题　目：A Multi-Treatment Forest Approach for Analyzing the Heterogeneous Effects of Team Familiarity

嘉　宾：Minmin Zhang, Ph.D. Candidate, The University of Texas at Dallas

主持人：花成 副教授 开云网页登录 安泰经济与管理学院

时　间：2024年11月27日（周三）14:00-15:30pm

地　点：安泰楼A503室

内容简介：

Extensive research has revealed that prior collaborative experiences among team members (called "team familiarity") enhance outcomes of group work in many different environments. In this study, we examine the effect of team familiarity on surgery duration and extend the literature on team dynamics by examining whether the effect of team familiarity is heterogeneous across patients. Because we use multiple variables to measure team familiarity (i.e., multiple treatments of interest), we first develop a new approach, which we call the "MT forest" approach, to estimate the heterogeneous effects of multiple treatments and demonstrate the effectiveness of this approach using synthetic data. Then, we apply the MT forest approach to an orthopedic surgery setting to estimate the heterogeneous effects of team familiarity on surgery duration, and investigate how the effect varies across patient features. We find (1) an increase in team familiarity score, especially the anesthesiologist-nurse and surgeon-anesthesiologist familiarity scores, significantly reduces surgery duration, and (2) the effect of team familiarity is heterogeneous across patients with different features. Finally, we develop an optimization model to assess the value of leveraging the heterogeneous effects of team familiarity to better match surgical teams with patients. This research contributes to the academic literature by providing a new approach to estimating the heterogeneous effects of multiple treatments and by providing empirical evidence that the effect of team familiarity is heterogeneous across patients. Our results are also of potential value to healthcare providers because they imply that leveraging the heterogeneous effects of team familiarity to better match surgical teams with patients can improve hospital operational efficiency.

演讲人简介：

Minmin Zhang is a Ph.D. candidate in Operations Management at Naveen Jindal School of Management, the University of Texas at Dallas, where he is advised by Guihua Wang and Elena Katok. His research addresses challenges in healthcare operations management with techniques from machine learning and empirical econometrics. He develops new machine-learning algorithms to support precision healthcare and evaluates the impact of policy interventions to provide actionable insights into operations. His work has been recognized as the winner of the INFORMS Health Applications Society Student Paper Competition, the runner-up of the POMS College of Healthcare Operations Management Best Paper Award and the POMS College of Service Operations Management Best Student Paper Award, and the finalist of the INFORMS Service Science Best Student Paper Award Competition and the POMS College of Operational Excellence Junior Scholar Best Paper Competition. Before his doctoral studies, he completed a bachelor's degree in mechanical engineering from Shanghai Jiao Tong University and a bachelor's and a master's degree in industrial and operations engineering from the University of Michigan.

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