Evidence map›Paper›PMID 30014160›Full record

ArticleWorld journal of urology2019

Following the crowd: patterns of crowdsourcing on Twitter among urologists.

Kevin Koo, Kevin Shee, E Ann Gormley

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Article in World journal of urology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

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3citing papers in PubMed
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3 · Its place in the literature

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3 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.

Kevin KooDartmouth-Hitchcock Medical Center, Lebanon, NH, USA. kkoo@jhmi.edu.ORCID http://orcid.org/0000-0003-0627-5518
Kevin SheeGeisel School of Medicine at Dartmouth, Hanover, NH, USA.
E Ann GormleyDartmouth-Hitchcock Medical Center, Lebanon, NH, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeTo examine patterns of crowdsourcing on the social media platform Twitter among urologists.

methodsUrologists' public Twitter accounts were reviewed for original posts seeking clinical advice or feedback, and associated reply posts, before and after the 140-character-limit expansion in 2017. Predictors of responses to crowdsourcing requests were determined using multivariable regression. When patient data were posted, we noted whether consent was documented.

resultsA total of 276 posts in 23 crowdsourcing requests prior to character-limit expansion were analyzed. Reasons for crowdsourcing included requesting solutions to a clinical dilemma (82 posts, 30%); advice seeking about a surgical plan (77 posts, 28%); surveying colleagues' experiences with a new product (64 posts, 23%); and soliciting feedback about a proposed course of action (53 posts, 19%). Recent completion of training (as a proxy for inexperience) did not appear to disproportionately motivate crowdsourcing; authors' median time in practice was 7 years, and authors practicing for ≤ 7 years initiated 57% of requests. 22 (96%) crowdsourcing requests received ≥ 1 reply. Of 15 requests about a specific patient, eight included imaging, but only one cited patient consent. A second analysis of 184 posts in 17 crowdsourcing requests initiated after character-limit expansion demonstrated significantly more authors replying per request (P = 0.01), but no change in the frequency of patient-specific crowdsourcing or citation of consent.

conclusionsUrologists are leveraging Twitter for crowdsourcing clinical guidance and experiential knowledge. Nearly all requests were answered, suggesting low barriers to entry for novice users. Even after character-limit expansion, dissemination of potentially identifiable patient data remains a concern.

Indexed as

InternetUrologistsConfidentialityCrowdsourcingHumansInformed ConsentMultivariate AnalysisSocial MediaCrowdsourcingEducationInternetSocial mediaTwitter

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