Evidence map›Paper›PMID 36759680›Full record

ArticleScientific reports2023

Social network analysis of nationwide interhospital emergency department transfers in Taiwan.

Chu-Lin Tsai, Ming-Tai Cheng, Shu-Hsien Hsu, Tsung-Chien Lu, Chien-Hua Huang, Yueh-Ping Liu, Chung-Liang Shih, Cheng-Chung Fang

Open access · goldAbstract read
In one paragraph

Article in Scientific reports, 2023. 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
0.7field-weighted citation impact, top 32% of its field
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, 2 citations in OpenAlex.

  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

8 authors at 2 institutions in 1 country.

Chu-Lin TsaiDepartment of Emergency Medicine, National Taiwan University Hospital, 7 Zhongshan S. Rd, Taipei, 100, Taiwan.
Ming-Tai ChengDepartment of Emergency Medicine, National Taiwan University Hospital, 7 Zhongshan S. Rd, Taipei, 100, Taiwan.
Shu-Hsien HsuDepartment of Emergency Medicine, National Taiwan University Hospital, 7 Zhongshan S. Rd, Taipei, 100, Taiwan.
Tsung-Chien LuDepartment of Emergency Medicine, National Taiwan University Hospital, 7 Zhongshan S. Rd, Taipei, 100, Taiwan.
Chien-Hua HuangDepartment of Emergency Medicine, National Taiwan University Hospital, 7 Zhongshan S. Rd, Taipei, 100, Taiwan.
Yueh-Ping LiuDepartment of Emergency Medicine, National Taiwan University Hospital, 7 Zhongshan S. Rd, Taipei, 100, Taiwan.
Chung-Liang ShihDepartment of Emergency Medicine, National Taiwan University Hospital, 7 Zhongshan S. Rd, Taipei, 100, Taiwan.
Cheng-Chung FangDepartment of Emergency Medicine, National Taiwan University Hospital, 7 Zhongshan S. Rd, Taipei, 100, Taiwan. conrad@ntu.edu.tw.
National Taiwan University Hospital · TWMinistry of Health and Welfare · TW

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Transferring patients between emergency departments (EDs) is a complex but important issue in emergency care regionalization. Social network analysis (SNA) is well-suited to characterize the ED transfer pattern. We aimed to unravel the underlying transfer network structure and to identify key network metrics for monitoring network functions. This was a retrospective cohort study using the National Electronic Referral System (NERS) database in Taiwan. All interhospital ED transfers from 2014 to 2016 were included and transfer characteristics were retrieved. Descriptive statistics and social network analysis were used to analyze the data. There were a total of 218,760 ED transfers during the 3-year study period. In the network analysis, there were a total of 199 EDs with 9516 transfer ties between EDs. The network demonstrated a multiple hub-and-spoke, regionalized pattern, with low global density (0.24), moderate centralization (0.57), and moderately high clustering of EDs (0.63). At the ED level, most transfers were one-way, with low reciprocity (0.21). Sending hospitals had a median of 5 transfer-out partners [interquartile range (IQR) 3-7), while receiving hospitals a median of 2 (IQR 1-6) transfer-in partners. A total of 16 receiving hospitals, all of which were designated base or co-base hospitals, had 15 or more transfer-in partners. Social network analysis of transfer patterns between hospitals confirmed that the network structure largely aligned with the planned regionalized transfer network in Taiwan. Understanding the network metrics helps track the structure and process aspects of regionalized care.

Indexed as

Patient TransferSocial Network AnalysisEmergency Service, HospitalHumansRetrospective StudiesTaiwan

Identifiers

PMID36759680
PMCPMC9909649
OpenAlexW4319748385

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Registered trials

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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.