Thompson William K, Rasmussen Luke V, Pacheco Jennifer A, Peissig Peggy L, Denny Joshua C, Kho Abel N, Miller Aaron, Pathak Jyotishman
Northwestern University, Chicago, IL, USA.
AMIA Annu Symp Proc. 2012;2012:911-20. Epub 2012 Nov 3.
The development of Electronic Health Record (EHR)-based phenotype selection algorithms is a non-trivial and highly iterative process involving domain experts and informaticians. To make it easier to port algorithms across institutions, it is desirable to represent them using an unambiguous formal specification language. For this purpose we evaluated the recently developed National Quality Forum (NQF) information model designed for EHR-based quality measures: the Quality Data Model (QDM). We selected 9 phenotyping algorithms that had been previously developed as part of the eMERGE consortium and translated them into QDM format. Our study concluded that the QDM contains several core elements that make it a promising format for EHR-driven phenotyping algorithms for clinical research. However, we also found areas in which the QDM could be usefully extended, such as representing information extracted from clinical text, and the ability to handle algorithms that do not consist of Boolean combinations of criteria.
基于电子健康记录（EHR）的表型选择算法的开发是一个复杂且高度迭代的过程，涉及领域专家和信息学家。为了便于在不同机构间移植算法，使用一种明确的形式规范语言来表示它们是很有必要的。为此，我们评估了最近开发的、用于基于EHR的质量指标的国家质量论坛（NQF）信息模型：质量数据模型（QDM）。我们选择了9种先前作为eMERGE联盟一部分开发的表型算法，并将它们转换为QDM格式。我们的研究得出结论，QDM包含几个核心元素，使其成为用于临床研究的EHR驱动的表型算法的一种有前景的格式。然而，我们也发现了QDM可以有效扩展的领域，例如表示从临床文本中提取的信息，以及处理不由标准的布尔组合构成的算法的能力。
