Evidence map›Paper›PMID 38303968›Full record

ArticleDigital health

Internet hospital response to the COVID-19 pandemic in a tertiary hospital in China: Perspectives based on a mixed-methods.

Xiaolong Wu, Yulin Kuang, Yonglin Guo, Ji Wu, Li Xiao

Open access · goldAbstract read
In one paragraph

Article in Digital health. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
2.2field-weighted citation impact, top 14% 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

3 citing papers in PubMed, 4 citations in OpenAlex.

  1. Observational
  2. Article
  3. 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

5 authors at 1 institution in 1 country.

Xiaolong WuBusiness School, Sun Yat-sen University, Shenzhen, China.ORCID https://orcid.org/0000-0001-5824-0566
Yulin KuangBusiness School, Sun Yat-sen University, Shenzhen, China.
Yonglin GuoSchool of Government, Sun Yat-sen University, Guangzhou, China.
Ji WuSchool of Business, Sun Yat-sen University, Guangzhou, China.
Li XiaoOffice of the SYSU CPC Committee, Sun Yat-sen University, Guangzhou, China.
Sun Yat-sen University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objective: This study aimed to summarize the characteristics of the Internet hospital services of the Seventh Affiliated Hospital of Sun Yat-sen University (SAHSYSU), describe diagnosis and treatment patterns in each department, determine SAHSYSU Internet hospital's role in pandemic control, and explore development strategies in non-pandemic situations. Methods: Mixed-methods was used in this study. Qualitative organizational behavior analysis was conducted on hospital meeting records and semi-structured interview records to determine the research analysis indicators. We quantitatively analyzed online consultation record data of SAHSYSU Internet hospital from January to December 2020, and conduct classification analysis on departmental case studies using K-means clustering algorithm. Results: 29,944 patient data items were retrieved. Internet hospital services synchronized with COVID-19 pandemic development in China and Guangdong province. The service volume peaked during the period of January to March, which coincided with the height of the pandemic. Out of the total visits, 58.90% were conducted during office hours while 41.10% were conducted during non-office hours. The majority of the patients came from Guangdong (19.67%) and Hubei (9.09%) provinces. The cluster analysis identified three clusters, each with different change rates and magnitudes of change for various departments. Conclusion: Internet hospitals complemented conventional medical services, providing crucial medical care during the COVID-19 pandemic. Internet hospitals are the future trend of medical services and should be improved based on each department's treatment characteristics.

Indexed as

COVID-19Internet hospitalpandemicpublic healthtelemedicine

Identifiers

PMID38303968
PMCPMC10832419
OpenAlexW4391443156

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.