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Research of an emergency medical system for mass casualty incidents in Shanghai, China: a system dynamics model

Overview of attention for article published in Patient preference and adherence, January 2018
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59 Mendeley
Title
Research of an emergency medical system for mass casualty incidents in Shanghai, China: a system dynamics model
Published in
Patient preference and adherence, January 2018
DOI 10.2147/ppa.s155603
Pubmed ID
Authors

Wenya Yu, Yipeng Lv, Chaoqun Hu, Xu Liu, Haiping Chen, Chen Xue, Lulu Zhang

Abstract

Emergency medical system for mass casualty incidents (EMS-MCIs) is a global issue. However, China lacks such studies extremely, which cannot meet the requirement of rapid decision-support system. This study aims to realize modeling EMS-MCIs in Shanghai, to improve mass casualty incident (MCI) rescue efficiency in China, and to provide a possible method of making rapid rescue decisions during MCIs. This study established a system dynamics (SD) model of EMS-MCIs using the Vensim DSS program. Intervention scenarios were designed as adjusting scales of MCIs, allocation of ambulances, allocation of emergency medical staff, and efficiency of organization and command. Mortality increased with the increasing scale of MCIs, medical rescue capability of hospitals was relatively good, but the efficiency of organization and command was poor, and the prehospital time was too long. Mortality declined significantly when increasing ambulances and improving the efficiency of organization and command; triage and on-site first-aid time were shortened if increasing the availability of emergency medical staff. The effect was the most evident when 2,000 people were involved in MCIs; however, the influence was very small under the scale of 5,000 people. The keys to decrease the mortality of MCIs were shortening the prehospital time and improving the efficiency of organization and command. For small-scale MCIs, improving the utilization rate of health resources was important in decreasing the mortality. For large-scale MCIs, increasing the number of ambulances and emergency medical professionals was the core to decrease prehospital time and mortality. For super-large-scale MCIs, increasing health resources was the premise.

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 59 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 59 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 8 14%
Student > Doctoral Student 7 12%
Other 5 8%
Researcher 5 8%
Student > Ph. D. Student 5 8%
Other 14 24%
Unknown 15 25%
Readers by discipline Count As %
Medicine and Dentistry 16 27%
Engineering 7 12%
Business, Management and Accounting 4 7%
Nursing and Health Professions 3 5%
Agricultural and Biological Sciences 2 3%
Other 9 15%
Unknown 18 31%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 04 February 2018.
All research outputs
#22,767,715
of 25,382,440 outputs
Outputs from Patient preference and adherence
#1,648
of 1,757 outputs
Outputs of similar age
#389,382
of 449,550 outputs
Outputs of similar age from Patient preference and adherence
#32
of 32 outputs
Altmetric has tracked 25,382,440 research outputs across all sources so far. This one is in the 1st percentile – i.e., 1% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,757 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 7.5. This one is in the 1st percentile – i.e., 1% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 449,550 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 32 others from the same source and published within six weeks on either side of this one. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.