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Understanding Data Differences across the ENACT Federated Research Network.

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Federated research networks can now identify data quality issues using patient counts. This novel, privacy-preserving method helps sites improve electronic health record data for better clinical trial accrual.

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Area of Science:

  • Health Informatics
  • Clinical Research
  • Data Science

Background:

  • Federated research networks facilitate medical research by exchanging electronic health record (EHR) data.
  • Poor data quality in EHRs can significantly hinder research goals and patient accrual.
  • Existing data quality solutions often rely on rigid standards, limiting adaptability.

Purpose of the Study:

  • To develop and implement a novel, data-centric method for identifying data quality issues in federated research networks.
  • To create a privacy-preserving pipeline that leverages patient counts and network statistics for data quality investigation.
  • To establish a flexible and adaptable approach applicable to diverse research networks.

Main Methods:

  • Distributed high-performance patient counting scripts (Integrating Biology at the Bedside - i2b2) across Evolve to Next-Gen Accrual of patients to Clinical Trials (ENACT) sites.
  • Aggregated site-contributed patient counts at the ENACT Hub to generate network statistics.
  • Developed the Data Quality Explorer (DQE) web application to ingest network statistics and facilitate site-specific data quality analysis.

Main Results:

  • Thirteen ENACT sites have contributed patient counts, with seven actively using the Data Quality Explorer (DQE) for analysis.
  • The implemented metric allows for data quality investigation relative to network statistics.
  • The system demonstrated successful aggregation of counts and provided a platform for data quality assessment.

Conclusions:

  • A novel, privacy-preserving metric using patient counts and network statistics was successfully implemented for data quality investigation in the ENACT network.
  • This data-centric, organically evolving method offers a flexible and low-barrier approach to data quality management.
  • The underlying pipeline design is generalizable to other federated research networks, promoting broader adoption and improved data quality.