NINR Big Data Boot Camp: Part 1 — Intro & Overview – Dr. Mary Engler & Dr. Patricia A. Grady

[music playing]>>Mary Engler:
Well, again, good morning and a big
welcome to NINR’s 2015 Big Data and Symptoms
Research Boot Camp. I’m Dr. Mary Engler and I’m
NINR’s training director and chief of the vascular
biology unit in the Division of Intramural Research. And I’m delighted to
be your program host. I hope you had good travels
to Bethesda and our NIH campus and I want to send
out a special welcome to our live video cast participants
and viewers today. We are just thrilled about
the big turnout and how much interest and popularity
there has been for this big data boot camp. I appreciate the time
all of you are taking to participate this week as
well as the time that all of our speakers have taken
to prepare for this great event. I also want to thank so many
for all of their tremendous hard work in planning and
preparing for this week’s big data boot camp including
the FAES administrative directors and staff
especially Dr. Amy Himes in the back there and
Ashanti Edwards. There’s a number —
Mary Ann, Carlene, Carlo — I hope I didn’t
forget anyone else, but thank you all so much. And also, our NINR divisions
including the extramural science programs and our
office of communication and public liaison and our
Division of Intramural Research Office of Training
Programs especially our deputy training director,
Dr. Pamela Tamez. Pam, could you stand up? And I also want to thank [applause] our NINR leadership for all of their
great support as well. Now, just to give you a
little bit of background on our Big Data and
Symptoms Research Boot Camp, it’s part of our NINR
Symptom Research Methodology series, which began
about five years ago. And I’m sure some of you
have attended the ones in the past but we had a focus
on pain and fatigue and sleep. But these past two years,
including this year, we focused on big data. And amazingly registration
filled up this year within two hours of registration. And it beat the record for
last year of about six hours. So, we’re very glad you all
made it and registered and we’re really happy to be
able to videocast today’s sessions for those that were
not able to register or attend in person. So, typically, a boot camp
is a camp for new military recruits to receive their
very rigorous, disciplined, intensive basic
training, right? And it’s very — extremely
challenging both mentally and physically. And I’m seeing a lot of
worried faces right now. [laughter] What’s coming up? But did you know — I did
a little research and, you know, just to give us
a historical perspective there’s also that the boot
camp is defined as a program that helps people become
much better at doing something in a short
period of time. So, without all the long
marches, sit-ups, push-ups, and pull-ups in this 100
degree weather that’s our goal for our NINR 2015 Big
Data Boot Camp — to provide you with a foundation in
data science focusing on methodologies and strategies
for incorporating novel methods into your research. We hope to increase your
research capacity and capability in big data
whether you are a graduate student, a university
faculty, or clinician. So, today’s plan as you
can see on our agenda, is to provide you an
introduction and foundation to the era of big data. Tomorrow’s session will
focus on clinical practice, big data, and
symptom research. Our third day will
focus on ethical, legal, and regulatory
aspects and data use. Then on Thursday,
or our fourth day, we will focus on data mining
as a tool for research and knowledge development. Lastly, on Friday, we will
be bringing it all together and we will also provide you
with information on funding opportunities for training,
career development, and big data research. So we have an exciting week
planned for you and I hope you have a wonderful
experience. It is my distinct honor to
officially open our NINR 2015 Big Data and Symptoms
Research Boot Camp and to introduce our first speaker,
and Director of NINR, Dr. Patricia Grady. [applause] Dr. Grady was
appointed Director of NINR in 1995. She’s a true champion for
nursing research and has led the NINR to astounding
heights and worldwide recognition. She is hardworking and
tireless in energy and enthusiasm for advancing
nursing science. We are so lucky to have
Dr. Grady with her exceptional leadership
at the helm of NINR. Please join me in a warm
welcome for Dr. Grady, our Director. [applause]>>Patricia Grady:
Thank you so much, Mary. It really is a pleasure
to be here this morning. I get as excited as you can
tell that Mary is and most of the staff you’ve
talked to when we have an opportunity to have all of
you come and to discuss some of the really pressing
issues that will help to move nursing science forward
and certainly most of you have become very familiar
with the terms big data over the last several years
particularly as the drum beats do increase
in tenor and speed. And also you’ll know — by
the time you leave this week you’ll be calling it data
science and not big data. Because we as always in
new areas have sort of an evolution of terminology but
