Reconciling evidence-based medicine and precision medicine in the era of big data:

 

Contributing to care delivery innovations, improved health preservation, and shifting the emphasis from  clinical therapy to prevention, and from disease to wellness. Challenges and opportunities:

Abstract:
This era of groundbreaking scientific developments in high-resolution, high-throughput technologies is allowing the cost-effective collection and analysis of huge, disparate datasets on individual health. Proper data mining and translation of the vast datasets into clinically actionable knowledge will require the application of clinical bioinformatics. These developments have triggered multiple national initiatives in precision medicine—a data-driven approach centering on the individual. However, clinical implementation of precision medicine poses numerous challenges. Foremost, precision medicine needs to be contrasted with the powerful and widely used practice of evidence-based medicine, which is informed by meta-analyses or group-centered studies from which mean recommendations are derived. This “one size fits all” approach can provide inadequate solutions for outliers. Such outliers, which are far from an oddity as all of us fall into this category for some traits, can be better managed using precision medicine. Here, we argue that it is necessary and possible to bridge between precision medicine and evidence-based medicine. This will require worldwide
and responsible data sharing, as well as regularly updated training programs. We also discuss the challenges and opportunities for achieving clinical utility in precision medicine. We project that, through collection, analyses and sharing of standardized medically relevant data globally, evidence-based precision medicine will shift progressively from therapy to prevention, thus leading eventually to improved, clinician-to-patient communication, citizen-centered healthcare and sustained well-being.

Glossary:
Clinical bioinformatics Bioinformatics mining and use of omics and other high-throughput data in a clinical setting, integrating various
standardized and interoperable datasets, extracting valuable clinically useful medical knowledge from these data
resources, and providing clinical-grade analyses or decision-support tools.
Clinical utility The relevance and utility of an intervention in patient care; the likelihood that an intervention will improve patient outcomes.
Evidence-based medicine The use of evidence from well-designed and well-conducted research (such as from meta-analyses, systematic
reviews, and randomized controlled trials) to optimize decision-making in medicine
Electronic health record (EHR) Digital version of data pertaining to the health status of patients (such as medical and treatment histories), and
allowing easy and secure information retrieval.
Incidentalome Ensemble of abnormal secondary incidental findings.
Interoperability Ability to exchange electronic information, based on implementation of standards, without special effort on the part of the user.
Metabolomics The high-throughput identification and quantification of small-molecule metabolites or exogenous substances present in cells, tissues, biofluids, and organisms.
Microbiome The collective genome of our indigenous microbes present in a biological specimen or organism. Medicine Acronym referring to predictive, preventive, personalized, and participatory  medicine, a systems approach that is
proactive and individualized, with an emphasis not only on disease, but also on wellness.
Personalized medicine Medical interventions tailored to a specific patient based on the individual characteristics of this patient and their
inferred response or risk of disease.
Precision medicine Precision medicine seeks to move away from symptom-based taxonomies towards the development of
individualized care, to be achieved through the molecular characterization of individuals in a multi-layered
patient-centered system, with customized medical interventions, taking into account a myriad of factors
(such as the patient’s genome, environment, and lifestyle) that can influence development of disease or treatment
response and thereby improve health (modified from [101]).
Quantified self Self-monitoring and data acquisition on, among others, vital signs, behavior, and lifestyle, as a means to improve health and fitness.
Stratified medicine While there may be subtle differences in the literal meanings of the terms “personalized medicine”, “precision medicine”,
and “stratified medicine”, they usually refer to the same concept when applied in practice. Stratified medicine (mainly used in the UK) is more treatment-dependent, targeting it according to relevant (biological, clinical, and other)
characteristics of subgroups of patients.

Background:
Since the writings of the Greek physician and philosopher
Galen in around 150–200 AD, healthcare has been largely
influenced by organ-based anatomy. This is reflected
throughout the world both in medical specialties and
disease classification and in the organic structures of most
hospitals [1]. The successful implementation of evidencebased
medicine allowed a departure from the classic empirical
practice that dominated medical history for centuries.
However, it did not modify this centuries-old organ-based
paradigm. Consequently, medicine deals essentially with
fragmented data. Moreover, despite the broad general
knowledge necessary for the practice of general practitioners
and hospitalists—specialties organized around a
patient and at the site of care—these experts cannot master
all the required knowledge. As a result, akin to the parable
of the blind men and the elephant, the fate and medical
trajectory of a given patient can vary depending not
only on the healthcare institution in which they are
being seen, but also on which specialty portal they are
first confronted with.
