A Hybrid Process Mining Framework for Automated Simulation Modelling for Healthcare

Advances in data and process mining algorithms combined with the availability of sophisticated information systems have created an encouraging environment for innovations in simulation modelling. Researchers have investigated the integration between such algorithms and business process modelling to facilitate the automation of building simulation models. These endeavors have resulted in a prototype termed Auto Simulation Model Builder (ASMB) for DES models. However, this prototype has limitations that undermine applying it on complex systems. This paper presents an extension of the ASMB framework previously developed by authors adopted for healthcare systems. The proposed framework offers a comprehensive solution for resources handling to support complex decision-making processes around hospital staff planning. The framework also introduces a machine learning real-time data-driven prediction approach for system performance using advanced activity blocks for the auto-generated model, based on live-streams of patient data. This prediction can be useful for both single and multiple healthcare units management.

[1]  Amr Arisha,et al.  Hybrid modeling for vineyard harvesting operations , 2016, 2016 Winter Simulation Conference (WSC).

[2]  Amr Arisha,et al.  A hybrid process-mining approach for simulation modeling , 2017, 2017 Winter Simulation Conference (WSC).

[3]  Amr Arisha,et al.  Towards Operations Excellence: Optimising Staff Scheduling For New Emergency Department , 2013 .

[4]  Angel A. Juan,et al.  SYMBIOTIC SIMULATION SYSTEM: HYBRID SYSTEMS MODEL MEETS BIG DATA ANALYTICS , 2018, 2018 Winter Simulation Conference (WSC).

[5]  N. R. T. P. van Beest,et al.  Redesigning business processes: a methodology based on simulation and process mining techniques , 2009, Knowledge and Information Systems.

[6]  Bernard P. Zeigler,et al.  DESIGNING CARE PATHWAYS USING SIMULATION MODELING AND MACHINE LEARNING , 2018, 2018 Winter Simulation Conference (WSC).

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

[8]  Susan McKeever,et al.  PRESENTING A HYBRID PROCESSING MINING FRAMEWORK FOR AUTOMATED SIMULATION MODEL GENERATION , 2018, 2018 Winter Simulation Conference (WSC).

[9]  Roger Jianxin Jiao,et al.  Workflow simulation for operational decision support using event graph through process mining , 2012, Decis. Support Syst..

[10]  D. Lane,et al.  Simulation Applied to Health Services: Opportunities for Applying the System Dynamics Approach , 1998, Journal of health services research & policy.

[11]  Patrick T. Hester,et al.  Towards a theory of multi-method M&S approach: Part I , 2014, Proceedings of the Winter Simulation Conference 2014.

[12]  Christopher J. Lynch,et al.  A multi-paradigm modeling framework for modeling and simulating problem situations , 2014, Proceedings of the Winter Simulation Conference 2014.

[13]  Jorge Alvarado,et al.  Combination of Process Mining and Simulation Techniques for Business Process Redesign: A Methodological Approach , 2012, SIMPDA.

[14]  Vlatka Hlupic,et al.  Business process modelling and analysis using discrete-event simulation , 1998, 1998 Winter Simulation Conference. Proceedings (Cat. No.98CH36274).

[15]  Bernardo Almada-Lobo,et al.  Hybrid simulation-optimization methods: A taxonomy and discussion , 2014, Simul. Model. Pract. Theory.

[16]  Björn Johansson,et al.  A methodology for input data management in discrete event simulation projects , 2008, 2008 Winter Simulation Conference.