Social media language of healthcare super-utilizers

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. Patie...

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Autores principales:	Sharath Chandra Guntuku, Elissa V. Klinger, Haley J. McCalpin, Lyle H. Ungar, David A. Asch, Raina M. Merchant
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Computer applications to medicine. Medical informatics R858-859.7
Acceso en línea:	https://doaj.org/article/0544e8a7a4844d41bc82ce107c49ccf4
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spelling	oai:doaj.org-article:0544e8a7a4844d41bc82ce107c49ccf42021-12-02T17:04:08ZSocial media language of healthcare super-utilizers10.1038/s41746-021-00419-22398-6352https://doaj.org/article/0544e8a7a4844d41bc82ce107c49ccf42021-03-01T00:00:00Zhttps://doi.org/10.1038/s41746-021-00419-2https://doaj.org/toc/2398-6352Abstract 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.Sharath Chandra GuntukuElissa V. KlingerHaley J. McCalpinLyle H. UngarDavid A. AschRaina M. MerchantNature PortfolioarticleComputer applications to medicine. Medical informaticsR858-859.7ENnpj Digital Medicine, Vol 4, Iss 1, Pp 1-6 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Computer applications to medicine. Medical informatics R858-859.7
spellingShingle	Computer applications to medicine. Medical informatics R858-859.7 Sharath Chandra Guntuku Elissa V. Klinger Haley J. McCalpin Lyle H. Ungar David A. Asch Raina M. Merchant Social media language of healthcare super-utilizers
description	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.
format	article
author	Sharath Chandra Guntuku Elissa V. Klinger Haley J. McCalpin Lyle H. Ungar David A. Asch Raina M. Merchant
author_facet	Sharath Chandra Guntuku Elissa V. Klinger Haley J. McCalpin Lyle H. Ungar David A. Asch Raina M. Merchant
author_sort	Sharath Chandra Guntuku
title	Social media language of healthcare super-utilizers
title_short	Social media language of healthcare super-utilizers
title_full	Social media language of healthcare super-utilizers
title_fullStr	Social media language of healthcare super-utilizers
title_full_unstemmed	Social media language of healthcare super-utilizers
title_sort	social media language of healthcare super-utilizers
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/0544e8a7a4844d41bc82ce107c49ccf4
work_keys_str_mv	AT sharathchandraguntuku socialmedialanguageofhealthcaresuperutilizers AT elissavklinger socialmedialanguageofhealthcaresuperutilizers AT haleyjmccalpin socialmedialanguageofhealthcaresuperutilizers AT lylehungar socialmedialanguageofhealthcaresuperutilizers AT davidaasch socialmedialanguageofhealthcaresuperutilizers AT rainammerchant socialmedialanguageofhealthcaresuperutilizers
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Social media language of healthcare super-utilizers

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