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FasterCures
Still Thinking Research
Strategies to Advance the Use of Electronic Health Records to
Bridge Patient Care and Research
Preface
FasterCures is not just our name—it's our mission.
We are an “action tank” that works to improve the medical research system so that
we can speed up the time it takes to get important new medicines from discovery to
patients. One of our key strategic objectives is to increase patient engagement in
research and optimize use of patient data.
For the medical research system to work, it has to be patient-focused, and
patient-driven. In fighting disease, patience is not a virtue—patients are. Through
our Patients Helping Doctors (PHD) program, we focus our efforts on unlocking
patient information—medical records and biological material such as tissue, blood,
and DNA—and making it available to clinical researchers in a meaningful way.
This report is a central component of the PHD. An update to the 2005 FasterCures
Think Research analysis, it continues to call for integrating research into the health
information technology framework to achieve earlier and better diagnosis and
more effective prevention strategies and cures. This report examines current policies
and existing practices, presents a platform for discussion, and provides clear-cut
recommendations for action.
We thank the team who developed this report—Adam Clark, Ph.D., Kathi E.
Hanna, M.S., Ph.D., and Gillian Parrish. And we thank everyone who agreed to
be interviewed for their time and, more importantly, for their leadership and
contributions to improving the medical research system.
TABLE OF CONTENTS
01
06
09
16
Introduction
New Developments in Federal Health IT
Leveraging Health IT for Networked
Research Opportunities
Meeting the Challenges:
Institutional Pioneers, Innovators, and
Transformational Models
27
28
29
36
37
38
Lessons Learned
What’s Next: Actions and Opportunities
Recommendations for Action
In Sum
Acknowledgments
References
Introduction
FasterCures has perceived an enormous opportunity emerging
from the confluence of three transformative breakthroughs—
the completion of the Human Genome Project in 2003, the slow
but steady rise in the use of EHRs, and the expansion of health
information technology (health IT).
But how do we make the most of these developments? How can
scientists and clinicians possibly begin to make sense of all the
new data from the human genome, for example, without more
efficient means of linking these molecular data to other types
of data about medicine and health status? The relative effects of
single genes on complex diseases, such as cancer, diabetes, mental
illness, and heart disease, are often weak.
In every clinical encounter between a patient and a healthcare
provider, valuable information—personal medical histories, unique
genetic backgrounds, lifestyle factors, responses to different treatments—is collected and stored. This information not only improves
the quality and coordination of a patient's care, but, when applied
appropriately, also holds the key to understanding and managing
disease across groups of patients and populations. When clinical
information is digitized in the form of an electronic health record
(EHR) it becomes more valuable because it can be compared,
“It is unacceptable and
counter-intuitive that we
should not be learning
more of medicine, and
amassing the state of art
of medicine, with every
patient encounter. Every
clinical encounter is
extensive enough so that
to ignore what we learn
and not apply it to
advancing medicine
seems to be a wasteful
and harmful state. What
we need is buy-in so
that the default position
is that research is run
hand-in-hand with every
[patient] encounter.”
searched, and queried in ways that can benefit the patient, other
patients with the same disease or disorder, and the research enterprise, which is aiming to develop better diagnostics and therapies.
ISAAC S. KOHANE, M.D., PH.D.,
PROFESSOR OF PEDIATRICS, HARVARD
MEDICAL SCHOOL (MAY 2010)
Thus, data from many thousands of people (as many as half a million) may be needed to nail down the size and nature of the effect
of certain genes on the development of disease.1 Moreover, as we
begin to sort out the relative effects of genes and the environment
on health, we need standardized means for comparing and testing
hypotheses about disease initiation and progression. This requires
researchers to have access to clinical data from large cohorts of
patients. The EHR can be the vehicle for capturing these vital data.
1
THINK RESEARCH
In 2005, FasterCures urged health systems to “think research”
when developing or implementing EHR systems so as not to
foreclose a golden opportunity to connect clinical data with
research needs.2
To understand the relationships between molecular information
and human health, population-based and clinical studies are
needed, requiring the generation, storage, and analysis of enormous quantities of epidemiologic, genotypic, and phenotypic data.
To understand the connections between genes, proteins, and the
environment, sophisticated comparisons must be conducted, and
these comparisons cannot be done by hand or by eye or patient
by patient. It is the collective observations of hundreds, even
thousands, of patients that will shine a light on these associations.
In 2005, FasterCures
urged health systems
to “think research”
when developing or
implementing EHR
systems so as not to
foreclose a golden
opportunity to connect
clinical data with
research needs.
“Think”
“Think” is the motto chosen by Thomas J. Watson while heading the sales and
advertising departments at National Cash Register. Watson said, “Thought has
been the father of every advance since time began. 'I didn't think' has cost the
world millions of dollars.” Watson would bring the motto with him as founder
of IBM, where it became the company's philosophy and motto.
At the time, FasterCures saw the opportunity for EHRs to not only
provide a link between genes and disease, but also to:
• Monitor the health of the populations and detect emerging
health problems.
• Identify populations at risk of disease, or those who might
benefit most from therapies.
• Assess the usefulness of diagnostic tests and screening programs.
• Form hypotheses about disease initiation and progression.
• Conduct post-marketing surveillance studies of new drugs to
identify adverse events, improve prescribing practices, or make
labeling more accurate and complete.
• Identify potential study participants for clinical research.
2
STILL THINKING RESEARCH
Strategies to Advance the Use of Electronic Health Records to Bridge Patient Care and Research
The last item in this list seemed the most immediately promising
and feasible in 2005. Recruiting patients to clinical trials is often
time consuming and restricted to the site participating in the
research study, effectively eliminating many community clinics
from enrolling patients in clinical trials. Fewer than one-third of
clinical trial sites are able to recruit their original target population
within the time specified.3 In fact, a review of cancer clinical trials
sponsored by the National Cancer Institute of the National
Institutes of Health (NIH) showed that nearly 40 percent of these
trials failed to accrue enough patients and, thus, closed without
completing the study.4 This not only wastes the time, money, and
effort of researchers and drug sponsors, but it also is a disservice
to the patients who did enroll in the trial, but whose participation
will not advance cancer science or care.5
Information contained in a patient's EHR contains much of the
information needed to screen and match a patient to a trial.
Relying on clinical information derived from a paper-based
research environment is inefficient and impractical in the 21st
century. Theoretically, the increasingly widespread development
and implementation of EHR systems over the past several years
provide an avenue to speed data acquisition and searching,
conduct mass computing and sampling, and carry out more
Relying on clinical
information derived
from a paper-based
research environment
is inefficient and
impractical in the
21st century.
efficient, effective, and economical clinical trials.
In 2005, FasterCures located several pioneers and innovators in
the use of EHRs in research. We also identified barriers they had
overcome to some extent, but that were still faced by others.
Beyond the problem of overall low adoption rates of EHRs
across health systems, challenges included:
• The art and practice of medicine, which is not uniform.
• Reliability and completeness of the EHR.
• Limits of administrative and claims databases, which are not
responsive to research needs.
• Unstructured text.
• Integration of practice databases for data mining.
• Research regulations governing human subjects research and
patient privacy.
3
FasterCures recommended several possible solutions for moving the
field forward, such as:
• Integrating practice databases for data mining.
• Developing more sophisticated abstraction and encryption
systems to protect privacy.
• Developing database connection tools.
• Creating translational systems.
• Formulating online informed consent procedures.
• Evolving data mining and pattern recognition systems.
• Developing interactive patient query programs.
• Creating patient databases/warehouses/registries.
• Creating directories of clinical databases.
Although the horizon
has some bright spots,
and there is more activity
in this area than in 2005,
the health IT infrastructure as it exists today is
still falling short of its
potential to increase
understanding of disease
progression and advance
biomedical innovation.
STILL THINKING RESEARCH: ABOUT THIS REPORT
In 2010, five years after Think Research, FasterCures decided to
check in on the status of the use of EHRs in research to assess
whether anyone was, in fact, thinking about and exploiting the
research potential of EHRs. FasterCures conducted a number of
informational discussions with technology and research experts
from across the public, private, government, and academic sectors,
and conducted a media and literature review of related material
that has appeared over the past five years. We also studied organizations that have developed innovative approaches to gathering,
capturing, and using digital clinical data for research purposes.
Although the horizon has some bright spots, and there is more
activity in this area than in 2005, the health IT infrastructure as it
exists today is still falling short of its potential to increase understanding of disease progression and advance biomedical innovation.
As the volume of digitized patient data grows, a lack of functionality
and user interfaces that allow investigators to interact with and study
those data are leading to a host of missed research opportunities.
Unless electronic health information systems are set up to behave
as integrated laboratories, with the power to collect and analyze
research-quality data across systems, this potential will not be met.
And as more EHR systems are rolled out, not thinking about how to
enable research will cost not millions, but possibly billions of dollars in
opportunity costs, not to mention lives lost or shortened by disease.
4
STILL THINKING RESEARCH
Strategies to Advance the Use of Electronic Health Records to Bridge Patient Care and Research
Responsible access to clinical data by researchers stands to
accelerate the discovery and development of new interventions
and therapies in ways previously unimaginable.6 Moreover,
since 2005, a new trend has emerged that highlights even
more the need to use EHRs to bridge patient care and research—
the growing recognition of the critical role of translational
research.
Translational research is the process of applying ideas, insights,
and discoveries generated through basic scientific inquiry to the
treatment and prevention of human disease—the critical bridge
between basic and clinical research. This phase encompasses
the bidirectional steps taken between basic and clinical studies
to understand how fundamental biology can lead to better
understanding of disease and health, and how manifestations
of disease and health can inform fundamental understanding
of human biology. EHRs can be a critical tool for making these
connections in many types of research.
