Abstract
Long waiting queues have been a stressful problem in many tertiary public hospitals, which significantly impact the accessibility and quality of health care. One of the key challenges to solve this problem is to provide enough registration windows to serve hospital visit demand under the limited medical and human resources. Traditional window shift scheduling methods are usually based on experiences and biased historical data, which may not accurately reflect the actual hospital visit demand. In this work, we propose a demand-responsive window scheduling framework by accurately modeling and forecasting the fine-grained hospital visit demand from real-world human mobility data. Specifically, in the first phase, we extract hospital visit demand from taxi drop-off events around hospitals, and build a graph model to capture their spatiotemporal patterns. In the second phase, we propose a spatiotemporal graph neural network (ST-GNN) to accurately forecast the hospital visit demand, which simultaneously captures the spatial correlation by graph convolutional networks (GCN) and the temporal dependency by gated recurrent units (GRU). Finally, we exploit a queuing theory model to achieve demand-responsive windows scheduling. Evaluation results using real-world data from Xiamen City show that our framework accurately forecasts hospital visit demand, and effectively schedules hospital registration windows, which consistently outperforms the baselines.
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Acknowledgement
We would like to thank the reviewers for their constructive suggestions. This research is supported by NSF of China No. 61802325, NSF of Fujian Province No. 2018J01105, and the China Fundamental Research Funds for the Central Universities No. 20720170040.
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Wang, Z., Guo, R., Hong, L., Wang, C., Chen, L. (2020). Demand-Responsive Windows Scheduling in Tertiary Hospital Leveraging Spatiotemporal Neural Networks. In: Yu, Z., Becker, C., Xing, G. (eds) Green, Pervasive, and Cloud Computing. GPC 2020. Lecture Notes in Computer Science(), vol 12398. Springer, Cham. https://doi.org/10.1007/978-3-030-64243-3_18
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