Impact of COVID-19 pandemic early response measures on myocardial infarctions and acute cardiac care in Singapore.

The COVID -19 pandemic impacted acute myocardial infarction (AMI) attendances, ST-elevation myocardial infarction (STEMI) treatments, and outcomes. We collated data from majority of primary percutaneous coronary intervention (PPCI)-capable public healthcare centres in Singapore to understand the initial impact COVID-19 had on essential time-critical emergency services. We present data comparisons from 'Before Disease Outbreak Response System Condition (DORSCON) Orange', 'DORSCON Orange to start of circuit breaker (CB)', and during the first month of 'CB'. We collected aggregate numbers of weekly elective PCI from four centres and AMI admissions, PPCI, and in-hospital mortality from five centres. Exact door-to-balloon (DTB) times were recorded for one centre; another two reported proportions of DTB times exceeding targets. Median weekly elective PCI cases significantly decreased from 'Before DORSCON Orange' to 'DORSCON Orange to start of CB' (34 vs 22.5, P = 0.013). Median weekly STEMI admissions and PPCI did not change significantly. In contrast, the median weekly non-STEMI (NSTEMI) admissions decreased significantly from 'Before DORSCON Orange' to 'DORSCON Orange to start of CB' (59 vs 48, P = 0.005) and were sustained during CB (39 cases). Exact DTB times reported by one centre showed no significant change in the median. Out of three centres, two reported significant increases in the proportion that exceeded DTB targets. In-hospital mortality rates remained static. In Singapore, STEMI and PPCI rates remained stable, while NSTEMI rates decreased during DORSCON Orange and CB. The severe acute respiratory syndrome (SARS) experience may have helped prepare us to maintain essential services such as PPCI during periods of acute healthcare resource strain. However, data must be monitored and increased pandemic preparedness measures must be explored to ensure that AMI care is not adversely affected by continued COVID fluctuations and future pandemics.

Development and testing of a multi-lingual Natural Language Processing-based deep learning system in 10 languages for COVID-19 pandemic crisis: A multi-center study

Purpose The COVID-19 pandemic has drastically disrupted global healthcare systems. With the higher demand for healthcare and misinformation related to COVID-19, there is a need to explore alternative models to improve communication. Artificial Intelligence (AI) and Natural Language Processing (NLP) have emerged as promising solutions to improve healthcare delivery. Chatbots could fill a pivotal role in the dissemination and easy accessibility of accurate information in a pandemic. In this study, we developed a multi-lingual NLP-based AI chatbot, DR-COVID, which responds accurately to open-ended, COVID-19 related questions. This was used to facilitate pandemic education and healthcare delivery. Methods First, we developed DR-COVID with an ensemble NLP model on the Telegram platform (https://t.me/drcovid_nlp_chatbot). Second, we evaluated various performance metrics. Third, we evaluated multi-lingual text-to-text translation to Chinese, Malay, Tamil, Filipino, Thai, Japanese, French, Spanish, and Portuguese. We utilized 2,728 training questions and 821 test questions in English. Primary outcome measurements were (A) overall and top 3 accuracies;(B) Area Under the Curve (AUC), precision, recall, and F1 score. Overall accuracy referred to a correct response for the top answer, whereas top 3 accuracy referred to an appropriate response for any one answer amongst the top 3 answers. AUC and its relevant matrices were obtained from the Receiver Operation Characteristics (ROC) curve. Secondary outcomes were (A) multi-lingual accuracy;(B) comparison to enterprise-grade chatbot systems. The sharing of training and testing datasets on an open-source platform will also contribute to existing data. Results Our NLP model, utilizing the ensemble architecture, achieved overall and top 3 accuracies of 0.838 [95% confidence interval (CI): 0.826–0.851] and 0.922 [95% CI: 0.913–0.932] respectively. For overall and top 3 results, AUC scores of 0.917 [95% CI: 0.911–0.925] and 0.960 [95% CI: 0.955–0.964] were achieved respectively. We achieved multi-linguicism with nine non-English languages, with Portuguese performing the best overall at 0.900. Lastly, DR-COVID generated answers more accurately and quickly than other chatbots, within 1.12–2.15 s across three devices tested. Conclusion DR-COVID is a clinically effective NLP-based conversational AI chatbot, and a promising solution for healthcare delivery in the pandemic era.

Development and testing of a multi-lingual Natural Language Processing-based deep learning system in 10 languages for COVID-19 pandemic crisis: A multi-center study.

Purpose: The COVID-19 pandemic has drastically disrupted global healthcare systems. With the higher demand for healthcare and misinformation related to COVID-19, there is a need to explore alternative models to improve communication. Artificial Intelligence (AI) and Natural Language Processing (NLP) have emerged as promising solutions to improve healthcare delivery. Chatbots could fill a pivotal role in the dissemination and easy accessibility of accurate information in a pandemic. In this study, we developed a multi-lingual NLP-based AI chatbot, DR-COVID, which responds accurately to open-ended, COVID-19 related questions. This was used to facilitate pandemic education and healthcare delivery. Methods: First, we developed DR-COVID with an ensemble NLP model on the Telegram platform (https://t.me/drcovid_nlp_chatbot). Second, we evaluated various performance metrics. Third, we evaluated multi-lingual text-to-text translation to Chinese, Malay, Tamil, Filipino, Thai, Japanese, French, Spanish, and Portuguese. We utilized 2,728 training questions and 821 test questions in English. Primary outcome measurements were (A) overall and top 3 accuracies; (B) Area Under the Curve (AUC), precision, recall, and F1 score. Overall accuracy referred to a correct response for the top answer, whereas top 3 accuracy referred to an appropriate response for any one answer amongst the top 3 answers. AUC and its relevant matrices were obtained from the Receiver Operation Characteristics (ROC) curve. Secondary outcomes were (A) multi-lingual accuracy; (B) comparison to enterprise-grade chatbot systems. The sharing of training and testing datasets on an open-source platform will also contribute to existing data. Results: Our NLP model, utilizing the ensemble architecture, achieved overall and top 3 accuracies of 0.838 [95% confidence interval (CI): 0.826-0.851] and 0.922 [95% CI: 0.913-0.932] respectively. For overall and top 3 results, AUC scores of 0.917 [95% CI: 0.911-0.925] and 0.960 [95% CI: 0.955-0.964] were achieved respectively. We achieved multi-linguicism with nine non-English languages, with Portuguese performing the best overall at 0.900. Lastly, DR-COVID generated answers more accurately and quickly than other chatbots, within 1.12-2.15 s across three devices tested. Conclusion: DR-COVID is a clinically effective NLP-based conversational AI chatbot, and a promising solution for healthcare delivery in the pandemic era.

