About the Summer School

The Columbia OHDSI Summer School provides health professionals, researchers, and industry practitioners with an immersive, hands-on training to working with real-world health data and generating real-world evidence (RWE). Participants will explore the types of healthcare data captured during routine clinical care—such as electronic health records and administrative claims—and learn how to standardize these data using the OMOP Common Data Model to support collaborative, distributed research as part of a data network.

Over the course of the week, participants will engage with three real-world analytic use cases:

• Clinical characterization – using descriptive epidemiology to study disease natural history and treatment patterns
• Population-level estimation – applying causal inference to assess drug safety and comparative effectiveness
• Patient-level prediction – leveraging machine learning for early disease detection and precision medicine

Participants will be guided through the full RWE study lifecycle: from designing observational studies tailored to each use case, to applying open-source tools form the OHDSI community, and executing analyses across real-world data sources.

The curriculum combines foundational lectures on analytical methods with hands-on, interactive, faculty-led group exercises. In addition, participants will have dedicated time to develop and advance their own study concepts with personalized feedback and mentoring.

Audience & Prerequisites

The Summer School is designed for individuals interested in learning how to design and execute observational database studies focused on clinical characterization, population-level estimation, and patient-level prediction using distributed real-world data networks. Participants will also learn how to assess the reliability and interpretability of real-world evidence.

The program is ideal for clinicians, data scientists, statisticians, epidemiologists, informaticians, and health policy researchers from academia, the pharmaceutical and medical technology industries, health systems, and government agencies.

Study design and implementation will use OHDSI’s open-source tools, including ATLAS and the HADES R packages. No prior programming experience is required.

Faculty