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7
Perspectives on Innovation
INTRODUCTION
Health information technology (HIT) is a rapidly developing field pro-
pelled by continual innovation. Participants in this session were invited
to give informal remarks based on their observations of the workshop as
well as personal experiences. These comments are summarized briefly in
this chapter. The papers point to the need for novel approaches to the ag-
gregation of health data to improve population health, offer observations
on challenges and opportunities given the current state of HIT, and provide
perspective on the opportunities afforded by the vast quantities of health and
health-related information collected by individuals and available on the web.
Drawing from the assertion that population health is more than the
aggregation of individual disease and, therefore, an understanding that
population health cannot simply be gleaned by aggregating patient care
data, Population and Public Health Information Services’ Daniel Friedman
advocates for the creation of a U.S. population health record. He empha-
sizes that while the United States has large amounts of publicly accessible
population-level disease-related data, challenges for population health in-
clude a lack of that same level of granularity for functional status and
well-being as well as problems of data integration and integrity. In order
to address these issues he proposes the establishment of a single source of
population health data backed by an overarching data model and theoreti-
cal framework. Data would be drawn from a number of different sources
including those not typically integrated with clinical data, such as environ-
mental sampling and census data.
185
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186 DIGITAL INFRASTRUCTURE FOR THE LEARNING HEALTH SYSTEM
In her remarks Molly Coye, formerly from the Public Health Institute
(now the University of California, Los Angeles), identifies what she sees as
three areas of opportunity for HIT innovation. Citing the need to improve
the current state of clinical decision support she suggests areas where inno-
vation could help meet this goal: how to recognize and deal with incorrect
or missing data, integration of a single patient’s data from multiple sources,
and how to turn data into clinical guidance. Dr. Coye cites the need for re-
search to be integrated into care processes and for evidence generated to be
fed back in a continuous, seamless process that supports informed, shared
decision making. Lastly, she points to the movement of healthcare delivery
to integrated models—such as accountable care organizations—which in-
crease the need for remote data collection, diagnosis, consultation, and
treatment. Dr. Coye concludes by stressing that many of these challenges are
social rather than technical in nature, and therefore successful approaches
will need to take into account the complex character of these systems.
The growing prevalence of personal information ecologies provides
the context for the remarks made by the Institute of the Future’s Michael
Liebhold. He notes that these ecologies are composed of digital artifacts not
only related to health and fitness, but also to social activities, media use,
and even civic life. Mr. Liebhold observes that citizens are ready and willing
to collect and share their health information and, with the encouragement
of industry and employers, to become more actively involved in their own
health. However, effectively integrating information from all of these sources
in a meaningful way presents a formidable challenge. Technologies such as
those that underlie the semantic web hold much promise, but still face chal-
lenges, especially in the areas of privacy and security. Looking to the future,
Mr. Leibhold notes the need for methods to curate web-based health infor-
mation, for interoperable health app stores, and for the development of a
web of linked, open healthcare information and knowledge interoperability.
CONCEPTUALIzING A U.S. POPULATION HEALTH RECORD
Daniel J. Friedman, Ph.D.
Population and Public Health Information Services
This paper presents a concept for a U.S. population health record
(PopHR), an idea initially presented in a recent article coauthored with
Gib Parrish (Friedman and Parrish, 2010). Before presenting the concept
of a PopHR, it is necessary to define population health. Our definition is
the level and distribution of disease, functional status, and well-being of a
population. This definition focuses on (a) functional status and well-being
as well as disease; and (b) the level and distribution of each, allowing for
knowledge of disparities and equity.
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PERSPECTIVES ON INNOVATION
Based on this definition, it becomes equally important to be explicit
about what constitutes a population. We use the definition: all of the inhab-
itants of a given country or area taken together. In this definition, an area
can be a province, state, neighborhood, city, or town and can include groups
within the overall geography such as demographically bounded groups.
Health care is just one of many influences on population health. Influ-
ences such as the context of the population (natural environment, cultural
context, political context) and community attributes (social and collective
lifestyles, the environment, economic structures, education) all have a bear-
ing on the health of a population. Simply put, population health is more
than the aggregation of individual disease. As a result, the aggregation of
patient care data provides only an incomplete understanding of population
health.
