Rasmussen Luke V, Brandt Pascal S, Jiang Guoqian, Kiefer Richard C, Pacheco Jennifer A, Adekkanattu Prakash, Ancker Jessica S, Wang Fei, Xu Zhenxing, Pathak Jyotishman, Luo Yuan
Northwestern University, Chicago, IL.
AMIA Annu Symp Proc. 2020 Mar 4;2019:755-764. eCollection 2019.
With the increased adoption of electronic health records, data collected for routine clinical care is used for health outcomes and population sciences research, including the identification of phenotypes. In recent years, research networks, such as eMERGE, OHDSI and PCORnet, have been able to increase statistical power and population diversity by combining patient cohorts. These networks share phenotype algorithms that are executed at each participating site. Here we observe experiences with phenotype algorithm portability across seven research networks and propose a generalizable framework for phenotype algorithm portability. Several strategies exist to increase the portability of phenotype algorithms, reducing the implementation effort needed by each site. These include using a common data model, standardized representation of the phenotype algorithm logic, and technical solutions to facilitate federated execution of queries. Portability is achieved by tradeoffs across three domains: Data, Authoring and Implementation, and multiple approaches were observed in representing portable phenotype algorithms. Our proposed framework will help guide future research in operationalizing phenotype algorithm portability at scale.
随着电子健康记录的采用率不断提高，为常规临床护理收集的数据被用于健康结果和人群科学研究，包括表型识别。近年来，诸如eMERGE、OHDSI和PCORnet等研究网络通过合并患者队列，提高了统计效力和人群多样性。这些网络共享在每个参与站点执行的表型算法。在此，我们观察了七个研究网络中表型算法可移植性的经验，并提出了一个可推广的表型算法可移植性框架。存在多种策略来提高表型算法的可移植性，减少每个站点所需的实施工作量。这些策略包括使用通用数据模型、表型算法逻辑的标准化表示以及便于联合执行查询的技术解决方案。可移植性是通过在三个领域进行权衡实现的：数据、编写与实施，并且在表示可移植表型算法方面观察到了多种方法。我们提出的框架将有助于指导未来大规模实施表型算法可移植性的研究。
