title: Development of emergency department load relief area--gauging benefits in empirical terms.
abstract: BACKGROUND
  The primary goal of this investigation was to develop a simulation model to evaluate the various internal and external factors affecting patient flow and crowding in the emergency department (ED). In addition, a few recommendations are proposed to reconfigure the patient flow to improve ED capacity while maintaining service quality.
  
  
  METHODS
  In this research, we present a simulation study conducted in the ED at the "S Hospital" located in Seoul. Based on patient flow data and process analysis, a simulation model of patient throughput in the ED has been developed. We evaluated simulations of diverting the specific patient load in the light of our proposed recommendations to a separately managed area named as the ED load relief area (ED-LRA) and analyzing potential effects on overall length of stay (LOS) and waiting time (WT).
  
  
  RESULTS
  What-if analyses have been proposed to identify key issues and investigate the improvements as per our proposed recommendations. The simulation results suggest that specific patient load diversion is needed to ensure desired outcomes. With the diversion of specific patient load to ED-LRA, there is a reduction of 40.60% in mean LOS and 42.5% in WT with improved resource utilization. As a result, opening of an ED-LRA is justified.
  
  
  CONCLUSIONS
  Real-world systems are often too intricate for analytical models and often too expensive to trial with directly. Simulation models allow the modeling of this intricacy and enable experimentation to make inferences about how the actual system might perform. Our simulation study modeled that diverting the specific patient load to ED-LRA produced an improvement in overall ED's LOS and WT.
authors.affiliation: Department of Information and Industrial Engineering, Yonsei University College of Engineering, 50 Yonsei-ro, Seodaemun-gu, Seoul 120-749, Republic of Korea. farrukhrasheed@yonsei.ac.kr
authors.forename: Farrukh
authors.initials: F
authors.lastname: Rasheed
authors.forename: Young Hoon
authors.initials: YH
authors.lastname: Lee
authors.forename: Seung Ho
authors.initials: SH
authors.lastname: Kim
authors.forename: In Cheol
authors.initials: IC
authors.lastname: Park
mesh_terms: D003198:Computer Simulation; D003441:Crowding; D017598:Efficiency, Organizational; D004636:Emergency Service, Hospital / Q000458:organization & administration* / Q000706:statistics & numerical data; D006785:Hospitals, University / Q000458:organization & administration / Q000706:statistics & numerical data; D006786:Hospitals, Urban / Q000458:organization & administration / Q000706:statistics & numerical data; D006801:Humans; D007902:Length of Stay; D017146:Models, Organizational; D010343:Patient Admission / Q000592:standards / Q000706:statistics & numerical data; D010351:Patient Discharge / Q000592:standards / Q000706:statistics & numerical data; D010360:Patient Transfer / Q000458:organization & administration* / Q000592:standards / Q000706:statistics & numerical data; D011785:Quality Assurance, Health Care / Q000379:methods / Q000458:organization & administration* / Q000592:standards; D012720:Severity of Illness Index; D013997:Time Factors; D015599:Trauma Severity Indices; D014218:Triage / Q000379:methods* / Q000458:organization & administration / Q000592:standards; D000078329:Workforce