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Synopsis and Highlights
INTRODUCTION AND OVERVIEW
Health and health care are going digital. As multiple intersecting plat-
forms evolve to form a novel operational foundation for health and health
care—the nation’s digital health utility—the stage is set for fundamental
and unprecedented transformation. Most changes will occur virtually out
of sight, and the pace and profile of the transformation will be determined
by stewardship that fosters alignment of technology, science, and culture
in support of a continuously learning health system. In the context of
growing concerns about the quality and costs of care, the nation’s health
and economic security are interdependently linked to the success of that
stewardship.
Progress in computational science, information technology (IT), and
biomedical and health research methods have made it possible to foresee
the emergence of a learning health system that enables both the seamless
and efficient delivery of best care practices and the real-time generation
and application of new knowledge. Increases in the complexity and costs
of care compel such a system. With rapid advances in approaches to di-
agnosis (such as molecular diagnostics), therapeutics, genetic insights into
individual variation, and emerging measurement modalities (such as within
proteomics and imaging), clinicians and patients must sort through expo-
nentially increasing numbers of factors with each clinical decision. At the
same time, healthcare costs are draining the purchasing power of consum-
ers and handicapping the competitiveness of U.S. businesses, yet health
outcomes are falling far short of the possible.
1
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2 DIGITAL INFRASTRUCTURE FOR THE LEARNING HEALTH SYSTEM
Against this backdrop of opportunity and urgency, the Institute of
Medicine (IOM) of the National Academies, sponsored by the Office of
the National Coordinator for Health Information Technology (ONC), con-
vened a series of expert meetings to explore strategies for accelerating the
development of the digital infrastructure for the learning health system.
Presentations and major elements of those discussions are summarized in
this publication, Digital Infrastructure for the Learning Health System:
The Foundation for Continuous Improvement in Health and Health Care.
The Learning Health System
In 2001, the IOM report Crossing the Quality Chasm called national
attention to untenable deficiencies in health care, noting that every patient
should expect care that is safe, effective, patient-centered, timely, efficient,
and equitable (IOM, 2001). Based on the determination that health care is
a complex adaptive system—one in which progress on its central purpose
is shaped by tenets that are few, simple, and basic—the report identified
several rules to guide health care. In particular, these rules underscore the
importance of issues related to the locus of decisions, patient perspectives,
evidence, transparency, and waste reduction. The report envisioned, in ef-
fect, engaging patients, providers, and policy makers alike to ensure that
every healthcare decision is guided by timely, accurate, and comprehensive
health information provided in real time to ensure constantly improving
delivery of the right care to the right person for the right price.
The release of the IOM Chasm report stimulated broad activities re-
lated to clinical quality improvement and the effectiveness of health care,
including the eventual creation by the IOM of the Roundtable on Value
& Science-Driven Health Care. Begun in 2006 as the IOM Roundtable on
Evidence-Based Medicine, it has explored ways to improve the evidence
base for medical decisions and sought the development of a learning health
system “designed to generate and apply the best evidence for collabora-
tive health choices of each patient and provider; to drive the process of
discovery as a natural outgrowth of patient care; and to ensure innovation,
quality, safety, and value in health care.” From its inception, the Round-
table has conducted The Learning Health System Series of public meetings
to consider the capture of emerging innovations—such as those occurring
in IT, research methods, and care delivery—as building blocks in the foun-
dation of a learning health system. Characteristics of such a system are
noted in Box S-1 and in matrix form in Appendix A. In broad terms, they
represent delivery of best practice guidance at the point of choice, continu-
ous learning and feedback in both health and health care, and seamless,
ongoing communication among participants, all facilitated through the
application of IT.
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3
SYNOPSIS AND HIGHLIGHTS
BOX S-1
Learning Health System Characteristics
Culture: participatory, team-based, transparent, improving
Design and processes: patient-anchored and tested
Patients and public: fully and actively engaged
Decisions: informed, facilitated, shared, and coordinated
Care: starting with the best practice, every time
Outcomes and costs: transparent and constantly assessed
Knowledge: ongoing, seamless product of services and research
Digital technology: the engine for continuous improvement
Health information: a reliable, secure, and reusable resource
The Data utility: data stewarded and used for the common good
Trust fabric: strong, protected, and actively nurtured
Leadership: multi-focal, networked, and dynamic
SOURCE: Adapted from The Learning Healthcare System (IOM, 2007).
Because IT serves as the functional engine for the continuous learn-
ing system, this ONC-commissioned exploration was broadly conceived
to consider the issues and strategies required for the emergence of a digital
infrastructure that allows data collected during activities in various settings—
clinical, research, and public health—to be integrated, analyzed, and broadly
applied (“collect once, use for multiple purposes”) to inform and improve
clinical care decisions, promote patient education and self-management,
design public health strategies, and support research and knowledge devel-
opment efforts in a timely manner.
The Digital Health Infrastructure
The digital infrastructure for the learning health system will not solely
be the result of features designed and built de novo. Existing initiatives and
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4 DIGITAL INFRASTRUCTURE FOR THE LEARNING HEALTH SYSTEM
resources are actively in play at multiple levels—including electronic health
records (EHRs); personal health records (PHRs); telehealth; health informa-
tion portals; electronic monitoring devices; biobanks; health information
databases maintained by large health systems, private insurers, and regula-
tory agencies; and advances in molecular diagnostics. Each adds important
capacity for clinical care, clinical and health services research, public health
surveillance and intervention, patient education and self-management, and
safety and cost monitoring.
