Using a virtual hospital for patient flow management decision support
- Prof. Shaowen Qin, Flinders University
- Time: 2019-11-15 17:00
- Host: Prof. Xiaotie Deng
- Venue: Room 101, Courtyard No.5, Jingyuan
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. We have developed HESMAD (Hospital Event Simulation Model: Arrivals to Discharge), a virtual hospital based on a typical Australian hospital, for this purpose. We present a couple of examples of its application to test patient flow decongestion solutions and provide some experience-based discussions on various issues involved in simulation modelling, such as model validation and interpretation of simulation results.
Shaowen is an Associate Professor at the College of Science and Engineering of Flinders University, Australia. Her research interests include workflow and process optimisation; simulation modelling of dynamic behaviour of complex systems; and artificial intelligence (AI) based decision recommendation. Shaowen studied Engineering Mechanics at Tsinghua University, Applied Mathematics at Illinois Institute of Technology (US), and Theoretical and Applied Mechanics at Northwestern University (US). She worked at the National University of Singapore and Motorola Global Software Group in its U.S., Singapore and Australia Software Centres before joining Flinders University. With a multi-disciplinary background and industrial work experience, she aspires to solve complex real-word problems, especially those faced by the healthcare industry, using advanced research findings and computing technologies.