Prediction of hospitalization and waiting time within 24 hours of emergency department patients with unstructured text data

Hyeram Seo, Imjin Ahn, Hansle Gwon, Hee Jun Kang, Yunha Kim, Ha Na Cho, Heejung Choi, Minkyoung Kim, Jiye Han, Gaeun Kee, Seohyun Park, Dong-Woo Seo, Tae Joon Jun () and Young-Hak Kim ()
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Hyeram Seo: Asan Medical Institute of Convergence Science and Technology, Asan Medical Center, University of Ulsan College of Medicine
Imjin Ahn: Asan Medical Center, University of Ulsan College of Medicine
Hansle Gwon: Asan Medical Center, University of Ulsan College of Medicine
Hee Jun Kang: Asan Medical Center, University of Ulsan College of Medicine
Yunha Kim: Asan Medical Center, University of Ulsan College of Medicine
Ha Na Cho: Asan Medical Center, University of Ulsan College of Medicine
Heejung Choi: Asan Medical Institute of Convergence Science and Technology, Asan Medical Center, University of Ulsan College of Medicine
Minkyoung Kim: Asan Medical Institute of Convergence Science and Technology, Asan Medical Center, University of Ulsan College of Medicine
Jiye Han: Asan Medical Institute of Convergence Science and Technology, Asan Medical Center, University of Ulsan College of Medicine
Gaeun Kee: Asan Medical Center, University of Ulsan College of Medicine
Seohyun Park: Asan Medical Center, University of Ulsan College of Medicine
Dong-Woo Seo: Asan Medical Center, University of Ulsan College of Medicine
Tae Joon Jun: Asan Institute for Life Sciences, Asan Medical Center
Young-Hak Kim: Asan Medical Center, University of Ulsan College of Medicine

Health Care Management Science, 2024, vol. 27, issue 1, No 6, 114-129

Abstract: Abstract Overcrowding of emergency departments is a global concern, leading to numerous negative consequences. This study aimed to develop a useful and inexpensive tool derived from electronic medical records that supports clinical decision-making and can be easily utilized by emergency department physicians. We presented machine learning models that predicted the likelihood of hospitalizations within 24 hours and estimated waiting times. Moreover, we revealed the enhanced performance of these machine learning models compared to existing models by incorporating unstructured text data. Among several evaluated models, the extreme gradient boosting model that incorporated text data yielded the best performance. This model achieved an area under the receiver operating characteristic curve score of 0.922 and an area under the precision-recall curve score of 0.687. The mean absolute error revealed a difference of approximately 3 hours. Using this model, we classified the probability of patients not being admitted within 24 hours as Low, Medium, or High and identified important variables influencing this classification through explainable artificial intelligence. The model results are readily displayed on an electronic dashboard to support the decision-making of emergency department physicians and alleviate overcrowding, thereby resulting in socioeconomic benefits for medical facilities.

Keywords: Hospital admission prediction; Electronic medical record; Emergency department; Machine learning; Explainable artificial intelligence; Natural language processing (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s10729-023-09660-5

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