Still, these capacities are relatively early in their development, and
progress depends on improvements on several dimensions. As of 2009, only
about 12% of hospitals and 6% of clinician offices had an EHR in place
(DesRoches et al., 2008; Jha et al., 2010) and only about 1 in 14 Americans
had electronic access to any patient-oriented version of their health record
(CHCF, 2010). On the other hand, since 2000, the number of Americans
who have access to the internet has jumped from 46% to 74%, and the
number of American adults who have looked online for health informa-
tion has jumped from 25% to 61% (Fox, 2010). Wireless technology is
quickening the pace of change. With 6 in 10 American adults using wireless
capability with a laptop or mobile device (Smith, 2010), mobile applica-
tions are rapidly developing the potential for remote site access to health
information, as well as diagnostic and even treatment services.
This developing potential presents opportunities and challenges for
stewardship. Issues related to interoperability, governance, patient and
public engagement, and privacy and security concerns, among others, will
need to be better addressed for successful progress toward a learning health
system. Approaches and lessons from sectors outside health include those
from energy and the financial sector, two examples discussed in the meet-
ings and summarized in this publication (see Appendix B). VISA used a
minimalist approach, crafted on the combination of mutual self-interest and
basic rules-of-play, to build its platform for a global credit card network.
Consumer Energy’s work in the Smart Grid Initiative applied an analytically
driven approach to accommodate and network a wide variety of legacy
nodes in growing the electronic platform operating the nation’s energy
system. Background on the Smart Grid Initiative is presented in Box S-2.
Regardless of the model, a key rationale for the workshop discussions
was the reality that effective and efficient progress in the growth and de-
velopment of our national and global digital health infrastructure requires
active cooperation, collaboration, and role delineation among many orga-
nizations, companies, and agencies—private and public—at the cutting edge
of using health IT to improve health and health care.
The striking, and accelerating, progress in the capacity and transfor-
mative influences of IT on society over the past three decades is a blended
product of interrelated initiatives arising from within the commercial, in-
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5
SYNOPSIS AND HIGHLIGHTS
BOX S-2
Case: The Smart Grid
The Smart Grid is a long-term, complex systems development project to grow
the electronic platform operating the nation’s energy system using an engineering
approach to accommodate a wide variety of legacy nodes that are organic—con-
stantly growing and evolving, much like a biological system. This continuous evolu-
tion allows the Smart Grid’s architecture to preserve and encourage the capacity
of each node to innovate locally and deal with complexity in a way that suits local
and grid needs. As conceived, the Smart Grid will
• Enable active participation by consumers
• Accommodate all generation and storage options
• Enable new products, services, and markets
• Provide power equality for the digital economy
• Optimize asset utilization and operate efficiently
• Anticipate and respond to system disturbances (self-heal)
• Operate resiliently against attack and natural disaster
Because there is no need for consensus among the nodes on how they
should operate within local boundaries, the Smart Grid development methodology
is not based on comprehensive internal design and operating standards for each
node on the Grid to follow. Instead, the approach accommodates highly diverse
nodes connecting to the Smart Grid using open data translation protocols that
standardize information management, rather than using the internal workings of
each node. The Grid becomes a communications bus to which each node must
be able to write, and from which each node must be able to read. This architecture
preserves capacities for local operating autonomy and innovation throughout the
Smart Grid. It also manages a standardized communications capacity among
complex, and otherwise noninteroperable, legacy nodes on the Grid. These fea-
tures are all characteristics of ultra-large-scale (ULS) software-intensive systems.
dependent, and public sectors. Leaps in the speed, power, and efficiency of
information processing, the development of the Internet and World Wide
Web, and its use to facilitate near-universally available real-time access to
information, have spawned a new economy and new vehicles for progress.
Health information vendors, large and small, have emerged to meet the
growing demand for capacity to manage the retrieval, storage, and delivery
of information for agencies, institutions, professionals, and individuals in
virtually every aspect of health and health care. The range of newly digi-
talized services—and the growth of vendors to provide them—is startling.
Through technologies developed by companies such as Google, Microsoft,
and Yahoo, the amount of web-based health information accessed daily
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6 DIGITAL INFRASTRUCTURE FOR THE LEARNING HEALTH SYSTEM
by individuals and clinicians is already transforming the care process.
Beyond the publicly available digital resources, a vast array of specialized
care management products have emerged for activities such as scheduling
and billing; claims processing and payment; supply and equipment inven-
tory maintenance; individual patient charting; medication prescribing and
tracking; family and personal health records; clinician-patient communica-
tion; clinician and patient decision support; robotics-assisted procedures;
telehealth for remote site diagnosis and treatment; disease surveillance;
vital statistics reporting; postmarket product monitoring; safety and hazard
exposure monitoring; clinical research protocols; disease and intervention
registries; and data aggregation, analysis, and modeling.
Various large academic health centers and healthcare delivery organi-
zations—Veterans Health Administration (VHA), Kaiser Permanente (see
Box S-3), Geisinger Health System, Vanderbilt, MD Anderson, Palo Alto
Medical Foundation, Group Health Cooperative, several Harvard facilities,
Children’s Hospital of Philadelphia, Virginia Mason, and the Mayo Clinic,
to name a few—have invested substantially in the creation of advanced
digital resources for administrative, patient care, and research functions.
Additionally, some related collaborative research networks have begun
to develop. Nonetheless, the diversity and limited compatibility of the
products, and the lack of economic incentives for their use have, to date,
restrained the broader uptake, application, and functional utility of digital
capacity across the system.
