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4
How Do We Get There?
After reaching consensus on the need for a New Taxonomy, the Committee
deliberated extensively on the question “How do we get there?” In this context,
“there” refers to successful creation of a system for acquiring and analyzing in -
formation relating the molecular profiles and health histories of large numbers
of individuals. In Chapter 3, we describe the properties we would expect a
Knowledge Network of Disease and the New Taxonomy to have and the type of
Information Commons that would be needed to create them. However, we also
emphasized that these resources will forever remain “works in progress.” As
information technology, basic science, health research, and medicine undergo
successive waves of change, both the content and structure of the New Tax -
onomy and Information Commons are expected to evolve, likely in directions
that are presently impossible to envision. Consider, by analogy, early attempts
to conceptualize the world-wide web compared to the use of the internet today.
The Committee’s view is that we presently lack the infrastructure required to
produce a dramatically improved disease taxonomy. Rather, we propose a path
forward to develop the infrastructure and research system needed to create the
Knowledge Network of Disease that we believe would be an essential under-
pinning of a molecularly-based taxonomy. We also address the sustainability of
this initiative. Just as public leadership and investment played essential roles in
bringing the world-wide web into existence, we believe such investment will
be critical if we are to achieve a grand synthesis of data-intensive biology and
medicine. However, we also recognize that, just as the world-wide web needed
to pay its own way before it could truly flourish, the Knowledge Network and
its underlying Information Commons will need to do the same.
The Committee believes that initiatives will be required in three areas to
exploit the wealth of information now emerging on molecular mechanisms of
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disease by creating a dynamic and comprehensive, yet practical and widely-
used, Knowledge Network:
1. Design of appropriate strategies to collect and integrate disease-relevant
information. The Information Commons would be developed by link-
ing molecular data to patient information on a massive scale. Creat -
ing a system for establishing this linkage for increasing numbers of
individuals—and making the resulting data widely available to re-
searchers—is the key step in moving toward a Knowledge Network
and New Taxonomy. Such coupled data can be generated in several
ways—including the modest-scale, targeted molecular studies on pa -
tient materials that dominate current practice. However, the most
direct and effective discovery paradigm involves observational studies
that seek to relate molecular data to complete patient medical records
available as by-products of routine health care. Effective follow-up of
the most promising hypotheses generated through such studies will
require laboratory-based biological investigations designed to seek
explanations at the biochemical or physiological levels.
2. Implementation of pilot studies to establish a practical framework to
discover relationships between and among molecular and other patient-
specific data, patient diagnoses, and clinical outcomes. The new discov-
ery model will involve the mining of large sets of patient data acquired
during the ordinary course of health care. This is a novel, largely
untested discovery approach. Pilot studies designed to identify and
overcome obstacles to successful implementation of this approach will
be required before a set of “best practices” can emerge.
3. Gradual elimination of institutional, cultural, and regulatory barriers
to widespread sharing of the molecular profiles and health histories
of individuals, while still protecting patients’ rights. The sharing of
data about individual patients among multiple parties—including pa -
tients, physicians, insurance companies, the pharmaceutical industry,
and academic research groups—will be essential. Current policies on
consent, confidentiality, data protection and ownership, health cost
reimbursement, and intellectual-property will need to be modified to
ensure the free flow of research data between all stakeholders without
compromising patient interests.
A NEW DISCOVERY MODEL FOR DISEASE RESEARCH
The current model for relating molecular data to diagnoses and clinical
outcomes typically involves abstracting clinical data for a modest number of
patients from a clinical to a research setting, then attempting to draw correla -
tions between the abstracted clinical data and molecular data such as genetic
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polymorphisms, gene-expression levels, and metabolomic profiles. When dis -
coveries are judged definitive and potentially useful, an effort is made to return
this information to the clinical setting—for example, as a genetic or genomic
diagnostic test. This model creates a large gulf between the point of discovery
and the point of care with many opportunities for mis- and even non-commu -
nication between key stakeholders. For example, there have been approxi -
mately ten times more genome-wide association studies (GWASs) performed
on individuals of European ancestry than other groups (Need and Goldstein
2009). The current model also fails to exploit the wealth of molecular data that
are likely to be generated routinely in the future as personalized genomics and
perhaps other personalized “omics” become routine in clinical settings. Perhaps
most seriously, the current discovery model offers no path toward economically
sustainable integration of data-intensive biology with medicine.
