Healthcare Professionals’ Perceptions of Telehealth: Analysis of Tweets from Pre- and During the COVID-19 Pandemic

Larson, Sarah; Popov, Vitaliy; Ali, Azliza Mohd; Ramanathan, Parameswaran; Jung, Hee Soo

doi:10.1007/978-3-030-67788-6_27

Cited by 5 publications

(2 citation statements)

References 18 publications

(21 reference statements)

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“…this method to analyse communication in healthcare teams and simulations. [13][14][15] In contrast to static, descriptive models, ONA reveals dynamic differences in how shared understanding develops in healthcare teams. The rich outputs spotlight precise communication patterns that succeed or fail at establishing shared understanding and coordination of care.…”

Section: Open Accessmentioning

confidence: 99%

Applying ordered network analysis to video-recorded physician–nurse interactions to examine communication patterns associated with shared understanding in inpatient oncology care settings

Popov,

Tan,

Manojlovich

2024

BMJ Open

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ObjectivesThe main aim of this study was to demonstrate how ordered network analysis of video-recorded interactions combined with verbal response mode (VRM) coding (eg, edification, disclosure, reflection and interpretation) can uncover specific communication patterns that contribute to the development of shared understanding between physicians and nurses. The major hypothesis was that dyads that reached shared understanding would exhibit different sequential relationships between VRM codes compared with dyads that did not reach shared understanding.DesignObservational study design with the secondary analysis of video-recorded interactions.SettingThe study was conducted on two oncology units at a large Midwestern academic health care system in the USA.ParticipantsA total of 33 unique physician–nurse dyadic interactions were included in the analysis. Participants were the physicians and nurses involved in these interactions during patient care rounds.Primary and secondary outcome measuresThe primary outcome measure was the development of shared understanding between physicians and nurses, as determined by prior qualitative analysis. Secondary measures included the frequencies, orders and co-occurrences of VRM codes in the interactions.ResultsA Mann-Whitney U test showed that dyads that reached shared understanding (N=6) were statistically significantly different (U=148, p=0.00, r=0.93) from dyads that did not reach shared understanding (N=25) in terms of the sequential relationships between edification and disclosure, edification and advisement, as well as edification and questioning. Dyads that reached shared understanding engaged in more edification followed by disclosure, suggesting the importance of this communication pattern for reaching shared understanding.ConclusionsThis novel methodology demonstrates a robust approach to inform interventions that enhance physician–nurse communication. Further research could explore applying this approach in other healthcare settings and contexts.

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Section: Open Accessmentioning

confidence: 99%

Applying ordered network analysis to video-recorded physician–nurse interactions to examine communication patterns associated with shared understanding in inpatient oncology care settings

Popov,

Tan,

Manojlovich

2024

BMJ Open

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“…Furthermore, neither study considered the sentiment content of the tweets, which could provide more precise insights into how Twitter users perceive telemedicine. A separate evaluation of the sentiment of telemedicine-related tweets within a subpopulation of health care providers found overall positive opinions that focused on safety, accessibility, and implementation strategies [13]. However, because this study only focused on providers, there exists a dearth of research on the sentiment toward telemedicine expressed by the general Twitter population.…”

Section: Introductionmentioning

confidence: 95%

Language and Sentiment Regarding Telemedicine and COVID-19 on Twitter: Longitudinal Infodemiology Study

Pollack¹,

Gilbert‐Diamond²,

Alford‐Teaster³

et al. 2021

J Med Internet Res

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Background The COVID-19 pandemic has necessitated a rapid shift in how individuals interact with and receive fundamental services, including health care. Although telemedicine is not a novel technology, previous studies have offered mixed opinions surrounding its utilization. However, there exists a dearth of research on how these opinions have evolved over the course of the current pandemic. Objective This study aims to evaluate how the language and sentiment surrounding telemedicine has evolved throughout the COVID-19 pandemic. Methods Tweets published between January 1, 2020, and April 24, 2021, containing at least one telemedicine-related and one COVID-19–related search term (“telemedicine-COVID”) were collected from the Twitter full archive search (N=351,718). A comparator sample containing only COVID-19 terms (“general-COVID”) was collected and sampled based on the daily distribution of telemedicine-COVID tweets. In addition to analyses of retweets and favorites, sentiment analysis was performed on both data sets in aggregate and within a subset of tweets receiving the top 100 most and least retweets. Results Telemedicine gained prominence during the early stages of the pandemic (ie, March through May 2020) before leveling off and reaching a steady state from June 2020 onward. Telemedicine-COVID tweets had a 21% lower average number of retweets than general-COVID tweets (incidence rate ratio 0.79, 95% CI 0.63-0.99; P=.04), but there was no difference in favorites. A majority of telemedicine-COVID tweets (180,295/351,718, 51.3%) were characterized as “positive,” compared to only 38.5% (135,434/351,401) of general-COVID tweets (P<.001). This trend was also true on a monthly level from March 2020 through April 2021. The most retweeted posts in both telemedicine-COVID and general-COVID data sets were authored by journalists and politicians. Whereas the majority of the most retweeted posts within the telemedicine-COVID data set were positive (55/101, 54.5%), a plurality of the most retweeted posts within the general-COVID data set were negative (44/89, 49.4%; P=.01). Conclusions During the COVID-19 pandemic, opinions surrounding telemedicine evolved to become more positive, especially when compared to the larger pool of COVID-19–related tweets. Decision makers should capitalize on these shifting public opinions to invest in telemedicine infrastructure and ensure its accessibility and success in a postpandemic world.

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Leveraging Epistemic Network Analysis to Discern the Development of Shared Understanding Between Physicians and Nurses

Popov

Sobetski

Jones

et al. 2023

Communications in Computer and Information Science

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Healthcare Professionals’ Perceptions of Telehealth: Analysis of Tweets from Pre- and During the COVID-19 Pandemic

Cited by 5 publications

References 18 publications

Applying ordered network analysis to video-recorded physician–nurse interactions to examine communication patterns associated with shared understanding in inpatient oncology care settings

Applying ordered network analysis to video-recorded physician–nurse interactions to examine communication patterns associated with shared understanding in inpatient oncology care settings

Language and Sentiment Regarding Telemedicine and COVID-19 on Twitter: Longitudinal Infodemiology Study

Leveraging Epistemic Network Analysis to Discern the Development of Shared Understanding Between Physicians and Nurses

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