This report updates the status of existing and new activities in the
realm of EHRs and research, again shines a spotlight on some star
performers in the field, and makes several recommendations for
the future. It finds that much progress has been made in pushing
greater adoption of EHRs in clinical care. Publicly-funded efforts
Responsible access
to clinical data by
researchers stands to
accelerate the discovery
and development of new
interventions and therapies in ways previously
unimaginable.
are aiming to capitalize on digitized health data and several institutions are pushing the frontiers in this important approach to
research; yet there is more work to be done.
In developing this report, we found that:
• Vendors of new EHR systems are not building research
capacity into the architecture.
• The clinical research community is not actively involved in
or does not have incentives to push for research-friendly
EHR systems.
• Standards and universal exchange systems still challenge
the actual transfer and translation of research-relevant data.
• Existing EHR systems are not being leveraged to screen,
match, and enroll patients in clinical trials.
• The patient community is not fully engaged in or aware of
the need to share their clinical data to advance research.
5
We end this report with four recommendations for action:
• Clinical trial screening and matching should be included as
a measure for “Meaningful Use” of electronic health record
systems.
• The National Institutes of Health (NIH) should articulate a strategy
that will align its programs with the recommendations of the
Office of the National Coordinator (ONC) Federal Health IT
Strategic Plan.
• The ONC should develop an initiative with pilot projects that
would create medical research IT modules.
“In 2005, there was hope
that people would use
good information tools
and systems to deliver
care and advance medicine, and there were—
and are—islands of
excellence in the use of
those tools. With the
passage of the HITECH
Act, this is no longer just
considered a good thing
that we should do, but it
is expected that within
five to ten years, this will
become the standard.”
• Nationwide Health Information Network (NwHIN) should expand
its standards and policies to include clinical research and research
centers in the network of information exchange.
When implemented, we believe these will provide us with opportunities to create a medical research system that will be able to meet
and respond to the needs of patients, and accelerate the process of
turning a scientific breakthrough into a life-saving therapy.
New Developments in
Federal Health IT
There has been incredible progress in the adoption of EHRs in the
clinical setting from 2005 to 2011.
The primary purpose of EHR systems—improved healthcare—is
better served as more providers come online. Ideally, patients will
JONATHAN WHITE, DIRECTOR OF THE
AGENCY FOR HEALTHCARE RESEARCH
AND QUALITY'S HIT PORTFOLIO (MAY
2010)
benefit from a healthcare system that is integrated across time,
professionals, and institutions. The EHR has the potential to
recognize the interdependence of the many working parts of the
healthcare system to effectively manage the entire continuum of
care. These systems, able to communicate across providers, can
allow patient information to flow across all components of care,
across geographic sites, and across discrete patient care incidents.
At the federal level, a number of activities have been under way
to promote and facilitate clinical adoption of EHRs, which was
advancing slowly in the early part of the last decade.
6
STILL THINKING RESEARCH
Strategies to Advance the Use of Electronic Health Records to Bridge Patient Care and Research
THE HEALTH INFORMATION TECHNOLOGY FOR
ECONOMIC AND CLINICAL HEALTH (HITECH) ACT
In 2004, the Office of the National Coordinator for
Health Information Technology (ONC) was established
to promote the development of a national health IT infrastructure. As a result of the 2009 passage of the HITECH
Act—part of the American Recovery and Reinvestment
(ARRA) Act—the Department of Health and Human
Services (HHS) committed to investing $27 billion in
EHR adoption in medical practices across the nation with
the goal of having an EHR for every American by 2014. 7
HITECH allows hospitals and healthcare providers to qualify for reimbursements from the Centers for Medicare and
Medicaid Services if they adopt EHR systems deemed capable of achieving “meaningful use.” This set of standards and
certification criteria, determined by ONC with help from
external stakeholders and released in 2010, ensures that
EHR systems are capable of performing certain required
functions. HHS has instituted incentives to encourage
adoption of these EHR systems. For example, physicians
and other eligible professionals can qualify for incentive
payments totaling as much as $44,000 through Medicare
or $63,750 through Medicaid. Hospitals can qualify for millions of dollars of incentive payments for implementing
and becoming meaningful users of EHR technology.
HITECH included a number of other programs intended to
accelerate the adoption of electronic health systems.8
The Beacon Community Cooperative Agreement Program
The Beacon Community program provided grants to create
demonstration communities that use health IT to achieve
measurable improvements in quality and health outcomes
so as to demonstrate the ability of health IT to transform
the healthcare system. The program funds communities
that support the development of secure, private, and
accurate EHR systems and health information exchanges
to improve care coordination, increase the quality of care,
and slow the growth of healthcare spending.
health information—for example, medical history, physician
notes, laboratory data—is stored in multiple locations
making it difficult to consolidate and use for other
applications, including research. The Mayo SHARP project
is attempting to create a unified EHR that will allow for the
exchange of patient information among care providers,
government agencies, insurers, and other stakeholders.
The Nationwide Health Information Network (NwHIN)
The 2005 FasterCures report recommended that the needs
of medical researchers be built into individual EHR system
architecture, as well as that of the NwHIN. NwHIN, still
in development, will create a national common platform
for health information exchange across diverse entities
to promote a more effective network marketplace. It will
be the foundation to build standards, services, and policies
to ensure secure health information exchange over the
Internet. A group of government agencies along with
information exchange organizations are currently
establishing exchange networks to test the processes
involved in securely exchanging health information
among the various participating organizations.
The Direct Project, a part of NwHIN, was launched in 2010
as a pilot to demonstrate the successful delivery of specific
electronic clinical information through the network across
different settings, including primary care providers,
specialists, medical centers and hospitals, public health
departments, and laboratories.
The Nationwide Health Information Network Exchange pilot
initiative is demonstrating secure information exchange
through specific standards, services, and policies. The
exchange network is composed of federal agencies and
private organizations with capabilities to exchange
such information as:
• Summary patient records to gather disability informa-
tion through the Social Security Administration.
The Strategic Health IT Advanced Research Program
(SHARP)
SHARP supports research projects on breakthrough
advances in health IT that foster adoption, including security, patient support, healthcare applications, and network
design, and secondary use of EHR data, for example, for
research purposes. One of the signature programs funded
through SHARP is a project on the secondary use of health
data at the Mayo Clinic. Traditionally, patient electronic
• Streamlined transition of health records from active
duty personnel in the Department of Defense to
veteran status covered by the Veterans Administration
(a project called the Joint Lifetime Electronic Health
Record Program).
• Submission of de-identified quality assessment data to
Medicare and Medicaid.
• Biosurveillance and reporting to the Centers for
Disease Control and Prevention (CDC).9
7
“I was invited to a meeting
about building a learning
healthcare system for
cancer, and was asked to
speak about how the
ONC's activities are going
to create a learning healthcare system. So as more
and more data, maybe
even information, is available in EHR systems, what
are we doing to make that
data useful for research?
After a few hours of working on my speech it hit
me— we aren't; ONC is
going to fall short of that
goal. So I changed my way
of thinking. Everybody is
focused right now on
getting eligible professionals and hospitals to the
state of meaningful use
and many can't fathom
dealing with other uses
of the data on top of that,
at least not yet. But my
answer is we can't afford
not to do this. We are so
sub-optimizing and failing
to take full advantage of
our investment.”
DR. CHARLES FRIEDMAN, OFFICE OF
THE NATIONAL COORDINATOR FOR HEALTH
INFORMATION TECHNOLOGY, HHS
(JUNE 2010)
8
STILL THINKING RESEARCH
Other federal efforts besides those within the HITECH programs
include the CDC's Public Health Informatics and Technology
Program. This program is developing a public health standard to
align with certified EHR technologies. The standard will allow for
the exchange of information among providers, public health
agencies, and laboratories for public health reporting and awareness.10 The program also aims to advance the field of public health
informatics through applied research and innovation, and program managers are working to create a Public Health Information
Network to support exchange and harmonize public health information with the broader national health information network so
that electronic health information can be exchanged seamlessly
among public health agencies.11
Despite these enormous federal efforts to promote EHR adoption,
overall rates of uptake are still low.
The estimates on physician adoption vary depending on how the
electronic health system is defined, but substantial improvements
in the widespread use of EHR systems by the medical community
are yet to be achieved. According to recent estimates, approximately four percent of physicians have a comprehensive EHR
system and 13 percent have a basic system.12 Similarly, about two
percent of hospitals have a comprehensive system and eight
percent have a basic system.13 The adoption rates have been slow
for a variety of reasons, such as high cost of purchasing an EHR
system, change to clinical workflow, lack of incentives or
reimbursements, and uncertainty about the features needed for
a certified, comprehensive system.
As mentioned, the passage of HITECH in 2009 requires EHR
systems to meet minimal functionalities in order to be certified,
and physicians will receive reimbursements through Medicare and
Medicaid to encourage their adoption. Early indications show
that this lever to create adoption incentives may be working, as
81 percent of hospitals and 41 percent of office-based physicians
have said that they plan to adopt the systems and qualify for
the incentive payments.14
* Dr. Friedman was Chief Science Officer at the Office of the National Coordinator for
Health Information Technology, HHS from 2009-Summer 2011. He now heads a graduate program in health informatics at the University of Michigan.
Strategies to Advance the Use of Electronic Health Records to Bridge Patient Care and Research
The value of these efforts to improved health is obvious. However,
it is shortsighted if we do not also consider the potential vast
benefits such systems could provide clinical researchers. With the
majority of systems to date built to focus on reimbursement claims,
clinical workflow improvement, and outcomes reporting, their
potential use for research continues to be overlooked. As institutions struggle to adopt and implement EHR systems, it is crucial
that they consider the needs and seek the advice of the research
community and that the research community advocate on its behalf
and force its needs into ongoing plans to develop EHR systems.
As the political and technological architecture for EHRs continues
to solidify, the window of opportunity to capitalize on health
IT's promise for research could be getting smaller. If we don't
go the extra distance to harness EHRs effectively for learning
and research purposes before standards, incentives, and norms
are institutionalized, vast stores of information that could be
mapped to biology, medicine, and health will remain
unconnected and untapped.