The Committee views it as both desirable and ultimately inevitable that
this discovery model be fundamentally transformed. Instead of moving clinical
data and patient samples to research groups to allow analysis, the molecular
data of patients should instead be directly available to researchers and health-
care providers. The Committee recognizes that this is a radical departure from
current practice and one that faces significant challenges, nonetheless, because
we believe this new discovery model would have dramatic benefits, we believe
that aggressive steps should be taken to implement it.
The changes in science, information technology, medicine and social
attitudes—as discussed in Chapter 2—provides the opportunity to implement
this model. Indeed, there are concrete instances of research initiatives already
underway that substantiate the Committee’s belief that a special effort to imple-
ment its core recommendations can be achieved. In addition to the eMERGE
Consortium discussed in Chapter 2, an excellent example is a collaboration be -
tween Kaiser-Permanente Northern California and the University of California
at San Francisco (UCSF). Kaiser members were asked to participate in a study
that would allow genetic and other molecular data to be compared with their
full electronic health records. The study has faced major hurdles, and required
more than ten years to progress from its conceptualization to large-scale acqui -
sition of genetic data. A pivotal challenge was to build trust between Kaiser’s
members, management, and oversight groups such as the relevant Institutional
Review Boards. While all parties recognized it was essential that the Kaiser
members who were being asked to “opt in” to the research study be fully aware
of its aims, the outreach infrastructure required to educate members had to be
created nearly from scratch. A second major challenge was acquiring funding
to cover the cost of generating extensive molecular data that lacked direct and
immediate relevance to patient care—a responsibility that Kaiser itself could
not be expected to take on given the pressure to constrain health-care costs.
Moreover, changing perceptions about what constitutes appropriate informed
consent required costly and time-consuming reconsenting of the participants.
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Nonetheless, the ability of committed investigators—working within strongly
supportive institutions—to overcome these obstacles has been impressive:
nearly 200,000 Kaiser members have joined the study and large-scale data col -
lection is now underway.
The pioneering UCSF-Kaiser study makes clear that a discovery model
based on direct use of patient data is possible, even as its implementation faces
significant hurdles. In order to address and resolve these hurdles, the Commit -
tee envisions the design of several targeted pilot studies. These studies would
probe key aspects of this new research paradigm and demonstrate to health-
care providers the value of a molecularly informed taxonomy of disease. By
demonstrating value for patients, the pilot studies will seek to lay the ground -
work for a sustainable discovery model in which relevant clinically validated
molecular data are routinely generated at the “point of care” because they meet
the commonly accepted risk-benefit criteria that apply to all clinical test results.
PILOT STUDIES SHOULD DRAW UPON OBSERVATIONAL STUDIES
As emphasized above, the Committee believes that much of the initial work
necessary to develop the Information Commons should take the form of obser-
vational studies. In this context, what we mean by observational studies is that,
although molecular and other patient-specific data would be collected from
individuals in the normal course of health care, no changes in the treatment
of the individuals would be contingent on the data collected. This approach
to discovery is already in use today, although most current initiatives draw in
a very limited range of clinical data. Notably, many GWASs have compared
the genetic make-ups of individuals who receive a diagnosis of a disease to
those who do not (McCarthy et al. 2008). For example, GWASs comparing
individuals with and without a diagnosis of Crohn’s disease securely identified
a number of gene variants that implicate autophagy in the pathophysiology of
Crohn’s disease while similar comparisons for Age-Related Macular Degenera -
tion implicated complement factor H (McCarthy et al. 2008; Ryu et al. 2010). In
other instances, clinically relevant genotype-phenotype correlations have been
discovered in the course of observational studies performed during randomized
clinical trials. For example, a randomized clinical trial was performed to com -
pare the efficacy of different formulations of interferon alpha in the treatment
of chronic infection with hepatitis C. A subsequent observational study used
a GWAS to identify variation near the IL28B gene as strongly correlated with
response to treatment (Ge et al. 2009). Tests for the genetic variants identified
in this study are already in widespread clinical use (PRNewswire 2011; Scripps
Health 2011).