Recent technological, scientific, and social developments
are likely to change this paradigm. First, a flurry of
revolutionary, high-resolution, high-throughput datagenerating
technologies keep emerging, allowing costeffective
generation of huge datasets (often referred to as
“big data” [4]). Second, these developments are paralleled
by continuous innovations in information sciences (such
as sophisticated new algorithms and methodologies and
faster miniaturized processors, sensors, and cloud computing),
resulting in high-velocity and high-capacity computation
facilities. The third major factor is embodied by
the patients or citizens themselves. Individuals, empowered
by the proliferation of social media (see, for instance,
and connected technologies and electronic devices *
manifest a growing will to participate in managing
their own health and to also interact with others afflicted
with similar diseases. While each of these developments is
disruptive on its own, together they ensure that we are
living in perhaps one of the most profound periods of
advancement in biology and medicine, leading to a medical
revolution that will contribute to precision medicine
and transform health and medicine.
The long-term goals of precision medicine are numerous.
They include better disease delineation and stratification,
detection and monitoring of disease symptoms as early as
possible, identification of presymptomatic individuals (that
is, (long) before the disease is clearly manifest), monitoring
and modeling the dynamics of disease evolution, and
improved surveillance and management of disease. Prominent
among these goals are to provide better-adapted,
personalized surveillance measures and therapies and to
significantly delay disease onset and, whenever possible, to
prevent it. Consequently, it can be envisaged that the main
focus in healthcare will progressively shift—in a safe, efficient
and cost-effective manner—from treating disease to
managing health. Realization of these ambitious objectives
will result in significantly improved health outcomes and
patient satisfaction overall.
To achieve these overarching goals, it will be necessary
to bridge current evidence-based medical practice with
precision medicine and share, in a standardized
format and fashion, data across centers and countries.
Here, we outline our view of the challenges that need to
be surmounted to transform these opportunities into
real clinical benefits allowing the practice of evidencebased
precision medicine.
The role of clinical bioinformatics in precision
medicine
The capacity to produce and interpret the wealth of data
produced by these technological and scientific innovations
has already profoundly modified the scientific

These developments are also likely to impact medical
practice. The wealth of data (both multi-scale and multilevel),
which is theoretically already available for any
given individual, requires increasingly complex, sophisticated,
multidimensional analyses in order to convert
these heterogeneous, large-scale datasets into clinically
useful information. This is where clinical bioinformatics
(an essentially multidisciplinary approach whose key
functions include the utilization and integration of
laboratory and clinical data and the use of databases,
computational methods, algorithms, and other resources
and methods) enters the arena as an essential element of
data-driven precision medicine.
There are numerous definitions of clinical bioinformatics
or related terms (for example, see [12]). Our perception of
what this field entails is that by enabling the bioinformatics
mining and use of “omics” and other high-throughput data
in a clinical setting, by integrating various standardized and
interoperable datasets, by extracting valuable clinically useful
medical knowledge from these data resources, and by
providing clinical-grade analyses or decision-support tools,
clinical bioinformatics bridges the gap between medical
practitioners and the fruits of biological research. Hence, as
medicine tends progressively to be more and more intensively
data driven, clinical bioinformatics aims to support
diagnosis as well as tailored preventive and therapeutic
approaches in order to facilitate a personalized approach to
health.
Big data and clinically useful knowledge
It is not our purpose here to review these highthroughput
data-generating technologies, their limits, nor
all the potential they offer. It suffices to know that they
allow the generation of complex, heterogeneous big data
(such as high-resolution molecular omics, imaging, clinical,
and other, emerging data types), also referred to as a
“digital phenotype” [13]. Integration and analyses of these
multiple types of data sets, which include data ranging
from single cells [14, 15]—or parts thereof—to organs, entire
organisms, or populations, as well as including data
on individual lifestyles (for example, [16]), environments,
or social media (for example, [6, 13, 17, 18]), will provide
high-level views of exquisite granularity. It is logical to
expect that this ever-growing amount of high-resolution
data generated by these transformative tools can and will
lead to a profound shift in healthcare. This will be contingent
upon the data being eventually translated into clinical
benefits for patients as well as for individuals at
large—that is, be useful at the point of care as well as at
the population level [19].
