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  Artificial Intelligence (other than...

  Jul 13, 2020

  Public Perception of Artificial Intelligence in Medical Care: Content Analysis of Social Media

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  Authors List:

  • Shuqing Gao  
  • Lingnan He  
  • Yue Chen  
  • Dan Li  
  • Kaisheng Lai  

  Abstract:

  Background: High-quality medical resources are in high demand worldwide, and the application of artificial intelligence (AI) in medical care may help alleviate the crisis related to this shortage. The development of the medical AI industry depends to a certain extent on whether industry experts have a comprehensive understanding of the public’s views on medical AI. Currently, the opinions of the general public on this matter remain unclear. Objective: The purpose of this study is to explore the public perception of AI in medical care through a content analysis of social media data, including specific topics that the public is concerned about; public attitudes toward AI in medical care and the reasons for them; and public opinion on whether AI can replace human doctors. Methods: Through an application programming interface, we collected a data set from the Sina Weibo platform comprising more than 16 million users throughout China by crawling all public posts from January to December 2017. Based on this data set, we identified 2315 posts related to AI in medical care and classified them through content analysis. Results: Among the 2315 identified posts, we found three types of AI topics discussed on the platform: (1) technology and application (n=987, 42.63%), (2) industry development (n=706, 30.50%), and (3) impact on society (n=622, 26.87%). Out of 956 posts where public attitudes were expressed, 59.4% (n=568), 34.4% (n=329), and 6.2% (n=59) of the posts expressed positive, neutral, and negative attitudes, respectively. The immaturity of AI technology (27/59, 46%) and a distrust of related companies (n=15, 25%) were the two main reasons for the negative attitudes. Across 200 posts that mentioned public attitudes toward replacing human doctors with AI, 47.5% (n=95) and 32.5% (n=65) of the posts expressed that AI would completely or partially replace human doctors, respectively. In comparison, 20.0% (n=40) of the posts expressed that AI would not replace human doctors. Conclusions: Our findings indicate that people are most concerned about AI technology and applications. Generally, the majority of people held positive attitudes and believed that AI doctors would completely or partially replace human ones. Compared with previous studies on medical doctors, the general public has a more positive attitude toward medical AI. Lack of trust in AI and the absence of the humanistic care factor are essential reasons why some people still have a negative attitude toward medical AI. We suggest that practitioners may need to pay more attention to promoting the credibility of technology companies and meeting patients’ emotional needs instead of focusing merely on technical issues.

  • J Med Internet Res 2020;22(7):e16649

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  Web-based and Mobile Health...

  Jul 13, 2020

  Adherence to Established Treatment Guidelines Among Unguided Digital Interventions for Depression: Quality Evaluation of 28 Web-Based Programs and Mobile Apps

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  Authors List:

  • Stefan Bubolz  
  • Gwendolyn Mayer  
  • Nadine Gronewold  
  • Thomas Hilbel  
  • Jobst-Hendrik Schultz  

  Abstract:

  Background: Web-based interventions for depression have been widely tested for usability and functioning. However, the few studies that have addressed the therapeutic quality of these interventions have mainly focused on general aspects without consideration of specific quality factors related to particular treatment components. Clinicians and scientists are calling for standardized assessment criteria for web-based interventions to enable effective and trustworthy patient care. Therefore, an extensive evaluation of web-based interventions at the level of individual treatment components based on therapeutic guidelines and manuals is needed. Objective: The objective of this study was to evaluate the quality of unguided web-based interventions for depression at the level of individual treatment components based on their adherence to current gold-standard treatment guidelines and manuals. Methods: A comprehensive online search of popular app stores and search engines in January 2018 revealed 11 desktop programs and 17 smartphone apps that met the inclusion criteria. Programs and apps were included if they were available for German users, interactive, unguided, and targeted toward depression. All programs and apps were tested by three independent researchers following a standardized procedure with a predefined symptom trajectory. During the testing, all web-based interventions were rated with a standardized list of criteria based on treatment guidelines and manuals for depression. Results: Overall interrater reliability for all raters was substantial with an intraclass correlation coefficient of 0.73 and Gwet AC1 value of 0.80. The main features of web-based interventions included mood tracking (24/28, 86%), psychoeducation (21/28, 75%), cognitive restructuring (21/28, 75%), crisis management (20/28, 71%), behavioral activation (19/29, 68%), and relaxation training (18/28, 64%). Overall, therapeutic meaningfulness was rated higher for desktop programs (mean 4.13, SD 1.17) than for smartphone apps (mean 2.92, SD 1.46). Conclusions: Although many exercises from manuals are included in web-based interventions, the necessary therapeutic depth of the interventions is often not reached, and risk management is frequently lacking. There is a need for further research targeting general principles for the development and evaluation of therapeutically sound web-based interventions for depression.