The enrollment of individuals in these studies had no bearing on their
diagnoses, treatments, or in most cases, anything else in their lives. The goal
of these observational studies was simply to ask the question “Are there gene
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variants in the general population that are associated with who ends up with a
particular diagnosis or experiences a particular treatment response?”
While observational studies will be primary tools used to develop hypoth -
eses about new and clinically useful ways to group patients, the findings emerg -
ing from such studies will need confirmation and investigation using other
approaches. For example, there are likely to be a great many ways to classify
patients based on molecular data, and only some will have clinical utility. In
general, clinical utility will need to be evaluated using randomized clinical trials.
Observational studies will also need to be followed by functional studies
that seek to determine the mechanistic basis of observed molecular associations
with clinical outcomes. An example of this type of combined discovery path is
the identification of BCL11 as a modifier of the severity of sickle cell disease.
Initially implicated in this role in GWAS studies, the biological basis of the
association was quickly determined by focused analyses that established that
BCL11A acts as a repressor of fetal hemoglobin. It is the persistence of fetal
hemoglobin into adulthood in patients with particular variants at the BCL11
locus that ameliorates the symptoms of sickle cell disease (Sankaran et al. 2008).
We anticipate that laboratory-based research of this sort will be essential to elu-
cidate the underlying reasons for observed associations between molecular data
and clinical outcomes and that these mechanistic insights will play an essential
part in establishing the Knowledge Network and guiding its use.
The Committee envisions pilot studies that would:
1. Be of a sufficient size, as well as scientific and organizational complex -
ity, to reveal on the basis of actual experience the most significant
barriers to the development of point-of-care discovery efforts.
2. Address one or more unmet medical needs for which deeper biological
understanding of a disorder would likely lead to near-term changes in
treatment paradigms and health outcomes.
3. Include the generation and analysis of a range of molecular-data types
potentially including, but not limited to genomic data (sequence and
expression), metabolomic data, proteomic data, and/or microbiome
data.
4. Be led by an organization charged with delivering health care with
strong partnerships with researchers.
5. Be supported by research funding to establish a “proof of principle.”
6. Involve partnerships with a broad array of stakeholders, both public
and private, including health-care providers, patients, payers, and sci-
entists with expertise in genomics, epidemiology, social science, and
molecular biology.
7. Seek to remove barriers to data sharing and provide an ethical and
legal framework for protecting and respecting individual rights.
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8. Develop IT networks of sufficient scale to allow assembly analysis and
sharing of the integrated datasets.
9. Draw on laboratory research to assess the biological underpinnings of
associations between molecular data and clinical outcomes.
10. Establish validation standards for clinical, evidence-based decision-
making.
Below, we outline two example pilot studies; the first, “The Million Ameri -
can Genomes Initiative”, is selected to pilot the use of one of the key layers
of ‘omic information that is “ready to go”. This pilot project would help to
populate the Information Commons with relevant data and facilitate learning
how to establish connections with other layers. By focusing on health care
recipients in diverse states of health and disease, this project would also help
evaluate the new discovery paradigm by allowing correlations to be made
between germline sequences and a vast range of phenotypes. The second “Me-
tabolomic Profiles in Type 2 Diabetes” is disease specific and is designed to
ensure the early introduction of a different ‘omic layer (metabolomics) into the
Information Commons and to pilot evaluation of more targeted questions in
the new discovery paradigm.
