Version 2/28/08

**The Case for Cross-Collaboration between Population Sciences and
Informatics to Accelerate Cancer Control**

Michele R. Forman^1^, Sarah M. Greene^3^, Deborah M. Winn^5^, Paul
Courtney^2^, Nancy Avis^4^, Peter Schad^5^, Stephen Taplin^5^, Bradford
W. Hesse^5^

^1^M. D. Anderson Cancer Center, Houston, TX

^2^Dartmouth Medical School, Lebanon, NH

^3^Group Health Center for Health Studies, Seattle, WA

^4^Wake Forest University School of Medicine, NC

^5^National Cancer Institute, Bethesda MD

**Corresponding Author:**

Michele R. Forman, Ph.D., M.S.\
Department of Epidemiology\
UT M. D. Anderson Cancer Center\
PO Box 301439\
Houston, Texas 77230-1439\
Tel:  713-792-1860

E-mail: mforman@mdanderson.org

**Running Head:** Population Grid

**Manuscript Category:** Original Article. General topic: Cancer
Information Seeking

**Number of pages:** Text -- xxxx; Tables - x; Figures - x

**Acknowledgment:** This paper is a U.S. government work and, as such,
is in the public domain in the United States of America.

**Funding:** National Cancer Institute: Contract \# (insert caBIG #'s
here)

# Abstract 

> "*We as a Nation have a timely opportunity and an urgent need to build
> a 21st-century health support system---a comprehensive,
> knowledge-based system capable of providing information to all who
> need it to make sound decisions about health. Such a system can help
> realize the public interest related to disease prevention, health
> promotion, and population health."* --- National Committee on Vital
> Statistics, November 15, 2001(1).

# Introduction

## Proposal for a National Health Information Infrastructure

On November 15, 2001, just weeks after public web traffic flooded
servers at the Centers for Disease Control and Prevention in the wake of
a national health emergency (2), the National Committee on Vital
Statistics in the United States released its advisory report titled
"*Information for Health: A Strategy for Building the National Health
Information Infrastructure."* (1) The report examined the urgency for
information technology to electronically link data sources (connective
technologies) and users by adoption of Internet standards to improve
personal and population health in the United States. Framers of the
national report explained how, in the information-intensive world of
health, great strides had been made to develop cutting-edge applications
of information technology in medicine, biomedical research, and public
health surveillance. Yet, historically these efforts have been
disconnected and fragmented. Work was needed to connect these systems to
achieve economies of scale, and transform the ways in which research on
health and health care is translated into tangible benefits for
individuals and populations.

It is easy to see what is missing from the nation's health information
system by looking to other sectors in the economy (3). In 2001, when the
committee's report was released, a U.S. citizen could walk up to an
Automatic Teller Machine anywhere in the world and make a withdrawal
from a bank account half-a-world away. Yet, if that same citizen were to
become ill, there would be no provisions to transport his or her medical
records -- even to a provider two blocks away -- in a timely fashion.
Similarly, millions of Americans reap the benefits of connective
technologies in the geosciences that make it possible to read weather
maps from anywhere in the world in near real time. When it comes to
understanding the conditions that improve or worsen health, however,
early warning systems that might track epidemics or identify adverse
reactions to a newly marketed medication are just beginning to come
on-line. Our colleagues in Scandinavia have operated quite functionally
in this realm for decades. In 1973, the Norwegians established
registries for birth, cancer, and hospitalizations and can now link
information from birth throughout the life course. We have the
components to create such a system but there are challenges and
opportunities inherent in the development of such an infrastructure, and
attendant challenges in using this infrastructure to its full potential.

The 2001 report proclaimed that the health sector could do better. The
technologies exist that can tie together the nation's health
surveillance systems so that population scientists can understand
population dynamic at the demographic, epidemiologic and behavioral
levels with the same degree of acuity as biological scientists can
understand changes in gene expression. What is needed, according to the
report, is the will power and hard work to bring these disparate data
systems online into a shared lattice of standards and data.(4) According
to the committee's spokesperson, the events of 2001 had "dramatically
underscored the importance of an effective, comprehensive health
information infrastructure that links all health decision makers,
including the public" (p. v in the preamble).(1) The time had come to
"connect the dots" in public health and healthcare; to create a national
infrastructure made up of interoperable data systems that would use
science and empirical data to solve the complex health problems
confronting health scientists at the dawn of the new millennium. No
condition more than cancer could benefit from this health information
infrastructure. Cancer is a polygenic biosystem response to endogenous
and environmental cues over time. Recognizing the inherent interaction
of the biosystem to these cues on a daily basis, we now understand that
tailored prevention and intervention strategies are achievable goals.

## Connecting the Dots in Cancer Prevention and Control

In this paper, we envision a coordinated solution to the problem of
disconnected information systems and craft scenarios for cancer
prevention and control research based on the vision. To do this, we
apply ***informatics*** as the *lingua franca* of our transdisciplinary
discussion and as a catalyst for accelerated discovery in the population
sciences. The timing, we believe, is as propitious in cancer research as
it is in other areas of health. Consider the following:

-   In 2003, the U.S. National Cancer Institute (NCI) funded a five year
    pilot project to construct an infrastructure for sharing data and
    tools within the cancer research community. Termed the **ca**ncer
    **B**iomedical **I**nformatics **G**rid, or **caBIG**, the pilot
    project is investigating use of open source technologies that use a
    grid framework with databases and analytical services to enable
    queries across multiple datasets and coordinate massive data flows
    in cancer related molecular science. Within four years, the caBIG
    community grew to over 1,000 participants from 80 institutions with
    principal representation from the NCI's Comprehensive Cancer Centers
    (5). Shareable, analyzable data stores from information repositories
    for genomics and proteomics are beginning to come online for the
    benefit of the broader cancer research community (6).

-   The caBIG pilot project parallels efforts launched by the U.S.
    National Science Foundation in 2006 to open a new frontier in
    science by enhancing connections to data, resources, and people
    through "cyberinfrastructure"(7, 8). According to the National
    Science Foundation, many of the scientific problems confronting
    researchers in the upcoming century will be inherently
    data-intensive and complex. To tackle these problems, teams of
    researchers will need access to advanced computing systems,
    interoperable data structures, new information resources, digitally
    enabled-sensors, instruments, and virtual organizations. The
    creation of these components should give population scientists the
    tools to process the petabytes (i.e., 10^15^ bytes) of data likely
    to emerge from research in epigenetics, environmental determinants
    of behavior, health systems redesign, and the resolution of health
    disparities (9, 10).

