@Article{info:doi/10.2196/66509,
author="Raspado, Olivier
and Brack, Michel
and Brack, Olivier
and Vivancos, M{\'e}lanie
and Esparcieux, Aur{\'e}lie
and Cart-Tanneur, Emmanuelle
and Aouifi, Abdellah",
title="Oxidative Stress Markers and Prediction of Severity With a Machine Learning Approach in Hospitalized Patients With COVID-19 and Severe Lung Disease: Observational, Retrospective, Single-Center Feasibility Study",
journal="JMIR Form Res",
year="2025",
month="Apr",
day="11",
volume="9",
pages="e66509",
keywords="oxidative stress",
keywords="COVID-19",
keywords="SARS-CoV-2",
keywords="coronavirus",
keywords="respiratory",
keywords="infectious",
keywords="pulmonary",
keywords="respiration disorders",
keywords="hospitalization",
keywords="machine learning",
keywords="ML",
keywords="biomarker",
keywords="lung",
keywords="severity",
keywords="prediction",
abstract="Background: Serious pulmonary pathologies of infectious, viral, or bacterial origin are accompanied by inflammation and an increase in oxidative stress (OS). In these situations, biological measurements of OS are technically difficult to obtain, and their results are difficult to interpret. OS assays that do not require complex preanalytical methods, as well as machine learning methods for improving interpretation of the results, would be very useful tools for medical and care teams. Objective: We aimed to identify relevant OS biomarkers associated with the severity of hospitalized patients' condition and identify possible correlations between OS biomarkers and the clinical status of hospitalized patients with COVID-19 and severe lung disease at the time of hospital admission. Methods: All adult patients hospitalized with COVID-19 at the Infirmerie Protestante (Lyon, France) from February 9, 2022, to May 18, 2022, were included, regardless of the care service they used, during the respiratory infectious COVID-19 epidemic. We collected serous biomarkers from the patients (zinc [Zn], copper [Cu], Cu/Zn ratio, selenium, uric acid, high-sensitivity C-reactive protein [hs-CRP], oxidized low-density lipoprotein, glutathione peroxidase, glutathione reductase, and thiols), as well as demographic variables and comorbidities. A support vector machine (SVM) model was used to predict the severity of the patients' condition based on the collected data as a training set. Results: A total of 28 patients were included: 8 were asymptomatic at admission (grade 0), 14 had mild to moderate symptoms (grade 1) and 6 had severe to critical symptoms (grade 3). As the first outcome, we found that 3 biomarkers of OS were associated with severity (Zn, Cu/Zn ratio, and thiols), especially between grades 0 and 1 and between grades 0 and 2. As a second outcome, we found that the SVM model could predict the level of severity based on a biological analysis of the level of OS, with only 7\% misclassification on the training dataset. As an illustrative example, we simulated 3 different biological profiles (named A, B, and C) and submitted them to the SVM model. Profile B had significantly high Zn, low hs-CRP, a low Cu/Zn ratio, and high thiols, corresponding to grade 0. Profile C had low Zn, low selenium, high oxidized low-density lipoprotein, high glutathione peroxidase, a low Cu/Zn ratio, and low glutathione reductase, corresponding to grade 2. Conclusions: The level of severity of pulmonary damage in patients hospitalized with COVID-19 was predicted using an SVM model; moderate to severe symptoms in patients were associated with low Zn, low plasma thiol, increased hs-CRP, and an increased Cu/Zn ratio among a panel of 10 biomarkers of OS. Since this panel does not require a complex preanalytical method, it can be used and studied in other pathologies associated with OS, such as infectious pathologies or chronic diseases. ",
doi="10.2196/66509",
url="https://formative.jmir.org/2025/1/e66509"
}


@Article{info:doi/10.2196/63681,
author="Senathirajah, Yalini
and Kaufman, R. David
and Cato, Kenrick
and Daniel, Pia
and Roblin, Patricia
and Kushniruk, Andre
and Borycki, M. Elizabeth
and Feld, Emanuel
and Debi, Poli",
title="The Impact of the Burden of COVID-19 Regulatory Reporting in a Small Independent Hospital and a Large Network Hospital: Comparative Mixed Methods Study",
journal="Online J Public Health Inform",
year="2025",
month="Mar",
day="26",
volume="17",
pages="e63681",
keywords="regulatory reporting",
keywords="human factors",
keywords="reporting burden",
keywords="emergency response",
keywords="COVID-19",
keywords="hospital resilience",
keywords="pandemic response",
abstract="Background: During the COVID-19 pandemic in 2020, hospitals encountered numerous challenges that compounded their difficulties. Some of these challenges directly impacted patient care, such as the need to expand capacities, adjust services, and use new knowledge to save lives in an ever-evolving situation. In addition, hospitals faced regulatory challenges. Objective: This paper presents the findings of a qualitative study that aimed to compare the effects of reporting requirements on a small independent hospital and a large network hospital during the COVID-19 pandemic. Methods: We used both quantitative and qualitative analyses and conducted 51 interviews, which were thematically analyzed. We quantified the changes in regulatory reporting requirements during the first 14 months of the pandemic. Results: Reporting requirements placed a substantial time burden on key clinical personnel at the small independent hospital, consequently reducing the time available for patient care. Conversely, the large network hospital had dedicated nonclinical staff responsible for reporting duties, and their robust health information system facilitated this work. Conclusions: The discrepancy in health IT capabilities suggests that there may be significant institutional inequities affecting smaller hospitals' ability to respond to a pandemic and adequately support public health efforts. Electronic certification guidelines are essential to addressing the substantial equity issues. We discuss in detail the health care policy implications of these findings. ",
doi="10.2196/63681",
url="https://ojphi.jmir.org/2025/1/e63681",
url="http://www.ncbi.nlm.nih.gov/pubmed/40137048"
}


@Article{info:doi/10.2196/52972,
author="Rosenfeld, Daniel
and Brennan, Sean
and Wallach, Andrew
and Long, Theodore
and Keeley, Chris
and Kurien, Joseph Sarah",
title="COVID-19 Testing Equity in New York City During the First 2 Years of the Pandemic: Demographic Analysis of Free Testing Data",
journal="JMIR Public Health Surveill",
year="2025",
month="Mar",
day="13",
volume="11",
pages="e52972",
keywords="COVID-19 testing",
keywords="health disparities",
keywords="equity in testing",
keywords="New York City",
keywords="socioeconomic factors",
keywords="testing accessibility",
keywords="health care inequalities",
keywords="demographic analysis",
keywords="COVID-19 mortality",
keywords="coronavirus",
keywords="SARS-CoV-2",
keywords="pandemic",
keywords="equitable testing",
keywords="cost",
keywords="poor neighborhood",
keywords="resources",
abstract="Background: COVID-19 has caused over 46,000 deaths in New York City, with a disproportional impact on certain communities. As part of the COVID-19 response, the city has directly administered over 6 million COVID-19 tests (in addition to millions of indirectly administered tests not covered in this analysis) at no cost to individuals, resulting in nearly half a million positive results. Given that the prevalence of testing, throughout the pandemic, has tended to be higher in more affluent areas, these tests were targeted to areas with fewer resources. Objective: This study aimed to evaluate the impact of New York City's COVID-19 testing program; specifically, we aimed to review its ability to provide equitable testing in economically, geographically, and demographically diverse populations. Of note, in addition to the brick-and-mortar testing sites evaluated herein, this program conducted 2.1 million tests through mobile units to further address testing inequity. Methods: Testing data were collected from the in-house Microsoft SQL Server Management Studio 18 Clarity database, representing 6,347,533 total tests and 449,721 positive test results. These tests were conducted at 48 hospital system locations. Per capita testing rates by zip code tabulation area (ZCTA) and COVID-19 positivity rates by ZCTA were used as dependent variables in separate regressions. Median income, median age, the percentage of English-speaking individuals, and the percentage of people of color were used as independent demographic variables to analyze testing patterns across several intersecting identities. Negative binomial regressions were run in a Jupyter Notebook using Python. Results: Per capita testing inversely correlated with median income geographically. The overall pseudo r2 value was 0.1101 when comparing hospital system tests by ZCTA against the selected variables. The number of tests significantly increased as median income fell (SE 1.00000155; P<.001). No other variables correlated at a significant level with the number of tests (all P values were >.05). When considering positive test results by ZCTA, the number of positive test results also significantly increased as median income fell (SE 1.57e--6; P<.001) and as the percentage of female residents fell (SE 0.957; P=.001). The number of positive test results by ZCTA rose at a significant level alongside the percentage of English-only speakers (SE 0.271; P=.03). Conclusions: New York City's COVID-19 testing program was able to improve equity through the provision of no-cost testing, which focused on areas of the city that were disproportionately impacted by COVID-19 and had fewer resources. By detecting higher numbers of positive test results in resource-poor neighborhoods, New York City was able to deploy additional resources, such as those for contact tracing and isolation and quarantine support (eg, free food delivery and free hotel stays), early during the COVID-19 pandemic. Equitable deployment of testing is feasible and should be considered early in future epidemics or pandemics. ",
doi="10.2196/52972",
url="https://publichealth.jmir.org/2025/1/e52972"
}


@Article{info:doi/10.2196/63755,
author="Li, Wanxin
and Hua, Yining
and Zhou, Peilin
and Zhou, Li
and Xu, Xin
and Yang, Jie",
title="Characterizing Public Sentiments and Drug Interactions in the COVID-19 Pandemic Using Social Media: Natural Language Processing and Network Analysis",
journal="J Med Internet Res",
year="2025",
month="Mar",
day="5",
volume="27",
pages="e63755",
keywords="COVID-19",
keywords="natural language processing",
keywords="drugs",
keywords="social media",
keywords="pharmacovigilance",
keywords="public health",
abstract="Background: While the COVID-19 pandemic has induced massive discussion of available medications on social media, traditional studies focused only on limited aspects, such as public opinions, and endured reporting biases, inefficiency, and long collection times. Objective: Harnessing drug-related data posted on social media in real-time can offer insights into how the pandemic impacts drug use and monitor misinformation. This study aimed to develop a natural language processing (NLP) pipeline tailored for the analysis of social media discourse on COVID-19--related drugs. Methods: This study constructed a full pipeline for COVID-19--related drug tweet analysis, using pretrained language model--based NLP techniques as the backbone. This pipeline is architecturally composed of 4 core modules: named entity recognition and normalization to identify medical entities from relevant tweets and standardize them to uniform medication names for time trend analysis, target sentiment analysis to reveal sentiment polarities associated with the entities, topic modeling to understand underlying themes discussed by the population, and drug network analysis to dig potential adverse drug reactions (ADR) and drug-drug interactions (DDI). The pipeline was deployed to analyze tweets related to the COVID-19 pandemic and drug therapies between February 1, 2020, and April 30, 2022. Results: From a dataset comprising 169,659,956 COVID-19--related tweets from 103,682,686 users, our named entity recognition model identified 2,124,757 relevant tweets sourced from 1,800,372 unique users, and the top 5 most-discussed drugs: ivermectin, hydroxychloroquine, remdesivir, zinc, and vitamin D. Time trend analysis revealed that the public focused mostly on repurposed drugs (ie, hydroxychloroquine and ivermectin), and least on remdesivir, the only officially approved drug among the 5. Sentiment analysis of the top 5 most-discussed drugs revealed that public perception was predominantly shaped by celebrity endorsements, media hot spots, and governmental directives rather than empirical evidence of drug efficacy. Topic analysis obtained 15 general topics of overall drug-related tweets, with ``clinical treatment effects of drugs'' and ``physical symptoms'' emerging as the most frequently discussed topics. Co-occurrence matrices and complex network analysis further identified emerging patterns of DDI and ADR that could be critical for public health surveillance like better safeguarding public safety in medicines use. Conclusions: This study shows that an NLP-based pipeline can be a robust tool for large-scale public health monitoring and can offer valuable supplementary data for traditional epidemiological studies concerning DDI and ADR. The framework presented here aspires to serve as a cornerstone for future social media--based public health analytics. ",
doi="10.2196/63755",
url="https://www.jmir.org/2025/1/e63755",
url="http://www.ncbi.nlm.nih.gov/pubmed/40053730"
}


@Article{info:doi/10.2196/52119,
author="Tighe, Carlos
and Ngongalah, Lem
and Sent{\'i}s, Alexis
and Orchard, Francisco
and Pacurar, Gheorghe-Aurel
and Hayes, Conor
and Hayes, S. Jessica
and Toader, Adrian
and Connolly, A. M{\'a}ire",
title="Building and Developing a Tool (PANDEM-2 Dashboard) to Strengthen Pandemic Management: Participatory Design Study",
journal="JMIR Public Health Surveill",
year="2025",
month="Mar",
day="5",
volume="11",
pages="e52119",
keywords="pandemic preparedness and response",
keywords="COVID-19",
keywords="cross-border collaboration",
keywords="surveillance",
keywords="data collection",
keywords="data standardization",
keywords="data sharing",
keywords="dashboard",
keywords="IT system",
keywords="IT tools",
abstract="Background: The COVID-19 pandemic exposed challenges in pandemic management, particularly in real-time data sharing and effective decision-making. Data protection concerns and the lack of data interoperability and standardization hindered the collection, analysis, and interpretation of critical information. Effective data visualization and customization are essential to facilitate decision-making. Objective: This study describes the development of the PANDEM-2 dashboard, a system providing a standardized and interactive platform for decision-making in pandemic management. It outlines the participatory approaches used to involve expert end users in its development and addresses key considerations of privacy, data protection, and ethical and social issues. Methods: Development was informed by a review of 25 publicly available COVID-19 dashboards, leading to the creation of a visualization catalog. User requirements were gathered through workshops and consultations with 20 experts from various health care and public health professions in 13 European Union countries. These were further refined by mapping variables and indicators required to fulfill the identified needs. Through a participatory design process, end users interacted with a preprototype platform, explored potential interface designs, and provided feedback to refine the system's components. Potential privacy, data protection, and ethical and social risks associated with the technology, along with mitigation strategies, were identified through an iterative impact assessment. Results: Key variables incorporated into the PANDEM-2 dashboard included case rates, number of deaths, mortality rates, hospital resources, hospital admissions, testing, contact tracing, and vaccination uptake. Cases, deaths, and vaccination uptake were prioritized as the most relevant and readily available variables. However, data gaps, particularly in contact tracing and mortality rates, highlighted the need for better data collection and reporting mechanisms. User feedback emphasized the importance of diverse data visualization formats combining different data types, as well as analyzing data across various time frames. Users also expressed interest in generating custom visualizations and reports, especially on the impact of government interventions. Participants noted challenges in data reporting, such as inconsistencies in reporting levels, time intervals, the need for standardization between member states, and General Data Protection Regulation concerns for data sharing. Identified risks included ethical concerns (accessibility, user autonomy, responsible use, transparency, and accountability), privacy and data protection (security and access controls and data reidentification), and social issues (unintentional bias, data quality and accuracy, dependency on technology, and collaborative development). Mitigation measures focused on designing user-friendly interfaces, implementing robust security protocols, and promoting cross-member state collaboration. Conclusions: The PANDEM-2 dashboard provides an adaptable, user-friendly platform for pandemic preparedness and response. Our findings highlight the critical role of data interoperability, cross-border collaboration, and custom IT tools in strengthening future health crisis management. They also offer valuable insights into the challenges and opportunities in developing IT solutions to support pandemic preparedness. ",
doi="10.2196/52119",
url="https://publichealth.jmir.org/2025/1/e52119",
url="http://www.ncbi.nlm.nih.gov/pubmed/40053759"
}


@Article{info:doi/10.2196/60369,
author="Tan, Jin Rayner Kay
and Hensel, Devon
and Ivanova, Olena
and Bravo, Gomez Raquel
and Olumide, Adesola
and Adebayo, Emmanuel
and Cleeve, Amanda
and Gesselman, Amanda
and Shah, Jyoti Sonam
and Adesoba, Helen
and Marley, Gifty
and Tang, Weiming",
title="Telemedicine Use During the COVID-19 Pandemic in 8 Countries From the International Sexual Health and Reproductive Health Consortium: Web-Based Cross-Sectional Survey Study",
journal="J Med Internet Res",
year="2025",
month="Mar",
day="4",
volume="27",
pages="e60369",
keywords="COVID-19",
keywords="telemedicine",
keywords="sexual and reproductive health",
keywords="pandemic",
keywords="web-based survey",
keywords="sexual health",
keywords="reproductive health",
keywords="communication technology",
keywords="medical education",
keywords="contraception",
keywords="abortion",
keywords="health care delivery",
keywords="care",
keywords="chronic condition",
abstract="Background: Telemedicine is an important way to fill in the access gap to in-person health care services during challenging times like pandemics. Objective: This study aimed to investigate the role that telemedicine played during the COVID-19 pandemic by multicountry comparison of the use of telemedicine prior to and during the pandemic. Methods: This study analyzes data from the second wave of the International Sexual Health and Reproductive Health study. This included data collected between April 2021 and July 2022 in 8 countries, including Armenia (n=296), Egypt (n=889), Germany (n=138), Moldova (n=311), Nigeria (n=205), Portugal (n=951), Singapore (n=13), and Spain (n=54). This study covered sociodemographics, sexual and reproductive health (SRH), and telemedicine use. Descriptive statistics and multilevel modeling were used to assess the factors influencing the use of telemedicine. Results: Overall, 2857 participants were recruited. Approximately 57.6\% (n=1646) of participants had never used telemedicine prior to COVID-19 measures, while 45.9\% (n=1311) of participants required health care but reported not using telemedicine services following the introduction of COVID-19 measures. In high-income countries, the most common mode reported was audio-based telemedicine services, with 283 (71.8\%) and 417 (73.5\%) participants doing so before and during COVID-19, respectively. This was followed by text-based telemedicine services, with 152 (38.6\%) and 173 (30.5\%) participants doing so before and during COVID-19, respectively. In low- to middle-income countries, many participants also reported using audio-based telemedicine services, with 288 (35.3\%) and 237 (40.8\%) participants doing so before and during COVID-19, respectively. This was followed by chat-based telemedicine services, with 265 (32.4\%) and 217 (37.3\%) participants doing so before and during COVID-19, respectively. Multilevel modeling revealed that those who were older (adjusted odds ratio [aOR] 0.99, 95\% CI 0.99-1.00) and were in countries with a higher gross domestic product per capita (aOR 0.99, 95\% CI 0.98-1.00) were less likely to have ever used telemedicine. Participants who were of male sex assigned at birth (aOR 0.79, 95\% CI 0.65-0.96) were less likely to use telemedicine during the pandemic. Participants who perceived that they were worse off financially were more likely to have switched to telemedicine during COVID-19 (aOR 1.39, 95\% CI 1.02-1.89) and were more likely to report having a poor or fair experience of telemedicine services (aOR 1.75, 95\% CI 1.34-2.29). When sexual orientation was included in the model, nonheterosexual individuals were more likely to ever use telemedicine prior to COVID-19 (aOR 1.35, 95\% CI 1.08-1.69), more likely to have used telemedicine during COVID-19 (aOR 1.58, 95\% CI 1.24-2.02), and more likely to have switched to telemedicine during COVID-19 (aOR 1.55, 95\% CI 1.09-2.21). Conclusions: Telemedicine played a key role in addressing health care needs during the COVID-19 pandemic. Age, sex, economic status, and sexual orientation influenced its use. ",
doi="10.2196/60369",
url="https://www.jmir.org/2025/1/e60369",
url="http://www.ncbi.nlm.nih.gov/pubmed/40053813"
}


@Article{info:doi/10.2196/63996,
author="Amid, Clara
and van Roode, Y. Martine
and Rinck, Gabriele
and van Beek, Janko
and de Vries, D. Rory
and van Nierop, P. Gijsbert
and van Gorp, M. Eric C.
and Tobian, Frank
and Oude Munnink, B. Bas
and Sikkema, S. Reina
and Jaenisch, Thomas
and Cochrane, Guy
and Koopmans, G. Marion P.",
title="A Call for Action: Lessons Learned From a Pilot to Share a Complex, Linked COVID-19 Cohort Dataset for Open Science",
journal="JMIR Public Health Surveill",
year="2025",
month="Feb",
day="11",
volume="11",
pages="e63996",
keywords="data sharing",
keywords="data management",
keywords="open science",
keywords="COVID-19",
keywords="emerging infectious disease",
keywords="global health",
doi="10.2196/63996",
url="https://publichealth.jmir.org/2025/1/e63996"
}


@Article{info:doi/10.2196/63708,
author="Davoody, Nadia
and Stathakarou, Natalia
and Swain, Cara
and Bonacina, Stefano",
title="Exploring the Impact of the COVID-19 Pandemic on Learning Experience, Mental Health, Adaptability, and Resilience Among Health Informatics Master's Students: Focus Group Study",
journal="JMIR Med Educ",
year="2025",
month="Feb",
day="10",
volume="11",
pages="e63708",
keywords="COVID-19 pandemic",
keywords="eHealth",
keywords="blended learning",
keywords="health informatics",
keywords="higher education adaptation",
abstract="Background: The shift to online education due to the COVID-19 pandemic posed significant challenges and opportunities for students, affecting their academic performance, mental well-being, and engagement. Objective: This study aimed to explore the overall learning experience among health informatics master's students at Karolinska Institutet, Sweden, and the strategies they used to overcome learning challenges posed by the COVID-19 pandemic. Methods: Through 3 structured focus groups, this study explored health informatics master's students' experiences of shifting learning environments for classes that started in 2019, 2020, and 2021. All focus group sessions were recorded and transcribed verbatim. Inductive content analysis was used to analyze the data. Results: The results highlight the benefits of increased autonomy and flexibility and identify challenges such as technical difficulties, diminished social interactions, and psychological impacts. This study underscores the importance of effective online educational strategies, technological preparedness, and support systems to enhance student learning experiences during emergencies. The findings of this study highlight implications for educators, students, and higher education institutions to embrace adaptation and foster innovation. Implications for educators, students, and higher education institutions include the need for educators to stay current with the latest educational technologies and design teaching strategies and pedagogical approaches suited to both online and in-person settings to effectively foster student engagement. Students must be informed about the technological requirements for online learning and adequately prepared to meet them. Institutions play a critical role in ensuring equitable access to technology, guiding and supporting educators in adopting innovative tools and methods, and offering mental health resources to assist students in overcoming the challenges of evolving educational environments. Conclusions: This research contributes to understanding the complexities of transitioning to online learning in urgent circumstances and offers insights for better preparing educational institutions for future pandemics. ",
doi="10.2196/63708",
url="https://mededu.jmir.org/2025/1/e63708"
}


@Article{info:doi/10.2196/53434,
author="Alshanik, Farah
and Khasawneh, Rawand
and Dalky, Alaa
and Qawasmeh, Ethar",
title="Unveiling Topics and Emotions in Arabic Tweets Surrounding the COVID-19 Pandemic: Topic Modeling and Sentiment Analysis Approach",
journal="JMIR Infodemiology",
year="2025",
month="Feb",
day="10",
volume="5",
pages="e53434",
keywords="topic modeling",
keywords="sentiment analysis",
keywords="COVID-19",
keywords="social media",
keywords="Twitter",
keywords="public discussion",
abstract="Background: The worldwide effects of the COVID-19 pandemic have been profound, and the Arab world has not been exempt from its wide-ranging consequences. Within this context, social media platforms such as Twitter have become essential for sharing information and expressing public opinions during this global crisis. Careful investigation of Arabic tweets related to COVID-19 can provide invaluable insights into the common topics and underlying sentiments that shape discussions about the COVID-19 pandemic. Objective: This study aimed to understand the concerns and feelings of Twitter users in Arabic-speaking countries about the COVID-19 pandemic. This was accomplished through analyzing the themes and sentiments that were expressed in Arabic tweets about the COVID-19 pandemic. Methods: In this study, 1 million Arabic tweets about COVID-19 posted between March 1 and March 31, 2020, were analyzed. Machine learning techniques, such as topic modeling and sentiment analysis, were applied to understand the main topics and emotions that were expressed in these tweets. Results: The analysis of Arabic tweets revealed several prominent topics related to COVID-19. The analysis identified and grouped 16 different conversation topics that were organized into eight themes: (1) preventive measures and safety, (2) medical and health care aspects, (3) government and social measures, (4) impact and numbers, (5) vaccine development and research, (6) COVID-19 and religious practices, (7) global impact of COVID-19 on sports and countries, and (8) COVID-19 and national efforts. Across all the topics identified, the prevailing sentiments regarding the spread of COVID-19 were primarily centered around anger, followed by disgust, joy, and anticipation. Notably, when conversations revolved around new COVID-19 cases and fatalities, public tweets revealed a notably heightened sense of anger in comparison to other subjects. Conclusions: The study offers valuable insights into the topics and emotions expressed in Arabic tweets related to COVID-19. It demonstrates the significance of social media platforms, particularly Twitter, in capturing the Arabic-speaking community's concerns and sentiments during the COVID-19 pandemic. The findings contribute to a deeper understanding of the prevailing discourse, enabling stakeholders to tailor effective communication strategies and address specific public concerns. This study underscores the importance of monitoring social media conversations in Arabic to support public health efforts and crisis management during the COVID-19 pandemic. ",
doi="10.2196/53434",
url="https://infodemiology.jmir.org/2025/1/e53434",
url="http://www.ncbi.nlm.nih.gov/pubmed/39928401"
}


@Article{info:doi/10.2196/58656,
author="Kahlawi, Adham
and Masri, Firas
and Ahmed, Wasim
and Vidal-Alaball, Josep",
title="Cross-Cultural Sense-Making of Global Health Crises: A Text Mining Study of Public Opinions on Social Media Related to the COVID-19 Pandemic in Developed and Developing Economies",
journal="J Med Internet Res",
year="2025",
month="Jan",
day="27",
volume="27",
pages="e58656",
keywords="COVID-19",
keywords="SARS-CoV-2",
keywords="pandemic",
keywords="citizen opinion",
keywords="text mining",
keywords="LDA",
keywords="health crisis",
keywords="developing economies",
keywords="Italy",
keywords="Egypt",
keywords="UK",
keywords="dataset",
keywords="content analysis",
keywords="social media",
keywords="twitter",
keywords="tweet",
keywords="sentiment",
keywords="attitude",
keywords="perception",
keywords="perspective",
keywords="machine learning",
keywords="latent Dirichlet allocation",
keywords="vaccine",
keywords="vaccination",
keywords="public health",
keywords="infectious",
abstract="Background: The COVID-19 pandemic reshaped social dynamics, fostering reliance on social media for information, connection, and collective sense-making. Understanding how citizens navigate a global health crisis in varying cultural and economic contexts is crucial for effective crisis communication. Objective: This study examines the evolution of citizen collective sense-making during the COVID-19 pandemic by analyzing social media discourse across Italy, the United Kingdom, and Egypt, representing diverse economic and cultural contexts. Methods: A total of 755,215 social media posts from X (formerly Twitter) were collected across 3 time periods: the virus' emergence (February 15 to March 31, 2020), strict lockdown (April 1 to May 30, 2020), and the vaccine rollout (December 1, 2020 to January 15, 2021). In total, 284,512 posts from Italy, 261,978 posts from the United Kingdom, and 209,725 posts from Egypt were analyzed using the latent Dirichlet allocation algorithm to identify key thematic topics and track shifts in discourse across time and regions. Results: The analysis revealed significant regional and temporal differences in collective sense-making during the pandemic. In Italy and the United Kingdom, public discourse prominently addressed pragmatic health care measures and government interventions, reflecting higher institutional trust. By contrast, discussions in Egypt were more focused on religious and political themes, highlighting skepticism toward governmental capacity and reliance on alternative frameworks for understanding the crisis. Over time, all 3 countries displayed a shift in discourse toward vaccine-related topics during the later phase of the pandemic, highlighting its global significance. Misinformation emerged as a recurrent theme across regions, demonstrating the need for proactive measures to ensure accurate information dissemination. These findings emphasize the role of cultural, economic, and institutional factors in shaping public responses during health crises. Conclusions: Crisis communication is influenced by cultural, economic, and institutional contexts, as evidenced by regional variations in citizen engagement. Transparent and culturally adaptive communication strategies are essential to combat misinformation and build public trust. This study highlights the importance of tailoring crisis responses to local contexts to improve compliance and collective resilience. ",
doi="10.2196/58656",
url="https://www.jmir.org/2025/1/e58656"
}


@Article{info:doi/10.2196/50235,
author="Jefferson, Emily
and Milligan, Gordon
and Johnston, Jenny
and Mumtaz, Shahzad
and Cole, Christian
and Best, Joseph
and Giles, Charles Thomas
and Cox, Samuel
and Masood, Erum
and Horban, Scott
and Urwin, Esmond
and Beggs, Jillian
and Chuter, Antony
and Reilly, Gerry
and Morris, Andrew
and Seymour, David
and Hopkins, Susan
and Sheikh, Aziz
and Quinlan, Philip",
title="The Challenges and Lessons Learned Building a New UK Infrastructure for Finding and Accessing Population-Wide COVID-19 Data for Research and Public Health Analysis: The CO-CONNECT Project",
journal="J Med Internet Res",
year="2024",
month="Nov",
day="20",
volume="26",
pages="e50235",
keywords="COVID-19",
keywords="infrastructure",
keywords="trusted research environments",
keywords="safe havens",
keywords="feasibility analysis",
keywords="cohort discovery",
keywords="federated analytics",
keywords="federated discovery",
keywords="lessons learned",
keywords="population wide",
keywords="data",
keywords="public health",
keywords="analysis",
keywords="CO-CONNECT",
keywords="challenges",
keywords="data transformation",
doi="10.2196/50235",
url="https://www.jmir.org/2024/1/e50235"
}


@Article{info:doi/10.2196/49997,
author="Wen, Andrew
and Wang, Liwei
and He, Huan
and Fu, Sunyang
and Liu, Sijia
and Hanauer, A. David
and Harris, R. Daniel
and Kavuluru, Ramakanth
and Zhang, Rui
and Natarajan, Karthik
and Pavinkurve, P. Nishanth
and Hajagos, Janos
and Rajupet, Sritha
and Lingam, Veena
and Saltz, Mary
and Elowsky, Corey
and Moffitt, A. Richard
and Koraishy, M. Farrukh
and Palchuk, B. Matvey
and Donovan, Jordan
and Lingrey, Lora
and Stone-DerHagopian, Garo
and Miller, T. Robert
and Williams, E. Andrew
and Leese, J. Peter
and Kovach, I. Paul
and Pfaff, R. Emily
and Zemmel, Mikhail
and Pates, D. Robert
and Guthe, Nick
and Haendel, A. Melissa
and Chute, G. Christopher
and Liu, Hongfang
and ",
title="A Case Demonstration of the Open Health Natural Language Processing Toolkit From the National COVID-19 Cohort Collaborative and the Researching COVID to Enhance Recovery Programs for a Natural Language Processing System for COVID-19 or Postacute Sequelae of SARS CoV-2 Infection: Algorithm Development and Validation",
journal="JMIR Med Inform",
year="2024",
month="Sep",
day="9",
volume="12",
pages="e49997",
keywords="natural language processing",
keywords="clinical information extraction",
keywords="clinical phenotyping",
keywords="extract",
keywords="extraction",
keywords="NLP",
keywords="phenotype",
keywords="phenotyping",
keywords="narratives",
keywords="unstructured",
keywords="PASC",
keywords="COVID",
keywords="COVID-19",
keywords="SARS-CoV-2",
keywords="OHNLP",
keywords="Open Health Natural Language Processing",
abstract="Background: A wealth of clinically relevant information is only obtainable within unstructured clinical narratives, leading to great interest in clinical natural language processing (NLP). While a multitude of approaches to NLP exist, current algorithm development approaches have limitations that can slow the development process. These limitations are exacerbated when the task is emergent, as is the case currently for NLP extraction of signs and symptoms of COVID-19 and postacute sequelae of SARS-CoV-2 infection (PASC). Objective: This study aims to highlight the current limitations of existing NLP algorithm development approaches that are exacerbated by NLP tasks surrounding emergent clinical concepts and to illustrate our approach to addressing these issues through the use case of developing an NLP system for the signs and symptoms of COVID-19 and PASC. Methods: We used 2 preexisting studies on PASC as a baseline to determine a set of concepts that should be extracted by NLP. This concept list was then used in conjunction with the Unified Medical Language System to autonomously generate an expanded lexicon to weakly annotate a training set, which was then reviewed by a human expert to generate a fine-tuned NLP algorithm. The annotations from a fully human-annotated test set were then compared with NLP results from the fine-tuned algorithm. The NLP algorithm was then deployed to 10 additional sites that were also running our NLP infrastructure. Of these 10 sites, 5 were used to conduct a federated evaluation of the NLP algorithm. Results: An NLP algorithm consisting of 12,234 unique normalized text strings corresponding to 2366 unique concepts was developed to extract COVID-19 or PASC signs and symptoms. An unweighted mean dictionary coverage of 77.8\% was found for the 5 sites. Conclusions: The evolutionary and time-critical nature of the PASC NLP task significantly complicates existing approaches to NLP algorithm development. In this work, we present a hybrid approach using the Open Health Natural Language Processing Toolkit aimed at addressing these needs with a dictionary-based weak labeling step that minimizes the need for additional expert annotation while still preserving the fine-tuning capabilities of expert involvement. ",
doi="10.2196/49997",
url="https://medinform.jmir.org/2024/1/e49997"
}


@Article{info:doi/10.2196/54687,
author="Iyengar, Sriram M.
and Block Ngaybe, G. Maiya
and Gonzalez, Myla
and Arora, Mona",
title="Resilience Informatics: Role of Informatics in Enabling and Promoting Public Health Resilience to Pandemics, Climate Change, and Other Stressors",
journal="Interact J Med Res",
year="2024",
month="Aug",
day="12",
volume="13",
pages="e54687",
keywords="health informatics",
keywords="data science",
keywords="climate change",
keywords="pandemics",
keywords="COVID-19",
keywords="migrations",
keywords="mobile phone",
doi="10.2196/54687",
url="https://www.i-jmr.org/2024/1/e54687",
url="http://www.ncbi.nlm.nih.gov/pubmed/39133540"
}


@Article{info:doi/10.2196/57717,
author="Shang, Di
and Williams, Cynthia
and Culiqi, Hera",
title="Telehealth Uptake Among Hispanic People During COVID-19: Retrospective Observational Study",
journal="JMIR Med Inform",
year="2024",
month="Jul",
day="24",
volume="12",
pages="e57717",
keywords="telehealth",
keywords="telemedicine",
keywords="ICT",
keywords="eHealth",
keywords="e-health",
keywords="Hispanic",
keywords="health equity",
keywords="health access",
keywords="Hispanics",
keywords="digital divide",
keywords="usage",
keywords="utilization",
keywords="equity",
keywords="inequity",
keywords="inequities",
keywords="access",
keywords="accessibility",
keywords="Spanish",
keywords="observational",
keywords="demographic",
keywords="demographics",
keywords="socioeconomic",
keywords="socioeconomics",
keywords="information and communication technology",
abstract="Background: The Hispanic community represents a sizeable community that experiences inequities in the US health care system. As the system has moved toward digital health platforms, evaluating the potential impact on Hispanic communities is critical. Objective: The study aimed to investigate demographic, socioeconomic, and behavioral factors contributing to low telehealth use in Hispanic communities. Methods: We used a retrospective observation study design to examine the study objectives. The COVID-19 Research Database Consortium provided the Analytics IQ PeopleCore consumer data and Office Alley claims data. The study period was from March 2020 to April 2021. Multiple logistic regression was used to determine the odds of using telehealth services. Results: We examined 3,478,287 unique Hispanic patients, 16.6\% (577,396) of whom used telehealth. Results suggested that patients aged between 18 and 44 years were more likely to use telehealth (odds ratio [OR] 1.07, 95\% CI 1.05-1.1; P<.001) than patients aged older than 65 years. Across all age groups, patients with high incomes were at least 20\% more likely to use telehealth than patients with lower incomes (P<.001); patients who had a primary care physician (P=.01), exhibited high medical usage (P<.001), or were interested in exercise (P=.03) were more likely to use telehealth; patients who had unhealthy behaviors such as smoking and alcohol consumption were less likely to use telehealth (P<.001). Male patients were less likely than female patients to use telehealth among patients aged 65 years and older (OR 0.94, 95\% CI 0.93-0.95; P<.001), while male patients aged between 18 and 44 years were more likely to use telehealth (OR 1.05, 95\% CI 1.03-1.07; P<.001). Among patients younger than 65 years, full-time employment was positively associated with telehealth use (P<.001). Patients aged between 18 and 44 years with high school or less education were 2\% less likely to use telehealth (OR 0.98, 95\% CI 0.97-0.99; P=.005). Results also revealed a positive association with using WebMD (WebMD LLC) among patients aged older than 44 years (P<.001), while there was a negative association with electronic prescriptions among those who were aged between 18 and 44 years (P=.009) and aged between 45 and 64 years (P=.004). Conclusions: This study demonstrates that telehealth use among Hispanic communities is dependent upon factors such as age, gender, education, socioeconomic status, current health care engagement, and health behaviors. To address these challenges, we advocate for interdisciplinary approaches that involve medical professionals, insurance providers, and community-based services actively engaging with Hispanic communities and promoting telehealth use. We propose the following recommendations: enhance access to health insurance, improve access to primary care providers, and allocate fiscal and educational resources to support telehealth use. As telehealth increasingly shapes health care delivery, it is vital for professionals to facilitate the use of all available avenues for accessing care. ",
doi="10.2196/57717",
url="https://medinform.jmir.org/2024/1/e57717"
}


@Article{info:doi/10.2196/48464,
author="Oliveira, Alves Clara Rodrigues
and Pires, Carvalho Magda
and Meira, Cardoso Karina
and de Jesus, Cristina Jordana
and Borges, Nascimento Isabela
and Paix{\~a}o, Cristina Maria
and Mendes, Santos Mayara
and Ribeiro, Bonisson Leonardo
and Marcolino, Soriano Milena
and Alkmim, Moreira Maria Beatriz
and Ribeiro, Pinho Antonio Luiz",
title="Effect of a Structured Multilevel Telehealth Service on Hospital Admissions and Mortality During COVID-19 in a Resource-Limited Region in Brazil: Retrospective Cohort Study",
journal="J Med Internet Res",
year="2024",
month="Jun",
day="10",
volume="26",
pages="e48464",
keywords="COVID-19",
keywords="telehealth",
keywords="health care",
keywords="clinical outcomes",
keywords="hospital admission",
keywords="mortality",
keywords="adoption",
keywords="effectiveness",
keywords="digital health tool",
keywords="flu",
keywords="teleconsultation",
keywords="digital health",
keywords="digital literacy",
keywords="telemonitoring",
abstract="Background: The COVID-19 pandemic represented a great stimulus for the adoption of telehealth and many initiatives in this field have emerged worldwide. However, despite this massive growth, data addressing the effectiveness of telehealth with respect to clinical outcomes remain scarce.? Objective: The aim of this study was to evaluate the impact of the adoption of a structured multilevel telehealth service on hospital admissions during the acute illness course and the mortality of adult patients with flu syndrome in the context of the COVID-19 pandemic. Methods: A retrospective cohort study was performed in two Brazilian cities where a public COVID-19 telehealth service (TeleCOVID-MG) was deployed. TeleCOVID-MG was a structured multilevel telehealth service, including (1) first response and risk stratification through a chatbot software or phone call center, (2) teleconsultations with nurses and medical doctors, and (3) a telemonitoring system. For this analysis, we included data of adult patients registered in the Flu Syndrome notification databases who were diagnosed with flu syndrome between June 1, 2020, and May 31, 2021. The exposed group comprised patients with flu syndrome who used TeleCOVID-MG at least once during the illness course and the control group comprised patients who did not use this telehealth service during the respiratory illness course. Sociodemographic characteristics, comorbidities, and clinical outcomes data were extracted from the Brazilian official databases for flu syndrome, Severe Acute Respiratory Syndrome (due to any respiratory virus), and mortality. Models for the clinical outcomes were estimated by logistic regression. Results: The final study population comprised 82,182 adult patients with a valid registry in the Flu Syndrome notification system. When compared to patients who did not use the service (n=67,689, 82.4\%), patients supported by TeleCOVID-MG (n=14,493, 17.6\%) had a lower chance of hospitalization during the acute respiratory illness course, even after adjusting for sociodemographic characteristics and underlying medical conditions (odds ratio [OR] 0.82, 95\% CI 0.71-0.94; P=.005). No difference in mortality was observed between groups (OR 0.99, 95\% CI 0.86-1.12; P=.83). Conclusions: A telehealth service applied on a large scale in a limited-resource region to tackle COVID-19 was related to reduced hospitalizations without increasing the mortality rate. Quality health care using inexpensive and readily available telehealth and digital health tools may be delivered in areas with limited resources and should be considered as a potential and valuable health care strategy.?The success of a telehealth initiative relies on a partnership between the involved stakeholders to define the roles and responsibilities; set an alignment between the different modalities and levels of health care; and address the usual drawbacks related to the implementation process, such as infrastructure and accessibility issues. ",
doi="10.2196/48464",
url="https://www.jmir.org/2024/1/e48464",
url="http://www.ncbi.nlm.nih.gov/pubmed/38857068"
}


