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Related Experiment Video

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TransformEHRs: a flexible methodology for building transparent ETL processes for EHR reuse.

Miguel Pedrera-Jiménez1,2, Noelia García-Barrio1, Paula Rubio-Mayo1

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Summary

This study introduces a transparent methodology for creating extract, transform, and load (ETL) processes for electronic health records (EHRs). This ensures data extraction and transformation are understandable, reproducible, and auditable for clinical research.

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Area of Science:

  • Health Informatics
  • Data Science
  • Clinical Research

Background:

  • Electronic health record (EHR) data extraction for research often lacks transparency.
  • Existing methodologies are frequently 'black boxes,' hindering reproducibility and auditability.

Purpose of the Study:

  • To design and implement a FAIR Principles-compliant methodology for transparent and flexible EHR data extraction, transformation, and loading (ETL) processes.
  • To create a methodology applicable to any clinical condition and healthcare organization for secondary data use.

Main Methods:

  • Analysis of international projects to identify 17 essential data operations for EHR dataset generation.
  • Formalization of data operations using the ISO 13606 reference model and Detailed Clinical Models.
  • Agnostic development of operations using SQL and R, with automated ETL instantiation via XML configuration files.

Main Results:

  • Identified and formalized 17 data operations crucial for EHR data extraction and transformation.
  • Developed an agnostic data catalog using selected programming languages.
  • Implemented an automated ETL instantiation process based on a formal XML configuration file.

Conclusions:

  • Provided a transparent, flexible, understandable, auditable, and reproducible solution for secondary EHR data use.
  • The developed abstraction allows integration into existing EHR reuse methodologies.