From the webpage:
The Observational Medical Outcomes Partnership (OMOP) was a public-private partnership established to inform the appropriate use of observational healthcare databases for studying the effects of medical products. Over the course of the 5-year project and through its community of researchers from industry, government, and academia, OMOP successfully achieved its aims to:
- Conduct methodological research to empirically evaluate the performance of various analytical methods on their ability to identify true associations and avoid false findings
- Develop tools and capabilities for transforming, characterizing, and analyzing disparate data sources across the health care delivery spectrum, and
- Establish a shared resource so that the broader research community can collaboratively advance the science.
The results of OMOP's research has been widely published and presented at scientific conferences, including annual symposia.
The OMOP Legacy continues…
The community is actively using the OMOP Common Data Model for their various research purposes. Those tools will continue to be maintained and supported, and information about this work is available in the public domain.
The OMOP Research Lab, a central computing resource developed to facilitate methodological research, has been transitioned to the Reagan-Udall Foundation for the FDA under the Innovation in Medical Evidence Development and Surveillance (IMEDS) Program, and has been re-branded as the IMEDS Lab. Learn more at imeds.reaganudall.org.
Observational Health Data Sciences and Informatics (OHDSI) has been established as a multi-stakeholder, interdisciplinary collaborative to create open-source solutions that bring out the value of observational health data through large-scale analytics. The OHDSI collaborative includes all of the original OMOP research investigators, and will develop its tools using the OMOP Common Data Model. Learn more at ohdsi.org.
The OMOP Common Data Model will continue to be an open-source, community standard for observational healthcare data. The model specifications and associated work products will be placed in the public domain, and the entire research community is encouraged to use these tools to support everybody's own research activities.
One of the many data models that will no doubt be in play as work begins on searching for a common cancer research language.
Every data model has a constituency, the trick is to find two or more where cross-mapping has semantic and hopefully financial ROI.
I first saw this in a tweet by Christophe Lalanne.