A simulation-based decision support system to model complex demand driven healthcare facilities

Simulating healthcare processes is a sophisticated endeavor. Treatment processes and patient arrival patterns differ significantly in their statistical attributes and implicate a high degree of variability. In addition, there are several types of interconnected processes of medical staff involved that accompany a patient's journey through the healthcare facility. Replicating this behavior with process flow models in a discrete event simulation model is highly complex and therefore difficult to create while maintaining a high degree of precision. A framework is thus introduced which delivers an algorithm that allows to implement Multiple Participant Pathway Modeling (MPPM) under the consideration of Flexible Resource Allocation (FRA). This framework is applied on an Irish Emergency Department (ED), the outcome of which is presented here. Results show that scenarios can be investigated which impact several different process flows with a high precision. This provides a solid base for both the interpretation of results and decision making.

[1]  Paul R. Harper,et al.  Simulation model to investigate flexible workload management for healthcare and servicescape environment , 2009, Proceedings of the 2009 Winter Simulation Conference (WSC).

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

[3]  Sheldon Howard Jacobson,et al.  Application of discrete-event simulation in health care clinics: A survey , 1999, J. Oper. Res. Soc..

[4]  J. PAUL,et al.  A STUDY OF THE IMPACT OF INFORMATION TECHNOLOGY ON BUSINESS PROCESSES USING DISCRETE EVENT SIMULATION : A REPRISE JULIE EATOCK RAY , 2002 .

[5]  Peter Johnson,et al.  Towards a composite modelling approach for multitasking , 2004, TAMODIA '04.

[6]  Kevin Mackway-Jones,et al.  Emergency triage. , 2013, Emergency nurse : the journal of the RCN Accident and Emergency Nursing Association.

[7]  Wen-Shan Lin,et al.  Using simulation technique and genetic algorithm to improve the quality care of a hospital emergency department , 2007, Expert Syst. Appl..

[8]  Wil M. P. van der Aalst,et al.  Complexity metrics for Workflow nets , 2009, Inf. Softw. Technol..

[9]  F. P. Wieland,et al.  UNDERSTANDING ACCIDENT AND EMERGENCY DEPARTMENT PERFORMANCE USING SIMULATION , 2006 .

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

[11]  A. Bonato,et al.  Graphs and Hypergraphs , 2022 .

[12]  M. Thorwarth.  A Simulation-based Decision Support System to Improve Healthcare Facilities Performance – Elaborated on an Irish Emergency Department , 2011 .

[13]  Anas N. Al-Rabadi,et al.  A comparison of modified reconstructability analysis and Ashenhurst‐Curtis decomposition of Boolean functions , 2004 .

[14]  Toni Ruohonen,et al.  Simulation Model for Improving the Operation of the Emergency Department of Special Health Care , 2006, Proceedings of the 2006 Winter Simulation Conference.

[15]  Celia A. Glass,et al.  The nurse rostering problem: A critical appraisal of the problem structure , 2010, Eur. J. Oper. Res..

[16]  J. Banks,et al.  Discrete-Event System Simulation , 1995 .

[17]  Thomas R Rohleder,et al.  Modeling patient service centers with simulation and system dynamics , 2007, Health care management science.