A number of public, private, and independent sector initiatives have
emerged to accelerate stakeholder action on various dimensions important
to progress. To supplement the relatively limited pre-2009 public invest-
ments, independent sector leadership has come from foundations such
as the Markle Foundation, the Robert Wood Johnson Foundation, and
the California HealthCare Foundation. Furthermore, in addition to the
formation of capacity-building resources such as the Health Information
Exchanges, a number of facilitative stakeholder groups have emerged—for
example, the eHealth Initiative, the Clinical Data Interchange Standards
Consortium (CDISC), and the National eHealth Collaborative. On the
professional advancement dimension, the American Medical Informatics
Association has developed as a growing resource for the contributions of
biomedical and health informaticians working in activities to organize,
manage, analyze, and use information in health care. An example of the
coordinative potential of these groups is found in the development of inte-
gration profiles by Integrating the Healthcare Enterprise and CDISC to sup-
port the use of EHRs for clinical research, quality, and public health, and
the testing and demonstration of these profiles by several vendors including
Cerner, Allscripts, Greenway Medical, and General Electric Healthcare.
At the federal level, ONC was created in 2004 in the U.S. Department
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7
SYNOPSIS AND HIGHLIGHTS
BOX S-3
Case: Kaiser Permanente
In 2003, Kaiser Permanente (KP) launched a $4 billion health information
system called KP HealthConnect that links its facilities and clinicians throughout
their delivery system and represents the largest civilian installation of electronic
health records in the United States. The EHR at the heart of KP HealthConnect
provides a reliably accessible longitudinal record of member encounters across
clinical settings including laboratory, medication, and imaging data; as well as
supporting:
• lectronic prescribing and test ordering (computerized physician-order
E
entry) with standard order sets to promote evidence-based care
• opulation and patient-panel management tools such as disease regis-
P
tries to track patients with chronic conditions
• ecision support tools such as medication-safety alerts, preventive-care
D
reminders, and online clinical guidelines
• lectronic referrals that directly schedule patient appointments with spe-
E
cialty care physicians
• ersonal health records providing patients with the ability to view their
P
personal clinical information including lab results, plus linkage with phar-
macy, physician scheduling, and secure and confidential e-mail messag-
ing with clinicians.
• erformance monitoring and reporting capabilities
P
• atient registration and billing functions
P
Physician leaders report that access to the EHR in the exam room is helping
to promote compliance with evidence-based guidelines and treatment protocols,
eliminate duplicate tests, and enable physicians to handle multiple complaints
more efficiently within one visit. Ongoing evaluation by Kaiser indicates that pa-
tient satisfaction with outpatient physician encounters has increased and that the
combination of computerized physician-order entry, medication bar coding, and
electronic documentation tools is helping to reduce medication administration
errors in hospital care.
Overall, Kaiser’s experience suggests that use of the EHR and online portal
to support care management and new modes of patient encounters is having
positive effects on utilization of services and patient engagement. For example,
three-quarters or more of online users surveyed agreed that the portal enables
them to manage their health care effectively and that it makes interacting with the
healthcare team more convenient.
of Health and Human Services (HHS) to stimulate progress in the field.
Since 2009, with the enactment of the Health Information Technology
for Economic and Clinical Health Act (HITECH) as part of the American
Recovery and Reinvestment Act, the federal government leadership profile
has become especially prominent. This has included the commitment of
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8 DIGITAL INFRASTRUCTURE FOR THE LEARNING HEALTH SYSTEM
unprecedented resources for health information technology (HIT), admin-
istered through the leadership of ONC. Under HITECH, ONC was granted
$2 billion to facilitate the adoption and meaningful use of HIT. In addition,
an estimated $27 billion was designated for the Centers for Medicare &
Medicaid Services (CMS) to distribute as incentive payments for physicians
and hospitals to become meaningful users of HIT.
Designed as a set of staged requirements to qualify for CMS incentive
payments, the first-stage elements of “meaningful use” were released by
CMS on July 13, 2010. These established a core set of requirements for
eligible professionals and hospitals, as well as a menu of additional choices,
from which five are to be chosen. The stage 1 meaningful use target ele-
ments are listed in summary fashion in Box S-4, and details are contained in
Appendix D. The subsequent stages of meaningful use are currently under
development and are presented later in this summary, along with an indica-
tion of related issues flagged in workshop discussions.
In addition to the meaningful use requirements, ONC has funded a
series of grant programs through HITECH, including the Beacon Commu-
nity grants, aimed at demonstrating community-wide digital infrastructure
capacity and use for health improvement, and the Strategic Health Informa-
tion Technology Advanced Research Projects Program, to foster the capture
of technological advances to improve system performance. At the broader
level, ONC is pursuing a series of initiatives to foster health information
exchange among stakeholders, including the regional health information ex-
changes and under the Nationwide Health Information Network (NWHIN).
Several additional HHS agencies have activities important to the de-
velopment of the digital learning health system. CMS, in addition to estab-
lishing rules for meaningful use and requirements for uniform condition
identifiers central to healthcare payment and research, recently created
the Center for Medicare and Medicaid Innovation to test innovative pay-
ment and program service delivery methods. Within the National Institutes
of Health (NIH), the National Library of Medicine serves as the central
coordinating body for clinical terminology standards, and other NIH pro-
grams, such as the Clinical and Translational Science Awards Program, and
the National Cancer Institute’s Enterprise Vocabulary Series and cancer
Biomedical Informatics Grid (caBIG®, see Box S-5 and Appendix B for
additional information) serve as key contributors to building the capacity
to derive scientific discovery from patient care. Through its National Re-
source Center for Health IT and capacity initiatives on patient registries, the
Agency for Healthcare Research and Quality (AHRQ) supports a number
of programs to advance the digital utility for healthcare quality and safety.
