Face time with physicians: How do patients assess providers in video-visits?

Introduction: The COVID-19 pandemic has triggered a massive acceleration in the use of virtual and video-visits. As more patients and providers engage in video-visits over varied digital platforms, it is important to understand how patients assess their providers and the video-visit experiences. We also need to examine the relative importance of the factors that patients use in their assessment of video-visits in order to improve the overall healthcare experience and delivery. Methods: A data set of 5149 reviews of patients completing a video-visit was assembled through web scraping. Sentiment analysis was performed on the reviews and topic modeling was used to extract latent topics embedded in the reviews and their relative importance. Results: Most patient reviews (89.53%) reported a positive sentiment towards their providers in video-visits. Seven distinct topics underlying the reviews were identified: bedside manners, professional expertise, virtual experience, appointment scheduling and follow-up process, wait times, costs, and communication. Communication, bedside manners and professional expertise were the top factors patients alluded to in the positive reviews. Appointment-scheduling and follow-ups, wait-times, costs, virtual experience and professional expertise were important factors in the negative reviews. Discussion: To improve the overall experience of patients in video-visits, providers need to engage in clear communication, grow excellent bedside and webside manners, promptly attend the video-visit with minimal delays and follow-up with patients after the visit.

Do Patients Assess Physicians Differently in Video Visits as Compared with In-Person Visits? Insights from Text-Mining Online Physician Reviews.

Introduction: Use of both in-person and video visits have become a common norm in health care delivery, especially after the COVID-19 pandemic. It is imperative to understand how patients feel about their providers and their experiences during in-person and video visits. This study examines the important factors that patients use in their reviews and differences in the relative importance. Methods: We performed sentiment analysis and topic modeling on online physician reviews from April 2020 to April 2022. Our dataset comprised 34,824 reviews posted by patients after completing in-person or video visits. Results: Sentiment analysis yielded 27,507 (92.69%) positive and 2,168 (7.31%) negative reviews for in-person visits, and 4,610 (89.53%) positive and 539 (10.47%) negative reviews for video visits. Topic modeling identified seven factors patients used in their reviews: Bedside manners, Medical Expertise, Communication, Visit Environment, Scheduling and Follow-up, Wait times, and Costs and insurance. Patients who gave positive reviews after in-person consultations more frequently mentioned communication, office environment and staff, and bedside manners. Those who gave negative reviews after in-person visits mentioned longer wait times, providers' office and staff, medical expertise, and costs and insurance problems. Patients with positive reviews after video visits emphasized communication, bedside manners, and medical expertise. However, patients posting negative reviews after video visits frequently mentioned problems with appointment scheduling and follow-up, medical expertise, wait times, costs and insurance, and technical problems in video visits. Conclusions: This study identified key factors that influence patients' assessment of their providers in in-person and video visits. Paying attention to these factors can help improve the overall patient experience.

Examining Public Sentiments and Attitudes Toward COVID-19 Vaccination: Infoveillance Study Using Twitter Posts.

Background: A global rollout of vaccinations is currently underway to mitigate and protect people from the COVID-19 pandemic. Several individuals have been using social media platforms such as Twitter as an outlet to express their feelings, concerns, and opinions about COVID-19 vaccines and vaccination programs. This study examined COVID-19 vaccine-related tweets from January 1, 2020, to April 30, 2021, to uncover the topics, themes, and variations in sentiments of public Twitter users. Objective: The aim of this study was to examine key themes and topics from COVID-19 vaccine-related English tweets posted by individuals, and to explore the trends and variations in public opinions and sentiments. Methods: We gathered and assessed a corpus of 2.94 million COVID-19 vaccine-related tweets made by 1.2 million individuals. We used CoreX topic modeling to explore the themes and topics underlying the tweets, and used VADER sentiment analysis to compute sentiment scores and examine weekly trends. We also performed qualitative content analysis of the top three topics pertaining to COVID-19 vaccination. Results: Topic modeling yielded 16 topics that were grouped into 6 broader themes underlying the COVID-19 vaccination tweets. The most tweeted topic about COVID-19 vaccination was related to vaccination policy, specifically whether vaccines needed to be mandated or optional (13.94%), followed by vaccine hesitancy (12.63%) and postvaccination symptoms and effects (10.44%) Average compound sentiment scores were negative throughout the 16 weeks for the topics postvaccination symptoms and side effects and hoax/conspiracy. However, consistent positive sentiment scores were observed for the topics vaccination disclosure, vaccine efficacy, clinical trials and approvals, affordability, regulation, distribution and shortage, travel, appointment and scheduling, vaccination sites, advocacy, opinion leaders and endorsement, and gratitude toward health care workers. Reversal in sentiment scores in a few weeks was observed for the topics vaccination eligibility and hesitancy. Conclusions: Identification of dominant themes, topics, sentiments, and changing trends about COVID-19 vaccination can aid governments and health care agencies to frame appropriate vaccination programs, policies, and rollouts.

Topics, Trends, and Sentiments of Tweets About the COVID-19 Pandemic: Temporal Infoveillance Study.

BACKGROUND: With restrictions on movement and stay-at-home orders in place due to the COVID-19 pandemic, social media platforms such as Twitter have become an outlet for users to express their concerns, opinions, and feelings about the pandemic. Individuals, health agencies, and governments are using Twitter to communicate about COVID-19. OBJECTIVE: The aims of this study were to examine key themes and topics of English-language COVID-19-related tweets posted by individuals and to explore the trends and variations in how the COVID-19-related tweets, key topics, and associated sentiments changed over a period of time from before to after the disease was declared a pandemic. METHODS: Building on the emergent stream of studies examining COVID-19-related tweets in English, we performed a temporal assessment covering the time period from January 1 to May 9, 2020, and examined variations in tweet topics and sentiment scores to uncover key trends. Combining data from two publicly available COVID-19 tweet data sets with those obtained in our own search, we compiled a data set of 13.9 million English-language COVID-19-related tweets posted by individuals. We use guided latent Dirichlet allocation (LDA) to infer themes and topics underlying the tweets, and we used VADER (Valence Aware Dictionary and sEntiment Reasoner) sentiment analysis to compute sentiment scores and examine weekly trends for 17 weeks. RESULTS: Topic modeling yielded 26 topics, which were grouped into 10 broader themes underlying the COVID-19-related tweets. Of the 13,937,906 examined tweets, 2,858,316 (20.51%) were about the impact of COVID-19 on the economy and markets, followed by spread and growth in cases (2,154,065, 15.45%), treatment and recovery (1,831,339, 13.14%), impact on the health care sector (1,588,499, 11.40%), and governments response (1,559,591, 11.19%). Average compound sentiment scores were found to be negative throughout the examined time period for the topics of spread and growth of cases, symptoms, racism, source of the outbreak, and political impact of COVID-19. In contrast, we saw a reversal of sentiments from negative to positive for prevention, impact on the economy and markets, government response, impact on the health care industry, and treatment and recovery. CONCLUSIONS: Identification of dominant themes, topics, sentiments, and changing trends in tweets about the COVID-19 pandemic can help governments, health care agencies, and policy makers frame appropriate responses to prevent and control the spread of the pandemic.