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

Jie Xu, Luke V. Rasmussen, Pamela L. Shaw, Guoqian D Jiang, Richard C. Kiefer, Huan Mo, Jennifer A. Pacheco, Peter Speltz, Qian Zhu, Joshua C. Denny, Jyotishman Pathak, William K. Thompson, Enid Montague

Research output: Contribution to journalArticle

15 Citations (Scopus)

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.

Original languageEnglish (US)
Pages (from-to)1251-1260
Number of pages10
JournalJournal of the American Medical Informatics Association
Volume22
Issue number6
DOIs
StatePublished - 2015

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Translational Medical Research
Electronic Health Records
Phenotype
Software
PubMed
Publications
Research Personnel
Databases
Surveys and Questionnaires

Keywords

  • Clinical research
  • Electronic health records
  • Phenotype algorithm authoring tool
  • Phenotyping
  • Review

ASJC Scopus subject areas

  • Health Informatics

Cite this

Review and evaluation of electronic health records-driven phenotype algorithm authoring tools for clinical and translational research. / Xu, Jie; Rasmussen, Luke V.; Shaw, Pamela L.; Jiang, Guoqian D; Kiefer, Richard C.; Mo, Huan; Pacheco, Jennifer A.; Speltz, Peter; Zhu, Qian; Denny, Joshua C.; Pathak, Jyotishman; Thompson, William K.; Montague, Enid.

In: Journal of the American Medical Informatics Association, Vol. 22, No. 6, 2015, p. 1251-1260.

Research output: Contribution to journalArticle

Xu, J, Rasmussen, LV, Shaw, PL, Jiang, GD, Kiefer, RC, Mo, H, Pacheco, JA, Speltz, P, Zhu, Q, Denny, JC, Pathak, J, Thompson, WK & Montague, E 2015, 'Review and evaluation of electronic health records-driven phenotype algorithm authoring tools for clinical and translational research', Journal of the American Medical Informatics Association, vol. 22, no. 6, pp. 1251-1260. https://doi.org/10.1093/jamia/ocv070
Xu, Jie ; Rasmussen, Luke V. ; Shaw, Pamela L. ; Jiang, Guoqian D ; Kiefer, Richard C. ; Mo, Huan ; Pacheco, Jennifer A. ; Speltz, Peter ; Zhu, Qian ; Denny, Joshua C. ; Pathak, Jyotishman ; Thompson, William K. ; Montague, Enid. / Review and evaluation of electronic health records-driven phenotype algorithm authoring tools for clinical and translational research. In: Journal of the American Medical Informatics Association. 2015 ; Vol. 22, No. 6. pp. 1251-1260.
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AB - 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.

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