Blockchain applications in health care for COVID-19 and beyond: a systematic review.

The COVID-19 pandemic has had a substantial and global impact on health care, and has greatly accelerated the adoption of digital technology. One of these emerging digital technologies, blockchain, has unique characteristics (eg, immutability, decentralisation, and transparency) that can be useful in multiple domains (eg, management of electronic medical records and access rights, and mobile health). We conducted a systematic review of COVID-19-related and non-COVID-19-related applications of blockchain in health care. We identified relevant reports published in MEDLINE, SpringerLink, Institute of Electrical and Electronics Engineers Xplore, ScienceDirect, arXiv, and Google Scholar up to July 29, 2021. Articles that included both clinical and technical designs, with or without prototype development, were included. A total of 85 375 articles were evaluated, with 415 full length reports (37 related to COVID-19 and 378 not related to COVID-19) eventually included in the final analysis. The main COVID-19-related applications reported were pandemic control and surveillance, immunity or vaccine passport monitoring, and contact tracing. The top three non-COVID-19-related applications were management of electronic medical records, internet of things (eg, remote monitoring or mobile health), and supply chain monitoring. Most reports detailed technical performance of the blockchain prototype platforms (277 [66·7%] of 415), whereas nine (2·2%) studies showed real-world clinical application and adoption. The remaining studies (129 [31·1%] of 415) were themselves of a technical design only. The most common platforms used were Ethereum and Hyperledger. Blockchain technology has numerous potential COVID-19-related and non-COVID-19-related applications in health care. However, much of the current research remains at the technical stage, with few providing actual clinical applications, highlighting the need to translate foundational blockchain technology into clinical use.

Factors to Consider in the Use of Vital Signs Wearables to Minimize Contact With Stable COVID-19 Patients: Experience of Its Implementation During the Pandemic.

The COVID-19 pandemic has created a huge burden on the healthcare industry worldwide. Pressures to increase the isolation healthcare facility to cope with the growing number of patients led to an exploration of the use of wearables for vital signs monitoring among stable COVID-19 patients. Vital signs wearables were chosen for use in our facility with the purpose of reducing patient contact and preserving personal protective equipment. The process of deciding on the wearable solution as well as the implementation of the solution brought much insight to the team. This paper presents an overview of factors to consider in implementing a vital signs wearable solution. This includes considerations before deciding on whether or not to use a wearable device, followed by key criteria of the solution to assess. With the use of wearables rising in popularity, this serves as a guide for others who may want to implement it in their institutions.

Telehealth Demand Trends During the COVID-19 Pandemic in the Top 50 Most Affected Countries: Infodemiological Evaluation.

BACKGROUND: The COVID-19 pandemic has led to urgent calls for the adoption of telehealth solutions. However, public interest and demand for telehealth during the pandemic remain unknown. OBJECTIVE: We used an infodemiological approach to estimate the worldwide demand for telehealth services during COVID-19, focusing on the 50 most affected countries and comparing the demand for such services with the level of information and communications technology (ICT) infrastructure available. METHODS: We used Google Trends, the Baidu Index (China), and Yandex Keyword Statistics (Russia) to extract data on worldwide and individual countries' telehealth-related internet searches from January 1 to July 7, 2020, presented as relative search volumes (RSV; range 0-100). Daily COVID-19 cases and deaths were retrieved from the World Health Organization. Individual countries' ICT infrastructure profiles were retrieved from the World Economic Forum Report. RESULTS: Across the 50 countries, the mean RSV was 18.5 (SD 23.2), and the mean ICT index was 62.1 (SD 15.0). An overall spike in worldwide telehealth-related RSVs was observed from March 11, 2020 (RSV peaked to 76.0), which then tailed off in June-July 2020 (mean RSV for the period was 25.8), but remained higher than pre-March RSVs (mean 7.29). By country, 42 (84%) manifested increased RSVs over the evaluation period, with the highest observed in Canada (RSV=100) and the United States (RSV=96). When evaluating associations between RSV and the ICT index, both the United States and Canada demonstrated high RSVs and ICT scores (≥70.3). In contrast, European countries had relatively lower RSVs (range 3.4-19.5) despite high ICT index scores (mean 70.3). Several Latin American (Brazil, Chile, Colombia) and South Asian (India, Bangladesh, Pakistan) countries demonstrated relatively higher RSVs (range 13.8-73.3) but low ICT index scores (mean 44.6), indicating that the telehealth demand outstrips the current ICT infrastructure. CONCLUSIONS: There is generally increased interest and demand for telehealth services across the 50 countries most affected by COVID-19, highlighting the need to scale up telehealth capabilities, during and beyond the pandemic.