A Modular Architecture for Electronic Health Record-Driven Phenotyping

Luke V. RASMUSSEN; Richard C. KIEFER; Huan MO; Peter SPELTZ; William K. THOMPSON; Guoqian JIANG; Jennifer A. PACHECO; Jie XU; Qian ZHU; Joshua C. DENNY; Enid MONTAGUE; Jyotishman PATHAK

A Modular Architecture for Electronic Health Record-Driven Phenotyping

Luke V. RASMUSSEN
, Richard C. KIEFER
, Huan MO
, Peter SPELTZ
, William K. THOMPSON
, Guoqian JIANG
, Jennifer A. PACHECO
, Jie XU
, Qian ZHU
, Joshua C. DENNY
, Enid MONTAGUE
, Jyotishman PATHAK

Research output: Journal Publications › Journal Article (refereed) › peer-review

Abstract

Increasing interest in and experience with electronic health record (EHR)-driven phenotyping has yielded multiple challenges that are at present only partially addressed. Many solutions require the adoption of a single software platform, often with an additional cost of mapping existing patient and phenotypic data to multiple representations. We propose a set of guiding design principles and a modular software architecture to bridge the gap to a standardized phenotype representation, dissemination and execution. Ongoing development leveraging this proposed architecture has shown its ability to address existing limitations.

Original language	English
Pages (from-to)	147-151
Number of pages	5
Journal	AMIA Summits on Translational Science Proceedings
Publication status	E-pub ahead of print - 25 Mar 2015
Externally published	Yes

Funding

This work has been supported in part by funding from PhEMA (R01 GM105688) and eMERGE (U01 HG006379, U01 HG006378 and U01 HG006388).

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https://europepmc.org/article/PMC/4525215Licence: Unspecified

Cite this

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title = "A Modular Architecture for Electronic Health Record-Driven Phenotyping",

abstract = "Increasing interest in and experience with electronic health record (EHR)-driven phenotyping has yielded multiple challenges that are at present only partially addressed. Many solutions require the adoption of a single software platform, often with an additional cost of mapping existing patient and phenotypic data to multiple representations. We propose a set of guiding design principles and a modular software architecture to bridge the gap to a standardized phenotype representation, dissemination and execution. Ongoing development leveraging this proposed architecture has shown its ability to address existing limitations.",

author = "RASMUSSEN, \{Luke V.\} and KIEFER, \{Richard C.\} and Huan MO and Peter SPELTZ and THOMPSON, \{William K.\} and Guoqian JIANG and PACHECO, \{Jennifer A.\} and Jie XU and Qian ZHU and DENNY, \{Joshua C.\} and Enid MONTAGUE and Jyotishman PATHAK",

year = "2015",

month = mar,

day = "25",

language = "English",

pages = "147--151",

journal = "AMIA Summits on Translational Science Proceedings",

publisher = "American Medical Informatics Association",

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TY - JOUR

T1 - A Modular Architecture for Electronic Health Record-Driven Phenotyping

AU - RASMUSSEN, Luke V.

AU - KIEFER, Richard C.

AU - MO, Huan

AU - SPELTZ, Peter

AU - THOMPSON, William K.

AU - JIANG, Guoqian

AU - PACHECO, Jennifer A.

AU - XU, Jie

AU - ZHU, Qian

AU - DENNY, Joshua C.

AU - MONTAGUE, Enid

AU - PATHAK, Jyotishman

PY - 2015/3/25

Y1 - 2015/3/25

N2 - Increasing interest in and experience with electronic health record (EHR)-driven phenotyping has yielded multiple challenges that are at present only partially addressed. Many solutions require the adoption of a single software platform, often with an additional cost of mapping existing patient and phenotypic data to multiple representations. We propose a set of guiding design principles and a modular software architecture to bridge the gap to a standardized phenotype representation, dissemination and execution. Ongoing development leveraging this proposed architecture has shown its ability to address existing limitations.

AB - Increasing interest in and experience with electronic health record (EHR)-driven phenotyping has yielded multiple challenges that are at present only partially addressed. Many solutions require the adoption of a single software platform, often with an additional cost of mapping existing patient and phenotypic data to multiple representations. We propose a set of guiding design principles and a modular software architecture to bridge the gap to a standardized phenotype representation, dissemination and execution. Ongoing development leveraging this proposed architecture has shown its ability to address existing limitations.

M3 - Journal Article (refereed)

C2 - 26306258

SP - 147

EP - 151

JO - AMIA Summits on Translational Science Proceedings

JF - AMIA Summits on Translational Science Proceedings

ER -

A Modular Architecture for Electronic Health Record-Driven Phenotyping

Abstract

Funding

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