This vision has led to a refinement of the “classic” concept
of precision medicine, also referred to as personalized
medicine [20]—in our opinion, the terms “precision”,
“personalized”, and “stratified” medicine are essentially
equivalent and interchangeable, with preference for one or
the other reflecting more a matter of fashion or national
preference. Analyses, interpretation, and exploitation of
this wealth of (longitudinal) data (across cell types, organs,
tissues, individuals, cohorts, lifestyles, or environments),
embodied in activities regrouped under the term “clinical
bioinformatics”, are likely to provide unprecedented opportunities
for integrative approaches, allowing a shift
from the traditional organ-based paradigm to a more allinclusive
and systemic assessment of health and disease,
and the practice of systems medicine [21–23].
It is important to stress that improved understanding
and management of health-related issues, or any complex
biological situation, is best served by systemic explorations.
By “complex”, we refer to situations where
the whole is more than the sum of its constituent parts.
The latter can be intertwined and interrelated, forming
complex networks, and the resulting intricacies are often
not visible when these components are investigated individually—that
is, out of context. However, the utility of
reductionist approaches should not be downplayed.
Quite the opposite. Reductionist approaches, by providing
the elements required for the understanding of the
components of the larger system, can be, and have been,
incredibly successful, having paved the way to the
present scientific and medical knowledge. Yet, deconvoluting
this complexity to consider only its separable
components will often fail to capture the interactions
between all components (the “interactome”) and thus
the complexity of the entire unit, and therefore seriously
limit our capacity to understand, treat, or prevent
diseases. Therefore, both reductionist and integrative approaches
are necessary as they complement each other.
In medicine, this complexity pertains not only to the
individual as a whole, but should also include his/her
environment (or “exposome”) and behavior. We offer
below a selected set of examples of achievements made
thus far.
Genomic sequencing: With the continuously decreasing costs of next-generation
sequencing (NGS), much attention has been directed
towards clinical implementation of NGS of whole genomes
or fractions thereof. As a result, a number of lowBeckmann
and Lew Genome Medicine (2016) 8:134 Page 3 of 11
hanging fruits of NGS are already regularly collected and
used [24], resulting increasingly in actionable therapeutic
insights. After the widespread implementation of genetic
diagnoses for Mendelian diseases, NGS of circulating
fetal- or tumor-derived cell-free plasma DNA are prominent
and successful examples of non-invasive diagnostic applications
that are being used, respectively, for prenatal
diagnosis of fetal aneuploidies (for example, [25, 26]) or
cancer diagnosis (for example, [27–29]). Similarly, NGS of
cancer cells or tumors can suggest potential therapeutic
leads (for example, [30–35]). Another telling clinical
sequencing application is in the area of microbial pathogen
identification or for the epidemiologic analysis of
pathogen spread and evolution, thereby opening new avenues
for timely therapies, infection control, and publichealth
responses (for example, [36, 37]).
As cost-effectiveness increases, NGS is fast moving
from being essentially a research tool to being
adopted into clinical practice. These examples illustrate
the power of NGS-based analyses, which are
likely to become increasingly part of routine screening
in this fast-evolving landscape. Some advocate that, in
the foreseeable future, every individual could have
their genome sequenced (see, for instance, [38]), at
least once during their lifetime. This will, however,
require not only further substantial decreases in sequencing
costs, appropriate management of potential
iatrogenic harm, such as caused by the “incidentalome”,  but also new methods to convey
the information back to the tested individuals
with effective counseling and coaching strategies [41].