@Article{info:doi/10.2196/50897,
author="Ndlovu, Kagiso
and Mauco, Leonard Kabelo
and Makhura, Onalenna
and Hu, Robin
and Motlogelwa, Peace Nkwebi
and Masizana, Audrey
and Lo, Emily
and Mphoyakgosi, Thongbotho
and Moyo, Sikhulile",
title="Experiences, Lessons, and Challenges With Adapting REDCap for COVID-19 Laboratory Data Management in a Resource-Limited Country: Descriptive Study",
journal="JMIR Form Res",
year="2024",
month="Apr",
day="16",
volume="8",
pages="e50897",
keywords="REDCap",
keywords="DHIS2",
keywords="COVID-19",
keywords="National Health Laboratory",
keywords="eHealth",
keywords="interoperability",
keywords="data management",
keywords="Botswana",
abstract="Background: The COVID-19 pandemic brought challenges requiring timely health data sharing to inform accurate decision-making at national levels. In Botswana, we adapted and integrated the Research Electronic Data Capture (REDCap) and the District Health Information System version 2 (DHIS2) platforms to support timely collection and reporting of COVID-19 cases. We focused on establishing an effective COVID-19 data flow at the national public health laboratory, being guided by the needs of health care professionals at the National Health Laboratory (NHL). This integration contributed to automated centralized reporting of COVID-19 results at the Ministry of Health (MOH). Objective: This paper reports the experiences, challenges, and lessons learned while designing, adapting, and implementing the REDCap and DHIS2 platforms to support COVID-19 data management at the NHL in Botswana. Methods: A participatory design approach was adopted to guide the design, customization, and implementation of the REDCap platform in support of COVID-19 data management at the NHL. Study participants included 29 NHL and 4 MOH personnel, and the study was conducted from March 2, 2020, to June 30, 2020. Participants' requirements for an ideal COVID-19 data management system were established. NVivo 11 software supported thematic analysis of the challenges and resolutions identified during this study. These were categorized according to the 4 themes of infrastructure, capacity development, platform constraints, and interoperability. Results: Overall, REDCap supported the majority of perceived technical and nontechnical requirements for an ideal COVID-19 data management system at the NHL. Although some implementation challenges were identified, each had mitigation strategies such as procurement of mobile Internet routers, engagement of senior management to resolve conflicting policies, continuous REDCap training, and the development of a third-party web application to enhance REDCap's capabilities. Lessons learned informed next steps and further refinement of the REDCap platform. Conclusions: Implementation of REDCap at the NHL to streamline COVID-19 data collection and integration with the DHIS2 platform was feasible despite the urgency of implementation during the pandemic. By implementing the REDCap platform at the NHL, we demonstrated the possibility of achieving a centralized reporting system of COVID-19 cases, hence enabling timely and informed decision-making at a national level. Challenges faced presented lessons learned to inform sustainable implementation of digital health innovations in Botswana and similar resource-limited countries. ",
doi="10.2196/50897",
url="https://formative.jmir.org/2024/1/e50897",
url="http://www.ncbi.nlm.nih.gov/pubmed/38625736"
}


@Article{info:doi/10.2196/47846,
author="Oehm, Benedict Johannes
and Riepenhausen, Luise Sarah
and Storck, Michael
and Dugas, Martin
and Pryss, R{\"u}diger
and Varghese, Julian",
title="Integration of Patient-Reported Outcome Data Collected Via Web Applications and Mobile Apps Into a Nation-Wide COVID-19 Research Platform Using Fast Healthcare Interoperability Resources: Development Study",
journal="J Med Internet Res",
year="2024",
month="Feb",
day="27",
volume="26",
pages="e47846",
keywords="Fast Healthcare Interoperability Resources",
keywords="FHIR",
keywords="FHIR Questionnaire",
keywords="patient-reported outcome",
keywords="mobile health",
keywords="mHealth",
keywords="research compatibility",
keywords="interoperability",
keywords="Germany",
keywords="harmonized data collection",
keywords="findable, accessible, interoperable, and reusable",
keywords="FAIR data",
keywords="mobile phone",
abstract="Background: The Network University Medicine projects are an important part of the German COVID-19 research infrastructure. They comprise 2 subprojects: COVID-19 Data Exchange (CODEX) and Coordination on Mobile Pandemic Apps Best Practice and Solution Sharing (COMPASS). CODEX provides a centralized and secure data storage platform for research data, whereas in COMPASS, expert panels were gathered to develop a reference app framework for capturing patient-reported outcomes (PROs) that can be used by any researcher. Objective: Our study aims to integrate the data collected with the COMPASS reference app framework into the central CODEX platform, so that they can be used by secondary researchers. Although both projects used the Fast Healthcare Interoperability Resources (FHIR) standard, it was not used in a way that data could be shared directly. Given the short time frame and the parallel developments within the CODEX platform, a pragmatic and robust solution for an interface component was required. Methods: We have developed a means to facilitate and promote the use of the German Corona Consensus (GECCO) data set, a core data set for COVID-19 research in Germany. In this way, we ensured semantic interoperability for the app-collected PRO data with the COMPASS app. We also developed an interface component to sustain syntactic interoperability. Results: The use of different FHIR types by the COMPASS reference app framework (the general-purpose FHIR Questionnaire) and the CODEX platform (eg, Patient, Condition, and Observation) was found to be the most significant obstacle. Therefore, we developed an interface component that realigns the Questionnaire items with the corresponding items in the GECCO data set and provides the correct resources for the CODEX platform. We extended the existing COMPASS questionnaire editor with an import function for GECCO items, which also tags them for the interface component. This ensures syntactic interoperability and eases the reuse of the GECCO data set for researchers. Conclusions: This paper shows how PRO data, which are collected across various studies conducted by different researchers, can be captured in a research-compatible way. This means that the data can be shared with a central research infrastructure and be reused by other researchers to gain more insights about COVID-19 and its sequelae. ",
doi="10.2196/47846",
url="https://www.jmir.org/2024/1/e47846",
url="http://www.ncbi.nlm.nih.gov/pubmed/38411999"
}


@Article{info:doi/10.2196/47065,
author="Alqurashi, Heba
and Mohammed, Rafiuddin
and AlGhanmi, Shlyan Amany
and Alanazi, Farhan",
title="The Perception of Health Care Practitioners Regarding Telemedicine During COVID-19 in Saudi Arabia: Mixed Methods Study",
journal="JMIR Form Res",
year="2023",
month="Sep",
day="28",
volume="7",
pages="e47065",
keywords="telemedicine",
keywords="health care practitioners",
keywords="COVID-19",
keywords="Saudi Arabia",
keywords="mobile phone",
abstract="Background: Telemedicine is a rapidly evolving field that uses information and communication technology to provide remote health care services, such as diagnosis, treatment, consultation, patient monitoring, and medication delivery. With advancements in technology, telemedicine has become increasingly popular during the COVID-19 lockdown and has expanded beyond remote consultations via telephone or video to include comprehensive and reliable services. The integration of telemedicine platforms can enable patients and health care providers to communicate more efficiently and effectively. Objective: This study aims to investigate the awareness, knowledge, requirements, and perceptions of health care practitioners in Saudi Arabia during the pandemic health crisis from the end-user perspective. The findings of this study will inform policy makers regarding the sustainability of telemedicine and how it affects the process of provision of health care and improves the patients' journey. Methods: This study adopted a mixed methods design with a quantitative-based cross-sectional design and qualitative interviews to assess the perceptions of various health care professionals working in outpatient departments that have a telemedicine system that was used during the COVID-19 pandemic. For both approaches, ethics approval was obtained, and informed consent forms were signed. In total, 81 completed questionnaires were used in this study. In the second phase, general interviews were conducted with managerial staff and health care professionals to obtain their view of telemedicine services in their hospitals. Results: The study revealed that most participants (67/81, 83\%) were familiar with telemedicine technology, and the study proved to be statistically significant at P<.05 with a proportion of the participants (52/81, 64\%) believing that continuous training was essential for its effective use. The study also found that consultations (55/153, 35.9\%) and monitoring patients (35/153, 22.9\%) were the major components of telemedicine used by health care professionals, with telephones being the most commonly used mode of interaction with patients (74/117, 63.2\%). In addition, 54\% (44/81) of the respondents expressed concerns about patient privacy and confidentiality, highlighting this as a major issue. Furthermore, the majority of participants (58/81, 72\%) reported the necessity of implementing national standards essential for telemedicine technology in Saudi Arabia. The interviews conducted as part of the study revealed 5 major themes: culture, barriers and difficulties, communication, implementation, and evaluation. These themes highlighted the importance of a culture of acceptance and flexibility, effective communication, and ongoing evaluation of telemedicine technologies in health care systems. Conclusions: This study provides a crucial message with insights into the perceptions and experiences of health care professionals with telemedicine during the COVID-19 pandemic in Saudi Arabia. ",
doi="10.2196/47065",
url="https://formative.jmir.org/2023/1/e47065",
url="http://www.ncbi.nlm.nih.gov/pubmed/37768720"
}


@Article{info:doi/10.2196/46267,
author="Chang, Feier
and Krishnan, Jay
and Hurst, H. Jillian
and Yarrington, E. Michael
and Anderson, J. Deverick
and O'Brien, C. Emily
and Goldstein, A. Benjamin",
title="Comparing Natural Language Processing and Structured Medical Data to Develop a Computable Phenotype for Patients Hospitalized Due to COVID-19: Retrospective Analysis",
journal="JMIR Med Inform",
year="2023",
month="Aug",
day="22",
volume="11",
pages="e46267",
keywords="natural language processing",
keywords="NLP",
keywords="computable phenotype",
keywords="machine learning",
keywords="COVID",
keywords="coronavirus",
keywords="hospitalize",
keywords="hospitalization",
keywords="electronic health record",
keywords="EHR",
keywords="health record",
keywords="structured data",
keywords="data element",
keywords="free text",
keywords="unstructured data",
keywords="provider note",
keywords="classify",
keywords="classification",
keywords="algorithm",
keywords="COVID-19",
abstract="Background: Throughout the COVID-19 pandemic, many hospitals conducted routine testing of hospitalized patients for SARS-CoV-2 infection upon admission. Some of these patients are admitted for reasons unrelated to COVID-19 and incidentally test positive for the virus. Because COVID-19--related hospitalizations have become a critical public health indicator, it is important to identify patients who are hospitalized because of COVID-19 as opposed to those who are admitted for other indications. Objective: We compared the performance of different computable phenotype definitions for COVID-19 hospitalizations that use different types of data from electronic health records (EHRs), including structured EHR data elements, clinical notes, or a combination of both data types. Methods: We conducted a retrospective data analysis, using clinician chart review--based validation at a large academic medical center. We reviewed and analyzed the charts of 586 hospitalized individuals who tested positive for SARS-CoV-2 in January 2022. We used LASSO (least absolute shrinkage and selection operator) regression and random forests to fit classification algorithms that incorporated structured EHR data elements, clinical notes, or a combination of structured data and clinical notes. We used natural language processing to incorporate data from clinical notes. The performance of each model was evaluated based on the area under the receiver operator characteristic curve (AUROC) and an associated decision rule based on sensitivity and positive predictive value. We also identified top words and clinical indicators of COVID-19--specific hospitalization and assessed the impact of different phenotyping strategies on estimated hospital outcome metrics. Results: Based on a chart review, 38.2\% (224/586) of patients were determined to have been hospitalized for reasons other than COVID-19, despite having tested positive for SARS-CoV-2. A computable phenotype that used clinical notes had significantly better discrimination than one that used structured EHR data elements (AUROC: 0.894 vs 0.841; P<.001) and performed similarly to a model that combined clinical notes with structured data elements (AUROC: 0.894 vs 0.893; P=.91). Assessments of hospital outcome metrics significantly differed based on whether the population included all hospitalized patients who tested positive for SARS-CoV-2 or those who were determined to have been hospitalized due to COVID-19. Conclusions: These findings highlight the importance of cause-specific phenotyping for COVID-19 hospitalizations. More generally, this work demonstrates the utility of natural language processing approaches for deriving information related to patient hospitalizations in cases where there may be multiple conditions that could serve as the primary indication for hospitalization. ",
doi="10.2196/46267",
url="https://medinform.jmir.org/2023/1/e46267"
}


@Article{info:doi/10.2196/40676,
author="Jiang, Haoqiang
and Castellanos, Arturo
and Castillo, Alfred
and Gomes, J. Paulo
and Li, Juanjuan
and VanderMeer, Debra",
title="Nurses' Work Concerns and Disenchantment During the COVID-19 Pandemic: Machine Learning Analysis of Web-Based Discussions",
journal="JMIR Nursing",
year="2023",
month="Feb",
day="6",
volume="6",
pages="e40676",
keywords="text mining",
keywords="machine learning",
keywords="blog data",
keywords="COVID-19",
keywords="pandemic",
keywords="work concerns",
keywords="stressors",
keywords="natural language processing",
abstract="Background: Web-based forums provide a space for communities of interest to exchange ideas and experiences. Nurse professionals used these forums during the COVID-19 pandemic to share their experiences and concerns. Objective: The objective of this study was to examine the nurse-generated content to capture the evolution of nurses' work concerns during the COVID-19 pandemic. Methods: We analyzed 14,060 posts related to the COVID-19 pandemic from March 2020 to April 2021. The data analysis stage included unsupervised machine learning and thematic qualitative analysis. We used an unsupervised machine learning approach, latent Dirichlet allocation, to identify salient topics in the collected posts. A human-in-the-loop analysis complemented the machine learning approach, categorizing topics into themes and subthemes. We developed insights into nurses' evolving perspectives based on temporal changes. Results: We identified themes for biweekly periods and grouped them into 20 major themes based on the work concern inventory framework. Dominant work concerns varied throughout the study period. A detailed analysis of the patterns in how themes evolved over time enabled us to create narratives of work concerns. Conclusions: The analysis demonstrates that professional web-based forums capture nuanced details about nurses' work concerns and workplace stressors during the COVID-19 pandemic. Monitoring and assessment of web-based discussions could provide useful data for health care organizations to understand how their primary caregivers are affected by external pressures and internal managerial decisions and design more effective responses and planning during crises. ",
doi="10.2196/40676",
url="https://nursing.jmir.org/2023/1/e40676",
url="http://www.ncbi.nlm.nih.gov/pubmed/36608261"
}


@Article{info:doi/10.2196/37945,
author="Chiavi, Deborah
and Haag, Christina
and Chan, Andrew
and Kamm, Philipp Christian
and Sieber, Chlo{\'e}
and Staniki{\'c}, Mina
and Rodgers, Stephanie
and Pot, Caroline
and Kesselring, J{\"u}rg
and Salmen, Anke
and Rapold, Irene
and Calabrese, Pasquale
and Manjaly, Zina-Mary
and Gobbi, Claudio
and Zecca, Chiara
and Walther, Sebastian
and Stegmayer, Katharina
and Hoepner, Robert
and Puhan, Milo
and von Wyl, Viktor",
title="The Real-World Experiences of Persons With Multiple Sclerosis During the First COVID-19 Lockdown: Application of Natural Language Processing",
journal="JMIR Med Inform",
year="2022",
month="Nov",
day="10",
volume="10",
number="11",
pages="e37945",
keywords="natural language processing",
keywords="multiple sclerosis",
keywords="COVID-19",
keywords="nervous system disease",
keywords="nervous system disorder",
keywords="textual data",
keywords="health data",
keywords="patient data",
keywords="topic modeling",
keywords="sentiment analysis",
keywords="linguistic inquiry",
keywords="medical informatics",
keywords="clinical informatics",
abstract="Background: The increasing availability of ``real-world'' data in the form of written text holds promise for deepening our understanding of societal and health-related challenges. Textual data constitute a rich source of information, allowing the capture of lived experiences through a broad range of different sources of information (eg, content and emotional tone). Interviews are the ``gold standard'' for gaining qualitative insights into individual experiences and perspectives. However, conducting interviews on a large scale is not always feasible, and standardized quantitative assessment suitable for large-scale application may miss important information. Surveys that include open-text assessments can combine the advantages of both methods and are well suited for the application of natural language processing (NLP) methods. While innovations in NLP have made large-scale text analysis more accessible, the analysis of real-world textual data is still complex and requires several consecutive steps. Objective: We developed and subsequently examined the utility and scientific value of an NLP pipeline for extracting real-world experiences from textual data to provide guidance for applied researchers. Methods: We applied the NLP pipeline to large-scale textual data collected by the Swiss Multiple Sclerosis (MS) registry. Such textual data constitute an ideal use case for the study of real-world text data. Specifically, we examined 639 text reports on the experienced impact of the first COVID-19 lockdown from the perspectives of persons with MS. The pipeline has been implemented in Python and complemented by analyses of the ``Linguistic Inquiry and Word Count'' software. It consists of the following 5 interconnected analysis steps: (1) text preprocessing; (2) sentiment analysis; (3) descriptive text analysis; (4) unsupervised learning--topic modeling; and (5) results interpretation and validation. Results: A topic modeling analysis identified the following 4 distinct groups based on the topics participants were mainly concerned with: ``contacts/communication;'' ``social environment;'' ``work;'' and ``errands/daily routines.'' Notably, the sentiment analysis revealed that the ``contacts/communication'' group was characterized by a pronounced negative emotional tone underlying the text reports. This observed heterogeneity in emotional tonality underlying the reported experiences of the first COVID-19--related lockdown is likely to reflect differences in emotional burden, individual circumstances, and ways of coping with the pandemic, which is in line with previous research on this matter. Conclusions: This study illustrates the timely and efficient applicability of an NLP pipeline and thereby serves as a precedent for applied researchers. Our study thereby contributes to both the dissemination of NLP techniques in applied health sciences and the identification of previously unknown experiences and burdens of persons with MS during the pandemic, which may be relevant for future treatment. ",
doi="10.2196/37945",
url="https://medinform.jmir.org/2022/11/e37945",
url="http://www.ncbi.nlm.nih.gov/pubmed/36252126"
}


@Article{info:doi/10.2196/35622,
author="Higa, Eduardo
and Elb{\'e}ji, Abir
and Zhang, Lu
and Fischer, Aur{\'e}lie
and Aguayo, A. Gloria
and Nazarov, V. Petr
and Fagherazzi, Guy",
title="Discovery and Analytical Validation of a Vocal Biomarker to Monitor Anosmia and Ageusia in Patients With COVID-19: Cross-sectional Study",
journal="JMIR Med Inform",
year="2022",
month="Nov",
day="8",
volume="10",
number="11",
pages="e35622",
keywords="vocal biomarker",
keywords="COVID-19",
keywords="ageusia",
keywords="anosmia",
keywords="loss of smell",
keywords="loss of taste",
keywords="digital assessment tool",
keywords="digital health",
keywords="medical informatics",
keywords="telehealth",
keywords="telemonitoring",
keywords="biomarker",
keywords="pandemic",
keywords="symptoms",
keywords="tool",
keywords="disease",
keywords="noninvasive",
keywords="AI",
keywords="artificial intelligence",
keywords="digital",
keywords="device",
abstract="Background: The COVID-19 disease has multiple symptoms, with anosmia and ageusia being the most prevalent, varying from 75\% to 95\% and from 50\% to 80\% of infected patients, respectively. An automatic assessment tool for these symptoms will help monitor the disease in a fast and noninvasive manner. Objective: We hypothesized that people with COVID-19 experiencing anosmia and ageusia had different voice features than those without such symptoms. Our objective was to develop an artificial intelligence pipeline to identify and internally validate a vocal biomarker of these symptoms for remotely monitoring them. Methods: This study used population-based data. Participants were assessed daily through a web-based questionnaire and asked to register 2 different types of voice recordings. They were adults (aged >18 years) who were confirmed by a polymerase chain reaction test to be positive for COVID-19 in Luxembourg and met the inclusion criteria. Statistical methods such as recursive feature elimination for dimensionality reduction, multiple statistical learning methods, and hypothesis tests were used throughout this study. The TRIPOD (Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis) Prediction Model Development checklist was used to structure the research. Results: This study included 259 participants. Younger (aged <35 years) and female participants showed higher rates of ageusia and anosmia. Participants were aged 41 (SD 13) years on average, and the data set was balanced for sex (female: 134/259, 51.7\%; male: 125/259, 48.3\%). The analyzed symptom was present in 94 (36.3\%) out of 259 participants and in 450 (27.5\%) out of 1636 audio recordings. In all, 2 machine learning models were built, one for Android and one for iOS devices, and both had high accuracy---88\% for Android and 85\% for iOS. The final biomarker was then calculated using these models and internally validated. Conclusions: This study demonstrates that people with COVID-19 who have anosmia and ageusia have different voice features from those without these symptoms. Upon further validation, these vocal biomarkers could be nested in digital devices to improve symptom assessment in clinical practice and enhance the telemonitoring of COVID-19--related symptoms. Trial Registration: Clinicaltrials.gov NCT04380987; https://clinicaltrials.gov/ct2/show/NCT04380987 ",
doi="10.2196/35622",
url="https://medinform.jmir.org/2022/11/e35622",
url="http://www.ncbi.nlm.nih.gov/pubmed/36265042"
}


@Article{info:doi/10.2196/37770,
author="Gatto, Joseph
and Seegmiller, Parker
and Johnston, Garrett
and Preum, Masud Sarah",
title="Identifying the Perceived Severity of Patient-Generated Telemedical Queries Regarding COVID: Developing and Evaluating a Transfer Learning--Based Solution",
journal="JMIR Med Inform",
year="2022",
month="Sep",
day="2",
volume="10",
number="9",
pages="e37770",
keywords="natural language processing",
keywords="transfer learning",
keywords="telemedicine triage",
keywords="COVID-19",
keywords="health resource",
keywords="health care",
keywords="patient query",
keywords="learning solution",
keywords="telemedical",
keywords="lexical model",
keywords="machine learning",
keywords="BERT",
keywords="telehealth",
abstract="Background: Triage of textual telemedical queries is a safety-critical task for medical service providers with limited remote health resources. The prioritization of patient queries containing medically severe text is necessary to optimize resource usage and provide care to those with time-sensitive needs. Objective: We aim to evaluate the effectiveness of transfer learning solutions on the task of telemedical triage and provide a thorough error analysis, identifying telemedical queries that challenge state-of-the-art natural language processing (NLP) systems. Additionally, we aim to provide a publicly available telemedical query data set with labels for severity classification for telemedical triage of respiratory issues. Methods: We annotated 573 medical queries from 3 online health platforms: HealthTap, HealthcareMagic, and iCliniq. We then evaluated 6 transfer learning solutions utilizing various text-embedding strategies. Specifically, we first established a baseline using a lexical classification model with term frequency--inverse document frequency (TF-IDF) features. Next, we investigated the effectiveness of global vectors for text representation (GloVe), a pretrained word-embedding method. We evaluated the performance of GloVe embeddings in the context of support vector machines (SVMs), bidirectional long short-term memory (bi-LSTM) networks, and hierarchical attention networks (HANs). Finally, we evaluated the performance of contextual text embeddings using transformer-based architectures. Specifically, we evaluated bidirectional encoder representation from transformers (BERT), Bio+Clinical-BERT, and Sentence-BERT (SBERT) on the telemedical triage task. Results: We found that a simple lexical model achieved a mean F1 score of 0.865 (SD 0.048) on the telemedical triage task. GloVe-based models using SVMs, HANs, and bi-LSTMs achieved a 0.8-, 1.5-, and 2.1-point increase in the F1 score, respectively. Transformer-based models, such as BERT, Bio+Clinical-BERT, and SBERT, achieved a mean F1 score of 0.914 (SD 0.034), 0.904 (SD 0.041), and 0.917 (SD 0.037), respectively. The highest-performing model, SBERT, provided a statistically significant improvement compared to all GloVe-based and lexical baselines. However, no statistical significance was found when comparing transformer-based models. Furthermore, our error analysis revealed highly challenging query types, including those with complex negations, temporal relationships, and patient intents. Conclusions: We showed that state-of-the-art transfer learning techniques work well on the telemedical triage task, providing significant performance increase over lexical models. Additionally, we released a public telemedical triage data set using labeled questions from online medical question-and-answer (Q\&A) platforms. Our analysis highlights various avenues for future works that explicitly model such query challenges. ",
doi="10.2196/37770",
url="https://medinform.jmir.org/2022/9/e37770",
url="http://www.ncbi.nlm.nih.gov/pubmed/35981230"
}


@Article{info:doi/10.2196/37829,
author="Singhal, Aditya
and Baxi, Kaur Manmeet
and Mago, Vijay",
title="Synergy Between Public and Private Health Care Organizations During COVID-19 on Twitter: Sentiment and Engagement Analysis Using Forecasting Models",
journal="JMIR Med Inform",
year="2022",
month="Aug",
day="18",
volume="10",
number="8",
pages="e37829",
keywords="social media",
keywords="health care",
keywords="Twitter",
keywords="content analysis",
keywords="user engagement",
keywords="sentiment forecasting",
keywords="natural language processing",
keywords="public health",
keywords="pharmaceutical",
keywords="public engagement",
abstract="Background: Social media platforms (SMPs) are frequently used by various pharmaceutical companies, public health agencies, and nongovernment organizations (NGOs) for communicating health concerns, new advancements, and potential outbreaks. Although the benefits of using them as a tool have been extensively discussed, the online activity of various health care organizations on SMPs during COVID-19 in terms of engagement and sentiment forecasting has not been thoroughly investigated. Objective: The purpose of this research is to analyze the nature of information shared on Twitter, understand the public engagement generated on it, and forecast the sentiment score for various organizations. Methods: Data were collected from the Twitter handles of 5 pharmaceutical companies, 10 US and Canadian public health agencies, and the World Health Organization (WHO) from January 1, 2017, to December 31, 2021. A total of 181,469 tweets were divided into 2 phases for the analysis, before COVID-19 and during COVID-19, based on the confirmation of the first COVID-19 community transmission case in North America on February 26, 2020. We conducted content analysis to generate health-related topics using natural language processing (NLP)-based topic-modeling techniques, analyzed public engagement on Twitter, and performed sentiment forecasting using 16 univariate moving-average and machine learning (ML) models to understand the correlation between public opinion and tweet contents. Results: We utilized the topics modeled from the tweets authored by the health care organizations chosen for our analysis using nonnegative matrix factorization (NMF): cumass=--3.6530 and --3.7944 before and during COVID-19, respectively. The topics were chronic diseases, health research, community health care, medical trials, COVID-19, vaccination, nutrition and well-being, and mental health. In terms of user impact, WHO (user impact=4171.24) had the highest impact overall, followed by public health agencies, the Centers for Disease Control and Prevention (CDC; user impact=2895.87), and the National Institutes of Health (NIH; user impact=891.06). Among pharmaceutical companies, Pfizer's user impact was the highest at 97.79. Furthermore, for sentiment forecasting, autoregressive integrated moving average (ARIMA) and seasonal autoregressive integrated moving average with exogenous factors (SARIMAX) models performed best on the majority of the subsets of data (divided as per the health care organization and period), with the mean absolute error (MAE) between 0.027 and 0.084, the mean square error (MSE) between 0.001 and 0.011, and the root-mean-square error (RMSE) between 0.031 and 0.105. Conclusions: Our findings indicate that people engage more on topics such as COVID-19 than medical trials and customer experience. In addition, there are notable differences in the user engagement levels across organizations. Global organizations, such as WHO, show wide variations in engagement levels over time. The sentiment forecasting method discussed presents a way for organizations to structure their future content to ensure maximum user engagement. ",
doi="10.2196/37829",
url="https://medinform.jmir.org/2022/8/e37829",
url="http://www.ncbi.nlm.nih.gov/pubmed/35849795"
}


@Article{info:doi/10.2196/37365,
author="Ali, Hazrat
and Shah, Zubair",
title="Combating COVID-19 Using Generative Adversarial Networks and Artificial Intelligence for Medical Images: Scoping Review",
journal="JMIR Med Inform",
year="2022",
month="Jun",
day="29",
volume="10",
number="6",
pages="e37365",
keywords="augmentation",
keywords="artificial intelligence",
keywords="COVID-19",
keywords="diagnosis",
keywords="generative adversarial networks",
keywords="diagnostic",
keywords="lung image",
keywords="imaging",
keywords="data augmentation",
keywords="X-ray",
keywords="CT scan",
keywords="data scarcity",
keywords="image data",
keywords="neural network",
keywords="clinical informatics",
abstract="Background: Research on the diagnosis of COVID-19 using lung images is limited by the scarcity of imaging data. Generative adversarial networks (GANs) are popular for synthesis and data augmentation. GANs have been explored for data augmentation to enhance the performance of artificial intelligence (AI) methods for the diagnosis of COVID-19 within lung computed tomography (CT) and X-ray images. However, the role of GANs in overcoming data scarcity for COVID-19 is not well understood. Objective: This review presents a comprehensive study on the role of GANs in addressing the challenges related to COVID-19 data scarcity and diagnosis. It is the first review that summarizes different GAN methods and lung imaging data sets for COVID-19. It attempts to answer the questions related to applications of GANs, popular GAN architectures, frequently used image modalities, and the availability of source code. Methods: A search was conducted on 5 databases, namely PubMed, IEEEXplore, Association for Computing Machinery (ACM) Digital Library, Scopus, and Google Scholar. The search was conducted from October 11-13, 2021. The search was conducted using intervention keywords, such as ``generative adversarial networks'' and ``GANs,'' and application keywords, such as ``COVID-19'' and ``coronavirus.'' The review was performed following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews (PRISMA-ScR) guidelines for systematic and scoping reviews. Only those studies were included that reported GAN-based methods for analyzing chest X-ray images, chest CT images, and chest ultrasound images. Any studies that used deep learning methods but did not use GANs were excluded. No restrictions were imposed on the country of publication, study design, or outcomes. Only those studies that were in English and were published from 2020 to 2022 were included. No studies before 2020 were included. Results: This review included 57 full-text studies that reported the use of GANs for different applications in COVID-19 lung imaging data. Most of the studies (n=42, 74\%) used GANs for data augmentation to enhance the performance of AI techniques for COVID-19 diagnosis. Other popular applications of GANs were segmentation of lungs and superresolution of lung images. The cycleGAN and the conditional GAN were the most commonly used architectures, used in 9 studies each. In addition, 29 (51\%) studies used chest X-ray images, while 21 (37\%) studies used CT images for the training of GANs. For the majority of the studies (n=47, 82\%), the experiments were conducted and results were reported using publicly available data. A secondary evaluation of the results by radiologists/clinicians was reported by only 2 (4\%) studies. Conclusions: Studies have shown that GANs have great potential to address the data scarcity challenge for lung images in COVID-19. Data synthesized with GANs have been helpful to improve the training of the convolutional neural network (CNN) models trained for the diagnosis of COVID-19. In addition, GANs have also contributed to enhancing the CNNs' performance through the superresolution of the images and segmentation. This review also identified key limitations of the potential transformation of GAN-based methods in clinical applications. ",
doi="10.2196/37365",
url="https://medinform.jmir.org/2022/6/e37365",
url="http://www.ncbi.nlm.nih.gov/pubmed/35709336"
}


@Article{info:doi/10.2196/37196,
author="Dainty, N. Katie
and Seaton, Bianca M.
and Estacio, Antonio
and Hicks, K. Lisa
and Jamieson, Trevor
and Ward, Sarah
and Yu, H. Catherine
and Mosko, D. Jeffrey
and Kassardjian, D. Charles",
title="Virtual Specialist Care During the COVID-19 Pandemic: Multimethod Patient Experience Study",
journal="JMIR Med Inform",
year="2022",
month="Jun",
day="28",
volume="10",
number="6",
pages="e37196",
keywords="virtual care",
keywords="specialist care",
keywords="patient experience",
keywords="COVID-19",
keywords="medical care",
keywords="virtual health",
keywords="care data",
keywords="decision support",
keywords="telehealth",
keywords="video consultation",
abstract="Background: Transitioning nonemergency, ambulatory medical care to virtual visits in light of the COVID-19 global pandemic has been a massive shift in philosophy and practice that naturally came with a steep learning curve for patients, physicians, and clinic administrators. Objective: We undertook a multimethod study to understand the key factors associated with successful and less successful experiences of virtual specialist care, particularly as they relate to the patient experience of care. Methods: This study was designed as a multimethod patient experience study using survey methods, descriptive qualitative interview methodology, and administrative virtual care data collected by the hospital decision support team. Six specialty departments participated in the study (endoscopy, orthopedics, neurology, hematology, rheumatology, and gastroenterology). All patients who could speak and read English and attended a virtual specialist appointment in a participating clinic at St. Michael's Hospital (Toronto, Ontario, Canada) between October 1, 2020, and January 30, 2021, were eligible to participate. Results: During the study period, 51,702 virtual specialist visits were conducted in the departments that participated in the study. Of those, 96\% were conducted by telephone and 4\% by video. In both the survey and interview data, there was an overall consensus that virtual care is a satisfying alternative to in-person care, with benefits such as reduced travel, cost, time, and SARS-CoV-2 exposure, and increased convenience. Our analysis further revealed that the specific reason for the visit and the nature and status of the medical condition are important considerations in terms of guidance on where virtual care is most effective. Technology issues were not reported as a major challenge in our data, given that the majority of ``virtual'' visits reported by our participants were conducted by telephone, which is an important distinction. Despite the positive value of virtual care discussed by the majority of interview participants, 50\% of the survey respondents still indicated they would prefer to see their physician in person. Conclusions: Patient experience data collected in this study indicate a high level of satisfaction with virtual specialty care, but also signal that there are nuances to be considered to ensure it is an appropriate and sustainable part of the standard of care. ",
doi="10.2196/37196",
url="https://medinform.jmir.org/2022/6/e37196",
url="http://www.ncbi.nlm.nih.gov/pubmed/35482950"
}


@Article{info:doi/10.2196/35307,
author="Alvarez-Romero, Celia
and Martinez-Garcia, Alicia
and Ternero Vega, Jara
and D{\'i}az-Jim{\`e}nez, Pablo
and Jim{\`e}nez-Juan, Carlos
and Nieto-Mart{\'i}n, Dolores Mar{\'i}a
and Rom{\'a}n Villar{\'a}n, Esther
and Kovacevic, Tomi
and Bokan, Darijo
and Hromis, Sanja
and Djekic Malbasa, Jelena
and Besla{\'c}, Suzana
and Zaric, Bojan
and Gencturk, Mert
and Sinaci, Anil A.
and Ollero Baturone, Manuel
and Parra Calder{\'o}n, Luis Carlos",
title="Predicting 30-Day Readmission Risk for Patients With Chronic Obstructive Pulmonary Disease Through a Federated Machine Learning Architecture on Findable, Accessible, Interoperable, and Reusable (FAIR) Data: Development and Validation Study",
journal="JMIR Med Inform",
year="2022",
month="Jun",
day="2",
volume="10",
number="6",
pages="e35307",
keywords="FAIR principles",
keywords="research data management",
keywords="clinical validation",
keywords="chronic obstructive pulmonary disease",
keywords="privacy-preserving distributed data mining",
keywords="early predictive model",
abstract="Background: Owing to the nature of health data, their sharing and reuse for research are limited by legal, technical, and ethical implications. In this sense, to address that challenge and facilitate and promote the discovery of scientific knowledge, the Findable, Accessible, Interoperable, and Reusable (FAIR) principles help organizations to share research data in a secure, appropriate, and useful way for other researchers. Objective: The objective of this study was the FAIRification of existing health research data sets and applying a federated machine learning architecture on top of the FAIRified data sets of different health research performing organizations. The entire FAIR4Health solution was validated through the assessment of a federated model for real-time prediction of 30-day readmission risk in patients with chronic obstructive pulmonary disease (COPD). Methods: The application of the FAIR principles on health research data sets in 3 different health care settings enabled a retrospective multicenter study for the development of specific federated machine learning models for the early prediction of 30-day readmission risk in patients with COPD. This predictive model was generated upon the FAIR4Health platform. Finally, an observational prospective study with 30 days follow-up was conducted in 2 health care centers from different countries. The same inclusion and exclusion criteria were used in both retrospective and prospective studies. Results: Clinical validation was demonstrated through the implementation of federated machine learning models on top of the FAIRified data sets from different health research performing organizations. The federated model for predicting the 30-day hospital readmission risk was trained using retrospective data from 4.944 patients with COPD. The assessment of the predictive model was performed using the data of 100 recruited (22 from Spain and 78 from Serbia) out of 2070 observed (records viewed) patients during the observational prospective study, which was executed from April 2021 to September 2021. Significant accuracy (0.98) and precision (0.25) of the predictive model generated upon the FAIR4Health platform were observed. Therefore, the generated prediction of 30-day readmission risk was confirmed in 87\% (87/100) of cases. Conclusions: Implementing a FAIR data policy in health research performing organizations to facilitate data sharing and reuse is relevant and needed, following the discovery, access, integration, and analysis of health research data. The FAIR4Health project proposes a technological solution in the health domain to facilitate alignment with the FAIR principles. ",
doi="10.2196/35307",
url="https://medinform.jmir.org/2022/6/e35307",
url="http://www.ncbi.nlm.nih.gov/pubmed/35653170"
}