it does speak to the larger picture and all
the possibilities. We’re also — I was glad
that Mary went over the definitions and terminology
of boot camp because we really did intend for it
not to have any military connotation since we’re
not that kind of facility. But to really — we do —
because we are NIH and we have such a collection of
intellectual talent here we are better able to collect a
very distinguished roster of expertise in a short period
of time and aggregate. And so we do feel that we
have an opportunity to give you a lot of information
hopefully well meshed with what you’re bringing with
you either — between joint efforts, your efforts, and
our efforts too – so that you will leave with
something of a synthesized idea. Not completely synthesized
of course but something of an idea of how this
information will be useful to you, how you’re
going to put it to work, and also to be able to
network with a collection of people in your cohort here
as well as to be able to continue to be in contact
with the speakers who you feel will be most helpful to you. So I do want to add my
words of warm welcome to, Mary and all the staff. Actually, we do have a
collection — you’ll notice as you go through the week
— and aggregation of NINR staff here — either
participating, listening, speaking, and also available
for counseling — for grant counseling. So, I would like to add
my words of welcome. We also are lucky to have
a number of really expert speakers from around the
country as well as the NIH. So again, you really will
have a very full week and to be able to, as Mary said,
learn a lot in a short period of time. So with that, I will add my
official words of welcome to the 2015 Big Data and
Symptoms Research and Methodologies Boot Camp. So — and also thanking
ahead of time all of our speakers for their
time and expertise. So, moving forward today I
will address briefly each of these areas — the
challenges and opportunities for you — many of which
you’re equally as well aware of as I am and by the time
I finish talking about the challenges I want to make
sure that you focus not just on those and become
overwhelmed, but look at the
opportunities. Looking a little bit at what
we’re doing in the area of nursing science
and big data, and finishing up with some
of the ideas for what we can do to move things forward. So, why are we here? You’ve already heard a
little bit about this this morning from Mary,
but essentially, we do understand that in
nursing science we have a wonderful opportunity to
put big data to work and to improve health through
the use of big data. We also are looking at the
key role that we can play in this area and the major
thing — areas where we can have an impact. And so we do have a lot of
opportunity and a lot of talent in the area of
analytics and informatics for resource management. Some of those folks you’ll
hear from during this week. We also have made a great
deal of progress in terms of the science, in terms of
nursing science and we can use that nursing science by
collecting the information and the data, being able
to aggregate studies, being able to learn from
each other and we are creating through those
studies and also the electronic health record in
particular are collecting a great deal of information. Much more than we realize. And we need to figure out
ways to be able to put that to work to improve
patient outcomes. The other piece of this
which is becoming much more obvious — it’s been evident
in nursing for a while, but is also becoming much
more evident across the country and across other
disciplines is the importance of patient and
stakeholder engagement and participation. And most notably, we are
focusing on that and that’s getting a lot of press
lately for the areas around precision medicine and
building the very large million person cohort
— a million genomes but essentially that really
is a patient cohort. And how can we
do that better? We had a full two-day series
of meetings last week — two weeks ago —
workshop on this. And really, to determine how
best to enroll people who are walking around
very healthy, et cetera — to enroll them
in a data base where they can proactively and
preventatively think about their health and then — and
have that information be available when it will be
most helpful for them and for their interventions to
maintain and to restore health for them. So, much of what we do
in nursing is related to symptoms and
symptom science. That is — we’re disease
agnostic so we focus on symptoms and not particular
disease but there are so many commonalities
among symptoms. And we are attempting to
study those in ways that really will benefit
just as it turns out, will benefit greatly
by the use of big data. So, when we look at symptoms
as opposed to disease we have to understand
that they are complex, that they do cut cross
different health states and states of disorders. That they are co-morbid. Very few people — we know
now that anyone over 50 in our population — most
people over 50 are experiencing one or
more chronic illnesses. And these can range from the
very minor to the very life limiting. And so — so that’s
