How can we use health care data to generate reproducible scientific evidence or reliable clinical predictions? What innovative tools are available to allow us to efficiently work with our own data and in collaboration with others?

Observational Health Data Sciences and Informatics (OHDSI, pronounced Odyssey) is a rapidly expanding multi-sector research collaborative dedicated to uncovering the value of health data through large-scale analytics. The OHDSI community includes dozens of academic, corporate, and governmental institutions that use health data for research in the US and around the world. The community conducts methods research to identify best practices and builds state-of-the-art open source tools that implement those methods.

The following courses feature archived talks by principal developers Marc Suchard, MD, PhD, University of California at Los Angeles (UCLA); Martijn Schuemie, PhD, and Jenna Reps, PhD, Janssen Research and Development. Learn how to use OHDSI tools on data that conforms to the OHDSI community’s OMOP Common Data Model (CDM). In addition to implementing best practices, these tools are designed to simplify research processes by eliminating data wrangling and standardizing the parts of complex multistep processes that don’t require thoughtful consideration while informing many parts that do.

These talks provide tutorials for any researcher, statistician, analyst, methodology specialists, or staff who uses health care data for research. Learners are expected to have basic R experience and understanding of observational data, as well as prior experience analyzing observational data such as electronic health records.

OHDSI Part 2 - Patient-Level Prediction
How can OHDSI analytic tool sets be used in developing patient-level prediction models?
Instructor: Jenna Reps Course Type: Self-Paced Online
OHDSI Part 1 - OMOP Common Data Model, ATLAS, & Cohorts

 

What is the OMOP Common Data Model (CDM)?

Instructor: Christian Reich Course Type: Self-Paced Online