@Article{info:doi/10.2196/37042,
author="Ge, Fangmin
and Qian, Huan
and Lei, Jianbo
and Ni, Yiqi
and Li, Qian
and Wang, Song
and Ding, Kefeng",
title="Experiences and Challenges of Emerging Online Health Services Combating COVID-19 in China: Retrospective, Cross-Sectional Study of Internet Hospitals",
journal="JMIR Med Inform",
year="2022",
month="Jun",
day="1",
volume="10",
number="6",
pages="e37042",
keywords="COVID-19",
keywords="telehealth",
keywords="e-consultation",
keywords="dynamics of health care topics",
keywords="China health system",
abstract="Background: Internet-based online virtual health services were originally an important way for the Chinese government to resolve unmet medical service needs due to inadequate medical institutions. Its initial development was not well received. Then, the unexpected COVID-19 pandemic produced a tremendous demand for telehealth in a short time, which stimulated the explosive development of internet hospitals. The Second Affiliated Hospital of Zhejiang University (SAHZU) has taken a leading role in the construction of internet hospitals in China. The pandemic triggered the hospital to develop unique research on health service capacity under strict quarantine policies and to predict long-term trends. Objective: This study aims to provide policy enlightenment for the construction of internet-based health services to better fight against COVID-19 and to elucidate future directions through an in-depth analysis of 2 years of online health service data gleaned from SAHZU's experiences and lessons learned. Methods: We collected data from SAHZU Internet Hospital from November 1, 2019, to September 16, 2021. Data from over 900,000 users were analyzed with respect to demographic characteristics, demands placed on departments by user needs, new registrations, and consultation behaviors. Interrupted time series (ITS) analysis was adopted to evaluate the impact of this momentous emergency event and its long-term trends. With theme analysis and a defined 2D model, 3 investigations were conducted synchronously to determine users' authentic demands on online hospitals. Results: The general profile of internet hospital users is young or middle-aged women who live in Zhejiang and surrounding provinces. The ITS model indicated that, after the intervention (the strict quarantine policies) was implemented during the outbreak, the number of internet hospital users significantly increased ($\beta$\_2=105.736, P<.001). Further, long-term waves of COVID-19 led to an increasing number of users following the outbreak ($\beta$\_3=0.167, P<.001). In theme analysis, we summarized 8 major demands by users of the SAHZU internet hospital during the national shutdown period and afterwards. Online consultations and information services were persistent and universal demands, followed by concerns about medical safety and quality, time, and cost. Users' medical behavior patterns changed from onsite to online as internet hospital demands increased. Conclusions: The pandemic has spawned the explosive growth of telehealth; as a public tertiary internet hospital, the SAHZU internet hospital is partially and irreversibly integrated into the traditional medical system. As we shared the practical examples of 1 public internet hospital in China, we put forward suggestions about the future direction of telehealth. Vital experience in the construction of internet hospitals was provided in the normalization of COVID-19 prevention and control, which can be demonstrated as a model of internet hospital management practice for other medical institutions. ",
doi="10.2196/37042",
url="https://medinform.jmir.org/2022/6/e37042",
url="http://www.ncbi.nlm.nih.gov/pubmed/35500013"
}


@Article{info:doi/10.2196/34306,
author="Boukobza, Adrien
and Burgun, Anita
and Roudier, Bertrand
and Tsopra, Rosy",
title="Deep Neural Networks for Simultaneously Capturing Public Topics and Sentiments During a Pandemic: Application on a COVID-19 Tweet Data Set",
journal="JMIR Med Inform",
year="2022",
month="May",
day="25",
volume="10",
number="5",
pages="e34306",
keywords="neural network",
keywords="deep learning",
keywords="COVID-19",
keywords="explainable artificial intelligence",
keywords="decision support",
keywords="natural language processing",
abstract="Background: Public engagement is a key element for mitigating pandemics, and a good understanding of public opinion could help to encourage the successful adoption of public health measures by the population. In past years, deep learning has been increasingly applied to the analysis of text from social networks. However, most of the developed approaches can only capture topics or sentiments alone but not both together. Objective: Here, we aimed to develop a new approach, based on deep neural networks, for simultaneously capturing public topics and sentiments and applied it to tweets sent just after the announcement of the COVID-19 pandemic by the World Health Organization (WHO). Methods: A total of 1,386,496 tweets were collected, preprocessed, and split with a ratio of 80:20 into training and validation sets, respectively. We combined lexicons and convolutional neural networks to improve sentiment prediction. The trained model achieved an overall accuracy of 81\% and a precision of 82\% and was able to capture simultaneously the weighted words associated with a predicted sentiment intensity score. These outputs were then visualized via an interactive and customizable web interface based on a word cloud representation. Using word cloud analysis, we captured the main topics for extreme positive and negative sentiment intensity scores. Results: In reaction to the announcement of the pandemic by the WHO, 6 negative and 5 positive topics were discussed on Twitter. Twitter users seemed to be worried about the international situation, economic consequences, and medical situation. Conversely, they seemed to be satisfied with the commitment of medical and social workers and with the collaboration between people. Conclusions: We propose a new method based on deep neural networks for simultaneously extracting public topics and sentiments from tweets. This method could be helpful for monitoring public opinion during crises such as pandemics. ",
doi="10.2196/34306",
url="https://medinform.jmir.org/2022/5/e34306",
url="http://www.ncbi.nlm.nih.gov/pubmed/35533390"
}


@Article{info:doi/10.2196/37831,
author="Sauvayre, Romy
and Vernier, Jessica
and Chauvi{\`e}re, C{\'e}dric",
title="An Analysis of French-Language Tweets About COVID-19 Vaccines: Supervised Learning Approach",
journal="JMIR Med Inform",
year="2022",
month="May",
day="17",
volume="10",
number="5",
pages="e37831",
keywords="social media",
keywords="natural language processing",
keywords="public health",
keywords="vaccine",
keywords="machine learning",
keywords="CamemBERT language model",
keywords="method",
keywords="epistemology",
keywords="COVID-19",
keywords="disinformation",
keywords="language model",
abstract="Background: As the COVID-19 pandemic progressed, disinformation, fake news, and conspiracy theories spread through many parts of society. However, the disinformation spreading through social media is, according to the literature, one of the causes of increased COVID-19 vaccine hesitancy. In this context, the analysis of social media posts is particularly important, but the large amount of data exchanged on social media platforms requires specific methods. This is why machine learning and natural language processing models are increasingly applied to social media data. Objective: The aim of this study is to examine the capability of the CamemBERT French-language model to faithfully predict the elaborated categories, with the knowledge that tweets about vaccination are often ambiguous, sarcastic, or irrelevant to the studied topic. Methods: A total of 901,908 unique French-language tweets related to vaccination published between July 12, 2021, and August 11, 2021, were extracted using Twitter's application programming interface (version 2; Twitter Inc). Approximately 2000 randomly selected tweets were labeled with 2 types of categorizations: (1) arguments for (pros) or against (cons) vaccination (health measures included) and (2) type of content (scientific, political, social, or vaccination status). The CamemBERT model was fine-tuned and tested for the classification of French-language tweets. The model's performance was assessed by computing the F1-score, and confusion matrices were obtained. Results: The accuracy of the applied machine learning reached up to 70.6\% for the first classification (pro and con tweets) and up to 90\% for the second classification (scientific and political tweets). Furthermore, a tweet was 1.86 times more likely to be incorrectly classified by the model if it contained fewer than 170 characters (odds ratio 1.86; 95\% CI 1.20-2.86). Conclusions: The accuracy of the model is affected by the classification chosen and the topic of the message examined. When the vaccine debate is jostled by contested political decisions, tweet content becomes so heterogeneous that the accuracy of the model drops for less differentiated classes. However, our tests showed that it is possible to improve the accuracy by selecting tweets using a new method based on tweet length. ",
doi="10.2196/37831",
url="https://medinform.jmir.org/2022/5/e37831",
url="http://www.ncbi.nlm.nih.gov/pubmed/35512274"
}


@Article{info:doi/10.2196/38308,
author="Cuenca-Zald{\'i}var, Nicol{\'a}s Juan
and Torrente-Regidor, Maria
and Mart{\'i}n-Losada, Laura
and Fern{\'a}ndez-De-Las-Pe{\~n}as, C{\'e}sar
and Florencio, Lima Lidiane
and Sousa, Alexandre Pedro
and Palacios-Ce{\~n}a, Domingo",
title="Exploring Sentiment and Care Management of Hospitalized Patients During the First Wave of the COVID-19 Pandemic Using Electronic Nursing Health Records: Descriptive Study",
journal="JMIR Med Inform",
year="2022",
month="May",
day="12",
volume="10",
number="5",
pages="e38308",
keywords="electronic health records",
keywords="COVID-19",
keywords="pandemic",
keywords="content text analysis",
abstract="Background: The COVID-19 pandemic has changed the usual working of many hospitalization units (or wards). Few studies have used electronic nursing clinical notes (ENCN) and their unstructured text to identify alterations in patients' feelings and therapeutic procedures of interest. Objective: This study aimed to analyze positive or negative sentiments through inspection of the free text of the ENCN, compare sentiments of ENCN with or without hospitalized patients with COVID-19, carry out temporal analysis of the sentiments of the patients during the start of the first wave of the COVID-19 pandemic, and identify the topics in ENCN. Methods: This is a descriptive study with analysis of the text content of ENCN. All ENCNs between January and June 2020 at Guadarrama Hospital (Madrid, Spain) extracted from the CGM Selene Electronic Health Records System were included. Two groups of ENCNs were analyzed: one from hospitalized patients in post--intensive care units for COVID-19 and a second group from hospitalized patients without COVID-19. A sentiment analysis was performed on the lemmatized text, using the National Research Council of Canada, Affin, and Bing dictionaries. A polarity analysis of the sentences was performed using the Bing dictionary, SO Dictionaries V1.11, and Spa dictionary as amplifiers and decrementators. Machine learning techniques were applied to evaluate the presence of significant differences in the ENCN in groups of patients with and those without COVID-19. Finally, a structural analysis of thematic models was performed to study the abstract topics that occur in the ENCN, using Latent Dirichlet Allocation topic modeling. Results: A total of 37,564 electronic health records were analyzed. Sentiment analysis in ENCN showed that patients with subacute COVID-19 have a higher proportion of positive sentiments than those without COVID-19. Also, there are significant differences in polarity between both groups (Z=5.532, P<.001) with a polarity of 0.108 (SD 0.299) in patients with COVID-19 versus that of 0.09 (SD 0.301) in those without COVID-19. Machine learning modeling reported that despite all models presenting high values, it is the neural network that presents the best indicators (>0.8) and with significant P values between both groups. Through Structural Topic Modeling analysis, the final model containing 10 topics was selected. High correlations were noted among topics 2, 5, and 8 (pressure ulcer and pharmacotherapy treatment), topics 1, 4, 7, and 9 (incidences related to fever and well-being state, and baseline oxygen saturation) and topics 3 and 10 (blood glucose level and pain). Conclusions: The ENCN may help in the development and implementation of more effective programs, which allows patients with COVID-19 to adopt to their prepandemic lifestyle faster. Topic modeling could help identify specific clinical problems in patients and better target the care they receive. ",
doi="10.2196/38308",
url="https://medinform.jmir.org/2022/5/e38308",
url="http://www.ncbi.nlm.nih.gov/pubmed/354869"
}


@Article{info:doi/10.2196/37771,
author="Van Olmen, Josefien
and Van Nooten, Jens
and Philips, Hilde
and Sollie, Annet
and Daelemans, Walter",
title="Predicting COVID-19 Symptoms From Free Text in Medical Records Using Artificial Intelligence: Feasibility Study",
journal="JMIR Med Inform",
year="2022",
month="Apr",
day="27",
volume="10",
number="4",
pages="e37771",
keywords="natural language processing",
keywords="text mining",
keywords="electronic medical records",
keywords="COVID-19",
keywords="structured registry",
keywords="coding procedure",
keywords="prediction model",
keywords="feasibility study",
keywords="precision model",
keywords="artificial intelligence",
keywords="primary care",
abstract="Background: Electronic medical records have opened opportunities to analyze clinical practice at large scale. Structured registries and coding procedures such as the International Classification of Primary Care further improved these procedures. However, a large part of the information about the state of patient and the doctors' observations is still entered in free text fields. The main function of those fields is to report the doctor's line of thought, to remind oneself and his or her colleagues on follow-up actions, and to be accountable for clinical decisions. These fields contain rich information that can be complementary to that in coded fields, and until now, they have been hardly used for analysis. Objective: This study aims to develop a prediction model to convert the free text information on COVID-19--related symptoms from out of hours care electronic medical records into usable symptom-based data that can be analyzed at large scale. Methods: The design was a feasibility study in which we examined the content of the raw data, steps and methods for modelling, as well as the precision and accuracy of the models. A data prediction model for 27 preidentified COVID-19--relevant symptoms was developed for a data set derived from the database of primary-care out-of-hours consultations in Flanders. A multiclass, multilabel categorization classifier was developed. We tested two approaches, which were (1) a classical machine learning--based text categorization approach, Binary Relevance, and (2) a deep neural network learning approach with BERTje, including a domain-adapted version. Ethical approval was acquired through the Institutional Review Board of the Institute of Tropical Medicine and the ethics committee of the University Hospital of Antwerpen (ref 20/50/693). Results: The sample set comprised 3957 fields. After cleaning, 2313 could be used for the experiments. Of the 2313 fields, 85\% (n=1966) were used to train the model, and 15\% (n=347) for testing. The normal BERTje model performed the best on the data. It reached a weighted F1 score of 0.70 and an exact match ratio or accuracy score of 0.38, indicating the instances for which the model has identified all correct codes. The other models achieved respectable results as well, ranging from 0.59 to 0.70 weighted F1. The Binary Relevance method performed the best on the data without a frequency threshold. As for the individual codes, the domain-adapted version of BERTje performs better on several of the less common objective codes, while BERTje reaches higher F1 scores for the least common labels especially, and for most other codes in general. Conclusions: The artificial intelligence model BERTje can reliably predict COVID-19--related information from medical records using text mining from the free text fields generated in primary care settings. This feasibility study invites researchers to examine further possibilities to use primary care routine data. ",
doi="10.2196/37771",
url="https://medinform.jmir.org/2022/4/e37771",
url="http://www.ncbi.nlm.nih.gov/pubmed/35442903"
}


@Article{info:doi/10.2196/33733,
author="R{\"o}bbelen, Alice
and Schmieding, L. Malte
and Kopka, Marvin
and Balzer, Felix
and Feufel, A. Markus",
title="Interactive Versus Static Decision Support Tools for COVID-19: Randomized Controlled Trial",
journal="JMIR Public Health Surveill",
year="2022",
month="Apr",
day="15",
volume="8",
number="4",
pages="e33733",
keywords="clinical decision support",
keywords="usability",
keywords="COVID-19",
keywords="consumer health",
keywords="medical informatic",
keywords="symptom checker",
keywords="decision support",
keywords="symptom",
keywords="support",
keywords="decision making",
keywords="algorithm",
keywords="flowchart",
keywords="agent",
abstract="Background: During the COVID-19 pandemic, medical laypersons with symptoms indicative of a COVID-19 infection commonly sought guidance on whether and where to find medical care. Numerous web-based decision support tools (DSTs) have been developed, both by public and commercial stakeholders, to assist their decision making. Though most of the DSTs' underlying algorithms are similar and simple decision trees, their mode of presentation differs: some DSTs present a static flowchart, while others are designed as a conversational agent, guiding the user through the decision tree's nodes step-by-step in an interactive manner. Objective: This study aims to investigate whether interactive DSTs provide greater decision support than noninteractive (ie, static) flowcharts. Methods: We developed mock interfaces for 2 DSTs (1 static, 1 interactive), mimicking patient-facing, freely available DSTs for COVID-19-related self-assessment. Their underlying algorithm was identical and based on the Centers for Disease Control and Prevention's guidelines. We recruited adult US residents online in November 2020. Participants appraised the appropriate social and care-seeking behavior for 7 fictitious descriptions of patients (case vignettes). Participants in the experimental groups received either the static or the interactive mock DST as support, while the control group appraised the case vignettes unsupported. We determined participants' accuracy, decision certainty (after deciding), and mental effort to measure the quality of decision support. Participants' ratings of the DSTs' usefulness, ease of use, trust, and future intention to use the tools served as measures to analyze differences in participants' perception of the tools. We used ANOVAs and t tests to assess statistical significance. Results: Our survey yielded 196 responses. The mean number of correct assessments was higher in the intervention groups (interactive DST group: mean 11.71, SD 2.37; static DST group: mean 11.45, SD 2.48) than in the control group (mean 10.17, SD 2.00). Decisional certainty was significantly higher in the experimental groups (interactive DST group: mean 80.7\%, SD 14.1\%; static DST group: mean 80.5\%, SD 15.8\%) compared to the control group (mean 65.8\%, SD 20.8\%). The differences in these measures proved statistically significant in t tests comparing each intervention group with the control group (P<.001 for all 4 t tests). ANOVA detected no significant differences regarding mental effort between the 3 study groups. Differences between the 2 intervention groups were of small effect sizes and nonsignificant for all 3 measures of the quality of decision support and most measures of participants' perception of the DSTs. Conclusions: When the decision space is limited, as is the case in common COVID-19 self-assessment DSTs, static flowcharts might prove as beneficial in enhancing decision quality as interactive tools. Given that static flowcharts reveal the underlying decision algorithm more transparently and require less effort to develop, they might prove more efficient in providing guidance to the public. Further research should validate our findings on different use cases, elaborate on the trade-off between transparency and convenience in DSTs, and investigate whether subgroups of users benefit more with 1 type of user interface than the other. Trial Registration: Deutsches Register Klinischer Studien DRKS00028136; https://tinyurl.com/4bcfausx (retrospectively registered) ",
doi="10.2196/33733",
url="https://publichealth.jmir.org/2022/4/e33733",
url="http://www.ncbi.nlm.nih.gov/pubmed/34882571"
}


@Article{info:doi/10.2196/35073,
author="Haithcoat, Timothy
and Liu, Danlu
and Young, Tiffany
and Shyu, Chi-Ren",
title="Investigating Health Context Using a Spatial Data Analytical Tool: Development of a Geospatial Big Data Ecosystem",
journal="JMIR Med Inform",
year="2022",
month="Apr",
day="6",
volume="10",
number="4",
pages="e35073",
keywords="context",
keywords="Geographic Information System",
keywords="big data",
keywords="equity",
keywords="population health",
keywords="public health",
keywords="digital health",
keywords="eHealth",
keywords="location",
keywords="geospatial",
keywords="data analytics",
keywords="analytical framework",
keywords="medical informatics",
keywords="research knowledgebase",
abstract="Background: Enabling the use of spatial context is vital to understanding today's digital health problems. Any given location is associated with many different contexts. The strategic transformation of population health, epidemiology, and eHealth studies requires vast amounts of integrated digital data. Needed is a novel analytical framework designed to leverage location to create new contextual knowledge. The Geospatial Analytical Research Knowledgebase (GeoARK), a web-based research resource has robust, locationally integrated, social, environmental, and infrastructural information to address today's complex questions, investigate context, and spatially enable health investigations. GeoARK is different from other Geographic Information System (GIS) resources in that it has taken the layered world of the GIS and flattened it into a big data table that ties all the data and information together using location and developing its context. Objective: It is paramount to build a robust spatial data analytics framework that integrates social, environmental, and infrastructural knowledge to empower health researchers' use of geospatial context to timely answer population health issues. The goal is twofold in that it embodies an innovative technological approach and serves to ease the educational burden for health researchers to think spatially about their problems. Methods: A unique analytical tool using location as the key was developed. It allows integration across source, geography, and time to create a geospatial big table with over 162 million individual locations (X-Y points that serve as rows) and 5549 attributes (represented as columns). The concept of context (adjacency, proximity, distance, etc) is quantified through geoanalytics and captured as new distance, density, or neighbor attributes within the system. Development of geospatial analytics permits contextual extraction and investigator-initiated eHealth and mobile health (mHealth) analysis across multiple attributes. Results: We built a unique geospatial big data ecosystem called GeoARK. Analytics on this big table occur across resolution groups, sources, and geographies for extraction and analysis of information to gain new insights. Case studies, including telehealth assessment in North Carolina, national income inequality and health outcome disparity, and a Missouri COVID-19 risk assessment, demonstrate the capability to support robust and efficient geospatial understanding of a wide spectrum of population health questions. Conclusions: This research identified, compiled, transformed, standardized, and integrated multifaceted data required to better understand the context of health events within a large location-enabled database. The GeoARK system empowers health professionals to engage more complex research where the synergisms of health and geospatial information will be robustly studied beyond what could be accomplished today. No longer is the need to know how to perform geospatial processing an impediment to the health researcher, but rather the development of how to think spatially becomes the greater challenge. ",
doi="10.2196/35073",
url="https://medinform.jmir.org/2022/4/e35073",
url="http://www.ncbi.nlm.nih.gov/pubmed/35311683"
}


@Article{info:doi/10.2196/36200,
author="Kruse, Scott Clemens
and Mileski, Michael
and Dray, Gevin
and Johnson, Zakia
and Shaw, Cameron
and Shirodkar, Harsha",
title="Physician Burnout and the Electronic Health Record Leading Up to and During the First Year of COVID-19: Systematic Review",
journal="J Med Internet Res",
year="2022",
month="Mar",
day="31",
volume="24",
number="3",
pages="e36200",
keywords="electronic health record",
keywords="physician burnout",
keywords="quality improvement",
keywords="psychiatry",
keywords="medical informatics",
keywords="COVID-19",
keywords="pandemic",
keywords="health informatic",
keywords="health care",
keywords="health care professional",
keywords="health care infrastructure",
keywords="health care system",
keywords="mental health",
keywords="cognitive fatigue",
abstract="Background: Physician burnout was first documented in 1974, and the electronic health record (EHR) has been known to contribute to the symptoms of physician burnout. Authors pondered the extent of this effect, recognizing the increased use of telemedicine during the first year of COVID-19. Objective: The aim of this review was to objectively analyze the literature over the last 5 years for empirical evidence of burnout incident to the EHR and to identify barriers to, facilitators to, and associated patient satisfaction with using the EHR to improve symptoms of burnout. Methods: No human participants were involved in this review; however, 100\% of participants in studies analyzed were adult physicians. We queried 4 research databases and 1 targeted journal for studies commensurate with the objective statement from January 1, 2016 through January 31, 2021 (n=25). Results: The hours spent in documentation and workflow are responsible for the sense of loss of autonomy, lack of work-life balance, lack of control of one's schedule, cognitive fatigue, a general loss of autonomy, and poor relationships with colleagues. Researchers have identified training, local customization of templates and workflow, and the use of scribes as strategies to alleviate the administrative burden of the EHR and decrease symptoms of burnout. Conclusions: The solutions provided in the literature only addressed 2 of the 3 factors (workflow and documentation time) but not the third factor (usability). Practitioners and administrators should focus on the former 2 factors because they are within their sphere of control. EHR vendors should focus on empirical evidence to identify and improve the usability features with the greatest impact. Researchers should design experiments to explore solutions that address all 3 factors of the EHR that contribute to burnout. Trial Registration: PROSPERO International Prospective Register of Systematic Reviews CRD42020201820; https://www.crd.york.ac.uk/prospero/display\_record.php?RecordID=201820 International Registered Report Identifier (IRRID): RR2-10.2196/15490 ",
doi="10.2196/36200",
url="https://www.jmir.org/2022/3/e36200",
url="http://www.ncbi.nlm.nih.gov/pubmed/35120019"
}


@Article{info:doi/10.2196/32949,
author="Jung, Christian
and Mamandipoor, Behrooz
and Fj{\o}lner, Jesper
and Bruno, Romano Raphael
and Wernly, Bernhard
and Artigas, Antonio
and Bollen Pinto, Bernardo
and Schefold, C. Joerg
and Wolff, Georg
and Kelm, Malte
and Beil, Michael
and Sviri, Sigal
and van Heerden, V. Peter
and Szczeklik, Wojciech
and Czuczwar, Miroslaw
and Elhadi, Muhammed
and Joannidis, Michael
and Oeyen, Sandra
and Zafeiridis, Tilemachos
and Marsh, Brian
and Andersen, H. Finn
and Moreno, Rui
and Cecconi, Maurizio
and Leaver, Susannah
and De Lange, W. Dylan
and Guidet, Bertrand
and Flaatten, Hans
and Osmani, Venet",
title="Disease-Course Adapting Machine Learning Prognostication Models in Elderly Patients Critically Ill With COVID-19: Multicenter Cohort Study With External Validation",
journal="JMIR Med Inform",
year="2022",
month="Mar",
day="31",
volume="10",
number="3",
pages="e32949",
keywords="machine-based learning",
keywords="outcome prediction",
keywords="COVID-19",
keywords="pandemic",
keywords="machine learning",
keywords="prediction models",
keywords="clinical informatics",
keywords="patient data",
keywords="elderly population",
abstract="Background: The COVID-19 pandemic caused by SARS-CoV-2 is challenging health care systems globally. The disease disproportionately affects the elderly population, both in terms of disease severity and mortality risk. Objective: The aim of this study was to evaluate machine learning--based prognostication models for critically ill elderly COVID-19 patients, which dynamically incorporated multifaceted clinical information on evolution of the disease. Methods: This multicenter cohort study (COVIP study) obtained patient data from 151 intensive care units (ICUs) from 26 countries. Different models based on the Sequential Organ Failure Assessment (SOFA) score, logistic regression (LR), random forest (RF), and extreme gradient boosting (XGB) were derived as baseline models that included admission variables only. We subsequently included clinical events and time-to-event as additional variables to derive the final models using the same algorithms and compared their performance with that of the baseline group. Furthermore, we derived baseline and final models on a European patient cohort, which were externally validated on a non-European cohort that included Asian, African, and US patients. Results: In total, 1432 elderly (?70 years old) COVID-19--positive patients admitted to an ICU were included for analysis. Of these, 809 (56.49\%) patients survived up to 30 days after admission. The average length of stay was 21.6 (SD 18.2) days. Final models that incorporated clinical events and time-to-event information provided superior performance (area under the receiver operating characteristic curve of 0.81; 95\% CI 0.804-0.811), with respect to both the baseline models that used admission variables only and conventional ICU prediction models (SOFA score, P<.001). The average precision increased from 0.65 (95\% CI 0.650-0.655) to 0.77 (95\% CI 0.759-0.770). Conclusions: Integrating important clinical events and time-to-event information led to a superior accuracy of 30-day mortality prediction compared with models based on the admission information and conventional ICU prediction models. This study shows that machine-learning models provide additional information and may support complex decision-making in critically ill elderly COVID-19 patients. Trial Registration: ClinicalTrials.gov NCT04321265; https://clinicaltrials.gov/ct2/show/NCT04321265 ",
doi="10.2196/32949",
url="https://medinform.jmir.org/2022/3/e32949",
url="http://www.ncbi.nlm.nih.gov/pubmed/35099394"
}


@Article{info:doi/10.2196/31557,
author="Lyu, Zeyu
and Takikawa, Hiroki",
title="The Disparity and Dynamics of Social Distancing Behaviors in Japan: Investigation of Mobile Phone Mobility Data",
journal="JMIR Med Inform",
year="2022",
month="Mar",
day="22",
volume="10",
number="3",
pages="e31557",
keywords="COVID-19",
keywords="social distancing",
keywords="mobility",
keywords="time series",
keywords="tracking",
keywords="policy",
abstract="Background: The availability of large-scale and fine-grained aggregated mobility data has allowed researchers to observe the dynamics of social distancing behaviors at high spatial and temporal resolutions. Despite the increasing attention paid to this research agenda, limited studies have focused on the demographic factors related to mobility, and the dynamics of social distancing behaviors have not been fully investigated. Objective: This study aims to assist in designing and implementing public health policies by exploring how social distancing behaviors varied among various demographic groups over time. Methods: We combined several data sources, including mobile tracking mobility data and geographical statistics, to estimate the visiting population of entertainment venues across demographic groups, which can be considered the proxy of social distancing behaviors. Next, we used time series analysis methods to investigate how voluntary and policy-induced social distancing behaviors shifted over time across demographic groups. Results: Our findings demonstrate distinct patterns of social distancing behaviors and their dynamics across age groups. On the one hand, although entertainment venues' population comprises mainly individuals aged 20-40 years, a more significant proportion of the youth has adopted social distancing behaviors and complied with policy implementations compared to older age groups. From this perspective, the increasing contribution to infections by the youth should be more likely to be attributed to their number rather than their violation of social distancing behaviors. On the other hand, although risk perception and self-restriction recommendations can induce social distancing behaviors, their impact and effectiveness appear to be largely weakened during Japan's second state of emergency. Conclusions: This study provides a timely reference for policymakers about the current situation on how different demographic groups adopt social distancing behaviors over time. On the one hand, the age-dependent disparity requires more nuanced and targeted mitigation strategies to increase the intention of elderly individuals to adopt mobility restriction behaviors. On the other hand, considering that the effectiveness of policy implementations requesting social distancing behaviors appears to decline over time, in extreme cases, the government should consider imposing stricter social distancing interventions, as they are necessary to promote social distancing behaviors and mitigate the transmission of COVID-19. ",
doi="10.2196/31557",
url="https://medinform.jmir.org/2022/3/e31557",
url="http://www.ncbi.nlm.nih.gov/pubmed/35297764"
}


@Article{info:doi/10.2196/32373,
author="Alhajri, Noora
and Simsekler, Emre Mecit Can
and Alfalasi, Buthaina
and Alhashmi, Mohamed
and Memon, Hamda
and Housser, Emma
and Abdi, Mustafa Abdulhamid
and Balalaa, Nahed
and Al Ali, Maryam
and Almaashari, Raghda
and Al Memari, Shammah
and Al Hosani, Farida
and Al Zaabi, Yousif
and Almazrouei, Shereena
and Alhashemi, Hamed",
title="Exploring Quality Differences in Telemedicine Between Hospital Outpatient Departments and Community Clinics: Cross-sectional Study",
journal="JMIR Med Inform",
year="2022",
month="Feb",
day="15",
volume="10",
number="2",
pages="e32373",
keywords="COVID-19",
keywords="patient satisfaction",
keywords="technology acceptance",
keywords="hospital",
keywords="community clinic",
keywords="video consultation",
keywords="audio consultation",
keywords="outpatient department",
keywords="OPD",
keywords="policy making",
keywords="UAE",
abstract="Background: Telemedicine is a care delivery modality that has the potential to broaden the reach and flexibility of health care services. In the United Arab Emirates, telemedicine services are mainly delivered through either integrated hospital outpatient department (OPDs) or community clinics. However, it is unknown if patients' perceptions of, and satisfaction with, telemedicine services differ between these two types of health care systems during the COVID-19 pandemic. Objective: We aimed to explore the differences in patients' perceptions of, and satisfaction with, telemedicine between hospital OPDs and community clinics during the COVID-19 pandemic. We also aimed to identify patient- or visit-related characteristics contributing to patient satisfaction with telemedicine. Methods: In this cross-sectional study that was conducted at Abu Dhabi health care centers, we invited outpatients aged 18 years or over, who completed a telemedicine visit during the COVID-19 pandemic, to participate in our study. Patients' perceptions of, and satisfaction with, telemedicine regarding the two system types (ie, hospital OPDs and community clinics) were assessed using an online survey that was sent as a link through the SMS system. Regression models were used to describe the association between patient- and visit-related characteristics, as well as the perception of, and satisfaction with, telemedicine services. Results: A total of 515 patients participated in this survey. Patients' satisfaction with telemedicine services was equally high among the settings, with no statistically significant difference between the two setting types (hospital OPDs: 253/343, 73.8\%; community clinics: 114/172, 66.3\%; P=.19). Video consultation was significantly associated with increased patient satisfaction (odds ratio [OR] 2.57, 95\% CI 1.04-6.33; P=.04) and patients' support of the transition to telemedicine use during and after the pandemic (OR 2.88, 95\% CI 1.18-7.07; P=.02). Patients who used video consultations were more likely to report that telemedicine improved access to health care services (OR 3.06, 95\% CI 1.71-8.03; P=.02), reduced waiting times and travel costs (OR 4.94, 95\% CI 1.15-21.19; P=.03), addressed patients' needs (OR 2.63, 95\% CI 1.13-6.11; P=.03), and eased expression of patients' medical concerns during the COVID-19 pandemic (OR 2.19, 95\% CI 0.89-5.38; P=.09). Surprisingly, middle-aged patients were two times more likely to be satisfied with telemedicine services (OR 2.12, 95\% CI 1.09-4.14; P=.03), as compared to any other age group in this study. Conclusions: These findings suggest that patient satisfaction was unaffected by the health system setting in which patients received the teleconsultations, whether they were at hospitals or community clinics. Video consultation was associated with increased patient satisfaction with telemedicine services. Efforts should be focused on strategic planning for enhanced telemedicine services, video consultation in particular, for both emergent circumstances, such as the COVID-19 pandemic, and day-to-day health care delivery. ",
doi="10.2196/32373",
url="https://medinform.jmir.org/2022/2/e32373",
url="http://www.ncbi.nlm.nih.gov/pubmed/34978281"
}


@Article{info:doi/10.2196/28183,
author="Wang, Qian
and Xie, Luyao
and Song, Bo
and Di, Jiangli
and Wang, Linhong
and Mo, Kit-Han Phoenix",
title="Effects of Social Media Use for Health Information on COVID-19--Related Risk Perceptions and Mental Health During Pregnancy: Web-Based Survey",
journal="JMIR Med Inform",
year="2022",
month="Jan",
day="13",
volume="10",
number="1",
pages="e28183",
keywords="COVID-19",
keywords="pregnant",
keywords="social media use",
keywords="risk perception",
keywords="worry",
keywords="depression",
abstract="Background: Social media has become an important source of health information during the COVID-19 pandemic. Very little is known about the potential mental impact of social media use on pregnant women. Objective: This study aims to examine the association between using social media for health information and risk perception for COVID-19, worry due to COVID-19, and depression among pregnant women in China. Methods: A total of 4580 pregnant women were recruited from various provinces of China. The participants completed a cross-sectional, web-based survey in March 2020. Results: More than one-third (1794/4580, 39.2\%) of the participants reported always using social media for obtaining health information. Results of structural equation modeling showed that the frequency of social media use for health information was positively associated with perceived susceptibility ($\beta$=.05; P<.001) and perceived severity ($\beta$=.12; P<.001) of COVID-19, which, in turn, were positively associated with worry due to COVID-19 ($\beta$=.19 and $\beta$=.72, respectively; P<.001). Perceived susceptibility ($\beta$=.09; P<.001), perceived severity ($\beta$=.08; P<.001), and worry due to COVID-19 ($\beta$=.15; P<.001) all had a positive association with depression. Bootstrapping analysis showed that the indirect effects of frequency of social media use for health information on both worry due to COVID-19 ($\beta$=.09, 95\% CI 0.07-0.12) and depression ($\beta$=.05, 95\% CI 0.02-0.07) were statistically significant. Conclusions: This study provides empirical evidence on how social media use for health information might have a negative impact on the mental health of pregnant women. Interventions are needed to equip this population with the skills to use social media properly and with caution. ",
doi="10.2196/28183",
url="https://medinform.jmir.org/2022/1/e28183",
url="http://www.ncbi.nlm.nih.gov/pubmed/34762065"
}


@Article{info:doi/10.2196/32726,
author="Kim, Jeongmin
and Lim, Hakyung
and Ahn, Jae-Hyeon
and Lee, Hwa Kyoung
and Lee, Suk Kwang
and Koo, Chul Kyo",
title="Optimal Triage for COVID-19 Patients Under Limited Health Care Resources With a Parsimonious Machine Learning Prediction Model and Threshold Optimization Using Discrete-Event Simulation: Development Study",
journal="JMIR Med Inform",
year="2021",
month="Nov",
day="2",
volume="9",
number="11",
pages="e32726",
keywords="COVID-19",
keywords="decision support techniques",
keywords="machine learning",
keywords="prediction",
keywords="triage",
abstract="Background: The COVID-19 pandemic has placed an unprecedented burden on health care systems. Objective: We aimed to effectively triage COVID-19 patients within situations of limited data availability and explore optimal thresholds to minimize mortality rates while maintaining health care system capacity. Methods: A nationwide sample of 5601 patients confirmed with COVID-19 until April 2020 was retrospectively reviewed. Extreme gradient boosting (XGBoost) and logistic regression analysis were used to develop prediction models for the maximum clinical severity during hospitalization, classified according to the World Health Organization Ordinal Scale for Clinical Improvement (OSCI). The recursive feature elimination technique was used to evaluate the maintenance of model performance when clinical and laboratory variables were eliminated. Using populations based on hypothetical patient influx scenarios, discrete-event simulation was performed to find an optimal threshold within limited resource environments that minimizes mortality rates. Results: The cross-validated area under the receiver operating characteristic curve (AUROC) of the baseline XGBoost model that utilized all 37 variables was 0.965 for OSCI ?6. Compared to the baseline model's performance, the AUROC of the feature-eliminated model that utilized 17 variables was maintained at 0.963 with statistical insignificance. Optimal thresholds were found to minimize mortality rates in a hypothetical patient influx scenario. The benefit of utilizing an optimal triage threshold was clear, reducing mortality up to 18.1\%, compared with the conventional Youden index. Conclusions: Our adaptive triage model and its threshold optimization capability revealed that COVID-19 management can be achieved via the cooperation of both the medical and health care management sectors for maximum treatment efficacy. The model is available online for clinical implementation. ",
doi="10.2196/32726",
url="https://medinform.jmir.org/2021/11/e32726",
url="http://www.ncbi.nlm.nih.gov/pubmed/34609319"
}


@Article{info:doi/10.2196/32303,
author="Luu, S. Hung
and Filkins, M. Laura
and Park, Y. Jason
and Rakheja, Dinesh
and Tweed, Jefferson
and Menzies, Christopher
and Wang, J. Vincent
and Mittal, Vineeta
and Lehmann, U. Christoph
and Sebert, E. Michael",
title="Harnessing the Electronic Health Record and Computerized Provider Order Entry Data for Resource Management During the COVID-19 Pandemic: Development of a Decision Tree",
journal="JMIR Med Inform",
year="2021",
month="Oct",
day="18",
volume="9",
number="10",
pages="e32303",
keywords="COVID-19",
keywords="computerized provider order entry",
keywords="electronic health record",
keywords="resource utilization",
keywords="personal protective equipment",
keywords="SARS-CoV-2 testing",
keywords="clinical decision support",
abstract="Background: The COVID-19 pandemic has resulted in shortages of diagnostic tests, personal protective equipment, hospital beds, and other critical resources. Objective: We sought to improve the management of scarce resources by leveraging electronic health record (EHR) functionality, computerized provider order entry, clinical decision support (CDS), and data analytics. Methods: Due to the complex eligibility criteria for COVID-19 tests and the EHR implementation--related challenges of ordering these tests, care providers have faced obstacles in selecting the appropriate test modality. As test choice is dependent upon specific patient criteria, we built a decision tree within the EHR to automate the test selection process by using a branching series of questions that linked clinical criteria to the appropriate SARS-CoV-2 test and triggered an EHR flag for patients who met our institutional persons under investigation criteria. Results: The percentage of tests that had to be canceled and reordered due to errors in selecting the correct testing modality was 3.8\% (23/608) before CDS implementation and 1\% (262/26,643) after CDS implementation (P<.001). Patients for whom multiple tests were ordered during a 24-hour period accounted for 0.8\% (5/608) and 0.3\% (76/26,643) of pre- and post-CDS implementation orders, respectively (P=.03). Nasopharyngeal molecular assay results were positive in 3.4\% (826/24,170) of patients who were classified as asymptomatic and 10.9\% (1421/13,074) of symptomatic patients (P<.001). Positive tests were more frequent among asymptomatic patients with a history of exposure to COVID-19 (36/283, 12.7\%) than among asymptomatic patients without such a history (790/23,887, 3.3\%; P<.001). Conclusions: The leveraging of EHRs and our CDS algorithm resulted in a decreased incidence of order entry errors and the appropriate flagging of persons under investigation. These interventions optimized reagent and personal protective equipment usage. Data regarding symptoms and COVID-19 exposure status that were collected by using the decision tree correlated with the likelihood of positive test results, suggesting that clinicians appropriately used the questions in the decision tree algorithm. ",
doi="10.2196/32303",
url="https://medinform.jmir.org/2021/10/e32303",
url="http://www.ncbi.nlm.nih.gov/pubmed/34546942"
}