While most attention is currently focused on DNA
sequencing [42], it should be emphasized that, however
valuable and informative our genomic DNA sequences
can be, our genomes do not encapsulate all the needed
information that determines our health status as not
everything is written in our genomes. While the
genotype-first approach [43] is attractive, the road from
genotype to phenotype is loaded with caveats and uncertainties
[44]—consider, for example, factors such as
incomplete penetrance, the description of clinically discordant
monozygotic twins (as reported, for instance,
among carriers of the ∂508 mutation in cystic fibrosis
[45]), the fact that a given mutation can result in clinically
distinct disease entities (see, for instance, [46–48])
or can lead to variable pleiotropic effects [49], the
description of resilient “superheroes” [50, 51], the difficulties
of interpreting variants of unknown significance
[52] or even of estimating the relative risks of known
pathogenic mutations [53], and the missing heritability
[54]. Furthermore, besides germ-line variants, somatic
mutation events may also need to be considered, even
beyond cancer, given the finding of somatic mosaicism
[55] and Venter and colleagues’ assertion of the
“dynamically changing nature of our genomes throughout
life” [56]. The detection and monitoring of such dynamic
changes will require more than one sequencing
test. These are but a series of examples demonstrating
the current limitations in the proper interpretation of
known genomic sequence variations, both for monogenic
entities and even more so for complex diseases
[19, 44].
Inherited and acquired elements
Predictive power will certainly improve with increased
integration of ancillary genome-derived information,
such as epigenetics and expression data (which we refer
to below as “meta-omics”). However, not every aspect of
health is determined, directly or indirectly, by the genome
inherited from our parents. The germline genome
is an essential component, but is only one of several
layers of information.
This is best illustrated with information that so far has
only just begun to enter the medical arena. In the past
decade, there has been an increasing awareness that, besides
our own inherited genome, each individual hosts
“alien” genes (our microbiome or metagenome), with
perhaps up to tenfold more microbial cells than human
cells, and the collective number of microbial proteincoding
genes (and thus gene complexity) is orders of
magnitude larger (close to 500 times) than that encoded
by our nuclear genome [57, 58]. Our microbiome, which
also contributes to a significant fraction of our metabolome
[59], assumes essential functions in regulating
growth and homeostasis. Its composition is influenced
by our own Mendelian genome [60], and yet no two individuals
are alike—that is, even monozygotic twins
might not share the exact same microbiome [60–63]. In
addition, while our inherited genome is, but for a few
exceptions, relatively static throughout life, our microbiome
is considerably more dynamic (for example,
[64–66])—its landscape changes and fluctuates in response
to a number of intrinsic and external factors
(such as disease, antibiotic therapy or other medications,
age, diet, lifestyle, socioeconomic status, and geography).
This plasticity endows the host with the capacity
to rapidly adapt and adjust to changing environments [67]
and to face adverse or stressful conditions (for example,
[65, 66]). In other words, the microbiome is a sensor for
the extrinsic and intrinsic ecosystem in which we dwell
and evolve; it can thus be seen as an essential element,
among other factors, ensuring the robustness needed for
survival in rapidly changing, and potentially adverse, environments.
The microbiome has also been causally implicated
in numerous diseases [65, 68], including brain
diseases (for example, [69–71]), as well as in response to
therapies [72, 73]. Hence, besides our inherited genome,
the microbiome could also be a major player in disease
Beckmann and Lew Genome Medicine (2016) 8:134 Page 4 of 11
and well-being, and this might justify that its composition
should become, in the foreseeable future, part of routine
clinical screening programs, allowing for a more comprehensive
understanding and management of individual
health.
Proper consideration of this complex multicomponent
partner—an integral element of our hologenome [67, 74],
which, in addition to our own genome, includes our
microbial flora as well as a largely unexplored viral component
[75]—will enable a more global approach to human
health. However, given the dynamic plasticity and
complexity of the microbiome, the data needed to capture
this information throughout an individual’s lifetime might
be orders of magnitude larger than those required for our
nuclear genome. This contrast is further exacerbated if all
associated meta-omics data are included.
While interest in the impact of the microbiome on health
has grown tremendously over the past few years, most of
this work still resides in the realm of basic science. Potential
clinical implementations are often emphasized, but their
realization requires further consolidation and validation.
Recent data suggest that anticancer therapies might need to
consider each patient’s microbiome as the latter might
shape immune-surveillance and autoimmunity and might
significantly affect the metabolism of anti-cancer agents
[76, 77]. If further substantiated and characterized, this area
could have a significant impact both on prophylaxis as well
as on treatment of cancer. It is also tempting to speculate
that oral decoctions in traditional Chinese medicine specifically
target the hologenome [78–80], but further studies
are necessary to clarify this point.
