Abstract
The current global pandemic of COVID-19 has caused significant strain on the medical resources of the healthcare providers, so more and more hospitals use telemedicine and virtual care for remote treatment (i.e. consulting, remote diagnosis, treatment, monitoring and follow-ups and so on) in response to COVID-19 pandemic, which is expected to deliver timely care while minimizing exposure to protect medical practitioners and patients. In this study, we study the telemedicine assignment between the patients and telemedical specialists by considering different sources of uncertainty, i.e. uncertain service duration and the no-show behavior of the doctors that is caused by the unexpected situations (i.e. emergency events). We propose a two-stage chance-constrained model with the assignment decisions in the recourse problem and employ an uncertainty set to capture the behavior of telemedical doctors, which finally gives rise to a two-stage binary integer program with binary variables in the recourse problem. We propose an enumeration-based column-and-constraint generation solution method to solve the resulting problem. A simple numerical study is done to illustrate our proposed framework. To the best of our knowledge, this is the first attempt to incorporate the behavior of doctors and uncertain service duration for the telemedicine assignment problem in the literature. We expect that this work could open an avenue for the research of telemedicine by incorporating different sources of uncertainty from an operations management viewpoint, especially in the context of a data-driven optimization framework.
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Acknowledgements
This study is fully supported by the project of “Scheduling and Optimization of Telemedicine Resource from a Data-Driven Perspective” from Natural Science Foundation of China [grant 71972012]. We thank this grant support very much.
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Ji, M., Li, J., Peng, C. (2022). Two-Stage Chance-Constrained Telemedicine Assignment Model with No-Show Behavior and Uncertain Service Duration. In: Yang, H., Qiu, R., Chen, W. (eds) AI and Analytics for Public Health. INFORMS-CSS 2020. Springer Proceedings in Business and Economics. Springer, Cham. https://doi.org/10.1007/978-3-030-75166-1_32
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