-   After two decades of exhortation by the medical informatics
    community,(11) the U.S. Department of Health and Human Services in
    conjunction with the Office of the President announced a goal in
    2004 to bring interoperable electronic health records to a majority
    of Americans within ten years. According to the Secretary of the
    Department of Health and Human Services, establishing an
    infrastructure to support the transfer of interoperable electronic
    health records will be a necessary prerequisite for delivering
    consumer-centric and information-rich healthcare (3) in an era of
    personalized medicine. Data analysis, conducted within such an
    infrastructure, will allow biomedical and population-based
    researchers to bring discovery from "bench to bedside to
    communities\" \--and back to the bench again\--through analyses of
    healthcare outcomes correlated with biobehavioral and molecular data
    into communities to improve the fit between discovery and practice
    (12).

-   In 2006, an estimated 75% of U.S. adults had Internet access \-- in
    just over ten years of diffusion, marking one of the fastest rates
    of diffusion for any new communication paradigm in recorded history
    (13). Indeed "cancer" was one of the top three health terms searched
    online by the public (14). Likewise, almost one half of the U.S.
    adult population (or an estimated 105 million Americans) reported
    having looked for information on cancer according to the NCI's
    Health Information National Trends Survey (HINTS) (15).

## Objectives of the Manuscript

Taking advantage of the paradigm shift embodied in discussions of a
national infrastructure of health information requires a common vision,
strategic thinking, collaboration, and hard work. In a significant
sense, these efforts are not new to cancer control. For instance,
painstaking progress has been made over the last three decades to create
and expand the Surveillance, Epidemiology, and End Results (SEER) data
system demonstrating how registry data improve understanding of cancer
outcomes at the population level.(16) Pilot projects have linked data
from Medicare to the SEER registry to answer crucial policy-relevant
questions.(17) What has been lacking is a consistent, inclusive way for
all population scientists to take full advantage of the benefits
afforded by a seamless, interoperable cancer resource online. Our
purpose in this manuscript is to move the process, initiated by NCI's
caBIG and NSF's cyberinfrastructure projects,(8) into the arena of
cancer prevention and control. Our aims are threefold:

-   [AIM 1:]{.underline} To describe the **background** to the national
    conversation on informatics for cancer prevention and control. In
    this aim we build on the framework articulated in the 2001 report by
    the National Committee on Vital Statistics.

-   [AIM 2:]{.underline} To describe several use cases that illustrate
    the need to develop informatics tools for a wide range of questions
    in cancer control and population sciences. In this section, we will
    illustrate how interoperability efforts in the population sciences
    have led or, indeed, can lead to improvements in population health
    and identify the lessons learned.

-   [AIM 3:]{.underline} To propose long term **solutions** to the
    challenges associated with connecting disparate data sources into an
    interoperable grid of population sciences resources. In this last
    aim, we do not just focus solely on technological solutions --
    though technology forms the enabling backdrop to our discussion --
    but we focus on the values, standards, practices, and
    compliance-behaviors needed for an informatics approach. Our purpose
    will be to present promising areas of research and development that
    can guide future efforts in the population sciences.

In creating this blueprint, we recognize the task represents a shift
from the comfort zone of one's own discipline to a transdisciplinary
approach in order to tackle the complex problems associated with cancer.
Data are required from complementary disciplines to fill in the
evidentiary base and accelerate progress in prevention, early detection,
diagnosis, treatment, and survivorship (9, 18-20). Thus, solutions will
arise from a multi-disciplinary perspective.(21, 22) The long-term goal
is to integrate the population sciences into an informatics space
currently shared by the 'omics', pathology and an ever increasing number
of other biomedical disciplines, with the ultimate payoff of unraveling
the complex process of cancer through transdisciplinary models.

# AIM 1: Background to the National Dialogue

## What is Informatics?

Origins of the term informatics can be traced back to German and French
scientists who began describing techniques for the automation of
information processes. Discussions transcended development of better
computer systems and emphasized the contributions of cognitive science
and a systems perspective to improve information access. Informatics
became the study of the structure, behavior, and interactions of natural
and artificial systems that store, process and communicate information.
This definition encompasses a vital system based on values, practices,
standards, data and the information flow across sectors. As the term
migrated into healthcare in the U.S., "medical informatics" became a
discipline dedicated to improving the flow of evidence-based health
information within and between biomedical settings (23). Applications
expanded to developments in artificial intelligence research, online
digital medical libraries, point-of-care diagnostics, error recognition,
decision support systems for coordinated care, electronic medical
records, and telemedicine to name just a few (11).

Through these applications, medical informaticians (i.e., practitioners
of medical informatics) emphasize the goal of the discipline is to
improve information process capabilities across natural (i.e., human)
and artificial (i.e., computer) systems. Thus, as one informatician put
it, health informatics is not about computers -- it is about "people,
tools, and conversations."(24) "Everyone thought IT was about computers,
but ... IT is about healthcare -- it is about the experience we really
have."(4). In other words, an informatics approach is a
"socio-technical" approach, not just a technical approach (25).

More recently, informatics has expanded to consider the application of
information processing for improving population health (6, 10, 26).
Congruent with the spirit of the approach, the conversations transcend
historical boundaries and emphasize how medical informatics systems can
work in tandem with public health and consumer informatics systems to
improve care processes and outcomes such as diabetic management (1), the
reach of screening (2), (3), the support of individuals living with
cancer (4), and the management of palliative care in the home (5). This
expanded dialogue includes consideration of how medical informatics
systems can work in tandem with public health and consumer informatics
systems to improve health at the individual [and]{.underline} population
level. But underlying this expanding view of information processing is
the need to communicate in the same language so that the conversations
are intelligible. *The goals of informatics frequently include achieving
"interoperability"---that is, the ability of two or more systems to
meaningfully exchange and use disparate terms, concepts, and data. To be
interoperable, systems must be able to exchange data in such a way that
the precise meaning of the data is readily accessible (semantic
interoperability), and the data can be translated by any of the
interacting systems into an understandable form (syntactic
interoperability).* This interoperability of data requires common
standards for the definition and description of data elements, and
agreement on data elements that measure complex concepts such as quality
of life or cancer staging. While interoperability is a goal of
technology, it remains a requirement for interdisciplinary research.
*Interoperability in informatics is simply the concrete manifestation of
our need to exchange information, concepts, and ideas using common
definitions in order to advance our scientific understanding of cancer.*
Yet, achieving semantic and syntactic interoperability is a great
challenge due to \"the decentralized nature of multidisciplinary
healthcare information systems (27).\"

## What research areas can benefit from a coordinated informatics solution?

In Figure 1, a Venn diagram borrowed in concept from the National Health
Information Network shows three primary partitions: one for healthcare
providers, one for individuals, and one for population health science.
Each sphere overlaps with other dimensions. Thus, data collected in the
population health sphere can inform the decision-making processes of
patients and medical staff. Likewise, data collected from individual
medical records can inform population scientists about the distribution
of risk factors or the relationship of personal behavior to perceptions
of disease risk. Data that live in the provider sphere can be exchanged
across care settings to enhance continuity and coordination of care, and
also yield patterns of care that can lead to identification of medical
errors or syndromic events. We discuss each of these three dimensions
below.

