Prediction of emergency department resource requirements during triage: An application of current natural language processing techniques.

• Type of Material: Article
• Authors: Nicholas W. Sterling, Emory University; Felix Brann, Vital Software Inc. Auckland; Rachel Patzer, Emory University; Mengyu Di, Emory University; Megan Koebbe, Emory University; Madalyn Burke, Emory University; Justin Schrager, Emory University
• Language: English
• Date: 2020-12
• Publisher: Wiley
• Publication Version: Final Published Version
• Copyright Statement: © 2020 The Authors. JACEP Open published by Wiley Periodicals LLC on behalf of the American College of Emergency Physicians.
• License: Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International
• Final Published Version (URL): http://dx.doi.org/10.1002/emp2.12253
• Title of Journal or Parent Work: Journal of the American College of Emergency Physicians Open
• Volume: 1
• Issue: 6
• Start Page: 1676
• End Page: 1683
• Keywords: triage; resources; emergency department; natural language processing; machine learning
• Research Categories: Health Sciences, Health Care Management

Abstract

Objective: Accurate triage in the emergency department (ED) is critical for medical safety and operational efficiency. We aimed to predict the number of future required ED resources, as defined by the Emergency Severity Index (ESI) triage protocol, using natural language processing of nursing triage notes. Methods: We constructed a retrospective cohort of all 265,572 consecutive ED encounters from 2015 to 2016 from 3 separate clinically heterogeneous academically affiliated EDs. We excluded encounters missing relevant information, leaving 226,317 encounters. We calculated the number of resources used by patients in the ED retrospectively and based outcome categories on criteria defined in the ESI algorithm: 0 (30,604 encounters), 1 (49,315 encounters), and 2 or more (146,398 encounters). A neural network model was trained on a training subset to predict the number of resources using triage notes and clinical variables at triage. Model performance was evaluated using the test subset and was compared with human ratings. Results: Overall model accuracy and macro F1 score for number of resources were 66.5% and 0.601, respectively. The model had similar macro F1 (0.589 vs 0.592) and overall accuracy (65.9% vs 69.0%) compared to human raters. Model predictions had slightly higher F1 scores and accuracy for 0 resources and were less accurate for 2 or more resources. Conclusions: Machine learning of nursing triage notes, combined with clinical data available at ED presentation, can be used to predict the number of required future ED resources. These findings suggest that machine learning may be a valuable adjunct tool in the initial triage of ED patients.