Evidence map›Paper›PMID 33417586›Full record

ArticleJournal of medical Internet research2021

Assessment of Internet Hospitals in China During the COVID-19 Pandemic: National Cross-Sectional Data Analysis Study.

Xingyan Xu, Yingying Cai, Siying Wu, Jianhui Guo, Le Yang, Jieli Lan, Yi Sun, Bingbing Wang, Jieyu Wu, Tinggui Wang and 6 more

Open access · goldAbstract read
In one paragraph

Article in Journal of medical Internet research, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 46 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
46citing papers in PubMed, 1 pooled it
4.6field-weighted citation impact, top 4% 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

46 citing papers in PubMed, 1 synthesis or guideline pooled it, 85 citations in OpenAlex.

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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

16 authors at 1 institution in 1 country.

Xingyan Xu *Department of Epidemiology and Health Statistics, School of Public Health, Fujian Medical University, Fuzhou, China.ORCID 0000-0002-4802-8542
Yingying Cai *Department of Epidemiology and Health Statistics, School of Public Health, Fujian Medical University, Fuzhou, China.ORCID 0000-0003-2892-8161
Siying Wu *Department of Epidemiology and Health Statistics, School of Public Health, Fujian Medical University, Fuzhou, China.ORCID 0000-0003-3560-5678
Jianhui GuoDepartment of Epidemiology and Health Statistics, School of Public Health, Fujian Medical University, Fuzhou, China.ORCID 0000-0001-5731-9328
Le YangDepartment of Epidemiology and Health Statistics, School of Public Health, Fujian Medical University, Fuzhou, China.ORCID 0000-0001-6739-6351
Jieli LanDepartment of Epidemiology and Health Statistics, School of Public Health, Fujian Medical University, Fuzhou, China.ORCID 0000-0002-8754-7564
Yi SunDepartment of Preventive Medicine, School of Public Health, Fujian Medical University, Fuzhou, China.ORCID 0000-0002-1396-7128
Bingbing WangDepartment of Epidemiology and Health Statistics, School of Public Health, Fujian Medical University, Fuzhou, China.ORCID 0000-0001-9659-8892
Jieyu WuDepartment of Epidemiology and Health Statistics, School of Public Health, Fujian Medical University, Fuzhou, China.ORCID 0000-0002-2231-9704
Tinggui WangDepartment of Epidemiology and Health Statistics, School of Public Health, Fujian Medical University, Fuzhou, China.ORCID 0000-0002-6435-1921
Shuna HuangDepartment of Clinical Research and Translation Center Office, The First Affiliated Hospital of Fujian Medical University, Fuzhou, China.ORCID 0000-0002-5420-0150
Yawen LinDepartment of Epidemiology and Health Statistics, School of Public Health, Fujian Medical University, Fuzhou, China.ORCID 0000-0002-2452-1885
Yuduan HuDepartment of Epidemiology and Health Statistics, School of Public Health, Fujian Medical University, Fuzhou, China.ORCID 0000-0002-6928-887X
Mingjun ChenDepartment of Epidemiology and Health Statistics, School of Public Health, Fujian Medical University, Fuzhou, China.ORCID 0000-0002-4617-9048
Xuecai GaoDepartment of Epidemiology and Health Statistics, School of Public Health, Fujian Medical University, Fuzhou, China.ORCID 0000-0001-6416-7684
Xiaoxu XieDepartment of Epidemiology and Health Statistics, School of Public Health, Fujian Medical University, Fuzhou, China.ORCID 0000-0002-3103-3724
Fujian Medical University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundInternet hospitals in China are being rapidly developed as an innovative approach to providing health services. The ongoing COVID-19 pandemic has triggered the development of internet hospitals that promote outpatient service delivery to the public via internet technologies. To date, no studies have assessed China's internet hospitals during the COVID-19 pandemic.

objectiveThis study aimed to elucidate the characteristics of China's internet hospitals and assess the health service capacity of these hospitals.

methodsData on 711 internet hospitals were collected from official websites, the WeChat (Tencent Inc) platform, smartphone apps, and the Baidu search engine until July 16, 2020.

resultsAs of July 16, 2020, 711 internet hospitals were developed in mainland China. More than half of these internet hospitals (421/711, 59.2%) were established during 2019 (206/711, 29%) and 2020 (215/711, 30.2%). Furthermore, about one-third (215/711, 30.2%) of internet hospitals were established at the beginning of 2020 as an emergency response to the COVID-19 epidemic. The 711 internet hospitals consisted of the following 3 types of hospitals: government-oriented (42/711, 5.91%), hospital-oriented (143/711, 20.11%), and enterprise-oriented internet hospitals (526/711, 73.98%). The vast majority of internet hospitals were traditional hospitals (526/711, 74%). Nearly 46.1% (221/711) of internet hospitals requested doctors to provide health services at a specific web clinic. Most patients (224/639, 35.1%) accessed outpatient services via WeChat. Internet hospitals' consulting methods included SMS text messaging consultations involving the use of graphics (552/570, 96.8%), video consultations (248/570, 43.5%), and telephone consultations (238/570, 41.8%). The median number of available web-based doctors was 43, and the median consultation fees of fever clinics and other outpatient clinics were ¥0 (US $0) per consultation and ¥6 (US $0.93) per consultation, respectively. Internet hospitals have provided various services during the COVID-19 pandemic, including medical prescription, drug delivery, and medical insurance services.

conclusionsThe dramatic increase of internet hospitals in China has played an important role in the prevention and control of COVID-19. Internet hospitals provide different and convenient medical services for people in need.

Indexed as

ChinaCOVID-19Cross-Sectional StudiesData AnalysisFemaleHospitalsHumansInternetMalePandemicsSARS-CoV-2TelemedicineaccessibilityChinacontrolCOVID-19cross-sectionaldigital healthhealth careinternet hospitalprevention

Identifiers

PMID33417586
PMCPMC7819672
OpenAlexW3108934293

What OpenQuestion holds

Textmetadata
LicenceCC BY
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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.