\< Place Figure 1 Here \>

### The Healthcare Provider Dimension 

There are opportunities to improve cancer care and the data base for
research with the introduction of electronic health records. Health care
providers interact with people moving across the cancer care continuum
from screening through a definitive diagnosis, cancer treatment,
monitoring for recurrences and cancer survivors.(6) High quality
information is critical to care providers as they address their
patients' needs and that same information can be used for research.(7)
Yet, primary care physicians report that clinical information is missing
in 14% of visits and the missing information results in repeat testing
(49%), additional visits or testing (46%,49%) and delays in care 56% of
the time.(7) In a series of reports on improving quality in healthcare
delivery, the Institute of Medicine described a healthcare "system" in
the U.S. that is fraught with error as individual providers struggle to
stay abreast of an explosion in biomedical research and individual
patient data(28, 29) because the information environment of healthcare
is not conducive to a "culture of safety." These safety problems can be
exacerbated by a fragmented healthcare environment that lacks the
infrastructure to ensure continuity of care for patients over their
lifespan. But information technology offers a potential infrastructure
for improving care. For example 90% of the medication errors were
reconciled through an electronic record that assisted with physician and
patient review, compared to 2% managed with usual care.(8) And when that
infrastructure exists it makes it possible to aggregate information
across individuals and conduct research as demonstrated by the advances
in psychiatric care, post market surveillance of medications and
vaccinations, and measurement of population-based effects of screening
implementation (9) (10) (3;11).

Discontinuities in care have serious consequences. For example,
providers' efforts to ensure that their patients adherence to
recommendations for routine screening can mean the difference between
finding the disease early, when something can be done about it or later
when prognosis is poor (30). They are equally problematic for cancer
survivors who find themselves "lost in transition" from cancer patient
to cancer survivor, when questions about monitoring for recurrence or
remaining vigilant to late effects are critically important (31). One
key response to these "lost in transition" concerns has been the efforts
by investigators at Baylor College of Medicine to provide cancer
patients with a standardized electronic summary of their treatment that
patients can bring with them to their health care providers who can also
use it to link to updated guidelines regarding oncology care for the
specific patient (32).

The Institute of Medicine's recommendations in light of these conditions
revolve around creating systemic solutions as support to healthcare
teams and their patients within the context of "healing relationships"
over time (28, 33). What the IOM means by this phrase is... Information
technologies will be an important part of the solution, according to the
report (28). For example, improved information technologies can be used
to get the right information to decision makers at the time they need
it. Additionally enhanced communication channels using informatics tools
can help maintain optimal healing relationships by providing transparent
reminders of milestone events, such as screening, and keeping all
members of the caretaking team on both the patient's side (e.g., family
members, personal caretakers) as well as the providers' side (e.g.,
primary care, specialty care, support staff) informed about next steps
and needed follow-up.

On the quality improvement side, as medicine evolves from
individual-based episodic care to planned population-based care of
individuals, enhanced data systems are a critical tool. Population-based
care if individuals combine epidemiologic principles, measurement, and
information technology to prioritize work and facilitate the
identification and management of individuals who fall outside specific
guidelines for care. Through the use of information systems it also
allows the care team to measure progress among the aggregated set of
individuals (12-14) (1). This is a cultural shift that depends on
information systems (10) that will ensure that health care practitioners
and practices adhere to standards of care and catches errors before they
become serious, Health provider office and hospital administrative staff
can use the informatics systems to support a learning environment that
measures and monitors care in a way that is tolerant of human error, and
that uses human factors principles to improve the quality of delivery
equitably across all patients (34).

At the regional, national, and health care system level, the data
collected by these real time statistical quality control processes as
well as other data captured by health care providers can be analyzed to
facilitate cancer control and population sciences research (28). In
fact, the capabilities of an informatics-infused delivery system to
support secondary data analysis will help blur the arbitrary
distinctions between basic and translational research, between
biomedical and public health research, and between research and clinical
practice (12).

A web-based EMR is another outgrowth of health informatics. The ideal
design of this web-based EMR application would include interfaces to
support clinician data entry (physician, pharmacist, nurse, therapist,
etc.), billing, and patient portal (including self-care instructions,
medication schedule, symptom/side effect log, health library, etc.)
Patients would give their providers the URL, account number, and
password to enter their visit and hospital stay data into their records.
The EMR system would also transmit the details of the encounter to
clinicians\' data warehouses for their business records.

For a web-based personal EMR to be successful interoperability must be
achieved between the EMR and each clinician\'s data warehouse, between
the EMR and each third-party payer\'s billing system, and between the
EMR and the patents\' web appliances. A reasonably standardized
clinician interface is required to reduce data entry burden on
clinicians since their patients will likely use different EMR products.
Moreover, the EMRs need to support connectivity with a variety of web
appliances, including PCs, web-enabled cell phones, PDAs, etc. Under
this scenario, patients would own their own health data. A potentially
useful feature would be the ability for caregivers of cognitively
impaired patients to establish EMR accounts for them, and manage their
health and medical care. Highly developed EMRs would also provide health
counseling to induce patients to adopt healthy behaviors, including
tobacco cessation, improved dietary habits, drug x food interactions,
first aid, managing minor illnesses, etc. The EMR could also contain
programs to improve medication adherence, blood pressure monitoring, and
other chronic disease self-management skills. In this sense, the EMR
becomes an automated health coach, supplementing the patient\'s
physician and nurse, and reducing reliance on the doctor office visit as
the dominant means of obtaining medical care.

To support research on web-based personal EMRs, these products would
have to build in tracking systems to capture the key clicks of every
user. Researchers could then obtain permission to access these data and
link them to the medical care data, as well as social surveys, and
community characteristics data. Interoperability among the EMR
transactions data warehouses would greatly facilitate research on their
use, effectiveness, and cost-effectiveness.

*The Personal Health Dimension*

The paradigm of care has moved away from the historic "medical model,"
in which patients were treated as passive recipients of care, to a
"patient-centric" model, in which patient empowerment is encouraged and
supported (35). The current model for chronic care has "activated
patients" interact proactively with a highly supportive healthcare
system to optimize the effectiveness of treatment and self-management
(36). The National Institutes of Health has identified agenda for
research oriented toward creating a 21^st^ Century model of medicine
that is personalized to consumers, predictive of upcoming risk, and
preemptive in approach to treatment (37).

