Improving triage accuracy of hospitalization and discharge decisions in the emergency department.

The initial severity triage of patients in the emergency department (ED) is implemented differently worldwide, according to the characteristics of each country. However, better classification methods are being studied due to various problems with the current system. Therefore, the aim of this study was to determine the usefulness of patients' severity assessment in a new way that gives appropriate values to factors that can be obtained in the ED. We collected data from 158,246 patients who visited the ED from 01 January 2016 to 31 December 2020. Using the appropriate values of various factors obtained using the Rasch analysis method, the cut-off values for predicting hospitalization and discharge at the ED of patients were determined. Furthermore, using artificial intelligence, the patients who were hospitalized and discharged from the ED were classified and compared with the results of the Rasch analysis. The accuracy of the algorithms was analyzed as a combination of factors that could be obtained during the initial stage of the patient's visits. The area under the curve (AUC) value for the prediction of hospitalization and discharge by a combination of factors immediately obtained from the visit was 0.611. In addition, using the factors that could be obtained after a certain period, the AUC value of hospitalization and discharge prediction was 0.767. The results of analysis using artificial intelligence were similar to or slightly higher than the AUC value of the Rasch analysis. The prediction of hospitalization and discharge in the ED using clinical parameters with Rasch analysis can be used for medical assistance, which is expected to help in the efficient operation of the ED. [ABSTRACT FROM AUTHOR]