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1
Introduction
Digital health data are the lifeblood of a continuous learning health
system. A steady flow of reliable data is necessary to coordinate and moni-
tor patient care, analyze and improve systems of care, conduct research to
develop new products and approaches, assess the effectiveness of medical
interventions, and advance population health. The totality of available
health data is a crucial resource that should be considered an invaluable
public asset in the pursuit of better care, improved health, and lower health
care costs (IOM, 2012). This publication summarizes discussions at the
March 2012 Institute of Medicine (IOM) workshop to identify and char-
acterize the current deficiencies in the reliability, availability, and usability
of digital health data and consider strategies, priorities, and responsibilities
to address such deficiencies.1
The ability to collect, share, and use digital health data is rapidly evolv-
ing. Increasing adoption of electronic health records (EHRs) is being driven
by the implementation of the Health Information Technology for Economic
and Clinical Health (HITECH) Act, which pays hospitals and individuals
incentives if they can demonstrate that they use EHRs in a meaningful way.
However, although more than half of office-based physicians were using
basic EHRs in 2011, only a third had access to the basic features necessary
1 The planning committee’s role was limited to planning the workshop, and the workshop
summary has been prepared by the workshop rapporteurs as a factual summary of what
occurred at the workshop. Statements, recommendations, and opinions expressed are those
of individual presenters and participants, and are not necessarily endorsed or verified by the
Institute of Medicine, and they should not be construed as reflecting any group consensus.
1
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2 DIGITAL DATA IMPROVEMENT PRIORITIES
to leverage this information for improvement, such as the ability to view
laboratory results, maintain problem lists, or manage prescription ordering,
(Decker et al., 2012).
In addition to increased data collection, more organizations are sharing
digital health data. Data collected to meet federal reporting requirements
or for administrative purposes are becoming more accessible. Efforts such
as Health.Data.gov provide access to government datasets for the devel-
opment of insights and software applications with the goal of improving
health. Within the private sector, at least one pharmaceutical company is
actively exploring release of some of its clinical trial data for research by
others.2 Data sharing partnerships are also opening up across organiza-
tions. The Care Connectivity Consortium, a group of five health systems at
the leading edge of using EHRs (Kaiser Permanente, Geisinger Health Sys-
tem, Mayo Clinic, Intermoutain Healthcare, and Group Health Coopera-
tive), have agreed to securely exchange clinical data for care coordination.
Sharing is also happening across industries. In the case of AstraZeneca and
WellPoint, a payer and a product manufacturer have initiated a study on the
clinical and cost effectiveness of treatments for some chronic and common
diseases. Finally, efforts to increase patient access to their own data, such
as the Blue Button initiative which allows patients to download their health
information with the click of a button, have been adopted by organizations
such as the Veterans Health Administration and UnitedHealthcare, and
included in the criteria for Meaningful Use.
The increased collection and sharing of health data is quickly moving
health care into the era of “big data.” This term refers to the huge volume
and diversity of data collected in increasingly connected digital technolo-
gies. The scale of “big data” has implications for analysis and learning in
a way that has been leveraged by other industries, such as intelligence, but
is only beginning in health care.
Increasing collection, sharing, and aggregation of data are being
matched by advances in methods for learning from these data. Clinical
and administrative data can be used for studies to assess the effectiveness
of health care interventions; identify product safety issues; detect emerging
epidemics; and measure health care utilization and value. Observational
methods that use data collected in the course of providing patient care are
increasingly appreciated as valuable contributors to generating and testing
hypotheses. The rapidly rising costs and extended duration of traditional
randomized control trials (RCTs) have contributed to the interests of inves-
tigators and funders, among whom there is a growing appreciation of the
need to harness big data for innovative streamlined approaches to testing
new interventions.
2 Personal communication, Joel Beetsch, Sanofi.
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INTRODUCTION 3
Crucial to all of these efforts is the appropriate alignment of data
sources with their intended use. Different uses have different requirements
of data, and therefore different priorities in terms of the evolving clinical
data utility. This challenge is magnified by the lack of lessons and best
practices for how to approach data quality assurances needed to support
the multiple facets of a learning health system. To address these issues and
gain a better understanding of the types, sources, applications, limitations,
appropriate uses, and quality improvement needs for digital health data,
the IOM’s Roundtable on Value & Science-Driven Health Care convened
a meeting on March 23, 2012, titled Digital Data Priorities for Continu-
ous Learning in Health and Health Care. This meeting followed a series
of related discussions summarized in the IOM publication titled Digital
Infrastructure for the Learning Health System (2011), and built on a body
of work done by the Roundtable on the centrality of a clinical data utility
to support continuous learning and improvement in health and health care
(IOM, 2010, 2011a,b,c).
