Telehealth adoption during the COVID-19 pandemic: A social media textual and network analysis

Remote Inclusion of Vulnerable Users in mHealth Intervention Design: Retrospective Case Analysis

BACKGROUND

Mobile health (mHealth) interventions that promote healthy behaviors or mindsets are a promising avenue to reach vulnerable or at-risk groups. In designing such mHealth interventions, authentic representation of intended participants is essential. The COVID-19 pandemic served as a catalyst for innovation in remote user-centered research methods. The capability of such research methods to effectively engage with vulnerable participants requires inquiry into practice to determine the suitability and appropriateness of these methods.

OBJECTIVE

In this study, we aimed to explore opportunities and considerations that emerged from involving vulnerable user groups remotely when designing mHealth interventions. Implications and recommendations are presented for researchers and practitioners conducting remote user-centered research with vulnerable populations.

METHODS

Remote user-centered research practices from 2 projects involving vulnerable populations in Norway and Australia were examined retrospectively using visual mapping and a reflection-on-action approach. The projects engaged low-income and unemployed groups during the COVID-19 pandemic in user-based evaluation and testing of interactive, web-based mHealth interventions.

RESULTS

Opportunities and considerations were identified as (1) reduced barriers to research inclusion; (2) digital literacy transition; (3) contextualized insights: a window into people's lives; (4) seamless enactment of roles; and (5) increased flexibility for researchers and participants.

CONCLUSIONS

Our findings support the capability and suitability of remote user methods to engage with users from vulnerable groups. Remote methods facilitate recruitment, ease the burden of research participation, level out power imbalances, and provide a rich and relevant environment for user-centered evaluation of mHealth interventions. There is a potential for a much more agile research practice. Future research should consider the privacy impacts of increased access to participants' environment via webcams and screen share and how technology mediates participants' action in terms of privacy. The development of support procedures and tools for remote testing of mHealth apps with user participants will be crucial to capitalize on efficiency gains and better protect participants' privacy.

Analysis of Telehealth Discussion Trends on Reddit (2019-2022)

Introduction: Use of telehealth increased during the COVID-19 pandemic and continues to be a popular health resource. This study analyzed the frequency and sentiment of telehealth discussions on Reddit. Methods: The data set included 13,071 publicly available Reddit submissions containing keywords related to telehealth over a 3-year period. We identified 173 unique subreddit communities, which were coded into mutually exclusive categories: (1) general telehealth, (2) individual care, (3) professional, (4) news, and (5) COVID-19. The Vader lexicon-based machine was used to assign sentiment scores. Results: Most subreddits were coded as individual care (n = 112), professional (n = 26), and news (n = 22). The frequency of submissions increased during the first 2 months of the pandemic and dropped in June 2020, but remained consistent through October 2022. Most Reddit submissions were positive in sentiment (56%). Conclusion: Findings show a mostly positive view of telehealth among Reddit users and an increase in telehealth-related discussions since the COVID-19 pandemic.

Impact of telemedicine on disease activity assessment: A case-crossover study nested within a cohort of patients with systemic lupus erythematosus

INTRODUCTION

The utilisation of telemedicine has been rapidly growing among patients with rheumatic diseases, especially following the corona virus disease 2019 pandemic. Ease and convenience appear to dominate the reasons for this growth. However, the effects of this approach in patients with systemic lupus erythematosus (SLE) are yet to be revealed. In this study, we examined the effect of telemedicine on disease activity assessment and damage scores in patients with SLE.

METHODS

This case-crossover study was nested within a national prospective cohort of patients with SLE in Saudi Arabia. Patients with SLE were included if they fulfilled the Systemic Lupus International Collaborating Clinics classification criteria between March 2020 and March 2021 and were assessed at three time points with 3 months between assessments, according to the standardised protocol of this cohort. Telemedicine was conducted for the first evaluation, while in-person assessments were used at the second and third visits. The primary outcome was the difference in the SLE disease activity index 2000 (SLEDAI-2K) score. The primary analysis was conducted using the repeated measure model and adjusted for potential confounders, including demographics, medications, and changes in steroid doses. Several sensitivity analyses were conducted to mitigate selection and time-varying confounders.

