School of Medicine, Universidade Federal de Minas Gerais, Belo Horizonte, Minas Gerais, Brazil.
Department of Medicine, School of Medicine, Medical College of Wisconsin, Wauwatosa, WI, United States.
J Med Internet Res. 2021 Apr 13;23(4):e27275. doi: 10.2196/27275.
Although the potential of big data analytics for health care is well recognized, evidence is lacking on its effects on public health.
The aim of this study was to assess the impact of the use of big data analytics on people's health based on the health indicators and core priorities in the World Health Organization (WHO) General Programme of Work 2019/2023 and the European Programme of Work (EPW), approved and adopted by its Member States, in addition to SARS-CoV-2-related studies. Furthermore, we sought to identify the most relevant challenges and opportunities of these tools with respect to people's health.
Six databases (MEDLINE, Embase, Cochrane Database of Systematic Reviews via Cochrane Library, Web of Science, Scopus, and Epistemonikos) were searched from the inception date to September 21, 2020. Systematic reviews assessing the effects of big data analytics on health indicators were included. Two authors independently performed screening, selection, data extraction, and quality assessment using the AMSTAR-2 (A Measurement Tool to Assess Systematic Reviews 2) checklist.
The literature search initially yielded 185 records, 35 of which met the inclusion criteria, involving more than 5,000,000 patients. Most of the included studies used patient data collected from electronic health records, hospital information systems, private patient databases, and imaging datasets, and involved the use of big data analytics for noncommunicable diseases. "Probability of dying from any of cardiovascular, cancer, diabetes or chronic renal disease" and "suicide mortality rate" were the most commonly assessed health indicators and core priorities within the WHO General Programme of Work 2019/2023 and the EPW 2020/2025. Big data analytics have shown moderate to high accuracy for the diagnosis and prediction of complications of diabetes mellitus as well as for the diagnosis and classification of mental disorders; prediction of suicide attempts and behaviors; and the diagnosis, treatment, and prediction of important clinical outcomes of several chronic diseases. Confidence in the results was rated as "critically low" for 25 reviews, as "low" for 7 reviews, and as "moderate" for 3 reviews. The most frequently identified challenges were establishment of a well-designed and structured data source, and a secure, transparent, and standardized database for patient data.
Although the overall quality of included studies was limited, big data analytics has shown moderate to high accuracy for the diagnosis of certain diseases, improvement in managing chronic diseases, and support for prompt and real-time analyses of large sets of varied input data to diagnose and predict disease outcomes.
International Prospective Register of Systematic Reviews (PROSPERO) CRD42020214048; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=214048.
尽管大数据分析在医疗保健领域的潜力得到了广泛认可，但关于其对公共卫生影响的证据尚缺乏。
本研究旨在根据世界卫生组织（WHO）2019/2023 年总工作方案和经成员国批准和通过的欧洲工作方案（EPW）中的健康指标和核心优先事项，以及与 SARS-CoV-2 相关的研究，评估大数据分析在人们健康方面的影响。此外，我们还试图确定这些工具对人们健康的最相关挑战和机遇。
从成立日期到 2020 年 9 月 21 日，我们在六个数据库（MEDLINE、Embase、Cochrane 图书馆中的 Cochrane 系统评价数据库、Web of Science、Scopus 和 Epistemonikos）中进行了搜索。纳入了评估大数据分析对健康指标影响的系统评价。两位作者使用 AMSTAR-2（评估系统评价的测量工具）检查表独立进行了筛选、选择、数据提取和质量评估。
文献检索最初产生了 185 条记录，其中 35 条符合纳入标准，涉及超过 500 万患者。大多数纳入的研究使用从电子健康记录、医院信息系统、私人患者数据库和成像数据集收集的患者数据，并涉及使用大数据分析进行非传染性疾病。“死于心血管、癌症、糖尿病或慢性肾脏疾病的概率”和“自杀死亡率”是世卫组织 2019/2023 年总工作方案和 2020/2025 年 EPW 中最常见的评估健康指标和核心优先事项。大数据分析在糖尿病并发症的诊断和预测以及精神障碍的诊断和分类方面显示出中等至高度的准确性；自杀企图和行为的预测；以及几种慢性疾病的诊断、治疗和重要临床结局的预测。25 项综述的结果置信度被评为“极低”，7 项综述的结果置信度被评为“低”，3 项综述的结果置信度被评为“中”。最常被识别的挑战是建立一个设计良好、结构合理的数据来源，以及一个安全、透明和标准化的患者数据数据库。
尽管纳入研究的总体质量有限，但大数据分析在某些疾病的诊断、慢性病管理的改善以及支持快速实时分析大量不同输入数据以诊断和预测疾病结果方面显示出中等至高度的准确性。
国际前瞻性系统评价注册（PROSPERO）CRD42020214048；https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=214048.