@Article{info:doi/10.2196/30157,
author="Sankaranarayanan, Saranya
and Balan, Jagadheshwar
and Walsh, R. Jesse
and Wu, Yanhong
and Minnich, Sara
and Piazza, Amy
and Osborne, Collin
and Oliver, R. Gavin
and Lesko, Jessica
and Bates, L. Kathy
and Khezeli, Kia
and Block, R. Darci
and DiGuardo, Margaret
and Kreuter, Justin
and O'Horo, C. John
and Kalantari, John
and Klee, W. Eric
and Salama, E. Mohamed
and Kipp, Benjamin
and Morice, G. William
and Jenkinson, Garrett",
title="COVID-19 Mortality Prediction From Deep Learning in a Large Multistate Electronic Health Record and Laboratory Information System Data Set: Algorithm Development and Validation",
journal="J Med Internet Res",
year="2021",
month="Sep",
day="28",
volume="23",
number="9",
pages="e30157",
keywords="COVID-19",
keywords="mortality",
keywords="prediction",
keywords="recurrent neural networks",
keywords="missing data",
keywords="time series",
keywords="deep learning",
keywords="machine learning",
keywords="neural network",
keywords="electronic health record",
keywords="EHR",
keywords="algorithm",
keywords="development",
keywords="validation",
abstract="Background: COVID-19 is caused by the SARS-CoV-2 virus and has strikingly heterogeneous clinical manifestations, with most individuals contracting mild disease but a substantial minority experiencing fulminant cardiopulmonary symptoms or death. The clinical covariates and the laboratory tests performed on a patient provide robust statistics to guide clinical treatment. Deep learning approaches on a data set of this nature enable patient stratification and provide methods to guide clinical treatment. Objective: Here, we report on the development and prospective validation of a state-of-the-art machine learning model to provide mortality prediction shortly after confirmation of SARS-CoV-2 infection in the Mayo Clinic patient population. Methods: We retrospectively constructed one of the largest reported and most geographically diverse laboratory information system and electronic health record of COVID-19 data sets in the published literature, which included 11,807 patients residing in 41 states of the United States of America and treated at medical sites across 5 states in 3 time zones. Traditional machine learning models were evaluated independently as well as in a stacked learner approach by using AutoGluon, and various recurrent neural network architectures were considered. The traditional machine learning models were implemented using the AutoGluon-Tabular framework, whereas the recurrent neural networks utilized the TensorFlow Keras framework. We trained these models to operate solely using routine laboratory measurements and clinical covariates available within 72 hours of a patient's first positive COVID-19 nucleic acid test result. Results: The GRU-D recurrent neural network achieved peak cross-validation performance with 0.938 (SE 0.004) as the area under the receiver operating characteristic (AUROC) curve. This model retained strong performance by reducing the follow-up time to 12 hours (0.916 [SE 0.005] AUROC), and the leave-one-out feature importance analysis indicated that the most independently valuable features were age, Charlson comorbidity index, minimum oxygen saturation, fibrinogen level, and serum iron level. In the prospective testing cohort, this model provided an AUROC of 0.901 and a statistically significant difference in survival (P<.001, hazard ratio for those predicted to survive, 95\% CI 0.043-0.106). Conclusions: Our deep learning approach using GRU-D provides an alert system to flag mortality for COVID-19--positive patients by using clinical covariates and laboratory values within a 72-hour window after the first positive nucleic acid test result. ",
doi="10.2196/30157",
url="https://www.jmir.org/2021/9/e30157",
url="http://www.ncbi.nlm.nih.gov/pubmed/34449401"
}


@Article{info:doi/10.2196/27977,
author="Gmunder, Nicole Kristin
and Ruiz, W. Jose
and Franceschi, Dido
and Suarez, M. Maritza",
title="Factors to Effective Telemedicine Visits During the COVID-19 Pandemic: Cohort Study",
journal="JMIR Med Inform",
year="2021",
month="Aug",
day="27",
volume="9",
number="8",
pages="e27977",
keywords="telemedicine",
keywords="COVID-19",
keywords="patient portals",
keywords="delivery of health care",
keywords="telehealth",
keywords="pandemic",
keywords="digital health",
abstract="Background: With COVID-19 there was a rapid and abrupt rise in telemedicine implementation often without sufficient time for providers or patients to adapt. As telemedicine visits are likely to continue to play an important role in health care, it is crucial to strive for a better understanding of how to ensure completed telemedicine visits in our health system. Awareness of these barriers to effective telemedicine visits is necessary for a proactive approach to addressing issues. Objective: The objective of this study was to identify variables that may affect telemedicine visit completion in order to determine actions that can be enacted across the entire health system to benefit all patients. Methods: Data were collected from scheduled telemedicine visits (n=362,764) at the University of Miami Health System (UHealth) between March 1, 2020 and October 31, 2020. Descriptive statistics, mixed effects logistic regression, and random forest modeling were used to identify the most important patient-agnostic predictors of telemedicine completion. Results: Using descriptive statistics, struggling telemedicine specialties, providers, and clinic locations were identified. Through mixed effects logistic regression (adjusting for clustering at the clinic site level), the most important predictors of completion included previsit phone call/SMS text message reminder status (confirmed vs not answered) (odds ratio [OR] 6.599, 95\% CI 6.483-6.717), MyUHealthChart patient portal status (not activated vs activated) (OR 0.315, 95\% CI 0.305-0.325), provider's specialty (primary care vs medical specialty) (OR 1.514, 95\% CI 1.472-1.558), new to the UHealth system (yes vs no) (OR 1.285, 95\% CI 1.201-1.374), and new to provider (yes vs no) (OR 0.875, 95\% CI 0.859-0.891). Random forest modeling results mirrored those from logistic regression. Conclusions: The highest association with a completed telemedicine visit was the previsit appointment confirmation by the patient via phone call/SMS text message. An active patient portal account was the second strongest variable associated with completion, which underscored the importance of patients having set up their portal account before the telemedicine visit. Provider's specialty was the third strongest patient-agnostic characteristic associated with telemedicine completion rate. Telemedicine will likely continue to have an integral role in health care, and these results should be used as an important guide to improvement efforts. As a first step toward increasing completion rates, health care systems should focus on improvement of patient portal usage and use of previsit reminders. Optimization and intervention are necessary for those that are struggling with implementing telemedicine. We advise setting up a standardized workflow for staff. ",
doi="10.2196/27977",
url="https://medinform.jmir.org/2021/8/e27977",
url="http://www.ncbi.nlm.nih.gov/pubmed/34254936"
}


@Article{info:doi/10.2196/30453,
author="Khuntia, Jiban
and Ning, Xue
and Stacey, Rulon",
title="Digital Orientation of Health Systems in the Post--COVID-19 ``New Normal'' in the United States: Cross-sectional Survey",
journal="J Med Internet Res",
year="2021",
month="Aug",
day="16",
volume="23",
number="8",
pages="e30453",
keywords="post--COVID-19",
keywords="digital orientation",
keywords="health systems",
keywords="digital transformation",
keywords="digital health",
keywords="telehealth",
keywords="telemedicine",
keywords="COVID-19",
keywords="impact",
keywords="insight",
keywords="cross-sectional",
keywords="survey",
keywords="United States",
keywords="electronic health record",
keywords="EHR",
abstract="Background: Almost all health systems have developed some form of customer-facing digital technologies and have worked to align these systems to their existing electronic health records to accommodate the surge in remote and virtual care deliveries during the COVID-19 pandemic. Others have developed analytics-driven decision-making capabilities. However, it is not clear how health systems in the United States are embracing digital technologies and there is a gap in health systems' abilities to integrate workflows with expanding technologies to spur innovation and futuristic growth. There is a lack of reliable and reported estimates of the current and futuristic digital orientations of health systems. Periodic assessments will provide imperatives to policy formulation and align efforts to yield the transformative power of emerging digital technologies. Objective: The aim of this study was to explore and examine differences in US health systems with respect to digital orientations in the post--COVID-19 ``new normal'' in 2021. Differences were assessed in four dimensions: (1) analytics-oriented digital technologies (AODT), (2) customer-oriented digital technologies (CODT), (3) growth and innovation--oriented digital technologies (GODT), and (4) futuristic and experimental digital technologies (FEDT). The former two dimensions are foundational to health systems' digital orientation, whereas the latter two will prepare for future disruptions. Methods: We surveyed a robust group of health system chief executive officers (CEOs) across the United States from February to March 2021. Among the 625 CEOs, 135 (22\%) responded to our survey. We considered the above four broad digital technology orientations, which were ratified with expert consensus. Secondary data were collected from the Agency for Healthcare Research and Quality Hospital Compendium, leading to a matched usable dataset of 124 health systems for analysis. We examined the relationship of adopting the four digital orientations to specific hospital characteristics and earlier reported factors as barriers or facilitators to technology adoption. Results: Health systems showed a lower level of CODT (mean 4.70) or GODT (mean 4.54) orientations compared with AODT (mean 5.03), and showed the lowest level of FEDT orientation (mean 4.31). The ordered logistic estimation results provided nuanced insights. Medium-sized (P<.001) health systems, major teaching health systems (P<.001), and systems with high-burden hospitals (P<.001) appear to be doing worse with respect to AODT orientations, raising some concerns. Health systems of medium (P<.001) and large (P=.02) sizes, major teaching health systems (P=.07), those with a high revenue (P=.05), and systems with high-burden hospitals (P<.001) have less CODT orientation. Health systems in the midwest (P=.05) and southern (P=.04) states are more likely to adopt GODT, whereas high-revenue (P=.004) and investor-ownership (P=.01) health systems are deterred from GODT. Health systems of a medium size, and those that are in the midwest (P<.001), south (P<.001), and west (P=.01) are more adept to FEDT, whereas medium (P<.001) and high-revenue (P<.001) health systems, and those with a high discharge rate (P=.04) or high burden (P=.003, P=.005) have subdued FEDT orientations. Conclusions: Almost all health systems have some current foundational digital technological orientations to glean intelligence or service delivery to customers, with some notable exceptions. Comparatively, fewer health systems have growth or futuristic digital orientations. The transformative power of digital technologies can only be leveraged by adopting futuristic digital technologies. Thus, the disparities across these orientations suggest that a holistic, consistent, and well-articulated direction across the United States remains elusive. Accordingly, we suggest that a policy strategy and financial incentives are necessary to spur a well-visioned and articulated digital orientation for all health systems across the United States. In the absence of such a policy to collectively leverage digital transformations, differences in care across the country will continue to be a concern. ",
doi="10.2196/30453",
url="https://www.jmir.org/2021/8/e30453",
url="http://www.ncbi.nlm.nih.gov/pubmed/34254947"
}


@Article{info:doi/10.2196/26336,
author="Walia, Bhavneet
and Shridhar, Anshu
and Arasu, Pratap
and Singh, Kaur Gursimar",
title="US Physicians' Perspective on the Sudden Shift to Telehealth: Survey Study",
journal="JMIR Hum Factors",
year="2021",
month="Aug",
day="12",
volume="8",
number="3",
pages="e26336",
keywords="physician survey",
keywords="US telehealth training",
keywords="US telehealth care",
keywords="COVID-19",
keywords="pandemic",
keywords="snowball sampling",
keywords="health care access",
keywords="health care quality",
keywords="telehealth",
keywords="telemedicine",
keywords="survey",
keywords="physician",
keywords="perspective",
keywords="recommendation",
keywords="policy",
keywords="public health",
keywords="implication",
keywords="quality",
keywords="access",
abstract="Background: Given the sudden shift to telemedicine during the early COVID-19 pandemic, we conducted a survey of practicing physicians' experience with telehealth during the prepandemic and early pandemic periods. Our survey estimates that most patient visits in the United States during the early COVID-19 pandemic period were conducted via telehealth. Given this magnitude and the potential benefits and challenges of telehealth for the US health care system, in this paper, we obtain, summarize, and analyze telehealth views and experiences of US-based practicing-physicians. Objective: The aim of this study was to examine the extent of shift toward telehealth training and care provision during the early pandemic from the US-based practicing physicians' perspective. We also sought to determine the short- and long-term implications of this shift on the quality, access, and mode of US health care delivery. Methods: We conducted a purposive, snowball-sampled survey of US practicing-physicians. A total of 148 physician completed the survey. Data were collected from July 17, 2020, through September 4, 2020. Results: Sample training intensity scaled 21-fold during the early pandemic period, and patient-care visits conducted via telehealth increased, on average, from 13.1\% directly before the pandemic to 59.7\% during the early pandemic period. Surveyed physician respondents reported that telehealth patient visits and face-to-face patient visits are comparable in quality. The difference was not statistically significant based on a nonparametric sign test (P=.11). Moreover, physicians feel that telehealth care should continue to play a larger role (44.9\% of total visits) in postpandemic health care in the United States. Our survey findings suggest a high market concentration in telehealth software, which is a market structural characteristic that may have implications on the cost and access of telehealth. The results varied markedly by physician employer type. Conclusions: During the shift toward telehealth, there has been a considerable discovery among physicians regarding US telehealth physicians. Physicians are now better prepared to undertake telehealth care from a training perspective. They are favorable toward a permanently expanded telehealth role, with potential for enhanced health care access, and the realization of enhanced access may depend on market structural characteristics of telehealth software platforms. ",
doi="10.2196/26336",
url="https://humanfactors.jmir.org/2021/3/e26336",
url="http://www.ncbi.nlm.nih.gov/pubmed/33938813"
}


@Article{info:doi/10.2196/29315,
author="Park, Jihwan
and Han, Jinhyun
and Kim, Yerin
and Rho, Jung Mi",
title="Development, Acceptance, and Concerns Surrounding App-Based Services to Overcome the COVID-19 Outbreak in South Korea: Web-Based Survey Study",
journal="JMIR Med Inform",
year="2021",
month="Jul",
day="30",
volume="9",
number="7",
pages="e29315",
keywords="COVID-19",
keywords="app-based services",
keywords="acceptance",
keywords="concerns",
keywords="epidemiological investigation, self-route management app, privacy",
abstract="Background: Since the COVID-19 outbreak, South Korea has been engaged in various efforts to overcome the pandemic. One of them is to provide app-based COVID-19--related services to the public. As the pandemic continues, a need for various apps has emerged, including COVID-19 apps that can support activities aimed at overcoming the COVID-19 pandemic. Objective: We aimed to determine which apps were considered the most necessary according to users and evaluate the current status of the development of COVID-19--related apps in South Korea. We also aimed to determine users' acceptance and concerns related to using apps to support activities to combat COVID-19. Methods: We collected data from 1148 users from a web-based survey conducted between November 11 and December 6, 2020. Basic statistical analysis, multiple response analysis, and the Wilcoxon rank sum test were performed using R software. We then manually classified the current status of the development of COVID-19--related apps. Results: In total, 68.4\% (785/1148) of the respondents showed high willingness to protect themselves from COVID-19 by using related apps. Users considered the epidemiological investigation app to be the most necessary app (709/1148, 61.8\%) overall, followed by the self-management app for self-isolation (613/1148, 53.4\%), self-route management app (605/1148, 52.7\%), COVID-19 symptom management app (483/1148, 42.1\%), COVID-19--related information provision app (339/1148, 29.5\%), and mental health management app (270/1148, 23.5\%). Despite the high intention to use these apps, users were also concerned about privacy issues and media exposure. Those who had an underlying disease and had experience using COVID-19--related apps showed significantly higher intentions to use those apps (P=.05 and P=.01, respectively). Conclusions: Targeting users is very important in order to design and develop the most necessary apps. Furthermore, to gain the public's trust and make the apps available to as many people as possible, it is vital to develop diverse apps in which privacy protection is maximized. ",
doi="10.2196/29315",
url="https://medinform.jmir.org/2021/7/e29315",
url="http://www.ncbi.nlm.nih.gov/pubmed/34137726"
}


@Article{info:doi/10.2196/29195,
author="Xiong, Ziyu
and Li, Pin
and Lyu, Hanjia
and Luo, Jiebo",
title="Social Media Opinions on Working From Home in the United States During the COVID-19 Pandemic: Observational Study",
journal="JMIR Med Inform",
year="2021",
month="Jul",
day="30",
volume="9",
number="7",
pages="e29195",
keywords="characterization",
keywords="COVID-19",
keywords="social media",
keywords="topic modeling",
keywords="Twitter",
keywords="work from home",
abstract="Background: Since March 2020, companies nationwide have started work from home (WFH) owing to the rapid increase of confirmed COVID-19 cases in an attempt to help prevent the disease from spreading and to rescue the economy from the pandemic. Many organizations have conducted surveys to understand people's opinions toward WFH. However, the findings are limited owing to a small sample size and the dynamic topics over time. Objective: This study aims to understand public opinions regarding WFH in the United States during the COVID-19 pandemic. Methods: We conducted a large-scale social media study using Twitter data to portray different groups of individuals who have positive or negative opinions on WFH. We performed an ordinary least squares regression analysis to investigate the relationship between the sentiment about WFH and user characteristics including gender, age, ethnicity, median household income, and population density. To better understand the public opinion, we used latent Dirichlet allocation to extract topics and investigate how tweet contents are related to people's attitude. Results: On performing ordinary least squares regression analysis using a large-scale data set of publicly available Twitter posts (n=28,579) regarding WFH during April 10-22, 2020, we found that the sentiment on WFH varies across user characteristics. In particular, women tend to be more positive about WFH (P<.001). People in their 40s are more positive toward WFH than those in other age groups (P<.001). People from high-income areas are more likely to have positive opinions about WFH (P<.001). These nuanced differences are supported by a more fine-grained topic analysis. At a higher level, we found that the most negative sentiment about WFH roughly corresponds to the discussion on government policy. However, people express a more positive sentiment when discussing topics on ``remote work or study'' and ``encouragement.'' Furthermore, topic distributions vary across different user groups. Women pay more attention to family activities than men (P<.05). Older people talk more about work and express a more positive sentiment regarding WFH. Conclusions: This paper presents a large-scale social media--based study to understand the public opinion on WFH in the United States during the COVID-19 pandemic. We hope that this study can contribute to policymaking both at the national and institution or company levels to improve the overall population's experience with WFH. ",
doi="10.2196/29195",
url="https://medinform.jmir.org/2021/7/e29195",
url="http://www.ncbi.nlm.nih.gov/pubmed/34254941"
}


@Article{info:doi/10.2196/20994,
author="Lu, Ding-Heng
and Hsu, Chia-An
and Yuan, J. Eunice
and Fen, Jun-Jeng
and Lee, Chung-Yuan
and Ming, Jin-Lain
and Chen, Tzeng-Ji
and Lee, Wui-Chiang
and Chen, Shih-Ann",
title="Experiences With Internet Triaging of 9498 Outpatients Daily at the Largest Public Hospital in Taiwan During the COVID-19 Pandemic: Observational Study",
journal="JMIR Med Inform",
year="2021",
month="Jul",
day="27",
volume="9",
number="7",
pages="e20994",
keywords="COVID-19",
keywords="hospital",
keywords="information services",
keywords="outpatients",
keywords="patient",
keywords="SARS-CoV-2",
keywords="triage",
keywords="virus",
abstract="Background: During pandemics, acquiring outpatients' travel, occupation, contact, and cluster histories is one of the most important measures in assessing the disease risk among incoming patients. Previous means of acquiring this information in the examination room have been insufficient in preventing disease spread. Objective: This study aimed to demonstrate the deployment of an automatic system to triage outpatients over the internet. Methods: An automatic system was incorporated in the existing web-based appointment system of the hospital and deployed along with its on-site counterpart. Automatic queries to the virtual private network travel and contact history database with each patient's national ID number were made for each attempt to acquire the patient's travel and contact histories. Patients with relevant histories were denied registration or entry. Text messages were sent to patients without a relevant history for an expedited route of entry if applicable. Results: A total of 127,857 visits were recorded. Among all visits, 91,195 were registered on the internet. In total, 71,816 of them generated text messages for an expedited route of entry. Furthermore, 65 patients had relevant histories, as revealed by the virtual private network database, and were denied registration or entry. Conclusions: An automatic triage system to acquire outpatients' relevant travel and contact histories was deployed rapidly in one of the largest academic medical centers in Taiwan. The updated system successfully denied patients with relevant travel or contact histories entry to the hospital, thus preventing long lines outside the hospital. Further efforts could be made to integrate the system with the electronic medical record system. ",
doi="10.2196/20994",
url="https://medinform.jmir.org/2021/7/e20994",
url="http://www.ncbi.nlm.nih.gov/pubmed/34043524"
}


@Article{info:doi/10.2196/29314,
author="Blease, Charlotte
and Salmi, Liz
and H{\"a}gglund, Maria
and Wachenheim, Deborah
and DesRoches, Catherine",
title="COVID-19 and Open Notes: A New Method to Enhance Patient Safety and Trust",
journal="JMIR Ment Health",
year="2021",
month="Jun",
day="21",
volume="8",
number="6",
pages="e29314",
keywords="COVID-19",
keywords="patient portals",
keywords="electronic health records",
keywords="patient safety",
keywords="patient-centered care",
doi="10.2196/29314",
url="https://mental.jmir.org/2021/6/e29314",
url="http://www.ncbi.nlm.nih.gov/pubmed/34081603"
}


@Article{info:doi/10.2196/28648,
author="Pollack, C. Catherine
and Gilbert-Diamond, Diane
and Alford-Teaster, A. Jennifer
and Onega, Tracy",
title="Language and Sentiment Regarding Telemedicine and COVID-19 on Twitter: Longitudinal Infodemiology Study",
journal="J Med Internet Res",
year="2021",
month="Jun",
day="21",
volume="23",
number="6",
pages="e28648",
keywords="telemedicine",
keywords="telehealth",
keywords="COVID-19 pandemic",
keywords="social media",
keywords="sentiment analysis",
keywords="Twitter",
keywords="COVID-19",
keywords="pandemic",
abstract="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. ",
doi="10.2196/28648",
url="https://www.jmir.org/2021/6/e28648",
url="http://www.ncbi.nlm.nih.gov/pubmed/34086591"
}


@Article{info:doi/10.2196/24251,
author="Welch, B. Sarah
and Kulasekere, Amanda Dinushi
and Prasad, Vara P. V.
and Moss, B. Charles
and Murphy, Leo Robert
and Achenbach, J. Chad
and Ison, G. Michael
and Resnick, Danielle
and Singh, Lauren
and White, Janine
and Issa, Z. Tariq
and Culler, Kasen
and Boctor, J. Michael
and Mason, Maryann
and Oehmke, Francis James
and Faber, Mitchell Joshua Marco
and Post, Ann Lori",
title="The Interplay Between Policy and COVID-19 Outbreaks in South Asia: Longitudinal Trend Analysis of Surveillance Data",
journal="JMIR Public Health Surveill",
year="2021",
month="Jun",
day="17",
volume="7",
number="6",
pages="e24251",
keywords="7-day lag",
keywords="acceleration",
keywords="Bangladesh",
keywords="Bhutan",
keywords="COVID-19 surveillance",
keywords="COVID-19",
keywords="dynamic panel data",
keywords="India",
keywords="jerk",
keywords="Maldives",
keywords="Pakistan",
keywords="South Asia",
keywords="speed",
keywords="Sri Lanka",
abstract="Background: COVID-19 transmission rates in South Asia initially were under control when governments implemented health policies aimed at controlling the pandemic such as quarantines, travel bans, and border, business, and school closures. Governments have since relaxed public health restrictions, which resulted in significant outbreaks, shifting the global epicenter of COVID-19 to India. Ongoing systematic public health surveillance of the COVID-19 pandemic is needed to inform disease prevention policy to re-establish control over the pandemic within South Asia. Objective: This study aimed to inform public health leaders about the state of the COVID-19 pandemic, how South Asia displays differences within and among countries and other global regions, and where immediate action is needed to control the outbreaks. Methods: We extracted COVID-19 data spanning 62 days from public health registries and calculated traditional and enhanced surveillance metrics. We use an empirical difference equation to measure the daily number of cases in South Asia as a function of the prior number of cases, the level of testing, and weekly shifts in variables with a dynamic panel model that was estimated using the generalized method of moments approach by implementing the Arellano--Bond estimator in R. Results: Traditional surveillance metrics indicate that South Asian countries have an alarming outbreak, with India leading the region with 310,310 new daily cases in accordance with the 7-day moving average. Enhanced surveillance indicates that while Pakistan and Bangladesh still have a high daily number of new COVID-19 cases (n=4819 and n=3878, respectively), their speed of new infections declined from April 12-25, 2021, from 2.28 to 2.18 and 3.15 to 2.35 daily new infections per 100,000 population, respectively, which suggests that their outbreaks are decreasing and that these countries are headed in the right direction. In contrast, India's speed of new infections per 100,000 population increased by 52\% during the same period from 14.79 to 22.49 new cases per day per 100,000 population, which constitutes an increased outbreak. Conclusions: Relaxation of public health restrictions and the spread of novel variants fueled the second wave of the COVID-19 pandemic in South Asia. Public health surveillance indicates that shifts in policy and the spread of new variants correlate with a drastic expansion in the pandemic, requiring immediate action to mitigate the spread of COVID-19. Surveillance is needed to inform leaders whether policies help control the pandemic. ",
doi="10.2196/24251",
url="https://publichealth.jmir.org/2021/6/e24251",
url="http://www.ncbi.nlm.nih.gov/pubmed/34081593"
}


@Article{info:doi/10.2196/29251,
author="Alhajri, Noora
and Simsekler, Emre Mecit Can
and Alfalasi, Buthaina
and Alhashmi, Mohamed
and AlGhatrif, Majd
and Balalaa, Nahed
and Al Ali, Maryam
and Almaashari, Raghda
and Al Memari, Shammah
and Al Hosani, Farida
and Al Zaabi, Yousif
and Almazroui, Shereena
and Alhashemi, Hamed
and Baltatu, C. Ovidiu",
title="Physicians' Attitudes Toward Telemedicine Consultations During the COVID-19 Pandemic: Cross-sectional Study",
journal="JMIR Med Inform",
year="2021",
month="Jun",
day="1",
volume="9",
number="6",
pages="e29251",
keywords="audio consultation",
keywords="clinical decision-making",
keywords="clinical training",
keywords="communication",
keywords="COVID-19",
keywords="outpatient department",
keywords="perception",
keywords="telemedicine",
keywords="United Arab Emirates",
keywords="video consultation",
abstract="Background: To mitigate the effect of the COVID-19 pandemic, health care systems worldwide have implemented telemedicine technologies to respond to the growing need for health care services during these unprecedented times. In the United Arab Emirates, video and audio consultations have been implemented to deliver health services during the pandemic. Objective: This study aimed to evaluate whether differences exist in physicians' attitudes and perceptions of video and audio consultations when delivering telemedicine services during the COVID-19 pandemic. Methods: This survey was conducted on a cohort of 880 physicians from outpatient facilities in Abu Dhabi, which delivered telemedicine services during the COVID-19 pandemic between November and December 2020. In total, 623 physicians responded (response rate=70.8\%). The survey included a 5-point Likert scale to measure physician's attitudes and perceptions of video and audio consultations with reference to the quality of the clinical consultation and the professional productivity. Descriptive statistics were used to describe physicians' sociodemographic characteristics (age, sex, designation, clinical specialty, duration of practice, and previous experience with telemedicine) and telemedicine modality (video vs audio consultations). Regression models were used to assess the association between telemedicine modality and physicians' characteristics with the perceived outcomes of the web-based consultation. Results: Compared to audio consultations, video consultations were significantly associated with physicians' confidence toward managing acute consultations (odds ratio [OR] 1.62, 95\% CI 1.2-2.21; P=.002) and an increased ability to provide patient education during the web-based consultation (OR 2.21, 95\% CI 1.04-4.33; P=.04). There was no significant difference in physicians' confidence toward managing long-term and follow-up consultations through video or audio consultations (OR 1.35, 95\% CI 0.88-2.08; P=.17). Video consultations were less likely to be associated with a reduced overall consultation time (OR 0.69, 95\% CI 0.51-0.93; P=.02) and reduced time for patient note-taking compared to face-to-face visits (OR 0.48, 95\% CI 0.36-0.65; P<.001). Previous experience with telemedicine was significantly associated with a lower perceived risk of misdiagnosis (OR 0.46, 95\% CI 0.3-0.71; P<.001) and an enhanced physician-patient rapport (OR 2.49, 95\% CI 1.26-4.9; P=.008). Conclusions: These results indicate that video consultations should be adopted frequently in the new remote clinical consultations. Previous experience with telemedicine was associated with a 2-fold confidence in treating acute conditions, less than a half of the perceived risk of misdiagnosis, and an increased ability to provide patients with health education and enhance the physician-patient rapport. Additionally, these results show that audio consultations are equivalent to video consultations in providing remote follow-up care to patients with chronic conditions. These findings may be beneficial to policymakers of e-health programs in low- and middle-income countries, where audio consultations may significantly increase access to geographically remote health services. ",
doi="10.2196/29251",
url="https://medinform.jmir.org/2021/6/e29251",
url="http://www.ncbi.nlm.nih.gov/pubmed/34001497"
}


@Article{info:doi/10.2196/26463,
author="Liu, Jialin
and Liu, Siru
and Zheng, Tao
and Bi, Yongdong",
title="Physicians' Perspectives of Telemedicine During the COVID-19 Pandemic in China: Qualitative Survey Study",
journal="JMIR Med Inform",
year="2021",
month="Jun",
day="1",
volume="9",
number="6",
pages="e26463",
keywords="telemedicine",
keywords="COVID-19",
keywords="survey",
keywords="physician",
abstract="Background: Generalized restriction of movement due to the COVID-19 pandemic, together with unprecedented pressure on the health system, has disrupted routine care for non--COVID-19 patients. Telemedicine should be vigorously promoted to reduce the risk of infections and to offer medical assistance to restricted patients. Objective: The purpose of this study was to understand physicians' attitudes toward and perspectives of telemedicine during and after the COVID-19 pandemic, in order to provide support for better implementation of telemedicine. Methods: We surveyed all physicians (N=148), from October 17 to 25, 2020, who attended the clinical informatics PhD program at West China Medical School, Sichuan University, China. The physicians came from 57 hospitals in 16 provinces (ie, municipalities) across China, 54 of which are 3A-level hospitals, two are 3B-level hospitals, and one is a 2A-level hospital. Results: Among 148 physicians, a survey response rate of 87.2\% (129/148) was attained. The average age of the respondents was 35.6 (SD 3.9) years (range 23-48 years) and 67 out of 129 respondents (51.9\%) were female. The respondents come from 37 clinical specialties in 55 hospitals located in 14 provinces (ie, municipalities) across Eastern, Central, and Western China. A total of 94.6\% (122/129) of respondents' hospitals had adopted a telemedicine system; however, 34.1\% (44/129) of the physicians had never used a telemedicine system and only 9.3\% (12/129) used one frequently (?1 time/week). A total of 91.5\% (118/129) and 88.4\% (114/129) of physicians were willing to use telemedicine during and after the COVID-19 pandemic, respectively. Physicians considered the inability to examine patients in person to be the biggest concern (101/129, 78.3\%) and the biggest barrier (76/129, 58.9\%) to implementing telemedicine. Conclusions: Telemedicine is not yet universally available for all health care needs and has not been used frequently by physicians in this study. However, the willingness of physicians to use telemedicine was high. Telemedicine still has many problems to overcome. ",
doi="10.2196/26463",
url="https://medinform.jmir.org/2021/6/e26463",
url="http://www.ncbi.nlm.nih.gov/pubmed/33945493"
}


@Article{info:doi/10.2196/29405,
author="Izquierdo, Luis Jose
and Soriano, B. Joan",
title="Authors' Reply to: Minimizing Selection and Classification Biases Comment on ``Clinical Characteristics and Prognostic Factors for Intensive Care Unit Admission of Patients With COVID-19: Retrospective Study Using Machine Learning and Natural Language Processing''",
journal="J Med Internet Res",
year="2021",
month="May",
day="26",
volume="23",
number="5",
pages="e29405",
keywords="artificial intelligence",
keywords="big data",
keywords="COVID-19",
keywords="electronic health records",
keywords="tachypnea",
keywords="SARS-CoV-2",
keywords="predictive model",
keywords="prognosis",
keywords="classification bias",
keywords="critical care",
doi="10.2196/29405",
url="https://www.jmir.org/2021/5/e29405",
url="http://www.ncbi.nlm.nih.gov/pubmed/33989164"
}


@Article{info:doi/10.2196/27142,
author="Martos P{\'e}rez, Francisco
and Gomez Huelgas, Ricardo
and Mart{\'i}n Escalante, Dolores Mar{\'i}a
and Casas Rojo, Manuel Jos{\'e}",
title="Minimizing Selection and Classification Biases. Comment on ``Clinical Characteristics and Prognostic Factors for Intensive Care Unit Admission of Patients With COVID-19: Retrospective Study Using Machine Learning and Natural Language Processing''",
journal="J Med Internet Res",
year="2021",
month="May",
day="26",
volume="23",
number="5",
pages="e27142",
keywords="artificial intelligence",
keywords="big data",
keywords="COVID-19",
keywords="electronic health records",
keywords="tachypnea",
keywords="SARS-CoV-2",
keywords="predictive model",
keywords="prognosis",
keywords="classification bias",
keywords="critical care",
doi="10.2196/27142",
url="https://www.jmir.org/2021/5/e27142",
url="http://www.ncbi.nlm.nih.gov/pubmed/33989163"
}


@Article{info:doi/10.2196/29072,
author="Espinosa-Gonzalez, Belen Ana
and Neves, Luisa Ana
and Fiorentino, Francesca
and Prociuk, Denys
and Husain, Laiba
and Ramtale, Christian Sonny
and Mi, Emma
and Mi, Ella
and Macartney, Jack
and Anand, N. Sneha
and Sherlock, Julian
and Saravanakumar, Kavitha
and Mayer, Erik
and de Lusignan, Simon
and Greenhalgh, Trisha
and Delaney, C. Brendan",
title="Predicting Risk of Hospital Admission in Patients With Suspected COVID-19 in a Community Setting: Protocol for Development and Validation of a Multivariate Risk Prediction Tool",
journal="JMIR Res Protoc",
year="2021",
month="May",
day="25",
volume="10",
number="5",
pages="e29072",
keywords="COVID-19 severity",
keywords="risk prediction tool",
keywords="early warning score",
keywords="hospital admission",
keywords="primary care",
keywords="electronic health records",
abstract="Background: During the pandemic, remote consultations have become the norm for assessing patients with signs and symptoms of COVID-19 to decrease the risk of transmission. This has intensified the clinical uncertainty already experienced by primary care clinicians when assessing patients with suspected COVID-19 and has prompted the use of risk prediction scores, such as the National Early Warning Score (NEWS2), to assess severity and guide treatment. However, the risk prediction tools available have not been validated in a community setting and are not designed to capture the idiosyncrasies of COVID-19 infection. Objective: The objective of this study is to produce a multivariate risk prediction tool, RECAP-V1 (Remote COVID-19 Assessment in Primary Care), to support primary care clinicians in the identification of those patients with COVID-19 that are at higher risk of deterioration and facilitate the early escalation of their treatment with the aim of improving patient outcomes. Methods: The study follows a prospective cohort observational design, whereby patients presenting in primary care with signs and symptoms suggestive of COVID-19 will be followed and their data linked to hospital outcomes (hospital admission and death). Data collection will be carried out by primary care clinicians in four arms: North West London Clinical Commissioning Groups (NWL CCGs), Oxford-Royal College of General Practitioners (RCGP) Research and Surveillance Centre (RSC), Covid Clinical Assessment Service (CCAS), and South East London CCGs (Doctaly platform). The study involves the use of an electronic template that incorporates a list of items (known as RECAP-V0) thought to be associated with disease outcome according to previous qualitative work. Data collected will be linked to patient outcomes in highly secure environments. We will then use multivariate logistic regression analyses for model development and validation. Results: Recruitment of participants started in October 2020. Initially, only the NWL CCGs and RCGP RSC arms were active. As of March 24, 2021, we have recruited a combined sample of 3827 participants in these two arms. CCAS and Doctaly joined the study in February 2021, with CCAS starting the recruitment process on March 15, 2021. The first part of the analysis (RECAP-V1 model development) is planned to start in April 2021 using the first half of the NWL CCGs and RCGP RSC combined data set. Posteriorly, the model will be validated with the rest of the NWL CCGs and RCGP RSC data as well as the CCAS and Doctaly data sets. The study was approved by the Research Ethics Committee on May 27, 2020 (Integrated Research Application System number: 283024, Research Ethics Committee reference number: 20/NW/0266) and badged as National Institute of Health Research Urgent Public Health Study on October 14, 2020. Conclusions: We believe the validated RECAP-V1 early warning score will be a valuable tool for the assessment of severity in patients with suspected COVID-19 in the community, either in face-to-face or remote consultations, and will facilitate the timely escalation of treatment with the potential to improve patient outcomes. Trial Registration: ISRCTN registry ISRCTN13953727; https://www.isrctn.com/ISRCTN13953727 International Registered Report Identifier (IRRID): DERR1-10.2196/29072 ",
doi="10.2196/29072",
url="https://www.researchprotocols.org/2021/5/e29072",
url="http://www.ncbi.nlm.nih.gov/pubmed/33939619"
}


@Article{info:doi/10.2196/27419,
author="Li, Junjiang
and Giabbanelli, Philippe",
title="Returning to a Normal Life via COVID-19 Vaccines in the United States: A Large-scale Agent-Based Simulation Study",
journal="JMIR Med Inform",
year="2021",
month="Apr",
day="29",
volume="9",
number="4",
pages="e27419",
keywords="agent-based model",
keywords="cloud-based simulations",
keywords="COVID-19",
keywords="large-scale simulations",
keywords="vaccine",
keywords="model",
keywords="simulation",
keywords="United States",
keywords="agent-based",
keywords="effective",
keywords="willingness",
keywords="capacity",
keywords="plan",
keywords="strategy",
keywords="outcome",
keywords="interaction",
keywords="intervention",
keywords="scenario",
keywords="impact",
abstract="Background: In 2020, COVID-19 has claimed more than 300,000 deaths in the United States alone. Although nonpharmaceutical interventions were implemented by federal and state governments in the United States, these efforts have failed to contain the virus. Following the Food and Drug Administration's approval of two COVID-19 vaccines, however, the hope for the return to normalcy has been renewed. This hope rests on an unprecedented nationwide vaccine campaign, which faces many logistical challenges and is also contingent on several factors whose values are currently unknown. Objective: We study the effectiveness of a nationwide vaccine campaign in response to different vaccine efficacies, the willingness of the population to be vaccinated, and the daily vaccine capacity under two different federal plans. To characterize the possible outcomes most accurately, we also account for the interactions between nonpharmaceutical interventions and vaccines through 6 scenarios that capture a range of possible impacts from nonpharmaceutical interventions. Methods: We used large-scale, cloud-based, agent-based simulations by implementing the vaccination campaign using COVASIM, an open-source agent-based model for COVID-19 that has been used in several peer-reviewed studies and accounts for individual heterogeneity and a multiplicity of contact networks. Several modifications to the parameters and simulation logic were made to better align the model with current evidence. We chose 6 nonpharmaceutical intervention scenarios and applied the vaccination intervention following both the plan proposed by Operation Warp Speed (former Trump administration) and the plan of one million vaccines per day, proposed by the Biden administration. We accounted for unknowns in vaccine efficacies and levels of population compliance by varying both parameters. For each experiment, the cumulative infection growth was fitted to a logistic growth model, and the carrying capacities and the growth rates were recorded. Results: For both vaccination plans and all nonpharmaceutical intervention scenarios, the presence of the vaccine intervention considerably lowers the total number of infections when life returns to normal, even when the population compliance to vaccines is as low as 20\%. We noted an unintended consequence; given the vaccine availability estimates under both federal plans and the focus on vaccinating individuals by age categories, a significant reduction in nonpharmaceutical interventions results in a counterintuitive situation in which higher vaccine compliance then leads to more total infections. Conclusions: Although potent, vaccines alone cannot effectively end the pandemic given the current availability estimates and the adopted vaccination strategy. Nonpharmaceutical interventions need to continue and be enforced to ensure high compliance so that the rate of immunity established by vaccination outpaces that induced by infections. ",
doi="10.2196/27419",
url="https://medinform.jmir.org/2021/4/e27419",
url="http://www.ncbi.nlm.nih.gov/pubmed/33872188"
}


