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  3. Understanding Data Differences Across The Enact Federated Research Network.
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  3. Understanding Data Differences Across The Enact Federated Research Network.

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Understanding data differences across the ENACT federated research network.

Taowei D Wang1, Darren W Henderson2, Griffin M Weber3

  • 1Department of Neurology, Massachusetts General Hospital, Boston, MA, 02114, United States.

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|April 6, 2026

View abstract on PubMed

Summary
This summary is machine-generated.

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 clinical trials.

Keywords:
ENACTdata qualitydata visualizationfederated networki2b2

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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 progress.
  • Existing data quality methods often rely on rigid standards, limiting adaptability.

Purpose of the Study:

  • To develop a novel, data-centric method for identifying data quality issues in federated research networks.
  • To create a privacy-preserving system applicable even to sites not yet fully integrated into the network.
  • To enhance the reliability of EHR data for clinical trial accrual.

Main Methods:

  • Distributed high-performance patient counting scripts (Integrating Biology at the Bedside - i2b2) across ENACT sites.
  • Aggregated site-contributed patient counts at the ENACT Hub to generate network statistics.
  • Developed the Data Quality Explorer (DQE) web application to visualize and investigate data quality relative to network benchmarks.

    • Main Results:

    • Thirteen ENACT sites contributed patient counts, with ten actively using DQE for data quality analysis.
    • The system successfully generates network statistics from aggregated patient counts.
    • The Data Quality Explorer (DQE) facilitates data quality investigation for participating sites.

    Conclusions:

    • Implemented a novel metric for data quality investigation in federated networks using patient counting and network statistics.
    • The developed end-to-end pipeline is privacy-preserving and adaptable to various research networks.
    • This approach offers a low-barrier, evolving solution to improve EHR data quality for clinical research.