Evidence map›Paper›PMID 25365059›Full record

ArticleInternational journal of environmental research and public health2014

Impact of vehicular networks on emergency medical services in urban areas.

Chun-Liang Lee, Chung-Yuan Huang, Tzu-Chien Hsiao, Chun-Yen Wu, Yaw-Chung Chen, I-Cheng Wang

Abstract read
In one paragraph

Article in International journal of environmental research and public health, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Chun-Liang LeeDepartment of Computer Science and Information Engineering, School of Electrical and Computer Engineering, College of Engineering, Chang Gung University, No. 259, Wen-Hwa 1st Road, Kwei-Shan, Tao-Yuan 33302, Taiwan. cllee@mail.cgu.edu.tw.
Chung-Yuan HuangDepartment of Computer Science and Information Engineering, School of Electrical and Computer Engineering, College of Engineering, Chang Gung University, No. 259, Wen-Hwa 1st Road, Kwei-Shan, Tao-Yuan 33302, Taiwan. gscott@mail.cgu.edu.tw.
Tzu-Chien HsiaoDepartment of Computer Science, Institute of Biomedical Engineering, National Chiao Tung University, 1001 University Road, Hsinchu 30010, Taiwan. labview@cs.nctu.edu.tw.
Chun-Yen WuDepartment of Computer Science, Institute of Biomedical Engineering, National Chiao Tung University, 1001 University Road, Hsinchu 30010, Taiwan. xxjyen@gmail.com.
Yaw-Chung ChenDepartment of Computer Science, National Chiao Tung University, 1001 University Road, Hsinchu 30010, Taiwan. ycchen@cs.nctu.edu.tw.
I-Cheng WangDepartment of Computer Science and Information Engineering, School of Electrical and Computer Engineering, College of Engineering, Chang Gung University, No. 259, Wen-Hwa 1st Road, Kwei-Shan, Tao-Yuan 33302, Taiwan. b0029018@stmail.cgu.edu.tw.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The speed with which emergency personnel can provide emergency treatment is crucial to reducing death and disability among acute and critically ill patients. Unfortunately, the rapid development of cities and increased numbers of vehicles are preventing emergency vehicles from easily reaching locations where they are needed. A significant number of researchers are experimenting with vehicular networks to address this issue, but in most studies the focus has been on communication technologies and protocols, with few efforts to assess how network applications actually support emergency medical care. Our motivation was to search the literature for suggested methods for assisting emergency vehicles, and to use simulations to evaluate them. Our results and evidence-based studies were cross-referenced to assess each method in terms of cumulative survival ratio (CSR) gains for acute and critically ill patients. Simulation results indicate that traffic light preemption resulted in significant CSR increases of between 32.4% and 90.2%. Route guidance was found to increase CSRs from 14.1% to 57.8%, while path clearing increased CSRs by 15.5% or less. It is our hope that this data will support the efforts of emergency medical technicians, traffic managers, and policy makers.

Indexed as

Computer Communication NetworksEmergency Medical ServicesGeographic Information SystemsMotor VehiclesCities

Identifiers

PMID25365059
PMCPMC4245616

What OpenQuestion holds

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LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.