Abstract

An understanding of healthcare super-utilizers’ online behaviors could better identify experiences to inform interventions. In this retrospective case-control study, we analyzed patients’ social media posts to better understand their day-to-day behaviors and emotions expressed online. Patients included those receiving care in an urban academic emergency department who consented to share access to their historical Facebook posts and electronic health records. Super-utilizers were defined as patients with more than six visits to the Emergency Department (ED) in a year. We compared posts by super-utilizers with a matched group using propensity scoring based on age, gender and Charlson comorbidity index. Super-utilizers were more likely to post about confusion and negativity (D = .65, 95% CI-[.38, .95]), self-reflection (D = .63 [.35, .91]), avoidance (D = .62 [.34, .90]), swearing (D = .52 [.24, .79]), sleep (D = .60 [.32, .88]), seeking help and attention (D = .61 [.33, .89]), psychosomatic symptoms, (D = .49 [.22, .77]), self-agency (D = .56 [.29, .85]), anger (D = .51, [.24, .79]), stress (D = .46, [.19, .73]), and lonely expressions (D = .44, [.17, .71]). Insights from this study can potentially supplement offline community care services with online social support interventions considering the high engagement of super-utilizers on social media.

Details

Title
Social media language of healthcare super-utilizers
Author
Guntuku Sharath Chandra 1   VIAFID ORCID Logo  ; Klinger, Elissa V 2 ; McCalpin, Haley J 2 ; Ungar, Lyle H 3 ; Asch, David A 4 ; Merchant, Raina M 5 

 Penn Medicine Center for Digital Health, Philadelphia, USA; University of Pennsylvania, Department of Computer and Information Science, Philadelphia, USA (GRID:grid.25879.31) (ISNI:0000 0004 1936 8972); University of Pennsylvania, Perelman School of Medicine, Philadelphia, USA (GRID:grid.25879.31) (ISNI:0000 0004 1936 8972) 
 Penn Medicine Center for Digital Health, Philadelphia, USA (GRID:grid.25879.31); Penn Medicine Center for Health Care Innovation, Philadelphia, USA (GRID:grid.25879.31) 
 Penn Medicine Center for Digital Health, Philadelphia, USA (GRID:grid.25879.31); University of Pennsylvania, Department of Computer and Information Science, Philadelphia, USA (GRID:grid.25879.31) (ISNI:0000 0004 1936 8972); University of Pennsylvania, The Wharton School, Philadelphia, USA (GRID:grid.25879.31) (ISNI:0000 0004 1936 8972) 
 University of Pennsylvania, Perelman School of Medicine, Philadelphia, USA (GRID:grid.25879.31) (ISNI:0000 0004 1936 8972); Penn Medicine Center for Health Care Innovation, Philadelphia, USA (GRID:grid.25879.31); University of Pennsylvania, The Wharton School, Philadelphia, USA (GRID:grid.25879.31) (ISNI:0000 0004 1936 8972); Cpl Michael J Crescenz VA Medical Center, Philadelphia, USA (GRID:grid.410355.6) (ISNI:0000 0004 0420 350X) 
 Penn Medicine Center for Digital Health, Philadelphia, USA (GRID:grid.410355.6); University of Pennsylvania, Perelman School of Medicine, Philadelphia, USA (GRID:grid.25879.31) (ISNI:0000 0004 1936 8972); Penn Medicine Center for Health Care Innovation, Philadelphia, USA (GRID:grid.25879.31) 
Publication year
2021
Publication date
Dec 2021
Publisher
Nature Publishing Group
e-ISSN
23986352
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
2531368391
Copyright
© The Author(s) 2021. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.