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美国普渡大学Nan Kong学术报告

2018年06月12日 14:11  点击:[]

报告题目:Patient Sequencing for Ancillary Service Providers in an Inpatient Unit

报告人:Nan Kong 副教授

邀请人:胡祥培 教授

报告时间:2018.6.21上午9:00-10:00

报告地点:大连理工大学管经学部新大楼B312

报告摘要:Ancillary service providers (ASPs) on an inpatient unit, such as social workers, care managers, and physical and occupational therapists, play crucial roles in the inpatient discharge process. However, these providers are also responsible for other patients besides those to be discharged. To aid ASPs in better handling their patient workload on a daily basis, we derive practical strategies through an optimization approach. Our proposed model extends the stochastic single machine sequencing problem with problem-specific constraints in order to minimize the total boarding time in upstream units. Using a scenario sampling approach and a simulated annealing algorithm, we derive near-optimal strategies and evaluate them using real data from a large hospital in Maine. Our experimental results suggest that prioritizing discharges immediately after patients with tight due-dates, and in a specific order, reduces upstream boarding. Further, as the number of patients to be seen on a day increases and/or the number of discharges among these patients increases, the discharge patients must be moved up in the sequence. Our proposed strategies also perform well on the boarding time objective compared to realistic ASP sequencing strategies derived based on our experience at that unit and classical machine scheduling rules.

This is a joint work with Drs. Pratik Parikh, Nicholas Ballester from Wright State University and Dr. Jordan Peck from MaineHealth. The work is under 2nd-round review in the IEEE Transactions on Automation Science and Engineering. If time allows, I will also talk about a research extension currently being investigated by Xiaochun Feng, a PhD candidate of DUT.

报告人简介:Professor Nan Kong is Associate Professor in the Weldon School of Biomedical Engineering at Purdue University. He received his PhD in industrial engineering from the University of Pittsburgh in 2006. His research interest includes applying operations research to healthcare systems operations management and policy development. Recently, he has expanded his research to machine learning for health care, particularly hyperspectral image analysis and individual patient record analysis. He has published close to 60 peer-reviewed journal articles and conference proceedings papers. He has received grants from the United States National Science Foundation, National Cancer Institute, Agency for Health Research and Quality, and Air Force Office of Scientific Research. He is currently President-Elect of the Public Sector Operations Research Section in the Institute for Operations Research and the Management Sciences (INFORMS). In the past, he was council member and sectary of the Health Application Society in INFORMS. He has organized tasks/clusters for several INFORMS meetings, including INFORMS 2016, 2017, INFORMS International 2012, 2015, and INFORMS Healthcare 2013, 2017.

 

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