TY - JOUR AU - Raspado, Olivier AU - Brack, Michel AU - Brack, Olivier AU - Vivancos, Mélanie AU - Esparcieux, Aurélie AU - Cart-Tanneur, Emmanuelle AU - Aouifi, Abdellah PY - 2025/4/11 TI - 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 JO - JMIR Form Res SP - e66509 VL - 9 KW - oxidative stress KW - COVID-19 KW - SARS-CoV-2 KW - coronavirus KW - respiratory KW - infectious KW - pulmonary KW - respiration disorders KW - hospitalization KW - machine learning KW - ML KW - biomarker KW - lung KW - severity KW - prediction N2 - 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. UR - https://formative.jmir.org/2025/1/e66509 UR - http://dx.doi.org/10.2196/66509 ID - info:doi/10.2196/66509 ER - TY - JOUR AU - Senathirajah, Yalini AU - Kaufman, R. David AU - Cato, Kenrick AU - Daniel, Pia AU - Roblin, Patricia AU - Kushniruk, Andre AU - Borycki, M. Elizabeth AU - Feld, Emanuel AU - Debi, Poli PY - 2025/3/26 TI - The Impact of the Burden of COVID-19 Regulatory Reporting in a Small Independent Hospital and a Large Network Hospital: Comparative Mixed Methods Study JO - Online J Public Health Inform SP - e63681 VL - 17 KW - regulatory reporting KW - human factors KW - reporting burden KW - emergency response KW - COVID-19 KW - hospital resilience KW - pandemic response N2 - 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. UR - https://ojphi.jmir.org/2025/1/e63681 UR - http://dx.doi.org/10.2196/63681 UR - http://www.ncbi.nlm.nih.gov/pubmed/40137048 ID - info:doi/10.2196/63681 ER - TY - JOUR AU - Rosenfeld, Daniel AU - Brennan, Sean AU - Wallach, Andrew AU - Long, Theodore AU - Keeley, Chris AU - Kurien, Joseph Sarah PY - 2025/3/13 TI - COVID-19 Testing Equity in New York City During the First 2 Years of the Pandemic: Demographic Analysis of Free Testing Data JO - JMIR Public Health Surveill SP - e52972 VL - 11 KW - COVID-19 testing KW - health disparities KW - equity in testing KW - New York City KW - socioeconomic factors KW - testing accessibility KW - health care inequalities KW - demographic analysis KW - COVID-19 mortality KW - coronavirus KW - SARS-CoV-2 KW - pandemic KW - equitable testing KW - cost KW - poor neighborhood KW - resources N2 - 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. UR - https://publichealth.jmir.org/2025/1/e52972 UR - http://dx.doi.org/10.2196/52972 ID - info:doi/10.2196/52972 ER - TY - JOUR AU - Li, Wanxin AU - Hua, Yining AU - Zhou, Peilin AU - Zhou, Li AU - Xu, Xin AU - Yang, Jie PY - 2025/3/5 TI - Characterizing Public Sentiments and Drug Interactions in the COVID-19 Pandemic Using Social Media: Natural Language Processing and Network Analysis JO - J Med Internet Res SP - e63755 VL - 27 KW - COVID-19 KW - natural language processing KW - drugs KW - social media KW - pharmacovigilance KW - public health N2 - 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. UR - https://www.jmir.org/2025/1/e63755 UR - http://dx.doi.org/10.2196/63755 UR - http://www.ncbi.nlm.nih.gov/pubmed/40053730 ID - info:doi/10.2196/63755 ER - TY - JOUR AU - Tighe, Carlos AU - Ngongalah, Lem AU - Sentís, Alexis AU - Orchard, Francisco AU - Pacurar, Gheorghe-Aurel AU - Hayes, Conor AU - Hayes, S. Jessica AU - Toader, Adrian AU - Connolly, A. Máire PY - 2025/3/5 TI - Building and Developing a Tool (PANDEM-2 Dashboard) to Strengthen Pandemic Management: Participatory Design Study JO - JMIR Public Health Surveill SP - e52119 VL - 11 KW - pandemic preparedness and response KW - COVID-19 KW - cross-border collaboration KW - surveillance KW - data collection KW - data standardization KW - data sharing KW - dashboard KW - IT system KW - IT tools N2 - 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. UR - https://publichealth.jmir.org/2025/1/e52119 UR - http://dx.doi.org/10.2196/52119 UR - http://www.ncbi.nlm.nih.gov/pubmed/40053759 ID - info:doi/10.2196/52119 ER - TY - JOUR AU - Tan, Jin Rayner Kay AU - Hensel, Devon AU - Ivanova, Olena AU - Bravo, Gomez Raquel AU - Olumide, Adesola AU - Adebayo, Emmanuel AU - Cleeve, Amanda AU - Gesselman, Amanda AU - Shah, Jyoti Sonam AU - Adesoba, Helen AU - Marley, Gifty AU - Tang, Weiming PY - 2025/3/4 TI - 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 JO - J Med Internet Res SP - e60369 VL - 27 KW - COVID-19 KW - telemedicine KW - sexual and reproductive health KW - pandemic KW - web-based survey KW - sexual health KW - reproductive health KW - communication technology KW - medical education KW - contraception KW - abortion KW - health care delivery KW - care KW - chronic condition N2 - 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. UR - https://www.jmir.org/2025/1/e60369 UR - http://dx.doi.org/10.2196/60369 UR - http://www.ncbi.nlm.nih.gov/pubmed/40053813 ID - info:doi/10.2196/60369 ER - TY - JOUR AU - Amid, Clara AU - van Roode, Y. Martine AU - Rinck, Gabriele AU - van Beek, Janko AU - de Vries, D. Rory AU - van Nierop, P. Gijsbert AU - van Gorp, M. Eric C. AU - Tobian, Frank AU - Oude Munnink, B. Bas AU - Sikkema, S. Reina AU - Jaenisch, Thomas AU - Cochrane, Guy AU - Koopmans, G. Marion P. PY - 2025/2/11 TI - A Call for Action: Lessons Learned From a Pilot to Share a Complex, Linked COVID-19 Cohort Dataset for Open Science JO - JMIR Public Health Surveill SP - e63996 VL - 11 KW - data sharing KW - data management KW - open science KW - COVID-19 KW - emerging infectious disease KW - global health UR - https://publichealth.jmir.org/2025/1/e63996 UR - http://dx.doi.org/10.2196/63996 ID - info:doi/10.2196/63996 ER - TY - JOUR AU - Davoody, Nadia AU - Stathakarou, Natalia AU - Swain, Cara AU - Bonacina, Stefano PY - 2025/2/10 TI - 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 JO - JMIR Med Educ SP - e63708 VL - 11 KW - COVID-19 pandemic KW - eHealth KW - blended learning KW - health informatics KW - higher education adaptation N2 - 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. UR - https://mededu.jmir.org/2025/1/e63708 UR - http://dx.doi.org/10.2196/63708 UR - http://www.ncbi.nlm.nih.gov/pubmed/ ID - info:doi/10.2196/63708 ER - TY - JOUR AU - Alshanik, Farah AU - Khasawneh, Rawand AU - Dalky, Alaa AU - Qawasmeh, Ethar PY - 2025/2/10 TI - Unveiling Topics and Emotions in Arabic Tweets Surrounding the COVID-19 Pandemic: Topic Modeling and Sentiment Analysis Approach JO - JMIR Infodemiology SP - e53434 VL - 5 KW - topic modeling KW - sentiment analysis KW - COVID-19 KW - social media KW - Twitter KW - public discussion N2 - 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. UR - https://infodemiology.jmir.org/2025/1/e53434 UR - http://dx.doi.org/10.2196/53434 UR - http://www.ncbi.nlm.nih.gov/pubmed/39928401 ID - info:doi/10.2196/53434 ER - TY - JOUR AU - Kahlawi, Adham AU - Masri, Firas AU - Ahmed, Wasim AU - Vidal-Alaball, Josep PY - 2025/1/27 TI - 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 JO - J Med Internet Res SP - e58656 VL - 27 KW - COVID-19 KW - SARS-CoV-2 KW - pandemic KW - citizen opinion KW - text mining KW - LDA KW - health crisis KW - developing economies KW - Italy KW - Egypt KW - UK KW - dataset KW - content analysis KW - social media KW - twitter KW - tweet KW - sentiment KW - attitude KW - perception KW - perspective KW - machine learning KW - latent Dirichlet allocation KW - vaccine KW - vaccination KW - public health KW - infectious N2 - 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. UR - https://www.jmir.org/2025/1/e58656 UR - http://dx.doi.org/10.2196/58656 UR - http://www.ncbi.nlm.nih.gov/pubmed/ ID - info:doi/10.2196/58656 ER - TY - JOUR AU - Jefferson, Emily AU - Milligan, Gordon AU - Johnston, Jenny AU - Mumtaz, Shahzad AU - Cole, Christian AU - Best, Joseph AU - Giles, Charles Thomas AU - Cox, Samuel AU - Masood, Erum AU - Horban, Scott AU - Urwin, Esmond AU - Beggs, Jillian AU - Chuter, Antony AU - Reilly, Gerry AU - Morris, Andrew AU - Seymour, David AU - Hopkins, Susan AU - Sheikh, Aziz AU - Quinlan, Philip PY - 2024/11/20 TI - 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 JO - J Med Internet Res SP - e50235 VL - 26 KW - COVID-19 KW - infrastructure KW - trusted research environments KW - safe havens KW - feasibility analysis KW - cohort discovery KW - federated analytics KW - federated discovery KW - lessons learned KW - population wide KW - data KW - public health KW - analysis KW - CO-CONNECT KW - challenges KW - data transformation UR - https://www.jmir.org/2024/1/e50235 UR - http://dx.doi.org/10.2196/50235 UR - http://www.ncbi.nlm.nih.gov/pubmed/ ID - info:doi/10.2196/50235 ER - TY - JOUR AU - Wen, Andrew AU - Wang, Liwei AU - He, Huan AU - Fu, Sunyang AU - Liu, Sijia AU - Hanauer, A. David AU - Harris, R. Daniel AU - Kavuluru, Ramakanth AU - Zhang, Rui AU - Natarajan, Karthik AU - Pavinkurve, P. Nishanth AU - Hajagos, Janos AU - Rajupet, Sritha AU - Lingam, Veena AU - Saltz, Mary AU - Elowsky, Corey AU - Moffitt, A. Richard AU - Koraishy, M. Farrukh AU - Palchuk, B. Matvey AU - Donovan, Jordan AU - Lingrey, Lora AU - Stone-DerHagopian, Garo AU - Miller, T. Robert AU - Williams, E. Andrew AU - Leese, J. Peter AU - Kovach, I. Paul AU - Pfaff, R. Emily AU - Zemmel, Mikhail AU - Pates, D. Robert AU - Guthe, Nick AU - Haendel, A. Melissa AU - Chute, G. Christopher AU - Liu, Hongfang AU - PY - 2024/9/9 TI - 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 JO - JMIR Med Inform SP - e49997 VL - 12 KW - natural language processing KW - clinical information extraction KW - clinical phenotyping KW - extract KW - extraction KW - NLP KW - phenotype KW - phenotyping KW - narratives KW - unstructured KW - PASC KW - COVID KW - COVID-19 KW - SARS-CoV-2 KW - OHNLP KW - Open Health Natural Language Processing N2 - 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. UR - https://medinform.jmir.org/2024/1/e49997 UR - http://dx.doi.org/10.2196/49997 UR - http://www.ncbi.nlm.nih.gov/pubmed/ ID - info:doi/10.2196/49997 ER - TY - JOUR AU - Iyengar, Sriram M. AU - Block Ngaybe, G. Maiya AU - Gonzalez, Myla AU - Arora, Mona PY - 2024/8/12 TI - Resilience Informatics: Role of Informatics in Enabling and Promoting Public Health Resilience to Pandemics, Climate Change, and Other Stressors JO - Interact J Med Res SP - e54687 VL - 13 KW - health informatics KW - data science KW - climate change KW - pandemics KW - COVID-19 KW - migrations KW - mobile phone UR - https://www.i-jmr.org/2024/1/e54687 UR - http://dx.doi.org/10.2196/54687 UR - http://www.ncbi.nlm.nih.gov/pubmed/39133540 ID - info:doi/10.2196/54687 ER - TY - JOUR AU - Shang, Di AU - Williams, Cynthia AU - Culiqi, Hera PY - 2024/7/24 TI - Telehealth Uptake Among Hispanic People During COVID-19: Retrospective Observational Study JO - JMIR Med Inform SP - e57717 VL - 12 KW - telehealth KW - telemedicine KW - ICT KW - eHealth KW - e-health KW - Hispanic KW - health equity KW - health access KW - Hispanics KW - digital divide KW - usage KW - utilization KW - equity KW - inequity KW - inequities KW - access KW - accessibility KW - Spanish KW - observational KW - demographic KW - demographics KW - socioeconomic KW - socioeconomics KW - information and communication technology N2 - 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. UR - https://medinform.jmir.org/2024/1/e57717 UR - http://dx.doi.org/10.2196/57717 ID - info:doi/10.2196/57717 ER - TY - JOUR AU - Oliveira, Alves Clara Rodrigues AU - Pires, Carvalho Magda AU - Meira, Cardoso Karina AU - de Jesus, Cristina Jordana AU - Borges, Nascimento Isabela AU - Paixão, Cristina Maria AU - Mendes, Santos Mayara AU - Ribeiro, Bonisson Leonardo AU - Marcolino, Soriano Milena AU - Alkmim, Moreira Maria Beatriz AU - Ribeiro, Pinho Antonio Luiz PY - 2024/6/10 