• Hao Wang, Naomi Alanis, Laura Haygood, Thomas K Swoboda, Nathan Hoot, Daniel Phillips, Heidi Knowles, Sara Ann Stinson, Prachi Mehta, and Usha Sambamoorthi.
• Department of Emergency Medicine, JPS Health Network, Fort Worth, Texas, USA.
• Acad Emerg Med. 2024 Jul 1; 31 (7): 696706696-706.

ObjectivesNatural language processing (NLP) represents one of the adjunct technologies within artificial intelligence and machine learning, creating structure out of unstructured data. This study aims to assess the performance of employing NLP to identify and categorize unstructured data within the emergency medicine (EM) setting.MethodsWe systematically searched publications related to EM research and NLP across databases including MEDLINE, Embase, Scopus, CENTRAL, and ProQuest Dissertations & Theses Global. Independent reviewers screened, reviewed, and evaluated article quality and bias. NLP usage was categorized into syndromic surveillance, radiologic interpretation, and identification of specific diseases/events/syndromes, with respective sensitivity analysis reported. Performance metrics for NLP usage were calculated and the overall area under the summary of receiver operating characteristic curve (SROC) was determined.ResultsA total of 27 studies underwent meta-analysis. Findings indicated an overall mean sensitivity (recall) of 82%-87%, specificity of 95%, with the area under the SROC at 0.96 (95% CI 0.94-0.98). Optimal performance using NLP was observed in radiologic interpretation, demonstrating an overall mean sensitivity of 93% and specificity of 96%.ConclusionsOur analysis revealed a generally favorable performance accuracy in using NLP within EM research, particularly in the realm of radiologic interpretation. Consequently, we advocate for the adoption of NLP-based research to augment EM health care management.© 2024 The Authors. Academic Emergency Medicine published by Wiley Periodicals LLC on behalf of Society for Academic Emergency Medicine.

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