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医疗超级使用者的社交媒体语言。

Social media language of healthcare super-utilizers.

作者信息

Guntuku Sharath Chandra, Klinger Elissa V, McCalpin Haley J, Ungar Lyle H, Asch David A, Merchant Raina M

机构信息

Penn Medicine Center for Digital Health, Philadelphia, PA, USA.

Department of Computer and Information Science, University of Pennsylvania, Philadelphia, PA, USA.

出版信息

NPJ Digit Med. 2021 Mar 25;4(1):55. doi: 10.1038/s41746-021-00419-2.

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.

摘要

了解医疗超级使用者的在线行为有助于更好地识别相关经历,为干预措施提供参考。在这项回顾性病例对照研究中,我们分析了患者的社交媒体帖子,以更好地了解他们在网上表达的日常行为和情绪。研究对象包括在城市学术急诊科接受治疗且同意分享其历史Facebook帖子和电子健康记录的患者。超级使用者被定义为一年内到急诊科就诊超过六次的患者。我们使用倾向评分法,根据年龄、性别和查尔森合并症指数,将超级使用者的帖子与匹配组进行比较。超级使用者更有可能发布关于困惑和消极情绪(D = 0.65,95%置信区间[0.38, 0.95])、自我反思(D = 0.63 [0.35, 0.91])、回避(D = 0.62 [0.34, 0.90])、咒骂(D = 0.52 [0.24, 0.79])、睡眠(D = 0.60 [0.32, 0.88])、寻求帮助和关注(D = 0.61 [0.33, 0.89])、心身症状(D = 0.49 [0.22, 0.77])、自我能动性(D = 0.56 [0.29, 0.85])、愤怒(D = 0.51,[0.24, 0.79])、压力(D = 0.46,[0.19, 0.73])和孤独情绪(D = 0.44,[0.17, 0.71])的内容。考虑到超级使用者在社交媒体上的高参与度,本研究的见解可能会通过在线社会支持干预来补充线下社区护理服务。

相似文献

1
Social media language of healthcare super-utilizers.医疗超级使用者的社交媒体语言。
NPJ Digit Med. 2021 Mar 25;4(1):55. doi: 10.1038/s41746-021-00419-2.

本文引用的文献

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Evaluating the predictability of medical conditions from social media posts.从社交媒体帖子评估医疗状况的可预测性。
PLoS One. 2019 Jun 17;14(6):e0215476. doi: 10.1371/journal.pone.0215476. eCollection 2019.

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