Abstract
BackgroundEmergency department (ED) visits show a high volatility over time. Therefore, EDs are likely to be crowded at peak-volume moments. ED crowding is a widely reported problem with negative consequences for patients as well as staff. Previous studies on the predictive value of weather variables on ED visits show conflicting results. Also, no such studies were performed in the Netherlands. Therefore, we evaluated prediction models for the number of ED visits in our large the Netherlands teaching hospital based on calendar and weather variables as potential predictors.MethodsData on all ED visits from June 2016 until December 31, 2019, were extracted. The 2016–2018 data were used as training set, the 2019 data as test set. Weather data were extracted from three publicly available datasets from the Royal Netherlands Meteorological Institute. Weather observations in proximity of the hospital were used to predict the weather in the hospital’s catchment area by applying the inverse distance weighting interpolation method. The predictability of daily ED visits was examined by creating linear prediction models using stepwise selection; the mean absolute percentage error (MAPE) was used as measurement of fit.ResultsThe number of daily ED visits shows a positive time trend and a large impact of calendar events (higher on Mondays and Fridays, lower on Saturdays and Sundays, higher at special times such as carnival, lower in holidays falling on Monday through Saturday, and summer vacation). The weather itself was a better predictor than weather volatility, but only showed a small effect; the calendar-only prediction model had very similar coefficients to the calendar+weather model for the days of the week, time trend, and special time periods (both MAPE’s were 8.7%).ConclusionsBecause of this similar performance, and the inaccuracy caused by weather forecasts, we decided the calendar-only model would be most useful in our hospital; it can probably be transferred for use in EDs of the same size and in a similar region. However, the variability in ED visits is considerable. Therefore, one should always anticipate potential unforeseen spikes and dips in ED visits that are not shown by the model.
Highlights
Emergency department (ED) visits show a high volatility over time
Variable selection Automated stepwise variable selection on dataset A showed that most variables present in dataset A were relevant for predicting daily ED visits, with the majority of the missing calendar-based variables being indicators for months
Automated stepwise variable selection on dataset B showed similar results, with generally one of these cities having a positive coefficient and one having an sized negative coefficient, illustrating that the volatility of the weather could be a meaningful predictor of daily ED visits
Summary
Emergency department (ED) visits show a high volatility over time. Previous studies on the predictive value of weather variables on ED visits show conflicting results. No such studies were performed in the Netherlands. We evaluated prediction models for the number of ED visits in our large the Netherlands teaching hospital based on calendar and weather variables as potential predictors. Large numbers of patients generally present at emergency departments (EDs). Many EDs have been staffed based on average patient volumes [5], resulting in EDs that are more likely to be crowded at peak-volume moments. ED crowding is a widely reported problem with negative consequences for patients as well as staff. Reported consequences for staff include higher stress levels, increased violence towards staff, and inability to adhere to guideline-recommended treatment [8]
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