Healthcare Data and Population Health
The United States has many blessings when it comes to population
health data. We have rich disease-level data which allow researchers to look
at causes of death, birth rates, and cancer prevalence down to the census
track. However, we also have some burdens—what you cannot see at the
local level is functional status and well-being. The level of granularity we
have for causes of death does not exist for depression, disabling lower-back
injuries, etc.
We are also blessed by a large amount of publicly accessible, web-
based population-level disease data. Currently, roughly 28 states have
web-based systems that provide public access to population health data.
These systems vary in quality, but some are quite exceptional—employing
sophisticated statistics and providing access to two dozen or more data-
sets. Additionally, the Department of Health and Human Services (HHS)
has roughly two dozen web-based population health data systems that are
publicly accessible. With so many different websites, datasets are often
duplicative, resulting in different definitions for statistical measures or
different definitions for the same variables.
The Population Health Record
These burdens could all be solved—not to mention the current benefits
enhanced—if there was a single easily accessible source with an overarch-
ing data model and theoretical framework. This is the motivation behind a
PopHR. The PopHR focuses on populations, not on individuals; it focuses
on population health as defined above; and it focuses on the influences on
population health enumerated above. Thus, we define PopHR as a reposi-
tory of statistics, measures, and indicators regarding the state of and influ-
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188 DIGITAL INFRASTRUCTURE FOR THE LEARNING HEALTH SYSTEM
ences on the health of a defined population, in computer-processable form,
stored and transmitted securely, and accessible by multiple authorized users.
Framework
Successfully developing a PopHR will require an explicit population
health framework that includes a schematic representation of factors that
will potentially influence population health. There are many different ver-
sions of this type of framework, with an example shown in Figure 7-1.
Building the model around population health and not the individual health
of members of the population will remedy gaps in our current knowledge
such as functional status and well-being.
Information Model and Content
A logical and agreed upon information model will also be necessary
for achieving a PopHR. As opposed to an individualized population health
records system absent standards, adopting a standardized and agreed upon
information model will reduce the burden of overlapping and inconsis-
FIGURE 7-1 Influences on population health.
SOURCE: Friedman et al. (2005).
Figure 7-1.eps
bitmap
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PERSPECTIVES ON INNOVATION
Dataset that is a source of content for the PopHR
FIGURE 7-2 PopHR and PopHR system showing collection, processing, and re-
trieval of information content from a PopHR.
SOURCE: Friedman and Parrish (2010).
tently defined variables. A good example of such a model is the Australian
Institute for Health and Welfare National Data Model and its more recent
metadata directory called Meteor.1 Itnew.eps
Figure 7-2 is also important to consider infor-
redrawn as vector from original cited source
mation content. The PopHR would need to include information on health,
and the determinants ofis outline rather than real type
NB: type health, from existing data sources such as ongoing
population surveys; public health surveillance systems; environmental sam-
pling; Medicare claims; and population census. These data sources could
be either geographically or individually based, but would be aggregated to
the population level in the PopHR—a process enabled by a standardized
information model.
Conceptual Model
Figure 7-2 presents a conceptual model of a PopHR. Data for the popu-
lation are collected using various methods—surveys, environmental moni-
toring, and abstraction of health records—and compiled and processed to
form a population dataset. The dataset is then analyzed to produce a set of
population health measures which are stored in the PopHR for later retrieval.
To increase retrieval efficiency and speed, the PopHR system might use in-
termediate datasets in which one or more large datasets would be reduced in
size by either selectively removing infrequently used data elements to form
1 See www.aihw.gov.au/publications/hwi/nhimv2 (accessed March 2, 2011).
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190 DIGITAL INFRASTRUCTURE FOR THE LEARNING HEALTH SYSTEM
TABLE 7-1 Possible System Architectures for a Population Health Record
(PopHR)
Model Information Storage Information Retrieval
1 Centralized Centralized
2 Distributed Centralized
3 Centralized Distributed
4 Distributed Distributed
SOURCE: Friedman and Parrish (2010).
an “abstracted dataset,” or pre-tabulating and indexing the dataset on fre-
quently retrieved data elements. In response to a user query of the PopHR,
the PopHR system would retrieve information from either the PopHR with
precalculated measures (a standard PopHR information retrieval), or one
or more primary or intermediate datasets (an “on the fly” PopHR informa-
tion retrieval). For some queries a combination of standard and “on the
fly” retrievals might be necessary. The retrieved information would then be
synthesized into a response and communicated to the user via the Internet.
