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Defining Diagnostic Uncertainty as a Discourse Type: a Transdisciplinary Approach to Analysing Clinical Narratives of Electronic Health Records
- Source :
-
Applied Linguistics . 2024 45(1):134-162. - Publication Year :
- 2024
-
Abstract
- 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.
Details
- Language :
- English
- ISSN :
- 0142-6001 and 1477-450X
- Volume :
- 45
- Issue :
- 1
- Database :
- ERIC
- Journal :
- Applied Linguistics
- Publication Type :
- Academic Journal
- Accession number :
- EJ1416341
- Document Type :
- Journal Articles<br />Reports - Evaluative
- Full Text :
- https://doi.org/10.1093/applin/amad012