A queuing network analysis model in emergency departments

This paper establishes a general multi-stage queuing network model with feedback patient flow to analyze the behavior of patients flow among the stages and the performance of emergency departments(ED) in a hospital. The queuing network is decomposed into a set of single queuing systems that include a tandem queuing and a closed queuing system. The parameter values of the system are obtained from observations through parameter estimation. The complex interaction between the neighbor stages is analyzed and the expression of the patients flow between different stages is calculated in steady state by routing probability in the system. The main performance indicator formulas are developed and results are derived by using the parameter values. Some insights are derived from experiment and numeric analysis in a case study.

[1]  Michel Bierlaire,et al.  An analytic finite capacity queueing network model capturing the propagation of congestion and blocking , 2009, Eur. J. Oper. Res..

[2]  Mark P. Van Oyen,et al.  Design and Analysis of Hospital Admission Control for Operational Effectiveness , 2011 .

[3]  Marcia J. Wilson,et al.  Perfecting Patient Flow: America's Safety Net Hospitals and Emergency Department Crowding , 2005 .

[4]  N. Koizumi,et al.  Modeling Patient Flows Using a Queuing Network with Blocking , 2005, Health Care Management Science.

[5]  Jonathan Patrick,et al.  Access to Long‐Term Care: The True Cause of Hospital Congestion? , 2011 .

[6]  Rommert Dekker,et al.  An analytic model for capacity planning of prisons in the Netherlands , 2000, J. Oper. Res. Soc..

[7]  D. Gupta.  Surgical Suites' Operations Management , 2007 .

[8]  Michael Harrington,et al.  Reducing Boarding in a Post‐Anesthesia Care Unit , 2011 .

[10]  Peter G. Harrison,et al.  A Queueing Network Model of Patient Flow in an Accident and Emergency Department , 2006 .

[11]  Marc Lambrecht,et al.  Modeling a hospital queueing network , 2011 .

[12]  Martin L. Puterman,et al.  Reducing Surgical Ward Congestion Through Improved Surgical Scheduling and Uncapacitated Simulation , 2011 .

[13]  J. Little.  A Proof for the Queuing Formula: L = λW , 1961 .

[14]  Randolph W. Hall,et al.  Modeling Patient Flows Through the Health care System , 2013 .

[15]  Kenneth J. Klassen,et al.  The Effect of Integrated Scheduling and Capacity Policies on Clinical Efficiency , 2011 .

[16]  J. R. Jackson.  Networks of Waiting Lines , 1957 .