Personal Health Records, analogous to the physician-oriented electronic
medical record, allow patients to gain direct access to their own
medical information in proactive, preventative ways. Remote wireless
technologies are being adopted to improve self-management skills in
areas such as diet and exercise (38). Economic predictions are that
these types of consumer-informatics solutions will proliferate as the
health-conscious and technology-savvy baby boomers age. New types of
social interfaces are predicted to diffuse within younger age groups as
discussions of health enter into the digital environments in which these
groups have been raised (39). This movement toward patient-empowerment,
enabled by the democratizing influence of connective information
technologies, has given rise to a new area of research referred to as
"Consumer Health Informatics", defined as \"the branch of medical
informatics that analyses consumers\' needs for information; studies and
implements methods of making information accessible to consumers; and
models and integrates consumers\' preferences into medical information
systems\"(40). Studies of consumer behavior in online health
environments are beginning to emerge. As more consumer behavior goes
online, it should be possible (with patients' consent) to collect data
unobtrusively on behavioral and biological effectiveness. The result
should accelerate discovery in the population sciences, which are
dependent on aggregations of individual data to reflect population
profiles, and should shorten the time it takes to improve the quality of
information delivery based on patients' needs.

### Population Health Dimension

One of the hallmarks of the public health system in the U.S. is the
series of surveillance mechanisms launched over the last half century to
monitor disease. The National Health Interview Survey (NHIS), for
example, has emerged as a gold standard for providing summary health
statistics for populations in the U.S. In a similar vein, the
state-based Behavioral Risk Factor Surveillance System (BRFSS) offers a
comprehensive look at states' prevalence rates of avoidable behavioral
risk factors. The Behavioral Risk Factor Surveillance System illustrates
the value of connective, informatics solutions in public health.
Although the BRFSS is a state-based data collection system, the surveys
collect comparable data from state to state. Data from one such measure,
the Body Mass Index (BMI), were in conjunction with standards for
Open-GIS (Geographic Information Systems) merged to present results over
time on a national map. The results offered scientists and policy makers
a compelling profile of the emerging obesity epidemic (41). Detection of
this long term trend, with direct implications for health promotion
across diseases including cancer, was made possible through an
informatics-oriented strategic effort. Similar work has been done to
array SEER cancer registry data to identify geographic pockets of risk
in an effort to identify health disparities (16, 42).

According to predictions, the future of cancer surveillance will be
driven even further by developments in information technology (20). Just
as the terabytes of data being produced by DNA microarray technology
will enable the discovery of gene expression patterns using new
algorithms and statistical modeling techniques, macroarrays of
interconnected data points, each comprised of biological, behavioral,
environmental and socioeconomic factors, should enable analysis of broad
profiles at the "populomics" level (43, 44). Bringing population science
onto to the same grid as other cancer-related research enterprises will
be a necessary precondition for enabling genome-wide association
studies, for understanding gene by environment interactions, and for
identifying opportunities for "treatment" or intervention. For example
aggregate cancer information on populations living in specific zip codes
could identify the impact of community health centers serving the under-
and uninsured, or large health plans serving the employed. .

## The evolution of the national dialogue on informatics

In 1949, the establishment of the National Committee on Vital and Health
Statistics was the initial step in the informatics dialog. This
committee was charged with advising the federal government on the
information needs underlying health policy. More recently, when the
"Health Insurance Portability and Accountability Act" (HIPAA) of 1996
was passed by Congress, the national committee was given an advisory
role on the collection and use of health information for the public's
good. Fulfillment of that advisory role resulted in the series of
reports and memoranda leading to discussions of a national health
information infrastructure.

In parallel, there have been efforts starting in the1960\'s to develop
hospital information systems and tools for decision support. These
systems focused on enabling transmission of orders in acute care and
tracking services rendered for billing purposes. More recently in the
late 1980\'s, with the focus of health care finance shifting from
fee-for-service to managed care, the systems had to be reengineered to
accommodate the computer-based patient record which could track patient
care in these new networks. Although this new framework has shown
promise in integration of subsystems (decision support, CPOE, etc.), it
has also been difficult to implement due to lack of compatibility
between these subsystems.(45)

In the last 10 years an accelerating number of federal initiatives have
begun to unlock the promise of informatics in healthcare.

-   In 2001, the NCVHS issued their report, \"Information for Health,"
    which described the blueprint for the National Health Information
    Infrastructure (NHII) that is oriented toward the needs of the three
    stakeholder groups (providers, patients, and population scientists).

-   In late 2002, the CDC issued its document about PHIN, \"Public
    Health Information Network Functions and Specifications\", which
    outlined functions and specifications consistent and compatible with
    standards promoted by the NHII.

-   In 2004, NCI launched the caBIG initiative to accelerate research
    discoveries and improve patient outcomes by linking researchers,
    physicians, and patients throughout the cancer
    community(https://cabig.nci.nih.gov/)

-   In 2004, the office of National Coordinator for Health Information
    Technology was created within the HHS to create a Strategic
    Framework with four goals that expanded upon the NCVHS vision. These
    goals were (1) Inform Clinical Practice, (2) Interconnect
    Clinicians, (3) Personalize Care and (4) Improve Population Health
    (see <http://www.hhs.gov/healthit/framework.html>).

-   The Health Information Technology Standards Panel was established by
    the ONC to identify and harmonize data and technical standards.

-   In 2005, the American Health Information Community (AHIC) was
    chartered to recommend how to accelerate the development and
    adoption of health information technology
    (http://www.hhs.gov/healthit/community/background/).

-   In November 2005, the ONC awarded four contracts for developing
    prototype architectures for a National Health Information Network
    (NHIN). A report on the initial successes of the contracts was
    issued May 31, 2007.

In addition, the commercial vendors that provide the information systems
for hospitals and clinicians have begun to respond to the government
initiatives listed above and realize they play a pivotal role in
improving the quality and safety of patient care by implementing
standards for interoperability. The HIMSS Electronic Health Record
Vendors Association issued their \"EHRVA Interoperability Roadmap\" in
October 2006 (<http://www.himssehrva.org/docs/roadmap_v2c.pdf> ). This
document describes a phased implementation, with Phase 4 including
public health surveillance and healthcare quality reporting.

As we complete our discussion of AIM 1, an emerging opportunity emerges
amid the healthcare provider, personal health and population science to
develop terminology, standards, and definitions in order to facilitate
our unified and comprehensive scientific understanding of health and
health care. According to discussions at the national level, public
health professionals (scientists at academic and research institutions,
federal, state and local governments, health care providers in public
and private sector) must be considered major stakeholders in the
enterprise of informatics technologies to improve population health.
Population scientists must be proactive in the national discussion to
ensure that the resultant infrastructure conforms to the field's needs,
applications, and values. To that end, caBIG has established a working
group of epidemiologists and health services researchers to identify
opportunities for leveraging informatics to accelerate cancer prevention
and control (hereinafter referred to as population sciences) and this
chapter is the beginning of their work.

