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J Am Med Inform Assoc. 2015 Nov;22(6):1251-60. doi: 10.1093/jamia/ocv070. Epub 2015 Jul 29.

Review and evaluation of electronic health records-driven phenotype algorithm authoring tools for clinical and translational research.

Author information

1
Department of Medicine, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA jie.xu@northwestern.edu.
2
Department of Preventive Medicine, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA.
3
Galter Health Science Library, Clinical and Translational Sciences Institute (NUCATS), Feinberg School of Medicine, Northwestern University, Chicago, IL, USA.
4
Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA.
5
Department of Biomedical Informatics, School of Medicine, Vanderbilt University, Nashville, TN, USA.
6
Center for Genetic Medicine, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA.
7
Department of Information Systems, University of Maryland, Baltimore County (UMBC), Baltimore, MD, USA.
8
Center for Biomedical Research Informatics, NorthShore University Health System, Evanston, IL, USA.
9
Department of Medicine, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA.

Abstract

OBJECTIVE:

To review and evaluate available software tools for electronic health record-driven phenotype authoring in order to identify gaps and needs for future development.

MATERIALS AND METHODS:

Candidate phenotype authoring tools were identified through (1) literature search in four publication databases (PubMed, Embase, Web of Science, and Scopus) and (2) a web search. A collection of tools was compiled and reviewed after the searches. A survey was designed and distributed to the developers of the reviewed tools to discover their functionalities and features.

RESULTS:

Twenty-four different phenotype authoring tools were identified and reviewed. Developers of 16 of these identified tools completed the evaluation survey (67% response rate). The surveyed tools showed commonalities but also varied in their capabilities in algorithm representation, logic functions, data support and software extensibility, search functions, user interface, and data outputs.

DISCUSSION:

Positive trends identified in the evaluation included: algorithms can be represented in both computable and human readable formats; and most tools offer a web interface for easy access. However, issues were also identified: many tools were lacking advanced logic functions for authoring complex algorithms; the ability to construct queries that leveraged un-structured data was not widely implemented; and many tools had limited support for plug-ins or external analytic software.

CONCLUSIONS:

Existing phenotype authoring tools could enable clinical researchers to work with electronic health record data more efficiently, but gaps still exist in terms of the functionalities of such tools. The present work can serve as a reference point for the future development of similar tools.

KEYWORDS:

clinical research; electronic health records; phenotype algorithm authoring tool; phenotyping; review

PMID:
26224336
PMCID:
PMC5009915
DOI:
10.1093/jamia/ocv070
[Indexed for MEDLINE]
Free PMC Article

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