@Article{info:doi/10.2196/21394,
author="Poly, Nasrin Tahmina
and Islam, Mohaimenul Md
and Li, Jack Yu-Chuan
and Alsinglawi, Belal
and Hsu, Min-Huei
and Jian, Shan Wen
and Yang, Hsuan-Chia",
title="Application of Artificial Intelligence for Screening COVID-19 Patients Using Digital Images: Meta-analysis",
journal="JMIR Med Inform",
year="2021",
month="Apr",
day="29",
volume="9",
number="4",
pages="e21394",
keywords="COVID-19",
keywords="SARS-CoV-2",
keywords="pneumonia",
keywords="artificial intelligence",
keywords="deep learning",
abstract="Background: The COVID-19 outbreak has spread rapidly and hospitals are overwhelmed with COVID-19 patients. While analysis of nasal and throat swabs from patients is the main way to detect COVID-19, analyzing chest images could offer an alternative method to hospitals, where health care personnel and testing kits are scarce. Deep learning (DL), in particular, has shown impressive levels of performance when analyzing medical images, including those related to COVID-19 pneumonia. Objective: The goal of this study was to perform a systematic review with a meta-analysis of relevant studies to quantify the performance of DL algorithms in the automatic stratification of COVID-19 patients using chest images. Methods: A search strategy for use in PubMed, Scopus, Google Scholar, and Web of Science was developed, where we searched for articles published between January 1 and April 25, 2020. We used the key terms ``COVID-19,'' or ``coronavirus,'' or ``SARS-CoV-2,'' or ``novel corona,'' or ``2019-ncov,'' and ``deep learning,'' or ``artificial intelligence,'' or ``automatic detection.'' Two authors independently extracted data on study characteristics, methods, risk of bias, and outcomes. Any disagreement between them was resolved by consensus. Results: A total of 16 studies were included in the meta-analysis, which included 5896 chest images from COVID-19 patients. The pooled sensitivity and specificity of the DL models in detecting COVID-19 were 0.95 (95\% CI 0.94-0.95) and 0.96 (95\% CI 0.96-0.97), respectively, with an area under the receiver operating characteristic curve of 0.98. The positive likelihood, negative likelihood, and diagnostic odds ratio were 19.02 (95\% CI 12.83-28.19), 0.06 (95\% CI 0.04-0.10), and 368.07 (95\% CI 162.30-834.75), respectively. The pooled sensitivity and specificity for distinguishing other types of pneumonia from COVID-19 were 0.93 (95\% CI 0.92-0.94) and 0.95 (95\% CI 0.94-0.95), respectively. The performance of radiologists in detecting COVID-19 was lower than that of the DL models; however, the performance of junior radiologists was improved when they used DL-based prediction tools. Conclusions: Our study findings show that DL models have immense potential in accurately stratifying COVID-19 patients and in correctly differentiating them from patients with other types of pneumonia and normal patients. Implementation of DL-based tools can assist radiologists in correctly and quickly detecting COVID-19 and, consequently, in combating the COVID-19 pandemic. ",
doi="10.2196/21394",
url="https://medinform.jmir.org/2021/4/e21394",
url="http://www.ncbi.nlm.nih.gov/pubmed/33764884"
}


@Article{info:doi/10.2196/26075,
author="Patr{\'i}cio, Andr{\'e}
and Costa, S. Rafael
and Henriques, Rui",
title="Predictability of COVID-19 Hospitalizations, Intensive Care Unit Admissions, and Respiratory Assistance in Portugal: Longitudinal Cohort Study",
journal="J Med Internet Res",
year="2021",
month="Apr",
day="28",
volume="23",
number="4",
pages="e26075",
keywords="COVID-19",
keywords="machine learning",
keywords="intensive care admissions",
keywords="respiratory assistance",
keywords="predictive models",
keywords="data modeling",
keywords="clinical informatics",
abstract="Background: In the face of the current COVID-19 pandemic, the timely prediction of upcoming medical needs for infected individuals enables better and quicker care provision when necessary and management decisions within health care systems. Objective: This work aims to predict the medical needs (hospitalizations, intensive care unit admissions, and respiratory assistance) and survivability of individuals testing positive for SARS-CoV-2 infection in Portugal. Methods: A retrospective cohort of 38,545 infected individuals during 2020 was used. Predictions of medical needs were performed using state-of-the-art machine learning approaches at various stages of a patient's cycle, namely, at testing (prehospitalization), at posthospitalization, and during postintensive care. A thorough optimization of state-of-the-art predictors was undertaken to assess the ability to anticipate medical needs and infection outcomes using demographic and comorbidity variables, as well as dates associated with symptom onset, testing, and hospitalization. Results: For the target cohort, 75\% of hospitalization needs could be identified at the time of testing for SARS-CoV-2 infection. Over 60\% of respiratory needs could be identified at the time of hospitalization. Both predictions had >50\% precision. Conclusions: The conducted study pinpoints the relevance of the proposed predictive models as good candidates to support medical decisions in the Portuguese population, including both monitoring and in-hospital care decisions. A clinical decision support system is further provided to this end. ",
doi="10.2196/26075",
url="https://www.jmir.org/2021/4/e26075",
url="http://www.ncbi.nlm.nih.gov/pubmed/33835931"
}


@Article{info:doi/10.2196/27468,
author="Ghaderzadeh, Mustafa
and Asadi, Farkhondeh
and Jafari, Ramezan
and Bashash, Davood
and Abolghasemi, Hassan
and Aria, Mehrad",
title="Deep Convolutional Neural Network--Based Computer-Aided Detection System for COVID-19 Using Multiple Lung Scans: Design and Implementation Study",
journal="J Med Internet Res",
year="2021",
month="Apr",
day="26",
volume="23",
number="4",
pages="e27468",
keywords="artificial intelligence",
keywords="classification",
keywords="computer-aided detection",
keywords="computed tomography scan",
keywords="convolutional neural network",
keywords="coronavirus",
keywords="COVID-19",
keywords="deep learning",
keywords="machine learning",
keywords="machine vision",
keywords="model",
keywords="pandemic",
abstract="Background: Owing to the COVID-19 pandemic and the imminent collapse of health care systems following the exhaustion of financial, hospital, and medicinal resources, the World Health Organization changed the alert level of the COVID-19 pandemic from high to very high. Meanwhile, more cost-effective and precise COVID-19 detection methods are being preferred worldwide. Objective: Machine vision--based COVID-19 detection methods, especially deep learning as a diagnostic method in the early stages of the pandemic, have been assigned great importance during the pandemic. This study aimed to design a highly efficient computer-aided detection (CAD) system for COVID-19 by using a neural search architecture network (NASNet)--based algorithm. Methods: NASNet, a state-of-the-art pretrained convolutional neural network for image feature extraction, was adopted to identify patients with COVID-19 in their early stages of the disease. A local data set, comprising 10,153 computed tomography scans of 190 patients with and 59 without COVID-19 was used. Results: After fitting on the training data set, hyperparameter tuning, and topological alterations of the classifier block, the proposed NASNet-based model was evaluated on the test data set and yielded remarkable results. The proposed model's performance achieved a detection sensitivity, specificity, and accuracy of 0.999, 0.986, and 0.996, respectively. Conclusions: The proposed model achieved acceptable results in the categorization of 2 data classes. Therefore, a CAD system was designed on the basis of this model for COVID-19 detection using multiple lung computed tomography scans. The system differentiated all COVID-19 cases from non--COVID-19 ones without any error in the application phase. Overall, the proposed deep learning--based CAD system can greatly help radiologists detect COVID-19 in its early stages. During the COVID-19 pandemic, the use of a CAD system as a screening tool would accelerate disease detection and prevent the loss of health care resources. ",
doi="10.2196/27468",
url="https://www.jmir.org/2021/4/e27468",
url="http://www.ncbi.nlm.nih.gov/pubmed/33848973"
}


@Article{info:doi/10.2196/25181,
author="Montazeri, Mahdieh
and ZahediNasab, Roxana
and Farahani, Ali
and Mohseni, Hadis
and Ghasemian, Fahimeh",
title="Machine Learning Models for Image-Based Diagnosis and Prognosis of COVID-19: Systematic Review",
journal="JMIR Med Inform",
year="2021",
month="Apr",
day="23",
volume="9",
number="4",
pages="e25181",
keywords="machine learning",
keywords="diagnosis",
keywords="prognosis",
keywords="COVID-19",
abstract="Background: Accurate and timely diagnosis and effective prognosis of the disease is important to provide the best possible care for patients with COVID-19 and reduce the burden on the health care system. Machine learning methods can play a vital role in the diagnosis of COVID-19 by processing chest x-ray images. Objective: The aim of this study is to summarize information on the use of intelligent models for the diagnosis and prognosis of COVID-19 to help with early and timely diagnosis, minimize prolonged diagnosis, and improve overall health care. Methods: A systematic search of databases, including PubMed, Web of Science, IEEE, ProQuest, Scopus, bioRxiv, and medRxiv, was performed for COVID-19--related studies published up to May 24, 2020. This study was performed in accordance with the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-analyses) guidelines. All original research articles describing the application of image processing for the prediction and diagnosis of COVID-19 were considered in the analysis. Two reviewers independently assessed the published papers to determine eligibility for inclusion in the analysis. Risk of bias was evaluated using the Prediction Model Risk of Bias Assessment Tool. Results: Of the 629 articles retrieved, 44 articles were included. We identified 4 prognosis models for calculating prediction of disease severity and estimation of confinement time for individual patients, and 40 diagnostic models for detecting COVID-19 from normal or other pneumonias. Most included studies used deep learning methods based on convolutional neural networks, which have been widely used as a classification algorithm. The most frequently reported predictors of prognosis in patients with COVID-19 included age, computed tomography data, gender, comorbidities, symptoms, and laboratory findings. Deep convolutional neural networks obtained better results compared with non--neural network--based methods. Moreover, all of the models were found to be at high risk of bias due to the lack of information about the study population, intended groups, and inappropriate reporting. Conclusions: Machine learning models used for the diagnosis and prognosis of COVID-19 showed excellent discriminative performance. However, these models were at high risk of bias, because of various reasons such as inadequate information about study participants, randomization process, and the lack of external validation, which may have resulted in the optimistic reporting of these models. Hence, our findings do not recommend any of the current models to be used in practice for the diagnosis and prognosis of COVID-19. ",
doi="10.2196/25181",
url="https://medinform.jmir.org/2021/4/e25181",
url="http://www.ncbi.nlm.nih.gov/pubmed/33735095"
}


@Article{info:doi/10.2196/25066,
author="Cummings, C. Brandon
and Ansari, Sardar
and Motyka, R. Jonathan
and Wang, Guan
and Medlin Jr, P. Richard
and Kronick, L. Steven
and Singh, Karandeep
and Park, K. Pauline
and Napolitano, M. Lena
and Dickson, P. Robert
and Mathis, R. Michael
and Sjoding, W. Michael
and Admon, J. Andrew
and Blank, Ross
and McSparron, I. Jakob
and Ward, R. Kevin
and Gillies, E. Christopher",
title="Predicting Intensive Care Transfers and Other Unforeseen Events: Analytic Model Validation Study and Comparison to Existing Methods",
journal="JMIR Med Inform",
year="2021",
month="Apr",
day="21",
volume="9",
number="4",
pages="e25066",
keywords="COVID-19",
keywords="biomedical informatics",
keywords="critical care",
keywords="machine learning",
keywords="deterioration",
keywords="predictive analytics",
keywords="informatics",
keywords="prediction",
keywords="intensive care unit",
keywords="ICU",
keywords="mortality",
abstract="Background: COVID-19 has led to an unprecedented strain on health care facilities across the United States. Accurately identifying patients at an increased risk of deterioration may help hospitals manage their resources while improving the quality of patient care. Here, we present the results of an analytical model, Predicting Intensive Care Transfers and Other Unforeseen Events (PICTURE), to identify patients at high risk for imminent intensive care unit transfer, respiratory failure, or death, with the intention to improve the prediction of deterioration due to COVID-19. Objective: This study aims to validate the PICTURE model's ability to predict unexpected deterioration in general ward and COVID-19 patients, and to compare its performance with the Epic Deterioration Index (EDI), an existing model that has recently been assessed for use in patients with COVID-19. Methods: The PICTURE model was trained and validated on a cohort of hospitalized non--COVID-19 patients using electronic health record data from 2014 to 2018. It was then applied to two holdout test sets: non--COVID-19 patients from 2019 and patients testing positive for COVID-19 in 2020. PICTURE results were aligned to EDI and NEWS scores for head-to-head comparison via area under the receiver operating characteristic curve (AUROC) and area under the precision-recall curve. We compared the models' ability to predict an adverse event (defined as intensive care unit transfer, mechanical ventilation use, or death). Shapley values were used to provide explanations for PICTURE predictions. Results: In non--COVID-19 general ward patients, PICTURE achieved an AUROC of 0.819 (95\% CI 0.805-0.834) per observation, compared to the EDI's AUROC of 0.763 (95\% CI 0.746-0.781; n=21,740; P<.001). In patients testing positive for COVID-19, PICTURE again outperformed the EDI with an AUROC of 0.849 (95\% CI 0.820-0.878) compared to the EDI's AUROC of 0.803 (95\% CI 0.772-0.838; n=607; P<.001). The most important variables influencing PICTURE predictions in the COVID-19 cohort were a rapid respiratory rate, a high level of oxygen support, low oxygen saturation, and impaired mental status (Glasgow Coma Scale). Conclusions: The PICTURE model is more accurate in predicting adverse patient outcomes for both general ward patients and COVID-19 positive patients in our cohorts compared to the EDI. The ability to consistently anticipate these events may be especially valuable when considering potential incipient waves of COVID-19 infections. The generalizability of the model will require testing in other health care systems for validation. ",
doi="10.2196/25066",
url="https://medinform.jmir.org/2021/4/e25066",
url="http://www.ncbi.nlm.nih.gov/pubmed/33818393"
}


@Article{info:doi/10.2196/26211,
author="Dom{\'i}nguez-Olmedo, L. Juan
and Gragera-Mart{\'i}nez, {\'A}lvaro
and Mata, Jacinto
and Pach{\'o}n {\'A}lvarez, Victoria",
title="Machine Learning Applied to Clinical Laboratory Data in Spain for COVID-19 Outcome Prediction: Model Development and Validation",
journal="J Med Internet Res",
year="2021",
month="Apr",
day="14",
volume="23",
number="4",
pages="e26211",
keywords="COVID-19",
keywords="electronic health record",
keywords="machine learning",
keywords="mortality",
keywords="prediction",
abstract="Background: The COVID-19 pandemic is probably the greatest health catastrophe of the modern era. Spain's health care system has been exposed to uncontrollable numbers of patients over a short period, causing the system to collapse. Given that diagnosis is not immediate, and there is no effective treatment for COVID-19, other tools have had to be developed to identify patients at the risk of severe disease complications and thus optimize material and human resources in health care. There are no tools to identify patients who have a worse prognosis than others. Objective: This study aimed to process a sample of electronic health records of patients with COVID-19 in order to develop a machine learning model to predict the severity of infection and mortality from among clinical laboratory parameters. Early patient classification can help optimize material and human resources, and analysis of the most important features of the model could provide more detailed insights into the disease. Methods: After an initial performance evaluation based on a comparison with several other well-known methods, the extreme gradient boosting algorithm was selected as the predictive method for this study. In addition, Shapley Additive Explanations was used to analyze the importance of the features of the resulting model. Results: After data preprocessing, 1823 confirmed patients with COVID-19 and 32 predictor features were selected. On bootstrap validation, the extreme gradient boosting classifier yielded a value of 0.97 (95\% CI 0.96-0.98) for the area under the receiver operator characteristic curve, 0.86 (95\% CI 0.80-0.91) for the area under the precision-recall curve, 0.94 (95\% CI 0.92-0.95) for accuracy, 0.77 (95\% CI 0.72-0.83) for the F-score, 0.93 (95\% CI 0.89-0.98) for sensitivity, and 0.91 (95\% CI 0.86-0.96) for specificity. The 4 most relevant features for model prediction were lactate dehydrogenase activity, C-reactive protein levels, neutrophil counts, and urea levels. Conclusions: Our predictive model yielded excellent results in the differentiating among patients who died of COVID-19, primarily from among laboratory parameter values. Analysis of the resulting model identified a set of features with the most significant impact on the prediction, thus relating them to a higher risk of mortality. ",
doi="10.2196/26211",
url="https://www.jmir.org/2021/4/e26211",
url="http://www.ncbi.nlm.nih.gov/pubmed/33793407"
}


@Article{info:doi/10.2196/25884,
author="Aktar, Sakifa
and Ahamad, Martuza Md
and Rashed-Al-Mahfuz, Md
and Azad, AKM
and Uddin, Shahadat
and Kamal, AHM
and Alyami, A. Salem
and Lin, Ping-I
and Islam, Shariful Sheikh Mohammed
and Quinn, MW Julian
and Eapen, Valsamma
and Moni, Ali Mohammad",
title="Machine Learning Approach to Predicting COVID-19 Disease Severity Based on Clinical Blood Test Data: Statistical Analysis and Model Development",
journal="JMIR Med Inform",
year="2021",
month="Apr",
day="13",
volume="9",
number="4",
pages="e25884",
keywords="COVID-19",
keywords="blood samples",
keywords="machine learning",
keywords="statistical analysis",
keywords="prediction",
keywords="severity",
keywords="mortality",
keywords="morbidity",
keywords="risk",
keywords="blood",
keywords="testing",
keywords="outcome",
keywords="data set",
abstract="Background: Accurate prediction of the disease severity of patients with COVID-19 would greatly improve care delivery and resource allocation and thereby reduce mortality risks, especially in less developed countries. Many patient-related factors, such as pre-existing comorbidities, affect disease severity and can be used to aid this prediction. Objective: Because rapid automated profiling of peripheral blood samples is widely available, we aimed to investigate how data from the peripheral blood of patients with COVID-19 can be used to predict clinical outcomes. Methods: We investigated clinical data sets of patients with COVID-19 with known outcomes by combining statistical comparison and correlation methods with machine learning algorithms; the latter included decision tree, random forest, variants of gradient boosting machine, support vector machine, k-nearest neighbor, and deep learning methods. Results: Our work revealed that several clinical parameters that are measurable in blood samples are factors that can discriminate between healthy people and COVID-19--positive patients, and we showed the value of these parameters in predicting later severity of COVID-19 symptoms. We developed a number of analytical methods that showed accuracy and precision scores >90\% for disease severity prediction. Conclusions: We developed methodologies to analyze routine patient clinical data that enable more accurate prediction of COVID-19 patient outcomes. With this approach, data from standard hospital laboratory analyses of patient blood could be used to identify patients with COVID-19 who are at high risk of mortality, thus enabling optimization of hospital facilities for COVID-19 treatment. ",
doi="10.2196/25884",
url="https://medinform.jmir.org/2021/4/e25884",
url="http://www.ncbi.nlm.nih.gov/pubmed/33779565"
}


@Article{info:doi/10.2196/23238,
author="He, Qian
and Du, Fei
and Simonse, L. Lianne W.",
title="A Patient Journey Map to Improve the Home Isolation Experience of Persons With Mild COVID-19: Design Research for Service Touchpoints of Artificial Intelligence in eHealth",
journal="JMIR Med Inform",
year="2021",
month="Apr",
day="12",
volume="9",
number="4",
pages="e23238",
keywords="COVID-19",
keywords="design",
keywords="eHealth",
keywords="artificial intelligence",
keywords="service design",
keywords="patient journey map",
keywords="user-centered design",
keywords="digital service solutions in health",
keywords="home isolation",
keywords="AI",
keywords="touchpoint",
abstract="Background: In the context of the COVID-19 outbreak, 80\% of the persons who are infected have mild symptoms and are required to self-recover at home. They have a strong demand for remote health care that, despite the great potential of artificial intelligence (AI), is not met by the current services of eHealth. Understanding the real needs of these persons is lacking. Objective: The aim of this paper is to contribute a fine-grained understanding of the home isolation experience of persons with mild COVID-19 symptoms to enhance AI in eHealth services. Methods: A design research method with a qualitative approach was used to map the patient journey. Data on the home isolation experiences of persons with mild COVID-19 symptoms was collected from the top-viewed personal video stories on YouTube and their comment threads. For the analysis, this data was transcribed, coded, and mapped into the patient journey map. Results: The key findings on the home isolation experience of persons with mild COVID-19 symptoms concerned  (1) an awareness period before testing positive, (2) less typical and more personal symptoms, (3) a negative mood experience curve, (5) inadequate home health care service support for patients, and (6) benefits and drawbacks of social media support. Conclusions: The design of the patient journey map and underlying insights on the home isolation experience of persons with mild COVID-19 symptoms serves health and information technology professionals in more effectively applying AI technology into eHealth services, for which three main service concepts are proposed: (1) trustworthy public health information to relieve stress, (2) personal COVID-19 health monitoring, and (3) community support. ",
doi="10.2196/23238",
url="https://medinform.jmir.org/2021/4/e23238",
url="http://www.ncbi.nlm.nih.gov/pubmed/33444156"
}


@Article{info:doi/10.2196/21547,
author="Reps, M. Jenna
and Kim, Chungsoo
and Williams, D. Ross
and Markus, F. Aniek
and Yang, Cynthia
and Duarte-Salles, Talita
and Falconer, Thomas
and Jonnagaddala, Jitendra
and Williams, Andrew
and Fern{\'a}ndez-Bertol{\'i}n, Sergio
and DuVall, L. Scott
and Kostka, Kristin
and Rao, Gowtham
and Shoaibi, Azza
and Ostropolets, Anna
and Spotnitz, E. Matthew
and Zhang, Lin
and Casajust, Paula
and Steyerberg, W. Ewout
and Nyberg, Fredrik
and Kaas-Hansen, Skov Benjamin
and Choi, Hwa Young
and Morales, Daniel
and Liaw, Siaw-Teng
and Abrah{\~a}o, Fernandes Maria Tereza
and Areia, Carlos
and Matheny, E. Michael
and Lynch, E. Kristine
and Arag{\'o}n, Mar{\'i}a
and Park, Woong Rae
and Hripcsak, George
and Reich, G. Christian
and Suchard, A. Marc
and You, Chan Seng
and Ryan, B. Patrick
and Prieto-Alhambra, Daniel
and Rijnbeek, R. Peter",
title="Implementation of the COVID-19 Vulnerability Index Across an International Network of Health Care Data Sets: Collaborative External Validation Study",
journal="JMIR Med Inform",
year="2021",
month="Apr",
day="5",
volume="9",
number="4",
pages="e21547",
keywords="external validation",
keywords="transportability",
keywords="COVID-19",
keywords="prognostic model",
keywords="prediction",
keywords="C-19",
keywords="modeling",
keywords="datasets",
keywords="observation",
keywords="hospitalization",
keywords="bias",
keywords="risk",
keywords="decision-making",
abstract="Background: SARS-CoV-2 is straining health care systems globally. The burden on hospitals during the pandemic could be reduced by implementing prediction models that can discriminate patients who require hospitalization from those who do not. The COVID-19 vulnerability (C-19) index, a model that predicts which patients will be admitted to hospital for treatment of pneumonia or pneumonia proxies, has been developed and proposed as a valuable tool for decision-making during the pandemic. However, the model is at high risk of bias according to the ``prediction model risk of bias assessment'' criteria, and it has not been externally validated. Objective: The aim of this study was to externally validate the C-19 index across a range of health care settings to determine how well it broadly predicts hospitalization due to pneumonia in COVID-19 cases. Methods: We followed the Observational Health Data Sciences and Informatics (OHDSI) framework for external validation to assess the reliability of the C-19 index. We evaluated the model on two different target populations, 41,381 patients who presented with SARS-CoV-2 at an outpatient or emergency department visit and 9,429,285 patients who presented with influenza or related symptoms during an outpatient or emergency department visit, to predict their risk of hospitalization with pneumonia during the following 0-30 days. In total, we validated the model across a network of 14 databases spanning the United States, Europe, Australia, and Asia. Results: The internal validation performance of the C-19 index had a C statistic of 0.73, and the calibration was not reported by the authors. When we externally validated it by transporting it to SARS-CoV-2 data, the model obtained C statistics of 0.36, 0.53 (0.473-0.584) and 0.56 (0.488-0.636) on Spanish, US, and South Korean data sets, respectively. The calibration was poor, with the model underestimating risk. When validated on 12 data sets containing influenza patients across the OHDSI network, the C statistics ranged between 0.40 and 0.68. Conclusions: Our results show that the discriminative performance of the C-19 index model is low for influenza cohorts and even worse among patients with COVID-19 in the United States, Spain, and South Korea. These results suggest that C-19 should not be used to aid decision-making during the COVID-19 pandemic. Our findings highlight the importance of performing external validation across a range of settings, especially when a prediction model is being extrapolated to a different population. In the field of prediction, extensive validation is required to create appropriate trust in a model. ",
doi="10.2196/21547",
url="https://medinform.jmir.org/2021/4/e21547",
url="http://www.ncbi.nlm.nih.gov/pubmed/33661754"
}


@Article{info:doi/10.2196/25696,
author="Huang, Yingxiang
and Radenkovic, Dina
and Perez, Kevin
and Nadeau, Kari
and Verdin, Eric
and Furman, David",
title="Modeling Predictive Age-Dependent and Age-Independent Symptoms and Comorbidities of Patients Seeking Treatment for COVID-19: Model Development and Validation Study",
journal="J Med Internet Res",
year="2021",
month="Mar",
day="25",
volume="23",
number="3",
pages="e25696",
keywords="clinical informatics",
keywords="predictive modeling",
keywords="COVID-19",
keywords="app",
keywords="model",
keywords="prediction",
keywords="symptom",
keywords="informatics",
keywords="age",
keywords="morbidity",
keywords="hospital",
abstract="Background: The COVID-19 pandemic continues to ravage and burden hospitals around the world. The epidemic started in Wuhan, China, and was subsequently recognized by the World Health Organization as an international public health emergency and declared a pandemic in March 2020. Since then, the disruptions caused by the COVID-19 pandemic have had an unparalleled effect on all aspects of life. Objective: With increasing total hospitalization and intensive care unit admissions, a better understanding of features related to patients with COVID-19 could help health care workers stratify patients based on the risk of developing a more severe case of COVID-19. Using predictive models, we strive to select the features that are most associated with more severe cases of COVID-19. Methods: Over 3 million participants reported their potential symptoms of COVID-19, along with their comorbidities and demographic information, on a smartphone-based app. Using data from the >10,000 individuals who indicated that they had tested positive for COVID-19 in the United Kingdom, we leveraged the Elastic Net regularized binary classifier to derive the predictors that are most correlated with users having a severe enough case of COVID-19 to seek treatment in a hospital setting. We then analyzed such features in relation to age and other demographics and their longitudinal trend. Results: The most predictive features found include fever, use of immunosuppressant medication, use of a mobility aid, shortness of breath, and severe fatigue. Such features are age-related, and some are disproportionally high in minority populations. Conclusions: Predictors selected from the predictive models can be used to stratify patients into groups based on how much medical attention they are expected to require. This could help health care workers devote valuable resources to prevent the escalation of the disease in vulnerable populations. ",
doi="10.2196/25696",
url="https://www.jmir.org/2021/3/e25696",
url="http://www.ncbi.nlm.nih.gov/pubmed/33621185"
}


@Article{info:doi/10.2196/22860,
author="Lu, Zhao-Hua
and Wang, Xiaoqing Jade
and Li, Xintong",
title="Revealing Opinions for COVID-19 Questions Using a Context Retriever, Opinion Aggregator, and Question-Answering Model: Model Development Study",
journal="J Med Internet Res",
year="2021",
month="Mar",
day="19",
volume="23",
number="3",
pages="e22860",
keywords="natural language processing",
keywords="question-answering systems",
keywords="language summarization",
keywords="machine learning",
keywords="life and medical sciences",
keywords="COVID-19",
keywords="public health",
keywords="coronavirus literature",
abstract="Background: COVID-19 has challenged global public health because it is highly contagious and can be lethal. Numerous ongoing and recently published studies about the disease have emerged. However, the research regarding COVID-19 is largely ongoing and inconclusive. Objective: A potential way to accelerate COVID-19 research is to use existing information gleaned from research into other viruses that belong to the coronavirus family. Our objective is to develop a natural language processing method for answering factoid questions related to COVID-19 using published articles as knowledge sources. Methods: Given a question, first, a BM25-based context retriever model is implemented to select the most relevant passages from previously published articles. Second, for each selected context passage, an answer is obtained using a pretrained bidirectional encoder representations from transformers (BERT) question-answering model. Third, an opinion aggregator, which is a combination of a biterm topic model and k-means clustering, is applied to the task of aggregating all answers into several opinions. Results: We applied the proposed pipeline to extract answers, opinions, and the most frequent words related to six questions from the COVID-19 Open Research Dataset Challenge. By showing the longitudinal distributions of the opinions, we uncovered the trends of opinions and popular words in the articles published in the five time periods assessed: before 1990, 1990-1999, 2000-2009, 2010-2018, and since 2019. The changes in opinions and popular words agree with several distinct characteristics and challenges of COVID-19, including a higher risk for senior people and people with pre-existing medical conditions; high contagion and rapid transmission; and a more urgent need for screening and testing. The opinions and popular words also provide additional insights for the COVID-19--related questions. Conclusions: Compared with other methods of literature retrieval and answer generation, opinion aggregation using our method leads to more interpretable, robust, and comprehensive question-specific literature reviews. The results demonstrate the usefulness of the proposed method in answering COVID-19--related questions with main opinions and capturing the trends of research about COVID-19 and other relevant strains of coronavirus in recent years. ",
doi="10.2196/22860",
url="https://www.jmir.org/2021/3/e22860",
url="http://www.ncbi.nlm.nih.gov/pubmed/33739287"
}


@Article{info:doi/10.2196/27079,
author="Shen, Lining
and Yao, Rui
and Zhang, Wenli
and Evans, Richard
and Cao, Guang
and Zhang, Zhiguo",
title="Emotional Attitudes of Chinese Citizens on Social Distancing During the COVID-19 Outbreak: Analysis of Social Media Data",
journal="JMIR Med Inform",
year="2021",
month="Mar",
day="16",
volume="9",
number="3",
pages="e27079",
keywords="COVID-19",
keywords="Sina Weibo",
keywords="social distancing measures",
keywords="emotional analysis",
keywords="machine learning",
keywords="moderating effects",
keywords="deep learning",
keywords="social media",
keywords="emotion",
keywords="attitude",
keywords="infodemiology",
keywords="infoveillance",
abstract="Background: Wuhan, China, the epicenter of the COVID-19 pandemic, imposed citywide lockdown measures on January 23, 2020. Neighboring cities in Hubei Province followed suit with the government enforcing social distancing measures to restrict the spread of the disease throughout the province. Few studies have examined the emotional attitudes of citizens as expressed on social media toward the imposed social distancing measures and the factors that affected their emotions. Objective: The aim of this study was twofold. First, we aimed to detect the emotional attitudes of different groups of users on Sina Weibo toward the social distancing measures imposed by the People's Government of Hubei Province. Second, the influencing factors of their emotions, as well as the impact of the imposed measures on users' emotions, was studied. Methods: Sina Weibo, one of China's largest social media platforms, was chosen as the primary data source. The time span of selected data was from January 21, 2020, to March 24, 2020, while analysis was completed in late June 2020. Bi-directional long short-term memory (Bi-LSTM) was used to analyze users' emotions, while logistic regression analysis was employed to explore the influence of explanatory variables on users' emotions, such as age and spatial location. Further, the moderating effects of social distancing measures on the relationship between user characteristics and users' emotions were assessed by observing the interaction effects between the measures and explanatory variables. Results: Based on the 63,169 comments obtained, we identified six topics of discussion---(1) delaying the resumption of work and school, (2) travel restrictions, (3) traffic restrictions, (4) extending the Lunar New Year holiday, (5) closing public spaces, and (6) community containment. There was no multicollinearity in the data during statistical analysis; the Hosmer-Lemeshow goodness-of-fit was 0.24 ($\chi$28=10.34, P>.24). The main emotions shown by citizens were negative, including anger and fear. Users located in Hubei Province showed the highest amount of negative emotions in Mainland China. There are statistically significant differences in the distribution of emotional polarity between social distancing measures ($\chi$220=19,084.73, P<.001), as well as emotional polarity between genders ($\chi$24=1784.59, P<.001) and emotional polarity between spatial locations ($\chi$24=1659.67, P<.001). Compared with other types of social distancing measures, the measures of delaying the resumption of work and school or travel restrictions mainly had a positive moderating effect on public emotion, while traffic restrictions or community containment had a negative moderating effect on public emotion. Conclusions: Findings provide a reference point for the adoption of epidemic prevention and control measures, and are considered helpful for government agencies to take timely actions to alleviate negative emotions during public health emergencies. ",
doi="10.2196/27079",
url="https://medinform.jmir.org/2021/3/e27079",
url="http://www.ncbi.nlm.nih.gov/pubmed/33724200"
}


@Article{info:doi/10.2196/22916,
author="Gbashi, Sefater
and Adebo, Ayodeji Oluwafemi
and Doorsamy, Wesley
and Njobeh, Berka Patrick",
title="Systematic Delineation of Media Polarity on COVID-19 Vaccines in Africa: Computational Linguistic Modeling Study",
journal="JMIR Med Inform",
year="2021",
month="Mar",
day="16",
volume="9",
number="3",
pages="e22916",
keywords="COVID-19",
keywords="coronavirus",
keywords="vaccine",
keywords="infodemiology",
keywords="infoveillance",
keywords="infodemic",
keywords="sentiment analysis",
keywords="natural language processing",
keywords="media",
keywords="computation",
keywords="linguistic",
keywords="model",
keywords="communication",
abstract="Background: The global onset of COVID-19 has resulted in substantial public health and socioeconomic impacts. An immediate medical breakthrough is needed. However, parallel to the emergence of the COVID-19 pandemic is the proliferation of information regarding the pandemic, which, if uncontrolled, cannot only mislead the public but also hinder the concerted efforts of relevant stakeholders in mitigating the effect of this pandemic. It is known that media communications can affect public perception and attitude toward medical treatment, vaccination, or subject matter, particularly when the population has limited knowledge on the subject. Objective: This study attempts to systematically scrutinize media communications (Google News headlines or snippets and Twitter posts) to understand the prevailing sentiments regarding COVID-19 vaccines in Africa. Methods: A total of 637 Twitter posts and 569 Google News headlines or descriptions, retrieved between February 2 and May 5, 2020, were analyzed using three standard computational linguistics models (ie, TextBlob, Valence Aware Dictionary and Sentiment Reasoner, and Word2Vec combined with a bidirectional long short-term memory neural network). Results: Our findings revealed that, contrary to general perceptions, Google News headlines or snippets and Twitter posts within the stated period were generally passive or positive toward COVID-19 vaccines in Africa. It was possible to understand these patterns in light of increasingly sustained efforts by various media and health actors in ensuring the availability of factual information about the pandemic. Conclusions: This type of analysis could contribute to understanding predominant polarities and associated potential attitudinal inclinations. Such knowledge could be critical in informing relevant public health and media engagement policies. ",
doi="10.2196/22916",
url="https://medinform.jmir.org/2021/3/e22916",
url="http://www.ncbi.nlm.nih.gov/pubmed/33667172"
}


@Article{info:doi/10.2196/24497,
author="Liu, Qinlai
and Sun, Wenping
and Du, Changqing
and Yang, Leiying
and Yuan, Na
and Cui, Haiqing
and Song, Wengang
and Ge, Li",
title="Medical Morphology Training Using the Xuexi Tong Platform During the COVID-19 Pandemic: Development and Validation of a Web-Based Teaching Approach",
journal="JMIR Med Inform",
year="2021",
month="Mar",
day="15",
volume="9",
number="3",
pages="e24497",
keywords="COVID-19",
keywords="histology and embryology",
keywords="pathology",
keywords="web-based teaching",
keywords="Xuexi Tong platform",
abstract="Background: Histology and Embryology and Pathology are two important basic medical morphology courses for studying human histological structures under healthy and pathological conditions, respectively. There is a natural succession between the two courses. At the beginning of 2020, the COVID-19 pandemic suddenly swept the world. During this unusual period, to ensure that medical students would understand and master basic medical knowledge and to lay a solid foundation for future medical bridge courses and professional courses, a web-based medical morphology teaching team, mainly including teachers of courses in Histology and Embryology and Pathology, was established. Objective: This study aimed to explore a new teaching mode of Histology and Embryology and Pathology courses during the COVID-19 pandemic and to illustrate its feasibility and acceptability. Methods: From March to July 2020, our team selected clinical medicine undergraduate students who started their studies in 2018 and 2019 as recipients of web-based teaching. Meanwhile, nursing undergraduate students who started their studies in 2019 and 2020 were selected for traditional offline teaching as the control group. For the web-based teaching, our team used the Xuexi Tong platform as the major platform to realize a new ``seven-in-one'' teaching method (ie, videos, materials, chapter tests, interactions,  homework, live broadcasts, and case analysis/discussion). This new teaching mode involved diverse web-based teaching methods and contents, including flipped classroom, screen-to-screen experimental teaching, a drawing competition, and a writing activity on the theme of ``What I Know About COVID-19.'' When the teaching was about to end, a questionnaire was administered to obtain feedback regarding the teaching performance. In the meantime, the final written pathology examination results of the web-based teaching and traditional offline teaching groups were compared to examine the mastery of knowledge of the students. Results: Using the Xuexi Tong platform as the major platform to conduct ``seven-in-one'' teaching is feasible and acceptable. With regard to the teaching performance of this new web-based teaching mode, students demonstrated a high degree of satisfaction, and the questionnaire showed that 71.3\% or more of the students in different groups reported a greater degree of satisfaction or being very satisfied. In fact, more students achieved high scores (90-100) in the web-based learning group than in the offline learning control group (P=.02). Especially, the number of students with objective scores >60 in the web-based learning group was greater than that in the offline learning control group (P=.045). Conclusions: This study showed that the web-based teaching mode was not inferior to the traditional offline teaching mode for medical morphology courses, proving the feasibility and acceptability of web-based teaching during the COVID-19 pandemic. Our findings lay a solid theoretical foundation for follow-up studies of medical students. ",
doi="10.2196/24497",
url="https://medinform.jmir.org/2021/3/e24497",
url="http://www.ncbi.nlm.nih.gov/pubmed/33566792"
}


