Deep Neural Networks for Simultaneously Capturing Public Topics and Sentiments During a Pandemic: Application on a COVID-19 Tweet Data Set.

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.

General practitioners' perceptions of using virtual primary care during the COVID-19 pandemic: An international cross-sectional survey study.

With the onset of COVID-19, general practitioners (GPs) and patients worldwide swiftly transitioned from face-to-face to digital remote consultations. There is a need to evaluate how this global shift has impacted patient care, healthcare providers, patient and carer experience, and health systems. We explored GPs' perspectives on the main benefits and challenges of using digital virtual care. GPs across 20 countries completed an online questionnaire between June-September 2020. GPs' perceptions of main barriers and challenges were explored using free-text questions. Thematic analysis was used to analyse the data. A total of 1,605 respondents participated in our survey. The benefits identified included reducing COVID-19 transmission risks, guaranteeing access and continuity of care, improved efficiency, faster access to care, improved convenience and communication with patients, greater work flexibility for providers, and hastening the digital transformation of primary care and accompanying legal frameworks. Main challenges included patients' preference for face-to-face consultations, digital exclusion, lack of physical examinations, clinical uncertainty, delays in diagnosis and treatment, overuse and misuse of digital virtual care, and unsuitability for certain types of consultations. Other challenges include the lack of formal guidance, higher workloads, remuneration issues, organisational culture, technical difficulties, implementation and financial issues, and regulatory weaknesses. At the frontline of care delivery, GPs can provide important insights on what worked well, why, and how during the pandemic. Lessons learned can be used to inform the adoption of improved virtual care solutions and support the long-term development of platforms that are more technologically robust and secure.

Evaluating the Impact of COVID-19 on the Adoption of Virtual Care in General Practice in 20 Countries (inSIGHT): Protocol and Rationale Study.

BACKGROUND: In recent decades, virtual care has emerged as a promising option to support primary care delivery. However, despite the potential, adoption rates remained low. With the outbreak of COVID-19, it has suddenly been pushed to the forefront of care delivery. As we progress into the second year of the COVID-19 pandemic, there is a need and opportunity to review the impact remote care had in primary care settings and reassess its potential future role. OBJECTIVE: This study aims to explore the perspectives of general practitioners (GPs) and family doctors on the (1) use of virtual care during the COVID-19 pandemic, (2) perceived impact on quality and safety of care, and (3) essential factors for high-quality and sustainable use of virtual care in the future. METHODS: This study used an online cross-sectional questionnaire completed by GPs distributed across 20 countries. The survey was hosted in Qualtrics and distributed using email, social media, and the researchers' personal contact networks. GPs were eligible for the survey if they were working mainly in primary care during the period of the COVID-19 pandemic. Descriptive statistical analysis will be performed for quantitative variables, and relationships between the use of virtual care and perceptions on impact on quality and safety of care and participants' characteristics may be explored. Qualitative data (free-text responses) will be analyzed using framework analysis. RESULTS: Data collection took place from June 2020 to September 2020. As of this manuscript's submission, a total of 1605 GP respondents participated in the questionnaire. Further data analysis is currently ongoing. CONCLUSIONS: The study will provide a comprehensive overview of the availability of virtual care technologies, perceived impact on quality and safety of care, and essential factors for high-quality future use. In addition, a description of the underlying factors that influence this adoption and perceptions, in both individual GP and family doctor characteristics and the context in which they work, will be provided. While the COVID-19 pandemic may prove the first great stress test of the capabilities, capacity, and robustness of digital systems currently in use, remote care will likely remain an increasingly common approach in the future. There is an imperative to identify the main lessons from this unexpected transformation and use them to inform policy decisions and health service design. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/30099.

A COVID-19 Decision Support System for Phone Call Triage, Designed by and for Medical Students.

During spring 2020, SARS-CoV-2 pandemic induced shortage of medical equipment, hospital capacity and staff. To tackle this issue, medical students have been strongly involved in early patient triage through medical phone call regulation. Here, we present an intelligent web-based decision support system for COVID-19 phone call regulation, developed by and for, medical students to help them during this difficult but crucial task. The system is divided into 5 tabs. The first tab displays administrative information, clinical data related to life-threatening emergency, and personalized recommendations for patient management. The second tab displays a PDF report summarizing the clinical situation; the third tab displays the guidelines used for establishing the recommendations, and the fourth tab displays the overall algorithm in the form of a decision tree. The fifth tab provided a short user guide. The system was assessed by 21 medical staff. More than 90% of them appreciated the navigation and the interface, and found the content relevant. 90,5% of them would like to use it during the medical regulation. In the future, we plan to use this system during simulation-based medical learning for the initial medical training of medical students.

Reorganisation of GP surgeries during the COVID-19 outbreak: analysis of guidelines from 15 countries.

BACKGROUND: General practitioners (GPs) play a key role in managing the COVID-19 outbreak. However, they may encounter difficulties adapting their practices to the pandemic. We provide here an analysis of guidelines for the reorganisation of GP surgeries during the beginning of the pandemic from 15 countries. METHODS: A network of GPs collaborated together in a three-step process: (i) identification of key recommendations of GP surgery reorganisation, according to WHO, CDC and health professional resources from health care facilities; (ii) collection of key recommendations included in the guidelines published in 15 countries; (iii) analysis, comparison and synthesis of the results. RESULTS: Recommendations for the reorganisation of GP surgeries of four types were identified: (i) reorganisation of GP consultations (cancelation of non-urgent consultations, follow-up via e-consultations), (ii) reorganisation of GP surgeries (area partitioning, visual alerts and signs, strict hygiene measures), (iii) reorganisation of medical examinations by GPs (equipment, hygiene, partial clinical examinations, patient education), (iv) reorganisation of GP staff (equipment, management, meetings, collaboration with the local community). CONCLUSIONS: We provide here an analysis of guidelines for the reorganisation of GP surgeries during the beginning of the COVID-19 outbreak from 15 countries. These guidelines focus principally on clinical care, with less attention paid to staff management, and the area of epidemiological surveillance and research is largely neglected. The differences of guidelines between countries and the difficulty to apply them in routine care, highlight the need of advanced research in primary care. Thereby, primary care would be able to provide recommendations adapted to the real-world settings and with stronger evidence, which is especially necessary during pandemics.

Natural Language Processing for Rapid Response to Emergent Diseases: Case Study of Calcium Channel Blockers and Hypertension in the COVID-19 Pandemic.

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.