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Area of Science:

  • Biomedical Informatics
  • Clinical Data Analysis
  • Health Data Standards

Background:

  • Phenotype algorithm implementation is labor-intensive and prone to errors.
  • Translating human-readable descriptions into computable phenotypes requires significant effort.
  • Developing portable algorithms is essential to reduce implementation burdens.

Purpose of the Study:

  • To analyze phenotype algorithm implementation efforts within the eMERGE network.
  • To develop a novel metric (KIP score) for quantifying algorithm portability.
  • To identify common customization tasks and evaluate the impact of common data models.

Main Methods:

  • Retrospective analysis of 55 phenotype algorithms from the eMERGE network.
  • Development of a Knowledge, Interpretation, and Programming (KIP) scoring system.
  • Grouping tasks into 20 representative categories and estimating time spent.

Main Results:

  • 1153 customization tasks were identified, with the majority related to knowledge conversion (613) and programming (469).
  • The average clause-wise KIP score was 1.37 ± 1.38, indicating moderate portability challenges.
  • 70% of tasks require days to months to complete, highlighting significant implementation efforts.

Conclusions:

  • The KIP score provides a novel metric to quantify phenotype algorithm portability and implementation efforts.
  • Phenotype developers should focus on optimizing portability across knowledge, interpretation, and programming aspects.
  • Common data models like OMOP can improve portability, particularly for knowledge-oriented tasks.