主题： Prediction-driven Surge Planning with Application in the Emergency Department

报告人：Yue HU, Postdoctoral Principal Researcher, Chicago Booth School of Business & Assistant Professor (starting summer 2023), Stanford Graduate School of Business

主持人：Jiaqi LU, Assistant Professor, School of Data Science, CUHK-Shenzhen

日期： 12 October (Wednesday), 2022

时间：11:00 AM to 12:00 PM, Beijing Time

形式：Hybrid 

线下地点： 103 Meeting Room, Daoyuan Building 

Zoom链接： https://cuhk-edu-cn.zoom.us/j/5304767369?pwd=aFErUGFSSDlLNWJld0VNNmpTL0k0UT09

Zoom会议号：5304767369

密码：852648

语言： English

摘要：

Determining emergency department (ED) nurse staffing decisions to balance the quality of service and staffing cost can be extremely challenging, especially when there is a high level of uncertainty in patient-demand. Increasing data availability and continuing advancements in predictive analytics provide an opportunity to mitigate demand uncertainty by utilizing demand forecasts. In this work, we study a two-stage prediction-driven staffing framework where the prediction models are integrated with the base (made weeks in advance) and surge (made nearly real-time) staffing decisions in the ED. We quantify the benefit of having the ability to use the more expensive surge staffing and identify the importance of balancing demand uncertainty versus demand stochasticity. We also propose a near-optimal two-stage staffing policy that is straightforward to interpret and implement. Lastly, we develop a unified framework that combines parameter estimation, real-time demand forecasts, and capacity sizing in the ED. High-fidelity simulation experiments for the ED demonstrate that the proposed framework can reduce annual staffing costs by 11%–16% ($2 M–$3 M) while guaranteeing timely access to care.

简介：

Yue Hu is a Postdoctoral Principal Researcher at Chicago Booth School of Business. Her research lies at the intersection of healthcare operations management and applied probability. With particular focus on patient-flow management and capacity sizing in healthcare delivery systems, she studies how to leverage predictive analytics to guide operational strategies and innovations. In addition to solving practically relevant problems, she conducts research in developing new methodologies for the approximation and control of stochastic systems.
Hu’s research has been recognized in a number of competitions, including as the finalist of the 2022 INFORMS Doing Good with Good OR Competition, winner of the 2020 INFORMS APS Best Student Paper Award, finalist of the 2019 INFORMS IBM Best Student Paper Award, and honorable mention in the 2017 INFORMS Undergraduate Operations Research Prize. Hu received her PhD from the Decision, Risk and Operations Division at the Graduate School of Business, Columbia University. Prior to pursuing her PhD, she received a BS from the Department of Industrial Engineering and Management Sciences at Northwestern University. In Summer 2023, she will join Stanford Graduate School of Business as an Assistant Professor of Operations, Information & Technology.