# AIM 2: Lessons Learned from Use Cases in Population Science and Bioinformatics

Compelling "use cases" are described in this section to illustrate the
need for population scientists to utilize informatics and create
opportunities to develop new models for cancer etiology and control, as
well as stimulate more population scientists to engage in the effort.
For each use case, we start with the research question and then describe
either informatics approaches that have proven successful in moving the
content area forward or the pressing need for new informatics tools. In
the successful case series, we describe the strategies that have
potential for use in other research settings. Finally we draw upon the
use cases to review lessons learned in the application of informatics to
population science. Thus we hope that the lessons learned from the use
case illustrations can be applied to other research questions in
population sciences. The use cases are solely illustrative and are not
intended to be all- encompassing of research issues for application of
bioinformatics tools. The use cases that are described below include: 1)
energy balance and cancer prevention; 2) understanding the role of
gene-environment interactions in cancer epidemiology; 3)
patient-reported outcomes measurement system -- PROMIS- in population
sciences, and 4) optimizing health care systems for patients and the
population sciences.

## Use Case 1. Collecting accurate data on energy balance in cancer prevention 

*The Research Question:* The epidemic of obesity in the United States
along with the associations between obesity and mortality from cancer of
the breast (postmenopausal), endometrium, kidney, prostate, and colon
are major factors behind the effort to examine the role of energy
balance in cancer (46, 47). Indeed energy de-regulation has become a new
hallmark for cancer (48). Considerable research in population sciences
is focused on all or one of the components of energy balance, notably --
diet, physical activity, thermo-regulation, and metabolism -- and their
role in cancer susceptibility. Further, behavioral and other forms of
interventions endeavor to modify diet and increase physical activity.
Some of the research issues that are hampering detection of the energy
balance-cancer associations and potentially attenuating the effects of
cancer prevention trials include: 1) measurement error in dietary and
physical activity assessment; 2) recall and other sources of bias in
reporting diet and physical activity from questionnaires; 3) trends in
(different) age-specific anthropometric measures of body mass; 4)
differences in measurements of body size; and therefore a lack of
standardized tools to measure and common data elements for energy
balance at the population level.

*The obstacles:* In the arena of energy balance, participants typically
respond to a self-administered or interviewer-administered questionnaire
by reporting physical activities or food intake in the recent or distant
past. Traditional questionnaires may be lengthy often including long
lists of foods and requisite portion sizes (49). Studies of energy
balance and cancer risk or survival may be biased by differences in
recall by cancer patients or their surrogates and healthy controls.
Patients may be searching for the 'cause' of their cancer, with
potential differences from controls in exposure assessment of diet in
the past, whereas the controls may simply be burdened or unable to
anchor themselves to the referent period and respond to questions on
diet or physical activity (50). Dietary questionnaires are developed
using tools such as food records or using biomarkers of intake to
'validate' responses from food frequency questionnaires. Energy
expenditure as measured by the gold standard of the doubly labeled water
method has been used to validate one food frequency questionnaire (51).
Yet too few validation studies have had a gold standard for comparison
with a food frequency or other dietary questionnaire.

Another indicator of energy balance, anthropometric status (e.g. weight,
height, waist to hip ratio) varies by the tools used, by quality control
in measurement, and has different measurements and categories for lean,
normal, overweight, and obese for children and adults. For example,
children who are categorized between the 85^th^ and 95^th^ percentiles
and those at or above the 95^th^ percentiles of weight for height were
classified as 'at risk for' and 'overweight', respectively, until 2008,
when these two categories were changed to the 'overweight' and the
'obese'. The change in nomenclature and in the referent population for
these two categories of body size has implications for trends in
anthropometric status in children, let alone parental 'awareness' of the
body size of their child. Also, weight and height in adults or length in
infants are measured using equipment that requires regular (e.g. daily)
calibration, and measured by individuals who have been trained and
re-trained in anthropometric assessment. This level of quality control
in measurement and instrumentation is not uniformly employed or
reported. Furthermore, no one institution agrees upon the standard
location for waist circumference for waist to hip ratios or abdominal
obesity (52). Without accord on the location and technique for measuring
waist circumference, research on the role of abdominal obesity in cancer
and other chronic disease will lead to incomparable findings.

*Informatics approaches that provide potential solutions:* Study
participants now use cell phones or personal digital assistants ("PDAs")
to report food consumption in real time, and investigators rapidly
download and analyze dietary and other data from these tools. Use of
cameras on phones provides the opportunity for the participant to
photograph the actual portion of food before eaten and the remainder of
the food after the meal, thereby easing the path away from estimation of
portion size and concern for portion size distortion in reported intakes
(53). Computerized food recalls using the 5-pass system that is directly
linked to a food composition database enable the dietician to record,
analyze and provide recommendations to the participant about his/her
dietary intake in real time (54). These are but two examples of tools
that capture research data in real time. PDA's have the capacity to
'remind' the participant to write down his/her intake after a certain
period of time has elapsed since its use and to jog memory about other
activities such as biospecimen collections. Finally data from PDA's and
from camera-phones can be downloaded to a modem for data analysis. Thus
bioinformatics contribute to the design system for the user interface of
the tool and integrate data systems that can accommodate large volumes
of dietary and other data in a rapid fashion to reduce the time till
results are provided to the lay public.

Although the devices noted above have appeal for techno-comfortable
participants who use portable electronic devices such as PDAs,
challenges remain. Bioinformatics approaches are needed for the
development of culture-specific instructional modules to record
lifestyle behaviors and devices that the elderly and others with less
expertise can use, thereby creating the opportunity for assessment of
one's energy expenditure as part of his/her daily routine.

Population scientists need to join forces with information technology
experts to forge ahead developing common definitions, cut-offs, and
quality control standards for measurement of nutritional status. Teams
of experts need to establish standards for dietary and physical activity
assessment based on evaluation of different modalities and tools
followed by validation across diverse populations. With these systems in
place, monitoring and surveillance of energy balance can occur in the
population and optimally be linked to cancer surveillance and health
care systems.

**Use Case 2: Understanding the role of gene-environment interactions in
cancer etiology**

*The research question:* Cancer is the result of a complex interplay of
gene-environment interactions, and bioinformatics tools are essential
for evaluating these relationships. Given the number and complexity of
genetic and environmental factors that appear to be involved, it has
become very important that cancer epidemiologists have extremely large
sample sizes (55) in their studies and be able to manipulate extremely
large amounts of data. Newer technologies such as those for assessing
epigenetic and other heritable characteristics that do not involve DNA
sequence have been developed to examine their role in cancer risk. It is
now possible to conduct genome wide association studies that compare
persons with and with cancer across 550,000 places in the genome. The
application of these technologies has begun to lead to new ways of
thinking about cancer etiology.

