Abstract: Over the past few years, healthcare providers have significantly expanded telemedicine adoptions. On one hand, telemedicine has the potential to increase the accessibility of medical appointments. On the other hand, due to the limitation of diagnosis and treatment methods, telemedicine may be insufficient for patients' treatment needs and necessitate subsequent in-person follow-up visits. To better understand this tradeoff, we model the healthcare system as a queueing network providing two types of service: telemedicine and in-person consultations. We assume that an in-person visit guarantees successful treatment, whereas a telemedicine visit may fail to replace in-person care with a probability that is contingent on the patient's features. We formulate patients' strategic choices between these care modalities as a queueing game, and characterize the game-theoretic equilibrium and the socially optimal patients' choices. We further examine how improving patients' understanding of their telemedicine suitability through predictive analytics at the online triage stage affects system performance. We find that increasing information granularity maximizes the stability region of the system but may not always be optimal in reducing the average waiting time. This limitation, however, can be overcome by simultaneously deploying a priority rule that induces the social optimum under specific conditions. Finally, leveraging real-world data from a large academic hospital in the United States, we perform a comprehensive case study that encompasses both the development of a prediction model for in-person follow-up needs and the implementation of effective information provision and patient scheduling strategies.

Bio: Yue Hu is an Assistant Professor of Operations, Information &Technology at Stanford Graduate School of Business. Her research lies at the intersection of healthcare operations management and applied probability. With particular focus on scheduling, staffing, and patient-flow management 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.