Time-series cohort study to forecast emergency department visits in the city of Milan and predict high demand: a 2-day warning system
R Murtas, S Tunesi, A Andreano, AG Russo - BMJ open, 2022 - bmjopen.bmj.com
R Murtas, S Tunesi, A Andreano, AG Russo
BMJ open, 2022•bmjopen.bmj.comObjectives The emergency department (ED) is one of the most critical areas in any hospital.
Recently, many countries have seen a rise in the number of ED visits, with an increase in
length of stay and a detrimental effect on quality of care. Being able to forecast future
demands would be a valuable support for hospitals to prevent high demand, particularly in a
system with limited resources where use of ED services for non-urgent visits is an important
issue. Design Time-series cohort study. Setting We collected all ED visits between January …
Recently, many countries have seen a rise in the number of ED visits, with an increase in
length of stay and a detrimental effect on quality of care. Being able to forecast future
demands would be a valuable support for hospitals to prevent high demand, particularly in a
system with limited resources where use of ED services for non-urgent visits is an important
issue. Design Time-series cohort study. Setting We collected all ED visits between January …
Objectives
The emergency department (ED) is one of the most critical areas in any hospital. Recently, many countries have seen a rise in the number of ED visits, with an increase in length of stay and a detrimental effect on quality of care. Being able to forecast future demands would be a valuable support for hospitals to prevent high demand, particularly in a system with limited resources where use of ED services for non-urgent visits is an important issue.
Design
Time-series cohort study.
Setting
We collected all ED visits between January 2014 and December 2019 in the five larger hospitals in Milan. To predict daily volumes, we used a regression model with autoregressive integrated moving average errors. Predictors included were day of the week and year-round seasonality, meteorological and environmental variables, information on influenza epidemics and festivities. Accuracy of prediction was evaluated with the mean absolute percentage error (MAPE).
Primary outcome measures
Daily all-cause EDs visits.
Results
In the study period, we observed 2 223 479 visits. ED visits were most likely to occur on weekends for children and on Mondays for adults and seniors. Results confirmed the role of meteorological and environmental variables and the presence of day of the week and year-round seasonality effects. We found high correlation between observed and predicted values with a MAPE globally smaller than 8.1%.
Conclusions
Results were used to establish an ED warning system based on past observations and indicators of high demand. This is important in any health system that regularly faces scarcity of resources, and it is crucial in a system where use of ED services for non-urgent visits is still high.
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