Visualizing Emergency Department Admission through Data Mining

Abstract

Emergency Department (ED) boarding –the inability to transfer emergency patients to inpatient beds- is a key factor contributing to ED overcrowding. This paper presents a novel approach to improving hospital operational efficiency and, therefore, to decreasing ED boarding. Using the historic data of 15,000 patients, admission results and patient information are correlated in order to identify important admission predictor factors. For example, the type of radiology exams prescribed by the ED physician is identified as among the most important predictors of admission. Based on these  factors, a  real-time prediction  model is  developed which  is able  to correctly predict  the  admission  result  of  four  out  of  every  five  ED  patients.  The  proposed admission  model  can  be  used  by inpatient  units  to  estimate  the  likelihood  of ED patients’ admission, and consequently, the number of incoming patients from ED in the near future. Using  similar prediction models,  hospitals can evaluate their short-time needs for inpatient care more accurately Emergency Department (ED) boarding – the inability to transfer emergency patients to inpatient beds- is a key factor contributing to ED overcrowding. This paper presents a novel approach to improving hospital operational efficiency and, therefore, to decreasing ED boarding. Using the historic data of 15,000 patients, admission results and patient information are correlated in order to identify important admission predictor factors. For example, the type of radiology exams prescribed by the ED physician is identified as among the most important predictors of admission. The proposed admission model can be used by inpatient units to estimate the likelihood of ED patients’ admission, and consequently, the number of incoming patients from ED in the near future. Using similar prediction models, hospitals can evaluate their short-time needs for inpatient care more accurately. We use three algorithms to build the predictive models: (1) logistic regression, (2) decision trees, and Analytic tools (accuracy=80.31%, AUC-ROC=0.859) than the decision tree accuracy=80.06%, AUC-ROC=0.824) and the logistic regression model (accuracy=79.94%, AUC-ROC=0.849). Drawing on logistic regression, we identify several factors related to hospital admissions including hospital site, age, arrival mode, triage category, care group, previous admission in the past month, and previous admission in the past year. From a different perspective, the research focuses on mobility data instead of personal data in general using Structural Equation Modelling analysis method. Based on this research finding, we identified an unexplored factor that can be used to predict the intention to disclose mobility data, and the result also confirmed that context aspects such as demographics and different personal data categories.