Clinical Concept Value Sets and Interoperability in Health Data Analytics

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.