RESULTS

A total of 92 participants were included in this study. Most patients were females (88%), with a mean (±standard deviation [SD]) age of 36 (±13) years. The mean (±SD) disease activity scores at baseline were as follows: SLEDAI-2K, 5 (±5); SLE responder index, 3.8 (±3.5); Systemic Lupus International Collaborating Clinics/American College of Rheumatology damage index, 1 (±1). The mean difference in SLEDAI-2K score was -1.641 (95% confidence interval -2.773 to -0.510, p = 0.005*) between telemedicine and follow-up visits. The results were consistent in all sensitivity analyses.

CONCLUSION

We found that telemedicine assessment was associated with a much higher disease activity score than subsequent assessments, which may suggest an overestimation of disease activity and later assessment accuracy. Cautious adoption has been suggested for SLE patients with active disease.

Experience and Impact of COVID-19 on a Newly Formed Rural University Medical Office: Survey Study

BACKGROUND

The COVID-19 pandemic had large social effects, particularly in the fields of medicine and medical education. Medical organizations in the United States operate in overlapping contexts with interrelated goals inside multiple organizations, and the context of work strongly influenced how organizations were able to respond to COVID-19 restrictions.

OBJECTIVE

This research examines the experience and impact of COVID-19 on the implementation of a Health Resources and Services Administration grant in a newly formed university medical office with the interrelated goals of health policy, health outreach, and medical education. The goal is to understand how COVID-19 created different experiences and challenges for leaders and their collaborators working in medical education compared to those working in public health outreach or health policy.

METHODS

A survey about COVID-19 opportunities and challenges was administered to work unit leaders and their project collaborators. The most common experiences and challenges are shown, direct educational and other respondents' experiences and challenges are compared, and open-ended comment segments are analyzed.

RESULTS

Helping others adjust to digital work, remoteness, and coordination were common experiences during COVID-19. Common challenges include coordination and an inability to make comparisons to previous program years. On average, respondents had 11.3 (SD 7.8) experiences and 8.3 (SD 6.9) challenges considered in the survey. While all units were influenced by COVID-19 restrictions, medical education units had more experiences and challenges. Those involved directly in medical education experienced 69% (18.6/27) of their possible experiences and 54% (14.7/27) of their possible challenges on average compared to 35% (7/20) and 21% (4.2/20) among other respondents (P<.001). COVID-19 restrictions increased the complexity of project work and presented challenges, especially in terms of coordinating responses and access to locations.

CONCLUSIONS

The findings suggest that COVID-19 made the overall administration of programs more complex and drew attention from other medical and public health programs. While remoteness is appropriate for some medical education tasks, it is less appropriate for clinical learning. Remoteness presents an especially large challenge to clinical education. Employees now have expectations for remoteness to be built into programs and workplaces. Program administrators will have to integrate remoteness' benefits and drawbacks into their organization for the foreseeable future.

Comparing the Discussion of Telehealth in Two Social Media Platforms: Social Listening Analysis

Background

Social media is used as a source of information and platform to discuss health care; however, there is little research on discussion of telehealth in social media. Past research has looked at individual platforms, but a comparison of discussion on two platforms (Reddit and Twitter) has not been performed. Understanding telehealth-related social media discourse and the differences between platforms may provide insights into how telehealth is characterized online and which platforms provide patient perspectives. The COVID-19 pandemic provides a unique case study to examine how social media users approached both Reddit and Twitter during an international health crisis. This study used natural language processing tools and two social media platforms to (1) characterize and contrast each platform's telehealth-related posts according to themes and (2) assess the frequency of telehealth and telehealth-related terms posts before and during the onset of the COVID-19 pandemic.

Methods

We collected 6 years (2016 through 2021) of social media posts from Twitter and Reddit. The themes of the corpus were extracted using hashtags, subreddits, and Latent Dirichlet Allocation (LDA) and were analyzed using descriptive statistics.

Results

Both Twitter and Reddit showed exponential growth in the use of the term "telehealth" and telehealth-related terms in early 2020. The use of telehealth-related terms and discussion of COVID-19 coincided in both social media sites; however, other themes were discussed, including how to use telehealth. Reddit LDA clusters showed greatest usage of "telehealth" when associated with using or suggesting telehealth for receiving therapy, counseling, or psychoanalysis while Twitter focused on sharing telehealth news, products, and services.

Discussion

Twitter and Reddit had extensive growth in the use of telehealth-related terms after the COVID-19 pandemic. Twitter and Reddit showed themes connecting COVID-19 to telehealth, especially in reference to services, therapy, and counseling, however, Reddit had more discussion suggesting use of telehealth services or requesting peer insights into how to use telehealth as compared with Twitter, which appeared more focused on telehealth as a business or product.

