Transcript NIBIB Health IT Initiatives and Medical Image Sharing The 4th US-China Roundtable Conference on Scientific Data Cooperation March 29-30, 2010 James Luo Ph.D. Program Director,

NIBIB Health IT Initiatives
and Medical Image Sharing
The 4th US-China Roundtable Conference on Scientific Data Cooperation
March 29-30, 2010
James Luo Ph.D.
Program Director, Biomedical Informatics Programs
National Institute of Biomedical Imaging and Bioengineering
National Institutes of Health
US Healthcare Expenditures
US total healthcare expenditures reached $2.3 trillion in 2008
$7,681 per person
16.2% of total GDP
US Health Care Expenditure
Expenditure ($bil)
$2,500.00
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$2,000.00
$1,500.00
$1,000.00
$500.00
2008
2007
2006
2005
2004
2003
2002
2001
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1999
1998
1997
1996
1995
1994
1993
1992
1991
1990
$0.00
Year
Projection: it will reach $4.48 trillions (19.3% of GDP) in 2019
Source: DHHS
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Health IT & ARRA
A 2005 RAND study projects that the adoption of Health
IT in healthcare sectors:
A mean annual savings of almost $42 billion in US
The American Recovery and Reinvestment Act of 2009
(ARRA) provides over $19 billion stimulus funds for the
development and adoption of Health IT
$2 billion for ONC to set up the standards and “meaningful use”
Healthcare Information Technology Standards Panel (HITSP)
IHE, HL7, DICOM, etc.
Nationwide Health Information Network (NHIN)
Establishment of Certification Programs for Health IT
Certification Commission for Healthcare IT (CCHIT)
$17 billion incentives for adoption of EHR
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Benefits of Health IT
Interoperable Health IT can improve individual patient
care in numerous ways:
Provide complete and accurate health information at the point of
care.
Allow secure exchange between patients and providers.
Allow more informed decision making to enhance the quality
and reliability, while reducing errors
Provide increased efficiencies in care and administration
Reduce unnecessary or repetitive tests.
Improve population health.
Integrated EHR systems with image, genomics,
pharmacogenomics (PGx) data and AHRQ clinical
guidelines support evidence-based clinical decision and
personalized medicine
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Kaiser Case Study
A Kaiser study published in Health Affairs
showed that using EHR in 2004 to 2007:
office visit rate in Kaiser’s Hawaii region dropped 26.2
percent.
“phone visits” increased more than 8 fold,
online messaging rose nearly 600 percent.
In 2007
office visits 66%
(compare to ~ 100% in 2004)
phone “visits” 30%
online consultations 4%
The use of EHR and better connectivity with
patients (phone, online) has made Kaiser more
efficient
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Kaiser Case Study 2
With complete patient information available to
them in the EHR, physicians can respond to
patients’ questions about minor problems
without seeing them.
These modes of patient contact don’t lower
either patient satisfaction or the quality of care.
it reduces errors.
Integrated EHR systems reap the benefits due to
increased efficiency, reducing office visits,
avoiding redundant tests and prescriptions
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Image in Health IT
Medical images play a critical role in:
diagnosis and prognosis of diseases
therapeutic planning
medical decision-making, safety assessment, and
risk management
clinical research to discover effective technologies,
therapeutics, diagnostics, and prevention strategies
for different populations
tracking specific diseases and response to drug
analyses the effectiveness of therapeutic
Important part of electronic health records (EHR)
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NIBIB’s Initiatives in Health IT
and Clinical Image Sharing

NIBIB launched its Health IT and clinical image
sharing program in 2009
 NIBIB–RSNA
research project: develop a network
for patient-controlled medical image sharing built
upon the IHE (Integrating the Healthcare Enterprise)
– HITSP and ONC accepted standards.
Allow image sharing across RHIOs: UCSF, U. Maryland,
Mayo, U. Chicago, Mount Sinai.
 NIBIB
awarded Grand Opportunity (GO) grants in
clinical image sharing.
Address image sharing in RHIOs:
 U. Alabama, Birmingham
 Wake Forest U.
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Objectives of Clinical Image
Sharing

