Data harmonization for COVID-19 and cancer research registries
Authors: Rimma Belenkaya, Adam Watson, Shantha Bethusamy, Meera Patel, Tatyana Sandler, Julian Schwartz, James Park, Maggie Dobbins, Molly Maloy, Michael Lam, Nadia Bahadur, John Philip
Journal: Clinical Cancer Research (2020)

Introduction: The need to rapidly collect, integrate, and share data on COVID-19 patients with cancer at scale hasgiven rise to multiple internal and cross-institutional research registries  These registries support use cases thatrequire data at different levels of granularity and are built using mixed standards  Ensuring semantic interoperabilityand quality of this data is critical for generating reliable and reproducible evidence  At MSK, we created a frameworkthat enabled the rapid development of semantically compatible COVID and cancer registries and data exchange  Background: Handling and harmonizing real-world data for COVID and cancer research presented with typicalchallenges: maintenance of complex patient cohorts;reconciling different levels of temporal and semanticgranularity;supporting crosswalks between different representations without information loss;and sharing itinternally and with research consortia  Solving these challenges for COVID and cancer studies necessitatedadvanced infrastructure and harmonization solutions  Methods: We used MSK Extract, our research platform, to create an integrated COVID and cancer data researchframework  It included a library of reusable standardized REDCap used in multiple RedCap instances supportingindividual research studies;PostgreSQL database containing patient cohorts and data from Electronic HealthRecords (EHR) standardized to OMOP;and ETL pipelines  Our approach to the REDCap design and datamanagement allowed for combined sets of detailed, atomic, and aggregate-level data through a combination of abstraction, curation, and extraction of data from different sources  We developed reconciliation methodologybetween initial curation, available raw data, and the subsequent abstraction  We enforced consistent temporalconstraints on data extraction and curation  We used the OMOP vocabulary for semantic harmonization, mappingmetadata from internal and external registries to OMOP concepts  We linked procedure and medication codes tohigh-level treatment groups leveraging classifications available in the OMOP vocabulary  Results: Our approach to the REDCap design supported various analytical use cases and enabled data sharingbetween different investigators and registries  Reuse of the data that was previously abstracted complemented withthe data extracted from EHR allowed investigators and their teams to quickly review, validate, and update the priorcuration  Explicit temporal constraints supported alignment between different registries  Using the OMOP standardsand high-level treatment classifications supported data conversion between various registries and integration of thedata collected via REDCap and sourced from EHR  Conclusion: Using real-world data for observational COVID and cancer research presented us with opportunities toimprove and mature our evolving research infrastructure and better support internal and distributed research, andhighlighted the need for uniform data standards in the cancer domain