Integrating real-world data to assess cardiac ablation device outcomes in a multicenter study using the OMOP common data model for regulatory decisions: implementation and evaluation

Author:

Yu Yue1,Jiang Guoqian2,Brandt Eric3,Forsyth Tom3,Dhruva Sanket S4ORCID,Zhang Shumin5,Chen Jiajing3,Noseworthy Peter A6,Doshi Amit A7,Collison-Farr Kimberly3,Kim Dure8,Ross Joseph S9,Coplan Paul M510,Drozda Joseph P3

Affiliation:

1. Department of Quantitative Health Sciences, Mayo Clinic , Rochester, Minnesota, USA

2. Department of Artificial Intelligence and Informatics, Mayo Clinic , Rochester, Minnesota, USA

3. Mercy Research, Mercy , Chesterfield, Missouri, USA

4. School of Medicine, University of California San Francisco, and Section of Cardiology, Department of Medicine, San Francisco Veterans Affairs Medical Center , San Francisco, California, USA

5. MedTech Epidemiology and Real-World Data Sciences, Office of the Chief Medical Officer, Johnson & Johnson , New Brunswick, New Jersey, USA

6. Department of Cardiovascular Medicine, Mayo Clinic , Rochester, Minnesota, USA

7. Mercy Clinic, Mercy , St. Louis, Missouri, USA

8. National Evaluation System for Health Technology Coordinating Center (NESTcc), Medical Device Innovation Consortium , Arlington, Virginia, USA

9. Department of Internal Medicine, Yale School of Medicine, and the Center for Outcomes Research and Evaluation, Yale-New Haven Hospital , New Haven, Connecticut, USA

10. Perelman School of Medicine, University of Pennsylvania , Philadelphia, Pennsylvania, USA

Abstract

Abstract 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.

Funder

Food and Drug Administration

Publisher

Oxford University Press (OUP)

Subject

Health Informatics

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