Emergency Physician Twitter Use in the COVID-19 Pandemic as a Potential Predictor of Impending Surge: Retrospective Observational Study

How do medical professionals make sense (or not) of AI? A social-media-based computational grounded theory study and an online survey

To investigate opinions and attitudes of medical professionals towards adopting AI-enabled healthcare technologies in their daily business, we used a mixed-methods approach. Study 1 employed a qualitative computational grounded theory approach analyzing 181 Reddit threads in the several subreddits of r/medicine. By utilizing an unsupervised machine learning clustering method, we identified three key themes: (1) consequences of AI, (2) physician-AI relationship, and (3) a proposed way forward. In particular Reddit posts related to the first two themes indicated that the medical professionals' fear of being replaced by AI and skepticism toward AI played a major role in the argumentations. Moreover, the results suggest that this fear is driven by little or moderate knowledge about AI. Posts related to the third theme focused on factual discussions about how AI and medicine have to be designed to become broadly adopted in health care. Study 2 quantitatively examined the relationship between the fear of AI, knowledge about AI, and medical professionals' intention to use AI-enabled technologies in more detail. Results based on a sample of 223 medical professionals who participated in the online survey revealed that the intention to use AI technologies increases with increasing knowledge about AI and that this effect is moderated by the fear of being replaced by AI.

Understanding uptake of information about innovations among emergency department clinicians during the COVID-19 pandemic

Objectives

Early in the COVID-19 pandemic, little was known about managing sick patients, but emergency department (ED) clinicians had to decide which treatments and care processes to adopt. Our objective was to describe how ED clinicians learned about innovations and how they assessed them for credibility during the pandemic.

Methods

We purposively sampled clinicians from hospital-based EDs to conduct focus groups with ED clinicians and staff. We used both inductive and deductive approaches to conduct thematic analysis of transcripts.

Results

We conducted focus groups with clinicians from eight EDs across the United States. We found that ED clinicians in our sample relied on friends and colleagues or departmental and institutional leadership for information on innovations. They used social media sources when they came from credible accounts but did not directly seek information from professional societies. Clinicians reported a range of challenges to obtain credible information during the pandemic, including a fractured and changing information environment, policies misaligned across clinical sites or that conflicted with clinical knowledge, high patient volume, fear of harming patients, and untimely information. Facilitators included access to experienced and trusted colleagues and leaders and practicing at multiple EDs.

Conclusion

Participants cited anecdotal evidence, institutional practice, and word-of-mouth-rather than peer-reviewed evidence and professional society communications-as their primary sources of information about care innovations during the early phases of the pandemic. These results underscore the importance of developing trusted local mechanisms and wider networks to identify and vet information for frontline clinicians during rapidly emerging public health emergencies.

Twitter Analysis of Health Care Workers' Sentiment and Discourse Regarding Post-COVID-19 Condition in Children and Young People: Mixed Methods Study

BACKGROUND

The COVID-19 pandemic has had a significant global impact, with millions of cases and deaths. Research highlights the persistence of symptoms over time (post-COVID-19 condition), a situation of particular concern in children and young people with symptoms. Social media such as Twitter (subsequently rebranded as X) could provide valuable information on the impact of the post-COVID-19 condition on this demographic.

OBJECTIVE

With a social media analysis of the discourse surrounding the prevalence of post-COVID-19 condition in children and young people, we aimed to explore the perceptions of health care workers (HCWs) concerning post-COVID-19 condition in children and young people in the United Kingdom between January 2021 and January 2022. This will allow us to contribute to the emerging knowledge on post-COVID-19 condition and identify critical areas and future directions for researchers and policy makers.

METHODS

From a pragmatic paradigm, we used a mixed methods approach. Through discourse, keyword, sentiment, and image analyses, using Pulsar and InfraNodus, we analyzed the discourse about the experience of post-COVID-19 condition in children and young people in the United Kingdom shared on Twitter between January 1, 2021, and January 31, 2022, from a sample of HCWs with Twitter accounts whose biography identifies them as HCWs.

