observation cohort study, we demonstrate how natural language processing and unsupervised machine learning methods can reveal underlying types of uncertainty stories in serious illness conversations.
Conversational Storytelling offers a meaningful analytic framework for scalable computational methods to study uncertainty in healthcare conversations.
理解参与式决策中的不确定性需要科学关注患者、家属和临床医生在对话中相互作用的实际情况，以及这些情况发生的多层次背景。要实现这一理解，需要在具有概念基础的前提下，采用可规模化的方法，以便在具有代表性的大量人群中使用，这些人群在文化、说话和决策规范以及临床情况方面存在多样性。
在这里，我们专注于严重疾病，并将对话故事描述为一种可规模化且具有概念基础的框架，用于描述这些临床环境中不确定性的表达。我们使用来自大型直接观察队列研究的实际对话，展示了自然语言处理和无监督机器学习方法如何揭示严重疾病对话中潜在的不确定性故事类型。
对话故事为研究医疗保健对话中的不确定性提供了有意义的分析框架，也为可规模化的计算方法提供了支持。