TI - 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 JO - J Med Internet Res SP - e48464 VL - 26 KW - COVID-19 KW - telehealth KW - health care KW - clinical outcomes KW - hospital admission KW - mortality KW - adoption KW - effectiveness KW - digital health tool KW - flu KW - teleconsultation KW - digital health KW - digital literacy KW - telemonitoring N2 - 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. UR - https://www.jmir.org/2024/1/e48464 UR - http://dx.doi.org/10.2196/48464 UR - http://www.ncbi.nlm.nih.gov/pubmed/38857068 ID - info:doi/10.2196/48464 ER - TY - JOUR AU - Ndlovu, Kagiso AU - Mauco, Leonard Kabelo AU - Makhura, Onalenna AU - Hu, Robin AU - Motlogelwa, Peace Nkwebi AU - Masizana, Audrey AU - Lo, Emily AU - Mphoyakgosi, Thongbotho AU - Moyo, Sikhulile PY - 2024/4/16 TI - Experiences, Lessons, and Challenges With Adapting REDCap for COVID-19 Laboratory Data Management in a Resource-Limited Country: Descriptive Study JO - JMIR Form Res SP - e50897 VL - 8 KW - REDCap KW - DHIS2 KW - COVID-19 KW - National Health Laboratory KW - eHealth KW - interoperability KW - data management KW - Botswana N2 - 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. UR - https://formative.jmir.org/2024/1/e50897 UR - http://dx.doi.org/10.2196/50897 UR - http://www.ncbi.nlm.nih.gov/pubmed/38625736 ID - info:doi/10.2196/50897 ER - TY - JOUR AU - Oehm, Benedict Johannes AU - Riepenhausen, Luise Sarah AU - Storck, Michael AU - Dugas, Martin AU - Pryss, Rüdiger AU - Varghese, Julian PY - 2024/2/27 TI - 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 JO - J Med Internet Res SP - e47846 VL - 26 KW - Fast Healthcare Interoperability Resources KW - FHIR KW - FHIR Questionnaire KW - patient-reported outcome KW - mobile health KW - mHealth KW - research compatibility KW - interoperability KW - Germany KW - harmonized data collection KW - findable, accessible, interoperable, and reusable KW - FAIR data KW - mobile phone N2 - 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. UR - https://www.jmir.org/2024/1/e47846 UR - http://dx.doi.org/10.2196/47846 UR - http://www.ncbi.nlm.nih.gov/pubmed/38411999 ID - info:doi/10.2196/47846 ER - TY - JOUR AU - Alqurashi, Heba AU - Mohammed, Rafiuddin AU - AlGhanmi, Shlyan Amany AU - Alanazi, Farhan PY - 2023/9/28 TI - The Perception of Health Care Practitioners Regarding Telemedicine During COVID-19 in Saudi Arabia: Mixed Methods Study JO - JMIR Form Res SP - e47065 VL - 7 KW - telemedicine KW - health care practitioners KW - COVID-19 KW - Saudi Arabia KW - mobile phone N2 - 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. UR - https://formative.jmir.org/2023/1/e47065 UR - http://dx.doi.org/10.2196/47065 UR - http://www.ncbi.nlm.nih.gov/pubmed/37768720 ID - info:doi/10.2196/47065 ER - TY - JOUR AU - Chang, Feier AU - Krishnan, Jay AU - Hurst, H. Jillian AU - Yarrington, E. Michael AU - Anderson, J. Deverick AU - O'Brien, C. Emily AU - Goldstein, A. Benjamin PY - 2023/8/22 TI - Comparing Natural Language Processing and Structured Medical Data to Develop a Computable Phenotype for Patients Hospitalized Due to COVID-19: Retrospective Analysis JO - JMIR Med Inform SP - e46267 VL - 11 KW - natural language processing KW - NLP KW - computable phenotype KW - machine learning KW - COVID KW - coronavirus KW - hospitalize KW - hospitalization KW - electronic health record KW - EHR KW - health record KW - structured data KW - data element KW - free text KW - unstructured data KW - provider note KW - classify KW - classification KW - algorithm KW - COVID-19 N2 - 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. UR - https://medinform.jmir.org/2023/1/e46267 UR - http://dx.doi.org/10.2196/46267 ID - info:doi/10.2196/46267 ER - TY - JOUR AU - Jiang, Haoqiang AU - Castellanos, Arturo AU - Castillo, Alfred AU - Gomes, J. Paulo AU - Li, Juanjuan AU - VanderMeer, Debra PY - 2023/2/6 TI - Nurses? Work Concerns and Disenchantment During the COVID-19 Pandemic: Machine Learning Analysis of Web-Based Discussions JO - JMIR Nursing SP - e40676 VL - 6 KW - text mining KW - machine learning KW - blog data KW - COVID-19 KW - pandemic KW - work concerns KW - stressors KW - natural language processing N2 - 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. UR - https://nursing.jmir.org/2023/1/e40676 UR - http://dx.doi.org/10.2196/40676 UR - http://www.ncbi.nlm.nih.gov/pubmed/36608261 ID - info:doi/10.2196/40676 ER - TY - JOUR AU - Chiavi, Deborah AU - Haag, Christina AU - Chan, Andrew AU - Kamm, Philipp Christian AU - Sieber, Chloé AU - Staniki?, Mina AU - Rodgers, Stephanie AU - Pot, Caroline AU - Kesselring, Jürg AU - Salmen, Anke AU - Rapold, Irene AU - Calabrese, Pasquale AU - Manjaly, Zina-Mary AU - Gobbi, Claudio AU - Zecca, Chiara AU - Walther, Sebastian AU - Stegmayer, Katharina AU - Hoepner, Robert AU - Puhan, Milo AU - von Wyl, Viktor PY - 2022/11/10 TI - The Real-World Experiences of Persons With Multiple Sclerosis During the First COVID-19 Lockdown: Application of Natural Language Processing JO - JMIR Med Inform SP - e37945 VL - 10 IS - 11 KW - natural language processing KW - multiple sclerosis KW - COVID-19 KW - nervous system disease KW - nervous system disorder KW - textual data KW - health data KW - patient data KW - topic modeling KW - sentiment analysis KW - linguistic inquiry KW - medical informatics KW - clinical informatics N2 - 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. UR - https://medinform.jmir.org/2022/11/e37945 UR - http://dx.doi.org/10.2196/37945 UR - http://www.ncbi.nlm.nih.gov/pubmed/36252126 ID - info:doi/10.2196/37945 ER - TY - JOUR AU - Higa, Eduardo AU - Elbéji, Abir AU - Zhang, Lu AU - Fischer, Aurélie AU - Aguayo, A. Gloria AU - Nazarov, V. Petr AU - Fagherazzi, Guy PY - 2022/11/8 TI - Discovery and Analytical Validation of a Vocal Biomarker to Monitor Anosmia and Ageusia in Patients With COVID-19: Cross-sectional Study JO - JMIR Med Inform SP - e35622 VL - 10 IS - 11 KW - vocal biomarker KW - COVID-19 KW - ageusia KW - anosmia KW - loss of smell KW - loss of taste KW - digital assessment tool KW - digital health KW - medical informatics KW - telehealth KW - telemonitoring KW - biomarker KW - pandemic KW - symptoms KW - tool KW - disease KW - noninvasive KW - AI KW - artificial intelligence KW - digital KW - device N2 - 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 UR - https://medinform.jmir.org/2022/11/e35622 UR - http://dx.doi.org/10.2196/35622 UR - http://www.ncbi.nlm.nih.gov/pubmed/36265042 ID - info:doi/10.2196/35622 ER - TY - JOUR AU - Gatto, Joseph AU - Seegmiller, Parker AU - Johnston, Garrett AU - Preum, Masud Sarah PY - 2022/9/2 TI - Identifying the Perceived Severity of Patient-Generated Telemedical Queries Regarding COVID: Developing and Evaluating a Transfer Learning?Based Solution JO - JMIR Med Inform SP - e37770 VL - 10 IS - 9 KW - natural language processing KW - transfer learning KW - telemedicine triage KW - COVID-19 KW - health resource KW - health care KW - patient query KW - learning solution KW - telemedical KW - lexical model KW - machine learning KW - BERT KW - telehealth N2 - 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. UR - https://medinform.jmir.org/2022/9/e37770 UR - http://dx.doi.org/10.2196/37770 UR - http://www.ncbi.nlm.nih.gov/pubmed/35981230 ID - info:doi/10.2196/37770 ER - TY - JOUR AU - Singhal, Aditya AU - Baxi, Kaur Manmeet AU - Mago, Vijay PY - 2022/8/18 TI - Synergy Between Public and Private Health Care Organizations During COVID-19 on Twitter: Sentiment and Engagement Analysis Using Forecasting Models JO - JMIR Med Inform SP - e37829 VL - 10 IS - 8 KW - social media KW - health care KW - Twitter KW - content analysis KW - user engagement KW - sentiment forecasting KW - natural language processing KW - public health KW - pharmaceutical KW - public engagement N2 - 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. UR - https://medinform.jmir.org/2022/8/e37829 UR - http://dx.doi.org/10.2196/37829 UR - http://www.ncbi.nlm.nih.gov/pubmed/35849795 ID - info:doi/10.2196/37829 ER - TY - JOUR AU - Ali, Hazrat AU - Shah, Zubair PY - 2022/6/29 TI - Combating COVID-19 Using Generative Adversarial Networks and Artificial Intelligence for Medical Images: Scoping Review JO - JMIR Med Inform SP - e37365 VL - 10 IS - 6 KW - augmentation KW - artificial intelligence KW - COVID-19 KW - diagnosis KW - generative adversarial networks KW - diagnostic KW - lung image KW - imaging KW - data augmentation KW - X-ray KW - CT scan KW - data scarcity KW - image data KW - neural network KW - clinical informatics N2 - 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. UR - https://medinform.jmir.org/2022/6/e37365 UR - http://dx.doi.org/10.2196/37365 UR - http://www.ncbi.nlm.nih.gov/pubmed/35709336 ID - info:doi/10.2196/37365 ER - TY - JOUR AU - Dainty, N. Katie AU - Seaton, Bianca M. AU - Estacio, Antonio AU - Hicks, K. Lisa AU - Jamieson, Trevor AU - Ward, Sarah AU - Yu, H. Catherine AU - Mosko, D. Jeffrey AU - Kassardjian, D. Charles PY - 2022/6/28 TI - Virtual Specialist Care During the COVID-19 Pandemic: Multimethod Patient Experience Study JO - JMIR Med Inform SP - e37196 VL - 10 IS - 6 KW - virtual care KW - specialist care KW - patient experience KW - COVID-19 KW - medical care KW - virtual health KW - care data KW - decision support KW - telehealth KW - video consultation N2 - 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. UR - https://medinform.jmir.org/2022/6/e37196 UR - http://dx.doi.org/10.2196/37196 UR - http://www.ncbi.nlm.nih.gov/pubmed/35482950 ID - info:doi/10.2196/37196 ER - TY - JOUR AU - Alvarez-Romero, Celia AU - Martinez-Garcia, Alicia AU - Ternero Vega, Jara AU - Díaz-Jimènez, Pablo AU - Jimènez-Juan, Carlos AU - Nieto-Martín, Dolores María AU - Román Villarán, Esther AU - Kovacevic, Tomi AU - Bokan, Darijo AU - Hromis, Sanja AU - Djekic Malbasa, Jelena AU - Besla?, Suzana AU - Zaric, Bojan AU - Gencturk, Mert AU - Sinaci, Anil A. AU - Ollero Baturone, Manuel AU - Parra Calderón, Luis Carlos PY - 2022/6/2 TI - 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 JO - JMIR Med Inform SP - e35307 VL - 10 IS - 6 KW - FAIR principles KW - research data management KW - clinical validation KW - chronic obstructive pulmonary disease KW - privacy-preserving distributed data mining KW - early predictive model N2 - 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. UR - https://medinform.jmir.org/2022/6/e35307 UR - http://dx.doi.org/10.2196/35307 UR - http://www.ncbi.nlm.nih.gov/pubmed/35653170 ID - info:doi/10.2196/35307 ER - TY - JOUR AU - Ge, Fangmin AU - Qian, Huan AU - Lei, Jianbo AU - Ni, Yiqi AU - Li, Qian AU - Wang, Song AU - Ding, Kefeng PY - 2022/6/1 TI - Experiences and Challenges of Emerging Online Health Services Combating COVID-19 in China: Retrospective, Cross-Sectional Study of Internet Hospitals JO - JMIR Med Inform SP - e37042 VL - 10 IS - 6 KW - COVID-19 KW - telehealth KW - e-consultation KW - dynamics of health care topics KW - China health system N2 - 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 (?_2=105.736, P<.001). Further, long-term waves of COVID-19 led to an increasing number of users following the outbreak (?