Public Officials' Engagement on Social Media During the Rollout of the COVID-19 Vaccine: A Content Analysis of Tweets

BACKGROUND

Social media is an important way for governments to communicate with the public. This is particularly true in times of crisis such as the COVID-19 pandemic, during which time government officials had a strong role in promoting public health measures such as vaccines.

OBJECTIVE

In Canada, provincial COVID-19 vaccine rollout was delivered in three phases aligned with federal government COVID-19 vaccine guidance for priority populations. In this study, we examined how Canadian public officials used Twitter to engage with the public about vaccine rollout and how this engagement has shaped public response to vaccines across jurisdictions.

METHODS

We conducted a content analysis of tweets posted between December 28, 2020, and August 31, 2021. Leveraging social media artificial intelligence (AI) tool Brandwatch Analytics©, we constructed a list of public officials in three jurisdictions (Ontario, Alberta and British Columbia) organized across six public official types, then conducted an English/French keyword search for tweets about vaccine rollout and delivery that mentioned, retweeted, or replied to the public officials. We identified the top 30 tweets with the highest impressions in each jurisdiction in each of the three phases (approximately a 26-day window) of the vaccine rollout. The metrics of engagement (impressions, retweets, likes, and replies) from the top 30 tweets per phase in each jurisdiction were then extracted for additional annotation. We specifically annotated sentiment towards public officials' vaccine response (I.e., positive, negative, neutral) in each tweet, and also annotated the type of social media engagement. A thematic analysis of tweets was then conducted to add nuance to extracted data characterizing sentiment and interaction type.

RESULTS

Of the six categories of public officials, 142 prominent accounts were included from Ontario, Alberta and British Columbia. 270 tweets were included in the content analysis. Public officials mostly used Twitter for information provision (65.6%), followed by horizontal engagement (17.5%), citizen engagement (11.3%), and public service announcements (5.7%). Information provision by government bodies (e.g., provincial government and public health authorities) or municipal leaders are more prominent than tweets by other public official groups. Neutral sentiment accounted for 51.1% of all tweets, while positive sentiment (43.2%) was the second most common sentiment. In Ontario, 60% of the tweets were positive. Negative sentiment (e.g., public officials criticizing vaccine rollout) accounted for 12.6% of all tweets.

CONCLUSIONS

As governments continue to promote the uptake of the COVID-19 "booster" doses, findings from this study are useful in informing how governments can best utilize social media to engage with the public to achieve democratic goals.

CLINICALTRIAL

COVID-19 and telemedicine: A netnography approach

The COVID-19 pandemic has consolidated some trends that already existed in our society. Perhaps one of the most visible is the transformation of society towards greater digitisation. Digitalisation has gained weight in all aspects of our lives, and from the point of view of the health system we find an example in the slow historical adoption of telemedicine, which contrasts sharply with the massive conversion to this technology as a tool for social distancing. In this sense, the homebound population is the one most affected by the pandemic and the one that could benefit the most from the use of telemedicine. Using a netnography approach and based on the stimulus-organism-response paradigm, this study proposes to analyse the evolution of perception about telemedicine using the opinions expressed on Twitter. The primary technical tasks of the study incorporate the analysis of topics and the review of emotions and positive image perception using natural language processing. Specifically, tweets about telemedicine generated by the Spanish community are analysed in this work. The findings show that the COVID-19 pandemic has affected emotions and topics of interest related to telemedicine. This has changed the image that it had and the behaviour of the Twitter community in Spain. In conclusion, the study results suggest that changes in health systems affect people's emotions and behaviours.

Exploring public opinion about telehealth during COVID-19 by social media analytics

While COVID-19 catalyzed the acceptance and use of telehealth, our understanding of how it is perceived by multi-stakeholders such as patients, clinicians, and health authorities is limited. Drawing on social media analytics, this research examines social media discourses and users' opinions about telehealth during the COVID-19 pandemic. It applies natural language processing and deep learning to explore word of mouth on telehealth with a contextualized focus on the COVID-19 pandemic. We conducted topic modeling, sentiment analysis, and emotion analysis (fearful, happy, sad, surprised, and angry emotions). The topic modeling analysis led to the identification of 18 topics, representing 6 themes of digital health service delivery, pandemic response, communication and promotion, government action, health service domains (e.g. mental health, cancer, aged care), as well as pharma and drug. The sentiment analysis revealed that while most opinions expressed in tweets were positive, the public expressed mostly negative opinions about certain aspects of COVID-19 such as lockdowns and cyberattacks. Emotion analysis of tweets showed a dominant pattern of fearful and sad emotions in particular topics. The results of this study that inductively emerged from our social media analysis can aid public health authorities and health professionals to address the concerns of telehealth users and improve their experiences.

