Data model harmonization for the All Of Us Research Program: Transforming i2b2 data into the OMOP common data model

Abstract

Background The All Of Us Research Program (AOU) is building a nationwide cohort of one million patients’ EHR and genomic data. Data interoperability is paramount to the program’s success. AOU is standardizing its EHR data around the Observational Medical Outcomes Partnership (OMOP) data model. OMOP is one of several standard data models presently used in national-scale initiatives. Each model is unique enough to make interoperability difficult. The i2b2 data warehousing and analytics platform is used at over 200 sites worldwide, which uses a flexible ontology-driven approach for data storage. We previously demonstrated this ontology system can drive data reconfiguration, to transform data into new formats without site-specific programming. We previously implemented this on our 12-site Accessible Research Commons for Health (ARCH) network to transform i2b2 into the Patient Centered Outcomes Research Network model. Methods and results Here, we leverage our investment in i2b2 high-performance transformations to support the AOU OMOP data pipeline. Because the ARCH ontology has gained widespread national interest (through the Accrual to Clinical Trials network, other PCORnet networks, and the Nebraska Lexicon), we leveraged sites’ existing investments into this standard ontology. We developed an i2b2-to-OMOP transformation, driven by the ARCH-OMOP ontology and the OMOP concept mapping dictionary. We demonstrated and validated our approach in the AOU New England HPO (NEHPO). First, we transformed into OMOP a fake patient dataset in i2b2 and verified through AOU tools that the data was structurally compliant with OMOP. We then transformed a subset of data in the Partners Healthcare data warehouse into OMOP. We developed a checklist of assessments to ensure the transformed data had self-integrity (e.g., the distributions have an expected shape and required fields are populated), using OMOP’s visual Achilles data quality tool. This i2b2-to-OMOP transformation is being used to send NEHPO production data to AOU. It is open-source and ready for use by other research projects.

Content Coverage Evaluation of the OMOP Vocabulary on the Transplant Domain Focusing on Concepts Relevant for Kidney Transplant Outcomes Analysis

“…There are many existing CDMs including PCORnet, informatics for integrating biology in the bedside (i2b2), and Observational Medical Outcomes Partnership (OMOP) CDM, which are used in large nationwide initiatives. 12 Previous studies have evaluated multiple CDMs on various quality dimensions and have demonstrated that the OMOP CDM best satisfies the quality dimensions and is an appropriate model for comparative effectiveness and outcomes research. 7,9,13,14 More importantly, there are a rich set of open-source analytic tools that leverage OMOP CDM, which is why OMOP CDM is often chosen over other data models.…”

“…7,9,13,14 More importantly, there are a rich set of open-source analytic tools that leverage OMOP CDM, which is why OMOP CDM is often chosen over other data models. 7,12,15 Thus, despite an existing effort to transform transplant data into the i2b2 format, the focus of this study will be on assessing the feasibility of mapping concepts from the UNOS database to concepts in the OMOP vocabulary. 16…”

The ENIGMA Stroke Recovery Working Group: Big data neuroimaging to study brain-behavior relationships after stroke

“…Key benefits of a SQLbased environment -compared to other database options, such as REDCap (Harris et al, 2009) (https://www.project-redcap.org/) -are that it is freely available (not institutionally constrained) and widely used across both research and industry, leading to many options for integrating SQL analyses with popular programming environments such as Python, and statistical packages such as R. SQLite runs quickly and does not require specialized computing resources, and SQLite databases can be easily shared. SQL-based databases are also being used in other large-scale data sharing projects, such as the NIH-funded All of Us research initiative, which aims to gather healthrelated data about one million or more people living in the United States (Klann, Joss, Embree, & Murphy, 2019). Given these factors, SQLite provides an accessible option for data analysis and for future data sharing and is aligned with existing big data initiatives.…”

Toolkit to Compute Time-Based Elixhauser Comorbidity Indices and Extension to Common Data Models

“…The OMOP CDM offers the highest domain coverage by representing and analyzing EHR data [19]. The I2B2 is the most widely adopted CDM due to the flexibility of representing non-standard and local data [20,21]. Extending CDMs to accommodate ECIs will provide the ability to easily identify clinical comorbidities and complications in multi-site studies.…”