_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. UR - https://medinform.jmir.org/2022/6/e37042 UR - http://dx.doi.org/10.2196/37042 UR - http://www.ncbi.nlm.nih.gov/pubmed/35500013 ID - info:doi/10.2196/37042 ER - TY - JOUR AU - Boukobza, Adrien AU - Burgun, Anita AU - Roudier, Bertrand AU - Tsopra, Rosy PY - 2022/5/25 TI - Deep Neural Networks for Simultaneously Capturing Public Topics and Sentiments During a Pandemic: Application on a COVID-19 Tweet Data Set JO - JMIR Med Inform SP - e34306 VL - 10 IS - 5 KW - neural network KW - deep learning KW - COVID-19 KW - explainable artificial intelligence KW - decision support KW - natural language processing N2 - 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. UR - https://medinform.jmir.org/2022/5/e34306 UR - http://dx.doi.org/10.2196/34306 UR - http://www.ncbi.nlm.nih.gov/pubmed/35533390 ID - info:doi/10.2196/34306 ER - TY - JOUR AU - Sauvayre, Romy AU - Vernier, Jessica AU - Chauvière, Cédric PY - 2022/5/17 TI - An Analysis of French-Language Tweets About COVID-19 Vaccines: Supervised Learning Approach JO - JMIR Med Inform SP - e37831 VL - 10 IS - 5 KW - social media KW - natural language processing KW - public health KW - vaccine KW - machine learning KW - CamemBERT language model KW - method KW - epistemology KW - COVID-19 KW - disinformation KW - language model N2 - 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. UR - https://medinform.jmir.org/2022/5/e37831 UR - http://dx.doi.org/10.2196/37831 UR - http://www.ncbi.nlm.nih.gov/pubmed/35512274 ID - info:doi/10.2196/37831 ER - TY - JOUR AU - Cuenca-Zaldívar, Nicolás Juan AU - Torrente-Regidor, Maria AU - Martín-Losada, Laura AU - Fernández-De-Las-Peñas, César AU - Florencio, Lima Lidiane AU - Sousa, Alexandre Pedro AU - Palacios-Ceña, Domingo PY - 2022/5/12 TI - Exploring Sentiment and Care Management of Hospitalized Patients During the First Wave of the COVID-19 Pandemic Using Electronic Nursing Health Records: Descriptive Study JO - JMIR Med Inform SP - e38308 VL - 10 IS - 5 KW - electronic health records KW - COVID-19 KW - pandemic KW - content text analysis N2 - 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. UR - https://medinform.jmir.org/2022/5/e38308 UR - http://dx.doi.org/10.2196/38308 UR - http://www.ncbi.nlm.nih.gov/pubmed/354869 ID - info:doi/10.2196/38308 ER - TY - JOUR AU - Van Olmen, Josefien AU - Van Nooten, Jens AU - Philips, Hilde AU - Sollie, Annet AU - Daelemans, Walter PY - 2022/4/27 TI - Predicting COVID-19 Symptoms From Free Text in Medical Records Using Artificial Intelligence: Feasibility Study JO - JMIR Med Inform SP - e37771 VL - 10 IS - 4 KW - natural language processing KW - text mining KW - electronic medical records KW - COVID-19 KW - structured registry KW - coding procedure KW - prediction model KW - feasibility study KW - precision model KW - artificial intelligence KW - primary care N2 - 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. UR - https://medinform.jmir.org/2022/4/e37771 UR - http://dx.doi.org/10.2196/37771 UR - http://www.ncbi.nlm.nih.gov/pubmed/35442903 ID - info:doi/10.2196/37771 ER - TY - JOUR AU - Röbbelen, Alice AU - Schmieding, L. Malte AU - Kopka, Marvin AU - Balzer, Felix AU - Feufel, A. Markus PY - 2022/4/15 TI - Interactive Versus Static Decision Support Tools for COVID-19: Randomized Controlled Trial JO - JMIR Public Health Surveill SP - e33733 VL - 8 IS - 4 KW - clinical decision support KW - usability KW - COVID-19 KW - consumer health KW - medical informatic KW - symptom checker KW - decision support KW - symptom KW - support KW - decision making KW - algorithm KW - flowchart KW - agent N2 - 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) UR - https://publichealth.jmir.org/2022/4/e33733 UR - http://dx.doi.org/10.2196/33733 UR - http://www.ncbi.nlm.nih.gov/pubmed/34882571 ID - info:doi/10.2196/33733 ER - TY - JOUR AU - Haithcoat, Timothy AU - Liu, Danlu AU - Young, Tiffany AU - Shyu, Chi-Ren PY - 2022/4/6 TI - Investigating Health Context Using a Spatial Data Analytical Tool: Development of a Geospatial Big Data Ecosystem JO - JMIR Med Inform SP - e35073 VL - 10 IS - 4 KW - context KW - Geographic Information System KW - big data KW - equity KW - population health KW - public health KW - digital health KW - eHealth KW - location KW - geospatial KW - data analytics KW - analytical framework KW - medical informatics KW - research knowledgebase N2 - 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. UR - https://medinform.jmir.org/2022/4/e35073 UR - http://dx.doi.org/10.2196/35073 UR - http://www.ncbi.nlm.nih.gov/pubmed/35311683 ID - info:doi/10.2196/35073 ER - TY - JOUR AU - Kruse, Scott Clemens AU - Mileski, Michael AU - Dray, Gevin AU - Johnson, Zakia AU - Shaw, Cameron AU - Shirodkar, Harsha PY - 2022/3/31 TI - Physician Burnout and the Electronic Health Record Leading Up to and During the First Year of COVID-19: Systematic Review JO - J Med Internet Res SP - e36200 VL - 24 IS - 3 KW - electronic health record KW - physician burnout KW - quality improvement KW - psychiatry KW - medical informatics KW - COVID-19 KW - pandemic KW - health informatic KW - health care KW - health care professional KW - health care infrastructure KW - health care system KW - mental health KW - cognitive fatigue N2 - 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 UR - https://www.jmir.org/2022/3/e36200 UR - http://dx.doi.org/10.2196/36200 UR - http://www.ncbi.nlm.nih.gov/pubmed/35120019 ID - info:doi/10.2196/36200 ER - TY - JOUR AU - Jung, Christian AU - Mamandipoor, Behrooz AU - Fjølner, Jesper AU - Bruno, Romano Raphael AU - Wernly, Bernhard AU - Artigas, Antonio AU - Bollen Pinto, Bernardo AU - Schefold, C. Joerg AU - Wolff, Georg AU - Kelm, Malte AU - Beil, Michael AU - Sviri, Sigal AU - van Heerden, V. Peter AU - Szczeklik, Wojciech AU - Czuczwar, Miroslaw AU - Elhadi, Muhammed AU - Joannidis, Michael AU - Oeyen, Sandra AU - Zafeiridis, Tilemachos AU - Marsh, Brian AU - Andersen, H. Finn AU - Moreno, Rui AU - Cecconi, Maurizio AU - Leaver, Susannah AU - De Lange, W. Dylan AU - Guidet, Bertrand AU - Flaatten, Hans AU - Osmani, Venet PY - 2022/3/31 TI - Disease-Course Adapting Machine Learning Prognostication Models in Elderly Patients Critically Ill With COVID-19: Multicenter Cohort Study With External Validation JO - JMIR Med Inform SP - e32949 VL - 10 IS - 3 KW - machine-based learning KW - outcome prediction KW - COVID-19 KW - pandemic KW - machine learning KW - prediction models KW - clinical informatics KW - patient data KW - elderly population N2 - 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 UR - https://medinform.jmir.org/2022/3/e32949 UR - http://dx.doi.org/10.2196/32949 UR - http://www.ncbi.nlm.nih.gov/pubmed/35099394 ID - info:doi/10.2196/32949 ER - TY - JOUR AU - Lyu, Zeyu AU - Takikawa, Hiroki PY - 2022/3/22 TI - The Disparity and Dynamics of Social Distancing Behaviors in Japan: Investigation of Mobile Phone Mobility Data JO - JMIR Med Inform SP - e31557 VL - 10 IS - 3 KW - COVID-19 KW - social distancing KW - mobility KW - time series KW - tracking KW - policy N2 - 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. UR - https://medinform.jmir.org/2022/3/e31557 UR - http://dx.doi.org/10.2196/31557 UR - http://www.ncbi.nlm.nih.gov/pubmed/35297764 ID - info:doi/10.2196/31557 ER - TY - JOUR AU - Alhajri, Noora AU - Simsekler, Emre Mecit Can AU - Alfalasi, Buthaina AU - Alhashmi, Mohamed AU - Memon, Hamda AU - Housser, Emma AU - Abdi, Mustafa Abdulhamid AU - Balalaa, Nahed AU - Al Ali, Maryam AU - Almaashari, Raghda AU - Al Memari, Shammah AU - Al Hosani, Farida AU - Al Zaabi, Yousif AU - Almazrouei, Shereena AU - Alhashemi, Hamed PY - 2022/2/15 TI - Exploring Quality Differences in Telemedicine Between Hospital Outpatient Departments and Community Clinics: Cross-sectional Study JO - JMIR Med Inform SP - e32373 VL - 10 IS - 2 KW - COVID-19 KW - patient satisfaction KW - technology acceptance KW - hospital KW - community clinic KW - video consultation KW - audio consultation KW - outpatient department KW - OPD KW - policy making KW - UAE N2 - 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. UR - https://medinform.jmir.org/2022/2/e32373 UR - http://dx.doi.org/10.2196/32373 UR - http://www.ncbi.nlm.nih.gov/pubmed/34978281 ID - info:doi/10.2196/32373 ER - TY - JOUR AU - Wang, Qian AU - Xie, Luyao AU - Song, Bo AU - Di, Jiangli AU - Wang, Linhong AU - Mo, Kit-Han Phoenix PY - 2022/1/13 TI - Effects of Social Media Use for Health Information on COVID-19?Related Risk Perceptions and Mental Health During Pregnancy: Web-Based Survey JO - JMIR Med Inform SP - e28183 VL - 10 IS - 1 KW - COVID-19 KW - pregnant KW - social media use KW - risk perception KW - worry KW - depression N2 - 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 (?=.05; P<.001) and perceived severity (?=.12; P<.001) of COVID-19, which, in turn, were positively associated with worry due to COVID-19 (?=.19 and ?=.72, respectively; P<.001). Perceived susceptibility (?=.09; P<.001), perceived severity (?=.08; P<.001), and worry due to COVID-19 (?=.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 (?=.09, 95% CI 0.07-0.12) and depression (?=.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. UR - https://medinform.jmir.org/2022/1/e28183 UR - http://dx.doi.org/10.2196/28183 UR - http://www.ncbi.nlm.nih.gov/pubmed/34762065 ID - info:doi/10.2196/28183 ER - TY - JOUR AU - Kim, Jeongmin AU - Lim, Hakyung AU - Ahn, Jae-Hyeon AU - Lee, Hwa Kyoung AU - Lee, Suk Kwang AU - Koo, Chul Kyo PY - 2021/11/2 TI - 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 JO - JMIR Med Inform SP - e32726 VL - 9 IS - 11 KW - COVID-19 KW - decision support techniques KW - machine learning KW - prediction KW - triage N2 - 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. UR - https://medinform.jmir.org/2021/11/e32726 UR - http://dx.doi.org/10.2196/32726 UR - http://www.ncbi.nlm.nih.gov/pubmed/34609319 ID - info:doi/10.2196/32726 ER - TY - JOUR AU - Luu, S. Hung AU - Filkins, M. Laura AU - Park, Y. Jason AU - Rakheja, Dinesh AU - Tweed, Jefferson AU - Menzies, Christopher AU - Wang, J. Vincent AU - Mittal, Vineeta AU - Lehmann, U. Christoph AU - Sebert, E. Michael PY - 2021/10/18 TI - Harnessing the Electronic Health Record and Computerized Provider Order Entry Data for Resource Management During the COVID-19 Pandemic: Development of a Decision Tree JO - JMIR Med Inform SP - e32303 VL - 9 IS - 10 KW - COVID-19 KW - computerized provider order entry KW - electronic health record KW - resource utilization KW - personal protective equipment KW - SARS-CoV-2 testing KW - clinical decision support N2 - 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. UR - https://medinform.jmir.org/2021/10/e32303 UR - http://dx.doi.org/10.2196/32303 UR - http://www.ncbi.nlm.nih.gov/pubmed/34546942 ID - info:doi/10.2196/32303 ER - TY - JOUR AU - Sankaranarayanan, Saranya AU - Balan, Jagadheshwar AU - Walsh, R. Jesse AU - Wu, Yanhong AU - Minnich, Sara AU - Piazza, Amy AU - Osborne, Collin AU - Oliver, R. Gavin AU - Lesko, Jessica AU - Bates, L. Kathy AU - Khezeli, Kia AU - Block, R. Darci AU - DiGuardo, Margaret AU - Kreuter, Justin AU - O?Horo, C. John AU - Kalantari, John AU - Klee, W. Eric AU - Salama, E. Mohamed AU - Kipp, Benjamin AU - Morice, G. William AU - Jenkinson, Garrett PY - 2021/9/28 TI - COVID-19 Mortality Prediction From Deep Learning in a Large Multistate Electronic Health Record and Laboratory Information System Data Set: Algorithm Development and Validation JO - J Med Internet Res SP - e30157 VL - 23 IS - 9 KW - COVID-19 KW - mortality KW - prediction KW - recurrent neural networks KW - missing data KW - time series KW - deep learning KW - machine learning KW - neural network KW - electronic health record KW - EHR KW - algorithm KW - development KW - validation N2 - 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. UR - https://www.jmir.org/2021/9/e30157 UR - http://dx.doi.org/10.2196/30157 UR - http://www.ncbi.nlm.nih.gov/pubmed/34449401 ID - info:doi/10.2196/30157 ER - TY - JOUR AU - Gmunder, Nicole Kristin AU - Ruiz, W. Jose AU - Franceschi, Dido AU - Suarez, M. Maritza PY - 2021/8/27 TI - Factors to Effective Telemedicine Visits During the COVID-19 Pandemic: Cohort Study JO - JMIR Med Inform SP - e27977 VL - 9 IS - 8 KW - telemedicine KW - COVID-19 KW - patient portals KW - delivery of health care KW - telehealth KW - pandemic KW - digital health N2 - 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. UR - https://medinform.jmir.org/2021/8/e27977 UR - http://dx.doi.org/10.2196/27977 UR - http://www.ncbi.nlm.nih.gov/pubmed/34254936 ID - info:doi/10.2196/27977 ER - TY - JOUR AU - Khuntia, Jiban AU - Ning, Xue AU - Stacey, Rulon PY - 2021/8/16 TI - Digital Orientation of Health Systems in the Post?COVID-19 ?New Normal? in the United States: Cross-sectional Survey JO - J Med Internet Res SP - e30453 VL - 23 IS - 8 KW - post?COVID-19 KW - digital orientation KW - health systems KW - digital transformation KW - digital health KW - telehealth KW - telemedicine KW - COVID-19 KW - impact KW - insight KW - cross-sectional KW - survey KW - United States KW - electronic health record KW - EHR N2 - 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. UR - https://www.jmir.org/2021/8/e30453 UR - http://dx.doi.org/10.2196/30453 UR - http://www.ncbi.nlm.nih.gov/pubmed/34254947 ID - info:doi/10.2196/30453 ER - TY - JOUR AU - Walia, Bhavneet AU - Shridhar, Anshu AU - Arasu, Pratap AU - Singh, Kaur Gursimar PY - 2021/8/12 TI - US Physicians? Perspective on the Sudden Shift to Telehealth: Survey Study JO - JMIR Hum Factors SP - e26336 VL - 8 IS - 3 KW - physician survey KW - US telehealth training KW - US telehealth care KW - COVID-19 KW - pandemic KW - snowball sampling KW - health care access KW - health care quality KW - telehealth KW - telemedicine KW - survey KW - physician KW - perspective KW - recommendation KW - policy KW - public health KW - implication KW - quality KW - access N2 - 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. UR - https://humanfactors.jmir.org/2021/3/e26336 UR - http://dx.doi.org/10.2196/26336 UR - http://www.ncbi.nlm.nih.gov/pubmed/33938813 ID - info:doi/10.2196/26336 ER - TY - JOUR AU - Park, Jihwan AU - Han, Jinhyun AU - Kim, Yerin AU - Rho, Jung Mi PY - 2021/7/30 TI - Development, Acceptance, and Concerns Surrounding App-Based Services to Overcome the COVID-19 Outbreak in South Korea: Web-Based Survey Study JO - JMIR Med Inform SP - e29315 VL - 9 IS - 7 KW - COVID-19 KW - app-based services KW - acceptance KW - concerns KW - epidemiological investigation, self-route management app, privacy N2 - 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. UR - https://medinform.jmir.org/2021/7/e29315 UR - http://dx.doi.org/10.2196/29315 UR - http://www.ncbi.nlm.nih.gov/pubmed/34137726 ID - info:doi/10.2196/29315 ER - TY - JOUR AU - Xiong, Ziyu AU - Li, Pin AU - Lyu, Hanjia AU - Luo, Jiebo PY - 2021/7/30 TI - Social Media Opinions on Working From Home in the United States During the COVID-19 Pandemic: Observational Study JO - JMIR Med Inform SP - e29195 VL - 9 IS - 7 KW - characterization KW - COVID-19 KW - social media KW - topic modeling KW - Twitter KW - work from home N2 - 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. UR - https://medinform.jmir.org/2021/7/e29195 UR - http://dx.doi.org/10.2196/29195 UR - http://www.ncbi.nlm.nih.gov/pubmed/34254941 ID - info:doi/10.2196/29195 ER - TY - JOUR AU - Lu, Ding-Heng AU - Hsu, Chia-An AU - Yuan, J. Eunice AU - Fen, Jun-Jeng AU - Lee, Chung-Yuan AU - Ming, Jin-Lain AU - Chen, Tzeng-Ji AU - Lee, Wui-Chiang AU - Chen, Shih-Ann PY - 2021/7/27 TI - Experiences With Internet Triaging of 9498 Outpatients Daily at the Largest Public Hospital in Taiwan During the COVID-19 Pandemic: Observational Study JO - JMIR Med Inform SP - e20994 VL - 9 IS - 7 KW - COVID-19 KW - hospital KW - information services KW - outpatients KW - patient KW - SARS-CoV-2 KW - triage KW - virus N2 - 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. UR - https://medinform.jmir.org/2021/7/e20994 UR - http://dx.doi.org/10.2196/20994 UR - http://www.ncbi.nlm.nih.gov/pubmed/34043524 ID - info:doi/10.2196/20994 ER - TY - JOUR AU - Blease, Charlotte AU - Salmi, Liz AU - Hägglund, Maria AU - Wachenheim, Deborah AU - DesRoches, Catherine PY - 2021/6/21 TI - COVID-19 and Open Notes: A New Method to Enhance Patient Safety and Trust JO - JMIR Ment Health SP - e29314 VL - 8 IS - 6 KW - COVID-19 KW - patient portals KW - electronic health records KW - patient safety KW - patient-centered care UR - https://mental.jmir.org/2021/6/e29314 UR - http://dx.doi.org/10.2196/29314 UR - http://www.ncbi.nlm.nih.gov/pubmed/34081603 ID - info:doi/10.2196/29314 ER - TY - JOUR AU - Pollack, C. Catherine AU - Gilbert-Diamond, Diane AU - Alford-Teaster, A. Jennifer AU - Onega, Tracy PY - 2021/6/21 TI - Language and Sentiment Regarding Telemedicine and COVID-19 on Twitter: Longitudinal Infodemiology Study JO - J Med Internet Res SP - e28648 VL - 23 IS - 6 KW - telemedicine KW - telehealth KW - COVID-19 pandemic KW - social media KW - sentiment analysis KW - Twitter KW - COVID-19 KW - pandemic N2 - 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. UR - https://www.jmir.org/2021/6/e28648 UR - http://dx.doi.org/10.2196/28648 UR - http://www.ncbi.nlm.nih.gov/pubmed/34086591 ID - info:doi/10.2196/28648 ER - TY - JOUR AU - Welch, B. Sarah AU - Kulasekere, Amanda Dinushi AU - Prasad, Vara P. V. AU - Moss, B. Charles AU - Murphy, Leo Robert AU - Achenbach, J. Chad AU - Ison, G. Michael AU - Resnick, Danielle AU - Singh, Lauren AU - White, Janine AU - Issa, Z. Tariq AU - Culler, Kasen AU - Boctor, J. Michael AU - Mason, Maryann AU - Oehmke, Francis James AU - Faber, Mitchell Joshua Marco AU - Post, Ann Lori PY - 2021/6/17 TI - The Interplay Between Policy and COVID-19 Outbreaks in South Asia: Longitudinal Trend Analysis of Surveillance Data JO - JMIR Public Health Surveill SP - e24251 VL - 7 IS - 6 KW - 7-day lag KW - acceleration KW - Bangladesh KW - Bhutan KW - COVID-19 surveillance KW - COVID-19 KW - dynamic panel data KW - India KW - jerk KW - Maldives KW - Pakistan KW - South Asia KW - speed KW - Sri Lanka N2 - 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. UR - https://publichealth.jmir.org/2021/6/e24251 UR - http://dx.doi.org/10.2196/24251 UR - http://www.ncbi.nlm.nih.gov/pubmed/34081593 ID - info:doi/10.2196/24251 ER - TY - JOUR AU - Alhajri, Noora AU - Simsekler, Emre Mecit Can AU - Alfalasi, Buthaina AU - Alhashmi, Mohamed AU - AlGhatrif, Majd AU - Balalaa, Nahed AU - Al Ali, Maryam AU - Almaashari, Raghda AU - Al Memari, Shammah AU - Al Hosani, Farida AU - Al Zaabi, Yousif AU - Almazroui, Shereena AU - Alhashemi, Hamed AU - Baltatu, C. Ovidiu PY - 2021/6/1 TI - Physicians? Attitudes Toward Telemedicine Consultations During the COVID-19 Pandemic: Cross-sectional Study JO - JMIR Med Inform SP - e29251 VL - 9 IS - 6 KW - audio consultation KW - clinical decision-making KW - clinical training KW - communication KW - COVID-19 KW - outpatient department KW - perception KW - telemedicine KW - United Arab Emirates KW - video consultation N2 - 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. UR - https://medinform.jmir.org/2021/6/e29251 UR - http://dx.doi.org/10.2196/29251 UR - http://www.ncbi.nlm.nih.gov/pubmed/34001497 ID - info:doi/10.2196/29251 ER - TY - JOUR AU - Liu, Jialin AU - Liu, Siru AU - Zheng, Tao AU - Bi, Yongdong PY - 2021/6/1 TI - Physicians? Perspectives of Telemedicine During the COVID-19 Pandemic in China: Qualitative Survey Study JO - JMIR Med Inform SP - e26463 VL - 9 IS - 6 KW - telemedicine KW - COVID-19 KW - survey KW - physician N2 - 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. UR - https://medinform.jmir.org/2021/6/e26463 UR - http://dx.doi.org/10.2196/26463 UR - http://www.ncbi.nlm.nih.gov/pubmed/33945493 ID - info:doi/10.2196/26463 ER - TY - JOUR AU - Izquierdo, Luis Jose AU - Soriano, B. Joan PY - 2021/5/26 TI - 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? JO - J Med Internet Res SP - e29405 VL - 23 IS - 5 KW - artificial intelligence KW - big data KW - COVID-19 KW - electronic health records KW - tachypnea KW - SARS-CoV-2 KW - predictive model KW - prognosis KW - classification bias KW - critical care UR - https://www.jmir.org/2021/5/e29405 UR - http://dx.doi.org/10.2196/29405 UR - http://www.ncbi.nlm.nih.gov/pubmed/33989164 ID - info:doi/10.2196/29405 ER - TY - JOUR AU - Martos Pérez, Francisco AU - Gomez Huelgas, Ricardo AU - Martín Escalante, Dolores María AU - Casas Rojo, Manuel José PY - 2021/5/26 TI - 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? JO - J Med Internet Res SP - e27142 VL - 23 IS - 5 KW - artificial intelligence KW - big data KW - COVID-19 KW - electronic health records KW - tachypnea KW - SARS-CoV-2 KW - predictive model KW - prognosis KW - classification bias KW - critical care UR - https://www.jmir.org/2021/5/e27142 UR - http://dx.doi.org/10.2196/27142 UR - http://www.ncbi.nlm.nih.gov/pubmed/33989163 ID - info:doi/10.2196/27142 ER - TY - JOUR AU - Espinosa-Gonzalez, Belen Ana AU - Neves, Luisa Ana AU - Fiorentino, Francesca AU - Prociuk, Denys AU - Husain, Laiba AU - Ramtale, Christian Sonny AU - Mi, Emma AU - Mi, Ella AU - Macartney, Jack AU - Anand, N. Sneha AU - Sherlock, Julian AU - Saravanakumar, Kavitha AU - Mayer, Erik AU - de Lusignan, Simon AU - Greenhalgh, Trisha AU - Delaney, C. Brendan PY - 2021/5/25 TI - 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 JO - JMIR Res Protoc SP - e29072 VL - 10 IS - 5 KW - COVID-19 severity KW - risk prediction tool KW - early warning score KW - hospital admission KW - primary care KW - electronic health records N2 - 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 UR - https://www.researchprotocols.org/2021/5/e29072 UR - http://dx.doi.org/10.2196/29072 UR - http://www.ncbi.nlm.nih.gov/pubmed/33939619 ID - info:doi/10.2196/29072 ER - TY - JOUR AU - Li, Junjiang AU - Giabbanelli, Philippe PY - 2021/4/29 TI - Returning to a Normal Life via COVID-19 Vaccines in the United States: A Large-scale Agent-Based Simulation Study JO - JMIR Med Inform SP - e27419 VL - 9 IS - 4 KW - agent-based model KW - cloud-based simulations KW - COVID-19 KW - large-scale simulations KW - vaccine KW - model KW - simulation KW - United States KW - agent-based KW - effective KW - willingness KW - capacity KW - plan KW - strategy KW - outcome KW - interaction KW - intervention KW - scenario KW - impact N2 - 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. UR - https://medinform.jmir.org/2021/4/e27419 UR - http://dx.doi.org/10.2196/27419 UR - http://www.ncbi.nlm.nih.gov/pubmed/33872188 ID - info:doi/10.2196/27419 ER - TY - JOUR AU - Poly, Nasrin Tahmina AU - Islam, Mohaimenul Md AU - Li, Jack Yu-Chuan AU - Alsinglawi, Belal AU - Hsu, Min-Huei AU - Jian, Shan Wen AU - Yang, Hsuan-Chia PY - 2021/4/29 TI - Application of Artificial Intelligence for Screening COVID-19 Patients Using Digital Images: Meta-analysis JO - JMIR Med Inform SP - e21394 VL - 9 IS - 4 KW - COVID-19 KW - SARS-CoV-2 KW - pneumonia KW - artificial intelligence KW - deep learning N2 - 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. UR - https://medinform.jmir.org/2021/4/e21394 UR - http://dx.doi.org/10.2196/21394 UR - http://www.ncbi.nlm.nih.gov/pubmed/33764884 ID - info:doi/10.2196/21394 ER - TY - JOUR AU - Patrício, André AU - Costa, S. Rafael AU - Henriques, Rui PY - 2021/4/28 TI - Predictability of COVID-19 Hospitalizations, Intensive Care Unit Admissions, and Respiratory