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A decision support model for staff allocation of mobile medical service

Author

Listed:
  • Paveena Chaovalitwongse

    (Chulalongkorn University)

  • Krongsin Somprasonk

    (Chulalongkorn University)

  • Naragain Phumchusri

    (Chulalongkorn University)

  • Joseph Heim

    (University of Washington)

  • Zelda B. Zabinsky

    (University of Washington)

  • W. Art Chaovalitwongse

    (University of Washington)

Abstract

Princess Mother’s Medical Volunteer (PMMV) Foundation is the most recognized and significant free-of-charge mobile medical service (MMS) provider in Thailand. They require volunteers from partner hospitals to give medical care to poor populations residing in remote areas of the country where access to general medical services is limited. Volunteers usually include four types of staff: doctors, dentists, nurses, and pharmacists. According to their operational plan, the PMMV and their working partners need to properly allocate/assign volunteer medical staff to operation sites according to site requirements. In current planning process, the PMMV has to organize massive amounts of data from different organizations in the country, resulting in a long processing time for allocation decisions. In addition, the current process does not allow decision makers to efficiently allocate medical staff with acceptable transportation cost. There is a significant opportunity to improve this process by using analytical models to support this decision making. Thus, this paper proposes a decision support model for staff allocation. The proposed model is in a form of computer information system (CIS) that is carefully developed to facilitate the access to heterogeneous data and ease of use by decision makers. The proposed CIS will assist the PMMV central offices and partners to manage massive data more efficiently and effectively, while the decision algorithm can facilitate planners to achieve the lowest possible cost associated with their decisions. The outcomes of this research were verified by potential users through a focus group manner. The result showed that the potential users were very satisfied with the overall performance of this system.

Suggested Citation

  • Paveena Chaovalitwongse & Krongsin Somprasonk & Naragain Phumchusri & Joseph Heim & Zelda B. Zabinsky & W. Art Chaovalitwongse, 2017. "A decision support model for staff allocation of mobile medical service," Annals of Operations Research, Springer, vol. 249(1), pages 433-448, February.
    • Handle: RePEc:spr:annopr:v:249:y:2017:i:1:d:10.1007_s10479-015-1991-5
    DOI: 10.1007/s10479-015-1991-5
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    References listed on IDEAS

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    1. Sanja Petrovic & Greet Berghe, 2012. "A comparison of two approaches to nurse rostering problems," Annals of Operations Research, Springer, vol. 194(1), pages 365-384, April.
    2. Linet Özdamar & Ediz Ekinci & Beste Küçükyazici, 2004. "Emergency Logistics Planning in Natural Disasters," Annals of Operations Research, Springer, vol. 129(1), pages 217-245, July.
    3. Tzeng, Gwo-Hshiung & Cheng, Hsin-Jung & Huang, Tsung Dow, 2007. "Multi-objective optimal planning for designing relief delivery systems," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 43(6), pages 673-686, November.
    4. D. Michael Warner, 1976. "Scheduling Nursing Personnel According to Nursing Preference: A Mathematical Programming Approach," Operations Research, INFORMS, vol. 24(5), pages 842-856, October.
    5. A.T. Ernst & H. Jiang & M. Krishnamoorthy & B. Owens & D. Sier, 2004. "An Annotated Bibliography of Personnel Scheduling and Rostering," Annals of Operations Research, Springer, vol. 127(1), pages 21-144, March.
    6. Rongbing Huang & Seokjin Kim & Mozart Menezes, 2010. "Facility location for large-scale emergencies," Annals of Operations Research, Springer, vol. 181(1), pages 271-286, December.
    7. Sheu, Jiuh-Biing, 2007. "An emergency logistics distribution approach for quick response to urgent relief demand in disasters," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 43(6), pages 687-709, November.
    8. Huguette Beaulieu & Jacques Ferland & Bernard Gendron & Philippe Michelon, 2000. "A mathematical programming approach for scheduling physicians in the emergency room," Health Care Management Science, Springer, vol. 3(3), pages 193-200, June.
    9. Kathrin Fischer, 2002. "Sequential Discrete p-Facility Models for Competitive Location Planning," Annals of Operations Research, Springer, vol. 111(1), pages 253-270, March.
    Full references (including those not matched with items on IDEAS)

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