7th Annual AMIA Invitational Health Policy Meeting December 12-13 ...

Project Overview Purpose/Type of Data Project Features Results Challenges
Patient characteristics associated with venous
thromboembolic events: a cohort study using pooled
EHR data. MetroHealth System (Case Western Reserve
Univ). Demonstrate potential of de-identified clinical
data from multiple healthcare systems with different
EHRs for use in large retrospective studies. 4
BioSense 2.0. CDC program tracks health problems as
they evolve and provides public health officials with data
and tools to better prepare and coordinate responses.
Mandated in the Public Health Security and Bioterrorism
Preparedness and Response Act of 2002. Administered
by ASTHO, Council of State and Territorial
Epidemiologists, NACCHO, Int’l Soc for Disease
Surveillance. 5
BioVU. Vanderbilt Univ. Med. Ctr biorepository, funded
by NIH and CTSA; member of NGHRI eMERGE
consortium. Collection of patient samples for
biorepository using opt-out approach. 6
AMIA Invitational Health Policy Meeting 2012
Page 36 12/10/2012
Use of aggregated,
standardized, normalized
EHR data from different
health systems to perform
clinical research.
Use of EHR data for
biosurveillance; public
health syndromic
surveillance messaging for
MU. Nationwide integrated
system for early detection
and assessment of
bioterrorism-related
illnesses. Cloud-based
environment provides
situation awareness for allhazard
health-related
threats.
Biorepository includes
clinical-derived DNA
samples linked to deidentified
EHRs to support
genotype-phenotype
research.
Used pooled, de-identified
data from multiple systems
with distinct EHRs. Data
were standardized and
normalized via common
ontologies, and searchable
via HIPAA-compliant,
patient de-identified web
app.
Distributed environment,
(joint state, local and
federal) provides secure
data storage space, analytic
tools, and shared
environment for public
health surveillance.
Systematically collects large
volumes of EHR data from
clinical and non-clinical
services (ED, inpatient,
ambulatory) in near “real
time.”
Uses patient opt-out model
combined with deidentification
of
corresponding health
records and a substantial
patient notification
program.
Cohort study of EHR data
from 1 million patients was
much larger, performed with
much fewer resources, and
much faster than equivalent
prospective cohort study.
Enables monitoring of
numerous health conditions,
and rapid sharing of data by
state and local health depts.
and CDC. Examples of use:
BioSense data was used to
enhance surveillance for
dengue by identifying people
with dengue symptoms; to
monitor levels of heatrelated
illness during heat
wave.
As of 9/2012, contained DNA
samples from over 150,000
individuals and EHR data from 2
million people who did not opt
out. Over 65 papers report
results leveraging BioVU. Enables
pooling of data and samples
from different institutions in
biobank. Opt-out rate of 15%
over past 5 years.
� Study did not look at
unstructured data (e.g.,
imaging, autopsy results,
VTE signs) (natural
language processing not
supported)
� Although clinical data
may not meet research
standards (precision,
accuracy), large quantity
of data may mitigate this
None noted.
Uncertainty surrounding
implications of adopting opt-out
approach. Office of Human
Research Protections and DHHS
are seeking input on suitability of
opt out approach, consideration of
public education campaign, etc.