Foundations
Translating raw healthcare data into regulatory-grade Real-World Evidence (RWE) requires a strong grounding in epidemiological study design, evidence hierarchies, methodological bias mitigation, data source taxonomies, and legal governance.
This module provides the foundational concepts that underpin observational health research and the OMOP Common Data Model (CDM).
Module Roadmap
The foundations curriculum is organized into three comprehensive chapters:
| Chapter | Topic | Key Concepts Covered |
|---|---|---|
| 1. Introduction to Real-World Evidence | Methodological Foundations | Evidence-based medicine, Grimes & Schulz taxonomy, evidence pyramid, Efficacy vs. Effectiveness, epidemiological biases & mitigations, RWD taxonomy, and EU/Spanish legal frameworks. |
| 2. Real-World Data at Scale | Scaling & Network Studies | The 2.3 Exabyte challenge, process-centric vs. patient-centric architecture, the reproducibility crisis, and landmark multinational COVID-19 network studies (BMJ 2021, BMJ 2022). |
| 3. The Open Science Ecosystem | Community & Global Networks | The global OHDSI community (+1B patients), federated “code-to-data” architecture, and the European health data journey (EHDEN $\rightarrow$ DARWIN EU $\rightarrow$ EHDS / HealthData@EU). |
How This Connects to the Toolkit
Once you master the theoretical principles in this section, continue to the Introductory Guide to OMOP to learn how clinical ideas are represented as standard concept_ids, followed by the Performing an Analysis guide to write executable R workflows with DARWIN-EU / OHDSI packages.