Leveraging Health IT
for Networked Research
Opportunities
As institutions struggle
to adopt and implement
EHR systems, it is crucial
that they consider the
needs and seek the advice
of the research community and that the research
community advocate on
its behalf and force its
needs into ongoing plans
to develop EHR systems.
Fortunately, some people in the public sector have been thinking
a lot about how to create national opportunities to link
researchers and patients through the EHR and digital databases.
Many other possibilities also are on the horizon.
A sampling of federally supported efforts and opportunities to
use digital health information for research are described below.
>
THE HEALTH MAINTENANCE ORGANIZATION RESEARCH
NETWORK (HMORN)
HMORN is a consortium of 16 HMOs with the goal of improving
healthcare through various partnerships covering health services,
epidemiology, clinical, and behavioral research.15 Funded by NIH,
HMORN has developed the Virtual Data Warehouse to connect the
9
network sites and support multi-center research activities. The
warehouse uses a federated model to transform data captured
in patient EHRs and medical claims databases (such as diagnoses, demographics, medical procedures, tumor grade, and
laboratory results) from individual healthcare systems into a
standard format available to researchers in the network.
This federated model offers an effective mechanism to protect
the identity of patients, providers, and health plans while
allowing researchers to access relevant medical information
through the network.16 For example, the Cancer Research
Network, a consortium of collaborators within HMORN, is work-
With patient consent,
researchers will investigate genetic variants
associated with 40 more
disease characteristics
and symptoms and aim
to develop strategies to
apply this information to
adjust patient treatment
strategies.
ing on a pilot project in Hawaii that would extract electronic
data from the Virtual Data Warehouse and transmit it to the
central cancer registry. This will allow investigators to conduct
analyses on diagnosis, treatment, comorbidities, and other outcomes associated with cancer.17
>
THE DEcIDE (DEVELOPING EVIDENCE TO INFORM
DECISIONS ABOUT EFFECTIVENESS) NETWORK
The DEcIDE Network is a collection of research centers supported
by the Agency for Healthcare Research and Quality (AHRQ) to
conduct studies on the outcomes, effectiveness, safety, and usefulness of medical treatments and services. The DEcIDE Network
connects research-based health organizations with electronic
access to health information databases, thereby enhancing the
ability to conduct comparative effectiveness research.
In one program within the network, researchers are using a distributed research network (DRN) model to generate evidence
on the effects of new treatments and other medical technologies. A DRN model allows observation and study of large populations of patients through routine collection of patient data
(demographic, eligibility, and claims) available through EHRs
and processed for researcher access and use.
One of the groups within the DEcIDE Network is applying this
model in a pilot project to assess the comparative effectiveness
and safety of various second line anti-hypertensive therapies.18
For example, through this pilot, a researcher may query data
10
STILL THINKING RESEARCH
Strategies to Advance the Use of Electronic Health Records to Bridge Patient Care and Research
for patients with a diagnosis of hypertension who are given at
least one of several anti-hypertensive medications. The
researcher could then compare diagnoses with treatments and
follow-up occurrences to determine response outcomes and
ENHANCING PRIVACY,
IMPROVING HEALTH
THROUGH RESEARCH
assess if there were comparative differences in the medications
without knowing the location of the data, how the data are
stored, or how the query is distributed.
>
THE eMERGE (ELECTRONIC MEDICAL RECORDS AND
GENOMICS) NETWORK
One visionary goal of advanced use of electronic health information is the capability to align de-identified personal health
information with genetic information. The eMERGE Network,
supported by NIH's National Human Genome Research Institute,
aims to develop approaches to linking biorepositories to EHR
systems for large-scale genomic sequencing.19 This network is
composed of five sites and supported through an administrative
coordinating center. Each site has its own biorepository and
uses a common genotyping platform to sequence the DNA
samples from the specimens. The first phase of the project
demonstrated that data contained in patient EHRs can be used
with patients’ genetic information in large scale studies.
The eMERGE Network has already identified genetic variants
associated with dementia, cataracts, high-density lipoprotein
(HDL) cholesterol, peripheral arterial disease, white blood cell
count, type 2 diabetes and cardiac conduction defects.20
The eMERGE Network is now moving into its second phase of
development by expanding the use of genomic information in
clinical research and care. With patient consent, researchers will
investigate genetic variants associated with 40 more disease characteristics and symptoms and aim to develop strategies to apply
this information to adjust patient treatment strategies. Although
the network is still learning the challenges of sharing information
in a research network, particularly with regard to the quality of
data captured in an EHR versus the quality needed for “researchgrade” analyses, it has developed approaches to share de-identified genetic and patient medical information through the net-
In 2009, the Institute of Medicine
(IOM) released a report evaluating the impact of the Health
Insurance Portability and
Accountability Act of 1996 Privacy
Rule on research. The purpose
of the HIPAA Privacy Rule is to
protect an individual's personally
identifiable health information
and ensure that health insurance
is portable, that is, not necessarily
linked to or dependent on employment. The IOM committee
responsible for the review concluded that the Privacy Rule not
only does not protect privacy as
well as it should, but also, as currently implemented, impedes
important health research.
The report called for a new
approach to protecting privacy
that would harmonize existing
regulations and apply them
uniformly to all health research.
IOM supported making distinctions between information-based
research (such as electronic
medical data or stored biological
specimens) and interventional
research involving active patient
participation in an experimental
treatment setting. In arguing for
the need to improve researcher
access to medical and health information, the report stated that “If
society seeks to derive the benefits
of medical research in the form of
improved health and healthcare,
information should be shared to
achieve the greater good, and governing regulations should support
the use of such information, with
appropriate oversight.” 21
work and are leading efforts to ensure privacy and address public
concerns regarding the use of genomic information.
11
>
THE INFORMATICS FOR INTEGRATING BIOLOGY AND
THE BEDSIDE (I2B2) PROGRAM
i2b2, based out of the Partners HealthCare System in Boston, was
funded by NIH as part of its 2004 Roadmap Initiative to provide
researchers access to medical and research databases through a
public portal.22 The goal of i2b2 is to support exchange of clinical
and research information among participating organizations and
entities to drive research studies and hypothesis generation.
The goal of i2b2 is to
support exchange of
clinical and research
information among participating organizations
and entities to drive
research studies and
hypothesis generation.
The i2b2 platform bridges clinical data with research data, such
as genomics databases,23 across institutions into an integrated
database. i2b2 uses computational tools to de-identify electronic
patient records by removing private health information while still
preserving clinical information. This enables clinical researchers to
use existing patient records for discovery research and hypotheses
generation and testing. It allows researchers to query the database
to find smaller subsets of patients of interest (e.g., patients who
did not respond to a standard line of therapy) for further research
studies. It also can allow a researcher to investigate deeper into
the phenotype of certain sets of patients to support genomic,
outcome, or environmental studies.
I2B2 SUCCESS STORIES
12
•
Partners HealthCare researchers used data
extracted from 9,000 patient EHRs in i2b2 to create a
model for predicting chronic obstructive pulmonary
disease in certain asthma patients.24
•
Partners HealthCare's Driving Biology Projects
is using i2b2 to look for genotypes from asthma
patients who are unresponsive to conventional
medication to determine whether they have a
different genetic background resulting in the lack
of response.
STILL THINKING RESEARCH
•
Researchers at the University of Utah are
testing capabilities of i2b2 as an open source tool
for bench-to-bed-side research conducted outside
the Partners HealthCare network. They tested the
i2b2 platform to see if it was suitable for identifying potential patient cohorts based on clinical
information contained in the EHR, for example,
demographics, laboratory test results, medication
lists, and diagnostic data. The researchers found
that 44 percent of research data requests could be
captured through the i2b2, providing a useful
patient cohort selection tool.25
Strategies to Advance the Use of Electronic Health Records to Bridge Patient Care and Research
>
NIH'S BIOMEDICAL TRANSLATIONAL RESEARCH
INFORMATION SYSTEM (BTRIS)
BTRIS is a repository developed from a complex network of information systems supporting clinical care and research data collection from NIH-sponsored clinical trials conducted by the NIH
Clinical Center and the agency's intramural research program.
BTRIS functionality is twofold. It can provide an individual clinical
investigator with access to identifiable data for patients on his
or her own study protocols, and it can also provide all NIH
investigators with access to de-identified data across all protocols.
BTRIS will give investigators easy access to vast amounts of data, such
as demographics, vital signs, laboratory test results, and medication
history and allow researchers to generate reports across clinical trials.
>
THE BIOMEDICAL RESEARCH INTEGRATED
DOMAIN GROUP (BRIDG)
The BRIDG project is a collaborative public-private initiative to
develop a shared model that supports clinical research across IT
platforms. The BRIDG model was first released in 2007 and has been
used to support interoperability across applications developed by
By harmonizing existing
standards, the BRIDG
model can remove barriers that limit clinical and
translational research.
It demonstrated practical
success in its early
implementation with
the Clinical Trials
Management System
interoperability project.
the National Cancer Institute's Cancer Bioinformatics Grid (caBIG).
By harmonizing existing standards, the BRIDG model can remove
barriers that limit clinical and translational research. It demonstrated practical success in its early implementation with the Clinical
Trials Management System interoperability project. In this demonstration, data were successfully exchanged across clinical trials
management systems that supported the registration of a patient
to the trial, the import of laboratory data related to that patient,
and evaluation of the laboratory data to report any potential
adverse events related to the trial.26
>
THE FDA SENTINEL INITIATIVE
The U.S. Food and Drug Administration (FDA) Sentinel Initiative was
launched in 2008 as a surveillance system to monitor FDA-regulated
products through electronic healthcare data.27 The Sentinel System
allows FDA to monitor the safety of drugs and medical products
13
through data captured at academic medical centers, healthcare
systems, federal agencies, and insurance companies.
