Since there is wide variation in disease severity and acuity among patients presenting at the ED, clinical services and resources required will likewise vary considerably. The experiences gained from studies carried out in Western countries may not necessarily apply to local conditions, as there are multiple factors that might contribute to the fluctuation of the daily attendances at an ED in Singapore. The purpose of this paper is to identify the local factors associated with the daily attendances at ED, and to make predictions based on these local factors. As resources are dependent on patient Inhibitors,research,lifescience,medical acuity levels, the forecast is also

stratified by patient acuity categories (PAC). Methods Setting The study was carried out in an emergency department in a major public sector acute care regional general hospital in Singapore. The hospital has the highest number of ED attendances and the highest proportion of acutely ill patients among five public sector acute care general hospitals in Singapore. Permission

Inhibitors,research,lifescience,medical to conduct the study was granted by the Chairman, Medical Board of the hospital. Data Data used in the study was counts of daily patient attendances at ED between July 2005 and March 2008 (1,005 days), extracted Inhibitors,research,lifescience,medical from the ED administrative database. Patients who presented at the ED were classified as P1, P2 and P3 by the patient acuity category scale (PACS) used in all public sector hospital emergency departments in Singapore for resource Inhibitors,research,lifescience,medical allocation. P1 cases are most acutely ill and need immediate clinical services and treatment, P2 being acutely ill but can wait to be treated, and P3 being the less acutely ill patients who can wait longer to receive services (Table ​(Table1).1). Other data Selleckchem FK228 collected for the study included public holiday,

and local weather factors (ambient temperature, ambient air quality measured Inhibitors,research,lifescience,medical by PSI, and relative humidity). The selection of the potential predictors was based on literature, local observation and availability of data. Singapore is a tropical country where the range in daily temperature throughout the year does not vary very much, hence daily average temperature was used. Table 1 Patient classification by patient acuity category* Study design and methods Univariate analysis of daily ED attendances and their association Rolziracetam with potential predictors was carried out using general linear model, and significance testing using t-test where probabilities > 0.05 was considered statistically significant. Time series analysis for identifying significant predictors as well as for forecasting daily ED attendances were carried out using established time series analysis procedures, the most popular time series analysis technique being auto regression integrated moving average (ARIMA) [11] model.