DATA SOURCES IN THE DIGITAL HEALTH UTILITY
Digital health data are produced in a variety of different environments,
which impact greatly the characteristics of the data. Who collects the data,
how it is collected, why it is collected, and what is collected are some of
the ways that digital health data differ depending on their source and have
implications for the use of that data. Understanding these characteristics is
necessary to match data users with appropriate sources, and to understand
limitations and barriers in data analysis.
The increased adoption of EHRs has given data from routine care
increasing prominence as a potential component of the data utility. Data
collected in the course of delivering patient care come from a variety of
sources such as clinician offices, ambulatory procedure centers, hospitals,
and nursing and extended care facilities. The types of data vary by care
setting, but generally include both clinical and administrative elements.
Clinical elements include structured fields and free text notes, laboratory
results, images, and diagnostic test results. Administrative information in-
cludes process performance metrics, and details needed for billing, such as
International Classification of Diseases (ICD) codes.
Also growing in importance is data originating directly from patients.
These data can be captured through the use of personal health records or
patient portals, in clinical records as recorded by healthcare personnel,
or in records external to the health system. They can include personal re-
ports of current health status and wellness, family history, and remote site
laboratory readings, as well as health-related data such as socioeconomic,
environmental, and lifestyle factors. There is increasing interest in including
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4 DIGITAL DATA IMPROVEMENT PRIORITIES
patient-generated data in data sources such as the use of patient-reported
outcomes in research studies.
Ongoing and completed clinical trial data offer yet another major
source of new data insights—even beyond the immediate study focus. Trials
funded by public or private sponsors are largely carried out either at aca-
demic institutions or in community settings. Clinical trial data are typically
collected in addition to those already collected in routine care, usually fol-
low a standardized protocol, and are recorded in a case report form. When
the trial is performed for the approval or assessment of a regulated medical
product such as a new drug or device, the Food and Drug Administration
(FDA) closely regulates how and what data are collected. In addition to
traditional clinical trials, registries for quality activities, research, or post-
marketing surveillance are a parallel source of enriched clinical data.
Employers, as the purchasers of health insurance for much of the
population, often possess data on employees’ health care utilization, basic
health status, and associated expenses which can be used for knowledge
development. In addition, these data can have greater longitudinal richness
than records from clinical care providers.
A final source of health data is population health data routinely col-
lected through the public health system and its surveys and surveillance
activities. These data provide information on overall health trends, such
as births and deaths, disease prevalence, community health, environmental
health, and access to care, as well as disease incidence and threat data. The
collection and reporting of this information is increasingly digital, either
through freestanding systems and portals or as integrated parts of EHR
systems. Given the many levels at which public health works—local, county,
state, and federal—different data collection approaches and requirements
exist at the different levels. Other health-related community level data are
routinely collected by various organizations, departments, and agencies.
This includes community socioeconomic status profile data; community
physical profile data, such as density, design, and use; civic engagement
profiles; and community employer profile. Additionally, organizations col-
lect data on individuals through their commercial and social activities, such
as through supermarket rewards programs and Web-surfing patterns. These
data are used not only on their own, for insights into the community, but
in concert with other health data to yield a more complete understanding
of population health.
MOVING TO A CONTINUOUSLY LEARNING HEALTH SYSTEM
Although the collection of large amounts of health and health-related
data holds promise for both the scale and types of learning possible, data
alone are not sufficient for learning. Sharing, aggregation, analysis, and the
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INTRODUCTION 5
continuous management and improvement of these data are necessary to
enable the transition to a continuously learning health system.
The applications of digital health data in a learning system are multiple,
including care coordination; management of patient populations; associated
care and business processes; outcome, quality, and value assessments; gen-
eration of clinical evidence, including clinical trials, clinical effectiveness,
and genomic studies; surveillance and trend detection, including medical
products safety, syndromic and actionable surveillance, and hypothesis
generation; and public health program management. These differing uses
vary in their requirements for data quality and characteristics, but all share
common challenges related to data access, liquidity, interoperability, and
the development of innovative methods for analysis. These issues formed
the foundation for the presentations and discussions at the IOM public
workshop on Digital Data Priorities for Continuous Learning in Health
and Health Care.