Sentinel uses a “distributed data system” in which the initial
electronic patient data are housed at their existing secure
environments—not in one database. In this system, the data are
processed and sent to an FDA coordinating center to answer specific safety questions. By July 2010, FDA had built the framework
for active, near real-time safety surveillance to monitor data from
electronic healthcare systems for 25 million people, with a goal
of 100 million people by 2012. Because the system is built on EHR
platforms, it also will be able to identify health events that occur
For many academic
investigators involved
in clinical research,
electronic data capture
system setup for case
report forms and clinical
data management may
be expensive and possibly
prohibitive to conducting
research.
commonly in the general population (e.g., heart attacks) but that
are not normally reported to FDA as an adverse event, when, in
fact, the event may actually be related to the drug.
>
NIH'S CLINICAL AND TRANSLATIONAL SCIENCE AWARDS
Within the clinical research spectrum, a promising translational
research network, called the Clinical and Translational Science
Awards (CTSA), is underway. Adoption of a nationwide information exchange platform aligned with research standards stands
to benefit this network greatly. Begun in 2006 as part of the NIH
Roadmap, the CTSA program now funds nearly 60 centers, each
with its own program, but with an agreement to bridge the gap
between basic and clinical research by partnering as a network
of networks.
Collecting, submitting, and securely sharing research data across
sites and within collaborations is a difficult but important functionality for research systems. Among the strategic priorities outlined by the CTSA centers is a goal to enhance collaborations by
creating a data-sharing network among the sites.28 Compatible
and interoperable EHR systems could assist with research collaboration, patient recruitment into clinical trials, and retrospective
studies across the network of CTSA sites.29 However, the lack of
common standards, data elements, and application interfaces
make networked research studies a challenge.
A number of CTSA sites are developing platforms to overcome
some of these barriers and transform the goal into a reality.
14
STILL THINKING RESEARCH
Strategies to Advance the Use of Electronic Health Records to Bridge Patient Care and Research
Although the CTSA sites are not connected
tion of EHRs and health IT within the CTSA net-
through an integrated and interoperable infor-
work would allow for nationwide comparative
mation exchange network, a number of sites
effectiveness research to be conducted, and also
have deployed technologies to improve such
allow findings from such studies to be embedded
research collaborations.
and efficiently disseminated through EHR-based
clinical decision support to improve care.32
For example, The Ohio State University CTSA is
developing the Translational Informatics and Data
Additionally, a Research Electronic Data Capture
Management Grid (TRIAD), a “middleware” grid
(REDCap) Web-based software application has
system. In this grid system, TRIAD provides an
been adopted by a number of sites within the
interoperable service infrastructure to allow
CTSA network. Vanderbilt University initially
different researchers using different applications
developed REDCap to support data capture
to interact with the grid. If successfully adopted,
for clinical and translational research studies.33
TRIAD would provide a platform capable of sup-
For many academic investigators involved in
porting exchange, management, and integration
clinical research, electronic data capture system
of research data across participating CTSAs.
setup for case report forms and clinical data
30
management may be expensive and possibly
Another collaborative effort of the CTSA network
prohibitive to conducting research. REDCap
is a Web-based portal called ResearchMatch. This
was designed to support workflow methods
tool allows patients interested in medical research
for electronic data capture tools needed for
to register for studies and matches them to appro-
collecting, storing, and disseminating project-
priate investigators involved in clinical research.
specific research data to enable collaborative
31
studies. Since its pilot launch for collaborative
The CTSAs also have a rare capability to leverage
network research projects in 2006, REDCap has
health IT for national comparative effectiveness
grown to support more than 15,000 research
research efforts. The proper adoption and applica-
projects across the world.34
CTSA SUCCESS STORY: CAPTURING RADIOLOGY IMAGES
TO IMPROVE BREAST CANCER DIAGNOSIS
Researchers at the University of Pittsburgh's Clinical
and Translational Science Institute built an open source
software program called Diamond that could rapidly
scan large volumes of clinical data to identify and filter
specific items within a patient record, such as clinical
radiology images.35 In pilot testing, this program was
able to assemble a mammogram reference database of
more than 4,000 suspected breast mass regions using
an interactive search-assisted diagnosis system. The
program separated the images into histologically
confirmed malignances, benign masses, and false
positive mass regions. The program can efficiently
acompare mammograms with the reference database
and intelligently structure the search to improve
accuracy of breast cancer diagnosis.
15
Meeting the Challenges:
Institutional Pioneers,
Innovators, and
Transformational Models
We would accomplish
many more things if we
did not think of them as
impossible.
In the 2005 Think Research report, FasterCures profiled
a number of public and private systems that were at the
forefront of EHR implementation at the time. In many cases,
these systems also were spearheading attempts to integrate
clinical practice data with research goals. In 2005, forwardthinking clinical investigators from the profiled organizations—
VINCE LOMBARDI
ranging from private systems such as the Mayo Clinic and the
Geisinger Health System to public systems such as the Indian
Health Service and the Veterans Health Administration—
already were pioneering ways to safely use the information
stored in early stage EHRs and patient data warehouses
for relevant clinical studies.
85 percent
Since then, technology has improved and become more widespread, incentives to implement EHRs have been established
and applied, and patients have begun to take a more active
Patients understand the value
of health data in clinical research
to discover and develop new
treatments and improve care.
A 2009 national opinion poll
found that 85 percent of
respondents felt that it was
important that a nationally
coordinated health IT system
allow investigators to learn from
data to facilitate research and
track disease. 36 Most recent
surveys have shown strong public
support for patients to share
their anonymized personal health
information with researchers.
The survey responses ranged from
63 to 80 percent in support of
sharing this information. 37, 38
role in tracking and managing their data. These developments
have opened the doors to new means for using clinical and
health data in research.
The following case studies were selected because each model
demonstrates the value of the EHR to research.
HEALTHCARE SYSTEM MODELS
In many senses, Geisinger Health System, Kaiser Permanente,
and the Mayo Clinic are the grandparents of health IT and
its use in clinical and research practice. All three systems
have been at the leading edge of EHR adoption and, early
on, found ways to integrate clinical and research use of
electronic data. Each has the benefit of having a “captive
patient population” or closed system in which loyalty can
be built, trust established, and concerns about privacy and
confidentiality averted.
16
STILL THINKING RESEARCH
Strategies to Advance the Use of Electronic Health Records to Bridge Patient Care and Research
GEISINGER HEALTH SYSTEM:
Longevity and Loyalty of the Patient Population is Key
Geisinger Health System, an integrated health services
organization comprised of three medical center
campuses, a 700-member group practice, a nonprofit
health insurance company, and a Center for Health
Research, was an early pioneer in the emerging
EHR movement. The 2005 report, Think Research,
highlighted two interactive software programs being
piloted by Geisinger that aimed to use the organization's EHR to improve care for patients with chronic
heart disease and autism.
What started out as a system primarily designed to
improve the delivery of care has since extended its
reach to empower clinical research as well. Today,
Geisinger's EHR database contains information on
more than three million patients spread across central
and northeastern Pennsylvania—nearly 70,000 of
whom also use the patient portal, MyGeisinger, to
securely access their problem list, medications, allergies, immunizations, and test results.
Patients' EHR records are accessed by clinicians for
inpatient and outpatient care management, as well as
by investigators who identify and enroll thousands of
outpatients with common chronic conditions (e.g., diabetes, arthritis, vascular disease) in clinical trials. The
typical recruitment process involves writing unsolicited letters to patients who appear to meet broad inclusion/exclusion criteria based on a preliminary review of
their EHR—usually from a doctor or nurse who has
been involved in that patient's care—and urging interested respondents to opt in to the project.
“The challenge for us today is that most industry
sponsors are ill-equipped to take advantage of the
unique research opportunities a system like
Geisinger's can offer,” says Dr. Peter Berger, director
of Geisinger's Center for Clinical Studies. “We have a
fairly benevolent patient population in Pennsylvania,
and typically see response rates in the neighborhood
of 30 to 50 percent, which is incredibly high for industry standards.”
Another unique aspect of Geisinger's system is the low
inward and outward migration rate among its patients.
In the more than 14 years that the system has had
automated records, Geisinger researchers have been
able to keep and track a significant amount of longitudinal data, allowing for the discovery of trends and the
measurement of disease impact on various patient
populations. “We know, for example, that patients
who are very overweight are at risk of developing sleep
apnea,” said Berger, “because we reached out to
patients via our EHR who we knew had a high BMI and
asked them to voluntarily answer four questions related to assessing the symptoms of sleep apnea.
Respondents were then invited to undergo a sleep
study, and a high percentage were diagnosed with the
disease who may not have otherwise known about it
until symptoms began to appear.”
Efforts also are currently underway at Geisinger to
build a repository of blood samples (DNA and serum)
called Geisinger MyCode™. According to the Web
site, sample collection focuses on primary care
patients. MyCode™ is intended to serve as a resource
for research that combines information obtained from
DNA and serum with health information from the
EHR and other sources intended to improve the
prevention, diagnosis, and treatment of disease. The
blood samples—5,500 of which have already been
banked—will be stored so that they can be used for a
wide range of biochemical and genetic research for
many years in the future. Another 14,520 consents for
blood to be drawn have been collected, and additional
samples are being gathered.
17
KAISER PERMANENTE HEALTHCONNECT ® :
Taking Advantage of a Large, Integrated Clinical Network
When FasterCures first profiled Kaiser Permanente
(KP) HealthConnect® in 2005, it was already two years
into development, with several billion dollars invested
in automating records for the organization's then 8.4
million members across the country. At the time, Dean
Sittig of the Applied Research in Medical Informatics
Group said that Kaiser Permanente planned to use
Epic Systems' software and technology services to not
only integrate patient clinical records with appointment, scheduling, registration, and billing systems
across all of the healthcare system's eight regions, but
also to capture unstructured data for research exercises such as planning studies, patient care modeling,
outcomes tracking, disease management, post-marketing surveillance, identification of genetic factors,
and testing of exploratory data analyses.
