Brandt Pascal S, Kiefer Richard C, Pacheco Jennifer A, Adekkanattu Prakash, Sholle Evan T, Ahmad Faraz S, Xu Jie, Xu Zhenxing, Ancker Jessica S, Wang Fei, Luo Yuan, Jiang Guoqian, Pathak Jyotishman, Rasmussen Luke V
Biomedical Informatics and Medical Education University of Washington Seattle Washington USA.
Department of Health Sciences Research Mayo Clinic Rochester Minnesota USA.
Learn Health Syst. 2020 Jun 25;4(4):e10233. doi: 10.1002/lrh2.10233. eCollection 2020 Oct.
Electronic health record (EHR)-driven phenotyping is a critical first step in generating biomedical knowledge from EHR data. Despite recent progress, current phenotyping approaches are manual, time-consuming, error-prone, and platform-specific. This results in duplication of effort and highly variable results across systems and institutions, and is not scalable or portable. In this work, we investigate how the nascent Clinical Quality Language (CQL) can address these issues and enable high-throughput, cross-platform phenotyping.
We selected a clinically validated heart failure (HF) phenotype definition and translated it into CQL, then developed a CQL execution engine to integrate with the Observational Health Data Sciences and Informatics (OHDSI) platform. We executed the phenotype definition at two large academic medical centers, Northwestern Medicine and Weill Cornell Medicine, and conducted results verification (n = 100) to determine precision and recall. We additionally executed the same phenotype definition against two different data platforms, OHDSI and Fast Healthcare Interoperability Resources (FHIR), using the same underlying dataset and compared the results.
CQL is expressive enough to represent the HF phenotype definition, including Boolean and aggregate operators, and temporal relationships between data elements. The language design also enabled the implementation of a custom execution engine with relative ease, and results verification at both sites revealed that precision and recall were both 100%. Cross-platform execution resulted in identical patient cohorts generated by both data platforms.
CQL supports the representation of arbitrarily complex phenotype definitions, and our execution engine implementation demonstrated cross-platform execution against two widely used clinical data platforms. The language thus has the potential to help address current limitations with portability in EHR-driven phenotyping and scale in learning health systems.
电子健康记录（EHR）驱动的表型分析是从EHR数据中生成生物医学知识的关键第一步。尽管最近取得了进展，但当前的表型分析方法是手动的、耗时的、容易出错的且特定于平台。这导致了跨系统和机构的工作重复以及结果高度可变，并且不可扩展或移植。在这项工作中，我们研究了新兴的临床质量语言（CQL）如何解决这些问题并实现高通量、跨平台的表型分析。
我们选择了一个经过临床验证的心力衰竭（HF）表型定义并将其翻译成CQL，然后开发了一个CQL执行引擎以与观察性健康数据科学与信息学（OHDSI）平台集成。我们在两个大型学术医疗中心，即西北大学医学院和威尔康奈尔医学院执行了该表型定义，并进行了结果验证（n = 100）以确定精度和召回率。我们还使用相同的基础数据集在两个不同的数据平台OHDSI和快速医疗保健互操作性资源（FHIR）上执行相同的表型定义，并比较了结果。
CQL具有足够的表现力来表示HF表型定义，包括布尔运算符和聚合运算符以及数据元素之间的时间关系。语言设计还使得相对容易地实现自定义执行引擎，并且两个站点的结果验证表明精度和召回率均为100%。跨平台执行导致两个数据平台生成相同的患者队列。
CQL支持表示任意复杂的表型定义，并且我们的执行引擎实现展示了针对两个广泛使用的临床数据平台的跨平台执行。因此，该语言有潜力帮助解决当前EHR驱动的表型分析中便携性方面的限制以及学习健康系统中的扩展性问题。
