FHIR → OMOP: Healthcare ETL & Data Standardization

FHIR → OMOP: Healthcare ETL & Data Standardization

A technical portfolio project demonstrating how Data Engineering and ETL concepts can be applied to transform healthcare data from an exchange-oriented format such as FHIR into a standardized analytical data model such as the OMOP Common Data Model (OMOP CDM).

The project focuses on a major challenge in healthcare data: different healthcare systems may represent and structure information differently, making data integration, analysis, and research more difficult.

The workflow starts with healthcare information represented through FHIR Resources. The data then moves through a Raw/Staging layer, followed by data cleaning, transformation, standardization, concept mapping, and validation before being loaded into the OMOP Common Data Model.

The project clearly explains the different roles of FHIR and OMOP. FHIR is primarily designed to support the exchange of healthcare information between systems, while the OMOP Common Data Model provides a standardized structure for observational healthcare data used for analytics and research.

The project introduces important OMOP tables such as PERSON, VISIT_OCCURRENCE, CONDITION_OCCURRENCE, DRUG_EXPOSURE, MEASUREMENT, and OBSERVATION, explaining how healthcare information can be organized within the standardized model.

It also demonstrates the main stages of the ETL process: Extract, Transform, Mapping, Validation, and Load, while discussing data quality checks and the importance of validating transformed healthcare data.

The project includes conceptual examples of data transformation, concept mapping, validation checks, and SQL-based analysis after the data has been standardized.

The technical research is based on authoritative sources including HL7, OHDSI, the OMOP Common Data Model documentation, and the official FHIR-to-OMOP Implementation Guide, with references provided throughout the project.

Key Topics:

Healthcare ETL

FHIR

OMOP Common Data Model

Data Standardization

Data Mapping

Data Transformation

Data Validation

Data Quality

SQL

Relational Databases

Healthcare Analytics

Clinical Data Integration

Tools & Concepts: Python, Pandas, SQL, PostgreSQL, FHIR, OMOP CDM, ETL, Data Transformation, Data Quality