EXAMPLE PILOT STUDY 1:
THE MILLION AMERICAN GENOMES INITIATIVE (MAGI)
A natural pilot study that would contribute to the development of the
Information Commons and Knowledge Network of Disease would involve
the sequencing of the genomes of one million or more individuals and the
establishment of appropriate infrastructure for drawing correlations between
the sequence data and the medical histories of these individuals. In focusing
on a pilot study involving complete sequence data, we do not intend to elevate
sequence data above other data in their importance to the Knowledge Network.
Instead, this proposal recognizes that sequencing methods are “ready to go,” or
nearly so, for very-large-scale implementation and the acquisition of such data
in a point-of-care setting would, of necessity, require addressing key challenges
related to informed consent, protection of data, data storage, and data analysis
that will be common to all types of data. This proposal also recognizes that
sequencing on this scale will inevitably be undertaken in the near future in an
effort to make connections between human-genome-sequence data and com -
mon diseases. We view it as important to the development of the Knowledge
Network that this effort be grounded in the new discovery model, which would
make possible systematic comparisons of the molecular data with electronic
medical records, now and into the future: that is, the study design should al -
low correlations between genotypes determined now and health outcomes that
occur years or decades later.
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The sequencing of one million genomes would include a sufficient range
of individuals with different health outcomes and sufficient statistical power to
detect associations. For example, amoxicillin-clavulanic acid is a widely used
antibiotic that causes severe liver injury in one out of approximately 15,000
exposures. In a one-million-patient sample we would expect to include many
individuals with this—and other similarly rare—adverse drug reactions and
other medical conditions. It is also essential that the sample size be large enough
to build a concrete picture of the distribution of gene variants in individuals
free of specific diagnoses.
EXAMPLE PILOT STUDY 2:
METABOLOMIC PROFILES IN TYPE 2 DIABETES
Recent metabolomic profiling of blood samples from individuals who sub -
sequently developed type 2 diabetes showed marked differences in the charac -
teristics of branched-chain amino acids sampled from blood draws (Wang et
al. 2011). These early analyses suggest the potential of metabolomic analyses to
help identify those individuals at most risk of developing diabetes, and in par-
ticular, may help to elucidate the physiological steps involved in the transition
between insulin-resistant pre-diabetes and full-blown diabetes. We therefore
envision a pilot project focused on understanding this transition using me -
tabolomic profiles in blood. This work would begin with targeted quantitative
metabolomic studies transitioning toward more comprehensive metabolomic
profiles over time. Such an effort, combined with knowledge gained from Pilot
1 and research from other layers of the Information Commons (such as the
microbiome and exposome) could contribute substantially to strategies to delay
or prevent the development of type 2 diabetes.
ANTICIPATED OUTCOMES OF THE PILOT STUDIES
The pilot studies are intended to lead to new connections between genetic
or metabolomic variation and disease subclassifications, often with implications
for disease management and prevention. More importantly, they will provide
the lessons necessary to facilitate a more rapid transition in the way molecular
data are used. For example, pilot projects of sufficient scope and scale could
lead to the development of new discovery models, including those in which
patient groups self-organize in recognition of shared clinical features and then
pursue efforts to generate relevant molecular data. Such an initiative also would
permit many logistical, ethical, and bioinformatic challenges to be addressed in
ways that would benefit future efforts and lead toward the sustainable imple -
mentation of point-of-care discovery efforts.
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A RESEARCH MODEL BASED ON OPEN DATA
SHARING REQUIRES CHANGES TO DATA ACCESS,
CONSENT, AND SHARING POLICIES
Research to develop a Knowledge Network of Disease will need to resolve
complex ethical and policy challenges including consent, confidentiality, return
of individual results to patients, and oversight (Cambon-Thomsen et al. 2007;
Greely 2007; Hall et al. 2010).