  • J Med Internet Res 2020;22(7):e16136

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  Chatbots and Conversational Agents

  Jul 13, 2020

  Effectiveness and Safety of Using Chatbots to Improve Mental Health: Systematic Review and Meta-Analysis

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  Authors List:

  • Alaa Ali Abd-Alrazaq  
  • Asma Rababeh  
  • Mohannad Alajlani  
  • Bridgette M Bewick  
  • Mowafa Househ  

  Abstract:

  Background: The global shortage of mental health workers has prompted the utilization of technological advancements, such as chatbots, to meet the needs of people with mental health conditions. Chatbots are systems that are able to converse and interact with human users using spoken, written, and visual language. While numerous studies have assessed the effectiveness and safety of using chatbots in mental health, no reviews have pooled the results of those studies. Objective: This study aimed to assess the effectiveness and safety of using chatbots to improve mental health through summarizing and pooling the results of previous studies. Methods: A systematic review was carried out to achieve this objective. The search sources were 7 bibliographic databases (eg, MEDLINE, EMBASE, PsycINFO), the search engine “Google Scholar,” and backward and forward reference list checking of the included studies and relevant reviews. Two reviewers independently selected the studies, extracted data from the included studies, and assessed the risk of bias. Data extracted from studies were synthesized using narrative and statistical methods, as appropriate. Results: Of 1048 citations retrieved, we identified 12 studies examining the effect of using chatbots on 8 outcomes. Weak evidence demonstrated that chatbots were effective in improving depression, distress, stress, and acrophobia. In contrast, according to similar evidence, there was no statistically significant effect of using chatbots on subjective psychological wellbeing. Results were conflicting regarding the effect of chatbots on the severity of anxiety and positive and negative affect. Only two studies assessed the safety of chatbots and concluded that they are safe in mental health, as no adverse events or harms were reported. Conclusions: Chatbots have the potential to improve mental health. However, the evidence in this review was not sufficient to definitely conclude this due to lack of evidence that their effect is clinically important, a lack of studies assessing each outcome, high risk of bias in those studies, and conflicting results for some outcomes. Further studies are required to draw solid conclusions about the effectiveness and safety of chatbots. Trial Registration: PROSPERO International Prospective Register of Systematic Reviews CRD42019141219; https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42019141219

  • J Med Internet Res 2020;22(7):e16021

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  JMIR Theme Issue 2020: COVID-19...

  Jul 10, 2020

  Similarities and Differences in COVID-19 Awareness, Concern, and Symptoms by Race and Ethnicity in the United States: Cross-Sectional Survey

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  Authors List:

  • Jeb Jones  
  • Patrick S Sullivan  
  • Travis H Sanchez  
  • Jodie L Guest  
  • Eric W Hall  
  • Nicole Luisi  
  • Maria Zlotorzynska  
  • Gretchen Wilde  
  • Heather Bradley  
  • Aaron J Siegler  

  Abstract:

  Background: Existing health disparities based on race and ethnicity in the United States are contributing to disparities in morbidity and mortality during the coronavirus disease (COVID-19) pandemic. We conducted an online survey of American adults to assess similarities and differences by race and ethnicity with respect to COVID-19 symptoms, estimates of the extent of the pandemic, knowledge of control measures, and stigma. Objective: The aim of this study was to describe similarities and differences in COVID-19 symptoms, knowledge, and beliefs by race and ethnicity among adults in the United States. Methods: We conducted a cross-sectional survey from March 27, 2020 through April 1, 2020. Participants were recruited on social media platforms and completed the survey on a secure web-based survey platform. We used chi-square tests to compare characteristics related to COVID-19 by race and ethnicity. Statistical tests were corrected using the Holm Bonferroni correction to account for multiple comparisons. Results: A total of 1435 participants completed the survey; 52 (3.6%) were Asian, 158 (11.0%) were non-Hispanic Black, 548 (38.2%) were Hispanic, 587 (40.9%) were non-Hispanic White, and 90 (6.3%) identified as other or multiple races. Only one symptom (sore throat) was found to be different based on race and ethnicity (P=.003); this symptom was less frequently reported by Asian (3/52, 5.8%), non-Hispanic Black (9/158, 5.7%), and other/multiple race (8/90, 8.9%) participants compared to those who were Hispanic (99/548, 18.1%) or non-Hispanic White (95/587, 16.2%). Non-Hispanic White and Asian participants were more likely to estimate that the number of current cases was at least 100,000 (P=.004) and were more likely to answer all 14 COVID-19 knowledge scale questions correctly (Asian participants, 13/52, 25.0%; non-Hispanic White participants, 180/587, 30.7%) compared to Hispanic (108/548, 19.7%) and non-Hispanic Black (25/158, 15.8%) participants. Conclusions: We observed differences with respect to knowledge of appropriate methods to prevent infection by the novel coronavirus that causes COVID-19. Deficits in knowledge of proper control methods may further exacerbate existing race/ethnicity disparities. Additional research is needed to identify trusted sources of information in Hispanic and non-Hispanic Black communities and create effective messaging to disseminate correct COVID-19 prevention and treatment information.

  • J Med Internet Res 2020;22(7):e20001

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  New Methods

  Jul 10, 2020

  Diagnosing Parkinson Disease Through Facial Expression Recognition: Video Analysis

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  Authors List:

  • Bo Jin  
  • Yue Qu  
  • Liang Zhang  
  • Zhan Gao  

  Abstract:

  Background: The number of patients with neurological diseases is currently increasing annually, which presents tremendous challenges for both patients and doctors. With the advent of advanced information technology, digital medical care is gradually changing the medical ecology. Numerous people are exploring new ways to receive a consultation, track their diseases, and receive rehabilitation training in more convenient and efficient ways. In this paper, we explore the use of facial expression recognition via artificial intelligence to diagnose a typical neurological system disease, Parkinson disease (PD). Objective: This study proposes methods to diagnose PD through facial expression recognition. Methods: We collected videos of facial expressions of people with PD and matched controls. We used relative coordinates and positional jitter to extract facial expression features (facial expression amplitude and shaking of small facial muscle groups) from the key points returned by Face++. Algorithms from traditional machine learning and advanced deep learning were utilized to diagnose PD. Results: The experimental results showed our models can achieve outstanding facial expression recognition ability for PD diagnosis. Applying a long short-term model neural network to the positions of the key features, precision and F1 values of 86% and 75%, respectively, can be reached. Further, utilizing a support vector machine algorithm for the facial expression amplitude features and shaking of the small facial muscle groups, an F1 value of 99% can be achieved. Conclusions: This study contributes to the digital diagnosis of PD based on facial expression recognition. The disease diagnosis model was validated through our experiment. The results can help doctors understand the real-time dynamics of the disease and even conduct remote diagnosis.

  • J Med Internet Res 2020;22(7):e18697

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  Infodemiology and Infoveillance

  Jul 10, 2020

  Evaluation of Volume of News Reporting and Opioid-Related Deaths in the United States: Comparative Analysis Study of Geographic and Socioeconomic Differences

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  Authors List:

  • Yulin Hswen  
  • Amanda Zhang  
  • Clark Freifeld  
  • John S Brownstein  

  Abstract:

  Background: News media coverage is a powerful influence on public attitude and government action. The digitization of news media covering the current opioid epidemic has changed the landscape of coverage and may have implications for how to effectively respond to the opioid crisis. Objective: This study aims to characterize the relationship between volume of online opioid news reporting and opioid-related deaths in the United States and how these measures differ across geographic and socioeconomic county-level factors. Methods: Online news reports from February 2018 to April 2019 on opioid-related events in the United States were extracted from Google News. News data were aggregated at the county level and compared against opioid-related death counts. Ordinary least squares regression was used to model opioid-related death rate and opioid news coverage with the inclusion of socioeconomic and geographic explanatory variables. Results: A total of 35,758 relevant news reports were collected representing 1789 counties. Regression analysis revealed that opioid-related death rate was positively associated with news reporting. However, opioid-related death rate and news reporting volume showed opposite correlations with educational attainment and rurality. When controlling for variation in death rate, counties in the Northeast were overrepresented by news coverage. Conclusions: Our results suggest that regional variation in the volume of opioid-related news reporting does not reflect regional variation in opioid-related death rate. Differences in the amount of media attention may influence perceptions of the severity of opioid epidemic. Future studies should investigate the influence of media reporting on public support and action on opioid issues.

