Course Overview
OMOP Common Data Model (CDM) Fundamentals is a foundational course designed to introduce learners to the Observational Medical Outcomes Partnership (OMOP) Common Data Model and its role in standardizing healthcare data for analytics, research, and real-world evidence generation. This course explains how diverse clinical data from EHR systems, claims, and other sources is transformed into a common structure for large-scale analysis.
The course is ideal for learners who want to work with healthcare data standardization, analytics platforms, interoperability initiatives, and global health data projects.
Who This Course Is For
- Healthcare data and analytics professionals
- Digital health and healthcare IT professionals
- Clinical coders and CDI professionals transitioning into data roles
- Life science graduates entering health data and analytics fields
- Professionals working with EHR data, interoperability, or research platforms
No prior experience with OMOP is required. Basic understanding of healthcare data concepts is helpful.
What You’ll Learn
By the end of this course, you will be able to:
- Understand what OMOP CDM is and why it is used
- Explain the structure and components of the OMOP Common Data Model
- Understand observational healthcare data and real-world evidence
- Identify key OMOP tables and data domains
- Understand the process of mapping source data to OMOP
- Recognize how standardized data supports analytics and research
- Prepare for advanced healthcare data and analytics training
Course Curriculum
Module 1: Introduction to Healthcare Data & OMOP
- What is observational healthcare data
- Challenges of heterogeneous healthcare data
- Introduction to OMOP and OHDSI
Module 2: Overview of the OMOP Common Data Model
- Purpose and scope of OMOP CDM
- Benefits of using a common data model
- OMOP use cases in healthcare analytics
Module 3: OMOP CDM Structure & Domains
- Person, Visit, and Observation domains
- Condition, Procedure, Drug, and Measurement tables
- Vocabulary concepts and standardization
Module 4: Clinical Data Mapping Concepts
- Source data to OMOP transformation
- Overview of ETL processes
- Common mapping challenges and considerations
Module 5: OMOP Vocabularies & Terminologies
- Standard vs source concepts
- Role of SNOMED, RxNorm, LOINC in OMOP
- Concept relationships and mappings
Module 6: OMOP & Analytics Use Cases
- Population health analytics
- Real-world evidence (RWE)
- Observational research basics
Module 7: OMOP in Real-World Healthcare Systems
- OMOP and EHR data
- OMOP in interoperability and research platforms
- Career opportunities in OMOP-based projects
Tools & Standards Covered
- OMOP Common Data Model (CDM)
- Observational healthcare data concepts
- Clinical vocabularies and mappings
- Healthcare analytics frameworks
Career Outcomes
After completing this course, learners can pursue or progress into:
- Healthcare data analyst roles
- Clinical data standardization positions
- Digital health and analytics support roles
This course also prepares learners for advanced training in:
- Healthcare Interoperability Standards
- FHIR and HL7 integrations
- SNOMED CT and RxNorm
- Real-World Data (RWD) and Evidence (RWE)
Course Format
- Structured learning modules
- Concept-driven explanations
- Real-world healthcare data examples
- Downloadable reference materials
Certification
Certificate of Completion provided by ClinicalCoding.in
