The role of big
data in medicine
Technology is revolutionizing our
understanding and treatment of disease, says the founding director of the Icahn
Institute for Genomics and Multiscale Biology at New York’s Mount Sinai Health
System.
Most companies make a conscious and deliberate
decision to embrace digitization and the information revolution. Yet the role
of big data in medicine seems almost to compel organizations to become
involved. In this interview, Dr. Eric Schadt, the founding director of the
Icahn Institute for Genomics and Multiscale Biology at New York’s Mount Sinai
Health System, tells McKinsey’s Sastry Chilukuri how data-driven approaches to
research can help patients, in what ways technology has the potential to
transform medicine and the healthcare system, and how the Icahn Institute is
building its talent base. An edited transcript of Schadt’s remarks follows.
Evolution or revolution?
The role of big data in medicine is one where we can build
better health profiles and better predictive models around individual patients
so that we can better diagnose and treat disease.
One of the main limitations with medicine today and in the
pharmaceutical industry is our understanding of the biology of disease. Big
data comes into play around aggregating more and more information around
multiple scales for what constitutes a disease—from the DNA, proteins, and
metabolites to cells, tissues, organs, organisms, and ecosystems. Those are the
scales of the biology that we need to be modeling by integrating big data. If
we do that, the models will evolve, the models will build, and they will be
more predictive for given individuals.
It’s not going to be a discrete event—that all of a sudden we go
from not using big data in medicine to using big data in medicine. I view it as
more of a continuum, more of an evolution. As we begin building these models,
aggregating big data, we’re going to be testing and applying the models on
individuals, assessing the outcomes, refining the models, and so on. Questions
will become easier to answer. The modeling becomes more informed as we start
pulling in all of this information. We are at the very beginning stages of this
revolution, but I think it’s going to go very fast, because there’s great
maturity in the information sciences beyond medicine.
The life sciences are not the first to encounter big data. We
have information-power companies like Google and Amazon and Facebook, and a lot
of the algorithms that are applied there—to predict what kind of movie you like
to watch or what kind of foods you like to buy—use the same machine-learning
techniques. Those same types of methods, the infrastructure for managing the
data, can all be applied in medicine.
How wearables are poised to transform medicine
Wearable devices and engagement through mobile health apps
represent the future—not just of the research of diseases, but of medicine. I
can be confident in saying that, because today in medicine, a normal individual
who is generally healthy spends maybe ten minutes in front of a physician every
year. What that physician can possibly score you on to assess the state of your
health is very minimal. Unless something catastrophic is going on within
you—lipid levels that are way off the charts or glucose levels or something extreme—they’re
not doing much to assess what your state of well-being is, and the information
stored in medical records is not extensive enough.
What the wearable-device revolution provides is a way to
longitudinally monitor your state—with respect to many different dimensions of
your health—to provide a much better, much more accurate profile of who you
are, what your baseline is, and how deviations from that baseline may predict a
disease state or sliding into a disease state. That means we’ll be able to intervene
sooner to prevent you from that kind of slide. That sort of modeling would be
impossible unless you could phenotype individuals on a longitudinal and
long-term basis.
And while the wearable devices today are in this more
recreational-grade state, they’re changing incredibly rapidly into research
grade and ultimately clinical grade. There are already glucose monitors that
are FDA1 approved
that individuals can wear and that interface with digital apps, which then
connect directly with healthcare providers based on what they’re seeing with
your glucose profiles. You’ll see that kind of sensoring get better and better,
providing higher and higher grades and better and better profiles on
individuals over time. I estimate that in five to ten years, accurate
information about your health will exist more outside the health system than
inside the health system. And that will force the engagement of that
information by the medical community.
What big data means for patients, payers, and pharma
What I see for the future for patients is engaging them as a
partner in this new mode of understanding their health and wellness better and
understanding how to make better decisions around those elements.
Most of their data collection will be passive, so individuals
won’t have to be active every day—logging things, for example—but they’ll stay
engaged because they’ll get a benefit from it. They’ll agree to have their data
used in this way because they get some perceived benefit. Ultimately, that’ll
be the number of doctor visits you require, the number of times you were sick,
the number of times you progressed into a given disease state. All should
diminish. And there’s a benefit from being presented with the information, so
they’re looking at dashboards about themselves—they’re not blind to the
information or dependent on a physician to interpret it for them, they’re able
to see it every day and understand what it means.