  • J Med Internet Res 2020;22(7):e17693

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  Viewpoint

  Jul 10, 2020

  Blockchain in Health Care: Hope or Hype?

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  Authors List:

  • Rania El-Gazzar  
  • Karen Stendal  

  Abstract:

  There has been an increasing interest in blockchain technology from the health care sector in the last couple of years. The value proposition for using blockchain technology in the health care sector is to share sensitive patient data among health care entities securely and to empower patients. Blockchain technology allows patients to have an active role in developing and updating their own patient data. However, is blockchain technology really the silver bullet it seems to be? With this paper, we aim to understand the benefits and challenges of blockchain technology in the health care sector. We discuss innovation and security implications concerning blockchain technology in health care. Furthermore, we show that there is a need for more use cases to ensure the secure sharing of data within the health care sector. In our opinion, blockchain technology will not solve the issues encountered by the health care sector; in fact, it may raise more issues than it will solve.

  • J Med Internet Res 2020;22(7):e17199

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  General Original Articles (Editor: G....

  Jul 9, 2020

  Causal Effect of Honorary Titles on Physicians’ Service Volumes in Online Health Communities: Retrospective Study

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  Authors List:

  • Haiyan Yu  
  • Yali Wang  
  • Jying-Nan Wang  
  • Ya-Ling Chiu  
  • Hang Qiu  
  • Mingyue Gao  

  Abstract:

  Background: An OHC online health community (OHC) is an interactive platform for virtual communication between patients and physicians. Patients can typically search, seek, and share their experience and rate physicians, who may be involved in giving advice. Some OHC providers provide incentives in form of honorary titles to encourage the web-based involvement of physicians, but it is unclear whether the award of honorary titles has an impact on their consultation volume in an OHC. Objective: This study is designed to identify the differential treatment effect of the incentive policy on the service volumes for the subgroups of treatment and control in an OHC. This study aims to answer the following questions: Does an honorary title for physicians impact their service volumes in an OHC? During the period of discontinuity, can we identify the sharp effect of the incentive award on the outcomes of physicians’ service volumes? Methods: We acquired the targeted samples based on treatment, namely, physicians with an honorary title or not and outcomes measured before and after the award of the 2 subgroups. A regression discontinuity design was applied to investigate the impact of the honorary titles incentive as a treatment in an OHC. There was a sharply discontinuous effect of treatment on physicians’ online health service performance. The experimental data set consisted of 346 physicians in the treatment group (with honorary titles). Applying the propensity score matching method, the same size of physicians (n=346) was matched and selected as the control group. Results: A sharp discontinuity was found at the time of the physician receiving the honorary title. The results showed that the parametric estimates of the coefficient were significantly positively (P<.001) associated with monthly home page views. The jump in the monthly volumes of home page views was much sharper than that of the monthly consultations. Conclusions: The changes in the volumes of monthly consultations and home page views reflect the differential treatment effect of honorary titles on physicians’ service volumes. The effect of the incentive policy with honorary titles is objectively estimated from both the perspective of online and offline medical services in an OHC. Being named with honorary titles significantly multiplied monthly home page views, yet it did not significantly impact monthly consultations. This may be because consultation capacity is limited by the physician's schedule for consultations.

  • J Med Internet Res 2020;22(7):e18527

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  Clinical Information and Decision Making

  Jul 9, 2020

  Evaluating the Impact of the Grading and Assessment of Predictive Tools Framework on Clinicians and Health Care Professionals’ Decisions in Selecting...

