Analysis on Benefits and Costs of Machine Learning-Based Early Hospitalization Prediction

• Published in: IEEE access Vol. 10; pp. 32479 - 32493
• Main Authors: Kim, Eunbi, Han, Kap Su, Cheong, Taesu, Lee, Sung Woo, Eun, Joonyup, Kim, Su Jin
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Cost benefit analysis; Costs; Decision trees; Diseases; Emergency department; Emergency medical services; estimation of quantitative effects; Hospitalization; hospitalization prediction; Hospitals; Length of stay; Machine learning; Patient satisfaction; Prediction algorithms; Prediction models; Predictions; Predictive models; Radio frequency; Support vector machines
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2022.3160742

Overcrowding in emergency departments (EDs) has long been a problem worldwide and has serious consequences for patient satisfaction and safety. Typically, overcrowding is caused by delays in the boarding time of ED patients waiting for inpatient beds. If the hospitalization of patients is predicted early enough in EDs, inpatient beds can be prepared in advance and the boarding time can be reduced. We design machine learning-based hospitalization predictive models using data on 27,747 patients and compare the experimental results. Five predictive models are designed: 1) logistic regression, 2) XGBoost, 3) NGBoost, 4) support vector machine, and 5) decision tree models. Based on the predictive results, we estimate the quantitative effects of hospitalization predictions on EDs and wards. Using the data from the ED of a general hospital in South Korea, our experiments show that the ED length of stay of a patient can be reduced by 12.3 minutes on average and the ED can reduce the total length of stay by 340,147 minutes for a year.