Silver Medallion CRI
Poster Review
W. Ed Hammond, Ph.D.
Director, Duke Center for Health
Informatics
And assorted other things
Current Drivers
• Interoperability
– Terminology
• ‘Omics
• Data Quality
• Clinical Decision
Support
• NLP and Phenotypes
• Analytics
• Big Data
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EHR
Mobile Health
Consumer
GIS + Environment
Patient Identification
Usability
Cloud Computing
Social Networking
Privacy and Security
Hammond - CRI
From molecules to population
Molecular
Biology
Clinical
Research
Patient
Care
Public
Health
Population
Health
Individual, Family, Community, Societies
Site of Care: Intensive care, inpatient, ambulatory, intensive care,
emergency department, long term care, home care
Clinical Specialties
Global
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Unsolved Problems
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Terminology vs data elements
Patient Identification
Data quality, completeness, consistency
Structured vs unstructured data
Extracting knowledge from data
EHR
Sharing data - governance
Hammond - CRI
Poster Board 19
• Role of Named Entity Recognition in
Extraction of Diagnosis Codes from
Electronic Medical Records
– Daniel Harris, B.S1, Todd R. Johnson, Ph.D1,
and Ramakanth Kavuluru, Ph.D
– University of Kentucky
Hammond - CRI
Abstract
We present our findings in applying named entity
recognition (NER) techniques to extract ICD-9-CM
codes from Electronic Medical Records (EMRs). Using two
NER systems, MetaMap and cTAKES, we extract UMLS
concepts and map them to ICD-9-CM top level codes and
compare results with billing codes for each visit. These
unsupervised methods achieved EMR-based recall of 0.41
with precision 0.18. Our results point to the importance of
NER techniques as a first step in automatic extraction for
large coding schemes.
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Method
• 1000 clinical documents randomly chosen
• Text documents included discharge
summaries, operative reports, progress
notes
• Extracted ICD9 codes by human coding
experts
• Used MetaMap and cTakes on full EHR
and compared results
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Results
• Physician authored documents with union
of MetaMap and cTakes yielded a recall of
.41 and a precision of .18
• Using all documents increased recall to.43
and precision to .20
Hammond - CRI
Poster Board 25
• An Approach for the Mapping of CEM and
OpenEHR Archetypes
– Mari Carmen Legaz-García, Cui Tao, PhD ,
Marcos Menárguez-Tortosa , Jesualdo
Tomás Fernández-Breis, PhD , Christopher
G. Chute, MD, DrPH
– Mayo Clinic and Universidad de Murcia,
Murcia, Spain
1
2
2
1
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Abstract
• describe the first steps to build a system to
transform clinical models between Clinical
Element Model (CEM) and openEHR
archetypes by exploiting semantic web
technologies, which are currently being
used in many international efforts to
develop solutions for the semantic
interoperability of electronic healthcare
records.
Hammond - CRI
Introduction
• Clinical Element Model (CEM)
• openEHR Archetype Definition Language
(ADL) and archetypes
• Clinical Information Modelling Initiative
(CIMI)
• SHARPn
• Constraint Definition Language (CDL)
• Ontology Web Language (OWL)
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Methodology
• Map CEM to openEHR archetypes using
OWL. The OWL representation allows the
joint use of annotations in the CEM
models with external terminologies and
inference processes to identify the clinical
usage of the models and an openEHR
concept with similar meaning.
Hammond - CRI
Poster
• Automated Tools for Phenotype
Extraction from Medical Records
– Meliha Yetisgen-Yildiz, PhD, Cosmin A.
Bejan, PhD, Lucy Vanderwende, PhD, Fei
Xia, PhD, Heather L. Evans, MD, MS, Mark
M. Wurfel, MD, PhD
– University of Washington and Microsoft
Research
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Abstract
Clinical research studying critical illness phenotypes relies on
the identification of clinical syndromes defined by consensus
definitions. Historically, identifying phenotypes has required
manual chart review, a time and resource intensive process.
The overall research goal of Critical Illness PHenotype
ExtRaction (deCIPHER) project is to develop automated
approaches based on natural language processing and
machine learning that accurately identify phenotypes from
EMR. We chose pneumonia as our first critical illness
phenotype and conducted preliminary experiments to explore
the problem space. In this abstract, we outline the tools we
built for processing clinical records, present our preliminary
findings for pneumonia extraction, and describe future steps.
Hammond - CRI
Methods
• general purpose tools to process free-text
medical reports including
– a statistical section segmentation approach to
chunk a given medical record into its main
sections and
– an assertion analysis tool to analyze the
certainty level of a given concept in the
context it appears in text (e.g., present,
absent).
Hammond - CRI
Hammond - CRI
Poster Board 47
• Subject Identification Methods using
Electronic Data for Recruitment in a
Cross-institutional Intervention Study
– Adam B. Wilcox, PhD1, Margaret McDonald,
MSW, Elaine Fleck, MD, Melissa
Trachtenberg2, Penny H. Feldman, PhD
– Columbia University
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Abstract
• We compared different methods for
identifying subjects who were affiliated
with two different institutions as part of a
cross-institutional intervention study. Data
exchange methods, while more difficult in
getting approval, appear more efficient in
identifying potential subjects.
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Problem
• Patient-centered research may need to be
conducted across institutions, since
patients often receive different types of
clinical care from different types of
providers at different institutions. The
Washington Heights/Inwood Informatics
Infrastructure for Comparative
Effectiveness Research (WICER) project
is creating a research infrastructure to
support CER and PCOR.
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Method
• The WICER project is building a multiinstitutional informatics infrastructure and
demonstrating the feasibility of the
infrastructure to support comparative
effectiveness research (CER) through
three CER studies.
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• One method: shared patients were
identified from the VNSNY EHR, including
the referral source institution.
• Second method: the VNSNY patient
population was matched to CUMC patients,
using data from both EHRs.
• After patients are screened using
electronic data, they are interviewed
directly to determine if they are actually
receiving primary care from the
participating clinics.
Hammond - CRI
Results
For 110 patients that were screened for eligibility based
on where they received care. About half of patients
(51%) identified through the “VNSNY only” method were
not currently receiving care at the clinic, while only about
a third (36%) were not receiving care when using the
“VNSNY+CUMC” method. Patients did not pass the clinic
eligibility screening either because they did not receive
regular primary care at an ACN clinic (37%), or received
care at a non-recruiting clinic (39%). Other reasons were
that the patient changed physicians and was no longer
under the care of an ACN doctor (17%), or was seen by
an ACN physician, but in a private clinic (7%).
Hammond - CRI