A metadata framework for electronic phenotypes

As many phenotyping algorithms are being created to support precision
medicine or observational studies using electronic patient data, it is
getting increasingly difficult to identify the right algorithm for the
right task. A metadata framework promises to help curate phenotyping
algorithms to facilitate more efficient and accurate retrieval. We
recruited 20 researchers from two phenotyping communities, the eMERGE and
the OHDSI communities, and used a mixed-methods approach to develop the
metadata framework. Once we achieved a consensus of 39 metadata elements,
we surveyed 47 new researchers from these communities to evaluate the
utility of the metadata framework. Two researchers were also asked to use
it to annotate eight type 2 diabetes mellitus phenotypes. The survey
consisted of a series of multiple-choice questions, which allowed rating
of the utility of each element on a scale of 1-5, and open-ended
questions, which allowed for narrative responses. More than 90% of
respondents rated metadata elements concerning phenotype definition and
validation methods and metrics with a score of 4 or 5. Our thematic
analysis of the respondents’ feedback indicates that the strengths of the
metadata framework were its ability to capture rich descriptions,
explicitness, compliance with data standards, comprehensiveness in
validation metrics, and ability to enable cross-phenotype searches.
Limitations were its complexity for data collection and entailed costs.