A decision support model for staff allocation of mobile medical service

Paveena Chaovalitwongse (), Krongsin Somprasonk, Naragain Phumchusri, Joseph Heim, Zelda B. Zabinsky and W. Art Chaovalitwongse
Additional contact information
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

Annals of Operations Research, 2017, vol. 249, issue 1, No 21, 433-448

Abstract: 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.

Keywords: Mobile medical service; Staff allocation; Decision support; Transportation problem (search for similar items in EconPapers)
Date: 2017
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://link.springer.com/10.1007/s10479-015-1991-5 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:annopr:v:249:y:2017:i:1:d:10.1007_s10479-015-1991-5

Ordering information: This journal article can be ordered from
http://www.springer.com/journal/10479

DOI: 10.1007/s10479-015-1991-5

Access Statistics for this article

Annals of Operations Research is currently edited by Endre Boros

More articles in Annals of Operations Research from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().