BMJ Health & Care Informatics (Jul 2024)

Seamless EMR data access: Integrated governance, digital health and the OMOP-CDM

  • Douglas Boyle,
  • Siaw-Teng Liaw,
  • Graeme K Hart,
  • Christine Mary Hallinan,
  • Daniel Capurro,
  • Nicole Pratt,
  • Roger Ward,
  • Clair Sullivan,
  • Ashley P Ng,
  • Anton Van Der Vegt,
  • Oliver Daly,
  • Blanca Gallego Luxan,
  • David Bunker

DOI
https://doi.org/10.1136/bmjhci-2023-100953
Journal volume & issue
Vol. 31, no. 1

Abstract

Read online

Objectives In this overview, we describe theObservational Medical Outcomes Partnership Common Data Model (OMOP-CDM), the established governance processes employed in EMR data repositories, and demonstrate how OMOP transformed data provides a lever for more efficient and secure access to electronic medical record (EMR) data by health service providers and researchers.Methods Through pseudonymisation and common data quality assessments, the OMOP-CDM provides a robust framework for converting complex EMR data into a standardised format. This allows for the creation of shared end-to-end analysis packages without the need for direct data exchange, thereby enhancing data security and privacy. By securely sharing de-identified and aggregated data and conducting analyses across multiple OMOP-converted databases, patient-level data is securely firewalled within its respective local site.Results By simplifying data management processes and governance, and through the promotion of interoperability, the OMOP-CDM supports a wide range of clinical, epidemiological, and translational research projects, as well as health service operational reporting.Discussion Adoption of the OMOP-CDM internationally and locally enables conversion of vast amounts of complex, and heterogeneous EMR data into a standardised structured data model, simplifies governance processes, and facilitates rapid repeatable cross-institution analysis through shared end-to-end analysis packages, without the sharing of data.Conclusion The adoption of the OMOP-CDM has the potential to transform health data analytics by providing a common platform for analysing EMR data across diverse healthcare settings.