something that — in our clinical studies we have
made concerted efforts over the years in clinical trials
to limit the symptoms to a very narrow range. And so, what we’re
recognizing now is that in order to be realistic and
to be able to scale up, it is important to be able
to study those symptoms in clusters. The other thing is that
symptoms tend to be chronic. We spend much more time in
the area of chronic illness than we do in acute. And also, as I — as I
said — in addition to being co-morbid that they’re
often clustered and we’re, that’s a new area of
endeavor actually for nursing science. To determine what are those
clusters and how can we best address those as a group. The other part of this is —
and this very well speaks to our discipline — is that it
does require that we have interdisciplinary teams. We also have a
person-centered focus and as I mentioned before,
caregiver engagement is of particular
importance in this. We, as most of the
other disciplines, but particularly nursing
because it is so clinical focuses on a whole
spectrum of populations. It’s important that all
people have as high a level of quality of life and
health as possible. So, what are some of
those challenges and opportunities? They certainly are many and
most of you bring an idea of what those are when
you come to see us. But, one of the things that
we do spend a lot of time on, of course, our lives
mostly — our professional lives center certainly on
health and improvement of health and dealing
with health issues. But interestingly enough,
despite the fact that we as a country spend more money
per capita on health and also tend to have very high
levels of technology and resources available in
actually investigating and being able to
intervene and help, the indicators by
commonwealth and World Health Organization really
tell us that in terms of most of the major indicators
that we feel that we’re doing a good job in, in fact
we are not statistically in terms of particularly
quality of care whether it’s safe, effective, coordinated
— all of these kinds of things that we tend to pride
ourselves on as a discipline and as a country — turn out
when we compare with other countries. And these are in comparison
with another developed countries that are
comparable to us we actually do not fare as well as they
do even though we tend statistically to have a
greater resource store and also spend more money. So we do have our
work cut out for us. Now, there are a number of
ways that we can — that we collect big data. There are a number of
sources that are stockpiling all kinds of information
that we could put to work to help address these issues if
we had a better handle on what it was, where it
was, and how to do that. And among these enormous
databases that we are compiling include the
genomic databases. All of the information that
it has been and is being collected on patients
in clinical studies. And just — many people
now — if you talk to your neighbors and friends, the
23 and me is a very popular source for them. Many people voluntarily have
had their genomes mapped. The imaging data. Each time that you go just
for regular checkups and think about the number of
images that are taken. Each one of those images has
any number of pixels since we all have digital cameras
now we’re much more aware of what images actually mean. But each one of those is
broken down into myriad data points and that all provides
additional information. Also, we are exposed to
a number of different environmental influences and
we’re beginning to collect information on that as well. Not nearly as much
as we would like to. Phenotypical behavioral
information. Think about each time that
you see patients in your clinic settings or your
students see them, how many data points they’re
collecting and where is that stored and how well
is that systematized? Clinical settings. The number of technologies
when you walk into a room now compared to five or 10
years ago and each one of those technologies
essentially is collecting great deal of information
and much of it is digitized but it may not be
accessible to us. So, again, we have an
enormous horizon and much emphasis has been placed
on this recently in the publications in
science and nature. Two of our leading
publications in the field that really do say that we
are awash in data and that we need to determine how
best to handle that. So, we’ve tried a number
of different approaches. I tend to agree with leaders
who have gotten there before us. I think as Franklin
Roosevelt said, “Do something. If it works, do more of it. If it doesn’t, do
something else. And I think that’s really
the state that we are in now. Although we don’t have a
clear path for how best to do this, we do have a
number of strategies, some of which have worked,
some of which have not. And so moving forward we’ll
keep that in the path ahead of us. So, how do we do this
in terms of healthcare? How do we make
data available? How do we sort of datify? And what we’re really
looking at is an enormous volume of information. A variety of information. So how do you compare
like and unlike pools of information. Voracity, the data is being
