Applying Multi-phase DES Approach for Modelling the Patient Journey Through Accident and Emergency Departments

Accident and Emergency departments (A&ED) are in charge of providing access to patients requiring urgent acute care. A&ED are difficult to model due to the presence of interactions, different pathways and the multiple outcomes that patients may undertake depending on their health status. In addition, public concern has focused on the presence of overcrowding, long waiting times, patient dissatisfaction and cost overruns associated with A&ED. There is then a need for tackling these problems through developing integrated and explicit models supporting healthcare planning. However, the studies directly concentrating on modelling the A&EDs are largely limited. Therefore, this paper presents the use of a multi-phase DES framework for modelling the A&ED and facilitating the assessment of potential improvement strategies. Initially, the main components, critical variables and different states of the A&ED are identified to correctly model the entire patient journey. In this step, it is also necessary to characterize the demand in order to categorize the patients into pipelines. After this, a discrete-event simulation (DES) model is developed. Then, validation is conducted through the 2-sample t test to demonstrate whether the model is statistically comparable with the real-world A&ED department. This is followed by the use of Markov phase-type models for calculating the total costs of the whole system. Finally, various scenarios are explored to assess their potential impact on multiple outcomes of interest. A case study of a mixed-patient environment in a private A&E department is provided to validate the effectiveness of the multi-phase DES approach.

[1]  Ari B. Friedman,et al.  Urgent Care Needs Among Nonurgent Visits to the Emergency Department. , 2016, JAMA internal medicine.

[2]  Ian Higginson,et al.  Emergency department crowding , 2012, Emergency Medicine Journal.

[3]  John Devaney,et al.  Incorporating Discrete Event Simulation Into Quality Improvement Efforts in Health Care Systems , 2015, American journal of medical quality : the official journal of the American College of Medical Quality.

[4]  Chih-Hao Lin,et al.  Managing emergency department overcrowding via ambulance diversion: a discrete event simulation model. , 2015, Journal of the Formosan Medical Association = Taiwan yi zhi.

[5]  Derek Bell,et al.  The impact of changing the 4 h emergency access standard on patient waiting times in emergency departments in England , 2012, Emergency Medicine Journal.

[6]  R West,et al.  Objective standards for the emergency services: emergency admission to hospital. , 2001, Journal of the Royal Society of Medicine.

[7]  Navonil Mustafee,et al.  Applications of simulation within the healthcare context , 2010, J. Oper. Res. Soc..

[8]  Mark Fackrell,et al.  Modelling healthcare systems with phase-type distributions , 2009, Health care management science.

[9]  Mirsad Hadzikadic,et al.  Systems modeling and simulation applications for critical care medicine , 2012, Annals of Intensive Care.

[10]  Zhecheng Zhu,et al.  Estimating ICU bed capacity using discrete event simulation. , 2012, International journal of health care quality assurance.

[11]  Miguel Angel Ortiz Barrios,et al.  Discrete-Event Simulation to Reduce Waiting Time in Accident and Emergency Departments: A Case Study in a District General Clinic , 2017, UCAmI.

[12]  Chris D. Nugent,et al.  Reducing Appointment Lead-Time in an Outpatient Department of Gynecology and Obstetrics Through Discrete-Event Simulation: A Case Study , 2016, UCAmI.

[13]  Miguel A. Ortiz,et al.  Using Computer Simulation to Improve Patient Flow at an Outpatient Internal Medicine Department , 2016, UCAmI.

[14]  T. Olsen,et al.  Review of modeling approaches for emergency department patient flow and crowding research. , 2011, Academic emergency medicine : official journal of the Society for Academic Emergency Medicine.

[15]  Sally I. McClean,et al.  A modeling framework that combines markov models and discrete-event simulation for stroke patient care , 2011, TOMC.

[16]  Tobias Reggelin,et al.  Simulation and the Emergency Department Overcrowding Problem , 2017 .

[17]  Sima Ajami,et al.  Wait time in emergency department (ED) processes. , 2012, Medicinski arhiv.

[18]  Sally I. McClean,et al.  Using phase-type models to cost stroke patient care across health, social and community services , 2014, Eur. J. Oper. Res..

[19]  Dominik Aronsky,et al.  Forecasting emergency department crowding: a discrete event simulation. , 2008, Annals of emergency medicine.

[20]  Michael Pidd,et al.  Discrete event simulation for performance modelling in health care: a review of the literature , 2010, J. Simulation.

[21]  Renata Konrad,et al.  Modeling the impact of changing patient flow processes in an emergency department: Insights from a computer simulation study , 2013 .

[22]  Sameh Al-Shihabi,et al.  Reducing waiting time at an emergency department using design for Six Sigma and discrete event simulation , 2010 .