At the Food and Drug Administration (FDA), the Sentinel Initiative
(see Box S-6 and Appendix B) has been designed to build and implement
a national electronic system for postmarket surveillance of approved drugs
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SYNOPSIS AND HIGHLIGHTS
BOX S-4
Meaningful Use Requirement Categories
Core structured personal data (age, sex, ethnicity, smoking status)
Core list of active problems and diagnoses
Core structured clinical data (vital signs, meds, [labs])
Outpatient medications electronically prescribed
Automated medication safeguard/reconciliation
Clinical decision support
Care coordination support/interoperability
Visit-specific information to patients
Automated patient reminders
e-Record patient access (copy or patient portal)
Embedded measures for clinical quality reporting
Security safeguards
Examples of optional elements:
Advance directives for ages >65
Condition-specific data retrieval capacity
Public health reporting (reportable conditions)
SOURCE: Adapted from Blumenthal and Tavenner (2010). See Appendix D for details.
and other medical products. The Centers for Disease Control and Prevention
(CDC) supports several IT-based public health data collection and surveil-
lance programs and serves as the primary agency responsible for these track-
ing efforts, response and public health links to domestic and international
public health data systems, and the Health Resources and Services Admin-
istration (HRSA) has developed initiatives introducing HIT to improve care
access and coordination in rural areas and for underserved populations.
Efforts to promote the development, implementation, and widespread
adoption of HIT also build on a wide array of digital learning leadership
efforts by other federal agencies. In particular, important contributions stem
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10 DIGITAL INFRASTRUCTURE FOR THE LEARNING HEALTH SYSTEM
BOX S-5
Case: The National Cancer Institute’s caBIG® Initiative
The National Cancer Institute of the National Institutes of Health has devel-
oped an informatics program designed to improve patient care and accelerate
scientific discoveries by enabling the collection and analysis of large amounts of
biological and clinical information and facilitating connectivity and collaboration
among biomedical researchers and organizations. More than 700 different orga-
nizations are actively engaged in caBIG®, including basic and clinical researchers,
consumers, physicians, advocates, software architects and developers, bioinfor-
matics specialists and executives from academe, medical centers, government,
and commercial software, pharmaceutical, and biotechnology companies from the
United States and in 15+ countries around the globe.
At the heart of the caBIG® program is caGrid, a model-driven, service-
oriented architecture that provides standards-based core “services,” tools, and
interfaces so the community can connect to share data and analyses efficiently
and securely. More than 120 organizations are connected to caGrid. In partnership
with the American Society of Clincal Oncology, caBIG® is developing specifica-
tions and services to support oncology-extended EHRs that are being deployed
in community practice and hospital settings. caBIG® tools and technology are
also being used by researchers working on cardiovascular health, arthritis, and
AIDS. In addition, pilot projects have successfully connected caGrid to other net-
works, including the Nationwide Health Information Network, the CardioVascular
Research Grid, and the computational network TeraGrid.
from responsibilities and activities of the VHA—for example, the highly re-
garded Veterans Health Information Systems and Technology Architecture
system of IT supporting better care, as well as personal tools such as “My
HealtheVet” and the Virtual Lifetime Electronic Record programs—the
Department of Defense (DOD), the Federal Communications Commission
(FCC), and the National Science Foundation (NSF). The VHA and the
DOD have formed the Telemedicine and Advanced Technology Research
Center as a joint program to advance research and applications in health in-
formatics, telemedicine, and mobile health monitoring systems. Because of
the deep and broad set of capabilities and initiatives collectively sponsored
by federal agencies, their coordination and interface with private sector
activities offers a vital strategic opportunity to accelerate the development
of a learning health system.
Testament to the compelling priority of the prospects, in December
2010, the President’s Council of Advisors on Science and Technology
(PCAST) issued its report, Realizing the Full Potential of Health Informa-
tion Technology to Improve Healthcare for Americans: The Path Forward
(PCAST, 2010). The PCAST report examines the opportunities and needs
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SYNOPSIS AND HIGHLIGHTS
BOX S-6
Case: The FDA’s Sentinel Initiative
In 2008, the Department of Health and Human Services and the Food
and Drug Administration (FDA) announced the launch of FDA’s Sentinel Initia-
tive, a long-term program designed to build and implement a national electronic
s
ystem—the Sentinel System—for monitoring the safety of FDA-approved drugs
and other medical products. Data partners in the Sentinel System will include
organizations such as academic medical centers, healthcare systems, and health
insurance companies. As currently envisioned, participating data partners will
access, maintain, and protect their respective data, functioning as part of a “dis-
tributed system.”
In a related pilot activity, FDA is working with Harvard Pilgrim Health Care,
Inc. to develop a smaller working version of the future Sentinel System, dubbed
“Mini-Sentinel.” Through this pilot, FDA will learn more about some of the barriers
and challenges, both internal and external, to establishing a Sentinel System for
medical product safety monitoring. The Mini-Sentinel Coordinating Center (MSCC)
represents a consortium of more than 20 collaborating institutions, working with
participating data partners to use a common data model as the basis for their ap-
proach. Data partners transform their data into a standardized format, based upon
which the MSCC will write a single analytical software program for a given safety
question and provide it to each of the data partners. Each partner will conduct
analyses behind its existing, secure firewall and send only summary results to the
MSCC for aggregation and further evaluation.
