Evidence map›Paper›PMID 32978234›Full record

ArticleFamily medicine and community health2020

Exploring the structure of social media application-based information-sharing clinical networks in a community in Japan using a social network analysis approach.

Junji Haruta, Sho Tsugawa, Kazunari Ogura

Abstract read
In one paragraph

Article in Family medicine and community health, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

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0cells of the map it votes in
5citing papers in PubMed
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1 · What the graph read from it

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3 · Its place in the literature

Who cites it

5 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

3 authors.

Junji HarutaMedical Education Center, Keio University, Tokyo, Japan junharujp@keio.jp.ORCID 0000-0003-4176-7665
Sho TsugawaDivision of Information Engineering, University of Tsukuba, Tsukuba, Japan.
Kazunari OguraHachinohe Family Clinic, Hachinohe, Japan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveCurrently, use of social networking services (SNSs) for interprofessional collaboration is increasing. However, few studies have reported on virtual interprofessional interactions in community healthcare services. Revealing such structural characteristics of the networks can provide insight into the functions of the interprofessional information-sharing network and lead to smoother collaboration. Thus, we aimed to explore the structure of SNS-based information-sharing clinical networks.

designSocial network analysis (SNA).

settingWe selected a community in City X in Japan. DATA COLLECTION: We analysed SNS-based information-sharing clinical network data linked to patients receiving home medical care or care services between January and December 2018. A network was created for each patient to allow healthcare professionals to post and view messages on the web platform. In the SNA, healthcare professions registered in a patient group were represented as nodes, and message posting/viewing relationships were represented as links in the patient network. We investigated the structural characteristics of the target networks using several measures for SNA, including indegree centrality and outdegree centrality, which reflect the number of incoming and outgoing links to/from a node, respectively. Additionally, the professions forming the most central nodes were investigated based on their ranking to identify those with a central role in the networks. Finally, to compare the networks of nursing care levels 1-3 (lighter care requirement) and those with nursing care levels 4-5 (heavier care requirement), we analysed the structural differences in the networks and investigated the roles of healthcare professionals using centrality measures of nodes.

resultsAmong 844 groups, 247 groups with any nursing care level data were available for analysis. Increasing nursing care level showed higher density, reciprocity and lower centralisation. Healthcare professions with high indegree centrality (physicians, care workers and physical therapists) differed from those with high outdegree centrality (home care workers, physical therapists, and registered dieticians). Visiting nurses and nurses in the clinic played a central role, but visiting nurses tended to have higher indegree and outdegree centrality, while nurses in the clinic had higher closeness and betweenness centrality in networks with heavier care requirement.

conclusionThe SNS-based information-sharing clinical network structure showed that different professions played some form of a central role. Associations between network structures and patient outcomes, cost effectiveness and other factors warrant further investigation.

Indexed as

Social MediaSocial Network AnalysisSocial NetworkingAgedAged, 80 and overFemaleHealth PersonnelHumansInformation DisseminationJapanMaleMiddle Agedcommunity health servicesdelivery of health carehealth care evaluation mechanismshealth information exchangeintegrated

Identifiers

PMID32978234
PMCPMC7520901

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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.