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

Area of Science:

• Health Informatics
• Data Science
• Epidemiology

Background:

• Electronic medical record (EMR) data is complex and heterogeneous.
• Existing data repositories have established governance processes.
• Secure and efficient data access for research is a significant challenge.

Purpose of the Study:

• To describe the Observational Medical Outcomes Partnership Common Data Model (OMOP-CDM).
• To outline governance processes for EMR data repositories.
• To demonstrate how OMOP-CDM facilitates efficient and secure access to EMR data for health providers and researchers.

Main Methods:

• Utilizing pseudonymization and common data quality assessments.
• Converting complex EMR data into a standardized OMOP-CDM format.
• Developing shared end-to-end analysis packages for de-identified, aggregated data.

Main Results:

• OMOP-CDM provides a robust framework for data standardization and analysis.
• Enhanced data security and privacy through de-identified data sharing and local firewalls.
• Supports diverse research projects and health service operational reporting.

Conclusions:

• OMOP-CDM adoption transforms health data analytics by standardizing EMR data.
• Simplifies governance and facilitates rapid, repeatable cross-institutional analysis.
• Enables a common platform for analyzing EMR data across diverse healthcare settings.