ERIC Number: EJ1416341
Record Type: Journal
Publication Date: 2024
Pages: 29
Abstractor: As Provided
ISBN: N/A
ISSN: ISSN-0142-6001
EISSN: EISSN-1477-450X
Defining Diagnostic Uncertainty as a Discourse Type: a Transdisciplinary Approach to Analysing Clinical Narratives of Electronic Health Records
Lindsay C. Nickels; Trisha L. Marshall; Ezra Edgerton; Patrick W. Brady; Philip A. Hagedorn; James J. Lee
Applied Linguistics, v45 n1 p134-162 2024
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.
Descriptors: Clinical Diagnosis, Ambiguity (Context), Context Effect, Medicine, Electronic Publishing, Health Services, Records (Forms), Information Technology, Patients, Notetaking, Language Usage, Discourse Analysis, Artificial Intelligence, Algorithms, Classification
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Publication Type: Journal Articles; Reports - Evaluative
Education Level: N/A
Audience: N/A
Language: English
Sponsor: N/A
Authoring Institution: N/A
Grant or Contract Numbers: N/A