The Committee’s vision of a Knowledge Network of Disease and its as-
sociated benefits for future patients will become a reality only if the public
supports a new balance between research access to materials and clinical data
and respect for the values and preferences of donors. Ultimately, there should
be no dichotomy between “patient data or materials” and “those who benefit
from this research.” The patients who are giving their materials and data for
research would also receive the benefits of research leading to a Knowledge
Network and the resulting new molecularly-based taxonomy.
How might these ethical and policy challenges be resolved so that the pilot
studies described previously might be carried out? The Committee recommends
that an appropriate federal agency initiate a process to assess the privacy issues
associated with the research required to create the Knowledge Network and
Information Commons. Because these issues have been studied extensively, this
process need not start from scratch. However, in practical terms, investigators
who wish to participate in the pilot studies discussed above—and the Institu -
tional Review Boards who must approve their human-subjects protocols—will
need specific guidance on the range of informed-consent processes appropri -
ate for these projects. Subject to the constraints of current law and prevailing
ethical standards, the Committee encourages as much flexibility as possible
in the guidance provided. As much as possible, on-the-ground experience in
pilot projects carried out in diverse health-care settings, rather than top-down
dictates, should govern the emergence of best practices in this sensitive area,
whose handling will have a make-or-break influence on the entire Information
Commons/Knowledge Network/New Taxonomy initiative. Inclusion of health-
care providers and other stakeholders outside the academic community will be
essential.
An approach to these issues might include:
1. Intensive dialog about the benefits of an Information Commons con-
taining individual-centric data about health and disease. This dialog
should include researchers and the public, patient representatives, and
disease advocacy groups. Reaching out to communities that have been
suspicious of research because of historical abuses would strengthen
trust. At the workshop the Committee convened, we heard patient
advocates and public representatives argue forcefully that more trans -
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parency regarding research and more collaboration among researchers,
research institutions, and the public would facilitate research. For ex -
ample, when constructively engaged, advocacy groups have advanced
biomedical research by helping to design studies that are attractive
to patients, publicized the projects, helped to recruit participants,
and raised money to help pay for the research (Giusti 2011; Patients
LikeMe.com 2011).
2. Exploration of approaches to informed consent that would allow patients
to give broad consent for future studies whose details remain unspeci-
fied. Once provided with concise, understandable information on how
their data and biological materials would be used for research, many
patients are willing to consent provided they are treated as true part-
ners in an activity that will provide broad public benefit (IOM 2010a;
Trinidad et al. 2011). On the other hand, some patients will object gen-
erally to the research use of “leftover” specimens originally collected
for clinical purposes or, more narrowly, object to their use in certain
types of research. These concerns must be carefully addressed. Current
approaches to informed consent for research rely on long, complex
consent forms that may deter participation while doing little to help
participants understand the nature of the research. As noted below,
the Health Insurance Portability and Accountability Act (HIPAA)
requires authorization or waiver for each specific research study: com-
mon interpretations of this requirement are so restrictive that inves -
tigators and Institutional Review Boards thwart or substantially delay
research of the type that will be needed to develop the Information
Commons.
3. Strong public representation and input on oversight and governance.
Public participation in biobanks and research projects would build
trust (Levy et al. 2010) and help resolve issues that arise in the course
of research, such as whether to offer to return individual research
results to persons whose biological materials are analyzed (Beskow et
al. 2010a) As noted earlier, the gray areas around the potential that
researchers may have a “duty to inform” participants of clinically rel -
evant results need to be clarified.
The HIPAA required the federal government to develop regulations for
protecting the privacy of personal health information. The HIPAA privacy
regulations, which are intended to protect patient privacy, inhibit research
that requires widespread sharing and multi-purpose use of data on individual
patients in several ways (IOM 2009): First, rich molecular data about an indi -
vidual (particularly whole-genome sequencing) could be considered a unique
biological identifier under HIPAA, even if overt identifiers are removed. Al -
though a waiver of authorization to use identifiable health information may
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be granted under certain circumstances, many health-care organizations are
reluctant to participate. Secondly, because HIPAA does not allow authorization
for unspecified future research or for several projects at one time, authorization
must be given for each specific use of patient data. Thirdly, requirements for
“accounting” to patients for research uses of data are burdensome and discour-
age data sharing. These regulations are strong deterrents to the kinds of pilot
projects envisaged in this report.