As this pilot is being implemented, a governance structure is being developed
to ensure the activity encourages broad collaboration within appropriate guidelines
for the conduct of public health surveillance activities. In order to accomplish that,
the MSCC is developing a Statement of Principles and Policies that will include
descriptions of the organizational structure and policies related to communication,
privacy, confidentiality, data usage, conflicts of interest, and intellectual property.
for the use of HIT to improve healthcare quality and reduce cost, as well
as the activities and aligment of current federal programs with relevant re-
sponsibilities. It sets out a series of recommendations intended to facilitate
private, entrepreneurial initiatives through governmental action to speed
development of a “universal exchange language” for health information,
the application of which would maximize the ability to use existing and
developing electronic record systems. Specifically, it recommends action
by the federal government, especially ONC and CMS, in accelerating the
identification of standards required for health information exchange using
metadata-tagged data elements; mapping various existing semantic taxono-
mies onto the tagged elements; developing incentives for product use of
tagged elements; fostering use of metadata for security and safety protocols;
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42 DIGITAL INFRASTRUCTURE FOR THE LEARNING HEALTH SYSTEM
Priority Action Targets
The case: Analyses to assess the potential returns on health and economic
dimensions. Because of the centrality of broad-based support to progress,
and the “public good” nature of many of the activities, the need to demon-
strate a value proposition or business case for participation by stakeholders
in a digital learning health system was a topic of much discussion during
the workshop series. This emphasis was reinforced by the approach taken
by the PCAST report to encourage the development of a market around
digital health information exchange. Support of methods that apply seri-
ous analytical rigor to these issues and generate both technical and policy
suggestions were identified as being crucial to this effort. Researchers and
organizations such as think tanks were discussed as likely being the best
positioned to undertake the necessary analyses with support of a commis-
sioning resource.
Involvement: Initiative on citizens, patients, and clinicians as active learning
stakeholders. Many workshop discussions considered stakeholder invest-
ment to be a necessary component of any successful strategy. Participants
identified the need to redefine the roles of citizens, patients, and clinicians
in a way that activated their participation in their own health, and the
health of the population at large, through the facilitative properties of the
digital infrastructure. It was noted that patient and clinician groups can
play a crucial role in this effort by helping to convey the value proposi-
tion and ensuring that the interests of their constituents are represented in
the development and evolution of the system. Efforts that facilitate stake-
holder participation—such as increased control of health information by
patients and the use of patient-generated data in care plans and knowledge
generating processes—were discussed as priority next steps in stakeholder
engagement. Additionally, to attend to concerns around privacy, security,
trust, and additional work burden, participants stressed the importance of
honesty and transparency in facilitating support and understanding. Ulti-
mately, discussions noted that demonstrating the value of a digital health
infrastructure through the use of case studies that point to improved out-
comes and efficiency was likely the most compelling strategy to appeal to
stakeholders.
Functionality standards: Consensus on standards for core functionalities—
care, quality, public health, and research. Progress on the technical stan-
dards necessary to support the core functionalities of the learning health
system was continually referenced in workshop discussions. Participants
focused on the standards necessary not only to improve, monitor, and guide
care decisions but also to accelerate research, quality efforts, patient moni-
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SYNOPSIS AND HIGHLIGHTS
toring, and health surveillance. Related requirements include the ability to
exchange information through the use of minimal standards (such as those
to enable use of metadata-tagged information packets), query and analyze
distributed repositories of data for research purposes, ensure care decision
support, and enable quality improvement initiatives and public health sur-
veillance and reporting. Discussions also touched on the need for the digital
infrastructure to interface with next-generation systems including mobile
health applications and the way in which these and other capacities could
help engage patients and the public through improved information access.
Participants also underscored the strategic importance of adhering to a
minimal set of standards that support core functions but do not introduce
unnecessary barriers to progress.
Interoperability: Stakeholder vehicle to accelerate exchange and interoper-
ability specifications. System interoperability remains a major obstacle to
realizing a digital learning health system. When applying the ULS system
lens to this challenge, many participants stressed the need to develop a
parsimonious set of standards—such as those for metadata—to allow for
practical interoperability and information exchange across systems. Noting
that this issue lies in the realm of both technical capacity and governance
structure, several participants often compared this effort to the evolution
and governance of the Internet. While the differences between the digital
health infrastructure and the Internet were acknowledged, it was suggested
that the establishment and work of the Internet Engineering Task Force
might provide guidance for an industrial institution for the governance of
interoperability-related standards. Additionally, leveraging and coordinat-
ing existing progress and ongoing efforts in the areas of standards develop-
ment and facilitation were underscored as strategies to ensure that activities
progress as efficiently as possible.
ULS system test bed: Identify opportunities, implications, and test beds
for ULS system approach. As discussions focused on the characteriza-
tion of the health system as a complex sociotechnical ecosystem, analysis
was suggested on how the ULS approach might be applied to the health
system in both the short and long term. Mapping of a key ULS system
report (Northrop et al., 2006) to the learning health system through a col-
laborative effort between software engineers, computer scientists, medical
informaticians, and clinicians was offered as a starting point for this effort.
Furthermore, performing a rigorous engineering systems analysis leading
to a concept paper was suggested to clarify further the opportunities and
implications for the ULS system approach. Integral to the ULS approach
is the need to support rapid prototyping for continuous innovation. It was
suggested that test beds for the development, assessment, and dissemination
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44 DIGITAL INFRASTRUCTURE FOR THE LEARNING HEALTH SYSTEM
of these prototypes would be central to continual innovation. In this vein,
several participants pointed to the opportunity presented by the creation
of the Center for Medicare & Medicaid Innovation (CMMI). Certain
communities of excellence already provide some capacity in this area, and
participants often referenced ongoing activities at these institutions (see
Appendix B).
