Yu Yue, Jiang Guoqian, Brandt Eric, Forsyth Tom, Dhruva Sanket S, Zhang Shumin, Chen Jiajing, Noseworthy Peter A, Doshi Amit A, Collison-Farr Kimberly, Kim Dure, Ross Joseph S, Coplan Paul M, Drozda Joseph P
Department of Quantitative Health Sciences, Mayo Clinic, Rochester, Minnesota, USA.
Department of Artificial Intelligence and Informatics, Mayo Clinic, Rochester, Minnesota, USA.
JAMIA Open. 2023 Jan 10;6(1):ooac108. doi: 10.1093/jamiaopen/ooac108. eCollection 2023 Apr.
The objective of this study is to describe application of the Observational Medical Outcomes Partnership (OMOP) common data model (CDM) to support medical device real-world evaluation in a National Evaluation System for health Technology Coordinating Center (NESTcc) Test-Case involving 2 healthcare systems, Mercy Health and Mayo Clinic. CDM implementation was coordinated across 2 healthcare systems with multiple hospitals to aggregate both medical device data from supply chain databases and patient outcomes and covariates from electronic health record data. Several data quality assurance (QA) analyses were implemented on the OMOP CDM to validate the data extraction, transformation, and load (ETL) process. OMOP CDM-based data of relevant patient encounters were successfully established to support studies for FDA regulatory submissions. QA analyses verified that the data transformation was robust between data sources and OMOP CDM. Our efforts provided useful insights in real-world data integration using OMOP CDM for medical device evaluation coordinated across multiple healthcare systems.
本研究的目的是描述观察性医疗结果合作组织（OMOP）通用数据模型（CDM）的应用，以支持在涉及两个医疗系统（梅西健康和梅奥诊所）的国家卫生技术协调中心评估系统（NESTcc）测试案例中对医疗设备进行真实世界评估。CDM的实施在两个拥有多家医院的医疗系统之间进行协调，以汇总来自供应链数据库的医疗设备数据以及来自电子健康记录数据的患者结局和协变量。对OMOP CDM实施了多项数据质量保证（QA）分析，以验证数据提取、转换和加载（ETL）过程。成功建立了基于OMOP CDM的相关患者诊疗数据，以支持FDA监管申报研究。QA分析证实，数据源与OMOP CDM之间的数据转换是可靠的。我们的工作为使用OMOP CDM在多个医疗系统之间协调进行医疗设备评估的真实世界数据整合提供了有用的见解。