*The obstacles:* One approach that researchers are increasingly
embracing to achieve the large sample sizes for the statistical power to
test gene-environment interactions is the formation of research
consortia whose members agree to pool their data with that from multiple
other studies. NCI's web page for researcher consortia lists more than
30 such consortia <http://epi.grants.cancer.gov/Consortia/> . Some
consortia are focused around a particular cancer site, and often are
comprised of principal investigators of case-control studies of that
cancer. The numbers of case study participants in the combined
case-control studies can be greater than 10,000 and usually an
equivalent number of controls are involved in the studies. The members
of the consortium of cohorts are investigators of very large cohort
studies. Collectively, more than 2.6 million study participants are
included in the consortium of cohorts. Each of the consortia includes
studies that cover different time periods, and they differ in
geographical regions, eligibility requirements, and other factors.
However, they have many commonalities in terms of availability of DNA
and data on similar content domains, for example, tobacco use history by
study participants.

Pooling data from multiple existing studies poses special problems,
because questions in one study may or may not be identical to those in
another. In order to pool data for consortium studies, the investigators
must first harmonize the study variables, which involves understanding
the extent to which the variables in one study are similar to that in
another. In a recent article on human genome epidemiology, the authors
note that standardization of study protocols and instruments is best
achieved with intent from study inception, whereas harmonization across
studies is a process used when the studies were not (56). To harmonize
data, the investigators must examine each data item in each study in
each of the common content domains and determine whether the items are
capturing the same underlying piece of information. If they do, the
investigator must decide how to best combine the information in the data
items in the studies into new data items for the pooled data analysis
and identify any potential impact that the pooling may have on the
findings and their interpretation. For example, can the variable weight
in one study that abstracted the (measured) data from medical records as
the source be combined with self-reported weight data? Can the Yes/No
responses to the questions "have you ever smoked regularly" in one study
and "have you ever smoked 100 cigarettes or more in your life" in
another be considered comparable? Harmonizing questionnaire data is a
very painstaking and tedious process. Evaluating comparability of
genotyping and other laboratory-derived data across studies is also
important.

*Informatics approaches that provide potential solutions:*
Bioinformatics have promise to improve the laboriousness of
harmonization processes because of the development of tools and
resources for *creating common data elements and common vocabularies*. A
solution to the problem of obtaining ready *access by researchers to
data and metadata is dbGAP.* Accessing data and metadata for
epidemiologic research in cancer is a challenge, and bioinformatics
approaches are being developed to address this. Given the recognition by
epidemiology investigators that study data should be maximally used, and
more good ideas exist than can be tested in a given study,
epidemiologists are increasingly encouraging investigators outside of
their study team to have access to their data. NIH policies for data
sharing are also contributing to this trend. Creating easy access for
investigators is challenging given the size and complexity of the
databases and metadata, and issues related to the protection of the
privacy and identity of study subjects. Intellectual capital concerns
could also arise. dbGaP is one tool developed by the National Center for
Biotechnology Information at NIH to provide datasets and metadata on
epidemiologic studies examining genotype and phenotype associations
(<http://www.ncbi.nlm.nih.gov/sites/entrez?db=gap>). The web site notes
that "dbGaP provides two levels of access - open and controlled - in
order to allow broad release of non-sensitive data, while providing
oversight and investigator accountability for sensitive data sets
involving personal health information. Summaries of studies and the
contents of measured variables as well as original study documentation
are generally available to the public, while access to individual-level
data including phenotypic data and genotypes require varying levels of
access by committee to determine whether individual researchers can have
access to de-identified individual subject level data or pedigrees. Like
the Cancer Research Network (CRN)'s virtual data warehouse, this is a
pretty good solution**.**

**Use Case 3: Patient Reported Outcome Measurement Systems**

*The research issues:* Patient reported outcomes (PROs) are reports that
come directly from a patient about a health condition without the
intervention of an observer. They include for example, measures of
health related quality of life (HRQL), symptom severity and frequency,
and satisfaction with care. These measures are increasingly considered
important endpoints in observational studies of cancer patients and
clinical trials. PROs are particularly relevant in trials comparing
treatments that may be equally effective, but have different side
effects that affect HRQL. They provide important information to
clinicians and patients in evaluating treatment options. However, there
are many measures or indices for each of these concepts, that create
difficulties for the comparison of findings from one study to another.
For example, in the case of HRQL, measures vary in terms of the domains
assessed, individual items, and metrics. These differences make it
difficult to compare findings across studies that use different outcome
measures, thus reducing our ability to interpret results. To illustrate
the number of different measures used, a recent article reviewing HRQL
measurement in randomized clinical trials in surgical oncology reported
as many as 18 different HRQL measures in only 33 different trials (57).

One obvious solution to this situation is to have researchers use
identical measures across studies. However, not only is this very
unrealistic, but it does not make for good science. PROs differ in
subtle ways and a measure that may be most appropriate for one study,
may not be the best for another. Informatics provides the ability to
compare these different measures and thus move science forward at a
faster, more efficient, and cost effective pace.

*What are the obstacles?* Informatics provides the ability to harmonize
data so that variables mean the same thing. However, harmonizing data
involves examining each data item in each study to determine if the
items are capturing the same information. This is possible, but is a
very time consuming undertaking. In addition, some measures are
copyrighted and may not be available for this process.

*What are the possible solutions?* One example of a solution that uses
sophisticated psychometrics and informatics is seen in the
Patient-Reported Outcomes Measurement System (PROMIS) being developed as
part the NIH Roadmap Initiative. The goal of PROMIS is to build and
validate accessible item banks to measure key symptoms and health
concepts (58). PROMIS involves a network of clinicians, clinical
researchers and measurement experts organized around six primary
research site grants, a statistical coordinating center, NIH scientists,
and an Advisory Board. This Network has outlined a detailed and
systematic process for developing a new approach to PROs using computer
adaptive testing (CAT) that provides an individually tailored approach
to assessment without the loss of scale precision or content validity.
CAT is a system in which items are administered to respondents based on
the response to a previous item. For example, if a person says he or she
can walk a mile, the respondent is not asked about walking several
blocks or a single block. CAT provides an individually tailored approach
to assessment without the loss of scale precision or content validity
and reduces respondent burden. Based on the sophisticated development of
item banks, individuals only respond to a subset of questionnaire items
relevant to their situation, that are calibrated in such a way that
allows for standard metrics.