Engaging Patients via Online Healthcare Fora: Three Pharmacovigilance Use Cases

Increasingly, patient-generated safety insights are shared online, via general social media platforms or dedicated healthcare fora which give patients the opportunity to discuss their disease and treatment options. We evaluated three areas of potential interest for the use of social media in pharmacovigilance. To evaluate how social media may complement existing safety signal detection capabilities, we identified two use cases (drug/adverse event [AE] pairs) and then evaluated the frequency of AE discussions across a range of social media channels. Changes in frequency over time were noted in social media, then compared to frequency changes in Food and Drug Administration Adverse Event Reporting System (FAERS) data over the same time period using a traditional disproportionality method. Although both data sources showed increasing frequencies of AE discussions over time, the increase in frequency was greater in the FAERS data as compared to social media. To demonstrate the robustness of medical/AE insights of linked posts we manually reviewed 2,817 threads containing 21,313 individual posts from 3,601 unique authors. Posts from the same authors were linked together. We used a quality scoring algorithm to determine the groups of linked posts with the highest quality and manually evaluated the top 16 groups of posts. Most linked posts (12/16; 75%) contained all seven relevant medical insights assessed compared to only one (of 1,672) individual post. To test the capability of actively engage patients via social media to obtain follow-up AE information we identified and sent consents for follow-up to 39 individuals (through a third party). We sent target follow-up questions (identified by pharmacovigilance experts as critical for causality assessment) to those who consented. The number of people consenting to follow-up was low (20%), but receipt of follow-up was high (75%). We observed completeness of responses (37 out of 37 questions answered) and short average time required to receive the follow-up (1.8 days). Our findings indicate a limited use of social media data for safety signal detection. However, our research highlights two areas of potential value to pharmacovigilance: obtaining more complete medical/AE insights via longitudinal post linking and actively obtaining rapid follow-up information on AEs.

Self-Adaptive Telemedicine Specialist Recommendation Considering Specialist Activity and Patient Feedback

Purpose: With the rapid development of medical informatization, information overload and asymmetry have become major obstacles that limit patients' ability to find appropriate telemedicine specialists. Although doctor recommendation methods have been proposed, they fail to address data sparsity and cold-start issues, and electronic medical records (EMRs), patient preferences, potential interest of service providers and the changes over time are largely under-explored. Therefore, this study develops a self-adaptive telemedicine specialist recommendation method that incorporates specialist activity and patient utility feedback from the perspective of privacy protection to fill the research gaps. Methods: First, text vectorization, view similarity and probabilistic topic model are used to construct the patient and specialist feature models based on patients' EMRs and specialists' long- and short-term knowledge backgrounds, respectively. Second, the recommended specialist candidate set and recommendation index are obtained based on the similarity between patient features. Then, the specialist long-term knowledge feature model is used to update the newly registered specialist recommendation index and the recommended specialist candidate set to overcome the data sparsity and cold-start issues, and the specialist short-term knowledge feature model is adopted to extend the recommended specialist candidate set at the semantic level. Finally, we introduce the specialists' activity and patients' perceived utility feedback mechanism to construct a closed-loop adjusted and optimized specialist recommendation method. Results: An empirical study was conducted integrating EMRs of telemedicine patients from the National Telemedicine Center of China and specialists' profiles and ratings from an online healthcare platform. The proposed method successfully recommended relevant and active telemedicine specialists to the target patient, and increased the recommended opportunities for newly registered specialists to some extent. Conclusions: The proposed method emphasizes the adaptability and acceptability of the recommended results while ensuring their accuracy and relevance. Specialists' activity and patients' perceived utility jointly contribute to the acceptability of recommended results, and the recommendation strategy achieves the organic fusion of the two. Several comparative experiments demonstrate the effectiveness and operability of the hybrid recommendation strategy under the premise of data sparsity and privacy protection, enabling effective matching of patients' demand and service providers' capabilities, and providing beneficial insights for data-driven telemedicine services.