Five years later KP HealthConnect® is living up to its
vision. In March 2010, Kaiser Permanente completed
the implementation of what is now the largest private
deployment of an EHR system in the world. It securely
connects 8.8 million members to their healthcare
teams, including more than 100,000 physicians,
nurses, and care administrators in 431 medical
facilities and 35 hospitals across the country. It is
also deploying its system for research purposes.
disease, cancer, diabetes, high blood pressure,
Alzheimer's disease, asthma, and many others.
Based on the 3.3 million-member Kaiser
Permanente Medical Care Plan of Northern
California, the completed resource will link
together comprehensive EHRs, data on relevant
behavioral and environmental factors, and biobank
data (genetic information from saliva and blood)
from 500,000 consenting health plan members. KP
HealthConnect is the foundation for the RPGEH.
• All of Kaiser Permanente's eight regions currently
maintain disease registries (mainly for chronic
conditions such as diabetes, hypertension, heart
disease, asthma) which collect de-identified
information from KP HealthConnect® and use it to
generate information about population health that
can be applied on a reliable basis.
For example:
• Kaiser Permanente is building one of the world's
largest DNA databases to advance clinical research,
the Research Program on Genes, Environment, and
Health (RPGEH)—which allows scientists to study
the genetic and environmental factors that
influence common conditions such as heart
18
STILL THINKING RESEARCH
Strategies to Advance the Use of Electronic Health Records to Bridge Patient Care and Research
MAYO CLINIC:
Setting Data Standards
Long a pioneer in the use of automated records,
Mayo's medical records are among the most comprehensive in the world, providing the depth necessary to
enable a wide range of medical studies. The EHR system is designed to allow Mayo's researchers to efficiently search more than 6.5 million patient records by
condition, date of treatment, physician name, and test
category, in order to quickly find and leverage information that supports research efforts.
What started as a data-warehousing model in
collaboration with IBM has evolved into a complex
system that incorporates new techniques for harnessing patient data to improve diagnoses, find cures,
and individualize care. To address the need for
shared vocabulary and systematic capturing of
meta-data for complicated research queries, Mayo
created the Enterprise Data Trust (EDT), a repository
built using the HL7 Reference Information Model
and CDISC's BRIDG project. “What differentiates
the Mayo EDT from similar clinical data repositories
at peer academic health centers is our focus on data
governance,” said Dr. Christopher Chute, head
of the Division of Biomedical Informatics at the
Mayo Clinic. “We established a political process
within the organization which aligned with
extramural standards.”
• Mayo will use NIH's eMERGE Network to identify
novel genetic determinants of atherosclerotic
vascular disease in order to help identify high-risk
subjects who would benefit from aggressive
intervention to prevent peripheral arterial disease
(PAD). Using data captured in the Mayo EHR,
researchers will investigate how gene-environment
interactions influence susceptibility to PAD (for
example, does/how does smoking modify the
observation between genotype and atherosclerotic
vascular disease phenotypes), and look for
pathways that may serve as targets for new therapies.
• Through HHS's SHARP program, and collaborating
with Harvard University, the University of Texas
Health Science Center at Houston, and the
University of Illinois at Urbana-Champaign, Mayo
is working to identify short- and long-term
solutions to key challenges in health information
exchange. Mayo's role focuses on promoting the
secondary use of EHR data while maintaining privacy
and security, including developing strategies to
address natural language processing and data
normalization pipelines; improving clinical notes;
and expanding high-throughput phenotyping (by
compiling a library of standard phenotyping). The
goal is to integrate findings into medical practice
quickly across the nation.
19
PACeR:
A State-Level, Integrated Business Model for Electronic Research
THE PARTNERSHIP TO ADVANCE CLINICAL
ELECTRONIC RESEARCH (PACeR) was launched as
an electronic clinical research data network in New
York State to more efficiently identify candidates for
clinical research studies and manage their care.39
PACeR sought to build a model that recommends
solutions to technical, legal, regulatory, economic,
and operational issues in recruiting patients for
clinical research studies in the state. The goal of
PACeR is to improve patient care and the research
missions of participating institutions while
improving the efficiency and integrity of clinical
research across the network.
The PACeR network includes academic medical
centers, community hospitals, pharmaceutical companies, researchers, technology developers, state entities,
and patient representatives. The Healthcare
Association of New York State serves as the neutral
partner and coordinator, bringing multiple entities
together to drive a “market-based” approach to clinical
research networks. In doing so, PACeR aligned its
objectives to match investments and programs
developing under the HITECH Act as well as FDA
initiatives for safety surveillance and monitoring of
adverse events.
PACeR aligned its objectives to
match investments and programs
developing under the HITECH
Act as well as FDA initiatives
for safety surveillance and
monitoring of adverse events.
groups are currently under way establishing process
flows for querying research protocols for patient
identification for trial eligibility and closing the
“data gap” across institutions.
PACeR is designing demonstration projects to address
many of these barriers and develop an infrastructure
for a clinical research data network in New York.
Initial estimates suggest that PACeR's protocol modeling alone may produce more than $50 million annually
in revenue by providing one-stop access to higher
quality clinical data, a large statewide population of
eligible patients, and institution-specific understanding of patient availability. In addition, due to the efficiencies in patient enrollment and earlier product
launch, the program estimates millions in potential
savings for clinical trial sponsors using the network.
In its initial phase, PACeR identified barriers to the
secondary use of electronic health data for clinical
research capabilities and developed recommendations
to address those barriers. For example, the organization found that there are significant gaps in the design
of current EHR systems to support clinical research
that meets FDA requirements for data submission.
These systems also lacked standard terminology within software applications across providers, making data
collection and exchange inconsistent. PACeR working
20
STILL THINKING RESEARCH
Strategies to Advance the Use of Electronic Health Records to Bridge Patient Care and Research
PATIENT-CENTERED AND DIRECTED EFFORTS
plans and the desire to have some measure of
New models are emerging in health IT, taking
control over not only one's personal health
advantage of growing consumer savvy about and
information but also how it can be used to
comfort with Web-based, self-managed health
promote research.
MICROSOFT HEALTHVAULT
Launched in October 2008, MICROSOFT
HEALTHVAULT is a privacy- and security-enhanced
online service designed to give patients access to and
control over their health information and allow them
to share it with trusted entities. HealthVault can be
used to store copies of individual and family health
records obtained from providers, laboratories, health
plans, and pharmacies; exchange data with health and
fitness applications; and upload data from medical
devices. Account holders may also be authorized to
access records for multiple individuals, so that a
woman, for example, could manage records for each
of her children as well as an ailing parent.
Users are typically introduced to HealthVault
through one of the many applications that talk to
the HealthVault platform. These applications are
built in collaboration with healthcare companies,
nonprofit patient organizations, and device manufacturers. They address everything from managing
chronic conditions such as obesity, asthma, and diabetes; understanding and addressing diagnoses such
as cancer and heart disease; and tracking lifestyle
and fitness choices.
formats, including industry standards such as the
Continuity of Care Document and the Continuity
of Care Record.
Although HealthVault is primarily a tool for
coordinating and informing prevention and disease
management information based on consolidated,
personalized health data, it also can be used as a
clinical research resource through applications that
offer opportunities for account holders to voluntarily
engage in research initiatives. For example, TrialX
(from Applied Informatics, Inc.) automatically
matches participants to clinical trials based on their
personal health information. TrialX uses an up-to-date
database of more than 25,000 trials approved by FDA.
When an individual first uses a HealthVault
application, he or she is asked to authorize the
application to access a specific set of data types, and
those data types are the only ones the application can
use. HealthVault supports a number of data exchange
21
THE LOVE/AVON ARMY OF WOMEN
In 2008, the DR. SUSAN LOVE RESEARCH
FOUNDATION partnered with the AVON FOUNDATION
FOR WOMEN to launch the Love/Avon Army of Women, an
online initiative that aims to recruit one million women of
every age, ethnicity, and breast cancer history to take part in
breast cancer research. As of October 2010, more than
340,000 women had signed up, 44,000 of whom already
have participated in more than 35 studies.
study undergoes a rigorous scientific, safety, and
ethical review. Investigators agree to follow Health
Insurance Portability and Accountability Act (HIPAA)
guidelines to protect the confidentiality of study participants. Investigators also agree to create two Webcasts,
blogs, or videos for the Web site's Town Hall feature, which
explain the rationale of their research and summarize findings after the study is completed.
Women who are interested in participating register on the
Love/Avon Army of Women Web site, providing basic information. They then receive email updates announcing new
research studies and if they fit the criteria and want to participate, can click on a “sign me up” button that connects
them to study investigators for screening.
Though not a traditional EHR-based initiative, the Army of
Women has found a simple and elegant way of using technology to collect data and advance research. “In terms of
research and medical care, we are very behind in utilizing
the latest technology that is otherwise available in other
enterprises,” says Naz Sykes, executive director of the Dr.
Susan Love Research Foundation. “We need to make technology an accessible partner in our quest to find the cause
of many diseases.”
Scientists (academic, industry, nonprofit, and others) must
apply for the opportunity to recruit volunteers, and each
PATIENTSLIKEME.COM
PATIENTSLIKEME is a privately funded company dedi-
cated to providing patients who have life-changing illnesses with access to tools, information, and experiences to
take control of their disease. Co-founded in 2004 by MIT
engineer Jamie Heywood after his brother Stephen was
diagnosed with amyotrophic lateral sclerosis (ALS), this
unique platform enables patients to see and share outcomes-based data about their diseases in real time with
individuals who have similar conditions and with scientists working to find cures for those conditions.
The architecture creates a systems model that helps:
• Clinical researchers know whether their work has
modified outcomes
• Clinicians know what is wrong with a patient and how
to approach treatment
• Patients know what information is collected about
them and why
Today, tens of thousands of patients with a wide variety of
conditions use PatientsLikeMe to better manage their over-
22
STILL THINKING RESEARCH
all health, treatments, and symptoms, and to contribute
to the overall research effort.
