With Industry 4.0, big data and artificial intelligence have become paramount in the field of medicine. Electronic health records, the primary source of medical data, are not collected for research purposes but represent real-world data; therefore, they have various constraints. Although structured, laboratory data often contain unstandardized terminology or missing information. The major challenge lies in the lack of standardization of test results in terms of metrology, which complicates comparisons across laboratories. In this review, we delve into the essential components necessary for integrating real-world laboratory data into high-quality big data, including the standardization of terminology, data formats, equations, and the harmonization and standardization of results. Moreover, we address the transference and adjustment of laboratory results, along with the certification for quality of laboratory data. By discussing these critical aspects, we seek to shed light on the challenges and opportunities inherent to utilizing real-world laboratory data within the framework of healthcare big data and artificial intelligence.

中文翻译：

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在医疗大数据时代迈向高质量的真实世界实验室数据。


随着工业4.0的到来，大数据和人工智能在医学领域变得至关重要。电子健康记录是医疗数据的主要来源，收集的目的不是为了研究目的，而是代表真实世界的数据；因此，它们有各种限制。尽管是结构化的，但实验室数据通常包含不标准化的术语或缺失的信息。主要挑战在于计量方面的测试结果缺乏标准化，这使得实验室之间的比较变得复杂。在本次综述中，我们深入研究了将现实世界实验室数据整合为高质量大数据所需的基本组成部分，包括术语、数据格式、方程的标准化以及结果的协调和标准化。此外，我们还解决实验室结果的转移和调整，以及实验室数据质量的认证。通过讨论这些关键方面，我们试图阐明在医疗保健大数据和人工智能框架内利用真实世界实验室数据所固有的挑战和机遇。