Technical acceleration: Collaborative vehicle for computational scientists
and HIT community. Much of the work in the development of a digital
learning health system will necessitate interdisciplinary collaboration be-
tween academic, public, and private partners across the computer science,
HIT, science, and engineering communities. Participants suggested estab-
lishing a collaborative forum where these efforts can be initiated and de-
veloped. This forum could catalyze the interdisciplinary research program
necessary to develop the digital health infrastructure, and some participants
suggested that funding for such a forum and its associated activities might
best be served by collaborative efforts across relevant federal agencies (such
as NIH and NSF), relevant private sector partners, or both.
Quality measures: Consensus on embedded outcome-focused quality mea-
sures. Participants noted that the first step in determining the usefulness of
data collected by the digital health infrastructure is to identify the necessary
elements to collect. It was stated several times that in order to support the
quality improvement and research activities required for a learning system,
consensus around useful outcome-based measures is needed. Participants
suggested that this would motivate vendors and users to incorporate these
measures into their systems, driving seamless integration of quality mea-
surement and reporting into the digital infrastructure. Work at the NQF,
through the ONC HIT Policy Committee, and at CMS has already begun
addressing these needs.
Clinical research: Cooperative network to advance distributed research
capacity and core measures. Discussions often highlighted the centrality
of ongoing and continuous generation of knowledge from clinical data as
a central feature of the learning health system. Efforts to do research on
data held in distributed repositories, such as the HMO Research Network
and FDA’s Mini-Sentinel program, were pointed to as important early-stage
efforts in building systematic, larger scale capacity. Participants suggested
that a multidisciplinary, cooperative network of the relevant stakeholders—
principally computer scientists, clinical researchers, and data holders—
could be a starting point in accelerating progress in this dimension. It
was noted that this network would need to consider development of core
datasets to facilitate research and quality efforts, fostering consensus on
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SYNOPSIS AND HIGHLIGHTS
levels of consent and de-identification strategies necessary for effective re-
use of data, development of methodologies for query-based and automated
research and signal detection across distributed systems, development of
standards for distributed queries across the system, implications for a ULS
approach to existing and future distributed networks, and implications for
distributed research from possible advances in data structure and packaging
strategies for data interoperability and exchange across systems.
Identity resolution: Consortium to address patient identification across
the system. One of the major barriers discussed for several key system
functions—care appropriateness, continuity, quality assessment, and re-
search—relates to the current inability to reliably track and link individual
patients with their associated information across the health system. This
poses a problem for issues around care coordination, including the goal of
being able to make care decisions based on comprehensive health informa-
tion, as well as the development of a useful knowledge generation engine
that can incorporate all relevant information and deliver useful, accurate
support. Privacy and system security are paramount, but participants noted
that approaches are available to address these issues responsibly and the
barrier appears to be one of cultural hesitancy rather than a lack of tech-
nical capability. Targeting this issue through a consortium approach was
proposed as a way to provide the opportunity for stakeholder representa-
tion and engagement in an honest, transparent conversation about the
component value issues involved.
Governance and coordination: Determination and implementation of gov-
erning principles, priorities, system specifications, and cooperative strate-
gies. Workshop participants articulated the idea that governance principles
and priorities for a learning health system will require breaking new ground
both organizationally and functionally. Discussions identified the need to
improve coordination among key stakeholders to accelerate progress in
identifying and sharing lessons, examining commonalities, and exploiting
opportunities for efficiencies. It was noted that broad agreement will need
to be cooperatively marshaled to attend to principles and priorities that
support learning system functionalities such as data integrity, policies for
data use, human subjects research issues, and proprietary interests. In ad-
dition, discussions highlighted the role of governance in planning for and
mitigating system failures, an inevitable occurrence in all systems, but one
particularly well tolerated within the ULS system. Such failures would, of
course, be opportunities for learning, but are potentially alarming in the
context of health- and healthcare-associated information. An interdisciplin-
ary consortium of computer scientists and health infomaticians, such as
the one mentioned above, was suggested as a suitable place to engage this
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46 DIGITAL INFRASTRUCTURE FOR THE LEARNING HEALTH SYSTEM
issue on a technical level. However, addressing system failures in the health
system also has a deeply sociocultural component for which approaches
that emphasize honesty and transparency with patients and the public were
suggested. Education and outreach about this issue were identified as being
crucial in preventing irreparable tears in the trust fabric necessary to sup-
port a digital learning health system. In this respect, participants noted the
important contributions and potential of the Health IT Policy Committee’s
Governance Working Group. Discussions also underscored the potential
advantages of establishing a novel nongovernmental or public–private ven-
ture to foster the necessary governance capacity in this country and to work
with similar efforts internationally.
Opportunities in the Next Stages of Meaningful Use
In line with these priorities, discussions often focused on the ongoing
meaningful use requirement development process. Workshop participants
discussed the “beyond meaningful use” issue as key to increasing the utility
of digitally embedded clinical records in a learning health system. Specifi-
cally, since meaningful use is now such a well-established benchmark pro-
cess, elements of particular importance to the development of a learning
health system might not otherwise be addressed in the meaningful use pro-
cess if they are not called out for explicit attention in the upcoming stages.
Depicted in Box S-11 is a brief description of the meaningful use stages, the
current expected focus of the requirements for stages 2 and 3, and bullets
highlighting some key possibilities proposed by workshop participants.
Stage 2. Items that workshop participants felt were of particular importance
in enhancing the impact that stage 2 of meaningful use could have on the
progress of the digital learning health system cut across several dimen-
sions. Flagged as especially key were actions to accelerate standards for
semantic interoperability and exchange, as well as approaches for consistent
identification of patients. In order to further the utility of EHRs in clinical
research and population health, participants suggested core data elements
for EHRs, and seamless access to information from immunization registries.