PatientsLikeMe's technology creates the equivalent
of tags from patients to structure phenotypes in much
the same way that the Web creates hyperlinks between
documents. This allows the relationships between tags
to be studied and a holistic picture of the impact of a
disease to be assembled. Patients who willingly share
their experiences and data through the site do so with
the knowledge that it will be available in de-identified,
aggregated, and individual formats to investigators and
scientists studying their disease.
“The reason traditional electronic health records are not
advancing research in the way they could be is that we're
essentially asking monopolistic, private systems to generate
information sharing tools that will undercut their own
value,” says Heywood. “What we need to do is find a way to
create a viable competitive market for information.”
Strategies to Advance the Use of Electronic Health Records to Bridge Patient Care and Research
RESEARCH REGISTRIES
Research registries can provide
a warehouse of information
that can be queried, tapped,
and analyzed by researchers
looking for associations among
events, conditions, treatments,
or genes that might contribute
to the development and progression of disease. They can
be established retrospectively
(i.e., drawing off existing
records) or established prospectively and in real time. Some
66 percent
A recent survey from the Genetics and Public Policy Center found that 93
percent of respondents support the use of genetic testing by doctors to identify
a patient's risk of “having a bad reaction to a particular drug” and an equal
percentage support use of genetic data by researchers “to find new ways to
diagnose, prevent, or treat disease.” In this same survey, 66 percent of
respondents would trust researchers to have access to the genetic test results.
These results suggest a growing consumer/patient awareness of the use of
genetic information to improve care and discover new treatments, the
hallmark of bench-to-bedside-to-bench research. Since the publication of
Think Research in 2005, the Genetic Information Non-Discrimination Act of
2008 was passed, providing protection for most individuals to have the results
of genetic testing included as part of their medical record without fear that
such information could be used against them by employers or insurers.
examples appear below.
GENE PARTNERSHIP PROJECT:
Children's Hospital Boston
THE GENE PARTNERSHIP PROJECT (GPP) ,
a new model for recruiting patients into genomics
research, was launched in 2009 in the Developmental
Medicine Center and the Manton Center for Orphan
Disease Research at Children's Hospital Boston.
Though still in its pilot phase, the project's goal is to
eventually provide an opportunity for all patients
entering the hospital and their families to take part in
genetic research as an active participant.
Patients who opt in will get a lifelong EHR, called
Indivo, that is under their personal control (or, if
younger than age 18 years, that of a guardian). They
will be asked to provide a DNA sample, which will be
entered into a central repository, and to enroll in a
registry, giving researchers permission to include
them in studies.
According to GPP's principal investigator and director
of the Children's Hospital Informatics Program, Dr.
Isaac Kohane, this approach will maximize the significance of research findings by studying large populations
of patients, capturing all available phenotype and environmental information and, most importantly, giving relevant findings back to patients as they become available.
The GPP currently has more than 600 participants
enrolled in the pilot program, including patients, their
siblings, and their guardians. Rather than asking participants to donate DNA samples through a blanket consent
form, the project seeks to build “informed cohorts”—
large groups of subjects who are engaged and informed
about the research in which they are participating.
“Starting in childhood gives us a unique opportunity to
identify the effects of genetic variants before environmental exposures and lifestyle habits alter the picture,”
says Kohane. “By demystifying the research process and
increasing trust, we hope to improve patient willingness
to volunteer for studies.”
23
RESEARCH PATIENT DATA REGISTRY (RPDR):
Partners HealthCare
RPDR AT PARTNERS HEALTHCARE is a centralized
clinical data registry that gathers data from various
hospital legacy systems and stores it in one place.
Researchers are able to access these data using an
online query tool that provides access to aggregate
totals of patients with user-defined characteristics
(such as diagnoses, procedures, and/or laboratory
results) and, with proper Institutional Review Board
(IRB) approval, medical record data as well.
Many people view the RPDR as one of the first bestpractice models in medical informatics and research
data mining. Its operations are tightly integrated with
an IRB, and the data available to researchers are filtered with two-way anonymization as they are included in the system, so that records can be searched with
identifiable patient data when necessary, pending IRB
approval. Originally designed for the research community, the RPDR is technically and operationally compliant with HIPAA.
According to Dr. Shawn Murphy, associate director of
the Laboratory of Computer Science at Massachusetts
General Hospital (MGH), MGH has conducted return
on investment research based on current usage patterns and found that of the 600 to 700 studies conducted at the hospital each year, about 30 percent find
the RPDR critical to their research. Murphy also noted
that about half of the investigators using the RPDR do
so primarily for recruiting patients into clinical trials,
but that another handful of newer use cases have been
tested and are beginning to take root.
24
STILL THINKING RESEARCH
These include:
• Data mining
Using the data within the RPDR itself to establish
data-mining paradigms (e.g., usage patterns of
providers, medication recommendation frequency
and context) and conduct cutting-edge
comparative effectiveness research (e.g., does one
treatment/device provide better pain relief?)
• Surveillance applications
Post-market analysis of whether or how an FDAapproved medication is causing untoward effects
not demonstrated in a phase 3 study
• Genomic research
Repurposing samples collected in the course of
clinical care for research purposes and de-identified for research use.
In a paper published in 2010 by Arthritis Care &
Research, Murphy and his research team reported that
they were successfully able to use complete EHR data
to define a rheumatoid arthritis cohort with a positive
predictive value of 94 percent, which was superior to
an algorithm using codified data alone. 40
Strategies to Advance the Use of Electronic Health Records to Bridge Patient Care and Research
THE SHARED HEALTH RESEARCH INFORMATION NETWORK (SHRINE):
A Prototype Federated Query Tool for Clinical Data Repositories
Using NIH CTSA funds, Harvard Medical School has
been developing a working prototype of a query tool
that would be able to search the clinical data repositories of its three largest health centers—Beth Israel
Deaconess Medical Center, Children's Hospital
Boston, and Partners HealthCare System (PHS).
The lessons learned in creating the SHRINE
prototype are now being used as a road map for
other institutions developing their own clinical data
repositories, or integrating existing databases.
The plan is ultimately to replace the system with
a scalable peer-to-peer architecture and a
streamlined IRB process.
Given that each of these institutions operates
independently—with its own financial organizations,
databases, firewalls, access policies, and IRB—
Harvard determined that a combined data warehouse
would not be an effective solution. Instead, a
federated model was developed through which each
institution could manage and maintain control over
its local databases, but still issue distributed queries
through a standard Web service. Qualified investigators can use the SHRINE Web-based query tool to
determine the aggregate total number of patients
at participating hospitals who meet a given set of
inclusion and exclusion criteria for clinical studies
(e.g., demographics, diagnoses, medications, and
selected laboratory values). Because counts are
aggregate, patient privacy is protected.
These data will be most useful for investigators
interested in:
• Generating new research hypotheses
• Conducting research requiring large sample sizes
not easily available at any single institution
• Preparing grant applications that would benefit
from pre-identification and/or characterization of a
potential research cohort
• Identifying potential cohorts for clinical trials
25
CLINICAL TRIAL ALERT (CTA) SYSTEMS
For diseases such as cancer, the overwhelming majority of patients are treated in the community setting
where 20 to 40 percent may be eligible for participation in a clinical trial.41 However, the National Cancer
Institute has found that only approximately 3 percent
of adult cancer patients participate in a trial.42 An
Institute of Medicine study found that only 60 percent
of National Cancer Institute-sponsored phase 3 trials
are completed and published, with slow accrual or
insufficient accrual of patients being a reason.43 In
2010, NIH spent an estimated $3.3 billion dollars on
clinical trials. 44 This failure to complete a trial is a
tremendous waste of clinical time, financial resources,
and ultimately the patients' participation and contribution to advance medical research.
A patient may not participate in a trial for a variety
of reasons, including lack of physician awareness
of the trial and the patient's eligibility. In order to
overcome this barrier, the Institute of Medicine
recommended that tools be developed to “cue”
physicians through EHR systems that a patient
may be eligible for a trial.45
A Clinical Trial Alert (CTA) is a tool used to query the
EHR for matches to ongoing trials and notify the
physician of a match at the point of care with the
patient. Such systems have been shown to increase
physician referrals to a trial by 10-fold and double the
enrollment rate.46 Because these alert systems are
activated at the point of care, they also may prevent
the patient from undergoing initial therapies that may
prevent them from qualifying for the trial. Upon
enrollment in a clinical trial, an EHR also will have the
ability to signal a patient's primary care team or other
care coordinators of participation in the trial, accelerating identification of adverse events rather than identifying and reporting events only when the patient visits the clinical investigator.
26
STILL THINKING RESEARCH
CLINICAL TRIAL ALERT SUCCESS STORIES
• The Holston Medical Groups, a non-academic
healthcare organization located in Tennessee, uses
EHRs to speed the identification of patient candidates from its database as potential matches to a
trial. EHR data also help them assess the candidate's eligibility for the trial, which results in lower
“screen failures” and alerts physicians practicing
with the groups of a patient's involvement in a trial,
thereby decreasing protocol violations and
accelerating reporting of adverse events. 47 The
revenue generated from participating in clinical
trials was able to cover the cost of the research
department EHR system.
• Researchers at the University of Michigan
developed software called the Electronic Medical
Record Search Engine (EMERSE) as a tool to
extract research and clinical data from patient
records more efficiently than manual review. They
have applied this tool to screen EHRs from an
administrative database for depression research
studies and found significant time savings
compared to manual review. 48
• Research conducted at the Cleveland Clinic compared traditional recruitment to clinical trials to an
EHR-based CTA system. The study found that the
CTA caused a 10-fold increase in the physicians'
referral rate to clinical trials and a doubling of
the enrollment rate. The physician alert is a
critical process, because, while a small percentage
of patients report being offered the opportunity to
participate in a clinical research study, 63 percent of
those asked report participating, emphasizing the
importance of patient and physician awareness to a
trial.49 Furthermore, most physicians feel that a
CTA approach at point of care for trial recruitment
is easy to use, and 77 percent of physicians
appreciate being reminded about a clinical trial
through a CTA.50
Strategies to Advance the Use of Electronic Health Records to Bridge Patient Care and Research
Lessons Learned
These examples of innovation illustrate that EHRs and digital
clinical data can be incorporated into research in multiple
and varied ways. These illustrations should serve to encourage
those who might have been discouraged by the enormity
of the challenges posed in FasterCures's 2005 report.
