Publication: Physician workforce planning and allocation model using agent-based modeling: A case study in Thailand
Issued Date
2020-01-01
Resource Type
ISSN
10991751
07496753
07496753
DOI
Other identifier(s)
2-s2.0-85089494986
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Mahidol University
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SCOPUS
Bibliographic Citation
International Journal of Health Planning and Management. (2020)
Suggested Citation
Phanumas Thongsukdee, Waressara Weerawat Physician workforce planning and allocation model using agent-based modeling: A case study in Thailand. International Journal of Health Planning and Management. (2020). doi:10.1002/hpm.3041 Retrieved from: https://repository.li.mahidol.ac.th/handle/20.500.14594/59237
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Title
Physician workforce planning and allocation model using agent-based modeling: A case study in Thailand
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Abstract
© 2020 John Wiley & Sons Ltd Objective: To address the physician workforce challenges in Thailand using Agent-Based Modeling (ABM). Physician planning and allocating have been modeled with the life cycle framework starting from medical school intakes, residency training, and career retirement. Method: This study proposes a systematic approach to physician planning and allocating which are modeled with a framework starting from medical school intakes, residency training, and career retirement. Results: The ABM can estimate physician levels for provincial government hospitals (OPS-MOPH) and other sectors. The model can demonstrate any geographical imbalance issues at the provincial level under the existing rural recruitment program implementation. Based on the ABM simulation result, although person per physician ratios reached the target of 1800:1 at the national level in 2019, Bangkok is the only province that reached the target. For other provinces, the ratio of OPS-MOPH is far from the target. It will need 10 more years to reach the target, and a geographical imbalance will still exist. A solution of modified rural recruitment program with the limitation of hometown jobs has been proposed to reduce the geographical imbalance. With the proposed modified rural recruitment program, the model predicts that all provinces will reach the target in 2036.