Digital approaches in post-COVID healthcare: a systematic review of technological innovations in disease management.

IF 2.5 Q3 BIOCHEMICAL RESEARCH METHODS Biology Methods and Protocols Pub Date : 2024-10-01 eCollection Date: 2024-01-01 DOI:10.1093/biomethods/bpae070
Pamela Mfouth Kemajou, Armand Mbanya, Yves Coppieters
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Abstract

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.

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COVID 后医疗保健中的数字化方法:疾病管理技术创新的系统回顾。
大流行期间出现了后柯达病症(PCC),促使人们更多使用数字医疗技术(DHT)来管理封锁和医院人满为患的情况。实时跟踪和信息分析对于加强全球研究响应至关重要。本研究旨在绘制现代数字方法在估计 PCC 发病率、预测、诊断、治疗、监测和预后方面的应用图。根据世界卫生组织的定义,本综述通过搜索 PubMed 和 Scopus 数据库中与 DHT、智能医疗系统和 PCC 相关的关键词和同义词进行。根据预定义的纳入标准筛选了 2020 年 1 月 1 日至 2024 年 5 月 21 日期间发表的文章,并采用 PRISMA 框架报告了保留研究的结果。我们的搜索发现了 377 项研究,但保留了 23 项使用 DHT、人工智能 (AI) 和信息病理学来诊断、估计患病率、预测、治疗和监测 PCC 的研究。值得注意的是,少数干预措施使用信息病理学来识别疾病的临床表现,而大多数干预措施则利用电子健康记录和人工智能工具来估计诊断和患病率。然而,我们发现人工智能工具很少被用于监测症状,在中低收入国家(LMICs)也不存在涉及社会和人文科学的研究。这些研究结果表明,有几种 DHT 被用于医疗保健领域,但对于复杂的健康状况,尤其是在低收入和中等收入国家,迫切需要进一步开展社会和人文科学研究。加强 DHTs 并将人工智能与信息病理学相结合,为管理流行病和相关并发症(如 PCC)提供了前景广阔的途径。
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来源期刊
Biology Methods and Protocols
Biology Methods and Protocols Agricultural and Biological Sciences-Agricultural and Biological Sciences (all)
CiteScore
3.80
自引率
2.80%
发文量
28
审稿时长
19 weeks
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