Assistance in Portugal: Longitudinal Cohort Study JO - J Med Internet Res SP - e26075 VL - 23 IS - 4 KW - COVID-19 KW - machine learning KW - intensive care admissions KW - respiratory assistance KW - predictive models KW - data modeling KW - clinical informatics N2 - 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. UR - https://www.jmir.org/2021/4/e26075 UR - http://dx.doi.org/10.2196/26075 UR - http://www.ncbi.nlm.nih.gov/pubmed/33835931 ID - info:doi/10.2196/26075 ER - TY - JOUR AU - Ghaderzadeh, Mustafa AU - Asadi, Farkhondeh AU - Jafari, Ramezan AU - Bashash, Davood AU - Abolghasemi, Hassan AU - Aria, Mehrad PY - 2021/4/26 TI - Deep Convolutional Neural Network?Based Computer-Aided Detection System for COVID-19 Using Multiple Lung Scans: Design and Implementation Study JO - J Med Internet Res SP - e27468 VL - 23 IS - 4 KW - artificial intelligence KW - classification KW - computer-aided detection KW - computed tomography scan KW - convolutional neural network KW - coronavirus KW - COVID-19 KW - deep learning KW - machine learning KW - machine vision KW - model KW - pandemic N2 - 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. UR - https://www.jmir.org/2021/4/e27468 UR - http://dx.doi.org/10.2196/27468 UR - http://www.ncbi.nlm.nih.gov/pubmed/33848973 ID - info:doi/10.2196/27468 ER - TY - JOUR AU - Montazeri, Mahdieh AU - ZahediNasab, Roxana AU - Farahani, Ali AU - Mohseni, Hadis AU - Ghasemian, Fahimeh PY - 2021/4/23 TI - Machine Learning Models for Image-Based Diagnosis and Prognosis of COVID-19: Systematic Review JO - JMIR Med Inform SP - e25181 VL - 9 IS - 4 KW - machine learning KW - diagnosis KW - prognosis KW - COVID-19 N2 - 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. UR - https://medinform.jmir.org/2021/4/e25181 UR - http://dx.doi.org/10.2196/25181 UR - http://www.ncbi.nlm.nih.gov/pubmed/33735095 ID - info:doi/10.2196/25181 ER - TY - JOUR AU - Cummings, C. Brandon AU - Ansari, Sardar AU - Motyka, R. Jonathan AU - Wang, Guan AU - Medlin Jr, P. Richard AU - Kronick, L. Steven AU - Singh, Karandeep AU - Park, K. Pauline AU - Napolitano, M. Lena AU - Dickson, P. Robert AU - Mathis, R. Michael AU - Sjoding, W. Michael AU - Admon, J. Andrew AU - Blank, Ross AU - McSparron, I. Jakob AU - Ward, R. Kevin AU - Gillies, E. Christopher PY - 2021/4/21 TI - Predicting Intensive Care Transfers and Other Unforeseen Events: Analytic Model Validation Study and Comparison to Existing Methods JO - JMIR Med Inform SP - e25066 VL - 9 IS - 4 KW - COVID-19 KW - biomedical informatics KW - critical care KW - machine learning KW - deterioration KW - predictive analytics KW - informatics KW - prediction KW - intensive care unit KW - ICU KW - mortality N2 - 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. UR - https://medinform.jmir.org/2021/4/e25066 UR - http://dx.doi.org/10.2196/25066 UR - http://www.ncbi.nlm.nih.gov/pubmed/33818393 ID - info:doi/10.2196/25066 ER - TY - JOUR AU - Domínguez-Olmedo, L. Juan AU - Gragera-Martínez, Álvaro AU - Mata, Jacinto AU - Pachón Álvarez, Victoria PY - 2021/4/14 TI - Machine Learning Applied to Clinical Laboratory Data in Spain for COVID-19 Outcome Prediction: Model Development and Validation JO - J Med Internet Res SP - e26211 VL - 23 IS - 4 KW - COVID-19 KW - electronic health record KW - machine learning KW - mortality KW - prediction N2 - 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. UR - https://www.jmir.org/2021/4/e26211 UR - http://dx.doi.org/10.2196/26211 UR - http://www.ncbi.nlm.nih.gov/pubmed/33793407 ID - info:doi/10.2196/26211 ER - TY - JOUR AU - Aktar, Sakifa AU - Ahamad, Martuza Md AU - Rashed-Al-Mahfuz, Md AU - Azad, AKM AU - Uddin, Shahadat AU - Kamal, AHM AU - Alyami, A. Salem AU - Lin, Ping-I AU - Islam, Shariful Sheikh Mohammed AU - Quinn, MW Julian AU - Eapen, Valsamma AU - Moni, Ali Mohammad PY - 2021/4/13 TI - Machine Learning Approach to Predicting COVID-19 Disease Severity Based on Clinical Blood Test Data: Statistical Analysis and Model Development JO - JMIR Med Inform SP - e25884 VL - 9 IS - 4 KW - COVID-19 KW - blood samples KW - machine learning KW - statistical analysis KW - prediction KW - severity KW - mortality KW - morbidity KW - risk KW - blood KW - testing KW - outcome KW - data set N2 - 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. UR - https://medinform.jmir.org/2021/4/e25884 UR - http://dx.doi.org/10.2196/25884 UR - http://www.ncbi.nlm.nih.gov/pubmed/33779565 ID - info:doi/10.2196/25884 ER - TY - JOUR AU - He, Qian AU - Du, Fei AU - Simonse, L. Lianne W. PY - 2021/4/12 TI - 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 JO - JMIR Med Inform SP - e23238 VL - 9 IS - 4 KW - COVID-19 KW - design KW - eHealth KW - artificial intelligence KW - service design KW - patient journey map KW - user-centered design KW - digital service solutions in health KW - home isolation KW - AI KW - touchpoint N2 - 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. UR - https://medinform.jmir.org/2021/4/e23238 UR - http://dx.doi.org/10.2196/23238 UR - http://www.ncbi.nlm.nih.gov/pubmed/33444156 ID - info:doi/10.2196/23238 ER - TY - JOUR AU - Reps, M. Jenna AU - Kim, Chungsoo AU - Williams, D. Ross AU - Markus, F. Aniek AU - Yang, Cynthia AU - Duarte-Salles, Talita AU - Falconer, Thomas AU - Jonnagaddala, Jitendra AU - Williams, Andrew AU - Fernández-Bertolín, Sergio AU - DuVall, L. Scott AU - Kostka, Kristin AU - Rao, Gowtham AU - Shoaibi, Azza AU - Ostropolets, Anna AU - Spotnitz, E. Matthew AU - Zhang, Lin AU - Casajust, Paula AU - Steyerberg, W. Ewout AU - Nyberg, Fredrik AU - Kaas-Hansen, Skov Benjamin AU - Choi, Hwa Young AU - Morales, Daniel AU - Liaw, Siaw-Teng AU - Abrahão, Fernandes Maria Tereza AU - Areia, Carlos AU - Matheny, E. Michael AU - Lynch, E. Kristine AU - Aragón, María AU - Park, Woong Rae AU - Hripcsak, George AU - Reich, G. Christian AU - Suchard, A. Marc AU - You, Chan Seng AU - Ryan, B. Patrick AU - Prieto-Alhambra, Daniel AU - Rijnbeek, R. Peter PY - 2021/4/5 TI - Implementation of the COVID-19 Vulnerability Index Across an International Network of Health Care Data Sets: Collaborative External Validation Study JO - JMIR Med Inform SP - e21547 VL - 9 IS - 4 KW - external validation KW - transportability KW - COVID-19 KW - prognostic model KW - prediction KW - C-19 KW - modeling KW - datasets KW - observation KW - hospitalization KW - bias KW - risk KW - decision-making N2 - 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. UR - https://medinform.jmir.org/2021/4/e21547 UR - http://dx.doi.org/10.2196/21547 UR - http://www.ncbi.nlm.nih.gov/pubmed/33661754 ID - info:doi/10.2196/21547 ER - TY - JOUR AU - Huang, Yingxiang AU - Radenkovic, Dina AU - Perez, Kevin AU - Nadeau, Kari AU - Verdin, Eric AU - Furman, David PY - 2021/3/25 TI - Modeling Predictive Age-Dependent and Age-Independent Symptoms and Comorbidities of Patients Seeking Treatment for COVID-19: Model Development and Validation Study JO - J Med Internet Res SP - e25696 VL - 23 IS - 3 KW - clinical informatics KW - predictive modeling KW - COVID-19 KW - app KW - model KW - prediction KW - symptom KW - informatics KW - age KW - morbidity KW - hospital N2 - 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. UR - https://www.jmir.org/2021/3/e25696 UR - http://dx.doi.org/10.2196/25696 UR - http://www.ncbi.nlm.nih.gov/pubmed/33621185 ID - info:doi/10.2196/25696 ER - TY - JOUR AU - Lu, Zhao-Hua AU - Wang, Xiaoqing Jade AU - Li, Xintong PY - 2021/3/19 TI - Revealing Opinions for COVID-19 Questions Using a Context Retriever, Opinion Aggregator, and Question-Answering Model: Model Development Study JO - J Med Internet Res SP - e22860 VL - 23 IS - 3 KW - natural language processing KW - question-answering systems KW - language summarization KW - machine learning KW - life and medical sciences KW - COVID-19 KW - public health KW - coronavirus literature N2 - 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. UR - https://www.jmir.org/2021/3/e22860 UR - http://dx.doi.org/10.2196/22860 UR - http://www.ncbi.nlm.nih.gov/pubmed/33739287 ID - info:doi/10.2196/22860 ER - TY - JOUR AU - Shen, Lining AU - Yao, Rui AU - Zhang, Wenli AU - Evans, Richard AU - Cao, Guang AU - Zhang, Zhiguo PY - 2021/3/16 TI - Emotional Attitudes of Chinese Citizens on Social Distancing During the COVID-19 Outbreak: Analysis of Social Media Data JO - JMIR Med Inform SP - e27079 VL - 9 IS - 3 KW - COVID-19 KW - Sina Weibo KW - social distancing measures KW - emotional analysis KW - machine learning KW - moderating effects KW - deep learning KW - social media KW - emotion KW - attitude KW - infodemiology KW - infoveillance N2 - 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 (?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 (?220=19,084.73, P<.001), as well as emotional polarity between genders (?24=1784.59, P<.001) and emotional polarity between spatial locations (?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. UR - https://medinform.jmir.org/2021/3/e27079 UR - http://dx.doi.org/10.2196/27079 UR - http://www.ncbi.nlm.nih.gov/pubmed/33724200 ID - info:doi/10.2196/27079 ER - TY - JOUR AU - Gbashi, Sefater AU - Adebo, Ayodeji Oluwafemi AU - Doorsamy, Wesley AU - Njobeh, Berka Patrick PY - 2021/3/16 TI - Systematic Delineation of Media Polarity on COVID-19 Vaccines in Africa: Computational Linguistic Modeling Study JO - JMIR Med Inform SP - e22916 VL - 9 IS - 3 KW - COVID-19 KW - coronavirus KW - vaccine KW - infodemiology KW - infoveillance KW - infodemic KW - sentiment analysis KW - natural language processing KW - media KW - computation KW - linguistic KW - model KW - communication N2 - 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. UR - https://medinform.jmir.org/2021/3/e22916 UR - http://dx.doi.org/10.2196/22916 UR - http://www.ncbi.nlm.nih.gov/pubmed/33667172 ID - info:doi/10.2196/22916 ER - TY - JOUR AU - Liu, Qinlai AU - Sun, Wenping AU - Du, Changqing AU - Yang, Leiying AU - Yuan, Na AU - Cui, Haiqing AU - Song, Wengang AU - Ge, Li PY - 2021/3/15 TI - Medical Morphology Training Using the Xuexi Tong Platform During the COVID-19 Pandemic: Development and Validation of a Web-Based Teaching Approach JO - JMIR Med Inform SP - e24497 VL - 9 IS - 3 KW - COVID-19 KW - histology and embryology KW - pathology KW - web-based teaching KW - Xuexi Tong platform N2 - 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. UR - https://medinform.jmir.org/2021/3/e24497 UR - http://dx.doi.org/10.2196/24497 UR - http://www.ncbi.nlm.nih.gov/pubmed/33566792 ID - info:doi/10.2196/24497 ER - TY - JOUR AU - R Niakan Kalhori, Sharareh AU - Bahaadinbeigy, Kambiz AU - Deldar, Kolsoum AU - Gholamzadeh, Marsa AU - Hajesmaeel-Gohari, Sadrieh AU - Ayyoubzadeh, Mohammad Seyed PY - 2021/3/10 TI - Digital Health Solutions to Control the COVID-19 Pandemic in Countries With High Disease Prevalence: Literature Review JO - J Med Internet Res SP - e19473 VL - 23 IS - 3 KW - COVID-19 KW - digital health KW - information technology KW - telemedicine KW - electronic health N2 - 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. UR - https://www.jmir.org/2021/3/e19473 UR - http://dx.doi.org/10.2196/19473 UR - http://www.ncbi.nlm.nih.gov/pubmed/33600344 ID - info:doi/10.2196/19473 ER - TY - JOUR AU - Guzzi, H. Pietro AU - Tradigo, Giuseppe AU - Veltri, Pierangelo PY - 2021/3/9 TI - Regional Resource Assessment During the COVID-19 Pandemic in Italy: Modeling Study JO - JMIR Med Inform SP - e18933 VL - 9 IS - 3 KW - COVID-19 KW - data analysis KW - ICU KW - management KW - intensive care unit KW - pandemic KW - outbreak KW - infectious disease KW - resource KW - planning N2 - 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. UR - https://medinform.jmir.org/2021/3/e18933 UR - http://dx.doi.org/10.2196/18933 UR - http://www.ncbi.nlm.nih.gov/pubmed/33629957 ID - info:doi/10.2196/18933 ER - TY - JOUR AU - Yan, Chao AU - Zhang, Xinmeng AU - Gao, Cheng AU - Wilfong, Erin AU - Casey, Jonathan AU - France, Daniel AU - Gong, Yang AU - Patel, Mayur AU - Malin, Bradley AU - Chen, You PY - 2021/3/8 TI - Collaboration Structures in COVID-19 Critical Care: Retrospective Network Analysis Study JO - JMIR Hum Factors SP - e25724 VL - 8 IS - 1 KW - COVID-19 KW - intensive care unit KW - collaboration structure KW - critically ill patient KW - health care worker KW - network analysis KW - electronic health record KW - collaboration KW - critical care KW - relationship KW - safety KW - teamwork N2 - 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. UR - https://humanfactors.jmir.org/2021/1/e25724 UR - http://dx.doi.org/10.2196/25724 UR - http://www.ncbi.nlm.nih.gov/pubmed/33621187 ID - info:doi/10.2196/25724 ER - TY - JOUR AU - Kohane, S. Isaac AU - Aronow, J. Bruce AU - Avillach, Paul AU - Beaulieu-Jones, K. Brett AU - Bellazzi, Riccardo AU - Bradford, L. Robert AU - Brat, A. Gabriel AU - Cannataro, Mario AU - Cimino, J. James AU - García-Barrio, Noelia AU - Gehlenborg, Nils AU - Ghassemi, Marzyeh AU - Gutiérrez-Sacristán, Alba AU - Hanauer, A. David AU - Holmes, H. John AU - Hong, Chuan AU - Klann, G. Jeffrey AU - Loh, Will Ne Hooi AU - Luo, Yuan AU - Mandl, D. Kenneth AU - Daniar, Mohamad AU - Moore, H. Jason AU - Murphy, N. Shawn AU - Neuraz, Antoine AU - Ngiam, Yuan Kee AU - Omenn, S. Gilbert AU - Palmer, Nathan AU - Patel, P. Lav AU - Pedrera-Jiménez, Miguel AU - Sliz, Piotr AU - South, M. Andrew AU - Tan, Min Amelia Li AU - Taylor, M. Deanne AU - Taylor, W. Bradley AU - Torti, Carlo AU - Vallejos, K. Andrew AU - Wagholikar, B. Kavishwar AU - AU - Weber, M. Griffin AU - Cai, Tianxi PY - 2021/3/2 TI - What Every Reader Should Know About Studies Using Electronic Health Record Data but May Be Afraid to Ask JO - J Med Internet Res SP - e22219 VL - 23 IS - 3 KW - COVID-19 KW - electronic health records KW - real-world data KW - literature KW - publishing KW - quality KW - data quality KW - reporting standards KW - reporting checklist KW - review KW - statistics UR - https://www.jmir.org/2021/3/e22219 UR - http://dx.doi.org/10.2196/22219 UR - http://www.ncbi.nlm.nih.gov/pubmed/33600347 ID - info:doi/10.2196/22219 ER - TY - JOUR AU - Ikemura, Kenji AU - Bellin, Eran AU - Yagi, Yukako AU - Billett, Henny AU - Saada, Mahmoud AU - Simone, Katelyn AU - Stahl, Lindsay AU - Szymanski, James AU - Goldstein, Y. D. AU - Reyes Gil, Morayma PY - 2021/2/26 TI - Using Automated Machine Learning to Predict the Mortality of Patients With COVID-19: Prediction Model Development Study JO - J Med Internet Res SP - e23458 VL - 23 IS - 2 KW - automated machine learning KW - COVID-19 KW - biomarker KW - ranking KW - decision support tool KW - machine learning KW - decision support KW - Shapley additive explanation KW - partial dependence plot KW - dimensionality reduction N2 - 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. UR - https://www.jmir.org/2021/2/e23458 UR - http://dx.doi.org/10.2196/23458 UR - http://www.ncbi.nlm.nih.gov/pubmed/33539308 ID - info:doi/10.2196/23458 ER - TY - JOUR AU - Lim, Min Hooi AU - Teo, Hai Chin AU - Ng, Jenn Chirk AU - Chiew, Kian Thiam AU - Ng, Leik Wei AU - Abdullah, Adina AU - Abdul Hadi, Haireen AU - Liew, Sun Chee AU - Chan, Seng Chee PY - 2021/2/26 TI - An Automated Patient Self-Monitoring System to Reduce Health Care System Burden During the COVID-19 Pandemic in Malaysia: Development and Implementation Study JO - JMIR Med Inform SP - e23427 VL - 9 IS - 2 KW - COVID-19 KW - coronavirus disease KW - home monitoring KW - symptom monitoring KW - system KW - teleconsultation KW - development KW - eHealth KW - digital health KW - mHealth KW - health services research KW - telesurveillance KW - infectious disease KW - app N2 - 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. UR - https://medinform.jmir.org/2021/2/e23427 UR - http://dx.doi.org/10.2196/23427 UR - http://www.ncbi.nlm.nih.gov/pubmed/33600345 ID - info:doi/10.2196/23427 ER - TY - JOUR AU - Elhadi, Muhammed AU - Msherghi, Ahmed AU - Elhadi, Ahmed AU - Ashini, Aimen AU - Alsoufi, Ahmed AU - Bin Alshiteewi, Fatimah AU - Elmabrouk, Amna AU - Alsuyihili, Ali AU - Elgherwi, Alsafa AU - Elkhafeefi, Fatimah AU - Abdulrazik, Sarah AU - Tarek, Ahmed PY - 2021/2/26 TI - Utilization of Telehealth Services in Libya in Response to the COVID-19 Pandemic: Cross-sectional Analysis JO - JMIR Med Inform SP - e23335 VL - 9 IS - 2 KW - COVID-19 KW - cross-sectional study KW - resource-limited countries KW - SARS-CoV-2 KW - telehealth KW - telemedicine KW - transitional countries KW - usability N2 - 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. UR - https://medinform.jmir.org/2021/2/e23335 UR - http://dx.doi.org/10.2196/23335 UR - http://www.ncbi.nlm.nih.gov/pubmed/33606654 ID - info:doi/10.2196/23335 ER - TY - JOUR AU - Vahidy, Farhaan AU - Jones, L. Stephen AU - Tano, E. Mauricio AU - Nicolas, Carlos Juan AU - Khan, A. Osman AU - Meeks, R. Jennifer AU - Pan, P. Alan AU - Menser, Terri AU - Sasangohar, Farzan AU - Naufal, George AU - Sostman, Dirk AU - Nasir, Khurram AU - Kash, A. Bita PY - 2021/2/23 TI - 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) JO - JMIR Med Inform SP - e26773 VL - 9 IS - 2 KW - COVID-19 KW - SARS-CoV-2 KW - data science KW - data curation KW - electronic health records KW - learning health system KW - databases, factual N2 - 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. UR - https://medinform.jmir.org/2021/2/e26773 UR - http://dx.doi.org/10.2196/26773 UR - http://www.ncbi.nlm.nih.gov/pubmed/33544692 ID - info:doi/10.2196/26773 ER - TY - JOUR AU - Sang, Shengtian AU - Sun, Ran AU - Coquet, Jean AU - Carmichael, Harris AU - Seto, Tina AU - Hernandez-Boussard, Tina PY - 2021/2/22 TI - Learning From Past Respiratory Infections to Predict COVID-19 Outcomes: Retrospective Study JO - J Med Internet Res SP - e23026 VL - 23 IS - 2 KW - COVID-19 KW - invasive mechanical ventilation KW - all-cause mortality KW - machine learning KW - artificial intelligence KW - respiratory KW - infection KW - outcome KW - data KW - feasibility KW - framework N2 - 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. UR - https://www.jmir.org/2021/2/e23026 UR - http://dx.doi.org/10.2196/23026 UR - http://www.ncbi.nlm.nih.gov/pubmed/33534724 ID - info:doi/10.2196/23026 ER - TY - JOUR AU - Parikh, Soham AU - Davoudi, Anahita AU - Yu, Shun AU - Giraldo, Carolina AU - Schriver, Emily AU - Mowery, Danielle PY - 2021/2/22 TI - Lexicon Development for COVID-19-related Concepts Using Open-source Word Embedding Sources: An Intrinsic and Extrinsic Evaluation JO - JMIR Med Inform SP - e21679 VL - 9 IS - 2 KW - natural language processing KW - word embedding KW - COVID-19 KW - intrinsic KW - open-source KW - computation KW - model KW - prediction KW - semantic KW - syntactic KW - pattern N2 - 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. UR - https://medinform.jmir.org/2021/2/e21679 UR - http://dx.doi.org/10.2196/21679 UR - http://www.ncbi.nlm.nih.gov/pubmed/33544689 ID - info:doi/10.2196/21679 ER - TY - JOUR AU - Dai, Wanfa AU - Ke, Pei-Feng AU - Li, Zhen-Zhen AU - Zhuang, Qi-Zhen AU - Huang, Wei AU - Wang, Yi AU - Xiong, Yujuan AU - Huang, Xian-Zhang PY - 2021/2/22 TI - Establishing Classifiers With Clinical Laboratory Indicators to Distinguish COVID-19 From Community-Acquired Pneumonia: Retrospective Cohort Study JO - J Med Internet Res SP - e23390 VL - 23 IS - 2 KW - COVID-19 KW - clinical laboratory indicators KW - community-acquired pneumonia KW - classifier KW - classification algorithm N2 - 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. UR - https://www.jmir.org/2021/2/e23390 UR - http://dx.doi.org/10.2196/23390 UR - http://www.ncbi.nlm.nih.gov/pubmed/33534722 ID - info:doi/10.2196/23390 ER - TY - JOUR AU - Quiroz, Carlos Juan AU - Feng, You-Zhen AU - Cheng, Zhong-Yuan AU - Rezazadegan, Dana AU - Chen, Ping-Kang AU - Lin, Qi-Ting AU - Qian, Long AU - Liu, Xiao-Fang AU - Berkovsky, Shlomo AU - Coiera, Enrico AU - Song, Lei AU - Qiu, Xiaoming AU - Liu, Sidong AU - Cai, Xiang-Ran PY - 2021/2/11 TI - Development and Validation of a Machine Learning Approach for Automated Severity Assessment of COVID-19 Based on Clinical and Imaging Data: Retrospective Study JO - JMIR Med Inform SP - e24572 VL - 9 IS - 2 KW - algorithm KW - clinical data KW - clinical features KW - COVID-19 KW - CT scans KW - development KW - imaging KW - imbalanced data KW - machine learning KW - oversampling KW - severity assessment KW - validation N2 - 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. UR - http://medinform.jmir.org/2021/2/e24572/ UR - http://dx.doi.org/10.2196/24572 UR - http://www.ncbi.nlm.nih.gov/pubmed/33534723 ID - info:doi/10.2196/24572 ER - TY - JOUR AU - Bolourani, Siavash AU - Brenner, Max AU - Wang, Ping AU - McGinn, Thomas AU - Hirsch, S. Jamie AU - Barnaby, Douglas AU - Zanos, P. Theodoros AU - PY - 2021/2/10 TI - A Machine Learning Prediction Model of Respiratory Failure Within 48 Hours of Patient Admission for COVID-19: Model Development and Validation JO - J Med Internet Res SP - e24246 VL - 23 IS - 2 KW - artificial intelligence KW - prognostic KW - model KW - pandemic KW - severe acute respiratory syndrome coronavirus 2 KW - modeling KW - development KW - validation KW - COVID-19 KW - machine learning N2 - 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. UR - http://www.jmir.org/2021/2/e24246/ UR - http://dx.doi.org/10.2196/24246 UR - http://www.ncbi.nlm.nih.gov/pubmed/33476281 ID - info:doi/10.2196/24246 ER - TY - JOUR AU - Fernandes, Marta AU - Sun, Haoqi AU - Jain, Aayushee AU - Alabsi, S. Haitham AU - Brenner, N. Laura AU - Ye, Elissa AU - Ge, Wendong AU - Collens, I. Sarah AU - Leone, J. Michael AU - Das, Sudeshna AU - Robbins, K. Gregory AU - Mukerji, S. Shibani AU - Westover, Brandon M. PY - 2021/2/10 TI - Classification of the Disposition of Patients Hospitalized with COVID-19: Reading Discharge Summaries Using Natural Language Processing JO - JMIR Med Inform SP - e25457 VL - 9 IS - 2 KW - ICU KW - coronavirus KW - electronic health record KW - unstructured text KW - natural language processing KW - BoW KW - LASSO KW - feature selection KW - machine learning KW - intensive care unit KW - COVID-19 KW - EHR N2 - 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. UR - https://medinform.jmir.org/2021/2/e25457 UR - http://dx.doi.org/10.2196/25457 UR - http://www.ncbi.nlm.nih.gov/pubmed/33449908 ID - info:doi/10.2196/25457 ER - TY - JOUR AU - Idrees, Mohammad Sheikh AU - Nowostawski, Mariusz AU - Jameel, Roshan PY - 2021/2/9 TI - Blockchain-Based Digital Contact Tracing Apps for COVID-19 Pandemic Management: Issues, Challenges, Solutions, and Future Directions JO - JMIR Med Inform SP - e25245 VL - 9 IS - 2 KW - COVID-19 KW - digital contact tracing KW - privacy preservation KW - security KW - blockchain technology KW - blockchain KW - privacy KW - contact tracing KW - app KW - surveillance UR - https://medinform.jmir.org/2021/2/e25245 UR - http://dx.doi.org/10.2196/25245 UR - http://www.ncbi.nlm.nih.gov/pubmed/33400677 ID - info:doi/10.2196/25245 ER - TY - JOUR AU - Frick, J. Nicholas R. AU - Möllmann, L. Henriette AU - Mirbabaie, Milad AU - Stieglitz, Stefan PY - 2021/2/1 TI - Driving Digital Transformation During a Pandemic: Case Study of Virtual Collaboration in a German Hospital JO - JMIR Med Inform SP - e25183 VL - 9 IS - 2 KW - digital transformation KW - virtual collaboration KW - digital health KW - health care KW - COVID-19 KW - pandemic KW - hospital KW - collaboration KW - virtual heath KW - crisis KW - case study N2 - 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. UR - https://medinform.jmir.org/2021/2/e25183 UR - http://dx.doi.org/10.2196/25183 UR - http://www.ncbi.nlm.nih.gov/pubmed/33449905 ID - info:doi/10.2196/25183 ER - TY - JOUR AU - Reeves, Jeffery J. AU - Ayers, W. John AU - Longhurst, A. Christopher PY - 2021/2/1 TI - Telehealth in the COVID-19 Era: A Balancing Act to Avoid Harm JO - J Med Internet Res SP - e24785 VL - 23 IS - 2 KW - telehealth KW - patient safety KW - COVID-19 KW - coronavirus KW - informatics KW - safety KW - harm KW - risk KW - access KW - efficiency KW - virtual care UR - https://www.jmir.org/2021/2/e24785 UR - http://dx.doi.org/10.2196/24785 UR - http://www.ncbi.nlm.nih.gov/pubmed/33477104 ID - info:doi/10.2196/24785 ER - TY - JOUR AU - Ho, Thi Thao AU - Park, Jongmin AU - Kim, Taewoo AU - Park, Byunggeon AU - Lee, Jaehee AU - Kim, Young Jin AU - Kim, Beom Ki AU - Choi, Sooyoung AU - Kim, Hwan Young AU - Lim, Jae-Kwang AU - Choi, Sanghun PY - 2021/1/28 TI - Deep Learning Models for Predicting Severe Progression in COVID-19-Infected Patients: Retrospective Study JO - JMIR Med Inform SP - e24973 VL - 9 IS - 1 KW - COVID-19 KW - deep learning KW - artificial neural network KW - convolutional neural network KW - lung CT N2 - 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. UR - http://medinform.jmir.org/2021/1/e24973/ UR - http://dx.doi.org/10.2196/24973 UR - http://www.ncbi.nlm.nih.gov/pubmed/33455900 ID - info:doi/10.2196/24973 ER - TY - JOUR AU - Vaid, Akhil AU - Jaladanki, K. Suraj AU - Xu, Jie AU - Teng, Shelly AU - Kumar, Arvind AU - Lee, Samuel AU - Somani, Sulaiman AU - Paranjpe, Ishan AU - De Freitas, K. Jessica AU - Wanyan, Tingyi AU - Johnson, W. Kipp AU - Bicak, Mesude AU - Klang, Eyal AU - Kwon, Joon Young AU - Costa, Anthony AU - Zhao, Shan AU - Miotto, Riccardo AU - Charney, W. Alexander AU - Böttinger, Erwin AU - Fayad, A. Zahi AU - Nadkarni, N. Girish AU - Wang, Fei AU - Glicksberg, S. Benjamin PY - 2021/1/27 TI - Federated Learning of Electronic Health Records to Improve Mortality Prediction in Hospitalized Patients With COVID-19: Machine Learning Approach JO - JMIR Med Inform SP - e24207 VL - 9 IS - 1 KW - federated learning KW - COVID-19 KW - machine learning KW - electronic health records N2 - 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. UR - http://medinform.jmir.org/2021/1/e24207/ UR - http://dx.doi.org/10.2196/24207 UR - http://www.ncbi.nlm.nih.gov/pubmed/33400679 ID - info:doi/10.2196/24207 ER - TY - JOUR AU - Feldman, Jonah AU - Szerencsy, Adam AU - Mann, Devin AU - Austrian, Jonathan AU - Kothari, Ulka AU - Heo, Hye AU - Barzideh, Sam AU - Hickey, Maureen AU - Snapp, Catherine AU - Aminian, Rod AU - Jones, Lauren AU - Testa, Paul PY - 2021/1/27 TI - Giving Your Electronic Health Record a Checkup After COVID-19: A Practical Framework for Reviewing Clinical Decision Support in Light of the Telemedicine Expansion JO - JMIR Med Inform SP - e21712 VL - 9 IS - 1 KW - COVID-19 KW - EHR KW - clinical decision support KW - telemedicine KW - ambulatory care KW - electronic health record KW - framework KW - implementation N2 - 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. UR - http://medinform.jmir.org/2021/1/e21712/ UR - http://dx.doi.org/10.2196/21712 UR - http://www.ncbi.nlm.nih.gov/pubmed/33400683 ID - info:doi/10.2196/21712 ER - TY - JOUR AU - Guinez-Molinos, Sergio AU - Andrade, María José AU - Medina Negrete, Alejandro AU - Espinoza Vidal, Sonia AU - Rios, Elvis PY - 2021/1/20 TI - Interoperable Platform to Report Polymerase Chain Reaction SARS-CoV-2 Tests From Laboratories to the Chilean Government: Development and Implementation Study JO - JMIR Med Inform SP - e25149 VL - 9 IS - 1 KW - COVID-19 KW - SARS-CoV-2 KW - interoperability KW - laboratory information system KW - HL7 FHIR KW - PCR N2 - 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. UR - http://medinform.jmir.org/2021/1/e25149/ UR - http://dx.doi.org/10.2196/25149 UR - http://www.ncbi.nlm.nih.gov/pubmed/33417587 ID - info:doi/10.2196/25149 ER - TY - JOUR AU - Yang, Chuan AU - Zhang, Wei AU - Pang, Zhixuan AU - Zhang, Jing AU - Zou, Deling AU - Zhang, Xinzhong AU - Guo, Sicong AU - Wan, Jiye AU - Wang, Ke AU - Pang, Wenyue PY - 2021/1/19 TI - A Low-Cost, Ear-Contactless Electronic Stethoscope Powered by Raspberry Pi for Auscultation of Patients With COVID-19: Prototype Development and Feasibility Study JO - JMIR Med Inform SP - e22753 VL - 9 IS - 1 KW - stethoscope KW - auscultation KW - COVID-19 KW - Raspberry Pi KW - Python KW - ear-contactless KW - low-cost KW - phonocardiogram KW - digital health N2 - 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. UR - https://medinform.jmir.org/2021/1/e22753 UR - http://dx.doi.org/10.2196/22753 UR - http://www.ncbi.nlm.nih.gov/pubmed/33436354 ID - info:doi/10.2196/22753 ER - TY - JOUR AU - Syeda, Bareen Hafsa AU - Syed, Mahanazuddin AU - Sexton, Wayne Kevin AU - Syed, Shorabuddin AU - Begum, Salma AU - Syed, Farhanuddin AU - Prior, Fred AU - Yu Jr, Feliciano PY - 2021/1/11 TI - Role of Machine Learning Techniques to Tackle the COVID-19 Crisis: Systematic Review JO - JMIR Med Inform SP - e23811 VL - 9 IS - 1 KW - COVID-19 KW - coronavirus KW - SARS-CoV-2 KW - artificial intelligence KW - machine learning KW - deep learning KW - systematic review KW - epidemiology KW - pandemic KW - neural network N2 - 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. UR - http://medinform.jmir.org/2021/1/e23811/ UR - http://dx.doi.org/10.2196/23811 UR - http://www.ncbi.nlm.nih.gov/pubmed/33326405 ID - info:doi/10.2196/23811 ER - TY - JOUR AU - An, Ho Min AU - You, Chan Seng AU - Park, Woong Rae AU - Lee, Seongwon PY - 2021/1/8 TI - 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 JO - JMIR Med Inform SP - e25435 VL - 9 IS - 1 KW - telemedicine KW - telehealth KW - COVID-19 KW - pandemic KW - model KW - South Korea KW - acceptance KW - anxiety KW - cross-sectional N2 - 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. UR - http://medinform.jmir.org/2021/1/e25435/ UR - http://dx.doi.org/10.2196/25435 UR - http://www.ncbi.nlm.nih.gov/pubmed/33395397 ID - info:doi/10.2196/25435 ER - TY - JOUR AU - Ko, Hoon AU - Chung, Heewon AU - Kang, Seong Wu AU - Park, Chul AU - Kim, Wan Do AU - Kim, Eun Seong AU - Chung, Ryang Chi AU - Ko, Eun Ryoung AU - Lee, Hooseok AU - Seo, Ho Jae AU - Choi, Tae-Young AU - Jaimes, Rafael AU - Kim, Won Kyung AU - Lee, Jinseok PY - 2020/12/23 TI - 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 JO - J Med Internet Res SP - e25442 VL - 22 IS - 12 KW - COVID-19 KW - artificial intelligence KW - blood samples KW - mortality prediction N2 - 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. UR - http://www.jmir.org/2020/12/e25442/ UR - http://dx.doi.org/10.2196/25442 UR - http://www.ncbi.nlm.nih.gov/pubmed/33301414 ID - info:doi/10.2196/25442 ER - TY - JOUR AU - Lee, Hsiu-An AU - Kung, Hsin-Hua AU - Lee, Yuarn-Jang AU - Chao, C-J Jane AU - Udayasankaran, Ganesh Jai AU - Fan, Hueng-Chuen AU - Ng, Kwok-Keung AU - Chang, Yu-Kang AU - Kijsanayotin, Boonchai AU - Marcelo, B. Alvin AU - Hsu, Chien-Yeh PY - 2020/12/22 TI - Global Infectious Disease Surveillance and Case Tracking System for COVID-19: Development Study JO - JMIR Med Inform SP - e20567 VL - 8 IS - 12 KW - blockchain KW - infectious disease surveillance KW - international collaboration KW - HL7 FHIR KW - COVID-19 defense KW - COVID-19 N2 - 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. UR - http://medinform.jmir.org/2020/12/e20567/ UR - http://dx.doi.org/10.2196/20567 UR - http://www.ncbi.nlm.nih.gov/pubmed/33320826 ID - info:doi/10.2196/20567 ER - TY - JOUR AU - Gilson, F. Sarah AU - Umscheid, A. Craig AU - Laiteerapong, Neda AU - Ossey, Graeme AU - Nunes, J. Kenneth AU - Shah, D. Sachin PY - 2020/12/4 TI - Growth of Ambulatory Virtual Visits and Differential Use by Patient Sociodemographics at One Urban Academic Medical Center During the COVID-19 Pandemic: Retrospective Analysis JO - JMIR Med Inform SP - e24544 VL - 8 IS - 12 KW - telemedicine KW - telehealth KW - video visit KW - telephone visit KW - virtual visit KW - COVID-19 KW - age KW - sex KW - race KW - insurance KW - demographic KW - retrospective N2 - 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. UR - https://medinform.jmir.org/2020/12/e24544 UR - http://dx.doi.org/10.2196/24544 UR - http://www.ncbi.nlm.nih.gov/pubmed/33191247 ID - info:doi/10.2196/24544 ER - TY - JOUR AU - Alsharif, Abdullah PY - 2020/11/26 TI - Applying eHealth for Pandemic Management in Saudi Arabia in the Context of COVID-19: Survey Study and Framework Proposal JO - JMIR Med Inform SP - e19524 VL - 8 IS - 11 KW - COVID-19 KW - eHealth framework KW - infectious disease KW - pandemic KW - eHealth KW - public health N2 - 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. UR - http://medinform.jmir.org/2020/11/e19524/ UR - http://dx.doi.org/10.2196/19524 UR - http://www.ncbi.nlm.nih.gov/pubmed/33035174 ID - info:doi/10.2196/19524 ER - TY - JOUR AU - Li, Daowei AU - Zhang, Qiang AU - Tan, Yue AU - Feng, Xinghuo AU - Yue, Yuanyi AU - Bai, Yuhan AU - Li, Jimeng AU - Li, Jiahang AU - Xu, Youjun AU - Chen, Shiyu AU - Xiao, Si-Yu AU - Sun, Muyan AU - Li, Xiaona AU - Zhu, Fang PY - 2020/11/17 TI - Prediction of COVID-19 Severity Using Chest Computed Tomography and Laboratory Measurements: Evaluation Using a Machine Learning Approach JO - JMIR Med Inform SP - e21604 VL - 8 IS - 11 KW - COVID-19 KW - severe case prediction KW - computerized tomography KW - machine learning KW - CT KW - scan KW - detection KW - prediction KW - model N2 - 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. UR - http://medinform.jmir.org/2020/11/e21604/ UR - http://dx.doi.org/10.2196/21604 UR - http://www.ncbi.nlm.nih.gov/pubmed/33038076 ID - info:doi/10.2196/21604 ER - TY - JOUR AU - Khan, Younus Junaed AU - Khondaker, Islam Md Tawkat AU - Hoque, Tazim Iram AU - Al-Absi, H. Hamada R. AU - Rahman, Saifur Mohammad AU - Guler, Reto AU - Alam, Tanvir AU - Rahman, Sohel M. PY - 2020/11/10 TI - Toward Preparing a Knowledge Base to Explore Potential Drugs and Biomedical Entities Related to COVID-19: Automated Computational Approach JO - JMIR Med Inform SP - e21648 VL - 8 IS - 11 KW - COVID-19 KW - 2019-nCoV KW - coronavirus KW - SARS-CoV-2 KW - SARS KW - remdesivir KW - statin KW - statins KW - dexamethasone KW - ivermectin KW - hydroxychloroquine N2 - 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. UR - http://medinform.jmir.org/2020/11/e21648/ UR - http://dx.doi.org/10.2196/21648 UR - http://www.ncbi.nlm.nih.gov/pubmed/33055059 ID - info:doi/10.2196/21648 ER - TY - JOUR AU - Kim, Hyung-Jun AU - Han, Deokjae AU - Kim, Jeong-Han AU - Kim, Daehyun AU - Ha, Beomman AU - Seog, Woong AU - Lee, Yeon-Kyeng AU - Lim, Dosang AU - Hong, Ok Sung AU - Park, Mi-Jin AU - Heo, JoonNyung PY - 2020/11/9 TI - An Easy-to-Use Machine Learning Model to Predict the Prognosis of Patients With COVID-19: Retrospective Cohort Study JO - J Med Internet Res SP - e24225 VL - 22 IS - 11 KW - COVID-19 KW - machine learning KW - prognosis KW - SARS-CoV-2 KW - severe acute respiratory syndrome coronavirus 2 N2 - 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. UR - http://www.jmir.org/2020/11/e24225/ UR - http://dx.doi.org/10.2196/24225 