Reflecting the extensive discussion on the opportunity for using the digital
infrastructure to better engage patients in their health care, participants
suggested the addition of lay-interpretable language for patient-accessible
information, and incorporation of patient-generated data. Finally, discus-
sions emphasized the need for clinical decision support to be seamlessly
integrated into HIT systems to speed adoption.
Stage 3. Looking ahead to stage 3 of meaningful use, workshop participants
suggested deepening the focus on requirements related to demonstrating
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SYNOPSIS AND HIGHLIGHTS
BOX S-11
Meaningful Use and the Digital Learning Health System
Infrastructure
STAGE 1: 2011–2012
Stage 1 of meaningful use established 14–15 (eligible hospitals or eligible
professionals) required core functional components, focused on data capture and
sharing, along with a menu set of 10 additional components, from which 5 are to
be selected by the eligible hospitals or eligible professionals.
STAGE 2: 2013–2014
Stage 2 of meaningful use is under development by the HIT Policy Commit-
tee, including consideration of further focus on advanced clinical processes such
as clinical decision support, disease management, patient access to health infor-
mation, quality measurement, research, public health, and interoperability across
IT systems. The following are items underscored in IOM discussions as being of
particular and immediate importance to the impact of stage 2 enhancements on
progress toward the Digital Infrastructure for the Learning Health System:
• ntegration of semantic interoperability and exchange standards, including
I
data provenance and context
• lements fostering seamless integration of clinical decision support
E
• se of lay-interpretable language for patient-accessible EHR information
U
• ncorporation of patient-generated data, including patient preferences
I
• nclusion of core data elements that facilitate use of EHR data for clinical
I
research.
• trategy for seamless access to immunization history from immunization
S
registries
• trategy for consistent identification of patients
S
STAGE 3: 2015+
Stage 3 of meaningful use is expected to expand on requirements from
stages 1 and 2, with more direct emphasis on improved patient outcomes through
sharpened focus on quality, safety, efficiency, population health, and interoperabil-
ity. Following are items, in addition to those noted above for stage 2, underscored
in IOM discussions as being of particular and immediate importance to the impact
of stage 3 enhancements on progress toward the Digital Infrastructure for the
Learning Health System:
• bility to access comprehensive, longitudinal patient record at point of
A
care
• ncorporation of patient editing ability
I
• emonstration of baseline semantic interoperability and exchange capacity
D
among IT systems
• ntegration of nonmedical, health-related information
I
• eamless clinician–public health agency exchange on case-level informa-
S
tion and alerts
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48 DIGITAL INFRASTRUCTURE FOR THE LEARNING HEALTH SYSTEM
semantic interoperability and exchange capacity among systems, the ability
to access comprehensive patient records at the point of care, and seamless
exchange of cases and alerts between clinicians and public health agen-
cies. Participants also suggested strategies for including additional types
of data—including nonmedical, health-related data—as well as providing
patients with an annotated editing ability over their own records.
Stakeholder Responsibilities and Opportunities
Throughout each workshop, frequent reference was made to leadership
responsibilities that fell naturally to individual stakeholders, or groups of
stakeholders, to advance progress in the development of the digital infra-
structure for the learning health system. In many cases, this involves lever-
aging ongoing efforts or building upon them with an orientation toward
a continuous learning system. Summarized below are some of those most
often noted.
Federal Government
Even though participants noted the decentralized manner in which
localized innovation is likely to contribute to system progress, many of
the central strategy elements and priority action targets discussed require
strong leadership from federal agencies. Since a clear lead responsibility was
given to ONC and the Secretary of HHS by the HITECH statute, many
participants pointed to ONC as the natural leadership locus for activities
needing coordination at the national level. Opportunities to build on the
foundation laid by the HITECH requirements for work on standards,
requirements, and certification criteria in meaningful use of EHRs include
cooperation with other federal agencies in the development of a strategic
plan for national HIT efforts; establishment of a governance mechanism
for the NWHIN; accelerating, in cooperation with the National Institute for
Standards and Technology, work on standards for exchange and interop-
erability; and work with FCC, FDA, and CMS to identify standards and
reconcile regulations to facilitate wireless transmission of medical informa-
tion. Participants noted that as the HITECH funds are used, the coordinat-
ing capacity of ONC will take on even greater importance, as coalitions
will be needed to harmonize various key activities geared at developing the
standards, policies, governance, and research projects necessary for effective
progress toward a learning health system.
With respect to technical innovation, as the leading federal agency
for funding computer science and engineering research, NSF was noted
as a logical locus to work with ONC and NIH in the development of test
beds for the rapid deployment and evaluation of innovative technological
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SYNOPSIS AND HIGHLIGHTS
approaches. This work would have the potential to transform the function-
ality and capacity of the digital health infrastructure, as well as to shepherd
the establishment of collaborative vehicles for the ongoing partnerships
between the HIT and computational science communities.
Similarly, it was noted that progress in the quality and knowledge
generation dimensions of the digital platform will require leadership from
federal health agencies. AHRQ, working with ONC, professional societies,
and groups such as NQF and the National Committee for Quality As-
surance, is a natural steward for initiatives that enhance the utility of the
digital infrastructure for quality improvement and health services research.
The CDC’s focus on population health places it at the center of extend-
ing the scope of the digital infrastructure beyond health care. This carries
implications for almost all elements of the system, but will be especially
important for the support of public health processes and research as well
as public engagement. To these ends, participants suggested development
of templates and protocols for the integration of nonmedical population
health and demographic information into the system.
As the nation’s largest healthcare financing organization, CMS cur-
rently serves as the principal vehicle for applying economic incentives and
standards to accelerate application of the meaningful use requirements.
