Abstract:Background:
The coronavirus disease 2019 (COVID-19) had spread rapidly since late December 2019. Personal protective equipment was essential to prevent transmission. Owing to shortage of face masks, Taiwan government began to implement quasi rationing on February 6, 2020, by allowing each resident to purchase two masks in seven days. Taiwan National Health Insurance Administration offered online data with real-time updates on face mask availability in all contracted pharmacies and selected local he… Show more
“…Notably, there was a surprising prominence of reports about DCs (Fig. 2 ) for triage 14 – 16 , co-ordination 16 – 18 , and public health communication addressing misinformation, resource availability, and evolving guidelines 19 – 22 . Fig.…”
The coronavirus disease 2019 (COVID-19) pandemic has overwhelmed healthcare services, faced with the twin challenges in acutely meeting the medical needs of patients with COVID-19 while continuing essential services for non-COVID-19 illnesses. The need to re-invent, re-organize and transform healthcare and co-ordinate clinical services at a population level is urgent as countries that controlled initial outbreaks start to experience resurgences. A wide range of digital health solutions have been proposed, although the extent of successful real-world applications of these technologies is unclear. This study aims to review applications of artificial intelligence (AI), telehealth, and other relevant digital health solutions for public health responses in the healthcare operating environment amidst the COVID-19 pandemic. A systematic scoping review was performed to identify potentially relevant reports. Key findings include a large body of evidence for various clinical and operational applications of telehealth (40.1%, n = 99/247). Although a large quantity of reports investigated applications of artificial intelligence (AI) (44.9%, n = 111/247) and big data analytics (36.0%, n = 89/247), weaknesses in study design limit generalizability and translation, highlighting the need for more pragmatic real-world investigations. There were also few descriptions of applications for the internet of things (IoT) (2.0%, n = 5/247), digital platforms for communication (DC) (10.9%, 27/247), digital solutions for data management (DM) (1.6%, n = 4/247), and digital structural screening (DS) (8.9%, n = 22/247); representing gaps and opportunities for digital public health. Finally, the performance of digital health technology for operational applications related to population surveillance and points of entry have not been adequately evaluated.
“…Notably, there was a surprising prominence of reports about DCs (Fig. 2 ) for triage 14 – 16 , co-ordination 16 – 18 , and public health communication addressing misinformation, resource availability, and evolving guidelines 19 – 22 . Fig.…”
The coronavirus disease 2019 (COVID-19) pandemic has overwhelmed healthcare services, faced with the twin challenges in acutely meeting the medical needs of patients with COVID-19 while continuing essential services for non-COVID-19 illnesses. The need to re-invent, re-organize and transform healthcare and co-ordinate clinical services at a population level is urgent as countries that controlled initial outbreaks start to experience resurgences. A wide range of digital health solutions have been proposed, although the extent of successful real-world applications of these technologies is unclear. This study aims to review applications of artificial intelligence (AI), telehealth, and other relevant digital health solutions for public health responses in the healthcare operating environment amidst the COVID-19 pandemic. A systematic scoping review was performed to identify potentially relevant reports. Key findings include a large body of evidence for various clinical and operational applications of telehealth (40.1%, n = 99/247). Although a large quantity of reports investigated applications of artificial intelligence (AI) (44.9%, n = 111/247) and big data analytics (36.0%, n = 89/247), weaknesses in study design limit generalizability and translation, highlighting the need for more pragmatic real-world investigations. There were also few descriptions of applications for the internet of things (IoT) (2.0%, n = 5/247), digital platforms for communication (DC) (10.9%, 27/247), digital solutions for data management (DM) (1.6%, n = 4/247), and digital structural screening (DS) (8.9%, n = 22/247); representing gaps and opportunities for digital public health. Finally, the performance of digital health technology for operational applications related to population surveillance and points of entry have not been adequately evaluated.
“…The mask plan evolved to 2.0 and real-time mask maps were established to show the availability of masks. Figure 3 shows the user interface of one software application [ 17 ] for face mask availability, deployed on websites, social networking sites, and mobile apps in Taiwan [ 5 , 18 ]. As of April 2020, Taiwan had an adequate mask supply and could donate masks to help other countries.…”
Background
The COVID-19 pandemic is a severe global health crisis. Wearing a mask is a straightforward action that can be taken, but shortage of stock and equity of allocation were important issues in Taiwan. Furthermore, increased anxiety leading to the stockpiling of masks has been common during the pandemic.
Objective
We aim to summarize the name-based mask rationing plan implemented in Taiwan and explore the public’s perceived anxiety about mask shortages.
Methods
The government of Taiwan took action to control the supply and allocation of face masks. We summarize the timeline and important components of the mask rationing plan. A survey that aimed to investigate the overall response to the mask rationing plan was answered by 44 participants.
Results
The mask rationing plan was implemented in late January 2020. Daily production capacity was increased from 2 million masks to 16 million masks in April 2020. People could buy 9 masks in 14 days by verification via their National Health Insurance card. Digital face mask availability maps were created. Moreover, the mask plan safeguarded the purchase of masks and resulted in decreased anxiety about a mask shortage (4.05 [SD 1.15] points; 72.7% [n=32] of participants answered “agree” or “strongly agree”). The majority of people felt that the mask plan was satisfactory (4.2 [SD 0.92] points; 79.5% [n=35] of participants answered “agree” or “strongly agree”).
Conclusions
We found that the unique name-based mask rationing plan allowed for control of the production and supply of masks, and contributed to the appropriate allocation of masks. The mask rationing plan not only provided the public with physical protection, but also resulted in reduced anxiety about mask shortages during the pandemic.
“…To avoid panic buying, the Taiwan government provided open data on real-time mask availability with the location of pharmacies from February 2020. This helped to support the creation of 110 software projects to effectively visualize face mask distribution on the map until the mid-March, 2020 [ 57 ].…”
Objectives: The emerging COVID-19 pandemic has caused one of the world’s worst health disasters compounded by social confusion with misinformation, the so-called “Infodemic”. In this paper, we discuss how open technology approaches - including data sharing, visualization, and tooling - can address the COVID-19 pandemic and infodemic.
Methods: In response to the call for participation in the 2020 International Medical Informatics Association (IMIA) Yearbook theme issue on Medical Informatics and the Pandemic, the IMIA Open Source Working Group surveyed recent works related to the use of Free/Libre/Open Source Software (FLOSS) for this pandemic.
Results: FLOSS health care projects including GNU Health, OpenMRS, DHIS2, and others, have responded from the early phase of this pandemic. Data related to COVID-19 have been published from health organizations all over the world. Civic Technology, and the collaborative work of FLOSS and open data groups were considered to support collective intelligence on approaches to managing the pandemic.
Conclusion: FLOSS and open data have been effectively used to contribute to managing the COVID-19 pandemic, and open approaches to collaboration can improve trust in data.
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