We obtained information about hospital characteristics (e.g., urban versus rural, ownership

type, teaching status, bed size, and system member) from the 2008 American Hospital Association (AHA) Annual Survey Database and linked them to SEDD files using hospital identifiers. In addition, we obtained information about the trauma level of the hospital using the Trauma Information Exchange Program database (TIEP), collected by the Inhibitors,research,lifescience,medical American Trauma Society and the Johns Hopkins Center for Injury Research and Policy. Finally, we used the 2008 Area Resource File (ARF)e to obtain county-level income information. The proper measures of ED LOS and ED EGFR assay crowding are not straightforward [15]. Few investigators have attempted to develop models characterizing the completion times of different phases of emergency care. Multivariate linear regression techniques used to estimate ED waiting room time, treatment time, and boarding time for patients who were admitted or discharged from a hospital’s main ED or urgent care area [7]. Similarly, discrete-time survival analysis Inhibitors,research,lifescience,medical is applied to evaluate the effect of crowding on the different phases of ED care [4]. Both studies estimated Inhibitors,research,lifescience,medical the influence of various patient, temporal, and system factors on the mean or median completion times for different phases of emergency care. Few researchers [16] contributed to this literature

by demonstrating that the degree of crowding in a hospital can be accurately measured. Because the proper measures of ED LOS were not readily available in our data, we computed the duration for each visit by taking the difference between admission and discharge times, which is the total of the time patients waited Inhibitors,research,lifescience,medical in ED rooms plus their treatment time. Ideally, one would separate the times into components identified as important in the literature. Unfortunately, HCUP data lacks sufficient detail to do this. Statistical Analyses We initially performed extensive secondary data analyses to explore ED LOS by admission hour, day of the week, patient volume, patient

characteristics, hospital characteristics Inhibitors,research,lifescience,medical and area characteristics. The frequencies, means, medians, and 95% confidence intervals for several PAK6 of these variables were based on data for all T&R ED visits (excluding encounters where there was evidence that the patient also received observation services) in Arizona, Massachusetts, and Utah during 2008. Duration was expressed in minutes measured as the difference between admission time and discharge time. The mean (median) duration for a specific admission hour was measured as the mean (median) value of the durations of all visits admitted to EDs at that specific hour during 2008. The total volume of visits for a specific admission hour was measured as the total number of T&R visits to the EDs observed at that specific hour during 2008. (Note that it was not possible to include ED visits that resulted in subsequent admission to the hospital in the analysis.