PhenoFit: A Framework for Determining Computable Phenotyping Algorithm Fitness for Purpose and Reuse
Listed in
This article is not in any list yet, why not save it to one of your lists.Abstract
Background: Computational phenotyping from electronic health records (EHRs) is essential forclinical research, decision support, and quality/population health assessment, but theproliferation of algorithms for the same conditions makes it difficult to identify which algorithm ismost appropriate for reuse.Objective: To develop a framework for assessing phenotyping algorithm fitness for purpose andreuse.Fitness for Purpose: Phenotyping algorithms are fit for purpose when they identify theintended population with performance characteristics appropriate for the intended application.Fitness for Reuse: Phenotyping algorithms are fit for reuse when the algorithm isimplementable and generalizable - that is, it identifies the same intended population with similarperformance characteristics when applied to a new setting.Conclusions: The PhenoFit framework provides a structured approach to evaluate and adaptphenotyping algorithms for new contexts increasing efficiency and consistency of identifyingpatient populations from EHRs.