Integrating other data types
In addition to the inherited and acquired factors contributing
to the health of an individual, consideration of a
given cell type, tissue, or organ can now be envisaged
longitudinally, integrating the available information over
time and space with that of other body cells, tissues, or
fluids. Furthermore, the same tools provide the opportunity
to also take into account data on the environment,
as well as on individual activities, behavior, or
social networks. This will allow us to progressively depart
from anatomy-based medicine to eventually enable
a more all-inclusive, systemic assessment of a given individual’s
health, considering the whole rather than its
constitutive parts and leading to the development of
what others have called “P4” (predictive, preventive, personalized,
and participatory) medicine [81] targeting the
optimization and prolongation of wellness [82].
Indeed, being able to explore detailed datasets, including
those contributed by individuals themselves, should
enable more comprehensive, global (systemic) views of
their health status and thus enable both earlier and more
accurate diagnoses, as well as more appropriate, customized
therapeutic interventions, and thus contribute to the
development of P4 medicine.
Aggregation of heterogeneous data sets into
electronic health records
To achieve these objectives, these data, which are of multiple
heterogeneous types, formats, and sources, must be
integrated among themselves and with each individual’s
phenotypic and clinical records. The latter are currently
stored in electronic health records (EHRs). Yet, with over
700 distinct EHR vendors as well as numerous in-house
developments, medical data entry, database management,
and other processes can vary both from vendor to vendor
and from hospital to hospital. Hence, the market is alas
fragmented, often creating what amounts to “EHR silos”.

Examples of challenges and opportunities of evidence-based precision medicine:
•Multiplicity of stakeholders and disciplines
•Analyses of big data
•Heterogeneity of complex, multilayered data types,
and formats
•Harmonization of data semantics (clinical, laboratory,
and others): vocabularies, terminologies, classification
and coding systems, ontologies
•Standardization of data entry and storage
•Integration of multiple data types (such as laboratory,
clinical, behavioral, lifestyle, environmental)
•Secure, sustainable, and effective data storage and sharing
•Necessity for new analytic tools and algorithms
•Multiplicity and lack of semantic and technical interoperability
of electronic health record systems
•Extremely dynamic and fast-changing field, with
new tools constantly emerging
•Training and education of the different stakeholders
(medical staff, patients, and decision-makers)
•Ethical, legal, social, and consent issues
•Uberization of medicine
•Improved disease delineation, classification, and stratification
•Detection and monitoring of disease symptoms as early
as possible
•Non-invasive prenatal or cancer testing
•Identification of pre- or asymptomatic individuals
•Identification of new disease mechanisms and treatment modalities
•Monitoring and modeling the dynamics of disease evolution
•Improved, personalized surveillance and management of disease and therapies
•Significant delay of disease onset and, whenever possible, prevention
•Development of evidence-based precision medicine
•Shifting emphasis of medicine more from therapy to prevention, and
from disease to wellness
•Systemic view of medicine
•Patient participation
•Patient-centered medicine
Beckmann and Lew Genome Medicine (2016) 8:134 Page 5 of 11
are not easily achieved using current EHRs, resulting in a
growing trend to use ancillary data stores, such as picture
archiving computer systems (PACS) for imaging and
emerging warehouses for genomic data. Medical records
remain extremely heterogeneous, and integration of data
across EHRs can be daunting as current EHRs are neither
always interconnected nor mutually compatible or
interoperable.
This point is well summarized by the Community
Research and Development Information Service (CORDIS)
who stated that: “The clinical domain is probably
among the most complex from a semantic point of view.
Vocabularies, terminologies, classification and coding
systems, and ontologies have been developed by different
stakeholders to address different needs in different subdomains”
[84].
There is a clear necessity for robust standardized procedures,
formats, structured data, and nomenclature ontologies
to ensure reliability and efficiency [85]. However,
EHRs are not only heterogeneous but they can have
incomplete, inconsistent, or inaccurate data, as well as
additional limitations (reviewed elsewhere [86–88]) that
further hinder their applicability, scalability, and semantic
and technical interoperability. These issues can represent
major obstacles towards the efficient implementation of
precision medicine as the extraction of robust medical
information can often require cross-EHR meta-analyses,
justifying the call for common standards [89], as emphasized
below.
