Evidence map›Paper›PMID 39640833›Full record

ArticleHeliyon2024

Design of an emergency medical information system for mass gatherings.

Yu Tian, Zhuyi Shen, Yinghao Zhao, Tianshu Zhou, Qiang Li, Mao Zhang, Jingsong Li

Abstract read
In one paragraph

Article in Heliyon, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Article
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

7 authors.

Yu TianEngineering Research Center of EMR and Intelligent Expert System, Ministry of Education, College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, China.
Zhuyi ShenEngineering Research Center of EMR and Intelligent Expert System, Ministry of Education, College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, China.
Yinghao ZhaoResearch Center for Data Hub and Security, Zhejiang Lab, Hangzhou, China.
Tianshu ZhouResearch Center for Data Hub and Security, Zhejiang Lab, Hangzhou, China.
Qiang LiDepartments of Emergency Medicine, The Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, Zhejiang, 310009, China.
Mao ZhangDepartments of Emergency Medicine, The Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, Zhejiang, 310009, China.
Jingsong LiEngineering Research Center of EMR and Intelligent Expert System, Ministry of Education, College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: The frequency of mass gatherings is increasing. Such events often involve many people and carry the risk of mass casualty incidents, which require substantial medical resources from various healthcare institutions. The current medical system, while meeting daily needs, struggles to address the demand for a high volume of emergency resources and real-time data exchange. Objective: The aim of this study was to develop an emergency medical information system for mass gatherings. Methods: We developed an emergency medical information system for mass gatherings. Based on a unified prehospital and intrahospital emergency data exchange protocol, we can directly standardize medical information data and provide data support for the evacuation decision support algorithms of multiple institutions. Wearable devices, vehicle-mounted devices, video calling systems and surveillance systems are connected to capture real-time scenes. Results: We constructed the system via mobile applications and online platforms and deployed it in 3 hospitals, 5 ambulances and 17 on-site medical locations. We constructed a set of electronic medical records covering the whole first aid process according to the basic principles of first aid. The simulation results show that the proposed algorithm is suitable for mass gatherings. The overall survival rate of patients can be improved by 5 %, and the average evacuation efficiency of patients can be improved by 50 %. Furthermore, in a real-world environment, this method can ensure patient survival and achieve good convergence. Conclusions: Our system is capable of providing robust medical information support for emergency medical services during large-scale assembly events, ensuring a visualized full-process emergency response and decision-making for the diversion and subsequent transport of a large patient population.

Indexed as

Emergency medical service systemEvacuation decisionMass gatheringsPrehospital emergency medical service

Identifiers

PMID39640833
PMCPMC11620076

What OpenQuestion holds

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LicenceCC BY-NC-ND
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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.