Evaluating alternative resource allocation in an emergency department using discrete event simulation

Reducing emergency department (ED) overcrowding in the hope of improving the ED’s operational efficiency and healthcare delivery is an important objective for healthcare providers. This research analyzes resource allocation with the objective of reducing patient length-of-stay (LOS) and time to be seen by a physician or physician assistant (TBSPPA) while leveling resource utilization. Different levels of resources (physicians, physician assistants, and nurses) were changed in controlled experiments in order to analyze patients’ LOS and TBSPPA, as well as resource utilization. The experiments were performed using a simulation model based on data from an ED at a local hospital. The simulation model accounts for patients with different severity levels as well as different rates for patient arrivals. Based on the severity, patients are treated by combinations of multiple resources, often with interspersed waiting time. Results indicate that the simulation model can be used as a tool to help decision makers in the ED with the allocation of resources. The experiments show an average reduction of 14% in the average patients’ LOS, 16% in the average patients’ TBSPPA, and leveled resource utilization between 70% and 80% when allowing a restructure of the ED resource capacities.

[1]  Kao-Ping Chua,et al.  Overview of the U.S. Health Care System , 2006 .

[2]  N. Dellaert,et al.  Reducing emergency department waiting times by adjusting work shifts considering patient visits to multiple care providers , 2012 .

[3]  Gilles Reinhardt,et al.  Analysis of factors influencing length of stay in the emergency department. , 2003, CJEM.

[4]  Gillian Mould,et al.  Assessing the impact of systems modeling in the redesign of an Emergency Department , 2013 .

[5]  J. Grossman,et al.  Building a Better Delivery System: A New Engineering/Health Care Partnership , 2005 .

[6]  Young-Jun Son,et al.  Simulation-based optimal planning for material handling networks in mining , 2013, Simul..

[7]  Fatah Chetouane,et al.  Modeling and Improving Emergency Department Systems using Discrete Event Simulation , 2007, Simul..

[8]  Talal M. Alkhamis,et al.  Simulation optimization for an emergency department healthcare unit in Kuwait , 2009, Eur. J. Oper. Res..

[9]  Lori A. Clarke,et al.  Simulating patient flow through an Emergency Department using process-driven discrete event simulation , 2009, 2009 ICSE Workshop on Software Engineering in Health Care.

[10]  Javier Otamendi,et al.  meSO: simulation optimization using a multicriteria process capability index and evolutionary algorithms , 2013, Simul..

[11]  Gregory Gurevich,et al.  PERT-type projects: time–cost tradeoffs under uncertainty , 2013, Simul..

[12]  A. Garson,et al.  The US healthcare system 2010: problems, principles, and potential solutions. , 2000, Circulation.

[13]  Manuel D. Rossetti,et al.  Emergency department simulation and determination of optimal attending physician staffing schedules , 1999, WSC '99.

[14]  Taesik Lee,et al.  Reducing Emergency Department overcrowding - five patient buffer concepts in comparison , 2008, 2008 Winter Simulation Conference.

[15]  Rupa S. Valdez,et al.  Industrial and Systems Engineering and Health Care : Critical Areas of Research , 2010 .

[16]  Madhu C. Reddy,et al.  A Systematic Review of Simulation Studies Investigating Emergency Department Overcrowding , 2010, Simul..

[17]  Fanwen Meng UNDERSTANDING PATIENT FLOWS AND CONSULTATION PATTERNS FOR CARE DELIVERY IN EMERGENCY DEPARTMENT , 2014 .