Clinical informatics solutions in COVID-19 pandemic: Scoping literature review. (2022)

• Record Type: Journal Article
• Authors: Ganjali, Raheleh; Eslami, Saeid; Samimi, Tahereh; Sargolzaei, Mahdi; Firouraghi, Neda; MohammadEbrahimi, Shahab; khoshrounejad, Farnaz; Kheirdoust, Azam
• Is Part Of: Informatics in medicine unlocked. Volume 30(2022)
• Publication Date: 2022
• Subjects: COVID-19 -- Medical informatics -- Prediction -- Telehealth; Medical informatics -- Periodicals; 610.285
• DOI: 10.1016/j.imu.2022.100929
• Languages: English
• ISSNs: 2352-9148

Abstract

Abstract: Background: The 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. Methods: A 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. Results: In 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%). Conclusion: The 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.