How the COVID-19 pandemic is favoring the adoption of digital technologies in healthcare: a rapid literature review (preprint)

Background. Healthcare is responding to the COVID-19 pandemic through the fast adoption of digital solutions and advanced technology tools. The aim of this study is to describe which digital solutions have been reported in the scientific literature and to investigate their potential impact in the fight against the COVID-19 pandemic. Methods. We conducted a literature review searching PubMed and MedrXiv with terms considered adequate to find relevant literature on the use of digital technologies in response to COVID-19. We developed an impact score to evaluate the potential impact on COVID-19 pandemic of all the digital solutions addressed in the selected papers. Results. The search identified 269 articles, of which 145 full-text articles were assessed and 124 included in the review after screening and impact evaluation. Of selected articles, most of them addressed the use of digital technologies for diagnosis, surveillance and prevention. We report that digital solutions and innovative technologies have mainly been proposed for the diagnosis of COVID-19. In particular, within the reviewed articles we identified numerous suggestions on the use of artificial-intelligence-powered tools for the diagnosis and screening of COVID-19. Digital technologies are useful also for prevention and surveillance measures, for example through contact-tracing apps or monitoring of internet searches and social media usage. Discussion. It is worth taking advantage of the push given by the crisis, and mandatory to keep track of the digital solutions proposed today to implement tomorrow's best practices and models of care, and to be ready for any new moments of emergency.

The impact of early scientific literature in response to COVID-19: a scientometric perspective (preprint)

Background. In the early phases of a new pandemic, identifying the most relevant evidence and quantifying which studies are shared the most can help researchers and policy makers. The aim of this study is to describe and quantify the impact of early scientific production in response to COVID-19 pandemic. Methods. The study consisted of: 1) review of the scientific literature produced in the first 30 days since the first COVID-19 paper was published; 2) analysis of papers' metrics with the construction of a Computed-Impact-Score (CIS) that represents a unifying score over heterogeneous bibliometric indicators. In this study we use metrics and alternative metrics collected into five separate categories. On top of those categories we compute the CIS. Highest CIS papers are further analyzed. Results. 239 papers have been included in the study. The mean of citations, mentions and social media interactions resulted in 1.63, 10 and 1250, respectively. The paper with highest CIS resulted "Clinical features of patients[...]" by Chaolin Huang et al., which rated first also in citations and mentions. This is the first paper describing patients affected by the new disease and reporting data that are clearly of great interest to both the scientific community and the general population. Conclusions. The early response of scientific literature during an epidemic does not follow a pre- established pattern. Being able to monitor how communications spread from the scientific world toward the general population using both traditional and alternative metric measures is essential, especially in the early stages of a pandemic.

Using altmetrics for detecting impactful research in quasi-zero-day time-windows: the case of COVID-19 (preprint)

On December 31st 2019, the World Health Organization (WHO) China Country Office was informed of cases of pneumonia of unknown etiology detected in Wuhan City. The cause of the syndrome was a new type of coronavirus isolated on January 7th 2020 and named Severe Acute Respiratory Syndrome CoronaVirus 2 (SARS-CoV-2). SARS-CoV-2 is the cause of the coronavirus disease 2019 (COVID-19). Since January 2020 an ever increasing number of scientific works have appeared in literature. Identifying relevant research outcomes at very early stages is challenging. In this work we use COVID-19 as a use-case for investigating: (i) which tools and frameworks are mostly used for early scholarly communication; (ii) to what extent altmetrics can be used to identify potential impactful research in tight (i.e. quasi-zero-day) time-windows. A literature review with rigorous eligibility criteria is performed for gathering a sample composed of scientific papers about SARS-CoV-2/COVID-19 appeared in literature in the tight time-window ranging from January 15th 2020 to February 24th 2020. This sample is used for building a knowledge graph that represents the knowledge about papers and indicators formally. This knowledge graph feeds a data analysis process which is applied for experimenting with altmetrics as impact indicators. We find moderate correlation among traditional citation count, citations on social media, and mentions on news and blogs. This suggests there is a common intended meaning of the citational acts associated with aforementioned indicators. Additionally, we define a method that harmonises different indicators for providing a multi-dimensional impact indicator.