collected by a number of different sources,
technologically collected. Collected by humans. Collected robotically. And so how do you compare
that data and how do you make sure that it has a
certain level of accuracy, which you can rely on? The enormous speed, the
velocity at which this is coming at us, it is just
mind-boggling as Eric will probably tell you, Eric
Raymond as he speaks shortly will probably tell you a few
years ago we thought we had a big data problem and the
experts in the field were saying, “No, you don’t.” But now of course we do. And so then ultimately
what is — how can we make certain that we meet all of
these challenges so it does have the value
that we do need. So, there are a number of
approaches that have been tried. This article in JAMA is
extremely — looks very confusing but is actually
a really good attempt at looking at the number of
sources that are available and trying to make
sense of that. Because we know that
there is big promise for efficiency and
accountability in healthcare. But, this slide really shows
you just simply from a range of electronic health record
data what is available and how different these data
pools are and how comparable they could be but in
fact, the way that we’re collecting them often
are not that comparable. So, the big concern
really is two. May Correlations. Correlations are being made
each day and to make sure that they do actually
mean something. As Ronald Coase
in Economics said, torture the data and it
will confess to anything. And it really is sort of
like the original — one of the early textbooks on
statistics was “How to Lie with Statistics,” and we
really — the intent is to get the good data and to
make certain that what we — what it is telling us is
something that we can rely on. Another enormous challenge
is of course that of privacy — security and privacy. And of course, you know I
stand before you today as one of the, you know, 20
million people who’ve been hacked as part of the
federal government, so anything I say is
naturally suspect but we are — even to me —
but we are really, there’s an enormous amount
of effort that has been spent on security
and privacy. To be able to reassure
people to co-data. And that will continue. Obviously that will continue
to be an enormous challenge for us. So, we look at this. We say, “Okay, here are the
various populations that are interacting in terms of how
we get the data and how we process it. So, much of what we’re doing
starts with personal health records, electronic
health records. And that information goes
into a number of health information exchanges and
ultimately comes to us or agencies like us to be able
to integrate that and to figure out exactly what the
important information we can get — extract from that
is and how do we use that? So again, as I said before,
obviously the security and privacy concerns do remain
and we all — and this is a cartoon. “It’s not boring up here,
you get to look through everyone’s data.” And, even though
it is a joke, I think all of us at some
level worry about that and feel like this is exactly
what we are concerned about. In fact, on the way
in this morning, I was listening to an
interview with a physician who’s gone on record as he
and his family are making all of their data available. And the pros and cons
of making it available. And basically what
he said is, you know, he doesn’t know that it’ll
matter that much in his neighborhood, that people
will think less of his family if they have
hypertension, et cetera. But he also,
the bottom line, said is really there is a
certain limited level of privacy anyway. So, two points
of view on this. So moving into some examples
of the use of big data and nursing science, which has
been done — which we have done so far. Again, as part of the NIH
family it is extremely critical that all of us
are committed to turning discovery into health. That we do enormous number
of studies funded by NIH and have very startling results,
very positive results. But it is important that we
determine how we turn those into health. And so there are a number of
efforts at the NIH in terms of the leadership that is
committed to this area. Data commons, big data
to knowledge efforts. And some of those you’ll be
hearing about this week as you move forward
in the week. But we do have a number of
— and the BD2K as it’s affectionately called —
have a number of activities going on there which you
will hear about which are really directed
toward determining an infrastructure for NIH as a
whole and for the scientific community to be able to
determine which areas are important and how
to move forward. We have been very engaged
in that and we’re fortunate this morning to hear from
Dr. Eric Green who was the — we have– one of the
new positions at NIH is associate director
for data science. And Eric was the first
acting director of that office and chaired the
committee to select Dr. Phil Bourne who is our big
data associate director. But you will hear from
Eric this morning. So again, throughout the
field as you look through history, you know, there has
been an emphasis on big data although not identified as