Furthermore, much promise for future innovation in health IT to support
a learning system resides in the CMMI which provides an opportunity for
testing innovative approaches suggested by workshop participants. These
approaches include test beds for ULS-associated programs and new ap-
proaches to integrating clinical decision support with care coordination
and delivery models.
On the research front, both NIH and NSF have mandates and networks
to develop and demonstrate methods of improving the functionality of the
digital infrastructure for health research applications. NIH, VHA, DOD,
FDA, and AHRQ all have active programs under way that can evolve into
cooperative leadership efforts to expand the use of EHRs for research into
the clinical effectiveness of health interventions.
To build support and engagement among patients and the general
population, AHRQ, FDA, NIH, and ONC have each established links to
patient communities that can serve as the building blocks for a collabora-
tive initiative to better characterize and communicate the health and eco-
nomic advantages of public involvement in a digital platform for health
improvement.
Given this level of activity, and the number of central stakeholders,
the importance of ONC’s coordination mandate was often underscored.
Similarly emphasized was the need to cultivate strong counterpart
capacity outside of government to partner in coordination and governance
responsibilities.
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50 DIGITAL INFRASTRUCTURE FOR THE LEARNING HEALTH SYSTEM
State and Local Government Leadership
Given the regional emphasis of many of the ongoing efforts related to
the digital learning health system—such as the establishment of regional
health information exchanges—state and local governments and health
departments have experience establishing governance structures and devel-
oping programs for engaging local stakeholders. As a result, participants
noted, state and local bodies can function as resources and foundation
stones for broader efforts. By collaborating with ONC, CMS, HRSA, and
other federal initiatives, best practices and lessons learned can be leveraged
from state and local efforts. Additionally, it was suggested that some of the
more advanced local initiatives could serve as test beds for some of the in-
novative ULS-associated approaches suggested by participants.
Initiatives Outside Government
Outside of government, the entrepreneurial capacity of the commercial
sector will certainly be a major driver of progress. Similarly, the full po-
tential of the learning health system can only be achieved through the full
engagement of patients and the public. Workshop discussants frequently
underscored the roles of patient and clinician groups to facilitate dialogue
between stakeholders and mediate public engagement. In particular, by
using case studies to demonstrate the value of the digital infrastructure,
participants felt these organizations could help develop the shared learn-
ing culture and trust necessary for the learning system to function. Many
patient and clinician groups—such as the American College of Physicians,
the American College of Cardiology, the Society of Thoracic Surgeons, and
the National Partnership for Women and Families—are already involved in
this type of work. Participants noted that these existing activities could be
expanded to include issues of particular importance to a learning system.
Delivery systems, particularly those integrated across healthcare com-
ponents, have been at the cutting edge of innovative EHR use, quality
improvement, clinical data stewardship, patient engagement, quality ini-
tiatives, and distributed research efforts. Workshop conversations often
pointed to these efforts, such as those at Kaiser Permanente and Geisinger
Health System, suggesting that continued coordination between these deliv-
ery systems and relevant federal government agencies would be important
in growing the digital health infrastructure.
As the stewards of the largest stores of clinical and transactional in-
formation outside of the federal government, insurers, payers, and product
developers have an essential role to play in development of the digital
infrastructure. Their use of transactional health data to assess utilization
patterns, effectiveness, and efficiency is a foundational block on which
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SYNOPSIS AND HIGHLIGHTS
strategies for broader knowledge generation can build. Furthermore, com-
panies such as UnitedHealthcare have begun engaging the public in the use
of data in health. These efforts often were cited during discussions as crucial
first steps in establishing a learning culture.
Research is a fundamental aspect of the learning health system. Con-
sequently, participants noted the fundamental role researchers have in de-
veloping the infrastructure necessary for continuous knowledge generation
and application. Formation of multidisciplinary research communities was
often cited as a critical step in accelerating many of the strategies discussed.
Funding for these communities was noted as a clear opportunity for col-
laboration between NSF and NIH. Additionally, discussions highlighted
that much work remains to be done in order to maximize the knowledge
generation capabilities of the digital infrastructure, and that clinical re-
search and product development communities have an essential role in
building this capacity.
As much of the progress to date is a result of initiatives from many
independent organizations, their continued efforts as facilitators and inno-
vators were noted as crucial to accelerating progress. Reference was often
made to the importance of these organizations as the foundational elements
for coordination and governance leadership from outside government.
Finally, and ultimately of paramount importance, is the global perspec-
tive. As highlighted during workshop discussions and presentations (see
Chapter 8), meeting the goals of a learning health system will inevitably
require drawing upon resources and leadership of similar efforts through-
out the world. Some of this activity has begun in the limited arena of infec-
tious disease surveillance and monitoring and offers a hint of the potential
opportunities—and challenges—in developing a truly global clinical data
utility for health progress.
Collectively, the discussions captured in this publication represent un-
precedented promise for innovation and progress in health and health care.
Yet, the discussions also underscored that without successful efforts to cre-
ate the conditions necessary for seamless interoperability, to create the pro-
tocols for enhanced access and use of available information for knowledge
generation, and to build the culture of engagement and support on behalf
of the sort of information utility possible, the potential will go unmet. By
thoroughly and candidly engaging in discussions on the vision, the current
state of the system, the key priorities for future work, and the strategic ele-
ments for accelerating progress, participants have set in motion perspectives
that can quicken the progress in building the digital infrastructure required
for the continuously learning health system necessary—and possible—to
ensure better health for all.
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52 DIGITAL INFRASTRUCTURE FOR THE LEARNING HEALTH SYSTEM
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