Clinical epidemiology in the era of big data: new opportunities, familiar challenges.
Clinical epidemiology in the era of big data: new opportunities, familiar challenges.
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DOI：
10.2147/clep.s129779
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发表时间：
2017
影响因子：
3.9
通讯作者：
Pedersen L
中科院分区：
文献类型：
--
作者：
Ehrenstein V;Nielsen H;Pedersen AB;Johnsen SP;Pedersen L
常规记录的健康数据已从单纯的医疗保健提供或计费的副产品发展成为通过临床流行病学研究来研究和改善患者护理的强大研究工具。流行病学研究中的大数据意味着单个国家或跨国数据库网络内的大型可互连数据集。一些北欧、欧洲和其他跨国合作现已成熟。临床流行病学大数据的优势包括提高估计精度，这对于令人放心（“无效”）的结果尤其重要；对患者亚组进行有意义的分析的能力；并快速检测安全信号。大数据还将通过访问来自生物库、电子病历、患者报告的结果测量、自动和半自动电子监测设备以及社交媒体的链接信息，为研究提供新的可能性。然而，庞大的数据量并不能消除甚至可能放大系统误差。因此，解决系统误差、临床知识和潜在假设的方法比以往任何时候都更加重要，以确保可以辨别噪声背后的信号。
Routinely recorded health data have evolved from mere by-products of health care delivery or billing into a powerful research tool for studying and improving patient care through clinical epidemiologic research. Big data in the context of epidemiologic research means large interlinkable data sets within a single country or networks of multinational databases. Several Nordic, European, and other multinational collaborations are now well established. Advantages of big data for clinical epidemiology include improved precision of estimates, which is especially important for reassuring (“null”) findings; ability to conduct meaningful analyses in subgroup of patients; and rapid detection of safety signals. Big data will also provide new possibilities for research by enabling access to linked information from biobanks, electronic medical records, patient-reported outcome measures, automatic and semiautomatic electronic monitoring devices, and social media. The sheer amount of data, however, does not eliminate and may even amplify systematic error. Therefore, methodologies addressing systematic error, clinical knowledge, and underlying hypotheses are more important than ever to ensure that the signal is discernable behind the noise.