RESULTS

We obtained 300,000 tweets, out of which (after filtering for relevant tweets) we performed an in-depth qualitative sample analysis of 2588 tweets. The HCWs were responsive to announcements issued by the authorities regarding the management of the COVID-19 pandemic in the United Kingdom. The most frequent sentiment expressed was negative. The main themes were uncertainty about the future, policies and regulations, managing and addressing the COVID-19 pandemic and post-COVID-19 condition in children and young people, vaccination, using Twitter to share scientific literature and management strategies, and clinical and personal experiences.

CONCLUSIONS

The perceptions described on Twitter by HCWs concerning the presence of the post-COVID-19 condition in children and young people appear to be a relevant and timely issue and responsive to the declarations and guidelines issued by health authorities over time. We recommend further support and training strategies for health workers and school staff regarding the manifestations and treatment of children and young people with post-COVID-19 condition.

Cross sectional study of Twitter (X) use among academic anesthesiology departments in the United States

Twitter (recently renamed X) is used by academic anesthesiology departments as a social media platform for various purposes. We hypothesized that Twitter (X) use would be prevalent among academic anesthesiology departments and that the number of tweets would vary by region, physician faculty size, and National Institutes of Health (NIH) research funding rank. We performed a descriptive study of Twitter (X) use by academic anesthesiology departments (i.e. those with a residency program) in 2022. Original tweets were collected using a Twitter (X) analytics tool. Summary statistics were reported for tweet number and content. The median number of tweets was compared after stratifying by region, physician faculty size, and NIH funding rank. Among 166 academic anesthesiology departments, there were 73 (44.0%) that had a Twitter (X) account in 2022. There were 3,578 original tweets during the study period and the median number of tweets per department was 21 (25th-75th = 0, 75) with most tweets (55.8%) announcing general departmental news and a smaller number highlighting social events (12.5%), research (11.1%), recruiting (7.1%), DEI activities (5.2%), and trainee experiences (4.1%). There was no significant difference in the median number of tweets by region (P = 0.81). The median number of tweets differed significantly by physician faculty size (P<0.001) with larger departments tweeting more and also by NIH funding rank (P = 0.005) with highly funded departments tweeting more. In 2022, we found that less than half of academic anesthesiology departments had a Twitter (X) account, and the median number of annual tweets per account was relatively low. Overall, Twitter (X) use was less common than anticipated among academic anesthesiology departments and most tweets focused on promotion of departmental activities or individual faculty. There may be opportunities for more widespread and effective use of Twitter (X) by academic anesthesiology departments including education about anesthesiology as a specialty.

Longitudinal analysis of behavioral factors and techniques used to identify vaccine hesitancy among Twitter users: Scoping review

While vaccines have played a pivotal role in the fight against infectious diseases, individuals engage in online resources to find vaccine-related support and information. The benefits and consequences of these online peers are unclear and mainly cause a behavioral shift in user sentiment toward vaccination. This scoping review aims to identify the community and individual factors that longitudinally influence public behavior toward vaccination. The secondary aim is to gain insight into techniques and methodologies used to extract these factors from Twitter data. We followed PRISMA-ScR guidelines to search various online repositories. From this search process, a total of 28 most relevant articles out of 705 relevant studies. Three main themes emerged including individual and community factors influencing public attitude toward vaccination, and techniques employed to identify these factors. Anti-vax, Pro-vax, and neutral are the major communities, while misinformation, vaccine campaign, and user demographics are the common individual factors assessed during this reviewing process. Twitter user sentiment (positive, negative, and neutral) and emotions (fear, trust, sadness) were also discussed to identify the intentions to accept or refuse vaccines. SVM, LDA, BERT are the techniques used for topic modeling, while Louvain, NodeXL, and Infomap algorithms are used for community detection. This research is notable for being the first systematic review that emphasizes the dearth of longitudinal studies and the methodological and underlying practical constraints underpinning the lucrative implementation of an explainable and longitudinal behavior analysis system. Moreover, new possible research directions are suggested for the researchers to perform accurate human behavior analysis.

