The Emergency Department: Rising wait times, costs, and possible remedies

Emergency Departments (EDs) and Emergency Rooms (ERs) are designed to manage trauma, respond to disasters, and serve as the initial care for those with serious illnesses. However, because of many factors, the ED has become the doorway to the hospital and a “catch-all net” for patients including those with non-urgent needs. This increase in the population in the ED has lead to an increase in wait times for patients. It has been well documented that there has been a constant and consistent rise in the number of patients that frequent the ED (National Center for Health Statistics, 2002); the wait time for patients in the ED has increased (Pitts, Niska, Xu, & Burt, 2008); and the cost of the treatment in the ER has risen (Everett Clinic, 2008). Because the ED was designed to treat patients who need quick diagnoses and may be in potential life-threatening circumstances, management of time can be the ultimate enemy. If a system was implemented to decrease wait times in the ED, decrease the use of ED resources, and decrease costs endured by patients seeking care, better outcomes for patients and patient satisfaction could be achieved. The goal of this research was to explore potential changes and/or alternatives to relieve the burden endured by the ED. In order to explore these options, data was collected by conducting one-on-one interviews with seven physicians closely tied to a Level 1 ED (Emergency Room physicians, Trauma Surgeons and Primary Care physicians). A qualitative analysis was performed on the responses of one-on-one interviews with the aforementioned physicians. The interviews were standardized, open-ended questions that probe what makes an effective ED, possible solutions to improving patient care in the ED, potential remedies for the mounting problems that plague the ED, and the feasibility of bringing Primary Care Physicians to the ED to decrease the wait times experienced by the patient. From the responses, it is clear that there needs to be more research in this area, several areas need to be addressed, and a variety of solutions could be implemented. The most viable option seems to be making the ED its own entity (similar to the clinic or hospital) that includes urgent clinics as a part of the system, in which triage and better staffing would be the most integral part of its success.^

The number and timing of time -dependent covariate measurements for the Cox regression model

The problem of analyzing data with updated measurements in the time-dependent proportional hazards model arises frequently in practice. One available option is to reduce the number of intervals (or updated measurements) to be included in the Cox regression model. We empirically investigated the bias of the estimator of the time-dependent covariate while varying the effect of failure rate, sample size, true values of the parameters and the number of intervals. We also evaluated how often a time-dependent covariate needs to be collected and assessed the effect of sample size and failure rate on the power of testing a time-dependent effect.^ A time-dependent proportional hazards model with two binary covariates was considered. The time axis was partitioned into k intervals. The baseline hazard was assumed to be 1 so that the failure times were exponentially distributed in the ith interval. A type II censoring model was adopted to characterize the failure rate. The factors of interest were sample size (500, 1000), type II censoring with failure rates of 0.05, 0.10, and 0.20, and three values for each of the non-time-dependent and time-dependent covariates (1/4,1/2,3/4).^ The mean of the bias of the estimator of the coefficient of the time-dependent covariate decreased as sample size and number of intervals increased whereas the mean of the bias increased as failure rate and true values of the covariates increased. The mean of the bias of the estimator of the coefficient was smallest when all of the updated measurements were used in the model compared with two models that used selected measurements of the time-dependent covariate. For the model that included all the measurements, the coverage rates of the estimator of the coefficient of the time-dependent covariate was in most cases 90% or more except when the failure rate was high (0.20). The power associated with testing a time-dependent effect was highest when all of the measurements of the time-dependent covariate were used. An example from the Systolic Hypertension in the Elderly Program Cooperative Research Group is presented. ^