Reconciling evidence-based medicine and precision
medicine
The paradigms of evidence-based medicine and precision
medicine both have strengths and weaknesses. In
evidence-based medicine (Box 1), data are collected from
populations or large cohorts, from which mean values or
figures are derived to infer recommendations [20], which
will be applied to all, approximating the “one size fits all”
scenario. In general, outliers are essentially ignored. Although,
for most traits, each person might fall within the
mean estimates of evidence-based medicine, any individual
is nevertheless likely to be an outlier for one or several
conditions for which they might be a poor responder to
the recommended evidence-based medical practices.
Under these circumstances, evidence-based medicine
might fail to provide an adequate response.
This stands at odds with precision medicine, which focuses
on the individual, for which massive data-points
have been collected. However, precision medicine also
has its limitations. With millions of data-points per individual,
each case might be so particular or unique that
one might increasingly face “N-of-one” situations. In
view of the data scale and high level of complexity, this
can considerably limit the statistical power required to
set up and define appropriate evidence-based guidelines,
even if, for some patients, detailed functional investigation
might resolve this problem. Thus, the difficulties in
discriminating between significant and anecdotal inferences
based on “N-of-one” situations also creates a medical
problem. At best, the individual might occasionally
serve as their own proper control [90].
For medical practice to increasingly shift from generic
(“one size fits all”) to precision and personalized treatment,
there will be a need to reconcile evidence-based
medicine and precision medicine as, despite their respective
limitations, they can and should be viewed as
mutually complementary. The added value gained by
merging the strengths of both approaches relies on our
capacity to perform deep investigations of large cohorts
of patients. Indeed, the conversion from single cases to
an evidence-based approach will imply collation and
meta-analyses of big data from cross-institutional and
transnational large-scale registers and cohorts [91]. This
will allow detailed analyses of these very large population
samples, allowing, whenever possible, aggregation of
data from similar “N-of-one” cases, resulting in “N-of
many” paradigms, so that robust, reliable inferences can
be drawn for these stratified subgroups [90]. This transition
to an evidence-based precision medicine will, however,
necessitate standardization as well as responsible
sharing and mutualizing across numerous interoperable
data warehouses. Following the same rationale, the numerous
registries and large biobanks that are assembled
all over the world should also be constructed in such a
way as to warrant this need for inter-operability.
Thus, to safeguard optimal connectivity and utilization
of the vast amounts of data that can be collected in each
of these invaluable biobanks or clinical resources, it is
essential to ensure their accountability, transparency,
compatibility, and harmonized interoperability. To enable
access to this massive data set, it will be necessary
to create sustainable federated, safe data commons or
warehouses that will, under the appropriate conditions,
be open and available to all, thus facilitating responsible
data curation and sharing while duly respecting all ethical
and legal concerns and fully protecting the privacy
of the contributors [92]. Adequate solutions need to be
developed to meet these ambitious goals. Hopefully,
through federated efforts such as the Global Alliance for
Genomics and Health [93], these issues will be resolved,
allowing optimal use of these data for research (for example,
in disease modeling) as well as in clinical care.
Citizen-centered medicine
The patients or citizens embody an additional central element
of these quantitative as well as qualitative transformations
of healthcare. Active patient involvement will not
Beckmann and Lew Genome Medicine (2016) 8:134 Page 6 of 11
occur overnight. It might require adequate sensitization
and education, but will hopefully progressively settle in as
patients gradually and presumably irreversibly shift from
their ancient position of passive subjects to being active as
well as proactive participants and managers of their own
healthcare. This transition is enabled by the distribution
and diffusion of, and widespread access to, comfortable,
inconspicuous, user-friendly miniaturized connected mobile
devices or other wearable or implantable sensors
(among other technologies) that capture (often cheaply,
automatically, effortlessly, and continuously) geolocation,
pollution, environmental, behavioral, lifestyle, physiological,
or other clinically relevant data. This evolution has already
prompted the burgeoning fields of mobile health
(“mHealth”) and quantified self, opening the way to mobiledevice-based
non-invasive (self-) diagnosis and real-time or
remote monitoring [7, 8]. This might also contribute to a
transition from a classically paternalistic approach in medicine
to patient- or consumer-driven healthcare.
