Optimizing outpatient Department Staffing Level using Multi-Fidelity Models

Bowen Pang, Xiaolei Xie, Betnd Heidergott, Yijie Peng

Research output: Chapter in Book / Report / Conference proceedingConference contributionAcademicpeer-review

Abstract

The workload of the outpatient departments in Chinese large hospitals is extremely high. Patients often have to wait for a long time before getting their treatments. It is economically expensive to increase medical staffs including nurses and doctors. Therefore, it is critical to optimize staff planning in the outpatient departments to reduce excessive patient waiting time. A high-fidelity simulation model can accurately capture the features of the outpatient service system. But it is very time-consuming to obtain the optimal staff planning decision only based on the simulation model. A simplified queueing model might lead to an analytical solution for the optimal staff planning problem, but it can not fully capture the feature of the real outpatient service system. We propose to use the outputs of the high-fidelity simulation model to drive the output of the low-fidelity queueing model closer to that of the outpatient service system, and then use the data-driven queueing model to make the staff planning decision. Empirical studies on a major hospital are carried out, which demonstrate the effectiveness and efficiency of our method.

Original languageEnglish
Title of host publication2019 IEEE 15th International Conference on Automation Science and Engineering, CASE 2019 - Proceedings
PublisherIEEE Computer Society
Pages715-720
Number of pages6
ISBN (Electronic)9781728103556
DOIs
Publication statusPublished - 19 Sep 2019
Event15th IEEE International Conference on Automation Science and Engineering, CASE 2019 - Vancouver, Canada
Duration: 22 Aug 201926 Aug 2019

Publication series

NameIEEE International Conference on Automation Science and Engineering
Volume2019-August
ISSN (Print)2161-8070
ISSN (Electronic)2161-8089

Conference

Conference15th IEEE International Conference on Automation Science and Engineering, CASE 2019
CountryCanada
CityVancouver
Period22/08/1926/08/19

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Pang, B., Xie, X., Heidergott, B., & Peng, Y. (2019). Optimizing outpatient Department Staffing Level using Multi-Fidelity Models. In 2019 IEEE 15th International Conference on Automation Science and Engineering, CASE 2019 - Proceedings (pp. 715-720). [8842984] (IEEE International Conference on Automation Science and Engineering; Vol. 2019-August). IEEE Computer Society. https://doi.org/10.1109/COASE.2019.8842984
Pang, Bowen ; Xie, Xiaolei ; Heidergott, Betnd ; Peng, Yijie. / Optimizing outpatient Department Staffing Level using Multi-Fidelity Models. 2019 IEEE 15th International Conference on Automation Science and Engineering, CASE 2019 - Proceedings. IEEE Computer Society, 2019. pp. 715-720 (IEEE International Conference on Automation Science and Engineering).
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Pang, B, Xie, X, Heidergott, B & Peng, Y 2019, Optimizing outpatient Department Staffing Level using Multi-Fidelity Models. in 2019 IEEE 15th International Conference on Automation Science and Engineering, CASE 2019 - Proceedings., 8842984, IEEE International Conference on Automation Science and Engineering, vol. 2019-August, IEEE Computer Society, pp. 715-720, 15th IEEE International Conference on Automation Science and Engineering, CASE 2019, Vancouver, Canada, 22/08/19. https://doi.org/10.1109/COASE.2019.8842984

Optimizing outpatient Department Staffing Level using Multi-Fidelity Models. / Pang, Bowen; Xie, Xiaolei; Heidergott, Betnd; Peng, Yijie.

2019 IEEE 15th International Conference on Automation Science and Engineering, CASE 2019 - Proceedings. IEEE Computer Society, 2019. p. 715-720 8842984 (IEEE International Conference on Automation Science and Engineering; Vol. 2019-August).

Research output: Chapter in Book / Report / Conference proceedingConference contributionAcademicpeer-review

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Pang B, Xie X, Heidergott B, Peng Y. Optimizing outpatient Department Staffing Level using Multi-Fidelity Models. In 2019 IEEE 15th International Conference on Automation Science and Engineering, CASE 2019 - Proceedings. IEEE Computer Society. 2019. p. 715-720. 8842984. (IEEE International Conference on Automation Science and Engineering). https://doi.org/10.1109/COASE.2019.8842984