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Cohort Discovery Query Optimization via Computable Controlled Vocabulary Versioning.

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Clinical cohort discovery tools can now automatically account for evolving medical terminologies. Our computable system ensures accurate patient cohort identification despite changes in coding systems like ICD-10.

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Area of Science:

  • Health Informatics
  • Clinical Data Management
  • Biomedical Data Science

Background:

  • Self-service cohort discovery tools utilize Clinical Data Warehouses for research.
  • These tools commonly rely on controlled vocabularies (e.g., ICD-9-CM, CPT) for data retrieval.
  • Current tools often present only the latest vocabulary version, ignoring historical changes.

Purpose of the Study:

  • To address the challenge of evolving controlled vocabularies in cohort discovery.
  • To propose a system that automatically handles terminology versioning issues.
  • To enhance the accuracy and usability of self-service clinical cohort discovery tools.

Main Methods:

  • Development of a computable controlled vocabulary versioning system.
  • Integration of this system into self-service cohort discovery interfaces.
  • Automatic expansion of user queries to encompass relevant historical terminology versions.

Main Results:

  • Demonstrated a method for managing controlled vocabulary evolution within discovery tools.
  • Enabled cohort discovery tools to automatically adjust search criteria based on terminology changes.
  • Improved the robustness of cohort identification by accounting for historical coding variations.

Conclusions:

  • A computable versioning system can resolve challenges posed by evolving clinical terminologies.
  • This approach simplifies cohort discovery for researchers by abstracting away versioning complexities.
  • Enhanced cohort discovery tools improve the reliability of clinical research utilizing historical data.