SCCRIP (Sickle Cell Clinical Research and Intervention Program) established a longitudinal cohort at multiple sites with Sickle Cell Disease (SCD) in 2014 managed by St. Jude Clinical Hematology. A new collaborator for SCCRIP has longitudinal data for 600 SCD patients in OMOP CDM format and this effort is to convert OMOP CDM to SCCRIP format.
Build capacity and technical know-how among SCCRIP data management team and investigators in embracing Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM), an open community data standard, designed to standardize the structure and content of observational data and to enable efficient analyses that can produce reliable evidence.
OHDSI (Observational Health Data Sciences and Informatics) is a global, multi-disciplinary, interdisciplinary collaborative with a shared mission to improve health by empowering a community to collaboratively generate real-world evidence that promotes better health decisions and better care. Browse through https://ohdsi.org/ and Check out the video https://youtu.be/aSLLfbGhnGE
The Observational Health Data Sciences and Informatics (or OHDSI, pronounced "Odyssey") program is a multi-stakeholder, interdisciplinary collaborative to bring out the value of health data through large-scale analytics. All our solutions are open-source.
https://ohdsi.org/
Standardized Data: The OMOP Common Data Model
The Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM) is an open community data standard, designed to standardize the structure and content of observational data and to enable efficient analyses that can produce reliable evidence. A central component of the OMOP CDM is the OHDSI standardized
Read more about the OMOP Common Data ModelRead more about OHDSI's standardized vocabularies
vocabularies. The OHDSI vocabularies allow organization and standardization of medical terms to be used across the various clinical domains of the OMOP common data model and enable standardized analytics that leverage the knowledge base when constructing exposure and outcome phenotypes and other features within characterization, population-level effect estimation, and patient-level prediction studies.
GitHub https://github.com/OHDSI
Self-Paced Free courses:
the EHDEN Consortium to develop the EHDEN Academy, a set of free, on-demand training and development courses. These are open to anybody, but we always encourage new OHDSI collaborators to use this resource to learn about best practices towards our mission of improving health by empowering a community to collaboratively generate evidence that promotes better health decisions and better care.
• Our OHDSI News & Updates page keeps you informed of recent publications, upcoming studies and more, while also profiling collaborators and providing any other updates about our global efforts.
Build capacity and technical know-how among SCCRIP data management team and investigators in embracing Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM), an open community data standard, designed to standardize the structure and content of observational data and to enable efficient analyses that can produce reliable evidence.
Dr George Hripcsak shared attached presentation slides (April 2021) You may find much wider, and stronger adoption now) on Observational Health Data Sciences and Informatics, Interoperability, and Research https://www.ohdsi.org/wp-content/uploads/2021/04/[OHDSI-ONC-Hripcsak-2021.pdf](https://www.ohdsi.org/wp-content/uploads/2021/04/OHDSI-ONC-Hripcsak-2021.pdf)
https://ohdsi.org/join-the-journey/
https://ohdsi.org/education/
‘10-minute tutorials’ session. Those tutorials have been posted in both Teams ( Files -> Community Call Recordings -> April -> April 13, 2021) and our Community Calls page. I wanted to share them here as well. ATHENA https://www.youtube.com/embed/2WdwBASZYLk Cohort Definitions in ATLAS https://www.youtube.com/embed/35jtNdxwVEA ACHILLES https://www.youtube.com/embed/UyS-LAUql-A USAGI https://www.youtube.com/embed/O65_c3UX8Zs
The Book of OHDSI https://ohdsi.github.io/TheBookOfOhdsi/
The Observational Health Data Sciences and Informatics (or OHDSI, pronounced "Odyssey") program is a multi-stakeholder, interdisciplinary collaborative to bring out the value of health data through large-scale analytics. All our solutions are open-source. https://ohdsi.org/
Standardized Data: The OMOP Common Data Model
The Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM) is an open community data standard, designed to standardize the structure and content of observational data and to enable efficient analyses that can produce reliable evidence. A central component of the OMOP CDM is the OHDSI standardized Read more about the OMOP Common Data Model Read more about OHDSI's standardized vocabularies vocabularies. The OHDSI vocabularies allow organization and standardization of medical terms to be used across the various clinical domains of the OMOP common data model and enable standardized analytics that leverage the knowledge base when constructing exposure and outcome phenotypes and other features within characterization, population-level effect estimation, and patient-level prediction studies. GitHub https://github.com/OHDSI
Self-Paced Free courses:
the EHDEN Consortium to develop the EHDEN Academy, a set of free, on-demand training and development courses. These are open to anybody, but we always encourage new OHDSI collaborators to use this resource to learn about best practices towards our mission of improving health by empowering a community to collaboratively generate evidence that promotes better health decisions and better care.
• Our OHDSI News & Updates page keeps you informed of recent publications, upcoming studies and more, while also profiling collaborators and providing any other updates about our global efforts.