such and I mean you’d look at the technology available
at the time and the early — some of the early work
that was done literally by Florence Nightingale and
some of the flow sheets that she used and the data
points that were generated. If we had those — if we
were to look at that today with all the technology
available there would be an enormous mass of data that
would be readily available and interactive and
able to be used. And so starting from that to
the scatter diagrams and the neighborhood sociograms
that are now being used to collect data on healthy
people and communities regarding exercise,
diet, et cetera. We have a lot of information
and we just — but we do need to figure out how
to use that the best. So just quickly going over a
number of studies that have been funded that are
pointing us the way of some of the efforts that
are going forward. This is an example of one of
our early efforts of nurse scientists to enhance visual
display of quantitative information. And this nurse investigator
is working with the CTSI at Duke University and she’s
focusing primarily on infants with complex
life-threating conditions and is collecting all of
this data and trying to create a story to be able
to point forward on how you identify the complexity
of the disorder, the life threatening illness
as it changes over time. And to be able to — to be
able to tap into patterns and look at characteristics
that then can be more predictive. It also is a way of — one
of the interesting things is that big data is very
quantitative and yet we’re starting to talk about intuitions
because big data does give you a way to identify
potential trends to emerging trends and so it’s
interesting that it really — we’re trying to in
a sense teach clinical judgment and to these
sort of robotic systems. So it will be interesting to
see how that moves forward. But basically, this is a
way to take data that is collected from a number
of different sources and aggregate it and to be able
to create some patterns and some potential
predictive trends. Other areas that are
reasonably simple, but can be extremely
important is to be able to quantitate things
like blood pressure. This is from a paper on Sue
Bakken whom you will be hearing from later. Looking at the ability to
quantify and use blood pressure over
time to predict. We’ve been — that’s the
measurement that we’ve been measuring for years and
since about the 1920s it’s been readily available. A little bit later
than that, actually, but early WWI — has been
fairly common practice but now it’s more or less taken
for granted and we don’t — we use it as a single number
and an indicator when in fact it’s a very dynamic
measurement and could be much more useful if we could
— if we could practically measure it on a continual
basis of — So looking at some of the other papers
that have come out, we have enough examples
of the early efforts. Looking at being able to
construct metrics and algorithms to be able to use
this data – collect from different sources and to be
able to move forward with it. And so, such things as
accounting for information missing in logistical
regression. Rather than having
to throw out these, how can we account for that
missing data and is there some way that we
can reconstruct it. Looking for systematic
errors and heterogeneous populations. As I said earlier, this data
is being collected from a number of sources by a
number of different kinds of data collectors and we need
to figure out ways to check for accuracy as
we move forward. Now, several studies that
have received a number of attention over the last
year do have, in fact, were using big data as
some of the background. And a number of these have
to do with workforce issues or nurse staffing issues. And some of you are familiar
with the headlines since these have — most of what
you’ve seen in the popular press are the headlines. But the nurse staffing and
education mortality which is a global study done by Linda
Aiken out of University of Pennsylvania. It’s very interesting in
that it does quantitatively relate the number of nurse
staffing patient ratios, to identifiable
complications and even mortality. So that you can’t
quantify that, having less time from the
nurse or having the nurse have more patients
is in fact, not bad for your health
but it can be lethal. The other piece of this
which is interesting to us, of course, at NIH and those
of you at educational institutions is that it also
looks at the quality of the — or the quantity of
education so that the more prepared the practitioner is
the better off the outcome for patients both
morbidity and mortality. That should — that may seem
intuitively obvious but it’s something clearly that
patient care settings and hospitals have been a bit
resistant to hearing and so now that is quantified
through the use of this data and is beginning to make
a big impact in terms of practice. Another area as a result of
the institute of medicine study on the future of
nursing science – or the future of nursing – one of
the areas that they focused on was to encourage or
support the functioning of nurses up to the
