Defining diagnostic uncertainty as a discourse type: A transdisciplinary approach to analysing clinical narratives of Electronic Health Records
- Autores: Lindsay C Nickels, Trisha L Marshall, Ezra Edgerton, Patrick W Brady, Philip A Hagedorn, James J. Lee
- Localización: Applied linguistics, ISSN 0142-6001, Vol. 45, Nº 1, 2024, págs. 134-162
- Idioma: inglés
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Resumen
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.
