ArticleScientific reports2020
Variability in Language used on Social Media prior to Hospital Visits.
Article in Scientific reports, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 12 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
12 citing papers in PubMed, 43 citations in OpenAlex.
- Article
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- Pragmatic lessons for harnessing social media for health research through digital data donation post-API access.npj digital public health · 2026Review
- The Impact of Digital Transformation on Inpatient Care: Mixed Methods Study.JMIR public health and surveillance · 2023Review
- Using Facebook language to predict and describe excessive alcohol use.Alcoholism, clinical and experimental research · 2022Article
- The Reintegration Journey Following A Psychiatric Hospitalization: Examining the Role of Social Technologies.Proceedings of the ACM on human-computer interaction · 2022Article
- Social Media Discussions Predict Mental Health Consultations on College Campuses.Scientific reports · 2022Article
- Data Science Trends Relevant to Nursing Practice: A Rapid Review of the 2020 Literature.Applied clinical informatics · 2022Review
- Detecting Impending Stroke From Cognitive Traits Evident in Internet Searches: Analysis of Archival Data.Journal of medical Internet research · 2021Article
- A Social Media Study on Mental Health Status Transitions Surrounding Psychiatric Hospitalizations.Proceedings of the ACM on human-computer interaction · 2021Article
- Social media language of healthcare super-utilizers.NPJ digital medicine · 2021Article
- Psychosocial Effects of the COVID-19 Pandemic: Large-scale Quasi-Experimental Study on Social Media.Journal of medical Internet research · 2020Article
Corrections and comments
- Erratum issued
Authors and funding
8 authors at 4 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Forecasting healthcare utilization has the potential to anticipate care needs, either accelerating needed care or redirecting patients toward care most appropriate to their needs. While prior research has utilized clinical information to forecast readmissions, analyzing digital footprints from social media can inform our understanding of individuals' behaviors, thoughts, and motivations preceding a healthcare visit. We evaluate how language patterns on social media change prior to emergency department (ED) visits and inpatient hospital admissions in this case-crossover study of adult patients visiting a large urban academic hospital system who consented to share access to their history of Facebook statuses and electronic medical records. An ensemble machine learning model forecasted ED visits and inpatient admissions with out-of-sample cross-validated AUCs of 0.64 and 0.70 respectively. Prior to an ED visit, there was a significant increase in depressed language (Cohen's d = 0.238), and a decrease in informal language (d = 0.345). Facebook posts prior to an inpatient admission showed significant increase in expressions of somatic pain (d = 0.267) and decrease in extraverted/social language (d = 0.357). These results are a first step in developing methods to utilize user-generated content to characterize patient care-seeking context which could ultimately enable better allocation of resources and potentially early interventions to reduce unplanned visits.
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Registered trials
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.