[1]ZHANG Yue,WANG Zixiang,ZHOU Bo,et al.Algorithms for physician scheduling under the online and offline combined service mode based on machine learning[J].CAAI Transactions on Intelligent Systems,2025,20(4):800-812.[doi:10.11992/tis.202404032]
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Algorithms for physician scheduling under the online and offline combined service mode based on machine learning

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 20 Number of periods: 2025 4 Page number: 800-812 Column: 学术论文—机器学习 Public date: 2025-08-05
Title:
Algorithms for physician scheduling under the online and offline combined service mode based on machine learning
Author(s):
ZHANG Yue1;  WANG Zixiang2;  ZHOU Bo1;  LIU Ran1;  YANG Zhitao3
1. Department of Industrial Engineering and Management, Shanghai Jiao Tong University, Shanghai 200240, China;
2. Alibaba Business School, Hangzhou Normal University, Hangzhou 311121, China;
3. Department of Emergency, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
Keywords:
telemedicine; physician scheduling; time-varying queueing system; data-driven; deep learning; Markov decision process; approximate dynamic programming; heuristic
CLC:
TP18
DOI:
10.11992/tis.202404032
Abstract:
The online and offline combined medical service mode has become a new medical service mode generally adopted by large hospitals in China. Under this mode, large hospitals need to allocate physicians to online and offline services, and arrange online and offline scheduling plans for physicians while considering the switching of physicians between the two services. To address this problem, a Markov decision process model for physician scheduling with service level constraints was developed and an approximate dynamic programming algorithm was designed to solve the Markov decision process with high efficiency. Furthermore, considering multi-dimensional uncertainties such as highly time-varying patient arrival and service hours, a data-driven recurrent neural network was constructed based on the real-life data of the cooperative hospital as a performance evaluation method for the online and offline queueing systems. Numerical experiments show that the proposed methods can reduce the total working hours of physicians, effectively control the waiting time of patients, and ensure the high-efficiency operation of the system.
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