Characterizing variability of electronic health record-driven phenotype definitions

Area of Science:

• Health Informatics
• Computational Biology
• Clinical Informatics

Background:

• Phenotype definitions are crucial for research and clinical decision support.
• Variability in definition logic can hinder interoperability and reproducibility.
• Standardized representations are needed for computable phenotype definitions.

Purpose of the Study:

• To analyze the variability of logical constructs in rule-based phenotype definitions.
• To evaluate the characteristics of computable phenotype definitions.
• To identify components of phenotype definitions for standardization.

Main Methods:

• Analyzed 33 phenotype definitions represented in Fast Healthcare Interoperability Resources and Clinical Quality Language (CQL).
• Utilized automated analysis of computable representations within CQL libraries.
• Examined logical, data, and aggregate expressions, code usage, and terminology.

Main Results:

• Phenotype definitions predominantly use logical expressions and tabular data, with limited use of aggregate/arithmetic expressions.
• Expression depth varied significantly (4-27), with most using 4 or fewer medical terminologies.
• While specific operators varied, core components remained consistent across definitions.

Conclusions:

• All analyzed phenotype definitions comprise logical criteria and tabular data.
• Standardized, modular representations are essential for phenotype definitions.
• Modularity will support localization and shared logic for broader implementation.