@Article{info:doi/10.2196/19473,
author="R Niakan Kalhori, Sharareh
and Bahaadinbeigy, Kambiz
and Deldar, Kolsoum
and Gholamzadeh, Marsa
and Hajesmaeel-Gohari, Sadrieh
and Ayyoubzadeh, Mohammad Seyed",
title="Digital Health Solutions to Control the COVID-19 Pandemic in Countries With High Disease Prevalence: Literature Review",
journal="J Med Internet Res",
year="2021",
month="Mar",
day="10",
volume="23",
number="3",
pages="e19473",
keywords="COVID-19",
keywords="digital health",
keywords="information technology",
keywords="telemedicine",
keywords="electronic health",
abstract="Background: COVID-19, the disease caused by the novel coronavirus SARS-CoV-2, has become a global pandemic, affecting most countries worldwide. Digital health information technologies can be applied in three aspects, namely digital patients, digital devices, and digital clinics, and could be useful in fighting the COVID-19 pandemic. Objective: Recent reviews have examined the role of digital health in controlling COVID-19 to identify the potential of digital health interventions to fight the disease. However, this study aims to review and analyze the digital technology that is being applied to control the COVID-19 pandemic in the 10 countries with the highest prevalence of the disease. Methods: For this review, the Google Scholar, PubMed, Web of Science, and Scopus databases were searched in August 2020 to retrieve publications from December 2019 to March 15, 2020. Furthermore, the Google search engine was used to identify additional applications of digital health for COVID-19 pandemic control. Results: We included 32 papers in this review that reported 37 digital health applications for COVID-19 control. The most common digital health projects to address COVID-19 were telemedicine visits (11/37, 30\%). Digital learning packages for informing people about the disease, geographic information systems and quick response code applications for real-time case tracking, and cloud- or mobile-based systems for self-care and patient tracking were in the second rank of digital tool applications (all 7/37, 19\%). The projects were deployed in various European countries and in the United States, Australia, and China. Conclusions: Considering the potential of available information technologies worldwide in the 21st century, particularly in developed countries, it appears that more digital health products with a higher level of intelligence capability remain to be applied for the management of pandemics and health-related crises. ",
doi="10.2196/19473",
url="https://www.jmir.org/2021/3/e19473",
url="http://www.ncbi.nlm.nih.gov/pubmed/33600344"
}


@Article{info:doi/10.2196/18933,
author="Guzzi, H. Pietro
and Tradigo, Giuseppe
and Veltri, Pierangelo",
title="Regional Resource Assessment During the COVID-19 Pandemic in Italy: Modeling Study",
journal="JMIR Med Inform",
year="2021",
month="Mar",
day="9",
volume="9",
number="3",
pages="e18933",
keywords="COVID-19",
keywords="data analysis",
keywords="ICU",
keywords="management",
keywords="intensive care unit",
keywords="pandemic",
keywords="outbreak",
keywords="infectious disease",
keywords="resource",
keywords="planning",
abstract="Background: COVID-19 has been declared a worldwide emergency and a pandemic by the World Health Organization. It started in China in December 2019, and it rapidly spread throughout Italy, which was the most affected country after China. The pandemic affected all countries with similarly negative effects on the population and health care structures. Objective: The evolution of the COVID-19 infections and the way such a phenomenon can be characterized in terms of resources and planning has to be considered. One of the most critical resources has been intensive care units (ICUs) with respect to the infection trend and critical hospitalization. Methods: We propose a model to estimate the needed number of places in ICUs during the most acute phase of the infection. We also define a scalable geographic model to plan emergency and future management of patients with COVID-19 by planning their reallocation in health structures of other regions. Results: We applied and assessed the prediction method both at the national and regional levels. ICU bed prediction was tested with respect to real data provided by the Italian government. We showed that our model is able to predict, with a reliable error in terms of resource complexity, estimation parameters used in health care structures. In addition, the proposed method is scalable at different geographic levels. This is relevant for pandemics such as COVID-19, which has shown different case incidences even among northern and southern Italian regions. Conclusions: Our contribution can be useful for decision makers to plan resources to guarantee patient management, but it can also be considered as a reference model for potential upcoming waves of COVID-19 and similar emergency situations. ",
doi="10.2196/18933",
url="https://medinform.jmir.org/2021/3/e18933",
url="http://www.ncbi.nlm.nih.gov/pubmed/33629957"
}


@Article{info:doi/10.2196/25724,
author="Yan, Chao
and Zhang, Xinmeng
and Gao, Cheng
and Wilfong, Erin
and Casey, Jonathan
and France, Daniel
and Gong, Yang
and Patel, Mayur
and Malin, Bradley
and Chen, You",
title="Collaboration Structures in COVID-19 Critical Care: Retrospective Network Analysis Study",
journal="JMIR Hum Factors",
year="2021",
month="Mar",
day="8",
volume="8",
number="1",
pages="e25724",
keywords="COVID-19",
keywords="intensive care unit",
keywords="collaboration structure",
keywords="critically ill patient",
keywords="health care worker",
keywords="network analysis",
keywords="electronic health record",
keywords="collaboration",
keywords="critical care",
keywords="relationship",
keywords="safety",
keywords="teamwork",
abstract="Background: Few intensive care unit (ICU) staffing studies have examined the collaboration structures of health care workers (HCWs). Knowledge about how HCWs are connected to the care of critically ill patients with COVID-19 is important for characterizing the relationships among team structures, care quality, and patient safety. Objective: We aimed to discover differences in the teamwork structures of COVID-19 critical care by comparing HCW collaborations in the management of critically ill patients with and without COVID-19. Methods: In this retrospective study, we used network analysis methods to analyze the electronic health records (EHRs) of 76 critically ill patients (with COVID-19: n=38; without COVID-19: n=38) who were admitted to a large academic medical center, and to learn about HCW collaboration. We used the EHRs of adult patients who were admitted to the COVID-19 ICU at the Vanderbilt University Medical Center (Nashville, Tennessee, United States) between March 17, 2020, and May 31, 2020. We matched each patient according to age, gender, and their length of stay. Patients without COVID-19 were admitted to the medical ICU between December 1, 2019, and February 29, 2020. We used two sociometrics---eigencentrality and betweenness---to quantify HCWs' statuses in networks. Eigencentrality characterizes the degree to which an HCW is a core person in collaboration structures. Betweenness centrality refers to whether an HCW lies on the path of other HCWs who are not directly connected. This sociometric was used to characterize HCWs' broad skill sets. We measured patient staffing intensity in terms of the number of HCWs who interacted with patients' EHRs. We assessed the statistical differences in the core and betweenness statuses of HCWs and the patient staffing intensities of COVID-19 and non--COVID-19 critical care, by using Mann-Whitney U tests and reporting 95\% CIs. Results: HCWs in COVID-19 critical care were more likely to frequently work with each other (eigencentrality: median 0.096) than those in non--COVID-19 critical care (eigencentrality: median 0.057; P<.001). Internal medicine physicians in COVID-19 critical care had higher core statuses than those in non--COVID-19 critical care (P=.001). Nurse practitioners in COVID-19 care had higher betweenness statuses than those in non--COVID-19 care (P<.001). Compared to HCWs in non--COVID-19 settings, the EHRs of critically ill patients with COVID-19 were used by a larger number of internal medicine nurse practitioners (P<.001), cardiovascular nurses (P<.001), and surgical ICU nurses (P=.002) and a smaller number of resident physicians (P<.001). Conclusions: Network analysis methodologies and data on EHR use provide a novel method for learning about differences in collaboration structures between COVID-19 and non--COVID-19 critical care. Health care organizations can use this information to learn about the novel changes that the COVID-19 pandemic has imposed on collaboration structures in urgent care. ",
doi="10.2196/25724",
url="https://humanfactors.jmir.org/2021/1/e25724",
url="http://www.ncbi.nlm.nih.gov/pubmed/33621187"
}


@Article{info:doi/10.2196/22219,
author="Kohane, S. Isaac
and Aronow, J. Bruce
and Avillach, Paul
and Beaulieu-Jones, K. Brett
and Bellazzi, Riccardo
and Bradford, L. Robert
and Brat, A. Gabriel
and Cannataro, Mario
and Cimino, J. James
and Garc{\'i}a-Barrio, Noelia
and Gehlenborg, Nils
and Ghassemi, Marzyeh
and Guti{\'e}rrez-Sacrist{\'a}n, Alba
and Hanauer, A. David
and Holmes, H. John
and Hong, Chuan
and Klann, G. Jeffrey
and Loh, Will Ne Hooi
and Luo, Yuan
and Mandl, D. Kenneth
and Daniar, Mohamad
and Moore, H. Jason
and Murphy, N. Shawn
and Neuraz, Antoine
and Ngiam, Yuan Kee
and Omenn, S. Gilbert
and Palmer, Nathan
and Patel, P. Lav
and Pedrera-Jim{\'e}nez, Miguel
and Sliz, Piotr
and South, M. Andrew
and Tan, Min Amelia Li
and Taylor, M. Deanne
and Taylor, W. Bradley
and Torti, Carlo
and Vallejos, K. Andrew
and Wagholikar, B. Kavishwar
and 
and Weber, M. Griffin
and Cai, Tianxi",
title="What Every Reader Should Know About Studies Using Electronic Health Record Data but May Be Afraid to Ask",
journal="J Med Internet Res",
year="2021",
month="Mar",
day="2",
volume="23",
number="3",
pages="e22219",
keywords="COVID-19",
keywords="electronic health records",
keywords="real-world data",
keywords="literature",
keywords="publishing",
keywords="quality",
keywords="data quality",
keywords="reporting standards",
keywords="reporting checklist",
keywords="review",
keywords="statistics",
doi="10.2196/22219",
url="https://www.jmir.org/2021/3/e22219",
url="http://www.ncbi.nlm.nih.gov/pubmed/33600347"
}


@Article{info:doi/10.2196/23458,
author="Ikemura, Kenji
and Bellin, Eran
and Yagi, Yukako
and Billett, Henny
and Saada, Mahmoud
and Simone, Katelyn
and Stahl, Lindsay
and Szymanski, James
and Goldstein, Y. D.
and Reyes Gil, Morayma",
title="Using Automated Machine Learning to Predict the Mortality of Patients With COVID-19: Prediction Model Development Study",
journal="J Med Internet Res",
year="2021",
month="Feb",
day="26",
volume="23",
number="2",
pages="e23458",
keywords="automated machine learning",
keywords="COVID-19",
keywords="biomarker",
keywords="ranking",
keywords="decision support tool",
keywords="machine learning",
keywords="decision support",
keywords="Shapley additive explanation",
keywords="partial dependence plot",
keywords="dimensionality reduction",
abstract="Background: During a pandemic, it is important for clinicians to stratify patients and decide who receives limited medical resources. Machine learning models have been proposed to accurately predict COVID-19 disease severity. Previous studies have typically tested only one machine learning algorithm and limited performance evaluation to area under the curve analysis. To obtain the best results possible, it may be important to test different machine learning algorithms to find the best prediction model. Objective: In this study, we aimed to use automated machine learning (autoML) to train various machine learning algorithms. We selected the model that best predicted patients' chances of surviving a SARS-CoV-2 infection. In addition, we identified which variables (ie, vital signs, biomarkers, comorbidities, etc) were the most influential in generating an accurate model. Methods: Data were retrospectively collected from all patients who tested positive for COVID-19 at our institution between March 1 and July 3, 2020. We collected 48 variables from each patient within 36 hours before or after the index time (ie, real-time polymerase chain reaction positivity). Patients were followed for 30 days or until death. Patients' data were used to build 20 machine learning models with various algorithms via autoML. The performance of machine learning models was measured by analyzing the area under the precision-recall curve (AUPCR). Subsequently, we established model interpretability via Shapley additive explanation and partial dependence plots to identify and rank variables that drove model predictions. Afterward, we conducted dimensionality reduction to extract the 10 most influential variables. AutoML models were retrained by only using these 10 variables, and the output models were evaluated against the model that used 48 variables. Results: Data from 4313 patients were used to develop the models. The best model that was generated by using autoML and 48 variables was the stacked ensemble model (AUPRC=0.807). The two best independent models were the gradient boost machine and extreme gradient boost models, which had an AUPRC of 0.803 and 0.793, respectively. The deep learning model (AUPRC=0.73) was substantially inferior to the other models. The 10 most influential variables for generating high-performing models were systolic and diastolic blood pressure, age, pulse oximetry level, blood urea nitrogen level, lactate dehydrogenase level, D-dimer level, troponin level, respiratory rate, and Charlson comorbidity score. After the autoML models were retrained with these 10 variables, the stacked ensemble model still had the best performance (AUPRC=0.791). Conclusions: We used autoML to develop high-performing models that predicted the survival of patients with COVID-19. In addition, we identified important variables that correlated with mortality. This is proof of concept that autoML is an efficient, effective, and informative method for generating machine learning--based clinical decision support tools. ",
doi="10.2196/23458",
url="https://www.jmir.org/2021/2/e23458",
url="http://www.ncbi.nlm.nih.gov/pubmed/33539308"
}


@Article{info:doi/10.2196/23427,
author="Lim, Min Hooi
and Teo, Hai Chin
and Ng, Jenn Chirk
and Chiew, Kian Thiam
and Ng, Leik Wei
and Abdullah, Adina
and Abdul Hadi, Haireen
and Liew, Sun Chee
and Chan, Seng Chee",
title="An Automated Patient Self-Monitoring System to Reduce Health Care System Burden During the COVID-19 Pandemic in Malaysia: Development and Implementation Study",
journal="JMIR Med Inform",
year="2021",
month="Feb",
day="26",
volume="9",
number="2",
pages="e23427",
keywords="COVID-19",
keywords="coronavirus disease",
keywords="home monitoring",
keywords="symptom monitoring",
keywords="system",
keywords="teleconsultation",
keywords="development",
keywords="eHealth",
keywords="digital health",
keywords="mHealth",
keywords="health services research",
keywords="telesurveillance",
keywords="infectious disease",
keywords="app",
abstract="Background: During the COVID-19 pandemic, there was an urgent need to develop an automated COVID-19 symptom monitoring system to reduce the burden on the health care system and to provide better self-monitoring at home. Objective: This paper aimed to describe the development process of the COVID-19 Symptom Monitoring System (CoSMoS), which consists of a self-monitoring, algorithm-based Telegram bot and a teleconsultation system. We describe all the essential steps from the clinical perspective and our technical approach in designing, developing, and integrating the system into clinical practice during the COVID-19 pandemic as well as lessons learned from this development process. Methods: CoSMoS was developed in three phases: (1) requirement formation to identify clinical problems and to draft the clinical algorithm, (2) development testing iteration using the agile software development method, and (3) integration into clinical practice to design an effective clinical workflow using repeated simulations and role-playing. Results: We completed the development of CoSMoS in 19 days. In Phase 1 (ie, requirement formation), we identified three main functions: a daily automated reminder system for patients to self-check their symptoms, a safe patient risk assessment to guide patients in clinical decision making, and an active telemonitoring system with real-time phone consultations. The system architecture of CoSMoS involved five components: Telegram instant messaging, a clinician dashboard, system administration (ie, back end), a database, and development and operations  infrastructure. The integration of CoSMoS into clinical practice involved the consideration of COVID-19 infectivity and patient safety. Conclusions: This study demonstrated that developing a COVID-19 symptom monitoring system within a short time during a pandemic is feasible using the agile development method. Time factors and communication between the technical and clinical teams were the main challenges in the development process. The development process and lessons learned from this study can guide the future development of digital monitoring systems during the next pandemic, especially in developing countries. ",
doi="10.2196/23427",
url="https://medinform.jmir.org/2021/2/e23427",
url="http://www.ncbi.nlm.nih.gov/pubmed/33600345"
}


@Article{info:doi/10.2196/23335,
author="Elhadi, Muhammed
and Msherghi, Ahmed
and Elhadi, Ahmed
and Ashini, Aimen
and Alsoufi, Ahmed
and Bin Alshiteewi, Fatimah
and Elmabrouk, Amna
and Alsuyihili, Ali
and Elgherwi, Alsafa
and Elkhafeefi, Fatimah
and Abdulrazik, Sarah
and Tarek, Ahmed",
title="Utilization of Telehealth Services in Libya in Response to the COVID-19 Pandemic: Cross-sectional Analysis",
journal="JMIR Med Inform",
year="2021",
month="Feb",
day="26",
volume="9",
number="2",
pages="e23335",
keywords="COVID-19",
keywords="cross-sectional study",
keywords="resource-limited countries",
keywords="SARS-CoV-2",
keywords="telehealth",
keywords="telemedicine",
keywords="transitional countries",
keywords="usability",
abstract="Background: Health care systems in transitional countries have witnessed unprecedented challenges related to adequate and continuous health care provision during the COVID-19 pandemic. In many countries, including Libya, institutions and organizations have begun to implement telehealth technology for the first time. This serves to establish an alternative modality for direct physician-patient interviews to reduce the risk of COVID-19 transmission. Objective: This study aimed to assess the usability of telehealth services in Libya and to provide an overview of the current COVID-19 scenario. Methods: In this cross-sectional study, an anonymous web-based survey was administered to Libyan residents between April and May 2020. Participants were contacted through text messaging, emails, and social media. The survey items yielded information on the sociodemographic characteristics, availability and accessibility of health care services, effects of the COVID-19 pandemic on health care services, mental health status, and the feasibility and application of the telehealth system. Results: We obtained 2512 valid responses, of which 1721 (68.5\%) were from females. The participants were aged 28.2 (SD 7.6) years, of whom 2333 (92.9\%) were aged <40 years, and 1463 (58.2\%) were single. Regarding the health care services and their accessibility, 786 (31.1\%) participants reported having a poor health status in general, and 492 (19.6\%) reported having a confirmed diagnosis of at least one chronic disease. Furthermore, 498 (19.9\%) participants reported varying degrees of difficulty in accessing health care centers, and 1558 (62.0\%) could not access their medical records. Additionally, 1546 (61.6\%) participants experienced problems in covering medical costs, and 1429 (56.9\%) avoided seeking medical care owing to financial concerns. Regarding the feasibility of the telehealth system, approximately half of the participants reported that telehealth services were useful during the COVID-19 pandemic, and 1545 (61.5\%) reported that the system was an effective means of communication and of obtaining health care services. Furthermore, 1435 (57.1\%) participants felt comfortable using the telehealth system, and 1129 (44.9\%) felt that they were able to express themselves effectively. Moreover, 1389 (55.3\%) participants found the system easy to understand, and 1354 (53.9\%) reported having excellent communication with physicians through the telehealth system. However, only 1018 (40.5\%) participants reported that communication was better with the telehealth system than with traditional methods. Conclusions: Our study revealed high levels of usability and willingness to use the telemedicine system as an alternative modality to in-person consultations among the Libyan residents in this study. This system is advantageous because it helps overcome health care costs, increases access to prompt medical care and follow-up evaluation, and reduces the risk of COVID-19 transmission. However, internet connectivity and electricity issues could be a substantial barrier for many resource-limited communities, and further studies should address such obstacles. ",
doi="10.2196/23335",
url="https://medinform.jmir.org/2021/2/e23335",
url="http://www.ncbi.nlm.nih.gov/pubmed/33606654"
}


@Article{info:doi/10.2196/26773,
author="Vahidy, Farhaan
and Jones, L. Stephen
and Tano, E. Mauricio
and Nicolas, Carlos Juan
and Khan, A. Osman
and Meeks, R. Jennifer
and Pan, P. Alan
and Menser, Terri
and Sasangohar, Farzan
and Naufal, George
and Sostman, Dirk
and Nasir, Khurram
and Kash, A. Bita",
title="Rapid Response to Drive COVID-19 Research in a Learning Health Care System: Rationale and Design of the Houston Methodist COVID-19 Surveillance and Outcomes Registry (CURATOR)",
journal="JMIR Med Inform",
year="2021",
month="Feb",
day="23",
volume="9",
number="2",
pages="e26773",
keywords="COVID-19",
keywords="SARS-CoV-2",
keywords="data science",
keywords="data curation",
keywords="electronic health records",
keywords="learning health system",
keywords="databases, factual",
abstract="Background: The COVID-19 pandemic has exacerbated the challenges of meaningful health care digitization. The need for rapid yet validated decision-making requires robust data infrastructure. Organizations with a focus on learning health care (LHC) systems tend to adapt better to rapidly evolving data needs. Few studies have demonstrated a successful implementation of data digitization principles in an LHC context across health care systems during the COVID-19 pandemic. Objective: We share our experience and provide a framework for assembling and organizing multidisciplinary resources, structuring and regulating research needs, and developing a single source of truth (SSoT) for COVID-19 research by applying fundamental principles of health care digitization, in the context of LHC systems across a complex health care organization. Methods: Houston Methodist (HM) comprises eight tertiary care hospitals and an expansive primary care network across Greater Houston, Texas. During the early phase of the pandemic, institutional leadership envisioned the need to streamline COVID-19 research and established the retrospective research task force (RRTF). We describe an account of the structure, functioning, and productivity of the RRTF. We further elucidate the technical and structural details of a comprehensive data repository---the HM COVID-19 Surveillance and Outcomes Registry (CURATOR). We particularly highlight how CURATOR conforms to standard health care digitization principles in the LHC context. Results: The HM COVID-19 RRTF comprises expertise in epidemiology, health systems, clinical domains, data sciences, information technology, and research regulation. The RRTF initially convened in March 2020 to prioritize and streamline COVID-19 observational research; to date, it has reviewed over 60 protocols and made recommendations to the institutional review board (IRB). The RRTF also established the charter for CURATOR, which in itself was IRB-approved in April 2020. CURATOR is a relational structured query language database that is directly populated with data from electronic health records, via largely automated extract, transform, and load procedures. The CURATOR design enables longitudinal tracking of COVID-19 cases and controls before and after COVID-19 testing. CURATOR has been set up following the SSoT principle and is harmonized across other COVID-19 data sources. CURATOR eliminates data silos by leveraging unique and disparate big data sources for COVID-19 research and provides a platform to capitalize on institutional investment in cloud computing. It currently hosts deeply phenotyped sociodemographic, clinical, and outcomes data of approximately 200,000 individuals tested for COVID-19. It supports more than 30 IRB-approved protocols across several clinical domains and has generated numerous publications from its core and associated data sources. Conclusions: A data-driven decision-making strategy is paramount to the success of health care organizations. Investment in cross-disciplinary expertise, health care technology, and leadership commitment are key ingredients to foster an LHC system. Such systems can mitigate the effects of ongoing and future health care catastrophes by providing timely and validated decision support. ",
doi="10.2196/26773",
url="https://medinform.jmir.org/2021/2/e26773",
url="http://www.ncbi.nlm.nih.gov/pubmed/33544692"
}


@Article{info:doi/10.2196/23026,
author="Sang, Shengtian
and Sun, Ran
and Coquet, Jean
and Carmichael, Harris
and Seto, Tina
and Hernandez-Boussard, Tina",
title="Learning From Past Respiratory Infections to Predict COVID-19 Outcomes: Retrospective Study",
journal="J Med Internet Res",
year="2021",
month="Feb",
day="22",
volume="23",
number="2",
pages="e23026",
keywords="COVID-19",
keywords="invasive mechanical ventilation",
keywords="all-cause mortality",
keywords="machine learning",
keywords="artificial intelligence",
keywords="respiratory",
keywords="infection",
keywords="outcome",
keywords="data",
keywords="feasibility",
keywords="framework",
abstract="Background: For the clinical care of patients with well-established diseases, randomized trials, literature, and research are supplemented with clinical judgment to understand disease prognosis and inform treatment choices. In the void created by a lack of clinical experience with COVID-19, artificial intelligence (AI) may be an important tool to bolster clinical judgment and decision making. However, a lack of clinical data restricts the design and development of such AI tools, particularly in preparation for an impending crisis or pandemic. Objective: This study aimed to develop and test the feasibility of a ``patients-like-me'' framework to predict the deterioration of patients with COVID-19 using a retrospective cohort of patients with similar respiratory diseases. Methods: Our framework used COVID-19--like cohorts to design and train AI models that were then validated on the COVID-19 population. The COVID-19--like cohorts included patients diagnosed with bacterial pneumonia, viral pneumonia, unspecified pneumonia, influenza, and acute respiratory distress syndrome (ARDS) at an academic medical center from 2008 to 2019. In total, 15 training cohorts were created using different combinations of the COVID-19--like cohorts with the ARDS cohort for exploratory purposes. In this study, two machine learning models were developed: one to predict invasive mechanical ventilation (IMV) within 48 hours for each hospitalized day, and one to predict all-cause mortality at the time of admission. Model performance was assessed using the area under the receiver operating characteristic curve (AUROC), sensitivity, specificity, positive predictive value, and negative predictive value. We established model interpretability by calculating SHapley Additive exPlanations (SHAP) scores to identify important features. Results: Compared to the COVID-19--like cohorts (n=16,509), the patients hospitalized with COVID-19 (n=159) were significantly younger, with a higher proportion of patients of Hispanic ethnicity, a lower proportion of patients with smoking history, and fewer patients with comorbidities (P<.001). Patients with COVID-19 had a lower IMV rate (15.1 versus 23.2, P=.02) and shorter time to IMV (2.9 versus 4.1 days, P<.001) compared to the COVID-19--like patients. In the COVID-19--like training data, the top models achieved excellent performance (AUROC>0.90). Validating in the COVID-19 cohort, the top-performing model for predicting IMV was the XGBoost model (AUROC=0.826) trained on the viral pneumonia cohort. Similarly, the XGBoost model trained on all 4 COVID-19--like cohorts without ARDS achieved the best performance (AUROC=0.928) in predicting mortality. Important predictors included demographic information (age), vital signs (oxygen saturation), and laboratory values (white blood cell count, cardiac troponin, albumin, etc). Our models had class imbalance, which resulted in high negative predictive values and low positive predictive values. Conclusions: We provided a feasible framework for modeling patient deterioration using existing data and AI technology to address data limitations during the onset of a novel, rapidly changing pandemic. ",
doi="10.2196/23026",
url="https://www.jmir.org/2021/2/e23026",
url="http://www.ncbi.nlm.nih.gov/pubmed/33534724"
}


@Article{info:doi/10.2196/21679,
author="Parikh, Soham
and Davoudi, Anahita
and Yu, Shun
and Giraldo, Carolina
and Schriver, Emily
and Mowery, Danielle",
title="Lexicon Development for COVID-19-related Concepts Using Open-source Word Embedding Sources: An Intrinsic and Extrinsic Evaluation",
journal="JMIR Med Inform",
year="2021",
month="Feb",
day="22",
volume="9",
number="2",
pages="e21679",
keywords="natural language processing",
keywords="word embedding",
keywords="COVID-19",
keywords="intrinsic",
keywords="open-source",
keywords="computation",
keywords="model",
keywords="prediction",
keywords="semantic",
keywords="syntactic",
keywords="pattern",
abstract="Background: Scientists are developing new computational methods and prediction models to better clinically understand COVID-19 prevalence, treatment efficacy, and patient outcomes. These efforts could be improved by leveraging documented COVID-19--related symptoms, findings, and disorders from clinical text sources in an electronic health record. Word embeddings can identify terms related to these clinical concepts from both the biomedical and nonbiomedical domains, and are being shared with the open-source community at large. However, it's unclear how useful openly available word embeddings are for developing lexicons for COVID-19--related concepts. Objective: Given an initial lexicon of COVID-19--related terms, this study aims to characterize the returned terms by similarity across various open-source word embeddings and determine common semantic and syntactic patterns between the COVID-19 queried terms and returned terms specific to the word embedding source. Methods: We compared seven openly available word embedding sources. Using a series of COVID-19--related terms for associated symptoms, findings, and disorders, we conducted an interannotator agreement study to determine how accurately the most similar returned terms could be classified according to semantic types by three annotators. We conducted a qualitative study of COVID-19 queried terms and their returned terms to detect informative patterns for constructing lexicons. We demonstrated the utility of applying such learned synonyms to discharge summaries by reporting the proportion of patients identified by concept among three patient cohorts: pneumonia (n=6410), acute respiratory distress syndrome (n=8647), and COVID-19 (n=2397). Results: We observed high pairwise interannotator agreement (Cohen kappa) for symptoms (0.86-0.99), findings (0.93-0.99), and disorders (0.93-0.99). Word embedding sources generated based on characters tend to return more synonyms (mean count of 7.2 synonyms) compared to token-based embedding sources (mean counts range from 2.0 to 3.4). Word embedding sources queried using a qualifier term (eg, dry cough or muscle pain) more often returned qualifiers of the similar semantic type (eg, ``dry'' returns consistency qualifiers like ``wet'' and ``runny'') compared to a single term (eg, cough or pain) queries. A higher proportion of patients had documented fever (0.61-0.84), cough (0.41-0.55), shortness of breath (0.40-0.59), and hypoxia (0.51-0.56) retrieved than other clinical features. Terms for dry cough returned a higher proportion of patients with COVID-19 (0.07) than the pneumonia (0.05) and acute respiratory distress syndrome (0.03) populations. Conclusions: Word embeddings are valuable technology for learning related terms, including synonyms. When leveraging openly available word embedding sources, choices made for the construction of the word embeddings can significantly influence the words learned. ",
doi="10.2196/21679",
url="https://medinform.jmir.org/2021/2/e21679",
url="http://www.ncbi.nlm.nih.gov/pubmed/33544689"
}


@Article{info:doi/10.2196/23390,
author="Dai, Wanfa
and Ke, Pei-Feng
and Li, Zhen-Zhen
and Zhuang, Qi-Zhen
and Huang, Wei
and Wang, Yi
and Xiong, Yujuan
and Huang, Xian-Zhang",
title="Establishing Classifiers With Clinical Laboratory Indicators to Distinguish COVID-19 From Community-Acquired Pneumonia: Retrospective Cohort Study",
journal="J Med Internet Res",
year="2021",
month="Feb",
day="22",
volume="23",
number="2",
pages="e23390",
keywords="COVID-19",
keywords="clinical laboratory indicators",
keywords="community-acquired pneumonia",
keywords="classifier",
keywords="classification algorithm",
abstract="Background: The initial symptoms of patients with COVID-19 are very much like those of patients with community-acquired pneumonia (CAP); it is difficult to distinguish COVID-19 from CAP with clinical symptoms and imaging examination. Objective: The objective of our study was to construct an effective model for the early identification of COVID-19 that would also distinguish it from CAP. Methods: The clinical laboratory indicators (CLIs) of 61 COVID-19 patients and 60 CAP patients were analyzed retrospectively. Random combinations of various CLIs (ie, CLI combinations) were utilized to establish COVID-19 versus CAP classifiers with machine learning algorithms, including random forest classifier (RFC), logistic regression classifier, and gradient boosting classifier (GBC). The performance of the classifiers was assessed by calculating the area under the receiver operating characteristic curve (AUROC) and recall rate in COVID-19 prediction using the test data set. Results: The classifiers that were constructed with three algorithms from 43 CLI combinations showed high performance (recall rate >0.9 and AUROC >0.85) in COVID-19 prediction for the test data set. Among the high-performance classifiers, several CLIs showed a high usage rate; these included procalcitonin (PCT), mean corpuscular hemoglobin concentration (MCHC), uric acid, albumin, albumin to globulin ratio (AGR), neutrophil count, red blood cell (RBC) count, monocyte count, basophil count, and white blood cell (WBC) count. They also had high feature importance except for basophil count. The feature combination (FC) of PCT, AGR, uric acid, WBC count, neutrophil count, basophil count, RBC count, and MCHC was the representative one among the nine FCs used to construct the classifiers with an AUROC equal to 1.0 when using the RFC or GBC algorithms. Replacing any CLI in these FCs would lead to a significant reduction in the performance of the classifiers that were built with them. Conclusions: The classifiers constructed with only a few specific CLIs could efficiently distinguish COVID-19 from CAP, which could help clinicians perform early isolation and centralized management of COVID-19 patients. ",
doi="10.2196/23390",
url="https://www.jmir.org/2021/2/e23390",
url="http://www.ncbi.nlm.nih.gov/pubmed/33534722"
}


@Article{info:doi/10.2196/24572,
author="Quiroz, Carlos Juan
and Feng, You-Zhen
and Cheng, Zhong-Yuan
and Rezazadegan, Dana
and Chen, Ping-Kang
and Lin, Qi-Ting
and Qian, Long
and Liu, Xiao-Fang
and Berkovsky, Shlomo
and Coiera, Enrico
and Song, Lei
and Qiu, Xiaoming
and Liu, Sidong
and Cai, Xiang-Ran",
title="Development and Validation of a Machine Learning Approach for Automated Severity Assessment of COVID-19 Based on Clinical and Imaging Data: Retrospective Study",
journal="JMIR Med Inform",
year="2021",
month="Feb",
day="11",
volume="9",
number="2",
pages="e24572",
keywords="algorithm",
keywords="clinical data",
keywords="clinical features",
keywords="COVID-19",
keywords="CT scans",
keywords="development",
keywords="imaging",
keywords="imbalanced data",
keywords="machine learning",
keywords="oversampling",
keywords="severity assessment",
keywords="validation",
abstract="Background: COVID-19 has overwhelmed health systems worldwide. It is important to identify severe cases as early as possible, such that resources can be mobilized and treatment can be escalated. Objective: This study aims to develop a machine learning approach for automated severity assessment of COVID-19 based on clinical and imaging data. Methods: Clinical data---including demographics, signs, symptoms, comorbidities, and blood test results---and chest computed tomography scans of 346 patients from 2 hospitals in the Hubei Province, China, were used to develop machine learning models for automated severity assessment in diagnosed COVID-19 cases. We compared the predictive power of the clinical and imaging data from multiple machine learning models and further explored the use of four oversampling methods to address the imbalanced classification issue. Features with the highest predictive power were identified using the Shapley Additive Explanations framework. Results: Imaging features had the strongest impact on the model output, while a combination of clinical and imaging features yielded the best performance overall. The identified predictive features were consistent with those reported previously. Although oversampling yielded mixed results, it achieved the best model performance in our study. Logistic regression models differentiating between mild and severe cases achieved the best performance for clinical features (area under the curve [AUC] 0.848; sensitivity 0.455; specificity 0.906), imaging features (AUC 0.926; sensitivity 0.818; specificity 0.901), and a combination of clinical and imaging features (AUC 0.950; sensitivity 0.764; specificity 0.919). The synthetic minority oversampling method further improved the performance of the model using combined features (AUC 0.960; sensitivity 0.845; specificity 0.929). Conclusions: Clinical and imaging features can be used for automated severity assessment of COVID-19 and can potentially help triage patients with COVID-19 and prioritize care delivery to those at a higher risk of severe disease. ",
doi="10.2196/24572",
url="http://medinform.jmir.org/2021/2/e24572/",
url="http://www.ncbi.nlm.nih.gov/pubmed/33534723"
}


@Article{info:doi/10.2196/24246,
author="Bolourani, Siavash
and Brenner, Max
and Wang, Ping
and McGinn, Thomas
and Hirsch, S. Jamie
and Barnaby, Douglas
and Zanos, P. Theodoros
and ",
title="A Machine Learning Prediction Model of Respiratory Failure Within 48 Hours of Patient Admission for COVID-19: Model Development and Validation",
journal="J Med Internet Res",
year="2021",
month="Feb",
day="10",
volume="23",
number="2",
pages="e24246",
keywords="artificial intelligence",
keywords="prognostic",
keywords="model",
keywords="pandemic",
keywords="severe acute respiratory syndrome coronavirus 2",
keywords="modeling",
keywords="development",
keywords="validation",
keywords="COVID-19",
keywords="machine learning",
abstract="Background: Predicting early respiratory failure due to COVID-19 can help triage patients to higher levels of care, allocate scarce resources, and reduce morbidity and mortality by appropriately monitoring and treating the patients at greatest risk for deterioration. Given the complexity of COVID-19, machine learning approaches may support clinical decision making for patients with this disease. Objective: Our objective is to derive a machine learning model that predicts respiratory failure within 48 hours of admission based on data from the emergency department. Methods: Data were collected from patients with COVID-19 who were admitted to Northwell Health acute care hospitals and were discharged, died, or spent a minimum of 48 hours in the hospital between March 1 and May 11, 2020. Of 11,525 patients, 933 (8.1\%) were placed on invasive mechanical ventilation within 48 hours of admission. Variables used by the models included clinical and laboratory data commonly collected in the emergency department. We trained and validated three predictive models (two based on XGBoost and one that used logistic regression) using cross-hospital validation. We compared model performance among all three models as well as an established early warning score (Modified Early Warning Score) using receiver operating characteristic curves, precision-recall curves, and other metrics. Results: The XGBoost model had the highest mean accuracy (0.919; area under the curve=0.77), outperforming the other two models as well as the Modified Early Warning Score. Important predictor variables included the type of oxygen delivery used in the emergency department, patient age, Emergency Severity Index level, respiratory rate, serum lactate, and demographic characteristics. Conclusions: The XGBoost model had high predictive accuracy, outperforming other early warning scores. The clinical plausibility and predictive ability of XGBoost suggest that the model could be used to predict 48-hour respiratory failure in admitted patients with COVID-19. ",
doi="10.2196/24246",
url="http://www.jmir.org/2021/2/e24246/",
url="http://www.ncbi.nlm.nih.gov/pubmed/33476281"
}


@Article{info:doi/10.2196/25457,
author="Fernandes, Marta
and Sun, Haoqi
and Jain, Aayushee
and Alabsi, S. Haitham
and Brenner, N. Laura
and Ye, Elissa
and Ge, Wendong
and Collens, I. Sarah
and Leone, J. Michael
and Das, Sudeshna
and Robbins, K. Gregory
and Mukerji, S. Shibani
and Westover, Brandon M.",
title="Classification of the Disposition of Patients Hospitalized with COVID-19: Reading Discharge Summaries Using Natural Language Processing",
journal="JMIR Med Inform",
year="2021",
month="Feb",
day="10",
volume="9",
number="2",
pages="e25457",
keywords="ICU",
keywords="coronavirus",
keywords="electronic health record",
keywords="unstructured text",
keywords="natural language processing",
keywords="BoW",
keywords="LASSO",
keywords="feature selection",
keywords="machine learning",
keywords="intensive care unit",
keywords="COVID-19",
keywords="EHR",
abstract="Background: Medical notes are a rich source of patient data; however, the nature of unstructured text has largely precluded the use of these data for large retrospective analyses. Transforming clinical text into structured data can enable large-scale research studies with electronic health records (EHR) data. Natural language processing (NLP) can be used for text information retrieval, reducing the need for labor-intensive chart review. Here we present an application of NLP to large-scale analysis of medical records at 2 large hospitals for patients hospitalized with COVID-19. Objective: Our study goal was to develop an NLP pipeline to classify the discharge disposition (home, inpatient rehabilitation, skilled nursing inpatient facility [SNIF], and death) of patients hospitalized with COVID-19 based on hospital discharge summary notes. Methods: Text mining and feature engineering were applied to unstructured text from hospital discharge summaries. The study included patients with COVID-19 discharged from 2 hospitals in the Boston, Massachusetts area (Massachusetts General Hospital and Brigham and Women's Hospital) between March 10, 2020, and June 30, 2020. The data were divided into a training set (70\%) and hold-out test set (30\%). Discharge summaries were represented as bags-of-words consisting of single words (unigrams), bigrams, and trigrams. The number of features was reduced during training by excluding n-grams that occurred in fewer than 10\% of discharge summaries, and further reduced using least absolute shrinkage and selection operator (LASSO) regularization while training a multiclass logistic regression model. Model performance was evaluated using the hold-out test set. Results: The study cohort included 1737 adult patients (median age 61 [SD 18] years; 55\% men; 45\% White and 16\% Black; 14\% nonsurvivors and 61\% discharged home). The model selected 179 from a vocabulary of 1056 engineered features, consisting of combinations of unigrams, bigrams, and trigrams. The top features contributing most to the classification by the model (for each outcome) were the following: ``appointments specialty,'' ``home health,'' and ``home care'' (home); ``intubate'' and ``ARDS'' (inpatient rehabilitation); ``service'' (SNIF); ``brief assessment'' and ``covid'' (death). The model achieved a micro-average area under the receiver operating characteristic curve value of 0.98 (95\% CI 0.97-0.98) and average precision of 0.81 (95\% CI 0.75-0.84) in the testing set for prediction of discharge disposition. Conclusions: A supervised learning--based NLP approach is able to classify the discharge disposition of patients hospitalized with COVID-19. This approach has the potential to accelerate and increase the scale of research on patients' discharge disposition that is possible with EHR data. ",
doi="10.2196/25457",
url="https://medinform.jmir.org/2021/2/e25457",
url="http://www.ncbi.nlm.nih.gov/pubmed/33449908"
}


