Defining Diagnostic Uncertainty as a Discourse Type: a Transdisciplinary Approach to Analysing Clinical Narratives of Electronic Health Records

Oxford University Press. Great Clarendon Street, Oxford, OX2 6DP, UK. Tel: +44-1865-353907; Fax: +44-1865-353485; e-mail: [email protected]; Web site: http://applij.oxfordjournals.org/

Y

29

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

10.1093/applin/amad012

0142-6001
1477-450X

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.

As Provided

2024

EJ1416341