Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA; Dep. of Health Informatics and Administration, UW-Milwaukee, Milwaukee, WI, USA.
Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA.
Int J Med Inform. 2017 Dec;108:78-84. doi: 10.1016/j.ijmedinf.2017.10.004. Epub 2017 Oct 10.
Clinical registries are designed to collect information relating to a particular condition for research or quality improvement. Intuitively, informatics in the area of data management and extraction plays a central role in clinical registries. Due to various reasons such as lack of informatics awareness or expertise, there may be little informatics involvement in designing clinical registries. In this paper, we studied a clinical registry from two critical perspectives, data quality and interoperability, where informatics can play a role. We evaluated these two aspects of an existing registry, Gynecology Surgery Registry, by mapping data elements and value sets, used in the registry, to a standardized terminology, SNOMED-CT. The results showed that majority of the values are ad-hoc and only 6 of 91 procedures in the registry could be mapped to the SNOMED-CT. To tackle this issue, we assessed the feasibility of automated data abstraction process, by training machine learning classifiers, based on existing manually extracted data. These classifiers achieved a reasonable average F-measure of 0.94. We concluded that more informatics engagement is needed to improve the interoperability, reusability, and quality of the registry.
临床注册中心旨在为研究或质量改进收集与特定疾病相关的信息。直观地说,数据管理和提取领域的信息学在临床注册中心中起着核心作用。由于缺乏信息学意识或专业知识等各种原因,在设计临床注册中心时可能很少涉及信息学。在本文中,我们从数据质量和互操作性两个关键角度研究了一个临床注册中心,信息学可以在这两个角度发挥作用。我们通过将注册中心中使用的数据元素和值集映射到标准化术语 SNOMED-CT 来评估现有注册中心“妇科手术注册中心”的这两个方面。结果表明,大多数值都是特定的,在该注册中心的 91 个程序中只有 6 个可以映射到 SNOMED-CT。为了解决这个问题,我们通过基于现有手动提取的数据训练机器学习分类器来评估自动数据提取过程的可行性。这些分类器实现了合理的平均 F1 分数 0.94。我们得出结论,需要更多的信息学参与来提高注册中心的互操作性、可重用性和质量。