Predictive Models of Ward Admissions from the Emergency Department

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 model 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.