Some of the highlights we've gleaned as we developed
this report:
• The traditional closed healthcare system EHR models are still
growing and evolving, such as Geisinger Health System,
Kaiser Permanente, and the Mayo Clinic.
• Other models are emerging in which the patient is the
central player. That is, he or she volunteers to provide
information directly into a Web-based system for personal
health and research purposes. Examples include HealthVault,
The Love/Avon Army of Women, and PatientsLikeMe.
• Still other efforts focus on building clinical data registries or
databanks, where patient data can be stored and tracked
over time, queried by investigators, and research results
communicated back to participants. Examples include the
Gene Partnership Project, the Research Patient Data Registry,
Some institutions are
using their EHR systems
for clinical trial alerts, a
tool used to query the
EHR for matches to
ongoing trials and notify
the physician or other
health provider of a
match at the point of
care with the patient.
and the Shared Health Research Information Network.
• Finally, some institutions are using their EHR systems for
clinical trial alerts, a tool used to query the EHR for matches
to ongoing trials and notify the physician or other health
provider of a match at the point of care with the patient.
27
What's Next:
Actions and Opportunities
The potential to gather data on thousands—even millions—of
patient encounters provides an unprecedented opportunity to
make the connection between research and healthcare delivery.
At a time of significant scientific and technological advances, we
must continue to facilitate progress in the application of IT
capabilities to better manage and harness clinical data.
We are seeing more healthcare professionals and hospitals begin a
process to adopt systems to digitize interactions with patients. As
institutions move forward with adoption and implementation of
health IT systems, it is critical that they consider the needs of and
The potential to gather
data on thousands—
even millions—of patient
encounters provides an
unprecedented opportunity to make the connection between research
and healthcare delivery.
seek the advice of the research community. We need to build a
solid connection between the bench and bedside.
In this report, we found that the health IT infrastructure is still
falling short of its potential to leverage research capabilities to
increase our understanding of disease and accelerate therapies.
However, we spotlight models that have integrated research capacity into the health IT framework and yielded favorable patient outcomes. These tangible examples, along with insights gleaned from
leaders in healthcare, clinical research, and IT, provided us with a
framework for presenting the below recommendations for action.
• Clinical trial screening and matching should be included as
a measure for “Meaningful Use” of electronic health
record systems.
• NIH should articulate a strategy that will align its programs
with the recommendations of the ONC Federal Health IT
Strategic Plan.
• The Office of the National Coordinator should develop an
initiative with pilot projects that would create medical research
IT modules.
• Nationwide Health Information Network (NwHIN) should
expand its standards and policies to include clinical research and
research centers in the network of information exchange.
Each recommendation, implemented appropriately, is an opportunity to bring us closer to creating a medical research system that
28
STILL THINKING RESEARCH
Strategies to Advance the Use of Electronic Health Records to Bridge Patient Care and Research
will be able to meet and respond to the needs of patients, and
accelerate the process of turning a scientific breakthrough into a
life-saving therapy.
Recommendations for Action
>
CLINICAL TRIAL SCREENING AND MATCHING SHOULD
BE INCLUDED AS A MEASURE FOR “MEANINGFUL USE”
OF ELECTRONIC HEALTH RECORD SYSTEMS.
EHR systems for clinical research can improve the clinical trial process
by finding patients who match specific trial criteria, thereby speeding up accrual. This will ultimately expedite the submission of trial
data to the FDA in the format needed for review, while providing a
system that will monitor a patient's care while on a trial.
Clinical trial screening and matching is arguably the most readily
adaptable functionality to expand into the “Meaningful Use”
reimbursement requirements for clinical research. Currently, there
are 11,000 clinical trials recruiting and enrolling three million
patients.51 Integrating this recruitment function into electronic
health systems will expedite patient accrual into trials, and connect the trial to patient records, ensuring that patients and their
At a time of significant
scientific and technological advances, we must
continue to facilitate
progress in the application of IT capabilities to
better manage and harness clinical data.
providers are kept abreast of trial information and treatment protocols in real time. This would also allow researchers and trial
sponsors to shift resources previously dedicated for recruitment
strategies to other critically important efforts needed for successfully completing the trial.
To date, the Office of the National Coordinator for Health IT
(ONC) has focused its initial EHR strategies on data capturing and
sharing of a core set of variables such as vital signs, demographics,
lab results, and medication lists to improve and coordinate patient
care. As the ONC expands EHR functions to advanced processes,
they should explore solutions to include clinical trial matching in
the matrix of “meaningful use” requirements and invite input
from the clinical research and patient and medical research advocacy communities on such an initiative. Without such a requirement building these capabilities will likely not be a priority for
EHR developers.
29
The availability of $27 billion in HITECH funding for adoption of
EHRs over the next few years provides a strong incentive to bring
health IT into the clinic. By adopting clinical trial matching into
the “meaningful use” framework, these incentives could also
drive an increase in the number of patients and providers participating in clinical research. It is important to leverage this funding
stream to ensure that clinical research can connect seamlessly with
existing care systems.
Clinical research and patient advocacy communities play an essential role in ensuring this requirement is brought to fruition. As the
ultimate users and beneficiaries of an integrated and connected
clinical trial matching system, researchers and patient advocates
should actively monitor this issue diligently and create opportunities to provide input to ensure this becomes a priority.
We believe the NIH
can lead this two-way
interaction by articulating a vision that will
bring its researchers
and communities into
the national exchange
network.
>
NIH SHOULD ARTICULATE A STRATEGY THAT WILL ALIGN
ITS PROGRAMS WITH THE RECOMMENDATIONS OF THE
ONC FEDERAL HEALTH IT STRATEGIC PLAN.
In its 2011 Federal Health IT Strategic Plan, ONC specifically called
for “information exchange in support of research and the translation of research findings back into clinical practice” listing the need
for a two-way interaction between research and clinical care.52 As
the nation's medical research agency and the largest source of
funding for medical research in the world, the NIH is ideally poised
to facilitate efforts that integrate research with clinical care.
We believe the NIH can lead this two-way interaction by articulating a vision that will bring its researchers and communities into
the national exchange network. There are numerous NIH programs that support networked research studies, yet there isn't a
clearly defined strategy to bridge these programs with other federal health IT initiatives and ensure ongoing adoption of certified
electronic health systems*.
An NIH-driven strategy that would align research systems and certified electronic health systems could:
* A certified electronic health system
refers to systems that meet “meaningful
use” requirements as established by
the ONC.
30
STILL THINKING RESEARCH
•
improve workflow and reduce redundant data collection. In
many clinical studies, data are required to be entered into
Strategies to Advance the Use of Electronic Health Records to Bridge Patient Care and Research
two different databases—one for the research team and
one for the care team. This not only adds additional work
for clinical and research staff, but studies evaluating dual
database entry revealed error rates to be as high as 27 percent in some cases.53 Such errors in a clinical research study
could have a significant impact on the research conclusions.
•
bridge the traditional gap between vertical integration of
research and care within an institution and horizontal integration across institutions, particularly in community settings because community practitioners interact with all
points along the continuum from discovery to care delivery.54
One reason for the lack of broad IT adoption and interoperability in clinical research is the absence of financial incentives.55 Individual research centers may see value in vertical
alignment of their research information systems and their
clinical records systems within their individual center, but
the broader horizontal interoperability with other healthcare systems requires substantial resources and personnel
that individual centers are unlikely to invest in and have little incentive to do so.
•
expand sharing of clinical data for research purposes across
networks. For example, networks like the Clinical and
Translational Science Award (CTSA) program at NIH would
be a place to pilot initiatives that would align with certified
“meaningful use” systems. As illustrated earlier in the
report, a number of CTSA sites are developing novel
There are numerous
NIH programs that
support networked
research studies, yet
there isn't a clearly
defined strategy to
bridge these programs
with other federal health
IT initiatives and ensure
ongoing adoption of
certified electronic
health systems.
technologies and many of them are already participating
in collaborative research studies. The CTSAs could provide a
test-bed for the federal strategic plan calling for the twoway interaction between research and care data systems.
CTSAs, an established network of academic research centers,
are also linked to community-based settings, an important
consideration as majority of care delivered in the United
States is in physician offices and community clinics and
centers. Community practices refer patients, participate in
research agendas, translate research results into practice,
and communicate with and care for patients enrolled in a
31
clinical trial.56 NIH should include in its plan programs and
incentives to connect the community centers and their
patients with the research enterprise.
The NIH strategy should incorporate the insights of patients and
disease research organizations, and provide incentives to further
connect the community centers and their patients with the
research enterprise.
Patient advocacy groups and disease research organizations are
well-positioned to hold NIH accountable for ensuring its vision is
carried through and actualized across existing efforts and networks. They also play a pivotal role in amplifying the impact of
best practices, including those funded by the NIH, and ensure they
are broadly adopted.
One of the barriers
standing in the way of
research being integrated into an EHR system is
the need for high-quality
data that are annotated
with patient outcomes
and can be used for
research purposes.
>
THE OFFICE OF THE NATIONAL COORDINATOR SHOULD
DEVELOP AN INITIATIVE WITH PILOT PROJECTS THAT
WOULD CREATE MEDICAL RESEARCH IT MODULES.
