Dataset Information

SCOR: A secure international informatics infrastructure to investigate COVID-19.

ABSTRACT: Global pandemics call for large and diverse healthcare data to study various risk factors, treatment options, and disease progression patterns. Despite the enormous efforts of many large data consortium initiatives, scientific community still lacks a secure and privacy-preserving infrastructure to support auditable data sharing and facilitate automated and legally compliant federated analysis on an international scale. Existing health informatics systems do not incorporate the latest progress in modern security and federated machine learning algorithms, which are poised to offer solutions. An international group of passionate researchers came together with a joint mission to solve the problem with our finest models and tools. The SCOR Consortium has developed a ready-to-deploy secure infrastructure using world-class privacy and security technologies to reconcile the privacy/utility conflicts. We hope our effort will make a change and accelerate research in future pandemics with broad and diverse samples on an international scale.

SUBMITTER: Raisaro JL

PROVIDER: S-EPMC7454652 | biostudies-literature | 2020 Nov

REPOSITORIES: biostudies-literature

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SCOR: A secure international informatics infrastructure to investigate COVID-19.

Raisaro J L JL Marino Francesco F Troncoso-Pastoriza Juan J Beau-Lejdstrom Raphaelle R Bellazzi Riccardo R Murphy Robert R Bernstam Elmer V EV Wang Henry H Bucalo Mauro M Chen Yong Y Gottlieb Assaf A Harmanci Arif A Kim Miran M Kim Yejin Y Klann Jeffrey J Klersy Catherine C Malin Bradley A BA Méan Marie M Prasser Fabian F Scudeller Luigia L Torkamani Ali A Vaucher Julien J Puppala Mamta M Wong Stephen T C STC Frenkel-Morgenstern Milana M Xu Hua H Musa Baba Maiyaki BM Habib Abdulrazaq G AG Cohen Trevor T Wilcox Adam A Salihu Hamisu M HM Sofia Heidi H Jiang Xiaoqian X Hubaux J P JP

Journal of the American Medical Informatics Association : JAMIA 20201101 11

Global pandemics call for large and diverse healthcare data to study various risk factors, treatment options, and disease progression patterns. Despite the enormous efforts of many large data consortium initiatives, scientific community still lacks a secure and privacy-preserving infrastructure to support auditable data sharing and facilitate automated and legally compliant federated analysis on an international scale. Existing health informatics systems do not incorporate the latest progress in ...[more]

PMID: 32918447

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Project description:BackgroundThe global outbreak of COVID-19 (coronavirus disease 2019) disease has highlighted the importance of disease monitoring, diagnosing, treating, and screening. Technology-based instruments could efficiently assist healthcare systems during pandemics by allowing rapid and widespread transfer of information, real-time tracking of data transfer, and virtualization of meetings and patient visits. Therefore, this study was conducted to investigate the applications of clinical informatics (CI) during the COVID-19 outbreak.MethodsA comprehensive search was performed on Medline and Scopus databases in September 2020. Eligible studies were selected based on the inclusion and exclusion criteria. The extracted data from the studies reviewed were about study sample, study type, objectives, clinical informatics domain, applied method, sample size, outcomes, findings, and conclusion. The risk of bias was evaluated in the studies using appropriate instruments based on the type of each study. The selected studies were then subjected to thematic synthesis.ResultsIn this review study, 72 out of 2716 retrieved articles met the inclusion criteria for full-text analysis. Most of the articles reviewed were done in China and the United States of America. The majority of the studies were conducted in the following CI domains: prediction models (60%), telehealth (36%), and mobile health (4%). Most of the studies in telehealth domain used synchronous methods, such as online and phone- or video-call consultations. Mobile applications were developed as self-triage, self-scheduling, and information delivery tools during the COVID-19 pandemic. The most common types of prediction models among the reviewed studies were neural network (49%), classification (42%), and linear models (4.5%).ConclusionThe present study showed clinical informatics applications during COVID-19 and identified current gaps in this field. Health information technology and clinical informatics seem to be useful in assisting clinicians and managers to combat COVID-19. The most common domains in clinical informatics for research on the COVID-19 crisis were prediction models and telehealth. It is suggested that future researchers conduct scoping reviews to describe and analyze other levels of medical informatics, including bioinformatics, imaging informatics, and public health informatics.

Dataset Information

SCOR: A secure international informatics infrastructure to investigate COVID-19.

Publications

SCOR: A secure international informatics infrastructure to investigate COVID-19.

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