Linking social media and medical record data: a study of adults presenting to an academic, urban emergency department
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.
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“It has been strongly argued that important privacy concerns must be interpreted alongside the social good that can come from this kind of health research [27-30].”
BackgroundNair, R. A. S., Härtung, M., Heinisch, P., Jaskolski, J., Starke-Knäusel, C., Veríssimo, S., Schmidt, D. M., & Cimiano, P. (2025) · JMIR Medical Informatics · doi:10.2196/62909 · Cited by 6 · see citing works
“In a study with adults presenting to an academic, urban emergency department, Padrez et al [6] found that, among patients with a social media profile, 71% consented to sharing their social media data to compare it to their electronic medical records.”
McDonald, L., Behl, V., Sundar, V., Mehmud, F., Malcolm, B., & Ramagopalan, S. V. (2019) · JAMIA Open · doi:10.1093/jamiaopen/ooz013 · Cited by 6 · see citing works
“5% of retrieved Facebook posts were related to health.(6) With automated approaches such as natural language processing and machine learning the analysis of hundreds of thousands of text-based posts is now possible allowing for accelerated and more cost-effective data acquisition and analysis as compared to more tradi…”
BackgroundViews on social media and its linkage to longitudinal data from two generations of a UK cohort study
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A Short Review of Ethical Challenges in Clinical Natural Language Processing
Suster, S., Tulkens, S., & Daelemans, W. (2017) · Proceedings of the First ACL Workshop on Ethics in Natural Language Processing · doi:10.18653/v1/w17-1610 · Cited by 6 · see citing works
“Indeed, linking individuals’ health information from online resources to their health records to improve documentation is an active line of research (Padrez et al., 2015).”
BackgroundOni‐Orisan, A., Srinivas, N., Mehta, K., Das, J. L., Nguyen, T. T. X., Tison, G. H., Bauer, S. R., Burian, M., Funk, R. S., & Graham, R. (2021) · Clinical and Translational Science · doi:10.1111/cts.12973 · Cited by 4 · see citing works
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Can language use in social media help in the treatment of severe mental illness?
Kelly, D. L., Spaderna, M., Hodzic, V., Coppersmith, G., Chen, S., & Resnik, P. (2021) · Current research in psychiatry · doi:10.46439/psychiatry.1.001 · Cited by 5 · see citing works
“Furthermore, studies indicate that people are frequently willing to consent to use their private social media data for research [22-24], and are open to the idea of opt-in use of social media under clinical supervision [24,25].”
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New Trends and Challenges in Open Data
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“Thus, it is possible that social media research may be better suited to populations who more frequently use digital apps, which may explain the higher participation rates in previous studies of younger populations [7,14,17].”
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“Few studies of social media use and social media data sharing provide access to linked clinical data [42] or structured PROs, and to our knowledge, none have been undertaken among youth with rheumatic diseases.”
BackgroundLearning Representations of Social Media UsersPreprint
Benton, A. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1812.00436 · Cited by 1 · see citing works
“mith, and Dredze, 2017). One clever approach is to consider public messages self-reporting having a particular condition as genuine (Harman, Coppersmith, and Dredze, 2014; Coppersmith et al., 2015b). Padrez et al. (2015) assembled a parallel corpus of electronic health records alongside Twitter and Facebook posts. How…”
BackgroundThe Clinical Panel: Leveraging Psychological Expertise During NLP Research
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Self-citationAutomatic Tracking of Linguistic Changes for Monitoring Cognitive-Linguistic Health
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Modifiers of patients' Emergency Department care-seeking behavior
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Emerging and Innovative Technologies
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Sentiment Analysis of #Meanspo Tweets: Humans vs. Automatic Classifications
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The Role of Emotionality in the Self-reported and Actual Use of Facebook
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“These digital data may provide insights into symptoms, changes in behavior, and the decision to seek help [7,8], and using these data may tackle current research limitations in this area.”
When PCOS Meets Eating Disorders: An Explainable AI Approach to Detecting the Hidden Triple Burden
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“These discussions capture lived experiences of the triple burden in ways structured assessments may miss [11,12].”
- Authors
- Padrez, K. A., Ungar, L., Schwartz, H. A., Smith, R. J., Hill, S., Antanavicius, T., Brown, D. M., Crutchley, P., Asch, D. A., & Merchant, R. M.
- Affiliations
- University of California, San Francisco (US)
- University of Pennsylvania Health System (US)
- Philadelphia VA Medical Center (US)
- Corresponding author
- Merchant, Raina M
- ORCID
- Venue
- BMJ Quality & Safety
- Publisher
- BMJ
- Published
- 2015-10-13
- Volume
- 25
- Issue
- 6
- Pages
- 414-423
- ISSN
- 1475-3898 (ISSN-L)
- 2044-5415
- 2044-5423
- OpenAlex
- W2172403766
- Cited by
- 75
- References
- 42
- Citations by year
- 2026: 2 · 2025: 3 · 2024: 5 · 2023: 4 · 2022: 2 · 2021: 9 · 2020: 10 · 2019: 7 · 2018: 8 · 2017: 14 · 2016: 10 · 2015: 1
- FWCI
- 13.80
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- BMJ Quality & Safety (publishedVersion)
- PubMed (publishedVersion)
- HighWire Press Open Archive (submittedVersion)
- Funders
- National Heart, Lung, and Blood Institute (K23HL090832, R01HL122457)
- Topics
- Social Sciences › Health › Social Media in Health Education
- Health Sciences › Health Professions › Health Information Management › Electronic Health Records Systems
- Social Sciences › Psychology › Social Psychology › Mental Health via Writing
- Keywords
- social media, electronic health records, data linkage, Facebook, Twitter, emergency department patients, patient consent, health attitudes
- MeSH terms
- Academic Medical Centers, Adolescent, Adult, Attitude to Health, Emergency Service, Hospital, Female, Hospitals, Urban, Humans, Male, Middle Aged, Quality of Health Care, Information Storage and Retrieval, Patient Satisfaction, Young Adult, Electronic Health Records, Social Media
About these checks
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