Authors:
Laiene Azkue
1
;
2
;
Jon Kerexeta
1
;
3
;
Jorge Sampedro
4
;
Moisés Espejo
4
and
Nekane Larburu
1
;
3
Affiliations:
1
Vicomtech Foundation, Basque Research and Technology Alliance, (BRTA), 20009 Donostia, Spain
;
2
Biomedical Engineering Department, Mondragon Unibertsitatea, 20500 Mondragón, Spain
;
3
Biodonostia Health Research Institute, 20014 San Sebastián, Spain
;
4
Asunción Klinika, 20400 Tolosa, Spain
Keyword(s):
Emergency Department, Ward Admission, Predictive Models, Machine Learning, Artificial Intelligence.
Abstract:
The demand for emergency department (ED) care has increased significantly in recent years, mainly due to factors such as the increase in chronic diseases, aging population and urban population growth. The large influx of patients can lead to overcrowding and resource allocation problems, which impact the quality of care. A new tool to improve patient severity classification systems could improve ED care and avoid inappropriate admissions. Therefore, we propose the development of an artificial intelligence model to predict ED ward admissions. The proposed model uses electronic medical records from the Asunción Klinika in Spain and environmental data. Three models are created at different stages of ED: arrival model which predicts admission upon patient arrival, triage model which predicts admission after clinicians’ triage and the last one, laboratory model which make use of triage model data and laboratory analysis to estimate the risk among the most critical patients. The arrival mo
del achieved an AUC of 0.801, the triage model achieved an AUC of 0.854, and the laboratory model achieved an AUC of 0.781. These models provide valuable information for efficient patient management and resource allocation in the ED, contributing to improved patient care and the adequacy of hospital admissions.
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