scholarly journals Correlations Between Hospitals’ Social Media Presence and Reputation Score and Ranking: Cross-Sectional Analysis (Preprint)

2017 ◽  
Author(s):  
Justin D Triemstra ◽  
Rachel Stork Poeppelman ◽  
Vineet M Arora
Keyword(s):  
Social Media ◽  
Correlation Coefficients ◽  
Children's Hospitals ◽  
Cross Sectional ◽  
Cross Sectional Analysis ◽  
Children’S Hospitals ◽  
Reputation Score ◽  
The Us ◽  
Social Media Presence ◽  
Sectional Analysis

BACKGROUND The US News and World Report reputation score correlates strongly with overall rank in adult and pediatric hospital rankings. Social media affects how information is disseminated to physicians and is used by hospitals as a marketing tool to recruit patients. It is unclear whether the reputation score for adult and children’s hospitals relates to social media presence. OBJECTIVE The objective of our study was to analyze the association between a hospital’s social media metrics and the US News 2017-2018 Best Hospital Rankings for adult and children’s hospitals. METHODS We conducted a cross-sectional analysis of the reputation score, total score, and social media metrics (Twitter, Facebook, and Instagram) of hospitals who received at least one subspecialty ranking in the 2017-2018 US News publicly available annual rankings. Regression analysis was employed to analyze the partial correlation coefficients between social media metrics and a hospital’s total points (ie, rank) and reputation score for both adult and children’s hospitals while controlling for the bed size and time on Twitter. RESULTS We observed significant correlations for children’s hospitals’ reputation score and total points with the number of Twitter followers (total points: r=.465, P<.001; reputation: r=.524, P<.001) and Facebook followers (total points: r=.392, P=.002; reputation: r=.518, P<.001). Significant correlations for the adult hospitals reputation score were found with the number of Twitter followers (r=.848, P<.001), number of tweets (r=.535, P<.001), Klout score (r=.242, P=.02), and Facebook followers (r=.743, P<.001). In addition, significant correlations for adult hospitals total points were found with Twitter followers (r=.548, P<.001), number of tweets (r=.358, P<.001), Klout score (r=.203, P=.05), Facebook followers (r=.500, P<.001), and Instagram followers (r=.692, P<.001). CONCLUSIONS A statistically significant correlation exists between multiple social media metrics and both a hospital’s reputation score and total points (ie, overall rank). This association may indicate that a hospital’s reputation may be influenced by its social media presence or that the reputation or rank of a hospital drives social media followers.

scholarly journals Social Media Content of Idiopathic Pulmonary Fibrosis Groups and Pages on Facebook: A Cross Sectional Analysis (Preprint)

2020 ◽  
Author(s):  
Andrew Kochan ◽  
Shaun Ong ◽  
Sabina Guler ◽  
Kerri A. Johannson ◽  
Christopher J. Ryerson ◽  
...  
Keyword(s):  
Social Media ◽  
Idiopathic Pulmonary Fibrosis ◽  
Pulmonary Fibrosis ◽  
Medical Information ◽  
Scientific Information ◽  
User Engagement ◽  
Search Term ◽  
Cross Sectional ◽  
Cross Sectional Analysis ◽  
Sectional Analysis

BACKGROUND Patients use Facebook as a resource for medical information. We analyzed posts on idiopathic pulmonary fibrosis (IPF)-related Facebook groups and pages for presence of guideline content, user engagement, and usefulness. OBJECTIVE The objective of this study was to describe and analyze posts from Facebook groups and pages that primarily focus on IPF-related content. METHODS Cross-sectional analysis was performed on a single date, identifying Facebook groups and pages resulting from separately searching “IPF” and “idiopathic pulmonary fibrosis”. For inclusion, groups and pages needed to meet either search term and be in English, publicly available, and relevant to IPF. Every tenth post was assessed for general characteristics, source, focus, and user engagement metrics. Posts were analyzed for presence of IPF guideline content, useful scientific information (e.g. scientific publications), useful support information (e.g. information about support groups), and potentially harmful information. RESULTS Eligibility criteria were met by 12 groups and 27 pages, leading to analysis of 523 posts. Of these, 42% contained guideline content, 24% provided useful support, 20% provided useful scientific information, and 5% contained potentially harmful information. Post source was most commonly non-medical users (85%). Posts most frequently focused on IPF-related news (29%). Posts containing any guideline content had fewer likes or comments and a higher likelihood of containing potentially harmful content. Posts containing useful supportive information had more likes, shares, and comments. CONCLUSIONS Facebook contains useful information about IPF, but posts with misinformation and less guideline content have higher user engagement, making them more visible. Identifying ways to help IPF patients discriminate between useful and harmful information on Facebook and other social media platforms is an important task for healthcare professionals.

Evaluation, Treatment, and Insurance Coverage for Couples With Male Factor Infertility in the US: A Cross-Sectional Analysis of Survey Data

Urology ◽  
2020 ◽  
Vol 139 ◽  
pp. 97-103
Author(s):  
Clara Helene Glazer ◽  
Jake Anderson-Bialis ◽  
Deborah Anderson-Bialis ◽  
Michael L. Eisenberg
Keyword(s):  
Survey Data ◽  
Insurance Coverage ◽  
Male Factor Infertility ◽  
Male Factor ◽  
Cross Sectional ◽  
Cross Sectional Analysis ◽  
The Us ◽  
Sectional Analysis

scholarly journals Postmarketing commitments for novel drugs and biologics approved by the US Food and Drug Administration: a cross-sectional analysis

BMC Medicine ◽  
2019 ◽  
Vol 17 (1) ◽  
Author(s):  
Joshua D. Wallach ◽  
Anita T. Luxkaranayagam ◽  
Sanket S. Dhruva ◽  
Jennifer E. Miller ◽  
Joseph S. Ross
Keyword(s):  
Drug Administration ◽  
Food And Drug Administration ◽  
Cross Sectional ◽  
Cross Sectional Analysis ◽  
Novel Drugs ◽  
The Us ◽  
Sectional Analysis
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