Big data as a new approach in emergency medicine research

To examine weather effects on the daily demand for ambulance services in Hong Kong.

The daily ambulance demand for Hong Kong is rising, and it has been shown that weather factors (temperature and humidity) play a role in the demand for ambulance services. This study aimed at developing short-term forecasting models of daily ambulance calls using the 7-day weather forecast data as predictors. We employed the autoregressive integrated moving average (ARIMA) method to analyze over 1.3 million cases of emergency attendance in May 2006 through April 2009 and the 7-day weather forecast data for the same period. Our results showed that the ARIMA model could offer reasonably accurate forecasts of daily ambulance calls at 1–7 days ahead of time and with improved accuracy by including weather factors. Specifically, the inclusion of average temperature alone in our ARIMA model improved the predictability of the 1-day forecast when compared to that of a simple ARIMA model (8.8 % decrease in the root mean square error, RMSE = 53 vs 58). The improvement in the 7-day forecast with average temperature as a predictor was more pronounced, with a 10 % drop in prediction error (RMSE = 62 vs 69). These findings suggested that weather forecast data can improve the 1- to 7-day forecasts of daily ambulance demand. As weather forecast data are readily accessible from Hong Kong Observatory’s official website, there is virtually no cost to including them in the ARIMA models, which yield better prediction for forward planning and deployment of ambulance manpower.

An accurate projection for ambulance demand is essential to enable better resource planning for the future that strives to either maintain current levels of services or reconsider future standards and expectations. More than 2 million cases of emergency room attendance in 2008 were obtained from the Hong Kong Hospital Authority to project the demand for its ambulance services in 2036. The projection of ambulance demand in 2036 was computed in consideration of changes in the age-gender structure between 2008 and 2036. The quadratic relation between average daily temperature and daily ambulance demand in 2036 was further explored by including and excluding age-gender demographic changes. Without accounting for changes in the age-gender structure, the 2036 ambulance demand for age groups of 65 and above were consistently underestimated (by 38%-65%), whereas those of younger age groups were overestimated (by 6%-37%). Moreover, changes in the 2008 to 2036 age-gender structure also shift upward and emphasize relationships between average daily temperature and daily ambulance demand at both ends of the quadratic U-shaped curve. Our study reveals a potential societal implication of ageing population on the demand for ambulance services.