TY - GEN
T1 - Using a Virtual Hospital for Piloting Patient Flow Decongestion Interventions
AU - Qin, Shaowen
PY - 2019
Y1 - 2019
N2 - 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.
AB - 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.
KW - Decision support
KW - Optimization
KW - Patient flow
KW - Pilot study
KW - Simulation modelling
KW - Virtual experiments
UR - http://www.scopus.com/inward/record.url?scp=85076513350&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-35231-8_44
DO - 10.1007/978-3-030-35231-8_44
M3 - Conference contribution
AN - SCOPUS:85076513350
SN - 9783030352301
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 605
EP - 616
BT - Advanced Data Mining and Applications - 15th International Conference, ADMA 2019, Proceedings
A2 - Li, Jianxin
A2 - Wang, Sen
A2 - Qin, Shaowen
A2 - Li, Xue
A2 - Wang, Shuliang
PB - Springer
CY - Cham, Switzerland
T2 - 15th International Conference on Advanced Data Mining and Applications, ADMA 2019
Y2 - 21 November 2019 through 23 November 2019
ER -