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Related Experiment Video

Updated: May 24, 2026

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Development of an Airflow-Based Automated Pipeline for Constructing Common Data Model Integrating Structured and

Sunah Yang1,2,3, Kwangsoo Kim1,3,4, Chang Wook Jeong5,6

  • 1Department of Transdisciplinary Medicine, Seoul National University Hospital, Seoul, Republic of Korea.

Studies in Health Technology and Informatics
|May 23, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces an automated pipeline for constructing the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM). The workflow efficiently integrates diverse clinical data, enhancing reproducibility and scalability for multi-institutional use.

Keywords:
AirflowETL PipelineOMOP Common Data Model

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Published on: September 20, 2018

Area of Science:

  • Health Informatics
  • Clinical Data Management
  • Bioinformatics

Background:

  • Multimodal clinical data integration is crucial for research.
  • Standardized Common Data Models (CDMs) are needed for data harmonization.
  • Existing CDM construction methods can be manual and time-consuming.

Purpose of the Study:

  • To develop an automated pipeline for OMOP Common Data Model (CDM) construction.
  • To integrate structured Electronic Medical Record (EMR) data and unstructured clinical notes.
  • To enhance the reproducibility and scalability of CDM generation.

Main Methods:

  • Apache Airflow was utilized to build an automated data processing workflow.
  • The pipeline was designed for OMOP CDM version 5.4.
  • The system integrated structured EMR data and unstructured clinical text sources.

Main Results:

  • The pipeline successfully generated 47 OMOP CDM tables.
  • The system processed data from approximately 3.7 million patients at Seoul National University Hospital (2004-2024).
  • Nearly 10 million unstructured data instances were incorporated into the CDM.

Conclusions:

  • The automated pipeline streamlines OMOP CDM construction.
  • The workflow demonstrates improved reproducibility and scalability.
  • The approach supports future multi-institutional deployment and data sharing.