Evidence map›Paper›PMID 35904858›Full record

ArticleJMIR medical education2022

Use of Social Media for Implementing Diagnoses, Consultation, Training, and Case Reporting Among Medical Professionals to Improve Patient Care: Case Study of WeChat Groups Across Health Care Settings.

Lai Sze Tso

Open access · goldAbstract read
In one paragraph

Article in JMIR medical education, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
1.4field-weighted citation impact, top 15% 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

5 citing papers in PubMed, 18 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

1 author at 1 institution in 3 countries.

Lai Sze TsoGlobal Health & Medical Humanities Initiative, Massachusetts Institute of Technology, Cambridge, MA, United States.ORCID https://orcid.org/0000-0003-2739-6837
The Research Council of Norway · NO

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundHealth professionals in low- and middle-resource settings have limited access to up-to-date resources for diagnosing and treating illnesses, training medical staff, reviewing newly disseminated guidelines and publications, and preparing data for international disease reporting. A concomitant difficulty in high-resource settings is the need for continuing education and skills up-training in innovative procedures on unfamiliar social media platforms. These challenges can delay both patient care and epidemiological surveillance efforts. To overcome these challenges, health professionals have adapted WeChat Groups to implement timely, low-cost, and high-quality patient care.

objectiveThe primary study aim was to describe the processes taken by medical professionals across their diverse physical and virtual networks in adapting a bottom-up approach to collectively overcome resource shortages. The secondary study aim was to delineate the pathways, procedures, and resource/information sharing implemented by medical professionals using an international publicly available popular social media app (WeChat) to enhance performance of facility-based procedures and protocols for improved patient care.

methodsIn-depth interviews, observations, and digital ethnography of WeChat Groups communications were collected from medical professionals in interconnected networks of health care facilities. Participants' WeChat Groups usage and observations of their professional functions in interconnected networks were collected from November 2018 to 2019. Qualitative analysis and thematic coding were used to develop constructs and themes in NVivo. Constructs incorporated descriptions for the implementation and uses of WeChat Groups for professional connections, health care procedures, and patient care. Themes supporting the constructs focused on the pathways and venues used by medical professionals to build trust, to establish and solidify online networks, and to identify requests and resource sharing within WeChat Groups.

resultsThere were 58 participants (males 36 and females 22) distributed across 24 health care settings spanning geographical networks in south China. Analysis yielded 4 constructs and 11 themes delineating the bottom-up usage of WeChat Groups among clinicians, technicians, nurses, pharmacists, and public health administrators. Participants used WeChat Groups for collectively training hospital staff in complex new procedures, processing timely diagnoses of biological specimens, staying abreast of latest trends and clinical procedures and symptoms, and contributing to case reporting for emergent illnesses and international surveillance reporting. An unexpected strength of implementing clinical, training, and research support on a popular app with international coverage is the added ability to overcome administrative and geographic barriers in resource distribution. This advantage increased a network's access to WeChat Groups members both working within China and abroad, greatly expanding the scope of shared resources.

conclusionsThe organic, bottom-up approach of repurposing extant social media apps is low cost and efficient for timely implementation to improve patient care. WeChat's international user base enables medical staff to access widespread professional networks across geographic, administrative, and economic barriers, with potential to reduce health disparities in low-resource settings.

Indexed as

bottom-up approachdigital healthimplementation researchinnovative medical technologieslow-resource settingsmedical educationmHealthmobile health technologiessocial mediaWeChat

Identifiers

PMID35904858
PMCPMC9377440
OpenAlexW4205888413

What OpenQuestion holds

Textmetadata
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.