The Committee found a need to re-interpret—or perhaps reformulate—
HIPAA regulations, and is in agreement with the 2009 IOM report “Beyond
the HIPAA Privacy Rule: Enhancing Privacy, Improving Health Through Re -
search,” which found that the HIPAA privacy rule fails to protect privacy as in-
tended (IOM 2009), and, as currently implemented, impedes important health
research and imposes burdensome administrative requirements (IOM 2009).
This IOM report concluded that stricter security would be a better approach
to protect privacy than requiring patient authorization to use identifiable data
for research. It recommended that much research based on existing materials
and data be exempted from an amended HIPAA privacy rule (IOM 2009). For
example, the Committee suggested that researchers be allowed to work with
“secure, trusted, non-conflicted intermediaries that could develop a protocol,
or key,” for linking identifiable data from different sources (IOM 2009). A
biobank might serve as a trusted intermediary for the pilot projects described
above, giving researchers only data and materials without overt identifiers but
retaining a key to coded samples so they could update clinical information or
re-contact patients or donors when appropriate. Furthermore, the IOM re -
port recommended that “researchers, institutions, and organizations that store
personally identifiable data should establish strong security safeguards and set
limits on access to data” (IOM 2009). These precautions might include, for
example, requirements for physical security of data and provisions in materials
and data-transfer agreements that forbid researchers who receive de-identified
data from trying to re-identify patients or donors or to contact them directly.
Furthermore, new approaches to informed consent are being proposed and
tested. Some examples include: (1) incorporating highly specific patient prefer-
ences regarding use of their personal health information data (PCAST 2010),
(2) using a short form for informed consent for participating in biobanks, with
additional supplemental information for participants who desire more informa-
tion (Beskow et al. 2010b), (3) de-identified data-based, opt-out model used
by Vanderbilt and i2b2 (Pulley et al. 2010), and (4) consent for whole genome
sequencing and study of all phenotypes, coupled with respect for individualized
preferences regarding the return of clinically validated results (Biesecker et al.
2009). The Committee envisages that best practices and ultimately consensus
standards will emerge from the different models of consent and return of clini -
cally significant results to participants.
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PRECOMPETITIVE COLLABORATIONS
To accelerate the development of new tests and products based on a Knowl-
edge Network of Disease, precompetitive collaboration between nonprofits and
industry and among different for-profit companies would be desirable (IOM
2010b, 2011). The research needed to build the Information Commons, which
will require projects involving vast amounts of data from large numbers of
patients, will proceed more efficiently if such collaborations can be developed
both between academia and industry and among for-profit companies that have
historically been competitors (Altshuler et al. 2010).
These collaborations could include developing common standards and
database formats and building infrastructure to facilitate data sharing. Con -
sortia might be organized to share upstream research findings widely that
have no immediate market potential but are critical to downstream product
development. Examples of such upstream research include the identification
and validation of biomarkers and predictors of adverse drug reactions. To
build a flourishing culture of precompetitive collaboration, drug companies
will need to overcome their reluctance to share all data from completed clinical
trials, not just the selected data relevant to regulatory proceedings. Finally, and
most significantly, guidelines for intellectual property need to be clarified and
concerns about loss of intellectual-property rights addressed. Precompetitive
collaborations will only emerge if individuals and organizations have incentives
to join them (Vargas et al. 2010). The Committee believes that without such
incentives, it will prove difficult or impossible to collect the new information
that must be acquired before precision medicine, with its attendant benefits
in improved health outcomes and reduced health-care costs, can become a
widespread reality.
