Lauren F Laker1, Craig M Froehle2, Christopher J Lindsell3, Michael J Ward4. 1. Department of Emergency Medicine, University of Cincinnati, Cincinnati, OH; Lindner College of Business, University of Cincinnati, Cincinnati, OH. Electronic address: lolomathlaker@yahoo.com. 2. Department of Emergency Medicine, University of Cincinnati, Cincinnati, OH; Lindner College of Business, University of Cincinnati, Cincinnati, OH; James M. Anderson Center for Health Performance Excellence, Cincinnati Children's Hospital Medical Center, Cincinnati, OH. 3. Department of Emergency Medicine, University of Cincinnati, Cincinnati, OH. 4. Lindner College of Business, University of Cincinnati, Cincinnati, OH; Department of Emergency Medicine, Vanderbilt University, Nashville, TN.
Abstract
STUDY OBJECTIVE: Emergency departments (EDs) with both low- and high-acuity treatment areas often have fixed allocation of resources, regardless of demand. We demonstrate the utility of discrete-event simulation to evaluate flexible partitioning between low- and high-acuity ED areas to identify the best operational strategy for subsequent implementation. METHODS: A discrete-event simulation was used to model patient flow through a 50-bed, urban, teaching ED that handles 85,000 patient visits annually. The ED has historically allocated 10 beds to a fast track for low-acuity patients. We estimated the effect of a flex track policy, which involved switching up to 5 of these fast track beds to serving both low- and high-acuity patients, on patient waiting times. When the high-acuity beds were not at capacity, low-acuity patients were given priority access to flexible beds. Otherwise, high-acuity patients were given priority access to flexible beds. Wait times were estimated for patients by disposition and Emergency Severity Index score. RESULTS: A flex track policy using 3 flexible beds produced the lowest mean patient waiting time of 30.9 minutes (95% confidence interval [CI] 30.6 to 31.2 minutes). The typical fast track approach of rigidly separating high- and low-acuity beds produced a mean patient wait time of 40.6 minutes (95% CI 40.2 to 50.0 minutes), 31% higher than that of the 3-bed flex track. A completely flexible ED, in which all beds can accommodate any patient, produced mean wait times of 35.1 minutes (95% CI 34.8 to 35.4 minutes). The results from the 3-bed flex track scenario were robust, performing well across a range of scenarios involving higher and lower patient volumes and care durations. CONCLUSION: Using discrete-event simulation, we have shown that adding some flexibility into bed allocation between low and high acuity can provide substantial reductions in overall patient waiting and a more efficient ED.
STUDY OBJECTIVE: Emergency departments (EDs) with both low- and high-acuity treatment areas often have fixed allocation of resources, regardless of demand. We demonstrate the utility of discrete-event simulation to evaluate flexible partitioning between low- and high-acuity ED areas to identify the best operational strategy for subsequent implementation. METHODS: A discrete-event simulation was used to model patient flow through a 50-bed, urban, teaching ED that handles 85,000 patient visits annually. The ED has historically allocated 10 beds to a fast track for low-acuity patients. We estimated the effect of a flex track policy, which involved switching up to 5 of these fast track beds to serving both low- and high-acuity patients, on patient waiting times. When the high-acuity beds were not at capacity, low-acuity patients were given priority access to flexible beds. Otherwise, high-acuity patients were given priority access to flexible beds. Wait times were estimated for patients by disposition and Emergency Severity Index score. RESULTS: A flex track policy using 3 flexible beds produced the lowest mean patient waiting time of 30.9 minutes (95% confidence interval [CI] 30.6 to 31.2 minutes). The typical fast track approach of rigidly separating high- and low-acuity beds produced a mean patient wait time of 40.6 minutes (95% CI 40.2 to 50.0 minutes), 31% higher than that of the 3-bed flex track. A completely flexible ED, in which all beds can accommodate any patient, produced mean wait times of 35.1 minutes (95% CI 34.8 to 35.4 minutes). The results from the 3-bed flex track scenario were robust, performing well across a range of scenarios involving higher and lower patient volumes and care durations. CONCLUSION: Using discrete-event simulation, we have shown that adding some flexibility into bed allocation between low and high acuity can provide substantial reductions in overall patient waiting and a more efficient ED.
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