One of the barriers standing in the way of research being integrated into an EHR system is the need for high-quality data that
are annotated with patient outcomes and can be used for
research purposes. However, as we cite in this report, a number
of organizations are testing innovative models that link research
and care through imaging records, biospecimens, and genomic
databases with patients' EHRs.
To facilitate these activities and ensure widespread use across a
number of EHR platforms, ONC is well-positioned to fund pilot
programs that will create research modules that can be
“attached” (plug and play) or retrofitted to existing EHR systems
that were built without the capacity to accommodate research.
These pilot programs would allow connectivity to systems being
developed to meet meaningful use requirements and encourage
community practitioners to participate in clinical research.
To ensure this initiative reaches full potential, the ONC should consider the need to harmonize standards for collecting genomic and
molecular data and integrating these into an EHR. Genetic testing
32
STILL THINKING RESEARCH
Strategies to Advance the Use of Electronic Health Records to Bridge Patient Care and Research
is rapidly becoming part of routine medical care, and this will keep
expanding. The earliest possible incorporation of genetic and
molecular information into EHR exchange systems will accelerate
the translation of research into practice. EHR systems could
help to provide guidance to physicians on how best to interpret
genetic test results and guide patients to new geneticallytargeted treatments.
Patient and medical research advocates have an important role
to play in the priority-setting and decision-making process
necessary to develop meaningful research modules and harmonize
standards. Often, external pressure from informed advocates
can accelerate processes and ensure that efforts are designed
to deliver on the ultimate goal: improving patient outcomes.
They can also play a key role in developing tools to use electronic
health information to improve their constituents' health. These
groups should drive discussions with the ONC about mechanisms
to improve patient engagement, and the ONC should provide
funding opportunities to develop innovative tools for patient
interaction with health IT.
9 of the top 10
Recent research has demonstrated the ability to link genetic variants
in a DNA database associated with diseases like Crohn’s disease, multiple
sclerosis, or atrial fibrillation to a clinical diagnosis of these diseases in a
patient's EHR. 57 And for diseases like breast cancer, genetic and molecular
testing is becoming standard practice in routine care, clinical decisionmaking, and clinical research. However, despite the fact that 9 of the top
10 causes of death in the United States have genetic components, there is
no uniform and systematic effort to build capacity for incorporating
genomic and molecular data in the EHR.58 As more individuals undergo
genetic analyses and as more clinical information becomes linked to
genomics, it will be critical to bridge this genetic information with clinical
information collected in an EHR to improve our ability to understand
the genetic underpinnings of disease.
To ensure this initiative
reaches full potential, the
ONC should consider
the need to harmonize
standards for collecting
genomic and molecular
data and integrating
these into an EHR.
Genetic testing is rapidly
becoming part of routine
medical care, and this
will keep expanding.
33
>
NATIONWIDE HEALTH INFORMATION NETWORK (NwHIN)
SHOULD EXPAND ITS STANDARDS AND POLICIES TO
INCLUDE CLINICAL RESEARCH AND RESEARCH CENTERS
IN THE NETWORK OF INFORMATION EXCHANGE.
NwHIN was established to provide the infrastructure for the
exchange of health information across diverse entities. Efforts are
underway to standardize data, services, and policies in the delivery
of healthcare using EHRs. However, the initial set of services does
not include clinical research.
If research systems
are to effectively
exchange information
within and across the
healthcare enterprise,
there will need to be
agreement on the
standards and policies
needed for interoperable data exchange.
If research systems are to effectively exchange information within
and across the healthcare enterprise, there will need to be agreement on the standards and policies needed for interoperable data
exchange. Standards that could streamline data interchange
between electronic source data in EHRs and systems used by
clinical investigators (e.g., registries, distributed research networks,
public health databases) could facilitate and improve collaboration
and regulatory submission compliance. But there is much debate
within the research community as to what standards are needed
and how they are best implemented for research studies. NwHIN
could serve as a convening entity to harmonize standards for
clinical research and ensure that the research standards uniformly
integrate with electronic care systems. For example, the Clinical
Data Interchange Standards Consortium (CDISC) has developed
vendor-neutral medical research standards to support data
exchange for research studies and regulatory submission. Such
standards provide NwHIN with existing models to look at to
begin to bridge research into the larger network.
As standards are developed, consideration must be given to
research needs and research-quality data capture. Organizations
that are involved in clinical research, like the NIH and academic
medical centers, will need to provide input and direction to
NwHIN to help define the specifications necessary for clinical
research. The case studies mentioned earlier in this report illustrate the ability to proceed in some areas of clinical research without the tight and complete adherence to standards. There is an
opportunity to learn from these models to build a balanced model
for research standards that provides flexibility and innovation but
34
STILL THINKING RESEARCH
Strategies to Advance the Use of Electronic Health Records to Bridge Patient Care and Research
allows for a systemic exchange of information and data across
databases and networks.
Patient and medical research advocacy and disease research
organizations have a critical role to play cultivating a culture of
participation in research.
Patients who are part of a healthcare system that uses EHRs
already know the value of having a central record that can be
the
need for
standards
accessed by all healthcare providers. In addition, many consumers
are learning the value of controlling and having access to their
Standards experts argue that
own health information through self-managed personal health
common standards developed
records. These patients could be effective advocates for ensuring
for clinical use are necessary to
such access to valuable information could also be utilized to
provide the foundation for
provide vital data needed for research purposes.
research use.59 However, some
in the research community
counter that standards are secondary, are being developed at
the wrong level, or need not be
Ultimately, these recommendations are all designed to improve
fully completed before research
treatment options for patients. We urge policymakers to consider
can proceed. For example, the
the consequences of inaction and the real possibilities of lost
idea is not to connect one
opportunity to improve the way we search for treatments and cures.
EHR or a biobank to another,
But most importantly, we hope decisionmakers acknowledge the
but rather to have them both
tangible benefits of having a health IT system with integrated
use a common language and
research capabilities and seize opportunities to ensure we reap those
feed through external interfaces
benefits and translate them into better health outcomes for all.
to aggregate specific pieces
of information around an
individual. The “don't wait for
Patient groups play a unique role in bringing about change and
standards” camp argues that
ensuring accountability—they know the needs of their constituents
too much time and money is
and are trusted stewards in the interface with providers,
being spent trying to foster
researchers, and policymakers. They could develop educational
system-to-system integration
materials that help patients understand the value of clinical
around every bit of information
research, the importance of clinical trials, and the importance of
collected, and that the medical
aggregated clinical data to the research enterprise.
community should instead
agree on the need for a free-flow
of information across systems
to drive connectivity.
35
In Sum
FasterCures said the following in 2005, and will continue to say it:
As the healthcare system addresses the challenges of widespread
adoption of electronic patient records, research capacity should
be part of the architecture.
The complexity of issues that affect human health—from the
genomic and proteomic levels to the culture and locale of the
institutions that provide healthcare services—requires that there
be a more comprehensive and collaborative approach to connecting the worlds of science and the clinic.
The path to a [health IT] system that also serves the needs of
As the healthcare
system addresses the
challenges of widespread adoption of
electronic patient
records, research
capacity should be part
of the architecture.
researchers is a long one, but is one that must be mapped so that
no important opportunity is missed.
Clinical and health services researchers have been working toward
this goal for some time, with some impressive advances made in
recent years. Each year, bright ideas and new tools in the form of
information technology have arrived to help them. But we are
far from where we need to be.
As the clinical world continues to adopt health IT, the research
and patient advocacy communities need to push them to
think research.
36
STILL THINKING RESEARCH
Strategies to Advance the Use of Electronic Health Records to Bridge Patient Care and Research
Acknowledgments
The authors would like the thank the following individuals who granted time for interviews,
responded to email queries, and provided vital insights on the topics raised in this report.
Aaron Abend
Managing Director, Data
Warehousing
Recombinant Data Corp.
Peter B. Berger, M.D.
Associate Chief Research
Officer; Director, Center for
Clinical Studies
Geisinger Health System
Christopher G. Chute, M.D.,
Dr.P.H.
Professor of Medical
Informatics; Chair, Division
of Biomedical Informatics
Mayo Clinic College of
Medicine
James J. Cimino, M.D.
Chief, Laboratory for
Informatics Development
National Institutes of Health
Amy Compton-Phillips, M.D.
Associate Executive Director,
Quality
The Permanente Federation,
LLC
Lou Diamond, M.D.
Former Medical Director and
Vice President, Healthcare
Business
Thomson Reuters
Charles P. Friedman, Ph.D.
University of Michigan
Stephen H. Friend, M.D., Ph.D.
President, Co-Founder, and
Director
Sage Bionetworks
James “Jamie” Heywood
Co-Founder, Chairman
PatientsLikeMe
Michael Kahn, M.D., Ph.D.
Co-Director
Colorado Clinical and
Translational Sciences
Institute
Associate Professor,
Department of Pediatrics
University of Colorado,
Denver
William A. Knaus, M.D.
Evelyn Troup Hobson
Professor; Founding Chair,
Department of Public Health
Sciences
University of Virginia School
of Medicine
Isaac S. Kohane, M.D., Ph.D.
Director, Children's Hospital
Informatics Program;
Henderson Professor of
Health Sciences and
Technology; Harvard-MIT
Division of Health Sciences
and Technology;
Professor of Pediatrics
Harvard Medical School
Shawn Murphy, M.D., PhD
Associate Director,
Laboratory of Computer
Science—Clinical and
Research Informatics Division
Massachusetts General
Hospital
Assistant Professor,
Neurology
Harvard Medical School
Naz Sykes
Executive Director
Dr. Susan Love Research
Foundation
James Walker, M.D., FACP
Chief Health Information
Officer
Geisinger Health System
Jon White, M.D.
Director, Health Information
Technology Portfolio
Agency for Healthcare
Research and Quality
John Wilbanks
Vice President, Science
Creative Commons
Susan Love, M.D.
President and Medical
Director
Dr. Susan Love Research
Foundation
Deven McGraw
Director, Health Privacy
Project
Center for Democracy and
Technology
37
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