Implementation
There are various types of system architectures that could be employed
in a PopHR system (Table 7-1). In order to successfully implement a PopHR
it will be useful to start with the most practical model, and then build to the
nimblest and most versatile. In the near term (1 to 5 years) efforts should
focus on developing a population health framework and logical information
model as well as implementing Model 1 with core functionalities. Doing
so will require leveraging the existing HHS web-based query systems. Ef-
forts should be made to inventory the existing work and develop a logical
information model and metadata directory for these datasets.
As time progresses, efforts can be made to shift to more advanced
models—focusing first on developing and implementing Model 3 with core
and enhanced functionalities, and then on doing the same for Model 4.
ACCELERATING INNOVATION OUTSIDE THE PRIVATE SECTOR
Molly J. Coye, M.D., M.P.H.
Public Health Institute (formerly)
University of California, Los Angeles
Currently there is tremendous innovation going on in the private sector,
but we could be doing more to foster innovation in the public sector and
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PERSPECTIVES ON INNOVATION
at academic centers. This paper will address three challenges—which are
not so much daunting as exciting—facing the healthcare system that can
become loci for innovative projects. The first is decision support. Currently,
we are very far away from the goal of not just producing an array of data
but actually producing information that leads to change in the behavior of
clinicians and patients. The second concerns consolidating a health infor-
mation technology (HIT)-supported national knowledge base with parallel
efforts in effectiveness research. This too is far off, but the Patient-Centered
Outcomes Research Institute has the potential to help drive innovation.
Finally, we will be undertaking these efforts to build the digital infrastruc-
ture amidst pivotal transformations in delivery of health care. While this
certainly provides opportunities, we must also remain conscious of the
context of the Patient Protection and Affordable Care Act (ACA) if these
efforts are to be successful.
Decision Support
Decision support centers are turning healthcare data into healthcare
information. The limitations of our current decision support systems have
been very well described: they are klugy, physician-centric, and many phy-
sicians resist using them—often for good reason. Innovation in decision
support will need to move through three stages: avoiding bad decisions
caused by faulty or nonexistent data, integrating streams of data to pro-
vide optimally accurate and specific data, and supporting better decisions
with clinical guidance. Many organizations are actively involved in work
on all of these stages, with considerable progress being made on the first.
However, the second—to have data about the same patient coming from
multiple locations so that decisions are based on the most accurate and
specific data—is proving more elusive and will likely remain a challenge
for some time. While the meaningful use rules are encouraging progress,
the third stage will require that every point-of-care decision is informed not
by data, but by clinical guidance—again, turning data into information.
In order to activate innovation in decision support we need to do more
to stimulate the development of small, close-to-the-ground decision support
tools that will actually be used by physicians. To achieve this, it is necessary
to develop explicit clinical performance benchmarks in consultation with
physicians. Furthermore, it will be necessary to collaborate in design with
employers and health plans to ensure that there is a business case for use.
Unless providers who use these systems are rewarded for doing so, wide-
spread adoption will be hard to achieve.
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192 DIGITAL INFRASTRUCTURE FOR THE LEARNING HEALTH SYSTEM
Comparative Effectiveness Research
Attempting to integrate HIT and comparative effectiveness research
carries with it the unfortunate consequence of creating disillusionment. If
we are able to build transparency about how little evidence base there is for
much of our decisions into clinical decision support—and are transparent to
patients on this risk, too—we risk considerable disillusionment. This could
be ameliorated by integrating clinical research with the care encounter. Bill
Press has outlined a possible approach to this situation (Press, 2009). In his
model, when a patient comes to see a physician, the quality of evidence for
different diagnostic and therapeutic options is arrayed as probabilities—the
probability the diagnostic option will reveal, or the therapeutic option will
resolve, the problem at hand. When the patient makes a decision—which,
it should be noted, will be a shared and informed decision—the clinical en-
counter becomes part of a rolling clinical trial. As a result, the probabilities
evolve as the results of individual encounters and treatments are recorded
and reported. The result is a learning system, where evidence is continu-
ally generated and refined, and then fed back to clinicians and patients to
promote informed, shared decision making.
