Patient Electronic Health Data-Driven Approach to Clinical Decision Support

被引：5

作者：

Mane, Ketan K. ^{[1
]}

Bizon, Chris ^{[1
]}

Owen, Phillips ^{[1
]}

Gersing, Ken ^{[2
]}

Mostafa, Javed ^{[3
]}

Schmitt, Charles ^{[1
]}

机构：

[1] Univ N Carolina, Renaissance Comp Inst RENCI, Chapel Hill, NC 27515 USA

[2] Duke Univ, Dept Psychiat, Durham, NC 27706 USA

[3] Univ N Carolina, Sch Informat & Lib Sci, Chapel Hill, NC USA

来源：

CTS-CLINICAL AND TRANSLATIONAL SCIENCE | 2011年 / 4卷 / 05期

关键词：

electronic health records; patient data; clinical decision support; visual analytics; comparative effectiveness research; evidence-based medicine;

D O I：

10.1111/j.1752-8062.2011.00324.x

中图分类号：

R-3 [医学研究方法]; R3 [基础医学];

学科分类号：

1001 ;

摘要：

This article presents a novel visual analytics (VA)-based clinical decision support (CDS) tool prototype that was designed as a collaborative work between Renaissance Computing Institute and Duke University. Using Major Depressive Disorder data from MindLinc electronic health record system at Duke, the CDS tool shows an approach to leverage data from comparative population (patients with similar medical profile) to enhance a clinicians' decision making process at the point of care. The initial work is being extended in collaboration with the University of North Carolina CTSA to address the key challenges of CDS, as well as to show the use of VA to derive insight from large volumes of Electronic Health Record patient data. Clin Trans Sci 2011; Volume 4: 369-371

引用

页码：369 / 371

页数：3

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