Predicting hospital admissions to reduce crowding in the emergency departments
Visualitza/Obre
Cita com:
hdl:2117/375678
Tipus de documentArticle
Data publicació2022-10-24
EditorMultidisciplinary Digital Publishing Institute
Condicions d'accésAccés obert
Llevat que s'hi indiqui el contrari, els
continguts d'aquesta obra estan subjectes a la llicència de Creative Commons
:
Reconeixement 4.0 Internacional
Abstract
Having an increasing number of patients in the emergency department constitutes an obstacle to the admissions process and hinders the emergency department (ED)’s ability to deal with the continuously arriving demand for new admissions. In addition, forecasting is an important aid in many areas of hospital management, including elective surgery scheduling, bed management, and staff resourcing. Therefore, this paper aims to develop a precise prediction model for admissions in the Integral Healthcare System for Public Use in Catalonia. These models assist in reducing overcrowding in emergency rooms and improve the quality of care offered to patients. Data from 60 EDs were analyzed to determine the likelihood of hospital admission based on information readily available at the time of arrival in the ED. The first part of the study targeted the obtention of models with high accuracy and area under the curve (AUC), while the second part targeted the obtention of models with a sensitivity higher than 0.975 and analyzed the possible benefits that could come from the application of such models. From the 3,189,204 ED visits included in the study, 11.02% ended in admission to the hospital. The gradient boosting machine method was used to predict a binary outcome of either admission or discharge.
CitacióCusido, J. [et al.]. Predicting hospital admissions to reduce crowding in the emergency departments. "Applied sciences (Basel)", 24 Octubre 2022, vol. 12, núm. 21, article 10764, p. 1-17.
ISSN2076-3417
Versió de l'editorhttps://www.mdpi.com/2076-3417/12/21/10764
Fitxers | Descripció | Mida | Format | Visualitza |
---|---|---|---|---|
applsci-12-10764.pdf | 728,3Kb | Visualitza/Obre |