Ganjali Raheleh, Eslami Saeid, Samimi Tahereh, Sargolzaei Mahdi, Firouraghi Neda, MohammadEbrahimi Shahab, Khoshrounejad Farnaz, Kheirdoust Azam
Clinical Research Development Unit, Emam Reza Hospital, Mashhad University of Medical Sciences, Mashhad, Iran.
Department of Medical Informatics, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran.
Inform Med Unlocked. 2022;30:100929. doi: 10.1016/j.imu.2022.100929. Epub 2022 Mar 25.
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.
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.
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%).
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.
2019年冠状病毒病（COVID-19）的全球爆发凸显了疾病监测、诊断、治疗和筛查的重要性。基于技术的仪器可以通过实现信息的快速广泛传播、数据传输的实时跟踪以及会议和患者就诊的虚拟化，在大流行期间有效地协助医疗系统。因此，本研究旨在调查临床信息学（CI）在COVID-19疫情期间的应用。
2020年9月对Medline和Scopus数据库进行了全面检索。根据纳入和排除标准选择符合条件的研究。从所审查的研究中提取的数据包括研究样本、研究类型、目标、临床信息学领域、应用方法、样本量、结果、发现和结论。根据每项研究的类型，使用适当的工具评估研究中的偏倚风险。然后对所选研究进行主题综合分析。
在本综述研究中，2716篇检索到的文章中有72篇符合全文分析的纳入标准。大多数综述文章是在中国和美国完成的。大多数研究在以下临床信息学领域进行：预测模型（60%）、远程医疗（36%）和移动健康（4%）。远程医疗领域的大多数研究使用同步方法，如在线和电话或视频通话咨询。在COVID-19大流行期间，移动应用程序被开发为自我分诊、自我排班和信息传递工具。综述研究中最常见的预测模型类型是神经网络（49%）、分类（42%）和线性模型（4.5%）。
本研究展示了COVID-19期间临床信息学的应用，并确定了该领域当前的差距。健康信息技术和临床信息学似乎有助于临床医生和管理人员抗击COVID-19。临床信息学中针对COVID-19危机的最常见研究领域是预测模型和远程医疗。建议未来的研究人员进行范围综述，以描述和分析医学信息学的其他层面，包括生物信息学、影像信息学和公共卫生信息学。
