Understanding SQL, OMOP, & BigQuery | Office Hours 1.26.24

Welcome to another session of our Office Hours!
These recordings are your go-to place to discover new features and learn tips and tricks for using the All of Us Researcher Workbench.
In this video, Aymone Kouame, a data scientist on our team, provides an overview of concept relationships and mapping in Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM), example queries for All of Us EHR domains, and how to use BigQuery sessions efficiently.
Note: This Office Hours revisits themes presented in the previous “Introduction to Electronic Health Record (EHR) Data” Office Hours session on January 12, 2024.
During this Office Hours, you'll learn:
About OMOP and its structure
About concept hierarchy and concept_relationship and concept_ancestor tables
How to use BigQuery to query info into Jupyter Notebook
Quick links:
7:28 Review of concepts in OMOP
9:45 Understanding the OMOP Hierarchy
9:46 About the concept_relationship table
14:10 About the concept_ancestor table
16:22 Example of the concept_relationship and the concept_ancestor tables
20:09 Example queries for EHR domains - Conditions
21:47 Example queries for EHR domains - Procedures
22:34 Example queries for EHR domains - Drug
22:49 Example queries for EHR domains - Visits
23:10 Example queries for EHR domains - Measurements and Labs
23:57 Using BigQuery sessions for efficiency
25:23 Limitations of BigQuery sessions
27:20 (LIVE DEMO) Using BigQuery sessions in the Workbench
32:11 Live Q&A
If you have any questions, reach out to our support team at [e-mail]
#AllofUsResearch #ResearcherWorkbench #HealthResearch #OMOP #SQL #BigQuery #MedicalResearch #DataScience #BiomedicalResearch #ResearchCommunity
recorded January 26, 2024