Simulating the micro-level behavior of emergency department for macro-level features prediction

Emergency departments are currently facing major pressures due to rising demand caused by population growth, aging and high expectations of service quality. With changes continuing to challenge healthcare systems, developing solutions and formulating policies require a good understanding of the complex and dynamic nature of the relevant systems. However, as a typically complex system, it is hard to grasp the non-linear association between macro-level features and micro-level behavior for a systematic understanding. Instead of describing all the potential causes of this complex issue, in this paper we present a layer-based application framework to discover knowledge of an emergency department system through simulating micro-level behaviors of its components to facilitate a systematic understanding. Finally, case studies are used to demonstrate the potential use of the proposed approach. Results show that the proposed framework can significantly rtheeflect the non-linear association between micro-level behavior and macro-level features.

[1]  Xiaolan Xie,et al.  Hospitalization admission control of emergency patients using Markovian Decision Processes and discrete event simulation , 2014, Proceedings of the Winter Simulation Conference 2014.

[2]  Astrid Guttmann,et al.  Association between waiting times and short term mortality and hospital admission after departure from emergency department: population based cohort study from Ontario, Canada , 2011, BMJ : British Medical Journal.

[3]  R. Sánchez Bermejo,et al.  El triaje en urgencias en los hospitales españoles , 2013 .

[4]  Reinhard German,et al.  Prospective healthcare decision-making by combined system dynamics, discrete-event and agent-based simulation , 2013, 2013 Winter Simulations Conference (WSC).

[5]  Dietrich Dorner,et al.  The Logic of Failure: Why Things Go Wrong and What We Can Do to Make Them Right , 1996 .

[6]  Bruce L. Golden,et al.  Applications of Agent-Based Modeling and Simulation to Healthcare Operations Management , 2013 .

[7]  Peer-Olaf Siebers,et al.  Discrete-event simulation is dead, long live agent-based simulation! , 2010, J. Simulation.

[8]  Manel Taboada,et al.  Using an Agent-Based Simulation for Predicting the Effects of Patients Derivation Policies in Emergency Departments , 2013, ICCS.

[9]  F. Al-Shamali,et al.  Author Biographies. , 2015, Journal of social work in disability & rehabilitation.

[10]  Ron Goeree,et al.  Simulating an emergency department: the importance of modeling the interactions between physicians and delegates in a discrete event simulation , 2013, BMC Medical Informatics and Decision Making.

[11]  P Barach,et al.  Understanding the complexity of redesigning care around the clinical microsystem , 2006, Quality and Safety in Health Care.

[12]  Michael J. North,et al.  Agent-based modeling and simulation: introductory tutorial , 2013, WSC '13.

[13]  Emilio Luque,et al.  A Generalized Agent-Based Model to Simulate Emergency Departments , 2014 .

[14]  John D. Sterman,et al.  Learning in and about complex systems , 1994 .

[15]  Cynthia Rudin,et al.  Algorithms for interpretable machine learning , 2014, KDD.

[16]  Barry Richmond,et al.  Systems thinking: critical thinking skills for the 1990s and beyond , 1993 .

[17]  Robert L. Axtell,et al.  WHY AGENTS? ON THE VARIED MOTIVATIONS FOR AGENT COMPUTING IN THE SOCIAL SCIENCES , 2000 .

[18]  Emilio Luque,et al.  Quantitative Evaluation of Decision Effects in the Management of Emergency Department Problems , 2015, ICCS.

[19]  Sally C. Brailsford,et al.  Discrete-event simulation is alive and kicking! , 2014, J. Simulation.

[20]  M. Meehan.  General System Theory: Foundations, Development, Applications , 1969 .

[21]  Simon A. Levin,et al.  Complex adaptive systems: Exploring the known, the unknown and the unknowable , 2002 .

[22]  Ludwig von Bertalanffy,et al.  General System Theory , 1969 .