Open AccessPatient Electronic Health Data–Driven Approach to Clinical Decision Support
Author(s) -
Mane Ketan K.,
Bizon Chris,
Owen Phillips,
Gersing Ken,
Mostafa Javed,
Schmitt Charles
Publication year - 2011
Publication title -
clinical and translational science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.303
H-Index - 44
eISSN - 1752-8062
pISSN - 1752-8054
DOI - 10.1111/j.1752-8062.2011.00324.x
Subject(s) - leverage (statistics) , clinical decision support system , electronic health record , electronic medical record , decision support system , analytics , data science , computer science , health records , health care , medical record , medicine , medical emergency , data mining , artificial intelligence , radiology , economics , economic growth
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