Discrete Event Simulation Models for CT Examination Queuing in West China Hospital

In CT examination, the emergency patients (EPs) have highest priorities in the queuing system and thus the general patients (GPs) have to wait for a long time. This leads to a low degree of satisfaction of the whole patients. The aim of this study is to improve the patients' satisfaction by designing new queuing strategies for CT examination. We divide the EPs into urgent type and emergency type and then design two queuing strategies: one is that the urgent patients (UPs) wedge into the GPs' queue with fixed interval (fixed priority model) and the other is that the patients have dynamic priorities for queuing (dynamic priority model). Based on the data from Radiology Information Database (RID) of West China Hospital (WCH), we develop some discrete event simulation models for CT examination according to the designed strategies. We compare the performance of different strategies on the basis of the simulation results. The strategy that patients have dynamic priorities for queuing makes the waiting time of GPs decrease by 13 minutes and the degree of satisfaction increase by 40.6%. We design a more reasonable CT examination queuing strategy to decrease patients' waiting time and increase their satisfaction degrees.

[1]  Karina Thiebaut Sacramento,et al.  Simulation model of discret events applied to the planning and operation of a toll plaza , 2016 .

[2]  Simon Breslav,et al.  Coupling stochastic occupant models to building performance simulation using the discrete event system specification formalism , 2014 .

[3]  Zhang Guo-tong.  A Dynamic Self-adapting Queuing Model of Hospital Clinic , 2007 .

[4]  Keith A. Willoughby,et al.  Using simulation to test ideas for improving speech language pathology services , 2016, Eur. J. Oper. Res..

[5]  T. Rohleder,et al.  Outpatient appointment scheduling with urgent clients in a dynamic, multi‐period environment , 2004 .

[6]  Diwakar Gupta,et al.  Appointment scheduling in health care: Challenges and opportunities , 2008 .

[7]  C. John Rosenquist.  Queueing analysis: A useful planning and management technique for radiology , 2004, Journal of Medical Systems.

[8]  Paul R. Harper,et al.  Simulation in health-care: lessons from other sectors , 2012, Oper. Res..

[9]  Zied Jemaï,et al.  A simulation study to improve the performance of an emergency medical service: Application to the French Val-de-Marne department , 2014, Simul. Model. Pract. Theory.

[10]  Stefan Creemers,et al.  The modeling of interrupts and unplanned absences , 2011 .

[11]  Averill M. Law,et al.  Simulation Modeling and Analysis , 1982 .

[12]  Jolanta Hansen,et al.  Analysis of Current Practice of CT examinations , 2009, Acta oncologica.

[13]  R Ceglowski,et al.  Combining Data Mining and Discrete Event Simulation for a value-added view of a hospital emergency department , 2007, J. Oper. Res. Soc..

[15]  Neil Genzlinger.  A. and Q , 2006 .

[16]  Jing Wang,et al.  On the supply of China's healthcare resources in a decentralized healthcare system , 2015 .

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

[18]  Huchang Liao,et al.  Inpatient admission assessment in West China Hospital based on hesitant fuzzy linguistic VIKOR method , 2016, J. Intell. Fuzzy Syst..

[19]  Rainer Kolisch,et al.  Providing radiology health care services to stochastic demand of different customer classes , 2008, OR Spectr..

[20]  Yiyi Chen,et al.  Suggestions to ameliorate the inequity in urban/rural allocation of healthcare resources in China , 2014, International Journal for Equity in Health.

[21]  Paul R. Harper,et al.  Combining discrete-event simulation and system dynamics in a healthcare setting: A composite model for Chlamydia infection , 2014, Eur. J. Oper. Res..

[22]  Marc Lambrecht,et al.  The Modeling of Interrupts and Unplanned Absences in Health Care Operations , 2008 .

[23]  Y. Bar-dayan,et al.  Waiting time is a major predictor of patient satisfaction in a primary military clinic. , 2002, Military medicine.

[24]  Jingshan Li,et al.  A simulation study to improve quality of care in the emergency department of a community hospital. , 2012, Journal of emergency nursing: JEN : official publication of the Emergency Department Nurses Association.

[25]  Ming Lu,et al.  Modeling the Production Capacity of a Continuous Plant Using Discrete Event Simulation , 2013 .

[26]  Nico M. van Dijk,et al.  To pool or not to pool in hospitals: a theoretical and practical comparison for a radiotherapy outpatient department , 2010, Ann. Oper. Res..

[27]  Ben Wang,et al.  Managing Patient Service in a Diagnostic Medical Facility , 2006, Oper. Res..

[28]  D. Mcquarrie,et al.  Hospitalization utilization levels. The application of queuing. Theory to a controversial medical economic problem. , 1983, Minnesota medicine.

[29]  Mcquarrie Dg,et al.  Hospitalization utilization levels. The application of queuing. Theory to a controversial medical economic problem. , 1983 .

[30]  W. Marsden.  I and J , 2012 .

[31]  Nico M. van Dijk,et al.  Practical optimization by OR and simulation , 2008, Simul. Model. Pract. Theory.

[32]  Maurice Queyranne,et al.  Dynamic Multipriority Patient Scheduling for a Diagnostic Resource , 2008, Oper. Res..

[33]  Aimin Guo,et al.  Human Resource Staffing and Service Functions of Community Health Services Organizations in China , 2008, The Annals of Family Medicine.

[34]  Erwin W Hans,et al.  Applying the variety reduction principle to management of ancillary services. , 2007, Health care management review.

[35]  Tillal Eldabi,et al.  Simulation in manufacturing and business: A review , 2010, Eur. J. Oper. Res..