WORKSHOP SCOPE AND OBJECTIVES
Workshop participants included experts from across medicine, public
health, informatics, health information technology, health care services
research, health care quality reporting, biomedical research, clinical re-
search, statistics, medical product manufacturing, health care payment
and financing, and patient advocacy. Content was structured to explore
the data quality challenges and opportunities in a learning health system,
highlighting the opportunities and priorities beyond care coordination such
as population and care process management, clinical research, translational
informatics, and public health support at the national and state level. The
workshop also explored the potential for learning from large-scale health
datasets, focusing on innovative approaches to overcoming the challenges
of distributed data, data harmonization, and identity resolution.
The workshop statement of task can be found in Box 1-1, and the ele-
ments are reflected in the stated meeting objectives:
1. Discuss the current quality status of digital health data.
2. Explore challenges, and identify key questions related to data qual-
ity in the use of EHRs, patient registries, administrative data, and
public health sources for learning—continuous and episodic—and
for system operational and improvement purposes.
3. Engage individuals and organizations leading the way in improv-
ing the reliability, availability, and usability of digital health data
for real-time knowledge generation and health improvement in a
continuously learning health system.
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6 DIGITAL DATA IMPROVEMENT PRIORITIES
BOX 1-1
Statement of Task
An ad hoc committee will plan and conduct a public workshop to explore
the data quality issues and strategies central to the increasing capture and
use of digital clinical and patient-reported data for knowledge development. The
workshop will engage leading experts in reviewing the challenges, defining key
questions, and exploring a strategic framework for progress on the issue of health
data quality in a learning health system. Questions/topics of consideration could
include What are the data quality requirements to support the various knowledge
generation processes required by the learning health system (quality monitoring,
sentinel event detection, disease surveillance, clinical research)? What is known
about the current state of digital health data quality? What implications does this
have for short term uses? What analytical methods are available to assess data
quality? What novel analytical methods will need to be developed in order to meet
learning health system data-use needs? What are the essential components of
a strategy to achieve the necessary data quality levels? What lessons have been
learned by those organizations already undertaking learning health system–type
efforts? What foundational work has been done that can be built on/leveraged to
better meet learning health system data quality needs?
4. Identify and characterize the current deficiencies and consider strat-
egies, priorities, and responsibilities to address the deficiencies.
5. Initiate the development of a strategic framework for integrated
and networked stewardship of efforts to continuously increase
digital data utility.
Through a series of expert presentations and discussions, workshop
participants addressed issues of matching data quality to use, how these
needs align with current data sources, and what the potential and chal-
lenges are for leveraging digital health data for learning—both the short
and long term. The final workshop session included a moderated discussion
geared toward describing ways forward on the issues highlighted earlier in
the workshop.
ORGANIZATION OF THE SUMMARY
This publication summarizes the proceedings of the workshop on Digi-
tal Data Priorities for Continuous Learning in Health and Health Care, the
12th in the Learning Health System Series of publications by the Roundtable
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INTRODUCTION 7
on Value & Science-Driven Health Care. Chapters 2 through 5 summarize
the expert presentations at the workshop, and are organized by thematic
focus on the presentations, while Chapter 6 covers the concluding discus-
sion. Chapter 2 addresses data quality challenges and opportunities in a
learning health system, including explorations of data heterogeneity and the
importance of focusing on data of value to the patient. Chapter 3 focuses
on the many uses of the digital health data utility, covering the management
of patient populations, clinical research, translational informatics, and both
national and local public health efforts. Chapter 4 looks at emerging issues
and opportunities in the use of large datasets, including a discussion of the
challenge of data bias, and recent advances in mathematics that promise
to move research toward generating real time insights. Chapter 5 explores
emerging innovations in the use of digital health data including distributed
queries, data normalization, and data linkages. Chapter 6 summarizes the
concluding discussion in which many workshop participants suggested
potential strategies and actions to catalyze progress.
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IOM (Institute of Medicine). 2010. Redesigning the clinical effectiveness research paradigm:
Innovation and practice-based approaches. Washington, DC: The National Academies
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_______. 2011a. Clinical data as the basic staple for health learning: Creating and protecting
a public good. Washington, DC: The National Academies Press.
_______. 2011b. Digital infrastructure for the learning health system: The road to continuous
improvement in health and health care. Washington, DC: The National Academies Press.
_______. 2011c. Learning what works: Infrastructure required for comparative effectiveness
research. Washington, DC: The National Academies Press.
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