1. Clinician Decision Support Dashboard:
Extracting value from Electronic
Medical Records using Text Mining
Iccha Sethi and Harold R. Garner
(isethi@vbi.vt.edu and garner@vbi.vt.edu)
Virginia Bioinformatics Institute, Virginia Tech

2. Objective
• To leverage EMRs to enhance patient care.
• To realize a system that will analyze a patient’s
evolving EMR:
• in context with all available biomedical knowledge.
• using the accumulated experience recorded in the EMRs of other
patients.
• with the help of interactive, automated, actionable text mining
tools.
• using multidisciplinary approach. Computer
Science
• Text Mining
Marketing • HCI
Medicine
• Biomedical
research
Clinician Decision Support Dashboard

3. Flow Chart of the CDS Dashboard
The CDS Dashboard, in a secure network, will help physicians find de-identified
electronic medical records similar to their patient's medical record, relevant
medical literature, recent research findings, and clinical trials thereby aiding
them in diagnosis, treatment, prognosis and outcomes.
Patient EMR Clinician Input
INPUT
Query Assembly and Conditioning
SIMILARITY
eTBlast
ENGINE
De-
OUTPUT Related Case Clinical Customized
identified
Literature Reports Trials Features
EMRs
Figure 1: Flow chart of CDS (The Patient EMR is processed by the CDS and then compared
to various databases by eTBlast)

4. Challenges
Several challenges exist in creating a CDS Dashboard that can be universally used
including:
Variation in entries in the EMRs; each doctor uses their own set of acronyms and shortcuts.
The presence of billing and CPT codes.
Combinations of unstructured and structured laboratory data.
Presence of lexical variants and synonyms.

5. Goals
Our goal in building the CDS Dashboard was the ensure :
platform
portability
independence
can layer on
top of existing
electronic
modularity
medical record
management
software
Discussion
• The tool can be used in multiple ways:
• Teaching aid for medical students
• Exploratory tool for doctors and nurses