@Article{info:doi/10.2196/25245,
author="Idrees, Mohammad Sheikh
and Nowostawski, Mariusz
and Jameel, Roshan",
title="Blockchain-Based Digital Contact Tracing Apps for COVID-19 Pandemic Management: Issues, Challenges, Solutions, and Future Directions",
journal="JMIR Med Inform",
year="2021",
month="Feb",
day="9",
volume="9",
number="2",
pages="e25245",
keywords="COVID-19",
keywords="digital contact tracing",
keywords="privacy preservation",
keywords="security",
keywords="blockchain technology",
keywords="blockchain",
keywords="privacy",
keywords="contact tracing",
keywords="app",
keywords="surveillance",
doi="10.2196/25245",
url="https://medinform.jmir.org/2021/2/e25245",
url="http://www.ncbi.nlm.nih.gov/pubmed/33400677"
}


@Article{info:doi/10.2196/25183,
author="Frick, J. Nicholas R.
and M{\"o}llmann, L. Henriette
and Mirbabaie, Milad
and Stieglitz, Stefan",
title="Driving Digital Transformation During a Pandemic: Case Study of Virtual Collaboration in a German Hospital",
journal="JMIR Med Inform",
year="2021",
month="Feb",
day="1",
volume="9",
number="2",
pages="e25183",
keywords="digital transformation",
keywords="virtual collaboration",
keywords="digital health",
keywords="health care",
keywords="COVID-19",
keywords="pandemic",
keywords="hospital",
keywords="collaboration",
keywords="virtual heath",
keywords="crisis",
keywords="case study",
abstract="Background: The COVID-19 pandemic has not only changed the private lives of millions of people but has significantly affected the collaboration of medical specialists throughout health care systems worldwide. Hospitals are making changes to their regular operations to slow the spread of SARS-CoV-2 while ensuring the treatment of emergency patients. These substantial changes affect the typical work setting of clinicians and require the implementation of organizational arrangements. Objective: In this study, we aim to increase our understanding of how digital transformation drives virtual collaboration among clinicians in hospitals in times of crisis, such as the COVID-19 pandemic. Methods: We present the lessons learned from an exploratory case study in which we observed the introduction of an information technology (IT) system for enhancing collaboration among clinicians in a German hospital. The results are based on 16 semistructured interviews with physicians from various departments and disciplines; the interviews were generalized to better understand and interpret the meaning of the statements. Results: Three key lessons and recommendations explain how digital transformation ensures goal-driven collaboration among clinicians. First, we found that implementing a disruptive change requires alignment of the mindsets of the stakeholders. Second, IT-enabled collaboration presupposes behavioral rules that must be followed. Third, transforming antiquated processes demands a suitable technological infrastructure. Conclusions: Digital transformation is being driven by the COVID-19 pandemic. However, the rapid introduction of IT-enabled collaboration reveals grievances concerning the digital dissemination of medical information along the patient treatment path. To avoid being caught unprepared by future crises, digital transformation must be further driven to ensure collaboration, and the diagnostic and therapeutic process must be opened to disruptive strategies. ",
doi="10.2196/25183",
url="https://medinform.jmir.org/2021/2/e25183",
url="http://www.ncbi.nlm.nih.gov/pubmed/33449905"
}


@Article{info:doi/10.2196/24785,
author="Reeves, Jeffery J.
and Ayers, W. John
and Longhurst, A. Christopher",
title="Telehealth in the COVID-19 Era: A Balancing Act to Avoid Harm",
journal="J Med Internet Res",
year="2021",
month="Feb",
day="1",
volume="23",
number="2",
pages="e24785",
keywords="telehealth",
keywords="patient safety",
keywords="COVID-19",
keywords="coronavirus",
keywords="informatics",
keywords="safety",
keywords="harm",
keywords="risk",
keywords="access",
keywords="efficiency",
keywords="virtual care",
doi="10.2196/24785",
url="https://www.jmir.org/2021/2/e24785",
url="http://www.ncbi.nlm.nih.gov/pubmed/33477104"
}


@Article{info:doi/10.2196/24973,
author="Ho, Thi Thao
and Park, Jongmin
and Kim, Taewoo
and Park, Byunggeon
and Lee, Jaehee
and Kim, Young Jin
and Kim, Beom Ki
and Choi, Sooyoung
and Kim, Hwan Young
and Lim, Jae-Kwang
and Choi, Sanghun",
title="Deep Learning Models for Predicting Severe Progression in COVID-19-Infected Patients: Retrospective Study",
journal="JMIR Med Inform",
year="2021",
month="Jan",
day="28",
volume="9",
number="1",
pages="e24973",
keywords="COVID-19",
keywords="deep learning",
keywords="artificial neural network",
keywords="convolutional neural network",
keywords="lung CT",
abstract="Background: Many COVID-19 patients rapidly progress to respiratory failure with a broad range of severities. Identification of high-risk cases is critical for early intervention. Objective: The aim of this study is to develop deep learning models that can rapidly identify high-risk COVID-19 patients based on computed tomography (CT) images and clinical data. Methods: We analyzed 297 COVID-19 patients from five hospitals in Daegu, South Korea. A mixed artificial convolutional neural network (ACNN) model, combining an artificial neural network for clinical data and a convolutional neural network for 3D CT imaging data, was developed to classify these cases as either high risk of severe progression (ie, event) or low risk (ie, event-free). Results: Using the mixed ACNN model, we were able to obtain high classification performance using novel coronavirus pneumonia lesion images (ie, 93.9\% accuracy, 80.8\% sensitivity, 96.9\% specificity, and 0.916 area under the curve [AUC] score) and lung segmentation images (ie, 94.3\% accuracy, 74.7\% sensitivity, 95.9\% specificity, and 0.928 AUC score) for event versus event-free groups. Conclusions: Our study successfully differentiated high-risk cases among COVID-19 patients using imaging and clinical features. The developed model can be used as a predictive tool for interventions in aggressive therapies. ",
doi="10.2196/24973",
url="http://medinform.jmir.org/2021/1/e24973/",
url="http://www.ncbi.nlm.nih.gov/pubmed/33455900"
}


@Article{info:doi/10.2196/24207,
author="Vaid, Akhil
and Jaladanki, K. Suraj
and Xu, Jie
and Teng, Shelly
and Kumar, Arvind
and Lee, Samuel
and Somani, Sulaiman
and Paranjpe, Ishan
and De Freitas, K. Jessica
and Wanyan, Tingyi
and Johnson, W. Kipp
and Bicak, Mesude
and Klang, Eyal
and Kwon, Joon Young
and Costa, Anthony
and Zhao, Shan
and Miotto, Riccardo
and Charney, W. Alexander
and B{\"o}ttinger, Erwin
and Fayad, A. Zahi
and Nadkarni, N. Girish
and Wang, Fei
and Glicksberg, S. Benjamin",
title="Federated Learning of Electronic Health Records to Improve Mortality Prediction in Hospitalized Patients With COVID-19: Machine Learning Approach",
journal="JMIR Med Inform",
year="2021",
month="Jan",
day="27",
volume="9",
number="1",
pages="e24207",
keywords="federated learning",
keywords="COVID-19",
keywords="machine learning",
keywords="electronic health records",
abstract="Background: Machine learning models require large datasets that may be siloed across different health care institutions. Machine learning studies that focus on COVID-19 have been limited to single-hospital data, which limits model generalizability. Objective: We aimed to use federated learning, a machine learning technique that avoids locally aggregating raw clinical data across multiple institutions, to predict mortality in hospitalized patients with COVID-19 within 7 days. Methods: Patient data were collected from the electronic health records of 5 hospitals within the Mount Sinai Health System. Logistic regression with L1 regularization/least absolute shrinkage and selection operator (LASSO) and multilayer perceptron (MLP) models were trained by using local data at each site. We developed a pooled model with combined data from all 5 sites, and a federated model that only shared parameters with a central aggregator. Results: The LASSOfederated model outperformed the LASSOlocal model at 3 hospitals, and the MLPfederated model performed better than the MLPlocal model at all 5 hospitals, as determined by the area under the receiver operating characteristic curve. The LASSOpooled model outperformed the LASSOfederated model at all hospitals, and the MLPfederated model outperformed the MLPpooled model at 2 hospitals. Conclusions: The federated learning of COVID-19 electronic health record data shows promise in developing robust predictive models without compromising patient privacy. ",
doi="10.2196/24207",
url="http://medinform.jmir.org/2021/1/e24207/",
url="http://www.ncbi.nlm.nih.gov/pubmed/33400679"
}


@Article{info:doi/10.2196/21712,
author="Feldman, Jonah
and Szerencsy, Adam
and Mann, Devin
and Austrian, Jonathan
and Kothari, Ulka
and Heo, Hye
and Barzideh, Sam
and Hickey, Maureen
and Snapp, Catherine
and Aminian, Rod
and Jones, Lauren
and Testa, Paul",
title="Giving Your Electronic Health Record a Checkup After COVID-19: A Practical Framework for Reviewing Clinical Decision Support in Light of the Telemedicine Expansion",
journal="JMIR Med Inform",
year="2021",
month="Jan",
day="27",
volume="9",
number="1",
pages="e21712",
keywords="COVID-19",
keywords="EHR",
keywords="clinical decision support",
keywords="telemedicine",
keywords="ambulatory care",
keywords="electronic health record",
keywords="framework",
keywords="implementation",
abstract="Background: The transformation of health care during COVID-19, with the rapid expansion of telemedicine visits, presents new challenges to chronic care and preventive health providers. Clinical decision support (CDS) is critically important to chronic care providers, and CDS malfunction is common during times of change. It is essential to regularly reassess an organization's ambulatory CDS program to maintain care quality. This is especially true after an immense change, like the COVID-19 telemedicine expansion. Objective: Our objective is to reassess the ambulatory CDS program at a large academic medical center in light of telemedicine's expansion in response to the COVID-19 pandemic. Methods: Our clinical informatics team devised a practical framework for an intrapandemic ambulatory CDS assessment focused on the impact of the telemedicine expansion. This assessment began with a quantitative analysis comparing CDS alert performance in the context of in-person and telemedicine visits. Board-certified physician informaticists then completed a formal workflow review of alerts with inferior performance in telemedicine visits. Informaticists then reported on themes and optimization opportunities through the existing CDS governance structure. Results: Our assessment revealed that 10 of our top 40 alerts by volume were not firing as expected in telemedicine visits. In 3 of the top 5 alerts, providers were significantly less likely to take action in telemedicine when compared to office visits. Cumulatively, alerts in telemedicine encounters had an action taken rate of 5.3\% (3257/64,938) compared to 8.3\% (19,427/233,636) for office visits. Observations from a clinical informaticist workflow review included the following: (1) Telemedicine visits have different workflows than office visits. Some alerts developed for the office were not appearing at the optimal time in the telemedicine workflow. (2) Missing clinical data is a common reason for the decreased alert firing seen in telemedicine visits. (3) Remote patient monitoring and patient-reported clinical data entered through the portal could replace data collection usually completed in the office by a medical assistant or registered nurse. Conclusions: In a large academic medical center at the pandemic epicenter, an intrapandemic ambulatory CDS assessment revealed clinically significant CDS malfunctions that highlight the importance of reassessing ambulatory CDS performance after the telemedicine expansion. ",
doi="10.2196/21712",
url="http://medinform.jmir.org/2021/1/e21712/",
url="http://www.ncbi.nlm.nih.gov/pubmed/33400683"
}


@Article{info:doi/10.2196/25149,
author="Guinez-Molinos, Sergio
and Andrade, Mar{\'i}a Jos{\'e}
and Medina Negrete, Alejandro
and Espinoza Vidal, Sonia
and Rios, Elvis",
title="Interoperable Platform to Report Polymerase Chain Reaction SARS-CoV-2 Tests From Laboratories to the Chilean Government: Development and Implementation Study",
journal="JMIR Med Inform",
year="2021",
month="Jan",
day="20",
volume="9",
number="1",
pages="e25149",
keywords="COVID-19",
keywords="SARS-CoV-2",
keywords="interoperability",
keywords="laboratory information system",
keywords="HL7 FHIR",
keywords="PCR",
abstract="Background: Testing, traceability, and isolation actions are a central strategy defined by the World Health Organization to contain the COVID-19 pandemic. In this sense, the countries have had difficulties in counting the number of people infected with SARS-CoV-2. Errors in reporting results are a common factor, as well as the lack of interoperability between laboratories and governments. Approaches aimed at sending spreadsheets via email expose patients' privacy and have increased the probability of errors due to retyping, which generates a delay in the notification of results. Objective: This study aims to design and develop an interoperable platform to report polymerase chain reaction (PCR) SARS-CoV-2 tests from laboratories to the Chilean government. Methods: The methodology to design and develop the interoperable platform was comprised of six well-structured stages: (1) creation of a minimum data set for PCR SARS-CoV-2 tests, (2) modeling processes and end points where institutions interchange information, (3) standards and interoperability design, (4) software development, (5) software testing, and (6) software implementation. Results: The interoperable Fast Healthcare Interoperability Resources (FHIR) platform to report PCR SARS-CoV-2 tests from laboratories to the Chilean government was successfully implemented. The platform was designed, developed, tested, and implemented following a structured methodology. The platform's performance to 1000 requests resulted in a response time of 240 milliseconds, throughput of 28.3 requests per second, and process management time of 131 milliseconds. The security was assured through a private network exclusive to the Ministry of Health to ensure confidentiality and integrity. The authorization and authentication of laboratories were implemented with a JavaScript Object Notation Web Token. All the PCR SARS-CoV-2 tests were accessible through an application programming interface gateway with valid credentials and the right access control list. Conclusions: The platform was implemented and is currently being used by UC Christus Laboratory. The platform is secure. It was tested adequately for confidentiality, secure authorization, authentication, and message integrity. This platform simplifies the reporting of PCR SARS-CoV-2 tests and reduces the time and probability of mistakes in counting positive cases. The interoperable solution with FHIR is working successfully and is open for the community, laboratories, and any institution that needs to report PCR SARS-CoV-2 tests. ",
doi="10.2196/25149",
url="http://medinform.jmir.org/2021/1/e25149/",
url="http://www.ncbi.nlm.nih.gov/pubmed/33417587"
}


@Article{info:doi/10.2196/22753,
author="Yang, Chuan
and Zhang, Wei
and Pang, Zhixuan
and Zhang, Jing
and Zou, Deling
and Zhang, Xinzhong
and Guo, Sicong
and Wan, Jiye
and Wang, Ke
and Pang, Wenyue",
title="A Low-Cost, Ear-Contactless Electronic Stethoscope Powered by Raspberry Pi for Auscultation of Patients With COVID-19: Prototype Development and Feasibility Study",
journal="JMIR Med Inform",
year="2021",
month="Jan",
day="19",
volume="9",
number="1",
pages="e22753",
keywords="stethoscope",
keywords="auscultation",
keywords="COVID-19",
keywords="Raspberry Pi",
keywords="Python",
keywords="ear-contactless",
keywords="low-cost",
keywords="phonocardiogram",
keywords="digital health",
abstract="Background: Chest examination by auscultation is essential in patients with COVID-19, especially those with poor respiratory conditions, such as severe pneumonia and respiratory dysfunction, and intensive cases who are intubated and whose breathing is assisted with a ventilator. However, proper auscultation of these patients is difficult when medical workers wear personal protective equipment and when it is necessary to minimize contact with patients. Objective: The objective of our study was to design and develop a low-cost electronic stethoscope enabling ear-contactless auscultation and digital storage of data for further analysis. The clinical feasibility of our device was assessed in comparison to a standard electronic stethoscope. Methods: We developed a prototype of the ear-contactless electronic stethoscope, called Auscul Pi, powered by Raspberry Pi and Python. Our device enables real-time capture of auscultation sounds with a microspeaker instead of an earpiece, and it can store data files for later analysis. We assessed the feasibility of using this stethoscope by detecting abnormal heart and respiratory sounds from 8 patients with heart failure or structural heart diseases and from 2 healthy volunteers and by comparing the results with those from a 3M Littmann electronic stethoscope. Results: We were able to conveniently operate Auscul Pi and precisely record the patients' auscultation sounds. Auscul Pi showed similar real-time recording and playback performance to the Littmann stethoscope. The phonocardiograms of data obtained with the two stethoscopes were consistent and could be aligned with the cardiac cycles of the corresponding electrocardiograms. Pearson correlation analysis of amplitude data from the two types of phonocardiograms showed that Auscul Pi was correlated with the Littmann stethoscope with coefficients of 0.3245-0.5570 for healthy participants (P<.001) and of 0.3449-0.5138 among 4 patients (P<.001). Conclusions: Auscul Pi can be used for auscultation in clinical practice by applying real-time ear-contactless playback followed by quantitative analysis. Auscul Pi may allow accurate auscultation when medical workers are wearing protective suits and have difficulties in examining patients with COVID-19. Trial Registration: ChiCTR.org.cn ChiCTR2000033830; http://www.chictr.org.cn/showproj.aspx?proj=54971. ",
doi="10.2196/22753",
url="https://medinform.jmir.org/2021/1/e22753",
url="http://www.ncbi.nlm.nih.gov/pubmed/33436354"
}


@Article{info:doi/10.2196/23811,
author="Syeda, Bareen Hafsa
and Syed, Mahanazuddin
and Sexton, Wayne Kevin
and Syed, Shorabuddin
and Begum, Salma
and Syed, Farhanuddin
and Prior, Fred
and Yu Jr, Feliciano",
title="Role of Machine Learning Techniques to Tackle the COVID-19 Crisis: Systematic Review",
journal="JMIR Med Inform",
year="2021",
month="Jan",
day="11",
volume="9",
number="1",
pages="e23811",
keywords="COVID-19",
keywords="coronavirus",
keywords="SARS-CoV-2",
keywords="artificial intelligence",
keywords="machine learning",
keywords="deep learning",
keywords="systematic review",
keywords="epidemiology",
keywords="pandemic",
keywords="neural network",
abstract="Background: SARS-CoV-2, the novel coronavirus responsible for COVID-19, has caused havoc worldwide, with patients presenting a spectrum of complications that have pushed health care experts to explore new technological solutions and treatment plans. Artificial Intelligence (AI)--based technologies have played a substantial role in solving complex problems, and several organizations have been swift to adopt and customize these technologies in response to the challenges posed by the COVID-19 pandemic. Objective: The objective of this study was to conduct a systematic review of the literature on the role of AI as a comprehensive and decisive technology to fight the COVID-19 crisis in the fields of epidemiology, diagnosis, and disease progression. Methods: A systematic search of PubMed, Web of Science, and CINAHL databases was performed according to PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analysis) guidelines to identify all potentially relevant studies published and made available online between December 1, 2019, and June 27, 2020. The search syntax was built using keywords specific to COVID-19 and AI. Results: The search strategy resulted in 419 articles published and made available online during the aforementioned period. Of these, 130 publications were selected for further analyses. These publications were classified into 3 themes based on AI applications employed to combat the COVID-19 crisis: Computational Epidemiology, Early Detection and Diagnosis, and Disease Progression. Of the 130 studies, 71 (54.6\%) focused on predicting the COVID-19 outbreak, the impact of containment policies, and potential drug discoveries, which were classified under the Computational Epidemiology theme. Next, 40 of 130 (30.8\%) studies that applied AI techniques to detect COVID-19 by using patients' radiological images or laboratory test results were classified under the Early Detection and Diagnosis theme. Finally, 19 of the 130 studies (14.6\%) that focused on predicting disease progression, outcomes (ie, recovery and mortality), length of hospital stay, and number of days spent in the intensive care unit for patients with COVID-19 were classified under the Disease Progression theme. Conclusions: In this systematic review, we assembled studies in the current COVID-19 literature that utilized AI-based methods to provide insights into different COVID-19 themes. Our findings highlight important variables, data types, and available COVID-19 resources that can assist in facilitating clinical and translational research. ",
doi="10.2196/23811",
url="http://medinform.jmir.org/2021/1/e23811/",
url="http://www.ncbi.nlm.nih.gov/pubmed/33326405"
}


@Article{info:doi/10.2196/25435,
author="An, Ho Min
and You, Chan Seng
and Park, Woong Rae
and Lee, Seongwon",
title="Using an Extended Technology Acceptance Model to Understand the Factors Influencing Telehealth Utilization After Flattening the COVID-19 Curve in South Korea: Cross-sectional Survey Study",
journal="JMIR Med Inform",
year="2021",
month="Jan",
day="8",
volume="9",
number="1",
pages="e25435",
keywords="telemedicine",
keywords="telehealth",
keywords="COVID-19",
keywords="pandemic",
keywords="model",
keywords="South Korea",
keywords="acceptance",
keywords="anxiety",
keywords="cross-sectional",
abstract="Background: Although telehealth is considered a key component in combating the worldwide crisis caused by COVID-19, the factors that influence its acceptance by the general population after the flattening of the COVID-19 curve remain unclear. Objective: We aimed to identify factors affecting telehealth acceptance, including anxiety related to COVID-19, after the initial rapid spread of the disease in South Korea. Methods: We proposed an extended technology acceptance model (TAM) and performed a cross-sectional survey of individuals aged ?30 years. In total, 471 usable responses were collected. Confirmatory factor analysis was used to examine the validity of measurements, and the partial least squares (PLS) method was used to investigate factors influencing telehealth acceptance and the impacts of COVID-19. Results: PLS analysis showed that increased accessibility, enhanced care, and ease of telehealth use had positive effects on its perceived usefulness (P=.002, P<.001, and P<.001, respectively). Furthermore, perceived usefulness, ease, and privacy/discomfort significantly impacted the acceptance of telehealth (P<.001, P<.001, and P<.001, respectively). However, anxiety toward COVID-19 was not associated with telehealth acceptance (P=.112), and this insignificant relationship was consistent in the cluster (n=216, 46\%) of respondents with chronic diseases (P=.185). Conclusions: Increased accessibility, enhanced care, usefulness, ease of use, and privacy/discomfort are decisive variables affecting telehealth acceptance in the Korean general population, whereas anxiety about COVID-19 is not. This study may lead to a tailored promotion of telehealth after the pandemic subsides. ",
doi="10.2196/25435",
url="http://medinform.jmir.org/2021/1/e25435/",
url="http://www.ncbi.nlm.nih.gov/pubmed/33395397"
}


@Article{info:doi/10.2196/25442,
author="Ko, Hoon
and Chung, Heewon
and Kang, Seong Wu
and Park, Chul
and Kim, Wan Do
and Kim, Eun Seong
and Chung, Ryang Chi
and Ko, Eun Ryoung
and Lee, Hooseok
and Seo, Ho Jae
and Choi, Tae-Young
and Jaimes, Rafael
and Kim, Won Kyung
and Lee, Jinseok",
title="An Artificial Intelligence Model to Predict the Mortality of COVID-19 Patients at Hospital Admission Time Using Routine Blood Samples: Development and Validation of an Ensemble Model",
journal="J Med Internet Res",
year="2020",
month="Dec",
day="23",
volume="22",
number="12",
pages="e25442",
keywords="COVID-19",
keywords="artificial intelligence",
keywords="blood samples",
keywords="mortality prediction",
abstract="Background: COVID-19, which is accompanied by acute respiratory distress, multiple organ failure, and death, has spread worldwide much faster than previously thought. However, at present, it has limited treatments. Objective: To overcome this issue, we developed an artificial intelligence (AI) model of COVID-19, named EDRnet (ensemble learning model based on deep neural network and random forest models), to predict in-hospital mortality using a routine blood sample at the time of hospital admission. Methods: We selected 28 blood biomarkers and used the age and gender information of patients as model inputs. To improve the mortality prediction, we adopted an ensemble approach combining deep neural network and random forest models. We trained our model with a database of blood samples from 361 COVID-19 patients in Wuhan, China, and applied it to 106 COVID-19 patients in three Korean medical institutions. Results: In the testing data sets, EDRnet provided high sensitivity (100\%), specificity (91\%), and accuracy (92\%). To extend the number of patient data points, we developed a web application (BeatCOVID19) where anyone can access the model to predict mortality and can register his or her own blood laboratory results. Conclusions: Our new AI model, EDRnet, accurately predicts the mortality rate for COVID-19. It is publicly available and aims to help health care providers fight COVID-19 and improve patients' outcomes. ",
doi="10.2196/25442",
url="http://www.jmir.org/2020/12/e25442/",
url="http://www.ncbi.nlm.nih.gov/pubmed/33301414"
}


@Article{info:doi/10.2196/20567,
author="Lee, Hsiu-An
and Kung, Hsin-Hua
and Lee, Yuarn-Jang
and Chao, C-J Jane
and Udayasankaran, Ganesh Jai
and Fan, Hueng-Chuen
and Ng, Kwok-Keung
and Chang, Yu-Kang
and Kijsanayotin, Boonchai
and Marcelo, B. Alvin
and Hsu, Chien-Yeh",
title="Global Infectious Disease Surveillance and Case Tracking System for COVID-19: Development Study",
journal="JMIR Med Inform",
year="2020",
month="Dec",
day="22",
volume="8",
number="12",
pages="e20567",
keywords="blockchain",
keywords="infectious disease surveillance",
keywords="international collaboration",
keywords="HL7 FHIR",
keywords="COVID-19 defense",
keywords="COVID-19",
abstract="Background: COVID-19 has affected more than 180 countries and is the first known pandemic to be caused by a new virus. COVID-19's emergence and rapid spread is a global public health and economic crisis. However, investigations into the disease, patient-tracking mechanisms, and case report transmissions are both labor-intensive and slow. Objective: The pandemic has overwhelmed health care systems, forcing hospitals and medical facilities to find effective ways to share data. This study aims to design a global infectious disease surveillance and case tracking system that can facilitate the detection and control of COVID-19. Methods: The International Patient Summary (IPS; an electronic health record that contains essential health care information about a patient) was used. The IPS was designed to support the used case scenario for unplanned cross-border care. The design, scope, utility, and potential for reuse of the IPS for unplanned cross-border care make it suitable for situations like COVID-19. The Fast Healthcare Interoperability Resources confirmed that IPS data, which includes symptoms, therapies, medications, and laboratory data, can be efficiently transferred and exchanged on the system for easy access by physicians. To protect privacy, patient data are deidentified. All systems are protected by blockchain architecture, including data encryption, validation, and exchange of records. Results: To achieve worldwide COVID-19 surveillance, a global infectious disease information exchange must be enacted. The COVID-19 surveillance system was designed based on blockchain architecture. The IPS was used to exchange case study information among physicians. After being verified, physicians can upload IPS files and receive IPS data from other global cases. The system includes a daily IPS uploading and enhancement plan, which covers real-time uploading through the interoperation of the clinic system, with the module based on the Open Application Programming Interface architecture. Through the treatment of different cases, drug treatments, and the exchange of treatment results, the disease spread can be controlled, and treatment methods can be funded. In the Infectious Disease Case Tracking module, we can track the moving paths of infectious disease cases. The location information recorded in the blockchain is used to check the locations of different cases. The Case Tracking module was established for the Centers for Disease Control and Prevention to track cases and prevent disease spread. Conclusions: We created the IPS of infectious diseases for physicians treating patients with COVID-19. Our system can help health authorities respond quickly to the transmission and spread of unknown diseases, and provides a system for information retrieval on disease transmission. In addition, this system can help researchers form trials and analyze data from different countries. A common forum to facilitate the mutual sharing of experiences, best practices, therapies, useful medications, and clinical intervention outcomes from research in various countries could help control an unknown virus. This system could be an effective tool for global collaboration in evidence-based efforts to fight COVID-19. ",
doi="10.2196/20567",
url="http://medinform.jmir.org/2020/12/e20567/",
url="http://www.ncbi.nlm.nih.gov/pubmed/33320826"
}


@Article{info:doi/10.2196/24544,
author="Gilson, F. Sarah
and Umscheid, A. Craig
and Laiteerapong, Neda
and Ossey, Graeme
and Nunes, J. Kenneth
and Shah, D. Sachin",
title="Growth of Ambulatory Virtual Visits and Differential Use by Patient Sociodemographics at One Urban Academic Medical Center During the COVID-19 Pandemic: Retrospective Analysis",
journal="JMIR Med Inform",
year="2020",
month="Dec",
day="4",
volume="8",
number="12",
pages="e24544",
keywords="telemedicine",
keywords="telehealth",
keywords="video visit",
keywords="telephone visit",
keywords="virtual visit",
keywords="COVID-19",
keywords="age",
keywords="sex",
keywords="race",
keywords="insurance",
keywords="demographic",
keywords="retrospective",
abstract="Background: Despite widespread interest in the use of virtual (ie, telephone and video) visits for ambulatory patient care during the COVID-19 pandemic, studies examining their adoption during the pandemic by race, sex, age, or insurance are lacking. Moreover, there have been limited evaluations to date of the impact of these sociodemographic factors on the use of telephone versus video visits. Such assessments are crucial to identify, understand, and address differences in care delivery across patient populations, particularly those that could affect access to or quality of care. Objective: The aim of this study was to examine changes in ambulatory visit volume and type (ie, in-person vs virtual and telephone vs video visits) by patient sociodemographics during the COVID-19 pandemic at one urban academic medical center. Methods: We compared volumes and patient sociodemographics (age, sex, race, insurance) for visits during the first 11 weeks following the COVID-19 national emergency declaration (March 15 to May 31, 2020) to visits in the corresponding weeks in 2019. Additionally, for visits during the COVID-19 study period, we examined differences in visit type (ie, in-person versus virtual, and telephone versus video visits) by sociodemographics using multivariate logistic regression. Results: Total visit volumes in the COVID-19 study period comprised 51.4\% of the corresponding weeks in 2019 (n=80,081 vs n=155,884 visits). Although patient sociodemographics between the COVID-19 study period in 2020 and the corresponding weeks in 2019 were similar, 60.5\% (n=48,475) of the visits were virtual, compared to 0\% in 2019. Of the virtual visits, 61.2\% (n=29,661) were video based, and 38.8\% (n=18,814) were telephone based. In the COVID-19 study period, virtual (vs in-person) visits were more likely among patients with race categorized as other (vs White) and patients with Medicare (vs commercial) insurance and less likely for men, patients aged 0-17 years, 65-74 years, or ?75 years (compared to patients aged 18-45 years), and patients with Medicaid insurance or insurance categorized as other. Among virtual visits, compared to telephone visits, video visits were more likely to be adopted by patients aged 0-17 years (vs 18-45 years), but less likely for all other age groups, men, Black (vs White) patients, and patients with Medicare or Medicaid (vs commercial) insurance. Conclusions: Virtual visits comprised the majority of ambulatory visits during the COVID-19 study period, of which a majority were by video. Sociodemographic differences existed in the use of virtual versus in-person and video versus telephone visits. To ensure equitable care delivery, we present five policy recommendations to inform the further development of virtual visit programs and their reimbursement. ",
doi="10.2196/24544",
url="https://medinform.jmir.org/2020/12/e24544",
url="http://www.ncbi.nlm.nih.gov/pubmed/33191247"
}


@Article{info:doi/10.2196/19524,
author="Alsharif, Abdullah",
title="Applying eHealth for Pandemic Management in Saudi Arabia in the Context of COVID-19: Survey Study and Framework Proposal",
journal="JMIR Med Inform",
year="2020",
month="Nov",
day="26",
volume="8",
number="11",
pages="e19524",
keywords="COVID-19",
keywords="eHealth framework",
keywords="infectious disease",
keywords="pandemic",
keywords="eHealth",
keywords="public health",
abstract="Background: The increased frequency of epidemics such as Middle East respiratory syndrome, severe acute respiratory syndrome, Ebola virus, and Zika virus has created stress on health care management and operations as well as on relevant stakeholders. In addition, the recent COVID-19 outbreak has been creating challenges for various countries and their respective health care organizations in managing and controlling the pandemic. One of the most important observations during the recent outbreak is the lack of effective eHealth frameworks for managing and controlling pandemics. Objective: The aims of this study are to review the current National eHealth Strategy of Saudi Arabia and to propose an integrated eHealth framework that can be effective for managing health care operations and services during pandemics. Methods: A questionnaire-based survey was administered to 316 health care professionals to review the current national eHealth framework of Saudi Arabia and identify the objectives, factors, and components that are key for managing and controlling pandemics. Purposive sampling was used to collect responses from diverse experts, including physicians, technical experts, nurses, administrative experts, and pharmacists. The survey was administered at five hospitals in Saudi Arabia by forwarding the survey link using a web-based portal. A sample population of 350 was achieved, which was filtered to exclude incomplete and ineligible samples, giving a sample of 316 participants. Results: Of the 316 participants, 187 (59.2\%) found the current eHealth framework to be ineffective, and more than 50\% of the total participants stated that the framework lacked some essential components and objectives. Additional components and objectives focusing on using eHealth for managing information, creating awareness, increasing accessibility and reachability, promoting self-management and self-collaboration, promoting electronic services, and extensive stakeholder engagement were considered to be the most important factors by more than 80\% of the total participants. Conclusions: Managing pandemics requires an effective and efficient eHealth framework that can be used to manage various health care services by integrating different eHealth components and collaborating with all stakeholders. ",
doi="10.2196/19524",
url="http://medinform.jmir.org/2020/11/e19524/",
url="http://www.ncbi.nlm.nih.gov/pubmed/33035174"
}


@Article{info:doi/10.2196/21604,
author="Li, Daowei
and Zhang, Qiang
and Tan, Yue
and Feng, Xinghuo
and Yue, Yuanyi
and Bai, Yuhan
and Li, Jimeng
and Li, Jiahang
and Xu, Youjun
and Chen, Shiyu
and Xiao, Si-Yu
and Sun, Muyan
and Li, Xiaona
and Zhu, Fang",
title="Prediction of COVID-19 Severity Using Chest Computed Tomography and Laboratory Measurements: Evaluation Using a Machine Learning Approach",
journal="JMIR Med Inform",
year="2020",
month="Nov",
day="17",
volume="8",
number="11",
pages="e21604",
keywords="COVID-19",
keywords="severe case prediction",
keywords="computerized tomography",
keywords="machine learning",
keywords="CT",
keywords="scan",
keywords="detection",
keywords="prediction",
keywords="model",
abstract="Background: Most of the mortality resulting from COVID-19 has been associated with severe disease. Effective treatment of severe cases remains a challenge due to the lack of early detection of the infection. Objective: This study aimed to develop an effective prediction model for COVID-19 severity by combining radiological outcome with clinical biochemical indexes. Methods: A total of 46 patients with COVID-19 (10 severe, 36 nonsevere) were examined. To build the prediction model, a set of 27 severe and 151 nonsevere clinical laboratory records and computerized tomography (CT) records were collected from these patients. We managed to extract specific features from the patients' CT images by using a recently published convolutional neural network. We also trained a machine learning model combining these features with clinical laboratory results. Results: We present a prediction model combining patients' radiological outcomes with their clinical biochemical indexes to identify severe COVID-19 cases. The prediction model yielded a cross-validated area under the receiver operating characteristic (AUROC) score of 0.93 and an F1 score of 0.89, which showed a 6\% and 15\% improvement, respectively, compared to the models based on laboratory test features only. In addition, we developed a statistical model for forecasting COVID-19 severity based on the results of patients' laboratory tests performed before they were classified as severe cases; this model yielded an AUROC score of 0.81. Conclusions: To our knowledge, this is the first report predicting the clinical progression of COVID-19, as well as forecasting severity, based on a combined analysis using laboratory tests and CT images. ",
doi="10.2196/21604",
url="http://medinform.jmir.org/2020/11/e21604/",
url="http://www.ncbi.nlm.nih.gov/pubmed/33038076"
}


@Article{info:doi/10.2196/21648,
author="Khan, Younus Junaed
and Khondaker, Islam Md Tawkat
and Hoque, Tazim Iram
and Al-Absi, H. Hamada R.
and Rahman, Saifur Mohammad
and Guler, Reto
and Alam, Tanvir
and Rahman, Sohel M.",
title="Toward Preparing a Knowledge Base to Explore Potential Drugs and Biomedical Entities Related to COVID-19: Automated Computational Approach",
journal="JMIR Med Inform",
year="2020",
month="Nov",
day="10",
volume="8",
number="11",
pages="e21648",
keywords="COVID-19",
keywords="2019-nCoV",
keywords="coronavirus",
keywords="SARS-CoV-2",
keywords="SARS",
keywords="remdesivir",
keywords="statin",
keywords="statins",
keywords="dexamethasone",
keywords="ivermectin",
keywords="hydroxychloroquine",
abstract="Background: Novel coronavirus disease 2019 (COVID-19) is taking a huge toll on public health. Along with the non-therapeutic preventive measurements, scientific efforts are currently focused, mainly, on the development of vaccines and pharmacological treatment with existing drugs. Summarizing evidences from scientific literatures on the discovery of treatment plan of COVID-19 under a platform would help the scientific community to explore the opportunities in a systematic fashion. Objective: The aim of this study is to explore the potential drugs and biomedical entities related to coronavirus related diseases, including COVID-19, that are mentioned on scientific literature through an automated computational approach. Methods: We mined the information from publicly available scientific literature and related public resources. Six topic-specific dictionaries, including human genes, human miRNAs, diseases, Protein Databank, drugs, and drug side effects, were integrated to mine all scientific evidence related to COVID-19. We employed an automated literature mining and labeling system through a novel approach to measure the effectiveness of drugs against diseases based on natural language processing, sentiment analysis, and deep learning. We also applied the concept of cosine similarity to confidently infer the associations between diseases and genes. Results: Based on the literature mining, we identified 1805 diseases, 2454 drugs, 1910 genes that are related to coronavirus related diseases including COVID-19. Integrating the extracted information, we developed the first knowledgebase platform dedicated to COVID-19, which highlights potential list of drugs and related biomedical entities. For COVID-19, we highlighted multiple case studies on existing drugs along with a confidence score for their applicability in the treatment plan. Based on our computational method, we found Remdesivir, Statins, Dexamethasone, and Ivermectin could be considered as potential effective drugs to improve clinical status and lower mortality in patients hospitalized with COVID-19. We also found that Hydroxychloroquine could not be considered as an effective drug for COVID-19. The resulting knowledgebase is made available as an open source tool, named COVID-19Base. Conclusions: Proper investigation of the mined biomedical entities along with the identified interactions among those would help the research community to discover possible ways for the therapeutic treatment of COVID-19. ",
doi="10.2196/21648",
url="http://medinform.jmir.org/2020/11/e21648/",
url="http://www.ncbi.nlm.nih.gov/pubmed/33055059"
}