These devices offer new and exponentially expanding
opportunities and challenges in numerous areas,
including the health ecosystem. New mobile health
applications are constantly appearing (over 100,000
were available by the end of 2014 [94, 95]), including
applications for routine laboratory tests, such as for
blood chemistry. For these quantitative and qualitative
data to be of clinical utility, it will be necessary that
mobile health applications and devices be properly
evaluated and approved for the utility, safety, quality,
accuracy, and reliability of the collected information
[96]. With increased connectivity, use of certified connected
wearables and smart implants could allow
cheap, continuous, longitudinal harnessing of vast
amounts of high-quality clinically useful data, allowing
the collection of large-scale biologic, personal,
environmental, and social information. In the not-toodistant
future, these processes are likely to result in a
“medico-sphere” in which patients become big data
producers so that more medical data will be present
in individuals’ smartphones (or other devices) than in
their EHRs. This could lead to a major medical as
well as social shift in healthcare, as people take an increasing
role in their own health management.
While these applications might prove extremely useful
in several fields, such as in redesigning and evaluating
clinical trials or in critical care medicine [97], medical
knowledge might no longer be the prerogative of the
healthcare profession. Optimal use of this information
will still rest on the competences and skills of the medical
profession. However, with medical care progressively
becoming individual-driven, patients could increasingly
assume responsibilities for their own health, resulting in
a democratization of healthcare. The citizens could own
and control most of their personal medical data, which
could essentially no longer belong to the hospitals. This
could also impact the relationships between individuals
and their physicians, as the latter might turn increasingly
into a commodity or service, dissociated from hospitals,
with individuals rating, and selecting, their physicians.
This “uberization” of medicine [98] is thus likely to
impact current models and standards of healthcare
practice.
Furthermore, access to relevant, specialized medical
information will be easily enabled via the internet, which
will be further amplified via social media, including
disease-centered patient groups [6]. The capture of
personalized health-related records might incentivize individuals
to stay healthy. They might be able to monitor
vital parameters and, by proper lifestyle adjustments, to
reinforce positive behavior and to improve medical compliance
and thus maintain or ameliorate their general
health status. This might result in increased quality-oflife
and life expectancy. This could also facilitate “health
coaches” to intervene with adequate personalized advice.
Together, this might result progressively in increased
citizen empowerment and self-management.
Moreover, citizens might not only be actively engaged
in their own healthcare, they might also have the opportunity
to become research partners. With appropriate
regulation and consent, they might (and hopefully will)
opt to contribute their self-collected health-related data
to biomedical research (for example, [99, 100]), thereby
supporting citizen science. This would, once again,
necessitate (i) careful design of public data collection,
curation, storage, sharing, in due respect of the participants’
cultural sensitivities, privacy and consent, and
possibly control, and (ii) that the healthcare system be
able to integrate and use these data. Patient-centered
empowerment is thus likely to affect healthcare
dramatically.
The economics of precision medicine
Another essential driving force in precision medicine is
health economics. While combining evidence-based
medicine and precision medicine approaches will
optimize medical practice, there is a danger that this
might also signify the end of the era of the development
of blockbuster drugs, inasmuch as precision medicine
focuses on small, stratified sub-populations. In other
words, precision medicine pertains essentially to small
niches. Considering that there is already the problem of
the high cost of new medications, the ensuing reduction
of marketing niches could lead to serious economic and
social problems, as costs required for the development
and validation of new drugs, and thus their selling value,
might become prohibitively expensive. We surmise that,
despite this risk, the overall returns will shift the balance
towards important savings for healthcare. This rests on
Beckmann and Lew Genome Medicine (2016) 8:134 Page 7 of 11
the assumption that, although new niche medications
will continue to appear, the major benefits will not be in
the area of treatment but, as stated earlier and elsewhere
[82], in disease prevention or retardation.
In our opinion, the major goal of precision medicine is
not to extend life expectancy (although this is a likely
byproduct), but to improve long-term wellness. Diminishing
disease severity considerably or, even better, delaying
disease onset by one or several years (or almost totally)
are the major benefits to be gained from this precision
medicine paradigm. Moreover, this gain should be readily
implementable in all parts of the world, thereby contributing
to a global democratization of healthcare. According
to this scenario, medicine will continue to rely on adjusted
and personalized treatment, but the major goal of precision
medicine will focus on increased well-being.
