RT Journal Article
SR Electronic
T1 Progressive prediction of hospitalisation in the emergency department: uncovering hidden patterns to improve patient flow
JF Emergency Medicine Journal
JO Emerg Med J
FD BMJ Publishing Group Ltd and the British Association for Accident & Emergency Medicine
SP 308
OP 314
DO 10.1136/emermed-2014-203819
VO 34
IS 5
A1 Yuval Barak-Corren
A1 Shlomo Hanan Israelit
A1 Ben Y Reis
YR 2017
UL http://emj.bmj.com/content/34/5/308.abstract
AB Introduction One of the factors contributing to ED crowding is the lengthy delay in transferring an admitted patient from the ED to an inpatient department (ie, boarding time). An earlier start of the admission process using an automatic hospitalisation prediction model could potentially shorten these delays and reduce crowding.Methods Clinical, operational and demographic data were retrospectively collected on 80 880 visits to the ED of Rambam Health Care Campus in Haifa, Israel, from January 2011 to January 2012. Using these data, a logistic regression model was developed to predict patient disposition (hospitalisation vs discharge) at three progressive time points throughout the ED visit: within the first 10 min, within an hour and within 2 hours. The algorithm was trained on 50% of the data (n=40 440) and tested on the remaining 50%.Results During the study time period, 58 197 visits ended in discharge and 22 683 in hospitalisation. Within 1 hour of presentation, our model was able to predict hospitalisation with a specificity of 90%, sensitivity of 94% and an AUCof 0.97. Early clinical decisions such as testing for calcium levels were found to be highly predictive of hospitalisations. In the Rambam ED, the use of such a prediction system would have the potential to save more than 250 patient hours per day.Conclusions Data collected by EDs in electronic medical records can be used within a progressive modelling framework to predict patient flow and improve clinical operations. This approach relies on commonly available data and can be applied across different healthcare settings.