A flexible simulation platform to quantify and manage emergency department crowding

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 http://spark.rstudio.com/klopiano/EDsimulation/.

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.

Figure 1

Patient flow map. Patient paths are directed based on acuity and resource needs. If an ED does not utilize a Fast Track, all patients are assigned a bed in the Main Treatment Area.

Figure 2

A day in the life of an ED. Generated arrival functions (top), and 30-day simulated location of patients (middle) and idle resources (bottom) for nationally average and average academic ED settings. The nationally average setting is limited by providers, while beds are the primary bottleneck in the average academic setting.

Figure 3

Simulated door-to-event times. The radius of each dot corresponds to the number of patients in that demographic and the sizes are comparable across plots. The timeliness of care delivery is largely affected by patient acuity.

Figure 4

Effect of additional resources on patient flow. Simulated mean door-to-event times with additional resources for each ED environment. In both settings, a suite of resources is no more effective than a single, well-targeted resource.

Figure 5

Effect of fast track mechanisms on patient flow. Simulated effect of Fast Track mechanisms in the nationally average and average academic environments. Bold values are the standard settings for each environment. FT mechanisms in the bed-limited academic setting are a clear tradeoff between low-acuity and ESI-3 throughput.

Figure 6

Effect of reducing boarding times on patient flow. Simulated effect of boarding times on length of stay and LWBS in the nationally average and average academic ED settings. The dotted vertical line marks the standard mean boarding time for each setting. The bed-limited academic setting has a higher admit rate and shows greater sensitivity to boarding times.