The PROMIS item pools will consist of existing items from established
questionnaires and new items written by experts in patient populations.
Following the development of these item pools, item banks will be
developed using item-response theory (IRT). Once the CAT system is
developed, a web-based user-friendly repository of items will be
developed. The ultimate goal is to create a publicly-available system
that will allow researchers to access a common item repository and
computerized adaptive test. This item bank will allow for data to be
merged from different studies to perform comparative analyses. PROMIS is
currently focused on the following domains: physical function, symptoms
(pain, fatigue), emotional distress (depression, anxiety, anger), social
function, and social support. Eventually, using standardization
principles derived from informatics, researchers can call upon a
validated "item bank" of questionnaire items, thus improving
comparability across studies and allowing for data to be merged from
different studies to perform comparative or pooled analyses. Such
capability would be invaluable when research questions require large
samples of different racial and ethnic groups. Pooling of interoperable
data would facilitate analyses of population trends and comparisons of
outcomes across interventions where different measures have been used.

**Use Case 4: Health care systems data are invaluable to addressing
cancer control and population sciences research questions**

*The research area:* Health care systems are rich sources of data for
understanding the effectiveness of cancer control strategies as well as
observing real-world patterns of cancer care. Given that upwards of 80%
of cancer patients are cared for outside of cancer centers (need to
locate reference), the data from these health delivery systems provide
unparalleled opportunities to study a range of specific questions such
as trends in the use of aromatase inhibitors as adjuvant breast cancer
therapy or for chemoprevention; prevalence of cardiotoxicity or other
late effects of chemotherapeutic agents; or adherence to recommended
cancer screening guidelines. Often, questions like these benefit from
pooling and sharing data among a confederation of collaborating
institutions. The HMO Cancer Research Network (CRN) is one example of
such a population-based consortium, with 14 health care delivery systems
working together to address critical questions in cancer prevention and
control (59).

*The obstacles:* Population-based research across health systems
requires a high degree of cooperation and necessarily involves both
scientists and information technology (IT) personnel. Pooling data from
disparate legacy systems can be a time- and resource-intensive process,
and can be overwhelmingly redundant if this data aggregation task must
be done *de novo* for each new research question. Moreover, health
systems have legitimate concerns about sharing proprietary data on the
quality of their services and outcomes relative to their competitors.
Such concerns must be assuaged through robust technical and physical
safeguards of these data. In an era of heightened privacy and security
regulations and attendant penalties for violating these regulations,
healthcare systems may exhibit understandable reluctance to sharing
data. Finally, in the absence of nationally-mandated health IT
standards, there is little motivation for health systems to invest in
creating compatible or interoperable data systems.

*Informatics approaches that could address these obstacles:* A
distributed data warehouse, sometimes referred to as a virtual data
warehouse, is one approach to mitigating some of the obstacles
enumerated above. Hornbrook and colleagues (60) have described the CRN's
virtual data warehouse as a mechanism which maps the legacy data from
both electronic medical records and other computerized data sources to a
common set of variable definitions, labels, and standards. Then, since
the SAS programming language is universally used at all CRN sites, a
data analyst at one site can write a data extraction program that can be
applied at the other CRN sites with very minimal site-specific
customization. Hence, a data set can be created rapidly for use in a
multi-site project. This approach is considered a "virtual" warehouse,
insofar as there is no centrally-stored database aggregating all sites'
legacy data. In the CRN environment, the virtual data warehouse is
currently comprised of the following types of legacy data: tumor
registry, enrollment, demographics, pharmacy, encounters, and laboratory
values. These data marts are typically comprised of multiple underlying
data sources; for example, "encounters" will contain data from the
hospitalization system, ambulatory visit system, and claims data.

A specific utility within the virtual data warehouse is the
"counter"---a tool available to CRN researchers to gauge the feasibility
of certain events such as the number of cancer cases that meet
particular criteria (e.g., Stage I-II colon cancers diagnosed in men
over 75 between 2001 and 2005). Since some electronic medical record
data are text, rather than coded, other tools are needed to facilitate
data extraction. The application of natural language processing
algorithms, such as the cancer Text Information Extraction System
(caTIES) (61) holds great promise as a tool for searching chart notes,
pathology data, and other free-text elements of the EMR---such a tool
can facilitate rapid case ascertainment, and, by extension, provide an
opportunity to augment accrual into cancer clinical trials by matching
potentially-eligible patients with open trials in their region or health
care facility.

*Other resources needed to make more progress in this area:* The Cancer
Research Network and its virtual data warehouse are the products of a
long-standing funding mechanism from the National Cancer Institute. It
has taken thousands of person-hours to map the legacy data to common
formats and standards, and such capacity-building funds are increasingly
scarce in a tight health science budget climate. Yet the return on
investment for carefully-constructed data resources is incalculable.
Large medical datasets such as SEER (62), the SEER-Medicare database of
cancer care in the 65 and older population
(<http://healthservices.cancer.gov/seermedicare/overview/fact_sheet.html>),
and the Breast Cancer Surveillance Consortium (63) have proven their
value for enhanced understanding of the epidemiology and treatment of
cancer. Thus, the value of infrastructure must be recognized alongside
the need for informatics tools to leverage these potent data sources.

Multi-site research has proliferated over the last two decades, as
technologic innovations have made collaboration across wide geographic
areas more feasible. The research regulatory climate, however, was
founded in an era of single-institution research, and has not fully kept
pace with changes to the scientific enterprise. In an environment like
the CRN, with established processes for data aggregation, the
efficiencies gained through the virtual data warehouse have been offset
by the inefficiencies of having low-risk, limited data set studies
reviewed by multiple Institutional Review Boards (64). Indeed, the CRN's
experience is not unique ((65-68) To truly accelerate research progress,
regulatory requirements should be harmonized and streamlined wherever
possible (69).

**Summary of Lessons Learned from Use Cases:** In the four use cases,
several themes or lessons learned emerge, including: 1) a team approach
for solutions to obstacles is essential. The team has, at a minimum,
population science and information technology experts who can draw upon
each other's strengths to develop systems to pool large datasets for
analysis of gene-environment interactions; to facilitate the interface
of the user with a PDA or other technology; and to rapidly download data
for analysis and enhance turn-around of results to patients. One issue
in formulating the team is an ability as well as a willingness to
translate from one discipline to the other, spending time 'in training'
in the discipline of another to communicate effectively.

Another theme is the value-added to the development of a virtual data
warehouse in the CRN. The virtual data warehouse and similar distributed
data networks provide a range of opportunities for population scientists
from estimating the frequency of side-effects from a therapy to the
development of a full-blown data set for hypothesis testing. The
infrastructure of the CRN has unique features across the 14 member
institutions that enable this group to function as a working entity.
This has been achieved through careful attention to both socio-cultural
and technical aspects of collaboration. Several issues inherent to a
virtual data warehouse are: adherence to standards for data
specifications at the local level that could lead to incomparable data
across the network; and access to information at different levels, e.g.
de-identification of individual level data. These issues are faced by
others, including pooling projects/consortia, surveillance systems and
other registries in which data can be linked at the individual level.

