No abstract

Starting from the last decade of the twentieth century, most hospital Emergency Department (ED) budgets did not keep up with the demand for ED services made by growing populations and aging societies. Since labor consumes over 50% of the total monies invested in EDs and other healthcare systems, any downsizing, streamlining and reorganization plan needs to first address staffing issues such as determining the correct size of the workforce and its work shift scheduling. In this context, it is very important to remember that downsizing certainly does not mean a general cut-across-the-board. This study shows that a selective downsizing process in which each resource is treated separately (increasing the work capacity of some resources is also possible), based on its unique contribution to the overall ED operational performance, can approximately maintain current ED operational measures in terms patient length of stay (LOS) despite an overall reduction in staff hours. A linear optimization model (S-model) and a heuristic iterative simulation based algorithm (SWSSA) are used in this study for scheduling the resources' work shifts, one resource at a time. The algorithm was tested using data that was gathered from five general hospital EDs. By leveling the workload of the different resources in the ED, SWSSA was able to achieve LOS values within -19 to 4% of the original values despite a reduction of 8-17.5% in physicians' work hours and a reduction of 13-47% in the nurses' work hours.

In recent years hospitals have been vigorously searching for ways to reduce costs and improve productivity. One tool, simulation, is now widely accepted as an effective method for assisting management in evaluating different operational alternatives. It can help improve existing Emergency Departments (EDs) and assist in planning and designing new EDs. In order to increase the acceptance of simulation in healthcare systems in general and EDs in particular, hospital management should be directly involved in the development of these projects. Such involvement will also bolster the model's credibility. In addition, it is important to simplify simulation processes as much as is reasonably possible and use visual aids or animation that will heighten users' confidence in the model's ability. This study lays the foundation for the development of a simulation tool which is general, flexible, intuitive, simple to use and contains default values for most of the system's parameters.

Reducing Emergency Department (ED) overcrowding in the hope of improving the ED's operational efficiency and health care delivery ranks high on every health care decision maker's wish list. The current study concentrates on developing efficient work shift schedules that make the best use of current resource capacity with the objectives of reducing patient waiting time and leveling resource utilization as much as possible. The study introduces two iterative heuristic algorithms, which combine simulation and optimization models for scheduling the work shifts of the ED resources: physicians, nurses and technicians. The algorithms are distinctive because they account for patients being treated by multiple care providers, possibly over the course of several hours, often with interspersed waiting. In such instances, patient arrival time is not a good indicator of when the various care providers are needed. The algorithms were tested using a detailed simulation based on data from five general hospital EDs. A patient's Length of Stay (LOS) is measured as the time a patient spends in the ED until being admitted to the hospital or discharged. The first algorithm achieved an average reduction of between 20 and 45% in the total patient waiting time, which led to a reduction of between 7 and 17% in the combined average patient LOS. By allowing a restructure of the ED resource capacities, the second algorithm achieved an average reduction of between 20 and 64% in the total patient waiting time, leading to an 11 to 29% reduction in the combined average patient LOS. Copyright © 2012 Taylor and Francis Group, LLC

The study covers several hospitals and analyses over 20,000 process components; as such the common findings may serve as guidelines to other hospitals when addressing this issue.