The level of patient-fostered engagement in this approach is crucial to
promote innovation. If patients can be convinced of the benefits in such a
system they will not only be eager to participate, but will begin to demand
such capabilities from the healthcare system. With consumer demand we
might be able to accelerate work on the technical, political, social, and
economic dimensions of facilitating the rich exchange of data necessary to
enable such a system. This is an area of opportunity for academic medical
centers (because of informatics resources) and large medical groups (be-
cause of capitated care and large databases) to design closed-loop learning
systems that continually utilize data to evolve clinical understanding. Devel-
oping and refining this concept in small cases will begin to demonstrate the
utility to the general public, stimulating larger efforts.
Remote Models of Care
The third challenge facing health care is the reconfiguration of the
health delivery system toward integrated care models (such as accountable
care organizations) as a result of ACA. One of the defining characteristics
of these new delivery system models will be the remote nature of care. Func-
tions, not just data, will be liberated and redistributed. Furthermore, we
will likely see the rise of long-distance—or remote—diagnosis, consultation,
and treatment. This will require advanced health information exchange be-
tween and among organizations. The evolution will be a fluid process, but
it will also be rough. Considerable time and resources are being invested
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PERSPECTIVES ON INNOVATION
in the idea of distributed health information exchange, but this issue will
continue to be a continual source of difficulty.
The challenge will not be so much technical, as it will be political and
economic. Consequently, the Office of the National Coordinator for Health
Information Technology (ONC) should partner with the most advanced
systems using telemedicine, tele-ICU, tele-emergency and telehealth tech-
nologies to understand how the structures, regulations, and processes that
we are setting up now facilitate, or complicate, delivering networked care.
Conclusion
Addressing these challenges will test the limits of data integration with
electronic health records that live inside separate enterprises and support
learning and the dissemination of principles gleaned from data exchange.
Ultimately, successful approaches to these challenges will emerge from treat-
ing them as complex systems. Solutions will not involve rules and laws, but
will be centered on processes for solving complex and evolving problems.
COMBINATORIAL INNOVATION IN HEALTH
INFORMATION TECHNOLOGY
Michael Liebhold
Institute for the Future
The topic of this paper—combinatorial innovation—comes from a
concept introduced by Google chief economist Hal Varian. He postulates
that there is currently enough innovation available such that we do not
have to invent anything new to create disruption. This paper will begin by
addressing many elements that already exist today, but that in combination
can be disruptive, and then move on to a discussion of work going on at the
Institute for the Future as well as some priorities moving forward.
Capturing Personal Health Data
Discussions on the digital infrastructure for a learning health system
tend to focus on clinical information ecologies and the notion of stan-
dardized and interoperable electronic health records (EHRs). Much at-
tention, not to mention recent legislation, concerns the linkage between
evidence-based science and an interoperable EHR, but this is really only
half the picture. Something that is commonly ignored is personal informa-
tion ecologies.
Citizens are constantly creating digital artifacts. These are not just
health and fitness related, but come from their social life, shopping, media
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194 DIGITAL INFRASTRUCTURE FOR THE LEARNING HEALTH SYSTEM
use, vocational activities, and civic life. There is enormous interest—not
just in the healthcare community but across communities—in managing
these digital artifacts. Doing so will necessitate a holistic program which,
fortunately, has been acknowledged by the current administration. The re-
cently released a multiagency recommendation for a national identity and
security2 that advocates for a federated model for identity management as
an important step in harnessing the potential of these data.
When discussing patient engagement in health information technology,
many argue that only a minority of people will collect their data and want
to use it in their personal health record. Our indicators suggest that this
is not going to be the case. In the Silicon Valley, leading companies like
Intel, Cisco, Google, and others are providing real incentives for people
to get involved with their own data—body mass index, blood pressure,
cholesterol—and take control over their own health. This is viewed as a
corporate health issue, making it reasonable to assume that it can spread
to populations at large.
