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Assuring End-to-End Data Quality for Analytics on FHIR.

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Summary

This study introduces a pipeline to assess data completeness in oncological Real-World Data (RWD) transformations. It ensures reliable Real-World Evidence (RWE) generation by validating data quality from source to standardized formats.

Keywords:
Health Level Seven® FHIR®data qualityelectronic health recordsobservational study

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Area of Science:

  • Health Informatics
  • Data Science
  • Oncology

Background:

  • Real-World Data (RWD) from Electronic Health Records (EHRs) and registries holds significant potential for generating Real-World Evidence (RWE).
  • Data quality is paramount for robust RWE generation, particularly when transforming heterogeneous RWD into standardized, research-ready data models.
  • Ensuring data completeness is a critical first step in validating RWD quality.

Purpose of the Study:

  • To present a novel approach for assessing data completeness within an oncological Real-World Data (RWD) extraction and transformation pipeline.
  • To introduce a technical solution for evaluating data completeness at multiple stages of data transformation.

Main Methods:

  • Developed a modular pipeline for extracting and transforming oncological RWD.
  • Implemented a technical solution to assess data completeness across three transformation stages: initial data source, Health Level 7 (HL7) Fast Healthcare Interoperability Resources (FHIR), and Comma-Separated Values (CSV).
  • Utilized Trino, a distributed SQL engine, to compare cancer diagnosis counts for completeness evaluation.

Main Results:

  • The pipeline successfully enables the assessment of data completeness at critical transformation junctures.
  • The modular design facilitates compatibility with diverse data sources and aids in detecting errors within Extract, Transform, Load (ETL) processes.
  • Cancer diagnosis counts were used to quantitatively evaluate data completeness across transformation stages.

Conclusions:

  • The developed pipeline provides a robust method for assessing data completeness in oncological RWD.
  • This approach enhances the reliability of data analytics in federated environments.
  • Future work will extend the system to address other data quality dimensions like correctness and plausibility.