Adding More Beds to the Emergency Department or Reducing Admitted Patient Boarding Times: Which Has a More Significant Influence on Emergency Department Congestion?

Northwestern University, Department of Emergency Medicine, Feinberg School of Medicine, Chicago, IL, USA.
Annals of emergency medicine (Impact Factor: 4.68). 10/2008; 53(5):575-85. DOI: 10.1016/j.annemergmed.2008.07.009
Source: PubMed


We evaluate a computer simulation model designed to assess the effect on emergency department (ED) length of stay of varying the number of ED beds or altering the interval of admitted patient departure from the ED.
We created a computer simulation model (Med Model) based on institutional data and augmented by expert estimates and assumptions. We evaluated simulations of increasing the number of ED beds, increasing the admitted patient departure and increasing ED census, analyzing potential effects on overall ED length of stay. Multiple sensitivity analyses tested the robustness of the results to changes in model assumptions and institutional data.
With a constant ED departure rate at the base case and increasing ED beds, there is an increase in mean length of stay from 240 to 247 minutes (95% confidence interval [CI] 0.8 to 12.6 minutes). When keeping the number of beds constant at the base case and increasing the rate at which admitted patients depart the ED to their inpatient bed, the mean overall ED length of stay decreases from 240 to 218 minutes (95% CI 16.8 to 26.2 minutes). With a 15% increase in daily census, the trends are similar to the base case results. The sensitivity analyses reveal that despite a wide range of inputs, there are no differences from the base case.
Our computer simulation modeled that improving the rate at which admitted patients depart the ED produced an improvement in overall ED length of stay, whereas increasing the number of ED beds did not.

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    • "Table II a shows the admission probability which depends on patient type and urgency. As the urgency goes down, the admission probability also tends to go down (as also observed in [23] and [24]), except for the NI Red category of which 'only' 46 % is admitted. This, however, is due to the high mortality rate in this category. "
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    ABSTRACT: In this article, we present a decision support system (DSS) for improving patient flow in emergency departments (EDs). The core of the system is a discrete-event simulation (DES) model that aims to support capacity planning in the ED, in view of controlling patients' length of stay (LOS). Conceptually, it regards the patient LOS as the result of different queueing systems, the behaviour of which is influenced by different types of capacities. Taking inputs from ED patient record data, the DSS allows to analyse the impact of different capacity changes on patient flow, and to detect efficient capacity combinations using data envelopment analysis (DEA). We report on the insights obtained from a case study in a large regional hospital in Belgium.
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    • "It is currently accepted that improving ED quality includes discrete-event simulation and queuing systems [14]. Most simulations focus on EDs or on bed needs from an ED perspective [5] [15]. We hypothesized that hospitals have the potential to admit more unplanned patients. "
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    ABSTRACT: 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%.
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    • "To check for consistency with other existing models, we compared our results to those reported by Khare et al. [18], who published a model using parameters characterizing acuity-dependent arrivals, LWBS tolerance, treatment lengths, and admit rates. Accounting for ED beds, physicians, and boarding times, they concluded that reducing boarding times by 25% decreased mean length of stay by 22 minutes, while five additional beds increased mean length of stay by seven minutes. "
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    ABSTRACT: Background Hospital-based Emergency Departments are struggling to provide timely care to a steadily increasing number of unscheduled ED visits. Dwindling compensation and rising ED closures dictate that meeting this challenge demands greater operational efficiency. Methods Using techniques from operations research theory, as well as a novel event-driven algorithm for processing priority queues, we developed a flexible simulation platform for hospital-based EDs. We tuned the parameters of the system to mimic U.S. nationally average and average academic hospital-based ED performance metrics and are able to assess a variety of patient flow outcomes including patient door-to-event times, propensity to leave without being seen, ED occupancy level, and dynamic staffing and resource use. Results The causes of ED crowding are variable and require site-specific solutions. For example, in a nationally average ED environment, provider availability is a surprising, but persistent bottleneck in patient flow. As a result, resources expended in reducing boarding times may not have the expected impact on patient throughput. On the other hand, reallocating resources into alternate care pathways can dramatically expedite care for lower acuity patients without delaying care for higher acuity patients. In an average academic ED environment, bed availability is the primary bottleneck in patient flow. Consequently, adjustments to provider scheduling have a limited effect on the timeliness of care delivery, while shorter boarding times significantly reduce crowding. An online version of the simulation platform is available at Conclusion In building this robust simulation framework, we have created a novel decision-support tool that ED and hospital managers can use to quantify the impact of proposed changes to patient flow prior to implementation.
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