Diagnostic uncertainty is prevalent throughout medicine and significantly impacts patient care, especially when it goes unrecognized. However, we lack a reliable clinical means of identifying uncertainty. This study evaluates the narrative discourse within clinical notes in the Electronic Health Record as a means of identifying diagnostic uncertainty. Recognizing that discourse producers use language ‘semi-automatically’ (Partington et al. 2013), we hypothesized that clinicians include distinct indications of uncertainty in their written assessments, which could be elucidated by linguistic analysis. Using a cohort of patients prospectively identified as having an uncertain diagnosis (UD), we conducted a detailed corpus-assisted discourse analysis. The analysis revealed a set of linguistic indicators constitutive of diagnostic uncertainty including terms of modality, register-specific terms, and linguistically identifiable clinical behaviours. This dictionary of UD indicators was thoroughly tested, and its performance was compared with a matched-control dataset. Based on the findings, we built a machine learning classification algorithm with the ability to predict UD patient cohorts with 87.0% accuracy, effectively demonstrating the feasibility of using clinical discourse to classify patients and directly impact the clinical environment.

中文翻译：

---

将诊断不确定性定义为一种话语类型：一种分析电子健康记录临床叙述的跨学科方法

诊断不确定性在整个医学领域普遍存在，并且会显着影响患者护理，尤其是当它未被识别时。然而，我们缺乏一种可靠的临床方法来识别不确定性。本研究评估了电子健康记录中临床笔记中的叙述性话语，以此作为识别诊断不确定性的一种方式。认识到话语生产者“半自动”使用语言（Partington 等人，2013 年），我们假设临床医生在他们的书面评估中包含明显的不确定性迹象，这可以通过语言分析来阐明。我们使用前瞻性确定为不确定诊断 (UD) 的一组患者，进行了详细的语料库辅助话语分析。该分析揭示了一组构成诊断不确定性的语言指标，包括模态术语、特定语域和语言可识别的临床行为。这个 UD 指标字典已经过彻底测试，并将其性能与匹配控制数据集进行了比较。基于这些发现，我们构建了一种机器学习分类算法，能够以 87.0% 的准确率预测 UD 患者队列，有效地证明了使用临床话语对患者进行分类并直接影响临床环境的可行性。