Similar principles apply whether the collaborations involve commercial en-
tities or are confined to academia. To encourage the collection of materials and
data, organizations and researchers who collect them should have first access
to their use for research, while still ensuring their timely availability to others.
The Committee does not envision the desirability or need, in the context of the
research required to populate the Information Commons with data and derive a
Knowledge Network from it, for the instant-data-release model adopted during
the Human Genome Project. However, it does believe that timely, unrestricted
access to datasets by researchers with no connections to the investigators who
created them will be essential. The cost of populating the Information Com -
mons with data precludes extensive redundancy in publicly financed research
projects. At the same time, the size and complexity of these datasets—as well
as the need for diverse, competitive inputs to their analysis—precludes giving
any one group prolonged control over them. They must be regarded as public
resources available for widespread and diverse research into ways to improve
health care and to increase the efficiency of health care delivery.
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Because the Committee is skeptical that one-size-fits-all policies can ac -
commodate the conflicting values associated with incentivizing researchers and
insuring adequate access to data, it believes that pilot projects of increasing
scope and scale should put substantial emphasis on addressing the challenges
associated with data sharing, rather than focusing exclusively on data collection
and analysis.
COMPETITION AND SHARING IN THE HEALTH-CARE SYSTEM
A distinct and critical question is whether payers, such as health insurance
companies, will provide access to their vast databases of patient and outcomes
data and whether they will be willing to integrate these data with data from
other companies and researchers with the goal of creating Knowledge Networks
such as those described in Chapter 3. On one hand, these organizations recog -
nize the potential value and cost saving that could emerge from such an effort.
On the other hand there are considerable impediments. One of the main im -
pediments is cultural: many of these organizations view their data as a propriety
asset to be used in efforts to generate competitive advantages relative to other
organizations. For example, large health-care systems and insurance providers
are interested in developing decision-support tools for physicians that would
cut down on the substantial waste caused by misdiagnosis or inappropriate
treatment decisions. Integration of biological data, patient data, and outcomes
information into Knowledge Networks that aggregate data from many sources
could dramatically accelerate such efforts. However, if the data and the research
results are shared, it would undermine one type of competitive advantage
that large data providers might otherwise have. In this way, there is a tension
between the sharing that would be good for the health-care system as a whole
and the short-term competitive instincts of individual providers and payers.
Apart from the culture of competition there are other impediments related
to cost pressures. Cost pressures within the health-care system are such that
providers and payers are unlikely to be willing to invest substantially (or in
some cases, at all) in the collection of biological data for research purposes.
Over the long term, once such data have been shown to yield clinically useful
information, it will become justifiable to expend health-care resources on the
collection of actionable data, just as is presently done for standard diagnostic
tests. However, until such data are shown to be clinically useful, it is unrealistic
to expect that the Information Commons will become populated by biological
data (such as genome sequences) acquired from providers and payers. Simi -
larly, the information technology challenges associated with integration of large
datasets and new disease classification systems are substantial. For example,
Aetna is currently engaged in a multi-year effort to update its information
technology systems to support the planned conversion to the ICD-10 coding
standards. This effort alone will cost tens of millions of dollars. While the goals
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of integrating datasets and changing classification systems are achievable in
principle, they will be beyond the technical capacity of all but the largest and
most technologically sophisticated providers and payers. Thus, the transition to
non-proprietary Knowledge Networks into which all data would be deposited
would have to involve strong incentives for payers and providers. This may
mean that the government will ultimately need to require participation in such
Knowledge Networks for reimbursement of health care expenses. At an even
more fundamental level, the longstanding issue of equity in access to a suf-
ficiently advanced level of health care should also be addressed if the data in
the Knowledge Network is to adequately represent the diversity of our society.
THE DEVELOPMENT OF A KNOWLEDGE NETWORK OF
DISEASE WILL REQUIRE AND INFORM THE EDUCATION
OF HEALTH-CARE PROVIDERS AT ALL LEVELS
Decision-making based on a Knowledge Network of Disease and the New
Taxonomy, which will incorporate a multitude of parameters, will represent
a significant adjustment in the practical work of the primary care physician.
