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This paper highlights the importance of reusable value sets in health analytics. It addresses barriers to reuse and proposes recommendations for developing shared repositories to improve data analysis.

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Area of Science:

  • Health Informatics
  • Data Science
  • Clinical Analytics

Background:

  • Value sets are crucial for health analytics but face challenges in reuse.
  • The increasing use of Common Data Models and Distributed Research Networks amplifies the need for standardized value sets.
  • Existing ad hoc management of value sets incurs significant costs.

Purpose of the Study:

  • To emphasize the critical role of value sets in the health analytics ecosystem.
  • To discuss the development and adoption of shared, reusable value set repositories.
  • To present recommendations for overcoming barriers to value set reuse.

Main Methods:

  • The paper reviews the integration of value sets into health analytics tasks and landscapes.
  • It identifies barriers hindering the widespread reuse of value sets.
  • A concept-agnostic approach to vocabulary collections is introduced.

Main Results:

  • Reusable value sets are essential for advanced analytic resources like electronic phenotypes and clinical quality measures.
  • Significant benefits can be achieved through value set reuse, outweighing ad hoc management costs.
  • Recommendations for standards, infrastructure, and design are proposed to support reuse.

Conclusions:

  • Developing shared repositories and promoting the reuse of value sets can streamline health analytics.
  • Further work is needed to create comprehensive standards and infrastructure for value set reuse.
  • Adopting a concept-agnostic orientation can facilitate broader vocabulary collection use.