Challenges of evidence-based precision medicine
The challenge of understanding the underpinnings of
our health in increasing detail can be overwhelming, and
there could be significant potential pitfalls. The field of
precision medicine should advance with caution, avoiding
overselling and ensuring that any claim made rests
on solid grounds. The challenges ahead are numerous,
but so are the rewards—namely that each and every individual
should eventually be able to benefit from precision
medicine (Table 1). Probably no other medical field
has ever progressed so rapidly. Consequently, this has
ramifications that are likely to impact many aspects of
the entire medical domain.
To begin with, it will call for adequate educational and
training efforts and might necessitate frequent revisiting
and adjusting of educational programs to adequately
reflect the existing but constantly evolving state-of-the-art
of precision medicine. As the European Science Foundation
asserted: the “healthcare profession may, as a result,
need to undergo a radical overhaul” [101]. It is not unlikely
that, in a couple of years, we will see, akin to what
happened in the fields of radiology and imaging, the increasing
importance of the medical specialty (and possibly
sub-specialties) of clinical bioinformatics. This evolution
might require a profound reshaping of medical curricula
to train highly specialized experts, responsible for the interpretation
of the ever-increasing amount and complexity
of data, for modeling disease (onset, progression and treatment)
and for issuing reports that will serve as decision
support for clinical staff. However, this educational issue
concerns all other healthcare stakeholders as we will need
to ensure, on the one hand, the proper education and
training of experts in clinical bioinformatics and, on the
other, the familiarization of all concerned medical stakeholders
(MDs, nurses, technologists, lab technicians, pharmacists,
and other medical or clinical staff ) with this
specialty. Or, as stated elsewhere, “future education and
training must reflect the changing body of knowledge and
must be guided by changing day-to-day informatics challenges”
[102]. The days when a single physician (or even
hospitalist) could cover the entire gamut of medicine are
gone, as the practice of medicine becomes increasingly
multidisciplinary. The field of clinical bioinformatics is
likely to evolve rapidly and continuously, and will do so in
an unprecedented manner. To capitalize best on these
rapid advances, the corresponding future training curricula
will need to be reassessed and readjusted periodically
as well as adapted regularly for the continuous training of
practicing physicians and other healthcare professionals.
Given the potential future role of citizens in selfmanagement
of health as part of precision medicine,
it is also important that adequate attention and
resources are allocated to promote awareness of personal
health and to keep the public adequately
informed of the status of the field and how best to
benefit from it. Keeping the public sector abreast of
the field is a huge but necessary endeavor. It is essential
that the public is involved in this transformative
process as engaged partners rather than as bystanders.
Doing so will further incentivize them to participate
in this medical revolution. Only then will they be able
take full benefit of all the opportunities afforded by
this democratization of healthcare.

Conclusions
We consider that evidence-based precision medicine
rests on three pillars: (i) responsible inter-institutional
sharing of large clinical and laboratory interoperable,
harmonized data sets; (ii) data on vital signs and behavior
collected by empowered citizens; and (iii) clinical
bioinformatics required to convert this complex information
into clinically useful knowledge, which will be
returned by the medical practitioners to the individuals
concerned. As it becomes progressively easier to collect
huge amounts of disparate personal health- and
population-related information on a global scale, the real
challenge in clinical bioinformatics will be to curate,
store, federate, integrate, share, mine, interpret, and
transform these extensive heterogeneous data into scalable,
medically actionable resources, while paying due
respect to all legal, ethical, and privacy values [103, 104].
This could allow the transition from reactive to proactive
medicine. In other words, this knowledge could
lead us to revisit disease etiologies, to refine, stratify, or
reclassify diseases, and to identify new disease mechanisms
and treatment modalities. The net outcome could
be better clinical diagnosis or prognostication; this could
facilitate clinical decision-making, improve medical care
or treatment, and most importantly, could contribute to
disease delay or even prevention. Clinical bioinformatics
has a central role to play in this revolutionary personBeckmann
and Lew Genome Medicine (2016) 8:134 Page 8 of 11
centric effort by contributing to care delivery innovations
and improved health preservation, and shifting the
emphasis more and more from therapy to prevention,
and from disease to wellness.  via Jacques S. Beckmann* and Daniel Lew

Abbreviations
CB: Clinical bioinformatics; CORDIS: Community Research and Development
Information Service; EHR: Electronic health record; MD: Medical doctor;
mHealth: Mobile health; NGS: Next-generation sequencing; P4: Predictive,
preventive, personalized, and participatory; PACS: Picture archiving computer
systems

Advertisements