Given the demands on the time of physicians and other care-givers in the pres -
ent health-care environment, few are likely to have the time or to feel qualified
to interpret the results of “omics”-scale analyses of their patients. The impor-
tance of this issue will escalate over time as the Knowledge Network and its
linked molecular-based taxonomy evolve into a system whose sheer complexity
greatly exceeds current approaches to disease classification.
One concern is that the infusion of large molecular datasets into clinical
records will reinforce a tendency many perceive as already crediting genetic
and other molecular findings with more weight than they deserve. In extreme
cases, this cultural bias has enabled the promoting and marketing of “omic”
tests with no clinical value whatsoever (Kolata 2011). In other cases genetic or
“omic” tests with real value in specific contexts may be over-interpreted and
thereby occlude consideration of other relevant clinical data. To develop the
Knowledge Network of Disease and the New Taxonomy that will be derived
from it, health-care providers will need to develop much greater literacy in the
interpretation and application of molecular data.
To meet these challenges, health-care providers will require both decision-
support systems and new training paradigms. The decision-support systems will
need to provide useful information about the propensity of patients to develop
disease, facilitate a correct diagnosis, guide selection of the most appropriate
strategies for disease prevention or treatment, inform the patient about the
prognosis and management of the disease, and provide the opportunity for
both physicians and patients to access more detailed information about the
disease on an “as interested” or “as needed” basis. Whenever possible, such
decision-support systems should enable shared decision-making by patients and
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FIGURE 4-1 Curriculum for biomedical graduate program—proposed new model.
The current model of the first-year curriculum in a typical biomedical graduate program (top) and an alternative model (bottom). The
multicolored bars in the nodes and connections course represent fundamental principles and essential facts about each key process inte -
grated across scales.
SOURCE: Modified with permission from Lorsch 2011.
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HOW DO WE GET THERE?
their care-givers. Such systems should be readily updatable as more information
is acquired about disease classification, the ability of particular test results to
predict disease development, progression, or response to treatment, and the
success of particular disease-prevention and management strategies.
In order to prepare physicians for the use of a comprehensive, dynami -
cally changing Knowledge Network, biomedical education will need to adjust.
Lorsch and Nichols (2011) recently proposed that graduate and medical life sci-
ences curricula would significantly benefit from a major shift away from the cur-
rent discipline-specific model to a vertically integrated nodes-and-connections
framework (see Figure 4-1). This model is not the only possible way of reorga -
nizing instruction to reflect new knowledge about molecular processes, but it
demonstrates how the development of a molecularly-based taxonomy, and the
underlying Knowledge Network of Disease, could lead to major changes in
education, while preparing students pursuing research careers to function in a
scientific landscape that increasingly requires multidisciplinary approaches to
solve biomedical problems (NRC 2009; MIT 2011). It also would give future
physicians a more holistic view of biological processes, which reflects what
will be required to fulfill the promises of genomics and personalized medicine
(Ashley et al. 2010; Wiener et al. 2010).
The teaching model proposed by Lorsch and Nichols very closely mirrors
the properties of the Knowledge Network of Disease described in Chapter 3.
In this teaching model a given topic—for example, gene expression—would
be taught in a vertically integrated fashion, with essential information all the
way from the atomic to the whole-organism scale discussed. Adjusting teaching
strategies to reflect the biological reality of the material has the potential to cre -
ate significant synergies. Students may retain more knowledge of basic science
when this information is directly connected to medicine. The enhanced ability
to use the New Taxonomy in medical practice and research would reinforce the
student’s conception of biology. Although it is beyond the scope of this report
to suggest detailed reforms of the medical-school curriculum, the Committee
would like to emphasize that full realization of the power of the Knowledge
Network of Disease and the New Taxonomy derived from it would almost
certainly require a major shift in educational strategy.
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