To enable the sharing of radiology images across
health care institutions and vendor systems.
 To aim toward increasing the speed and accuracy of
data on which medical decisions are based,
 To reduce imaging redundancy and overutilization.
 To improve the quality of patient care by making
images immediately available.
 A key feature: patients control the access to and
sharing of the images, e.g.
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Consumer based control and ownership of their imaging
exams through Personal Health Records (PHRs)
Rural, underserved populations or academic patient
care environment image sharing is encouraged.
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Integrating Data, Models, and Reasoning
in Critical Care
• Challenges:
– “data overload”, false alarms, poor data
organization in the ICU
– early warning signs often difficult to recognize
• Opportunity:
Richness of ICU data makes
possible advanced
monitoring systems to
track and predict
pathophysiologic state of
patients.
Roger Mark, Massachusetts Institute Technology, R01 EB001659 (BRP)
Nursing notes and discharge
summaries are de-identified
automatically for the research
database.
Techniques to assess signal
quality
Predictive alerts for impending
hemo-dynamic instability
Hypotensive episode occurred
approximately 2 hours after alert
Roger Mark, Massachusetts Institute Technology, RO1-EB001659 (BRP)
MIMIC II Database: Multi-parameter Intelligent
Monitoring for Intensive Care
• a massive research-enabling database
• supports development and evaluation
of advanced patient monitoring
systems
• contents: 30,000 patient records;
4,000 include waveforms
• data includes: physiologic trends;
discharge summaries; nurses’ notes;
IV meds; physician orders; lab reports;
ventilator settings; etc.
De-identified database is made freely available to research community via
PhysioNet (www.physionet.org)
Roger Mark, Massachusetts Institute Technology, R01 EB001659 (BRP)
National Institute of
Biomedical Imaging
and Bioengineering
PhysioNet
the research resource for
complex physiologic signals
National Institute
of General
Medical
Sciences
Design of the PhysioNet Website
Scientific Community-at-Large
PhysioNet
Gateway to the
Resource
PhysioBank
Archive of
Physiologic
Signals and Time
Series
PhysioToolkit
Open Source
Software
For Data Analysis
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What is PhysioBank?
PhysioBank currently includes:
>40 collections of cardiopulmonary, neural, and
other biomedical signals from healthy subjects
and patients with a variety of conditions with
major public health implications, including
sudden cardiac death, congestive heart failure,
epilepsy, gait disorders, sleep apnea, and
aging.
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Who Uses PhysioNet / Where?
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>30,000 researchers, students,
manufacturers, educators, each month
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From all 50 US states and DC
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Users from >100 other countries
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Image Data Sharing in Research
Alzheimer's disease neuroimaging initative
(ADNI) – >$60 million, PPP
Goal: collection of data and samples (800 cases)
to establish a brain imaging biomarker,
to identify the best markers for following disease
progression and monitoring treatment response
Determine the optimum methods for acquiring,
processing, and distributing images and biomarkers
in conjunction with clinical and neuropsychological
data.
“Validate” imaging and biomarker data by correlating
with neuropsychological and clinical data.
Provide public access to all data and bio-specimens
http://www.loni.ucla.edu/ADNI/
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Hippocampal Atrophy as a Quantitative Trait in a Genome-Wide Association
Study Identifying Novel Susceptibility Genes for Alzheimer’s Disease
UC Irvine: S. Potkin, G Guffanti, A Lakatos, JA Turner, F Kruggel, JH Fallon,
Other Contributors: AJ Saykin, A Orro, S Lupoli, E Salvi, M Weiner, F Macciardi,
ADNI
• The case-control analysis
identified APOE and a recent
risk gene, TOMM40, at a
genome-wide significance level
of p-value ≤ 10−6
• The quantitative trait analysis
identified 21 genes or
chromosomal areas with at
least one SNP with a p-value ≤
10−6.
•Apoptosis, cell cycle
impairment and the alteration of
protein folding and degradation
through ubiquination are
among the candidate
pathophysiological
mechanisms
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Adapted
from: Potkin, Guffanti, et al. (2009) PLoS ONE 4(8): e6501
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Shen et al 2010: Overview
QC’ed genotyping data
Baseline MRI Scans
FreeSurfer: 56 volume or
cortical thickness measures
530,992 SNPs
142 QTs
GWAS of Imaging Phenotypes
Strong
associations
represented by
heat maps
VBM: 86 GM density measures
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GWAS
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VBM of candidate SNP
Refined modeling of
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candidate association
Shen et al 2010: Findings
• Whole genome, whole brain ROI analysis
– As expected, SNPs in the APOE and TOMM40 genes were confirmed
as markers strongly associated with multiple brain regions.
– Other top SNPs were proximal to the EPHA4, TP63 and NXPH1 genes.
• Refined analysis for a candidate SNP
– rs6463843 (flanking NXPH1) was associated with reduced global and
regional GM density across diagnostic groups in TT relative to GG
homozygotes.
– Interaction analysis indicated that AD patients homozygous for the T
allele showed differential vulnerability to right hippocampal GM
density loss.
– NXPH1 codes for a protein implicated in promotion of adhesion
between dendrites and axons, a key factor in synaptic integrity, the
loss of which is a hallmark of AD.
• A genome wide, whole brain search strategy has the potential to
reveal novel candidate genes and loci warranting further
investigation and replication.
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ADNI Genetics: UCLA, Thompson Lab
Voxelwise GWAS: Ran genomewide association for a quarter of a
million points across 700 subjects new gene discovery method; many
new SNPs; power calculations for
replication (Jason Stein et al,
NeuroImage, in press)
GRIN2b, a common glutamate
receptor genetic variant, is
associated with greater temporal
lobe atrophy and with AD; NMDAreceptor is a target for memantine
therapy (Jason Stein et al,
NeuroImage, in press)
FTO, an obesity risk gene carried
by 46% of Europeans, is associated
with 10-15% frontal and occipital
atrophy, and with a ~1.7kg weight
gain, on average (April Ho et al,
PNAS, in revision)
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Stein et al, NeuroImage, in press23