A better understanding of Alzheimer’s
disease
I believe payers are perhaps among the top of the chain as far
as who can benefit from this. Because, ultimately, payers want to constrain the
cost of each patient. They care about the health of the patient, but they want
to do whatever they can to motivate both the patients and the medical systems
that treat them to minimize the cost through better preventative measures,
better targeted therapies, and increased compliance for medication usage. So
now, payers are getting a better benefit from drugs being taken, because
they’re able to see that the drug is being taken as prescribed or that it’s not
having the effect on the patient so the patient can be switched earlier to a
more effective treatment. If you’re able to intervene sooner in the course of a
patient’s health, before they slide into a disease state, then you’re going to
save money on those unexpected hospitalizations or emergency-room visits or even
physician visits.
Then there’s just the general risk profiling of patients. Of
course, payers care a lot about understanding the overall risk of a patient and
what they’re likely to cost year over year. For example, say we’re able to
generate genomic information that tells us what the heritable cancer risk of
every patient is; you don’t need to wait until a lump is felt or the person’s
at a later stage of cancer, when it’s much more expensive. Those better risk
profiles will be an incentive for payers to pay attention and to actually be
involved in that development.
For device makers, I just see this as a revolution that’s theirs
to lose if they don’t embrace the development of consumer wearable devices or
sensors, more generally, in environments where every person in the US or on the
planet is buying a device versus one of a handful of medical systems. That’s a
better business model that’s going to generate lots of revenue. And so it’s up
to the device maker to embrace that revolution and even start transforming some
of the devices they’re already making into consumer-grade devices that can be
not just recreation grade but higher grade, on toward the clinical grade.
Finally, from the pharmaceutical standpoint, I think it’s major.
I mean, just look at Regeneron Pharmaceuticals and Geisinger engaging the
Geisinger Health System and sequencing everybody in that population to create a
better understanding of disease and protections against disease to do
therapeutics. What you’re seeing, at some level, is some embracing of this sort
of information revolution by the pharmaceutical companies. It’s doing it mainly
from the genomics arena, but it’s also approaching it from the standpoint of
better understanding disease, having a better understanding of the causal players
of disease, and using that or the causal protectants against disease to
directly develop therapeutics.
Bringing together the right talent
One of the most fun aspects of creating the Icahn Institute—and
growing it into the state it’s in today and where it’s heading—is creating the
right kind of ecosystem that can be comprised of highly diverse individuals
from the standpoint of different areas of expertise.
In the past three or four years, we’ve hired more than 300
people, spanning from the hardware side and big data computing to the sequence
informatics and bioinformatics to the CLIA-certified2 genomics
core—to generate the information—to the machine-learning and
predictive-modeling guys and the quantitative guys, to build the models. And
then we’ve linked that up to all the different disease-oriented institutes at
Mount Sinai, and to some of the clinics directly, to start pushing this
information-driven decision making into the clinical arena.
Not all the physicians were on board and, of course, there are
lots of people who will try to cause all sorts of fear about what kind of world
we’re going transform into if we are basing medical decisions on sophisticated
models where nobody really understands what’s happening. So it was all about
partnering with individuals such as key physicians who were viewed as thought
leaders—leading their area within the system—and carrying out the right kinds
of studies with those individuals.
In all of these different areas, we’re recruiting experts, and
we view what we build as sort of a hub node that we want linked to all the
different disease-oriented institutes to enable them to take advantage of this
great engine. But you need people to help translate it, and that’s what these
key hires have done. They have a strong foot within the Icahn Institute, but
they also care about disease. And so they form their whole lab around the idea
of how to more efficiently translate the information from the big information
hub out to the different disease areas. That’s still done mainly by training
individuals within those labs to be able to operate at a lower level. I think
what needs to happen beyond that is better engagement through software
engineering: user-interface designers, user-experience designers who can
develop the right kinds of interfaces to engage the human mind in that
information.
One of the biggest problems around big data, and the predictive
models that could build on that data, really centers on how you engage others
to benefit from that information. Beyond the tools that we need to engage
noncomputational individuals in this type of information and decision making,
training is another element. They’ve grown up in a system that is very counter
to this information revolution. So we’ve started placing much more emphasis on
the generation of coming physicians and on how we can transform the curriculum
of the medical schools. I think it’s a fundamental transformation of the
medical-school curriculum, and even the basic life sciences, where it becomes
more quantitative, more computational, and where everybody’s taking statistics
and combinatorics and machine learning and computing.
Those are just the tools you need to survive. And it has to
start at that earlier stage, because it’s very, very difficult to take somebody
already trained in biology or a physician and teach them the mathematics and
computer science that you need to play that game.
Dr. Eric
Schadt is the
founding director of the Icahn Institute for Genomics and Multiscale Biology at
New York’s Mount Sinai Health System. Sastry Chilukuri is a
principal in McKinsey’s New Jersey office.
FOR VIDEOS
http://www.mckinsey.com/Insights/Pharmaceuticals_and_Medical_Products/The_role_of_big_data_in_medicine?cid=digital-eml-alt-mip-mck-oth-1511
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