An Analytical Approach for Dispatch Operations of Emergency Medical Services: A Case Study of COVID-19

Jing Liu, Ruilin Ouyang, Chun-An Chou () and Jacqueline Griffin
Additional contact information
Jing Liu: Northeastern University
Ruilin Ouyang: Northeastern University
Chun-An Chou: Northeastern University
Jacqueline Griffin: Northeastern University

SN Operations Research Forum, 2023, vol. 4, issue 2, 1-36

Abstract: Abstract Emergency medical services (EMS) aims to deliver timely ambulatory care to incidents in communities. However, the operations of EMS may contend with suddenly increasing demands resulting from unexpected disasters such as disease outbreaks (e.g., COVID-19) or hurricanes. To this end, it usually requires better strategical decisions to dispatch, allocate, and reallocate EMS resources to meet the demand changes over time in terms of demographic and geographic distribution of incidents. In this study, we focus on the operation of the EMS resources (i.e., ambulance dispatch) in response to a demand disruption amid the COVID-19 pandemic. Specifically, we present a analytical framework to (1) analyze the underlying demographic and geographic patterns of emergency incidents and EMS resources; (2) develop a mathematical programming model to identify potential demand gaps of EMS coverage across different districts; and (3) provide a remedial reallocation solution to the EMS system with the existing ambulance capacity. The proposed method is validated with emergency response incident data in New York City for the first COVID-19 surge from March to April 2020. We found that it takes a long incident response time to scenes which reflects unexpected incident demands during COVID-19 surge. To cover such disruptive demands, ambulances need to be reallocated between service districts while meeting the response time standard. The proposed framework can be potentially applied to similar disruptive scenarios in the future and other operational systems disrupted by other disasters. Highlights We propose an analytical framework using optimization modeling and simulation techniques for EMS resource allocation in response to a demand disruption amid the COVID-19 pandemic. We propose mathematical programming models to identify potential demand gaps of EMS coverage across different districts. We provide a remedial reallocation solution to the EMS system with the existing ambulance capacity.

Keywords: Emergency medical services; Resource allocation; Ambulance dispatch; COVID-19; Optimization modeling; Simulation Analysis (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s43069-023-00218-3 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:snopef:v:4:y:2023:i:2:d:10.1007_s43069-023-00218-3

Ordering information: This journal article can be ordered from
https://www.springer.com/journal/43069

DOI: 10.1007/s43069-023-00218-3

Access Statistics for this article

SN Operations Research Forum is currently edited by Marco Lübbecke

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