Literature DB >> 26464519

Linking social media and medical record data: a study of adults presenting to an academic, urban emergency department.

Kevin A Padrez1, Lyle Ungar2, Hansen Andrew Schwartz2, Robert J Smith3, Shawndra Hill4, Tadas Antanavicius5, Dana M Brown6, Patrick Crutchley5, David A Asch7, Raina M Merchant6.   

Abstract

BACKGROUND: Social media may offer insight into the relationship between an individual's health and their everyday life, as well as attitudes towards health and the perceived quality of healthcare services.
OBJECTIVE: To determine the acceptability to patients and potential utility to researchers of a database linking patients' social media content with their electronic medical record (EMR) data.
METHODS: Adult Facebook/Twitter users who presented to an emergency department were queried about their willingness to share their social media data and EMR data with health researchers for the purpose of building a databank for research purposes. Shared posts were searched for select terms about health and healthcare.
RESULTS: Of the 5256 patients approached, 2717 (52%) were Facebook and/or Twitter users. 1432 (53%) of those patients agreed to participate in the study. Of these participants, 1008 (71%) consented to share their social media data for the purposes of comparing it with their EMR. Social media data consisted of 1 395 720 posts/tweets to Facebook and Twitter. Participants sharing social media data were slightly younger (29.1±9.8 vs 31.9±10.4 years old; p<0.001), more likely to post at least once a day (42% vs 29%; p=0.003) and more likely to present to the emergency room via self-arrival mode and have private insurance. Of Facebook posts, 7.5% (95% CI 4.8% to 10.2%) were related to health. Individuals with a given diagnosis in their EMR were significantly more likely to use terms related to that diagnosis on Facebook than patients without that diagnosis in their EMR (p<0.0008).
CONCLUSIONS: Many patients are willing to share and link their social media data with EMR data. Sharing patients have several demographic and clinical differences compared with non-sharers. A database that merges social media with EMR data has the potential to provide insights about individuals' health and health outcomes. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/

Entities:  

Keywords:  Chart review methodologies; Information technology; Social sciences

Mesh:

Year:  2015        PMID: 26464519     DOI: 10.1136/bmjqs-2015-004489

Source DB:  PubMed          Journal:  BMJ Qual Saf        ISSN: 2044-5415            Impact factor:   7.035


  25 in total

1.  Computational Approaches Toward Integrating Quantified Self Sensing and Social Media.

Authors:  Munmun De Choudhury; Mrinal Kumar; Ingmar Weber
Journal:  CSCW Conf Comput Support Coop Work       Date:  2017 Feb-Mar

2.  Using Facebook language to predict and describe excessive alcohol use.

Authors:  Rupa Jose; Matthew Matero; Garrick Sherman; Brenda Curtis; Salvatore Giorgi; Hansen Andrew Schwartz; Lyle H Ungar
Journal:  Alcohol Clin Exp Res       Date:  2022-05-16       Impact factor: 3.928

3.  Tweeting PP: an analysis of the 2015-2016 Planned Parenthood controversy on Twitter.

Authors:  Leo Han; Lisa Han; Blair Darney; Maria I Rodriguez
Journal:  Contraception       Date:  2017-09-01       Impact factor: 3.375

Review 4.  Using Patient-Reported Outcome Measures to Capture the Patient's Voice in Research and Care of Juvenile Idiopathic Arthritis.

Authors:  Aimee O Hersh; Parissa K Salimian; Elissa R Weitzman
Journal:  Rheum Dis Clin North Am       Date:  2016-03-18       Impact factor: 2.670

5.  Tweet Now, See You In the ED Later? Examining the Association Between Alcohol-related Tweets and Emergency Care Visits.

Authors:  Megan L Ranney; Brian Chang; Joshua R Freeman; Brian Norris; Mark Silverberg; Esther K Choo
Journal:  Acad Emerg Med       Date:  2016-06-20       Impact factor: 3.451

6.  Approaches to Link Geospatially Varying Social, Economic, and Environmental Factors with Electronic Health Record Data to Better Understand Asthma Exacerbations.

Authors:  Sherrie Xie; Blanca E Himes
Journal:  AMIA Annu Symp Proc       Date:  2018-12-05

7.  Leveraging innovative technology to generate drug response phenotypes for the advancement of biomarker-driven precision dosing.

Authors:  Akinyemi Oni-Orisan; Nithya Srinivas; Krina Mehta; Jesmin Lohy Das; Thu T Nguyen; Geoffrey H Tison; Scott R Bauer; Maria Burian; Ryan S Funk; Richard A Graham
Journal:  Clin Transl Sci       Date:  2021-02-12       Impact factor: 4.689

8.  Variations in Facebook Posting Patterns Across Validated Patient Health Conditions: A Prospective Cohort Study.

Authors:  Robert J Smith; Patrick Crutchley; H Andrew Schwartz; Lyle Ungar; Frances Shofer; Kevin A Padrez; Raina M Merchant
Journal:  J Med Internet Res       Date:  2017-01-06       Impact factor: 5.428

9.  Enhancing Electronic Health Record Data with Geospatial Information.

Authors:  Sherrie Xie; Rebecca Greenblatt; Michael Z Levy; Blanca E Himes
Journal:  AMIA Jt Summits Transl Sci Proc       Date:  2017-07-26

10.  Linked Patient-Reported Outcomes Data From Patients With Multiple Sclerosis Recruited on an Open Internet Platform to Health Care Claims Databases Identifies a Representative Population for Real-Life Data Analysis in Multiple Sclerosis.

Authors:  Valery Risson; Bhaskar Ghodge; Ian C Bonzani; Jonathan R Korn; Jennie Medin; Tanmay Saraykar; Souvik Sengupta; Deepanshu Saini; Melvin Olson
Journal:  J Med Internet Res       Date:  2016-09-22       Impact factor: 5.428

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