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Towards cross-application model-agnostic federated cohort discovery.

Nicholas J Dobbins1,2, Michele Morris3, Eugene Sadhu3

  • 1Department of Biomedical Informatics & Medical Education, University of Washington, Seattle, WA 98195, United States.

Journal of the American Medical Informatics Association : JAMIA
|August 7, 2024
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Summary

Leaf and Shared Health Research Information Network (SHRINE) demonstrate interoperability in federated networks. Leaf successfully translated SHRINE queries across heterogeneous data models with high accuracy, enabling broader cohort discovery.

Keywords:
clinical trialscohort discoveryelectronic health recordsinteroperability

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Area of Science:

  • Biomedical Informatics
  • Health Data Interoperability
  • Clinical Trial Recruitment

Background:

  • Federated data networks are crucial for large-scale clinical research.
  • Cohort discovery tools facilitate patient identification for trials.
  • Interoperability challenges hinder data sharing across diverse healthcare systems.

Purpose of the Study:

  • To demonstrate the interoperability of Leaf and Shared Health Research Information Network (SHRINE) cohort discovery tools.
  • To adapt Leaf to function within a SHRINE federated network and translate queries for heterogeneous data models.

Main Methods:

  • Leaf was modified to act as a node in a SHRINE network.
  • SHRINE queries were dynamically translated to other data models using a Python script.
  • 500 ENACT network queries were used for evaluation, with 100 for refinement.

Main Results:

  • 91.1% of translated queries yielded results within 5% of the i2b2 data model.
  • 91.3% query recall was achieved.
  • Most discrepancies were attributed to script/ETL errors, with Leaf's translation function later corrected.

Conclusions:

  • Leaf and SHRINE can effectively interoperate within federated data networks.
  • The study validates the dynamic translation of cohort discovery queries across heterogeneous data models.
  • This approach enhances the potential for large-scale, multi-institutional cohort identification.