Objectives. Emergency departments (EDs) and elective hospitalizations compete for beds. Our aim was to reduce hospital transfers using a queuing-model study. Methods. Macros were created to simulate four priority groups of patients according to hospitalization mode (elective, ED) and age (≥75 and <75 years), with randomization of number of admissions and length of stay (LOS). Those priorities were assigned regarding usual situations (ED admission with less priority than scheduled admission) not regarding clinical contexts. Simulations were based on actual data from an academic hospital. Models simulated ED boarder queue according to different scenarios based on number of hospital beds, LOS, and preventable hospitalizations. Results. Observed hospital-LOS was longer for patients ≥75 years (12.2 ± 3.6 days versus 11.4 ± 3.8 days; ) and for ED admissions (12.2 ± 0.6 versus 9.7 ± 0.6 days; ). In simulation models, two scenarios stabilized the beds demand after admissions: limitation of LOS to 30 days or 20% decrease in elective admissions among older patients. With these scenarios, the queue would be 25.2 patients for 361 beds (+2%) and 16.7 patients for 354 beds. Conclusions. Queuing models offer an interesting approach to bed management. A significant reduction in ED transfers is feasible, by limiting LOS to <30 days or by reducing elective hospitalizations of patients by 20%. 1. Introduction Access block is one of the principal factors influencing ED overcrowding [1, 2]. Access blocking and “boarding” have been previously associated with an increased risk of errors, delayed time to critical-care admission, and increased morbidity and mortality [1]. Hospitalizations through EDs (unplanned or nonelective) compete with elective admissions (planned or direct) for scarce medical and surgical ward beds [3]. Unplanned patients are the most affected because they frequently have acute medical conditions with older patients having an increased risk [4]. Factors influencing the availability of beds for nonelective hospitalizations and the rates of hospital bed occupancy have been outlined previously [5] and these factors extend the amount of time spent in the ED [6]. In order to forecast ED activity [7], a global hospital approach is required and the need for beds should be assessed on a daily basis [8]. There is great variability in admissions, type of patients, and distribution of hospital length of stay (LOS) depending on the day of admission [9] suggesting that computer simulation is the best tool to test different scenarios. Regarding the great variability in
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