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Abstract: |
Under the burgeoning paradigm of coordinated care, outpatient healthcare networks comprised of an integrated suite of specialist services are becoming more popular and face an increasingly diverse patient demand. This growing business model requires a sophisticated capacity allocation scheme for managing patients with different target deadlines for completing their itinerary of care in the network. A critical challenge is that patients need to visit multiple clinical services during their itinerary while the specific care pathway is not known when the capacity is being planned, because information about the patient’s condition evolves over the course of the patient’s itinerary and drives subsequent services required.?
In this work, we collaborate with the Mayo Clinic to develop a discrete-time queueing network to optimize the capacity allocation to improve itinerary completion rates. We characterize the itinerary completion time with a doubly-stochastic phase-type distribution and leverage a mean-field model to address computational intractability. We integrate the itinerary completion time model within a policy improvement framework to approximately solve a large-scale stochastic optimization that maximizes the proportion of on-time itinerary completions. We demonstrate, via a case study of the Mayo Clinic, that our solution approach can significantly improve on-time completion, from 70% under the current capacity allocation to more than 95% using our approach.?
报告人简介：
Pengyi Shi joined the Krannert School of Management at Purdue University as an Assistant Professor in January 2014. She received her Ph.D. degree in Industrial Engineering from Georgia Institute of Technology before joining Purdue. Her research interests include data-driven modeling and decision-making in healthcare operations. She has collaborated with practitioners and faculty members from different healthcare organizations, including major hospitals in the US, Singapore, and China. Her research has won the first place of INFORMS Pierskalla Best Paper Award in 2018 and the second place of POMS CHOM Best Paper Award in 2019.
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