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Public Disclosure of Identifiable Patient Information by Health Professionals on Social Media: A Content Analysis of Twitter Data

Lookup NU author(s): Dr Wasim Ahmed

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This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).


Abstract

BACKGROUND:Respecting patient privacy and confidentiality is critical for doctor-patient relationships and public trust in medical professionals. The frequency of potentially identifiable disclosures online during periods of active engagement is unknown.OBJECTIVE:The objective was to quantify potentially identifiable content shared by physicians and other health care providers on social media using the hashtag #ShareAStoryInOneTweet.METHODS:Symplur Signals software was used to access Twitter's API which searched for tweets including the hashtag #ShareAStoryInOneTweet. The study identified 1206 tweets by doctors, nurses, and other health professionals out of 43,374 tweets shared from May 1-31, 2018. Tweet content was evaluated in January 2019, eight months after the study period. To determine the incidence of sharing names or potentially identifiable information about patients, a content analysis of the 754 tweets in which tweets disclosed information about others was performed. The study also evaluated whether participants raised concerns about privacy breaches and estimated the frequency of deleted tweets. The study used a dual, blinded coding for a 10% sample to estimate inter-coder reliability for potential identifiability of tweet content using Cohen's kappa statistic.RESULTS:656 participants, including 486 doctors (74.1%) and 98 nurses (14.9%), shared 754 tweets disclosing information about others rather than themselves. Professional participants sharing stories about patient care disclosed the time frame in 95 (12.6%) and included patient names in 15 (2.0%) of tweets. It is estimated that friends or families could likely identify the clinical scenario described in 32.1% of the 754 tweets. Among 348 tweets about potentially living patients, it is estimated that 162 (46.6%) were likely identifiable by patients. Inter-coder reliability in rating the potential identifiability demonstrated 86.8% agreement, with a Cohen's Kappa of 0.8 suggesting substantial agreement of the 1206 tweets, the study identified that 78 (6.5%) had been deleted on the website but were still viewable in the analytics software dataset.CONCLUSIONS:During periods of active sharing online, nurses, physicians, and other health professionals may sometimes share more information than patients or families might expect. More study is needed to determine whether similar events arise frequently online and to understand how to best ensure that patients' rights are adequately respected.


Publication metadata

Author(s): Ahmed W, Jagsi R, Gutheil GT, Katz SM

Publication type: Article

Publication status: Published

Journal: Journal of Medical Internet Research

Year: 2020

Volume: 22

Issue: 9

Online publication date: 01/09/2020

Acceptance date: 23/07/2020

Date deposited: 20/11/2020

ISSN (print): 1439-4456

ISSN (electronic): 1438-8871

Publisher: JMIR Publications

URL: https://doi.org/10.2196/19746

DOI: 10.2196/19746


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