Harmonization of questionnaires and measures or indices is a theme that
emerges from two use cases. Harmonizing data is essential for pooling
large datasets to increase the sample size for the power to detect
associations. The process requires careful documentation of the research
studies including the content domain of measures and variables,
description of quality control in data collection, and other details
that challenge comparability of the same variable from one study to
another. The value added from data harmonization includes opportunities
for comparative assessment of measures in different populations. Item
banks of questionnaires for use in future research will enable more cost
effective and comparative research, greater specificity of items for use
in different populations, more data for pooling analysis, and
eventually, a shorter cycle and more efficient research enterprise. An
issue that has yet to be fully resolved in this arena is the ability to
use copyrighted scales or measures. Many of the most widely used
questionnaires are not open source, so to speak. This is a legitimate
consideration, as creators are rightfully concerned about proper
attribution, and ensuring that the face validity of the measure isn't
compromised. Yet copyright and related intellectual property issues may
impede overall progress in this area.

These illustrative use cases of the integration of informatics to the
population sciences, are merely a handful of examples of how informatics
could be appropriately leveraged to further population sciences
research. There are undoubtedly countless other examples. The themes
that arise apply to more than one use case and probably have
implications for other potential research areas in the population
sciences. If a matrix of the lessons learned and obstacles were created,
one might envision that most obstacles are classified as caveats more
than irreparable pieces.

Aim 3: The Way Forward: Implementing a Population Sciences Research
Framework that Embraces Informatics

1)  The solution is a process -- not a tool -- that links content and
    technical people. For example Promis -- based on a scientific
    theory... Content people need to drive it...Example is the caBIG
    process for developing body mass index. Body mass for kids.
    Straightforward index for adults; its historical genesis. One single
    CDE isn't going to work. Childhood growth is a process. A better way
    to do it is: a federal workgroup for development of (and
    responsibility for) data element/questionnaire item. How can greater
    scientific community weigh. Don't want one person in an office
    "solving the problem". Behavioral measures database (Hesse).
    Technology side -- to link to CADSR. New use case: NCI will require
    that a grant will use the data elements. Is it a controlled
    vocabulary (e.g., a single measure of depression) vs. an "i-tunes"
    way (most downloaded) -- It would need to have a threshold of
    documentation. Can have different versions (e.g., this one is used
    for this and this for that. Linkable to Pubmed. Frequency of
    downloads is one measure to help weigh quality (e.g., how frequently
    use). Could be multiple other measures of quality. Another example,
    Fagerstrom index for tobacco dependence.

2)  A taxonomy tree is needed: survey questionnaires (the items are used
    to compose the indices), indices (e.g., ses, quality of life, body
    mass, qualitative comparative (e.g., constant comparative approach
    -- for example focus groups by researchers trying to get people
    using PDAs. Does the instruction work?? Contextual UML), risk
    prediction models. Mention here Tobac/CDC/NCRR

3)  CaDSR needs a whole new strategy

We don't have applications that get the CDEs out into something useable.
Two examples That begin to address this issue are ePhocus
(http://cophin.uchsc.edu/ePHocus/) from the Colorado Public Health
Information Network and the University of Colorado Health Sciences
Center and PRESAGE from the Fox Chase Cancer Research Center in
Philadelphia, PA. Among the many features of these tools are... However,
a more comprehensive approach to combined CADSR type tools with
questionnaire building is critical.

4)  Copyrighted tools. Open-source is wonderful. But -- can't just
    download music at will. Pop sci tools don't have to be open source.
    The meta-data should be open-source, but OK to need to pay for the
    use of the tool. This is the standard -- it costs money to use the
    standard.

**Ramifications of Interoperability: Harmonizing Data Sharing Principles
with the Current Regulatory Framework**

Integrating breakthroughs in informatics with the proprietary and
regulatory concerns of the research community is a pivotal element in
this dialogue...

It is important to underscore that public health professions---including
scientists at academic and research institutions, federal state, and
local governments, and health care providers in the public and private
sectors---are major stakeholders in this enterprise, as highlighted in
both the National Health Information Infrastructure (NHII), and National
health Information Network (NHIN). The applications that meet public
health and health research needs may vary from those that meet
individual patient and clinician needs, but informatics can provide a
much-needed anchor and ensure that stakeholders' diverse needs are
harmonized.

-   Propose the implementation process inherent to an informatics
    approach---the framework that includes future research to integrate
    the population sciences into informatics arena and initiate the
    values, standards, practices and compliance behaviors to establish
    and continue to develop i-POP.

    -   Standards: Accepting the need for and development of a common
        language: Tools to move information across fields and translate
        results---what are culturally acceptable new measures i.e.
        'scientific units' to move data across? How do we make sure that
        everyone complies with these standards?

    -   Methodological Research: Designing and testing tools to
        integrate information into different model systems; establishing
        relationships for information flow is inherent to this research.

    -   Technology: Methods to utilize information collected at
        different levels even though the data are not necessarily
        collected from the same individual (for example?); we play with
        this a lot when we shift from animal to human research and back
        again. How do we identify a process whereby we agree on these
        technologies?

    -   Technology: Resources to develop or modify existing tools.

    -   Values: Who is God or how do we all play in the sandbox?
        Resources to address barriers to informatics development i.e. a
        set of guidelines for: use, not misuse, of heavily endorsed
        scales; embracing, adopting the practice of pooling (anonymized)
        data; protecting privacy so that data are not under-used
        especially for health disparities.

    -   Systems and Applications: More current information flow of vital
        and other health statistics; identification of new paradigms and
        of new populations for hypothesis testing; optimizing the sample
        size to address biological and statistical interactions by
        utilizing biospecimen analysis in gene-environment research.

    -   Leadership

    -   Commitment and compliance with values, practices, standards.

    -   Are there landmarks to measure success?

**Discussion**

-   Identify and describe potential binding sites for population
    sciences in industry and the government e.g. AHEC

-   Why population science has added value for individuals and therefore
    impact patient care

    -   e.g. rapid turn-around of findings from large population based
        studies; enhance the development of risk algorithms to assess
        probability of cancer given individual personal lifestyle and
        demographic characteristics

-   Summary table with opportunities and challenges (See Table 1 to
    NCVHS) as well as honest dialogue as to how we can move forward
    including additional resources, realistic appraisal of what it will
    take from population scientists thereby initiating a call for a
    strategy to address the opportunity.

**Figures:**

**Figure 1. Dimensions of Informatics Support in Cancer Control.**

![](media/image1.wmf){width="7.5in" height="6.361805555555556in"}

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