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  Authors List:

  • Mohamed Khalifa  
  • Farah Magrabi  
  • Blanca Gallego Luxan  

  Abstract:

  Background: While selecting predictive tools for implementation in clinical practice or for recommendation in clinical guidelines, clinicians and health care professionals are challenged with an overwhelming number of tools. Many of these tools have never been implemented or evaluated for comparative effectiveness. To overcome this challenge, the authors developed and validated an evidence-based framework for grading and assessment of predictive tools (the GRASP framework). This framework was based on the critical appraisal of the published evidence on such tools. Objective: The aim of the study was to examine the impact of using the GRASP framework on clinicians’ and health care professionals’ decisions in selecting clinical predictive tools. Methods: A controlled experiment was conducted through a web-based survey. Participants were randomized to either review the derivation publications, such as studies describing the development of the predictive tools, on common traumatic brain injury predictive tools (control group) or to review an evidence-based summary, where each tool had been graded and assessed using the GRASP framework (intervention group). Participants in both groups were asked to select the best tool based on the greatest validation or implementation. A wide group of international clinicians and health care professionals were invited to participate in the survey. Task completion time, rate of correct decisions, rate of objective versus subjective decisions, and level of decisional conflict were measured. Results: We received a total of 194 valid responses. In comparison with not using GRASP, using the framework significantly increased correct decisions by 64%, from 53.7% to 88.1% (88.1/53.7=1.64; t₁₉₃=8.53; P<.001); increased objective decision making by 32%, from 62% (3.11/5) to 82% (4.10/5; t₁₈₉=9.24; P<.001); decreased subjective decision making based on guessing by 20%, from 49% (2.48/5) to 39% (1.98/5; t₁₈₈=−5.47; P<.001); and decreased prior knowledge or experience by 8%, from 71% (3.55/5) to 65% (3.27/5; t₁₈₇=−2.99; P=.003). Using GRASP significantly decreased decisional conflict and increased the confidence and satisfaction of participants with their decisions by 11%, from 71% (3.55/5) to 79% (3.96/5; t₁₈₈=4.27; P<.001), and by 13%, from 70% (3.54/5) to 79% (3.99/5; t₁₈₈=4.89; P<.001), respectively. Using GRASP decreased the task completion time, on the 90th percentile, by 52%, from 12.4 to 6.4 min (t₁₉₃=−0.87; P=.38). The average System Usability Scale of the GRASP framework was very good: 72.5% and 88% (108/122) of the participants found the GRASP useful. Conclusions: Using GRASP has positively supported and significantly improved evidence-based decision making. It has increased the accuracy and efficiency of selecting predictive tools. GRASP is not meant to be prescriptive; it represents a high-level approach and an effective, evidence-based, and comprehensive yet simple and feasible method to evaluate, compare, and select clinical predictive tools.

  • J Med Internet Res 2020;22(7):e15770

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  Medicine 2.0: Social Media, Open,...

  Jul 9, 2020

  An Instagram-Based Study to Understand Betel Nut Use Culture in Micronesia: Exploratory Content Analysis

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  Authors List:

  • Wayne Buente  
  • Francis Dalisay  
  • Pallav Pokhrel  
  • Hanae Kurihara Kramer  
  • Ian Pagano  

  Abstract:

  Background: A 2012 World Health Organization report recognizes betel nut use as an urgent public health threat faced by the Western Pacific region. However, compared with other addictive substances, little is known about how betel nuts are depicted on social media platforms. In particular, image-based social media platforms can be powerful tools for health communication. Studying the content of substance use on visual social media may provide valuable insights into public health interventions. Objective: This study aimed to explore and document the ways that betel nut is portrayed on the photo-sharing site Instagram. The analysis focuses on the hashtag #pugua, which refers to the local term for betel nut in Guam and other parts of Micronesia. Methods: An exploratory content analysis of 242 Instagram posts tagged #pugua was conducted based on previous research on substance use and Instagram and betel nut practices in Micronesia. In addition, the study examined the social engagement of betel nut content on the image-based platform. Results: The study findings revealed content themes referencing the betel nut or betel nut tree, betel nut preparation practices, and the unique social and cultural context surrounding betel nut activity in Guam and Micronesia. In addition, certain practices and cultural themes encouraged social engagement on Instagram. Conclusions: The findings from this study emphasize the cultural relevance of betel nut use in Micronesia. These findings provide a basis for empirically testing hypotheses related to the etiological roles of cultural identity and pride in shaping betel nut use behavior among Micronesians, particularly youths and young adults. Such research is likely to inform the development of culturally relevant betel nut prevention and cessation programs.