@Article{info:doi/10.2196/24225,
author="Kim, Hyung-Jun
and Han, Deokjae
and Kim, Jeong-Han
and Kim, Daehyun
and Ha, Beomman
and Seog, Woong
and Lee, Yeon-Kyeng
and Lim, Dosang
and Hong, Ok Sung
and Park, Mi-Jin
and Heo, JoonNyung",
title="An Easy-to-Use Machine Learning Model to Predict the Prognosis of Patients With COVID-19: Retrospective Cohort Study",
journal="J Med Internet Res",
year="2020",
month="Nov",
day="9",
volume="22",
number="11",
pages="e24225",
keywords="COVID-19",
keywords="machine learning",
keywords="prognosis",
keywords="SARS-CoV-2",
keywords="severe acute respiratory syndrome coronavirus 2",
abstract="Background: Prioritizing patients in need of intensive care is necessary to reduce the mortality rate during the COVID-19 pandemic. Although several scoring methods have been introduced, many require laboratory or radiographic findings that are not always easily available. Objective: The purpose of this study was to develop a machine learning model that predicts the need for intensive care for patients with COVID-19 using easily obtainable characteristics---baseline demographics, comorbidities, and symptoms. Methods: A retrospective study was performed using a nationwide cohort in South Korea. Patients admitted to 100 hospitals from January 25, 2020, to June 3, 2020, were included. Patient information was collected retrospectively by the attending physicians in each hospital and uploaded to an online case report form. Variables that could be easily provided were extracted. The variables were age, sex, smoking history, body temperature, comorbidities, activities of daily living, and symptoms. The primary outcome was the need for intensive care, defined as admission to the intensive care unit, use of extracorporeal life support, mechanical ventilation, vasopressors, or death within 30 days of hospitalization. Patients admitted until March 20, 2020, were included in the derivation group to develop prediction models using an automated machine learning technique. The models were externally validated in patients admitted after March 21, 2020. The machine learning model with the best discrimination performance was selected and compared against the CURB-65 (confusion, urea, respiratory rate, blood pressure, and 65 years of age or older) score using the area under the receiver operating characteristic curve (AUC). Results: A total of 4787 patients were included in the analysis, of which 3294 were assigned to the derivation group and 1493 to the validation group. Among the 4787 patients, 460 (9.6\%) patients needed intensive care. Of the 55 machine learning models developed, the XGBoost model revealed the highest discrimination performance. The AUC of the XGBoost model was 0.897 (95\% CI 0.877-0.917) for the derivation group and 0.885 (95\% CI 0.855-0.915) for the validation group. Both the AUCs were superior to those of CURB-65, which were 0.836 (95\% CI 0.825-0.847) and 0.843 (95\% CI 0.829-0.857), respectively. Conclusions: We developed a machine learning model comprising simple patient-provided characteristics, which can efficiently predict the need for intensive care among patients with COVID-19. ",
doi="10.2196/24225",
url="http://www.jmir.org/2020/11/e24225/",
url="http://www.ncbi.nlm.nih.gov/pubmed/33108316"
}


@Article{info:doi/10.2196/24018,
author="Vaid, Akhil
and Somani, Sulaiman
and Russak, J. Adam
and De Freitas, K. Jessica
and Chaudhry, F. Fayzan
and Paranjpe, Ishan
and Johnson, W. Kipp
and Lee, J. Samuel
and Miotto, Riccardo
and Richter, Felix
and Zhao, Shan
and Beckmann, D. Noam
and Naik, Nidhi
and Kia, Arash
and Timsina, Prem
and Lala, Anuradha
and Paranjpe, Manish
and Golden, Eddye
and Danieletto, Matteo
and Singh, Manbir
and Meyer, Dara
and O'Reilly, F. Paul
and Huckins, Laura
and Kovatch, Patricia
and Finkelstein, Joseph
and Freeman, M. Robert
and Argulian, Edgar
and Kasarskis, Andrew
and Percha, Bethany
and Aberg, A. Judith
and Bagiella, Emilia
and Horowitz, R. Carol
and Murphy, Barbara
and Nestler, J. Eric
and Schadt, E. Eric
and Cho, H. Judy
and Cordon-Cardo, Carlos
and Fuster, Valentin
and Charney, S. Dennis
and Reich, L. David
and Bottinger, P. Erwin
and Levin, A. Matthew
and Narula, Jagat
and Fayad, A. Zahi
and Just, C. Allan
and Charney, W. Alexander
and Nadkarni, N. Girish
and Glicksberg, S. Benjamin",
title="Machine Learning to Predict Mortality and Critical Events in a Cohort of Patients With COVID-19 in New York City: Model Development and Validation",
journal="J Med Internet Res",
year="2020",
month="Nov",
day="6",
volume="22",
number="11",
pages="e24018",
keywords="machine learning",
keywords="COVID-19",
keywords="electronic health record",
keywords="TRIPOD",
keywords="clinical informatics",
keywords="prediction",
keywords="mortality",
keywords="EHR",
keywords="cohort",
keywords="hospital",
keywords="performance",
abstract="Background: COVID-19 has infected millions of people worldwide and is responsible for several hundred thousand fatalities. The COVID-19 pandemic has necessitated thoughtful resource allocation and early identification of high-risk patients. However, effective methods to meet these needs are lacking. Objective: The aims of this study were to analyze the electronic health records (EHRs) of patients who tested positive for COVID-19 and were admitted to hospitals in the Mount Sinai Health System in New York City; to develop machine learning models for making predictions about the hospital course of the patients over clinically meaningful time horizons based on patient characteristics at admission; and to assess the performance of these models at multiple hospitals and time points. Methods: We used Extreme Gradient Boosting (XGBoost) and baseline comparator models to predict in-hospital mortality and critical events at time windows of 3, 5, 7, and 10 days from admission. Our study population included harmonized EHR data from five hospitals in New York City for 4098 COVID-19--positive patients admitted from March 15 to May 22, 2020. The models were first trained on patients from a single hospital (n=1514) before or on May 1, externally validated on patients from four other hospitals (n=2201) before or on May 1, and prospectively validated on all patients after May 1 (n=383). Finally, we established model interpretability to identify and rank variables that drive model predictions. Results: Upon cross-validation, the XGBoost classifier outperformed baseline models, with an area under the receiver operating characteristic curve (AUC-ROC) for mortality of 0.89 at 3 days, 0.85 at 5 and 7 days, and 0.84 at 10 days. XGBoost also performed well for critical event prediction, with an AUC-ROC of 0.80 at 3 days, 0.79 at 5 days, 0.80 at 7 days, and 0.81 at 10 days. In external validation, XGBoost achieved an AUC-ROC of 0.88 at 3 days, 0.86 at 5 days, 0.86 at 7 days, and 0.84 at 10 days for mortality prediction. Similarly, the unimputed XGBoost model achieved an AUC-ROC of 0.78 at 3 days, 0.79 at 5 days, 0.80 at 7 days, and 0.81 at 10 days. Trends in performance on prospective validation sets were similar. At 7 days, acute kidney injury on admission, elevated LDH, tachypnea, and hyperglycemia were the strongest drivers of critical event prediction, while higher age, anion gap, and C-reactive protein were the strongest drivers of mortality prediction. Conclusions: We externally and prospectively trained and validated machine learning models for mortality and critical events for patients with COVID-19 at different time horizons. These models identified at-risk patients and uncovered underlying relationships that predicted outcomes. ",
doi="10.2196/24018",
url="https://www.jmir.org/2020/11/e24018",
url="http://www.ncbi.nlm.nih.gov/pubmed/33027032"
}


@Article{info:doi/10.2196/22280,
author="Golinelli, Davide
and Boetto, Erik
and Carullo, Gherardo
and Nuzzolese, Giovanni Andrea
and Landini, Paola Maria
and Fantini, Pia Maria",
title="Adoption of Digital Technologies in Health Care During the COVID-19 Pandemic: Systematic Review of Early Scientific Literature",
journal="J Med Internet Res",
year="2020",
month="Nov",
day="6",
volume="22",
number="11",
pages="e22280",
keywords="COVID-19",
keywords="SARS-CoV-2",
keywords="pandemic",
keywords="digital heath",
keywords="review",
keywords="literature",
keywords="mitigate",
keywords="impact",
keywords="eHealth",
abstract="Background: The COVID-19 pandemic is favoring digital transitions in many industries and in society as a whole. Health care organizations have responded to the first phase of the pandemic by rapidly adopting digital solutions and advanced technology tools. Objective: The aim of this review is to describe the digital solutions that have been reported in the early scientific literature to mitigate the impact of COVID-19 on individuals and health systems. Methods: We conducted a systematic review of early COVID-19--related literature (from January 1 to April 30, 2020) by searching MEDLINE and medRxiv with appropriate terms to find relevant literature on the use of digital technologies in response to the pandemic. We extracted study characteristics such as the paper title, journal, and publication date, and we categorized the retrieved papers by the type of technology and patient needs addressed. We built a scoring rubric by cross-classifying the patient needs with the type of technology. We also extracted information and classified each technology reported by the selected articles according to health care system target, grade of innovation, and scalability to other geographical areas. Results: The search identified 269 articles, of which 124 full-text articles were assessed and included in the review after screening. Most of the selected articles addressed the use of digital technologies for diagnosis, surveillance, and prevention. We report that most of these digital solutions and innovative technologies have been proposed for the diagnosis of COVID-19. In particular, within the reviewed articles, we identified numerous suggestions on the use of artificial intelligence (AI)--powered tools for the diagnosis and screening of COVID-19. Digital technologies are also useful for prevention and surveillance measures, such as contact-tracing apps and monitoring of internet searches and social media usage. Fewer scientific contributions address the use of digital technologies for lifestyle empowerment or patient engagement. Conclusions: In the field of diagnosis, digital solutions that integrate with traditional methods, such as AI-based diagnostic algorithms based both on imaging and clinical data, appear to be promising. For surveillance, digital apps have already proven their effectiveness; however, problems related to privacy and usability remain. For other patient needs, several solutions have been proposed, such as telemedicine or telehealth tools. These tools have long been available, but this historical moment may actually be favoring their definitive large-scale adoption. It is worth taking advantage of the impetus provided by the crisis; it is also important to keep track of the digital solutions currently being proposed to implement best practices and models of care in future and to adopt at least some of the solutions proposed in the scientific literature, especially in national health systems, which have proved to be particularly resistant to the digital transition in recent years. ",
doi="10.2196/22280",
url="http://www.jmir.org/2020/11/e22280/",
url="http://www.ncbi.nlm.nih.gov/pubmed/33079693"
}


@Article{info:doi/10.2196/23361,
author="Leslie, Heather",
title="openEHR Archetype Use and Reuse Within Multilingual Clinical Data Sets: Case Study",
journal="J Med Internet Res",
year="2020",
month="Nov",
day="2",
volume="22",
number="11",
pages="e23361",
keywords="openEHR",
keywords="archetype",
keywords="template",
keywords="reuse",
keywords="clinical informatics",
keywords="COVID-19",
keywords="standard",
keywords="crowd sourced",
keywords="data set",
keywords="data quality",
keywords="multilingual",
keywords="EHR",
keywords="electronic health record",
keywords="SARS-CoV-2",
abstract="Background: Despite electronic health records being in existence for over 50 years, our ability to exchange health data remains frustratingly limited. Commonly used clinical content standards, and the information models that underpin them, are primarily related to health data exchange, and so are usually document- or message-focused. In contrast, over the past 12 years, the Clinical Models program at openEHR International has gradually established a governed, coordinated, and coherent ecosystem of clinical information models, known as openEHR archetypes. Each archetype is designed as a maximal data set for a universal use-case, intended for reuse across various health data sets, known as openEHR templates. To date, only anecdotal evidence has been available to indicate if the hypothesis of archetype reuse across templates is feasible and scalable. As a response to the COVID-19 pandemic, between February and July 2020, 7 openEHR templates were independently created to represent COVID-19--related data sets for symptom screening, confirmed infection reporting, clinical decision support, and research. Each of the templates prioritized reuse of existing use-case agnostic archetypes found in openEHR International's online Clinical Knowledge Manager tool as much as possible. This study is the first opportunity to investigate archetype reuse within a range of diverse, multilingual openEHR templates. Objective: This study aims to investigate the use and reuse of openEHR archetypes across the 7 openEHR templates as an initial investigation about the reuse of information models across data sets used for a variety of clinical purposes. Methods: Analysis of both the number of occurrences of archetypes and patterns of occurrence within 7 discrete templates was carried out at the archetype or clinical concept level. Results: Across all 7 templates collectively, 203 instances of 58 unique archetypes were used. The most frequently used archetype occurred 24 times across 4 of the 7 templates. Total data points per template ranged from 40 to 179. Archetype instances per template ranged from 10 to 62. Unique archetype occurrences ranged from 10 to 28. Existing archetype reuse of use-case agnostic archetypes ranged from 40\% to 90\%. Total reuse of use-case agnostic archetypes ranged from 40\% to 100\%. Conclusions: Investigation of the amount of archetype reuse across the 7 openEHR templates in this initial study has demonstrated significant reuse of archetypes, even across unanticipated, novel modeling challenges and multilingual deployments. While the trigger for the development of each of these templates was the COVID-19 pandemic, the templates represented a variety of types of data sets: symptom screening, infection report, clinical decision support for diagnosis and treatment, and secondary use or research. The findings support the openEHR hypothesis that it is possible to create a shared, public library of standards-based, vendor-neutral clinical information models that can be reused across a diverse range of health data sets. ",
doi="10.2196/23361",
url="https://www.jmir.org/2020/11/e23361",
url="http://www.ncbi.nlm.nih.gov/pubmed/33035176"
}


@Article{info:doi/10.2196/21801,
author="Izquierdo, Luis Jose
and Ancochea, Julio
and 
and Soriano, B. Joan",
title="Clinical Characteristics and Prognostic Factors for Intensive Care Unit Admission of Patients With COVID-19: Retrospective Study Using Machine Learning and Natural Language Processing",
journal="J Med Internet Res",
year="2020",
month="Oct",
day="28",
volume="22",
number="10",
pages="e21801",
keywords="artificial intelligence",
keywords="big data",
keywords="COVID-19",
keywords="electronic health records",
keywords="tachypnea",
keywords="SARS-CoV-2",
keywords="predictive model",
abstract="Background: Many factors involved in the onset and clinical course of the ongoing COVID-19 pandemic are still unknown. Although big data analytics and artificial intelligence are widely used in the realms of health and medicine, researchers are only beginning to use these tools to explore the clinical characteristics and predictive factors of patients with COVID-19. Objective: Our primary objectives are to describe the clinical characteristics and determine the factors that predict intensive care unit (ICU) admission of patients with COVID-19. Determining these factors using a well-defined population can increase our understanding of the real-world epidemiology of the disease. Methods: We used a combination of classic epidemiological methods, natural language processing (NLP), and machine learning (for predictive modeling) to analyze the electronic health records (EHRs) of patients with COVID-19. We explored the unstructured free text in the EHRs within the Servicio de Salud de Castilla-La Mancha (SESCAM) Health Care Network (Castilla-La Mancha, Spain) from the entire population with available EHRs (1,364,924 patients) from January 1 to March 29, 2020. We extracted related clinical information regarding diagnosis, progression, and outcome for all COVID-19 cases. Results: A total of 10,504 patients with a clinical or polymerase chain reaction--confirmed diagnosis of COVID-19 were identified; 5519 (52.5\%) were male, with a mean age of 58.2 years (SD 19.7). Upon admission, the most common symptoms were cough, fever, and dyspnea; however, all three symptoms occurred in fewer than half of the cases. Overall, 6.1\% (83/1353) of hospitalized patients required ICU admission. Using a machine-learning, data-driven algorithm, we identified that a combination of age, fever, and tachypnea was the most parsimonious predictor of ICU admission; patients younger than 56 years, without tachypnea, and temperature <39 degrees Celsius (or >39 {\textordmasculine}C without respiratory crackles) were not admitted to the ICU. In contrast, patients with COVID-19 aged 40 to 79 years were likely to be admitted to the ICU if they had tachypnea and delayed their visit to the emergency department after being seen in primary care. Conclusions: Our results show that a combination of easily obtainable clinical variables (age, fever, and tachypnea with or without respiratory crackles) predicts whether patients with COVID-19 will require ICU admission. ",
doi="10.2196/21801",
url="http://www.jmir.org/2020/10/e21801/",
url="http://www.ncbi.nlm.nih.gov/pubmed/33090964"
}


@Article{info:doi/10.2196/23680,
author="Cha, Dongchul
and Shin, Ho Seung
and Kim, Jungghi
and Eo, Seong Tae
and Na, Gina
and Bae, Seonghoon
and Jung, Jinsei
and Kim, Huhn Sung
and Moon, Seok In
and Choi, Jaeyoung
and Park, Rang Yu",
title="Feasibility of Asynchronous and Automated Telemedicine in Otolaryngology: Prospective Cross-Sectional Study",
journal="JMIR Med Inform",
year="2020",
month="Oct",
day="19",
volume="8",
number="10",
pages="e23680",
keywords="telemedicine",
keywords="otolaryngology",
keywords="otology",
keywords="automated diagnosis",
keywords="asynchronous",
keywords="COVID-19",
keywords="diagnosis",
keywords="feasibility",
keywords="cross-sectional",
abstract="Background: COVID-19 often causes respiratory symptoms, making otolaryngology offices one of the most susceptible places for community transmission of the virus. Thus, telemedicine may benefit both patients and physicians. Objective: This study aims to explore the feasibility of telemedicine for the diagnosis of all otologic disease types. Methods: A total of 177 patients were prospectively enrolled, and the patient's clinical manifestations with otoendoscopic images were written in the electrical medical records. Asynchronous diagnoses were made for each patient to assess Top-1 and Top-2 accuracy, and we selected 20 cases to conduct a survey among four different otolaryngologists to assess the accuracy, interrater agreement, and diagnostic speed. We also constructed an experimental automated diagnosis system and assessed Top-1 accuracy and diagnostic speed. Results: Asynchronous diagnosis showed Top-1 and Top-2 accuracies of 77.40\% and 86.44\%, respectively. In the selected 20 cases, the Top-2 accuracy of the four otolaryngologists was on average 91.25\% (SD 7.50\%), with an almost perfect agreement between them (Cohen kappa=0.91). The automated diagnostic model system showed 69.50\% Top-1 accuracy. Otolaryngologists could diagnose an average of 1.55 (SD 0.48) patients per minute, while the machine learning model was capable of diagnosing on average 667.90 (SD 8.3) patients per minute. Conclusions: Asynchronous telemedicine in otology is feasible owing to the reasonable Top-2 accuracy when assessed by experienced otolaryngologists. Moreover, enhanced diagnostic speed while sustaining the accuracy shows the possibility of optimizing medical resources to provide expertise in areas short of physicians. ",
doi="10.2196/23680",
url="http://medinform.jmir.org/2020/10/e23680/",
url="http://www.ncbi.nlm.nih.gov/pubmed/33027033"
}


@Article{info:doi/10.2196/21628,
author="Nan, Shan
and Tang, Tianhua
and Feng, Hongshuo
and Wang, Yijie
and Li, Mengyang
and Lu, Xudong
and Duan, Huilong",
title="A Computer-Interpretable Guideline for COVID-19: Rapid Development and Dissemination",
journal="JMIR Med Inform",
year="2020",
month="Oct",
day="1",
volume="8",
number="10",
pages="e21628",
keywords="COVID-19",
keywords="guideline",
keywords="CDSS",
keywords="openEHR",
keywords="Guideline Definition Language",
keywords="development",
keywords="dissemination",
keywords="electronic health record",
keywords="algorithm",
abstract="Background: COVID-19 is a global pandemic that is affecting more than 200 countries worldwide. Efficient diagnosis and treatment are crucial to combat the disease. Computer-interpretable guidelines (CIGs) can aid the broad global adoption of evidence-based diagnosis and treatment knowledge. However, currently, no internationally shareable CIG exists. Objective: The aim of this study was to establish a rapid CIG development and dissemination approach and apply it to develop a shareable CIG for COVID-19. Methods: A 6-step rapid CIG development and dissemination approach was designed and applied. Processes, roles, and deliverable artifacts were specified in this approach to eliminate ambiguities during development of the CIG. The Guideline Definition Language (GDL) was used to capture the clinical rules. A CIG for COVID-19 was developed by translating, interpreting, annotating, extracting, and formalizing the Chinese COVID-19 diagnosis and treatment guideline. A prototype application was implemented to validate the CIG. Results: We used 27 archetypes for the COVID-19 guideline. We developed 18 GDL rules to cover the diagnosis and treatment suggestion algorithms in the narrative guideline. The CIG was further translated to object data model and Drools rules to facilitate its use by people who do not employ the non-openEHR archetype. The prototype application validated the correctness of the CIG with a public data set. Both the GDL rules and Drools rules have been disseminated on GitHub. Conclusions: Our rapid CIG development and dissemination approach accelerated the pace of COVID-19 CIG development. A validated COVID-19 CIG is now available to the public. ",
doi="10.2196/21628",
url="https://medinform.jmir.org/2020/10/e21628",
url="http://www.ncbi.nlm.nih.gov/pubmed/32931443"
}


@Article{info:doi/10.2196/20477,
author="Khurshid, Anjum",
title="Applying Blockchain Technology to Address the Crisis of Trust During the COVID-19 Pandemic",
journal="JMIR Med Inform",
year="2020",
month="Sep",
day="22",
volume="8",
number="9",
pages="e20477",
keywords="blockchain",
keywords="privacy",
keywords="trust",
keywords="contact tracing",
keywords="COVID-19",
keywords="coronavirus",
abstract="Background: The widespread death and disruption caused by the COVID-19 pandemic has revealed deficiencies of existing institutions regarding the protection of human health and well-being. Both a lack of accurate and timely data and pervasive misinformation are causing increasing harm and growing tension between data privacy and public health concerns. Objective: This aim of this paper is to describe how blockchain, with its distributed trust networks and cryptography-based security, can provide solutions to data-related trust problems. Methods: Blockchain is being applied in innovative ways that are relevant to the current COVID-19 crisis. We describe examples of the challenges faced by existing technologies to track medical supplies and infected patients and how blockchain technology applications may help in these situations. Results: This exploration of existing and potential applications of blockchain technology for medical care shows how the distributed governance structure and privacy-preserving features of blockchain can be used to create ``trustless'' systems that can help resolve the tension between maintaining privacy and addressing public health needs in the fight against COVID-19. Conclusions: Blockchain relies on a distributed, robust, secure, privacy-preserving, and immutable record framework that can positively transform the nature of trust, value sharing, and transactions. A nationally coordinated effort to explore blockchain to address the deficiencies of existing systems and a partnership of academia, researchers, business, and industry are suggested to expedite the adoption of blockchain in health care. ",
doi="10.2196/20477",
url="http://medinform.jmir.org/2020/9/e20477/",
url="http://www.ncbi.nlm.nih.gov/pubmed/32903197"
}


@Article{info:doi/10.2196/19588,
author="Fan, Tao
and Hao, Bo
and Yang, Shuo
and Shen, Bo
and Huang, Zhixin
and Lu, Zilong
and Xiong, Rui
and Shen, Xiaokang
and Jiang, Wenyang
and Zhang, Lin
and Li, Donghang
and He, Ruyuan
and Meng, Heng
and Lin, Weichen
and Feng, Haojie
and Geng, Qing",
title="Nomogram for Predicting COVID-19 Disease Progression Based on Single-Center Data: Observational Study and Model Development",
journal="JMIR Med Inform",
year="2020",
month="Sep",
day="8",
volume="8",
number="9",
pages="e19588",
keywords="coronavirus disease 2019",
keywords="COVID-19",
keywords="risk factors",
keywords="nomogram",
abstract="Background: In late December 2019, a pneumonia caused by SARS-CoV-2 was first reported in Wuhan and spread worldwide rapidly. Currently, no specific medicine is available to treat infection with COVID-19. Objective: The aims of this study were to summarize the epidemiological and clinical characteristics of 175 patients with SARS-CoV-2 infection who were hospitalized in Renmin Hospital of Wuhan University from January 1 to January 31, 2020, and to establish a tool to identify potential critical patients with COVID-19 and help clinical physicians prevent progression of this disease. Methods: In this retrospective study, clinical characteristics of 175 confirmed COVID-19 cases were collected and analyzed. Univariate analysis and least absolute shrinkage and selection operator (LASSO) regression were used to select variables. Multivariate analysis was applied to identify independent risk factors in COVID-19 progression. We established a nomogram to evaluate the probability of progression of the condition of a patient with COVID-19 to severe within three weeks of disease onset. The nomogram was verified using calibration curves and receiver operating characteristic curves. Results: A total of 18 variables were considered to be risk factors after the univariate regression analysis of the laboratory parameters (P<.05), and LASSO regression analysis screened out 10 risk factors for further study. The six independent risk factors revealed by multivariate Cox regression were age (OR 1.035, 95\% CI 1.017-1.054; P<.001), CK level (OR 1.002, 95\% CI 1.0003-1.0039; P=.02), CD4 count (OR 0.995, 95\% CI 0.992-0.998; P=.002), CD8 \% (OR 1.007, 95\% CI 1.004-1.012, P<.001), CD8 count (OR 0.881, 95\% CI 0.835-0.931; P<.001), and C3 count (OR 6.93, 95\% CI 1.945-24.691; P=.003). The areas under the curve of the prediction model for 0.5-week, 1-week, 2-week and 3-week nonsevere probability were 0.721, 0.742, 0.87, and 0.832, respectively. The calibration curves showed that the model had good prediction ability within three weeks of disease onset. Conclusions: This study presents a predictive nomogram of critical patients with COVID-19 based on LASSO and Cox regression analysis. Clinical use of the nomogram may enable timely detection of potential critical patients with COVID-19 and instruct clinicians to administer early intervention to these patients to prevent the disease from worsening. ",
doi="10.2196/19588",
url="http://medinform.jmir.org/2020/9/e19588/",
url="http://www.ncbi.nlm.nih.gov/pubmed/32866109"
}


@Article{info:doi/10.2196/22321,
author="Nakamoto, Ichiro
and Wang, Sheng
and Guo, Yan
and Zhuang, Weiqing",
title="A QR Code--Based Contact Tracing Framework for Sustainable Containment of COVID-19: Evaluation of an Approach to Assist the Return to Normal Activity",
journal="JMIR Mhealth Uhealth",
year="2020",
month="Sep",
day="7",
volume="8",
number="9",
pages="e22321",
keywords="COVID-19",
keywords="coronavirus",
keywords="symptom-based",
keywords="quick response",
keywords="eHealth",
keywords="digital health",
keywords="telesurveillance",
keywords="pandemic",
keywords="epidemic",
keywords="interoperability",
doi="10.2196/22321",
url="http://mhealth.jmir.org/2020/9/e22321/",
url="http://www.ncbi.nlm.nih.gov/pubmed/32841151"
}


@Article{info:doi/10.2196/20953,
author="Silven, V. Anna
and Petrus, J. Annelieke H.
and Villalobos-Quesada, Mar{\'i}a
and Dirikgil, Ebru
and Oerlemans, R. Carlijn
and Landstra, P. Cyril
and Boosman, Hileen
and van Os, A. Hendrikus J.
and Blanker, H. Marco
and Treskes, W. Roderick
and Bonten, N. Tobias
and Chavannes, H. Niels
and Atsma, E. Douwe
and Teng, Onno Y. K.",
title="Telemonitoring for Patients With COVID-19: Recommendations for Design and Implementation",
journal="J Med Internet Res",
year="2020",
month="Sep",
day="2",
volume="22",
number="9",
pages="e20953",
keywords="telemonitoring",
keywords="telemedicine",
keywords="eHealth",
keywords="digital health",
keywords="COVID-19",
doi="10.2196/20953",
url="https://www.jmir.org/2020/9/e20953",
url="http://www.ncbi.nlm.nih.gov/pubmed/32833660"
}


@Article{info:doi/10.2196/20992,
author="Lee, Won Seung
and Yuh, Tak Woon
and Yang, Myung Jee
and Cho, Yoon-Sik
and Yoo, Kyung In
and Koh, Yong Hyun
and Marshall, Dominic
and Oh, Donghwan
and Ha, Kyo Eun
and Han, Yong Man
and Yon, Keon Dong",
title="Nationwide Results of COVID-19 Contact Tracing in South Korea: Individual Participant Data From an Epidemiological Survey",
journal="JMIR Med Inform",
year="2020",
month="Aug",
day="25",
volume="8",
number="8",
pages="e20992",
keywords="COVID-19",
keywords="contact tracing",
keywords="coronavirus",
keywords="South Korea",
keywords="survey",
keywords="health data",
keywords="epidemiology",
keywords="transmission",
abstract="Background: Evidence regarding the effectiveness of contact tracing of COVID-19 and the related social distancing is limited and inconclusive. Objective: This study aims to investigate the epidemiological characteristics of SARS-CoV-2 transmission in South Korea and evaluate whether a social distancing campaign is effective in mitigating the spread of COVID-19. Methods: We used contract tracing data to investigate the epidemic characteristics of SARS-CoV-2 transmission in South Korea and evaluate whether a social distancing campaign was effective in mitigating the spread of COVID-19. We calculated the mortality rate for COVID-19 by infection type (cluster vs noncluster) and tested whether new confirmed COVID-19 trends changed after a social distancing campaign. Results: There were 2537 patients with confirmed COVID-19 who completed the epidemiologic survey: 1305 (51.4\%) cluster cases and 1232 (48.6\%) noncluster cases. The mortality rate was significantly higher in cluster cases linked to medical facilities (11/143, 7.70\% vs 5/1232, 0.41\%; adjusted percentage difference 7.99\%; 95\% CI 5.83 to 10.14) and long-term care facilities (19/221, 8.60\% vs 5/1232, 0.41\%; adjusted percentage difference 7.56\%; 95\% CI 5.66 to 9.47) than in noncluster cases. The change in trends of newly confirmed COVID-19 cases before and after the social distancing campaign was significantly negative in the entire cohort (adjusted trend difference --2.28; 95\% CI --3.88 to --0.68) and the cluster infection group (adjusted trend difference --0.96; 95\% CI --1.83 to --0.09). Conclusions: In a nationwide contact tracing study in South Korea, COVID-19 linked to medical and long-term care facilities significantly increased the risk of mortality compared to noncluster COVID-19. A social distancing campaign decreased the spread of COVID-19 in South Korea and differentially affected cluster infections of SARS-CoV-2. ",
doi="10.2196/20992",
url="http://medinform.jmir.org/2020/8/e20992/",
url="http://www.ncbi.nlm.nih.gov/pubmed/32784189"
}


@Article{info:doi/10.2196/20773,
author="Neuraz, Antoine
and Lerner, Ivan
and Digan, William
and Paris, Nicolas
and Tsopra, Rosy
and Rogier, Alice
and Baudoin, David
and Cohen, Bretonnel Kevin
and Burgun, Anita
and Garcelon, Nicolas
and Rance, Bastien
and ",
title="Natural Language Processing for Rapid Response to Emergent Diseases: Case Study of Calcium Channel Blockers and Hypertension in the COVID-19 Pandemic",
journal="J Med Internet Res",
year="2020",
month="Aug",
day="14",
volume="22",
number="8",
pages="e20773",
keywords="medication information",
keywords="natural language processing",
keywords="electronic health records",
keywords="COVID-19",
keywords="public health",
keywords="response",
keywords="emergent disease",
keywords="informatics",
abstract="Background: A novel disease poses special challenges for informatics solutions. Biomedical informatics relies for the most part on structured data, which require a preexisting data or knowledge model; however, novel diseases do not have preexisting knowledge models. In an emergent epidemic, language processing can enable rapid conversion of unstructured text to a novel knowledge model. However, although this idea has often been suggested, no opportunity has arisen to actually test it in real time. The current coronavirus disease (COVID-19) pandemic presents such an opportunity. Objective: The aim of this study was to evaluate the added value of information from clinical text in response to emergent diseases using natural language processing (NLP). Methods: We explored the effects of long-term treatment by calcium channel blockers on the outcomes of COVID-19 infection in patients with high blood pressure during in-patient hospital stays using two sources of information: data available strictly from structured electronic health records (EHRs) and data available through structured EHRs and text mining. Results: In this multicenter study involving 39 hospitals, text mining increased the statistical power sufficiently to change a negative result for an adjusted hazard ratio to a positive one. Compared to the baseline structured data, the number of patients available for inclusion in the study increased by 2.95 times, the amount of available information on medications increased by 7.2 times, and the amount of additional phenotypic information increased by 11.9 times. Conclusions: In our study, use of calcium channel blockers was associated with decreased in-hospital mortality in patients with COVID-19 infection. This finding was obtained by quickly adapting an NLP pipeline to the domain of the novel disease; the adapted pipeline still performed sufficiently to extract useful information. When that information was used to supplement existing structured data, the sample size could be increased sufficiently to see treatment effects that were not previously statistically detectable. ",
doi="10.2196/20773",
url="http://www.jmir.org/2020/8/e20773/",
url="http://www.ncbi.nlm.nih.gov/pubmed/32759101"
}


@Article{info:doi/10.2196/19866,
author="Ye, Jiancheng",
title="The Role of Health Technology and Informatics in a Global Public Health Emergency: Practices and Implications From the COVID-19 Pandemic",
journal="JMIR Med Inform",
year="2020",
month="Jul",
day="14",
volume="8",
number="7",
pages="e19866",
keywords="health technology",
keywords="health information system",
keywords="COVID-19",
keywords="artificial intelligence",
keywords="telemedicine",
keywords="big data",
keywords="privacy",
doi="10.2196/19866",
url="http://medinform.jmir.org/2020/7/e19866/",
url="http://www.ncbi.nlm.nih.gov/pubmed/32568725"
}


@Article{info:doi/10.2196/19938,
author="Bae, Seul Ye
and Kim, Hwan Kyung
and Choi, Won Sae
and Ko, Taehoon
and Jeong, Wook Chang
and Cho, BeLong
and Kim, Sun Min
and Kang, EunKyo",
title="Information Technology--Based Management of Clinically Healthy COVID-19 Patients: Lessons From a Living and Treatment Support Center Operated by Seoul National University Hospital",
journal="J Med Internet Res",
year="2020",
month="Jun",
day="12",
volume="22",
number="6",
pages="e19938",
keywords="COVID-19",
keywords="clinical informatics",
keywords="mobile app",
keywords="telemedicine",
keywords="hospital information system",
keywords="app",
keywords="health information technology",
abstract="Background: South Korea took preemptive action against coronavirus disease (COVID-19) by implementing extensive testing, thorough epidemiological investigation, strict social distancing, and rapid treatment of patients according to disease severity. The Korean government entrusted large-scale hospitals with the operation of living and treatment support centers (LTSCs) for the management for clinically healthy COVID-19 patients. Objective: The aim of this paper is to introduce our experience implementing information and communications technology (ICT)-based remote patient management systems at a COVID-19 LTSC. Methods: We adopted new electronic health record templates, hospital information system (HIS) dashboards, cloud-based medical image sharing, a mobile app, and smart vital sign monitoring devices. Results: Enhancements were made to the HIS to assist in the workflow and care of patients in the LTSC. A dashboard was created for the medical staff to view the vital signs and symptoms of all patients. Patients used a mobile app to consult with their physician or nurse, answer questionnaires, and input self-measured vital signs; the results were uploaded to the hospital information system in real time. Cloud-based image sharing enabled interoperability between medical institutions. Korea's strategy of aggressive mitigation has ``flattened the curve'' of the rate of infection. A multidisciplinary approach was integral to develop systems supporting patient care and management at the living and treatment support center as quickly as possible. Conclusions: Faced with a novel infectious disease, we describe the implementation and experience of applying an ICT-based patient management system in the LTSC affiliated with Seoul National University Hospital. ICT-based tools and applications are increasingly important in health care, and we hope that our experience will provide insight into future technology-based infectious disease responses. ",
doi="10.2196/19938",
url="http://www.jmir.org/2020/6/e19938/",
url="http://www.ncbi.nlm.nih.gov/pubmed/32490843"
}


@Article{info:doi/10.2196/20239,
author="Li, Mengyang
and Leslie, Heather
and Qi, Bin
and Nan, Shan
and Feng, Hongshuo
and Cai, Hailing
and Lu, Xudong
and Duan, Huilong",
title="Development of an openEHR Template for COVID-19 Based on Clinical Guidelines",
journal="J Med Internet Res",
year="2020",
month="Jun",
day="10",
volume="22",
number="6",
pages="e20239",
keywords="coronavirus disease",
keywords="COVID-19",
keywords="openEHR",
keywords="archetype",
keywords="template",
keywords="knowledge modeling",
keywords="clinical guidelines",
abstract="Background: The coronavirus disease (COVID-19) was discovered in China in December 2019. It has developed into a threatening international public health emergency. With the exception of China, the number of cases continues to increase worldwide. A number of studies about disease diagnosis and treatment have been carried out, and many clinically proven effective results have been achieved. Although information technology can improve the transferring of such knowledge to clinical practice rapidly, data interoperability is still a challenge due to the heterogeneous nature of hospital information systems. This issue becomes even more serious if the knowledge for diagnosis and treatment is updated rapidly as is the case for COVID-19. An open, semantic-sharing, and collaborative-information modeling framework is needed to rapidly develop a shared data model for exchanging data among systems. openEHR is such a framework and is supported by many open software packages that help to promote information sharing and interoperability. Objective: This study aims to develop a shared data model based on the openEHR modeling approach to improve the interoperability among systems for the diagnosis and treatment of COVID-19. Methods: The latest Guideline of COVID-19 Diagnosis and Treatment in China was selected as the knowledge source for modeling. First, the guideline was analyzed and the data items used for diagnosis and treatment, and management were extracted. Second, the data items were classified and further organized into domain concepts with a mind map. Third, searching was executed in the international openEHR Clinical Knowledge Manager (CKM) to find the existing archetypes that could represent the concepts. New archetypes were developed for those concepts that could not be found. Fourth, these archetypes were further organized into a template using Ocean Template Editor. Fifth, a test case of data exchanging between the clinical data repository and clinical decision support system based on the template was conducted to verify the feasibility of the study. Results: A total of 203 data items were extracted from the guideline in China, and 16 domain concepts (16 leaf nodes in the mind map) were organized. There were 22 archetypes used to develop the template for all data items extracted from the guideline. All of them could be found in the CKM and reused directly. The archetypes and templates were reviewed and finally released in a public project within the CKM. The test case showed that the template can facilitate the data exchange and meet the requirements of decision support. Conclusions: This study has developed the openEHR template for COVID-19 based on the latest guideline from China using openEHR modeling methodology. It represented the capability of the methodology for rapidly modeling and sharing knowledge through reusing the existing archetypes, which is especially useful in a new and fast-changing area such as with COVID-19. ",
doi="10.2196/20239",
url="http://www.jmir.org/2020/6/e20239/",
url="http://www.ncbi.nlm.nih.gov/pubmed/32496207"
}