Emergency department (ED) crowding is a well-recognized threat to patient
safety and it has been repeatedly associated with increased mortality. Accurate
forecasts of future service demand could lead to better resource management and
has the potential to improve treatment outcomes. This logic has motivated an
increasing number of research articles but there has been little to no effort
to move these findings from theory to practice. In this article, we present
first results of a prospective crowding early warning software, that was
integrated to hospital databases to create real-time predictions every hour
over the course of 5 months in a Nordic combined ED using Holt-Winters'
seasonal methods. We showed that the software could predict next hour crowding
with a nominal AUC of 0.98 and 24 hour crowding with an AUC of 0.79 using
simple statistical models. Moreover, we suggest that afternoon crowding can be
predicted at 1 p.m. with an AUC of 0.84.Comment: 15 pages, 6 figure
