Abstract
It is beyond the capacity of the human mind to process large amounts of interdependent information, such as predicting the dynamic behavior of a complex system and evaluating the short and long term effects of potential interventions aimed to improve its operations. At the same time, it is extremely costly to test these interventions with the real world system subject to improvement. Fortunately, we have moved to an era where advancements in computing and software technology have provided us the capabilities to build virtual complex systems (simulation models), that can serve as risk-free digital platforms for running pilot experiments with potential system interventions and obtain comparative data for decision support and optimization. This paper presents two case studies in a healthcare setting, where a simulation model named HESMAD (Hospital Event Simulation Model: Arrivals to Discharge) was applied to pilot potential interventions proposed by hospital professionals or researchers that are aimed at minimizing hospital patient flow congestion episodes. It was demonstrated that simulation modelling is not only an effective approach to conduct virtual experiments for evaluating proposed intervention ideas from healthcare professionals, but also an ideal vehicle for piloting scientific research outcomes from data science researchers. Some experience-based discussions on various issues involved in simulation modelling, such as validation of the simulation model and interpretation of simulation results are also provided.
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Acknowledgement
The author acknowledges contributions to HESMAD development and the case studies by Dr C. Thompson, Dr D. Ward, Dr T. Bogomolov, Mr. T.Y. Chen and other collaborators.
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Qin, S. (2019). Using a Virtual Hospital for Piloting Patient Flow Decongestion Interventions. In: Li, J., Wang, S., Qin, S., Li, X., Wang, S. (eds) Advanced Data Mining and Applications. ADMA 2019. Lecture Notes in Computer Science(), vol 11888. Springer, Cham. https://doi.org/10.1007/978-3-030-35231-8_44
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DOI: https://doi.org/10.1007/978-3-030-35231-8_44
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