There are also growing stores of health-related information that many
people do not normally consider. For example, since many people now
carry GPS devices in their pocket, we can mine those data and forecast
kinds of behaviors and activities in particular locations. Furthermore, some
individuals are beginning to wear sensors—not just for their health but for
fitness. In fact, there is a lot of new research in the area of using mobile
devices as hubs for a wearable network of sensors.
Making Sense of Captured Data
All of these new technologies generate a surplus of information. We do
not need all of the information, just the right information. Consequently,
we need to combine or orchestrate information, devices, and infrastructure
on a continual, real-time basis to deliver the right information to the pa-
tient/clinician at the right time. Fortunately, we are well on our way to do-
ing so. In online social networks, we are seeing the rise of social graphs—a
schematic that visualizes the kinds of linkages and relationships between
people on a dynamic and real-time basis. This technique is based on a very
common semantic web framework called Resource Description Framework
(RDF), a simple grammar for describing relationships in terms of the sub-
ject, predicate, and object.
RDF is also the basis for almost all semantic web applications used
for health information exchange. Soon, we will have a population of 500
million people who have a semantic web description of their relationships,
2See http://www.whitehouse.gov/blog/2010/06/25/national-strategy-trusted-identities-
cyberspace (accessed March 3, 2010).
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PERSPECTIVES ON INNOVATION
opening up unprecedented opportunities for data mining and sophisticated
inference on a real-time and continuous basis. Of course there are problems
with privacy and security if you put your data out there in a universal in-
frastructure—and there is a lot of work to be done on that front—but the
opportunity is immense.
Institute for the Future
In this new climate there are several major contact points that need to
be kept in mind: the relationship of our personal health information and the
public health commons, the relationship of our personal information and
contextual health information, the relationship of our clinical information
and contextual health, and the relationship of the scientific evidence base
with the clinical information. All of these pairs of relationships have to be
explored as a coherent system, and at the Institute for the Future we are
looking at what can be achieved with massive computing capability and an
abundance of rich data.3
The examples discussed above are the types of technologies our teams
have been working with—most recently in a project called Healthcare
2020—to develop tools for precise clinical health information and adaptive
health coaching. The result would be that your mobile device would know,
for example, that you are not supposed to drink and therefore advise you
against going to a bar. Similarly, if you are a diabetic it could coax you
to stay away from McDonald’s and, instead, go for a run. With technolo-
gies like these we can optimize our health spans, not just prevent morbid
conditions.
Priorities Moving Forward
As this field continues to grow, there will need to be a certification
process for curating public health information on the web. With so many
individuals getting health information on the web from dubious sources,
there is a new stewardship role that has to be fulfilled. The government
could take a leadership position and come up with standards to certify
aggregators and curators of information on the web. Furthermore, the fed-
eral government can prime the pump by opening an interoperable health
app store, providing tools for consumers to collect, report, generate, and
analyze their health, behavioral, dietary, and fitness data. Finally, as these
concepts are still in development, support for the development of a deep
healthcare web of linked open data and open frameworks for knowledge
interoperability, the roles and practices for real-time sensor data, and re-
3 For more information, see http://www.iftf.org/health (accessed March 3, 2011).
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196 DIGITAL INFRASTRUCTURE FOR THE LEARNING HEALTH SYSTEM
search on therapeutic health information patterns are all needed if we are
to harness the power of digital information for improvements in health and
health care.
REFERENCES
Friedman, D. J., and R. G. Parrish. 2010. The population health record: Concepts, definition,
design, and implementation. Journal of the American Medical Informatics Association
17(4):359-366.
Friedman, D. J., E. L. Hunter, and R. G. Parrish. 2005. Health statistics: Shaping policy and
practice to improve the population’s health. New York: Oxford University Press.
Press, W. H. 2009. Bandit solutions provide unified ethical models for randomized clinical
trials and comparative effectiveness research. Proceedings of the National Academy of
Sciences 106(52):22387-22392.
