How do I install the OMOP CDM on PostgreSQL without using R?

You can download pre-generated SQL DDLs from the GitHub releases tab or the master branch, specifically in the inst/ddl folder for your version, such as v5.4. This allows manual execution in your database without any R dependencies.

What's the difference between OMOP CDM v5.4 and v6.0?

The main difference is that v6.0 makes datetime fields mandatory instead of optional, which affects exposure and outcome timing assumptions. However, v6.0 is not fully supported by OHDSI tools, so the community recommends using v5.4 for now until tool compatibility is established.

OMOP CDM vs i2b2: which is better for clinical data warehousing?

OMOP CDM is optimized for observational research and interoperability with OHDSI analytics tools, while i2b2 is often used for cohort discovery and simpler queries. Choose OMOP for large-scale, cross-institutional studies, and i2b2 for faster deployment in specific clinical settings with less emphasis on standardization.

How to generate DDLs for a new CDM version using the R package?

First, install the CommonDataModel package from GitHub, then use the `buildRelease` function with the desired CDM version and dialect. Ensure you have updated the CSV files and rebuilt the package as per the steps in the README for model changes.

Is the OMOP CDM suitable for real-time health data analytics?

No, the OMOP CDM is designed for batch processing of observational data from sources like electronic health records, not real-time streams. It focuses on standardized schemas for historical analysis, making it inefficient for live monitoring or streaming applications.

What databases are supported by the OMOP CDM DDLs?

Supported dialects include PostgreSQL and others, as listed by the `listSupportedDialects()` function in the R package. The README shows examples with PostgreSQL, but you should run the function to see the full list of compatible SQL systems.

How do I submit a bug fix or proposal for the OMOP CDM?

Open an issue on the GitHub repository using the proposal template provided. For bug fixes, follow the detailed steps in the README to update CSV files and ensure changes are made in the source files, not the generated DDLs, to maintain consistency.

OMOP Common Data Model — Observational Health Data Standard

What is OMOP Common Data Model?

The OMOP Common Data Model is a standardized data model and set of SQL DDL scripts for structuring observational health data. It solves the problem of heterogeneous healthcare data formats by providing a consistent schema that enables large-scale analytics, population-level studies, and tool interoperability within the OHDSI ecosystem.

Target Audience

Clinical informaticians, data engineers, and researchers working with electronic health records or observational health data who need to transform disparate datasets into a standardized format for analysis.

Value Proposition

Developers choose this because it is the community-adopted standard for observational research, offering versioned, database-agnostic DDLs and programmatic tools that ensure data consistency and enable the use of the broader OHDSI tool suite.

Definition and DDLs for the OMOP Common Data Model (CDM)

Use Cases

Best For

Standardizing electronic health record data from multiple institutions
Enabling population-level health outcomes research across disparate datasets
Setting up a data warehouse for observational health analytics
Ensuring data quality and consistency for clinical studies
Integrating with OHDSI tools like ATLAS or the Data Quality Dashboard
Converting legacy healthcare databases to a community-standard schema

Not Ideal For

Projects requiring real-time data processing or streaming analytics on health data
Small, single-institution studies with homogeneous data formats that don't need cross-compatibility
Teams wanting to use the latest CDM v6.0 features with full community tool support
Environments where R is not available or teams prefer SQL-only workflows without programmatic generation

Pros & Cons

Pros

Standardized Schema

Defines a common set of tables and fields for observational health data, enabling large-scale analytics and interoperability across disparate datasets, as core to the OHDSI philosophy.

Multi-Dialect SQL DDLs

Generates Data Definition Language scripts for various SQL databases like PostgreSQL, shown in the `buildRelease` function with target dialects, ensuring database-agnostic deployment.

Version Management

Supports multiple CDM versions with tools like `listSupportedVersions()` to generate specific releases, facilitating upgrades and consistency in long-term projects.

Programmatic Deployment

Provides R functions such as `executeDdl` to directly create tables in databases, streamlining setup for supported dialects without manual SQL execution.

Model-Driven Consistency

Uses CSV files as the single source of truth for table and field definitions, ensuring consistency across DDLs, documentation, and data quality checks, as detailed in the update process.

Cons

R Dependency

Requires R-Studio and additional packages like DatabaseConnector and SqlRender for full functionality, adding complexity for teams not already using R in their stack.

Limited v6.0 Support

The README explicitly states that v6.0 is not fully supported by OHDSI tools, creating a gap for teams wanting to adopt the latest model with mandatory datetime fields.

Complex Update Workflow

Making changes to the model involves multiple steps with CSV files and R package rebuilding, as described in the bug fix section, which can be cumbersome and error-prone for quick iterations.

Ecosystem Lock-in

Heavily tied to the OHDSI community and tools like ATLAS, which might limit flexibility for projects that don't align with this specific ecosystem or need custom integrations.

Frequently Asked Questions

What is OMOP Common Data Model?

Target Audience

Value Proposition

Use Cases

Best For

Standardizing electronic health record data from multiple institutions
Enabling population-level health outcomes research across disparate datasets
Setting up a data warehouse for observational health analytics
Ensuring data quality and consistency for clinical studies
Integrating with OHDSI tools like ATLAS or the Data Quality Dashboard
Converting legacy healthcare databases to a community-standard schema

Not Ideal For

Projects requiring real-time data processing or streaming analytics on health data
Small, single-institution studies with homogeneous data formats that don't need cross-compatibility
Teams wanting to use the latest CDM v6.0 features with full community tool support
Environments where R is not available or teams prefer SQL-only workflows without programmatic generation

Pros & Cons

Pros

Standardized Schema

Defines a common set of tables and fields for observational health data, enabling large-scale analytics and interoperability across disparate datasets, as core to the OHDSI philosophy.

Multi-Dialect SQL DDLs

Generates Data Definition Language scripts for various SQL databases like PostgreSQL, shown in the `buildRelease` function with target dialects, ensuring database-agnostic deployment.

Version Management

Supports multiple CDM versions with tools like `listSupportedVersions()` to generate specific releases, facilitating upgrades and consistency in long-term projects.

Programmatic Deployment

Provides R functions such as `executeDdl` to directly create tables in databases, streamlining setup for supported dialects without manual SQL execution.

Model-Driven Consistency

Uses CSV files as the single source of truth for table and field definitions, ensuring consistency across DDLs, documentation, and data quality checks, as detailed in the update process.

Cons

R Dependency

Requires R-Studio and additional packages like DatabaseConnector and SqlRender for full functionality, adding complexity for teams not already using R in their stack.

Limited v6.0 Support

The README explicitly states that v6.0 is not fully supported by OHDSI tools, creating a gap for teams wanting to adopt the latest model with mandatory datetime fields.

Complex Update Workflow

Making changes to the model involves multiple steps with CSV files and R package rebuilding, as described in the bug fix section, which can be cumbersome and error-prone for quick iterations.

Ecosystem Lock-in

Heavily tied to the OHDSI community and tools like ATLAS, which might limit flexibility for projects that don't align with this specific ecosystem or need custom integrations.

Frequently Asked Questions

OMOP Common Data Model

What is OMOP Common Data Model?

Overview

Use Cases

Best For

Not Ideal For

Pros & Cons

Pros

Cons

Frequently Asked Questions

Found a gem we're missing?

OMOP Common Data Model

What is OMOP Common Data Model?

Overview

Use Cases

Best For

Not Ideal For

Pros & Cons

Pros

Cons

Frequently Asked Questions

Found a gem we're missing?