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Use of natural language processing in the emergency department: A clinical overview on the state of the art
Journal article   Open access

Use of natural language processing in the emergency department: A clinical overview on the state of the art

Carly Hudson, Adrian Goldsworthy, Thuy Linh Phan, Anu Joy, Oystein Tronstad and Marcus Randall
Artificial Intelligence in Health, Vol.3(2), 025450097
2026
DOI: https://doi.org/10.36922/AIH025450097
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Open Access CC BY V4.0

Abstract

Emergency department Healthcare data Natural language processing Triage text Unstructured data
Machine learning (ML) and artificial intelligence are increasingly ubiquitous in healthcare data analytics. To date, however, ML has been largely restricted to the analysis of structured data. While natural language processing (NLP) is gaining prominence in healthcare, substantial challenges remain in the generation and analysis of unstructured data. Emergency departments, which are increasingly under-resourced and overburdened, may benefit from the implementation of ML techniques that incorporate NLP to support clinical decision-making and improve patient care. Historically, regional and cultural variations have posed significant challenges to the widespread application of ML algorithms beyond their original training datasets. The rapidly increasing use of NLP within clinical note-taking applications provides avenues to assist in standardizing unstructured data and extracting meaningful insights to improve generalization and clinical translation.

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