Wide adoption of electronic health records (EHRs) has raised the expectation that data obtained during routine clinical care, termed "real-world" data, will be accumulated across health care systems and analyzed on a large scale to produce improvements in patient outcomes and the use of health care resources. To facilitate a learning health system, EHRs must contain clinically meaningful structured data elements that can be readily exchanged, and the data must be of adequate quality to draw valid inferences. At the present time, the majority of EHR content is unstructured and locked into proprietary systems that pose significant challenges to conducting accurate analyses of many clinical outcomes. This article details the current state of data obtained at the point of care and describes the changes necessary to use the EHR to build a learning health system.

中文翻译：

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构建学习健康系统所需数据​​的状态更新

电子健康记录 (EHR) 的广泛采用提高了人们的期望，即在常规临床护理期间获得的数据（称为“真实世界”数据）将在整个医疗保健系统中积累并进行大规模分析，以改善患者的治疗效果和卫生保健资源的利用。为了促进学习型健康系统，EHR 必须包含具有临床意义且易于交换的结构化数据元素，并且数据必须具有足够的质量以得出有效的推论。目前，大多数 EHR 内容都是非结构化的，并锁定在专有系统中，这对对许多临床结果进行准确分析构成了重大挑战。