5-Year Analysis of Twitter Usage Among Physicians: 2016-2020 (Preprint)

Background

Physicians are increasingly using Twitter as a channel for communicating with colleagues and the public. Identifying physicians on Twitter is difficult due to the varied and imprecise ways that people self-identify themselves on the social media platform. This is the first study to describe a reliable, repeatable methodology for identifying physicians on Twitter. By using this approach, we characterized the longitudinal activity of US physicians on Twitter.

Objective

We aimed to develop a reliable and repeatable methodology for identifying US physicians on Twitter and to characterize their activity on Twitter over 5 years by activity, tweeted topic, and account type.

Methods

In this study, 5 years of Twitter data (2016-2020) were mined for physician accounts. US physicians on Twitter were identified by using a custom-built algorithm to screen for physician identifiers in the Twitter handles, user profiles, and tweeted content. The number of tweets by physician accounts from the 5-year period were counted and analyzed. The top 100 hashtags were identified, categorized into topics, and analyzed.

Results

Approximately 1 trillion tweets were mined to identify 6,399,146 (<0.001%) tweets originating from 39,084 US physician accounts. Over the 5-year period, the number of US physicians tweeting more than doubled (ie, increased by 112%). Across all 5 years, the most popular themes were general health, medical education, and mental health, and in specific years, the number of tweets related to elections (2016 and 2020), Black Lives Matter (2020), and COVID-19 (2020) increased.

Conclusions

Twitter has become an increasingly popular social media platform for US physicians over the past 5 years, and their use of Twitter has evolved to cover a broad range of topics, including science, politics, social activism, and COVID-19. We have developed an accurate, repeatable methodology for identifying US physicians on Twitter and have characterized their activity.

Examining the Utility of Social Media in COVID-19 Vaccination: Unsupervised Learning of 672,133 Twitter Posts

BACKGROUND

Although COVID-19 vaccines have recently become available, efforts in global mass vaccination can be hampered by the widespread issue of vaccine hesitancy.

OBJECTIVE

The aim of this study was to use social media data to capture close-to-real-time public perspectives and sentiments regarding COVID-19 vaccines, with the intention to understand the key issues that have captured public attention, as well as the barriers and facilitators to successful COVID-19 vaccination.

METHODS

Twitter was searched for tweets related to "COVID-19" and "vaccine" over an 11-week period after November 18, 2020, following a press release regarding the first effective vaccine. An unsupervised machine learning approach (ie, structural topic modeling) was used to identify topics from tweets, with each topic further grouped into themes using manually conducted thematic analysis as well as guided by the theoretical framework of the COM-B (capability, opportunity, and motivation components of behavior) model. Sentiment analysis of the tweets was also performed using the rule-based machine learning model VADER (Valence Aware Dictionary and Sentiment Reasoner).

RESULTS

Tweets related to COVID-19 vaccines were posted by individuals around the world (N=672,133). Six overarching themes were identified: (1) emotional reactions related to COVID-19 vaccines (19.3%), (2) public concerns related to COVID-19 vaccines (19.6%), (3) discussions about news items related to COVID-19 vaccines (13.3%), (4) public health communications about COVID-19 vaccines (10.3%), (5) discussions about approaches to COVID-19 vaccination drives (17.1%), and (6) discussions about the distribution of COVID-19 vaccines (20.3%). Tweets with negative sentiments largely fell within the themes of emotional reactions and public concerns related to COVID-19 vaccines. Tweets related to facilitators of vaccination showed temporal variations over time, while tweets related to barriers remained largely constant throughout the study period.

CONCLUSIONS

The findings from this study may facilitate the formulation of comprehensive strategies to improve COVID-19 vaccine uptake; they highlight the key processes that require attention in the planning of COVID-19 vaccination and provide feedback on evolving barriers and facilitators in ongoing vaccination drives to allow for further policy tweaks. The findings also illustrate three key roles of social media in COVID-19 vaccination, as follows: surveillance and monitoring, a communication platform, and evaluation of government responses.