  • J Med Internet Res 2020;22(7):e13954

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  e-Learning and Medical Education

  Jul 8, 2020

  Benefits of Massive Open Online Course Participation: Deductive Thematic Analysis

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  Authors List:

  • Elizabeth R Blum  
  • Terese Stenfors  
  • Per J Palmgren  

  Abstract:

  Background: Massive open online courses (MOOCs), as originally conceived, promised to provide educational access to anyone with an internet connection. However, the expansiveness of MOOC education has been found to be somewhat limited. Nonetheless, leading universities continue to offer MOOCs, including many in the health sciences, on a number of private platforms. Therefore, research on online education must include thorough understanding of the role of MOOCs. To date, studies on MOOC participants have focused mainly on learners’ assessment of the course. It is known that MOOCs are not reaching the universal audiences that were predicted, and much knowledge has been gained about learners’ perceptions of MOOCs. However, there is little scholarship on what learners themselves gain from participating in MOOCs. Objective: As MOOC development persists and expands, scholars and developers should be made aware of the role of MOOCs in education by examining what these courses do offer their participants. The objective of this qualitative synthesis of a set of MOOC evaluation studies was to explore outcomes for MOOC learners, that is, how the learners themselves benefit from participating in MOOCs. Methods: To explore MOOC learners’ outcomes, we conducted a qualitative synthesis in the form of a deductive thematic analysis, aggregating findings from 17 individual studies selected from an existing systematic review of MOOC evaluation methods. We structured our inquiry using the Kirkpatrick model, considering Kirkpatrick levels 2, 3, and 4 as potential themes in our analysis. Results: Our analysis identified six types of Kirkpatrick outcomes in 17 studies. Five of these outcomes (learning/general knowledge, skills, attitudes, confidence, and commitment) fit into Kirkpatrick Level 2, while Kirkpatrick Level 3 outcomes concerning behavior/application were seen in four studies. Two additional themes were identified outside of the Kirkpatrick framework: culture and identity outcomes and affective/emotional outcomes. Kirkpatrick Level 4 was not represented among the outcomes we examined. Conclusions: Our findings point to some gains from MOOCs. While we can expect MOOCs to persist, how learners benefit from the experience of participating in MOOCs remains unclear.

  • J Med Internet Res 2020;22(7):e17318

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  Telehealth and Telemonitoring

  Jul 8, 2020

  The Effects of Telemonitoring on Patient Compliance With Self-Management Recommendations and Outcomes of the Innovative Telemonitoring Enhanced Care Program...

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  Authors List:

  • Hang Ding  
  • Rajiv Jayasena  
  • Sheau Huey Chen  
  • Andrew Maiorana  
  • Alison Dowling  
  • Jamie Layland  
  • Norm Good  
  • Mohanraj Karunanithi  
  • Iain Edwards  

  Abstract:

  Background: Telemonitoring enables care providers to remotely support outpatients in self-managing chronic heart failure (CHF), but the objective assessment of patient compliance with self-management recommendations has seldom been studied. Objective: This study aimed to evaluate patient compliance with self-management recommendations of an innovative telemonitoring enhanced care program for CHF (ITEC-CHF). Methods: We conducted a multicenter randomized controlled trial with a 6-month follow-up. The ITEC-CHF program comprised the provision of Bluetooth-enabled scales linked to a call center and nurse care services to assist participants with weight monitoring compliance. Compliance was defined a priori as weighing at least 4 days per week, analyzed objectively from weight recordings on the scales. The intention-to-treat principle was used to perform the analysis. Results: A total of 184 participants (141/184, 76.6% male), with a mean age of 70.1 (SD 12.3) years, were randomized to receive either ITEC-CHF (n=91) or usual care (control; n=93), of which 67 ITEC-CHF and 81 control participants completed the intervention. For the compliance criterion of weighing at least 4 days per week, the proportion of compliant participants in the ITEC-CHF group was not significantly higher than that in the control group (ITEC-CHF: 67/91, 74% vs control: 56/91, 60%; P=.06). However, the proportion of ITEC-CHF participants achieving the stricter compliance standard of at least 6 days a week was significantly higher than that in the control group (ITEC-CHF: 41/91, 45% vs control: 23/93, 25%; P=.005). Conclusions: ITEC-CHF improved participant compliance with weight monitoring, although the withdrawal rate was high. Telemonitoring is a promising method for supporting both patients and clinicians in the management of CHF. However, further refinements are required to optimize this model of care. Trial Registration: Australian New Zealand Clinical Trial Registry ACTRN12614000916640; https://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=366691

  • J Med Internet Res 2020;22(7):e17559

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