Health informatics and EHR to support clinical research in the COVID-19 pandemic: an overview

Abstract

The coronavirus disease 2019 (COVID-19) pandemic has clearly shown that major challenges and threats for humankind need to be addressed with global answers and shared decisions. Data and their analytics are crucial components of such decision-making activities. Rather interestingly, one of the most difficult aspects is reusing and sharing of accurate and detailed clinical data collected by Electronic Health Records (EHR), even if these data have a paramount importance. EHR data, in fact, are not only essential for supporting day-by-day activities, but also they can leverage research and support critical decisions about effectiveness of drugs and therapeutic strategies. In this paper, we will concentrate our attention on collaborative data infrastructures to support COVID-19 research and on the open issues of data sharing and data governance that COVID-19 had made emerge. Data interoperability, healthcare processes modelling and representation, shared procedures to deal with different data privacy regulations, and data stewardship and governance are seen as the most important aspects to boost collaborative research. Lessons learned from COVID-19 pandemic can be a strong element to improve international research and our future capability of dealing with fast developing emergencies and needs, which are likely to be more frequent in the future in our connected and intertwined world.

Identification of drugs associated with reduced severity of COVID-19 – a case-control study in a large population

“…In this large-scale retrospective study, we identified several drugs and products that are significantly associated with reduced odds for COVID-19 hospitalization, both in the general population, and in patients with laboratory proven SARS-CoV-2 infection. Several other research groups have recognized the potential for EHRs to enable large-scale studies in COVID-19 and the challenges of this sort of retrospective research are reviewed in (Dagliati et al, 2021;Ek Sudat et al, 2021). To give a few examples, EHRs have also been used to predict: i) COVID-19 mortality based on pre-existing conditions (Estiri et al, 2021;Osborne et al, 2020), ii) early diagnosis of COVID-19 based on clinical notes (Wagner et al, 2020) and iii) eligibility of COVID-19 patients for clinical trials by matching trial criteria with patient records (Kim et al, 2021).…”

Identification of drugs associated with reduced severity of COVID-19: A case-control study in a large population

“…In this large-scale retrospective study, we identified several drugs and products that are significantly associated with reduced odds for COVID-19 hospitalization, both in the general population, and in patients with laboratory proven SARS-CoV-2 infection. Several other research groups have recognized the potential for EHRs to enable large-scale studies in COVID-19 and the challenges of this sort of retrospective research are reviewed in (Dagliati et al, 2021; Sudat et al, 2021). To give a few examples, EHRs have also been used to predict: i) COVID-19 mortality based on pre-existing conditions (Estiri et al, 2021; Osborne et al, 2020), ii) early diagnosis of COVID-19 based on clinical notes (Wagner et al, 2020) and iii) eligibility of COVID-19 patients for clinical trials by matching trial criteria with patient records (Kim et al, 2021).…”

Artificial intelligence for COVID-19: battling the pandemic with computational intelligence

“…For example, Obeid et al [106] performed text information analysis based on patients’ self-reported symptoms to predict COVID-19 infection risk by a word embedding-based CNN. The unstructured information can be used as complementary information for structured information [107] . Integrating structured and unstructured information has the potential to completely represent the patient and improve model performance.…”