Mfouth Kemajou Pamela, Mbanya Armand, Coppieters Yves
School of Public Health, Centre for Research in Epidemiology, Biostatistics and Clinical Research, Université Libre de Bruxelles (ULB), Brussels, Belgium.
Health of Population in Transition Research Group, University of Yaounde I, Yaounde, Cameroon.
Biol Methods Protoc. 2024 Oct 1;9(1):bpae070. doi: 10.1093/biomethods/bpae070. eCollection 2024.
Post-COVID conditions (PCC) emerged during the pandemic, prompting a rise in the use of Digital Health Technologies (DHTs) to manage lockdowns and hospital overcrowding. Real-time tracking and information analyses were crucial to strengthening the global research response. This study aims to map the use of modern digital approaches in estimating the prevalence, predicting, diagnosing, treating, monitoring, and prognosis of PCC. This review was conducted by searching PubMed and Scopus databases for keywords and synonyms related to DHTs, Smart Healthcare Systems, and PCC based on the World Health Organization definition. Articles published from 1 January 2020 to 21 May 2024 were screened for eligibility based on predefined inclusion criteria, and the PRISMA framework was used to report the findings from the retained studies. Our search identified 377 studies, but we retained 23 studies that used DHTs, artificial intelligence (AI), and infodemiology to diagnose, estimate prevalence, predict, treat, and monitor PCC. Notably, a few interventions used infodemics to identify the clinical presentations of the disease, while most utilized Electronic Health Records and AI tools to estimate diagnosis and prevalence. However, we found that AI tools were scarcely used for monitoring symptoms, and studies involving SHS were non-existent in low- and middle-income countries (LMICs). These findings show several DHTs used in healthcare, but there is an urgent need for further research in SHS for complex health conditions, particularly in LMICs. Enhancing DHTs and integrating AI and infodemiology provide promising avenues for managing epidemics and related complications, such as PCC.
新冠后状况（PCC）在疫情期间出现，促使数字健康技术（DHTs）的使用增加，以应对封锁和医院过度拥挤的情况。实时跟踪和信息分析对于加强全球研究应对至关重要。本研究旨在梳理现代数字方法在估计PCC的患病率、预测、诊断、治疗、监测和预后方面的应用。本综述通过在PubMed和Scopus数据库中搜索与DHTs、智能医疗系统和基于世界卫生组织定义的PCC相关的关键词和同义词来进行。根据预先定义的纳入标准，筛选了2020年1月1日至2024年5月21日发表的文章以确定其是否符合资格，并使用PRISMA框架报告保留研究的结果。我们的搜索识别出377项研究，但我们保留了23项使用DHTs、人工智能（AI）和信息流行病学来诊断、估计患病率、预测、治疗和监测PCC的研究。值得注意的是，一些干预措施使用信息疫情学来识别该疾病的临床表现，而大多数则利用电子健康记录和AI工具来估计诊断和患病率。然而，我们发现AI工具很少用于监测症状，并且低收入和中等收入国家（LMICs）不存在涉及智能医疗系统（SHS）的研究。这些发现表明医疗保健中使用了几种DHTs，但迫切需要针对复杂健康状况在SHS方面进行进一步研究，特别是在LMICs。加强DHTs并整合AI和信息流行病学为管理疫情及相关并发症（如PCC）提供了有前景的途径。
