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Using RxNorm for cross-institutional formulary data normalization within a distributed grid-computing environment.

Rob Wynden1, Nick Anderson, Marco Casale

  • 1University of California San Francisco, CA, USA.

AMIA ... Annual Symposium Proceedings. AMIA Symposium
|December 24, 2011
PubMed
Summary
This summary is machine-generated.

Academic medical centers must normalize clinical formulary data from multiple institutions for aggregated analysis. This study presents a solution for generating normalized data views from distributed datasets for translational research.

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Published on: July 27, 2021

Area of Science:

  • Biomedical Informatics
  • Clinical Translational Science
  • Data Science

Background:

  • Academic medical centers in the Clinical Translational Sciences Awards (CTSA) program store clinical formulary data in Integrated Data Repositories (IDR).
  • Exposing this data via grid computing aids hypothesis generation and cohort selection.
  • Longitudinal data from multiple institutions necessitates term normalization for aggregation and comparison.

Purpose of the Study:

  • To address the challenge of aggregating and comparing large, distributed clinical formulary datasets.
  • To propose a solution for generating derived, aggregated, and normalized data views.
  • To facilitate the re-use of clinical formulary data in clinical translational research.

Main Methods:

  • Developing a data normalization strategy for clinical formulary terms.
  • Implementing a process for aggregating data from distributed Integrated Data Repositories.
  • Creating a framework for generating derived, normalized data views.

Main Results:

  • A method for normalizing diverse clinical formulary terminologies was established.
  • A system for aggregating normalized data from multiple institutions was successfully demonstrated.
  • Derived aggregated normalized views were generated for enhanced data re-use.

Conclusions:

  • The proposed solution effectively addresses the normalization and aggregation challenges of distributed clinical formulary data.
  • Generated normalized data views support hypothesis generation and cohort selection in translational research.
  • This approach enhances the utility of longitudinal, multi-institutional data for clinical research.