UR - http://www.ncbi.nlm.nih.gov/pubmed/33108316 ID - info:doi/10.2196/24225 ER - TY - JOUR AU - Vaid, Akhil AU - Somani, Sulaiman AU - Russak, J. Adam AU - De Freitas, K. Jessica AU - Chaudhry, F. Fayzan AU - Paranjpe, Ishan AU - Johnson, W. Kipp AU - Lee, J. Samuel AU - Miotto, Riccardo AU - Richter, Felix AU - Zhao, Shan AU - Beckmann, D. Noam AU - Naik, Nidhi AU - Kia, Arash AU - Timsina, Prem AU - Lala, Anuradha AU - Paranjpe, Manish AU - Golden, Eddye AU - Danieletto, Matteo AU - Singh, Manbir AU - Meyer, Dara AU - O'Reilly, F. Paul AU - Huckins, Laura AU - Kovatch, Patricia AU - Finkelstein, Joseph AU - Freeman, M. Robert AU - Argulian, Edgar AU - Kasarskis, Andrew AU - Percha, Bethany AU - Aberg, A. Judith AU - Bagiella, Emilia AU - Horowitz, R. Carol AU - Murphy, Barbara AU - Nestler, J. Eric AU - Schadt, E. Eric AU - Cho, H. Judy AU - Cordon-Cardo, Carlos AU - Fuster, Valentin AU - Charney, S. Dennis AU - Reich, L. David AU - Bottinger, P. Erwin AU - Levin, A. Matthew AU - Narula, Jagat AU - Fayad, A. Zahi AU - Just, C. Allan AU - Charney, W. Alexander AU - Nadkarni, N. Girish AU - Glicksberg, S. Benjamin PY - 2020/11/6 TI - Machine Learning to Predict Mortality and Critical Events in a Cohort of Patients With COVID-19 in New York City: Model Development and Validation JO - J Med Internet Res SP - e24018 VL - 22 IS - 11 KW - machine learning KW - COVID-19 KW - electronic health record KW - TRIPOD KW - clinical informatics KW - prediction KW - mortality KW - EHR KW - cohort KW - hospital KW - performance N2 - 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. UR - https://www.jmir.org/2020/11/e24018 UR - http://dx.doi.org/10.2196/24018 UR - http://www.ncbi.nlm.nih.gov/pubmed/33027032 ID - info:doi/10.2196/24018 ER - TY - JOUR AU - Golinelli, Davide AU - Boetto, Erik AU - Carullo, Gherardo AU - Nuzzolese, Giovanni Andrea AU - Landini, Paola Maria AU - Fantini, Pia Maria PY - 2020/11/6 TI - Adoption of Digital Technologies in Health Care During the COVID-19 Pandemic: Systematic Review of Early Scientific Literature JO - J Med Internet Res SP - e22280 VL - 22 IS - 11 KW - COVID-19 KW - SARS-CoV-2 KW - pandemic KW - digital heath KW - review KW - literature KW - mitigate KW - impact KW - eHealth N2 - 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. UR - http://www.jmir.org/2020/11/e22280/ UR - http://dx.doi.org/10.2196/22280 UR - http://www.ncbi.nlm.nih.gov/pubmed/33079693 ID - info:doi/10.2196/22280 ER - TY - JOUR AU - Leslie, Heather PY - 2020/11/2 TI - openEHR Archetype Use and Reuse Within Multilingual Clinical Data Sets: Case Study JO - J Med Internet Res SP - e23361 VL - 22 IS - 11 KW - openEHR KW - archetype KW - template KW - reuse KW - clinical informatics KW - COVID-19 KW - standard KW - crowd sourced KW - data set KW - data quality KW - multilingual KW - EHR KW - electronic health record KW - SARS-CoV-2 N2 - 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. UR - https://www.jmir.org/2020/11/e23361 UR - http://dx.doi.org/10.2196/23361 UR - http://www.ncbi.nlm.nih.gov/pubmed/33035176 ID - info:doi/10.2196/23361 ER - TY - JOUR AU - Izquierdo, Luis Jose AU - Ancochea, Julio AU - AU - Soriano, B. Joan PY - 2020/10/28 TI - Clinical Characteristics and Prognostic Factors for Intensive Care Unit Admission of Patients With COVID-19: Retrospective Study Using Machine Learning and Natural Language Processing JO - J Med Internet Res SP - e21801 VL - 22 IS - 10 KW - artificial intelligence KW - big data KW - COVID-19 KW - electronic health records KW - tachypnea KW - SARS-CoV-2 KW - predictive model N2 - 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 ºC 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. UR - http://www.jmir.org/2020/10/e21801/ UR - http://dx.doi.org/10.2196/21801 UR - http://www.ncbi.nlm.nih.gov/pubmed/33090964 ID - info:doi/10.2196/21801 ER - TY - JOUR AU - Cha, Dongchul AU - Shin, Ho Seung AU - Kim, Jungghi AU - Eo, Seong Tae AU - Na, Gina AU - Bae, Seonghoon AU - Jung, Jinsei AU - Kim, Huhn Sung AU - Moon, Seok In AU - Choi, Jaeyoung AU - Park, Rang Yu PY - 2020/10/19 TI - Feasibility of Asynchronous and Automated Telemedicine in Otolaryngology: Prospective Cross-Sectional Study JO - JMIR Med Inform SP - e23680 VL - 8 IS - 10 KW - telemedicine KW - otolaryngology KW - otology KW - automated diagnosis KW - asynchronous KW - COVID-19 KW - diagnosis KW - feasibility KW - cross-sectional N2 - 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. UR - http://medinform.jmir.org/2020/10/e23680/ UR - http://dx.doi.org/10.2196/23680 UR - http://www.ncbi.nlm.nih.gov/pubmed/33027033 ID - info:doi/10.2196/23680 ER - TY - JOUR AU - Nan, Shan AU - Tang, Tianhua AU - Feng, Hongshuo AU - Wang, Yijie AU - Li, Mengyang AU - Lu, Xudong AU - Duan, Huilong PY - 2020/10/1 TI - A Computer-Interpretable Guideline for COVID-19: Rapid Development and Dissemination JO - JMIR Med Inform SP - e21628 VL - 8 IS - 10 KW - COVID-19 KW - guideline KW - CDSS KW - openEHR KW - Guideline Definition Language KW - development KW - dissemination KW - electronic health record KW - algorithm N2 - 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. UR - https://medinform.jmir.org/2020/10/e21628 UR - http://dx.doi.org/10.2196/21628 UR - http://www.ncbi.nlm.nih.gov/pubmed/32931443 ID - info:doi/10.2196/21628 ER - TY - JOUR AU - Khurshid, Anjum PY - 2020/9/22 TI - Applying Blockchain Technology to Address the Crisis of Trust During the COVID-19 Pandemic JO - JMIR Med Inform SP - e20477 VL - 8 IS - 9 KW - blockchain KW - privacy KW - trust KW - contact tracing KW - COVID-19 KW - coronavirus N2 - 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. UR - http://medinform.jmir.org/2020/9/e20477/ UR - http://dx.doi.org/10.2196/20477 UR - http://www.ncbi.nlm.nih.gov/pubmed/32903197 ID - info:doi/10.2196/20477 ER - TY - JOUR AU - Fan, Tao AU - Hao, Bo AU - Yang, Shuo AU - Shen, Bo AU - Huang, Zhixin AU - Lu, Zilong AU - Xiong, Rui AU - Shen, Xiaokang AU - Jiang, Wenyang AU - Zhang, Lin AU - Li, Donghang AU - He, Ruyuan AU - Meng, Heng AU - Lin, Weichen AU - Feng, Haojie AU - Geng, Qing PY - 2020/9/8 TI - Nomogram for Predicting COVID-19 Disease Progression Based on Single-Center Data: Observational Study and Model Development JO - JMIR Med Inform SP - e19588 VL - 8 IS - 9 KW - coronavirus disease 2019 KW - COVID-19 KW - risk factors KW - nomogram N2 - 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. UR - http://medinform.jmir.org/2020/9/e19588/ UR - http://dx.doi.org/10.2196/19588 UR - http://www.ncbi.nlm.nih.gov/pubmed/32866109 ID - info:doi/10.2196/19588 ER - TY - JOUR AU - Nakamoto, Ichiro AU - Wang, Sheng AU - Guo, Yan AU - Zhuang, Weiqing PY - 2020/9/7 TI - A QR Code?Based Contact Tracing Framework for Sustainable Containment of COVID-19: Evaluation of an Approach to Assist the Return to Normal Activity JO - JMIR Mhealth Uhealth SP - e22321 VL - 8 IS - 9 KW - COVID-19 KW - coronavirus KW - symptom-based KW - quick response KW - eHealth KW - digital health KW - telesurveillance KW - pandemic KW - epidemic KW - interoperability UR - http://mhealth.jmir.org/2020/9/e22321/ UR - http://dx.doi.org/10.2196/22321 UR - http://www.ncbi.nlm.nih.gov/pubmed/32841151 ID - info:doi/10.2196/22321 ER - TY - JOUR AU - Silven, V. Anna AU - Petrus, J. Annelieke H. AU - Villalobos-Quesada, María AU - Dirikgil, Ebru AU - Oerlemans, R. Carlijn AU - Landstra, P. Cyril AU - Boosman, Hileen AU - van Os, A. Hendrikus J. AU - Blanker, H. Marco AU - Treskes, W. Roderick AU - Bonten, N. Tobias AU - Chavannes, H. Niels AU - Atsma, E. Douwe AU - Teng, Onno Y. K. PY - 2020/9/2 TI - Telemonitoring for Patients With COVID-19: Recommendations for Design and Implementation JO - J Med Internet Res SP - e20953 VL - 22 IS - 9 KW - telemonitoring KW - telemedicine KW - eHealth KW - digital health KW - COVID-19 UR - https://www.jmir.org/2020/9/e20953 UR - http://dx.doi.org/10.2196/20953 UR - http://www.ncbi.nlm.nih.gov/pubmed/32833660 ID - info:doi/10.2196/20953 ER - TY - JOUR AU - Lee, Won Seung AU - Yuh, Tak Woon AU - Yang, Myung Jee AU - Cho, Yoon-Sik AU - Yoo, Kyung In AU - Koh, Yong Hyun AU - Marshall, Dominic AU - Oh, Donghwan AU - Ha, Kyo Eun AU - Han, Yong Man AU - Yon, Keon Dong PY - 2020/8/25 TI - Nationwide Results of COVID-19 Contact Tracing in South Korea: Individual Participant Data From an Epidemiological Survey JO - JMIR Med Inform SP - e20992 VL - 8 IS - 8 KW - COVID-19 KW - contact tracing KW - coronavirus KW - South Korea KW - survey KW - health data KW - epidemiology KW - transmission N2 - 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. UR - http://medinform.jmir.org/2020/8/e20992/ UR - http://dx.doi.org/10.2196/20992 UR - http://www.ncbi.nlm.nih.gov/pubmed/32784189 ID - info:doi/10.2196/20992 ER - TY - JOUR AU - Neuraz, Antoine AU - Lerner, Ivan AU - Digan, William AU - Paris, Nicolas AU - Tsopra, Rosy AU - Rogier, Alice AU - Baudoin, David AU - Cohen, Bretonnel Kevin AU - Burgun, Anita AU - Garcelon, Nicolas AU - Rance, Bastien AU - PY - 2020/8/14 TI - Natural Language Processing for Rapid Response to Emergent Diseases: Case Study of Calcium Channel Blockers and Hypertension in the COVID-19 Pandemic JO - J Med Internet Res SP - e20773 VL - 22 IS - 8 KW - medication information KW - natural language processing KW - electronic health records KW - COVID-19 KW - public health KW - response KW - emergent disease KW - informatics N2 - 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. UR - http://www.jmir.org/2020/8/e20773/ UR - http://dx.doi.org/10.2196/20773 UR - http://www.ncbi.nlm.nih.gov/pubmed/32759101 ID - info:doi/10.2196/20773 ER - TY - JOUR AU - Ye, Jiancheng PY - 2020/7/14 TI - The Role of Health Technology and Informatics in a Global Public Health Emergency: Practices and Implications From the COVID-19 Pandemic JO - JMIR Med Inform SP - e19866 VL - 8 IS - 7 KW - health technology KW - health information system KW - COVID-19 KW - artificial intelligence KW - telemedicine KW - big data KW - privacy UR - http://medinform.jmir.org/2020/7/e19866/ UR - http://dx.doi.org/10.2196/19866 UR - http://www.ncbi.nlm.nih.gov/pubmed/32568725 ID - info:doi/10.2196/19866 ER - TY - JOUR AU - Bae, Seul Ye AU - Kim, Hwan Kyung AU - Choi, Won Sae AU - Ko, Taehoon AU - Jeong, Wook Chang AU - Cho, BeLong AU - Kim, Sun Min AU - Kang, EunKyo PY - 2020/6/12 TI - Information Technology?Based Management of Clinically Healthy COVID-19 Patients: Lessons From a Living and Treatment Support Center Operated by Seoul National University Hospital JO - J Med Internet Res SP - e19938 VL - 22 IS - 6 KW - COVID-19 KW - clinical informatics KW - mobile app KW - telemedicine KW - hospital information system KW - app KW - health information technology N2 - 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. UR - http://www.jmir.org/2020/6/e19938/ UR - http://dx.doi.org/10.2196/19938 UR - http://www.ncbi.nlm.nih.gov/pubmed/32490843 ID - info:doi/10.2196/19938 ER - TY - JOUR AU - Li, Mengyang AU - Leslie, Heather AU - Qi, Bin AU - Nan, Shan AU - Feng, Hongshuo AU - Cai, Hailing AU - Lu, Xudong AU - Duan, Huilong PY - 2020/6/10 TI - Development of an openEHR Template for COVID-19 Based on Clinical Guidelines JO - J Med Internet Res SP - e20239 VL - 22 IS - 6 KW - coronavirus disease KW - COVID-19 KW - openEHR KW - archetype KW - template KW - knowledge modeling KW - clinical guidelines N2 - 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. UR - http://www.jmir.org/2020/6/e20239/ UR - http://dx.doi.org/10.2196/20239 UR - http://www.ncbi.nlm.nih.gov/pubmed/32496207 ID - info:doi/10.2196/20239 ER -