HAS Online Seminars

Next Speaker: Qiushi Chen is the Korb Early Career Professor in Industrial Engineering and Associate Professor in the Harold and Inge Marcus Department of Industrial and Manufacturing Engineering at The Pennsylvania State University. He earned his PhD degree in Operations Research from the Georgia Institute of Technology in 2016 and completed his post-doctoral training at Massachusetts General Hospital and Harvard Medical School. His research has focused on utilizing innovative mathematical modeling, optimization, and data analytics tools, integrated with real-world healthcare datasets, to better inform clinical decisions and policy making in broad healthcare settings. He has been collaborating closely with researchers from medicine, health policy, health economics, and social sciences in multidisciplinary research projects on modeling for opioid and substance use, behavioral and mental health, and chronic diseases. His work has been supported by the National Institutes of Health and the National Science Foundation. He served as a council member of the INFORMS Health Applications Society (HAS) during 2021-2023 and currently serves as an associate editor of Health Care Management Science. 

Seminar Title: Leveraging Data and Decision Analytic Models to Address the Substance Misuse in the US

Date and Time: Friday, January 30 at 12:00 PM (EST)

Abstract: Substance misuse and drug overdose deaths have emerged as major public health threats in the United States over the past two decades. Despite a recent decline in overdose mortality, the overall burden remains alarmingly high, with over 80,000 overdose deaths in 2024 alone. While substantial efforts over a wide range of intervention strategies are underway to abate the harms of opioid and substance misuse in communities on the practical front, many existing and emerging questions require research utilizing advanced analytic modeling tools to guide evidence-based decisions. In this talk, I will present several data and decision analytic modeling studies spanning from measurement to resource allocation in the substance use epidemic. First, to inform local stakeholders with the burden of opioid misuse in communities, we develop a Bayesian hierarchical framework that integrates multiple publicly available data sources to provide nationwide county-level prevalence estimates of opioid misuse. Next, we will discuss a problem of allocating opioid settlement funds that originates from a real-world decision-making setting. We introduce two fairness metrics for the allocation and formulate the fair allocation decisions as a convex optimization problem. Finally, motivated by the challenges encountered by the addiction medicine team in the local health center, we study the prioritization decisions of peer recovery specialists in substance use treatment. We formulate a Markov decision process model to optimize which patients to release early and more efficiently utilize the limited capacity of peer recovery services.