Prediction of hospitalization using artificial intelligence for urgent patients in the emergency department

Abstract Timely assessment to accurately prioritize patients is crucial for emergency department (ED) management. Urgent (i.e., level-3, on a 5-level emergency severity index system) patients have become a challenge since under-triage and over-triage often occur. This study was aimed to develop a co...

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Autores principales:	Jung-Ting Lee, Chih-Chia Hsieh, Chih-Hao Lin, Yu-Jen Lin, Chung-Yao Kao
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/02e679f6ef55470f95856821d8d1eee4
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spelling	oai:doaj.org-article:02e679f6ef55470f95856821d8d1eee42021-12-02T18:51:29ZPrediction of hospitalization using artificial intelligence for urgent patients in the emergency department10.1038/s41598-021-98961-22045-2322https://doaj.org/article/02e679f6ef55470f95856821d8d1eee42021-09-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-98961-2https://doaj.org/toc/2045-2322Abstract Timely assessment to accurately prioritize patients is crucial for emergency department (ED) management. Urgent (i.e., level-3, on a 5-level emergency severity index system) patients have become a challenge since under-triage and over-triage often occur. This study was aimed to develop a computational model by artificial intelligence (AI) methodologies to accurately predict urgent patient outcomes using data that are readily available in most ED triage systems. We retrospectively collected data from the ED of a tertiary teaching hospital between January 1, 2015 and December 31, 2019. Eleven variables were used for data analysis and prediction model building, including 1 response, 2 demographic, and 8 clinical variables. A model to predict hospital admission was developed using neural networks and machine learning methodologies. A total of 282,971 samples of urgent (level-3) visits were included in the analysis. Our model achieved a validation area under the curve (AUC) of 0.8004 (95% CI 0.7963–0.8045). The optimal cutoff value identified by Youden's index for determining hospital admission was 0.5517. Using this cutoff value, the sensitivity was 0.6721 (95% CI 0.6624–0.6818), and the specificity was 0.7814 (95% CI 0.7777–0.7851), with a positive predictive value of 0.3660 (95% CI 0.3586–0.3733) and a negative predictive value of 0.9270 (95% CI 0.9244–0.9295). Subgroup analysis revealed that this model performed better in the nontraumatic adult subgroup and achieved a validation AUC of 0.8166 (95% CI 0.8199–0.8212). Our AI model accurately assessed the need for hospitalization for urgent patients, which constituted nearly 70% of ED visits. This model demonstrates the potential for streamlining ED operations using a very limited number of variables that are readily available in most ED triage systems. Subgroup analysis is an important topic for future investigation.Jung-Ting LeeChih-Chia HsiehChih-Hao LinYu-Jen LinChung-Yao KaoNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-8 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Jung-Ting Lee Chih-Chia Hsieh Chih-Hao Lin Yu-Jen Lin Chung-Yao Kao Prediction of hospitalization using artificial intelligence for urgent patients in the emergency department
description	Abstract Timely assessment to accurately prioritize patients is crucial for emergency department (ED) management. Urgent (i.e., level-3, on a 5-level emergency severity index system) patients have become a challenge since under-triage and over-triage often occur. This study was aimed to develop a computational model by artificial intelligence (AI) methodologies to accurately predict urgent patient outcomes using data that are readily available in most ED triage systems. We retrospectively collected data from the ED of a tertiary teaching hospital between January 1, 2015 and December 31, 2019. Eleven variables were used for data analysis and prediction model building, including 1 response, 2 demographic, and 8 clinical variables. A model to predict hospital admission was developed using neural networks and machine learning methodologies. A total of 282,971 samples of urgent (level-3) visits were included in the analysis. Our model achieved a validation area under the curve (AUC) of 0.8004 (95% CI 0.7963–0.8045). The optimal cutoff value identified by Youden's index for determining hospital admission was 0.5517. Using this cutoff value, the sensitivity was 0.6721 (95% CI 0.6624–0.6818), and the specificity was 0.7814 (95% CI 0.7777–0.7851), with a positive predictive value of 0.3660 (95% CI 0.3586–0.3733) and a negative predictive value of 0.9270 (95% CI 0.9244–0.9295). Subgroup analysis revealed that this model performed better in the nontraumatic adult subgroup and achieved a validation AUC of 0.8166 (95% CI 0.8199–0.8212). Our AI model accurately assessed the need for hospitalization for urgent patients, which constituted nearly 70% of ED visits. This model demonstrates the potential for streamlining ED operations using a very limited number of variables that are readily available in most ED triage systems. Subgroup analysis is an important topic for future investigation.
format	article
author	Jung-Ting Lee Chih-Chia Hsieh Chih-Hao Lin Yu-Jen Lin Chung-Yao Kao
author_facet	Jung-Ting Lee Chih-Chia Hsieh Chih-Hao Lin Yu-Jen Lin Chung-Yao Kao
author_sort	Jung-Ting Lee
title	Prediction of hospitalization using artificial intelligence for urgent patients in the emergency department
title_short	Prediction of hospitalization using artificial intelligence for urgent patients in the emergency department
title_full	Prediction of hospitalization using artificial intelligence for urgent patients in the emergency department
title_fullStr	Prediction of hospitalization using artificial intelligence for urgent patients in the emergency department
title_full_unstemmed	Prediction of hospitalization using artificial intelligence for urgent patients in the emergency department
title_sort	prediction of hospitalization using artificial intelligence for urgent patients in the emergency department
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/02e679f6ef55470f95856821d8d1eee4
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Prediction of hospitalization using artificial intelligence for urgent patients in the emergency department

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