PhenoFit: a framework for determining computable phenotyping algorithm fitness for purpose and reuse

TL;DR

PhenoFit is a framework to evaluate and adapt phenotyping algorithms for reuse in different clinical settings.

Contribution

PhenoFit introduces a structured approach to assess algorithm fitness for purpose and reuse in EHR-based phenotyping.

Findings

Phenotyping algorithms are fit for purpose when they identify the intended population with appropriate performance.

Algorithms are fit for reuse if they are implementable and generalizable across new settings.

PhenoFit increases efficiency and consistency in identifying patient populations from EHRs.

Abstract

Computational phenotyping from electronic health records (EHRs) is essential for clinical research, decision support, and quality/population health assessment, but the proliferation of algorithms for the same conditions makes it difficult to identify which algorithm is most appropriate for reuse.

To develop a framework for assessing phenotyping algorithm fitness for purpose and reuse.

Phenotyping algorithms are fit for purpose when they identify the intended population with performance characteristics appropriate for the intended application.

Phenotyping algorithms are fit for reuse when the algorithm is implementable and generalizable—that is, it identifies the same intended population with similar performance characteristics when applied to a new setting.

The PhenoFit framework provides a structured approach to evaluate and adapt phenotyping algorithms for new contexts increasing efficiency and consistency of identifying patient populations from EHRs.

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