level of preparation. And that certainly should
be true for all health team members, but one of the
areas in that is of course the advance practice
component of the field. Now, most of those we don’t
have as much to do with because they’re not as
frequently as part of the research team – although
that’s changing – but we have funded the studies
that do show that there are measurable differences. And to look at some
of the safety issues. And so that’s a really
important piece of the future. Now, we are part of a
national – as you might expect, a number of national
exchanges are being, or state exchanges,
are being established. And so, one of these is a
really important one that’s reported in on JAMA
at this past year. And that is the nation-wide
health information exchange. How do you collect
the information, how do you certify who does
it, how do you participate, and how do you query
this information? Ideally, information that is
amassed will be available to the public. That’s something that
NIH is very committed to, and we’re working very hard
to make that happen from our own studies that we fund. But we do not have control
of all of the information that is not collected under
our funding so that – a number of groups – this
effort is growing across the country and will be
extremely in moving big data forward. And in underscoring
the value of it. So, one of the areas that
you’re aware of is an important programmatic area
for NINR is End-of-Life and Palliative care. And that is an area which is
beginning to benefit through the Palliative Care
Cooperative research group, and other groups that
are being formed, is beginning to collect
a lot of data and shows enormous promise for
benefiting from this. We recall, this is an
area in which very little research had been done until
the last 10 or 15 years. And so much of the
early research was more qualitative. And through the use of —
through the advance of the science and also the use
of big data approaches, the timing is particularly
good because we will benefit from this. So, a number of — one of
the — in addition to the other factors that I
mentioned earlier in terms of the data collection, one
of the other issues that we struggle with is
site-specific where is the data collected. So there are a number — and
particularly in the area of End-of-Life, this is,
because that’s an area where the data may be collected
in acute hospital settings, intensive care units, all
the way from that to the home setting, to hospice, to
assisted living facilities. And so the circumstances
under which the data is collected are
quite variable. Much more so than
in many other areas. And so it is important to be
able to look at the quality of the data and also to be
able to use that data to predict, particularly one
of the areas now is that we understand that hospices are
underused because of some of the rules and regulations
that vary about admissions and discharges, and what
kind of care can be provided in those settings. And so the use of big data
in this area is hope that it will be able to better
predict who is in what stage of readiness, and who can
benefit from these different forms of care, and that we
can be more accurate in that. Now, we also are, as you
know we at NIH have a very robust intramural program. And our intramural program
is focused on a number of areas that lend themselves
as we move forward – will lend themselves to
this area of big data. And one of these is one
in which we are currently working, collaborating with
the uniformed services university across
the street, and also the
department of defense. And that’s in the
area of brain injury. And most of you have
read a great deal, or know that it’s a major
problem with soldiers returning from the war. And so that’s the major
focus that we’re starting with, with this group. But we also understand
that brain trauma, brain injury is a very
enormous challenge in pediatric population,
and also in our general population. In addition to the
football players, which we all know about. But, so we are– so the data
that we’re collecting in these studies is
contributing to a federal agency traumatic brain
injury research warehouse. It’s a big informatics
system in a data warehouse, and so we are already
participating in sharing data and benefiting
from shared data. Which is a really good
model as we move forward. This is also in
collaboration with the rehab medicine and the clinical
center as well as the military and
uniformed services. And so we’re just starting
now to amass an amount of data that we can
begin to learn from. In our Centers program
across the country, we are also starting an
experiment with moving toward our use of big data. The Centers program,
as you know, is a very active and very
interactive program across the country, and does focus
on such scientific areas of genomics of pain,
sleep-related symptoms, bio-behavioral symptom
management, cardio-vascular, and also adaptive leadership
and symptom science. And this group is
working together very collaboratively, and taken
on the challenge to identify common data elements
so that we can, potentially — all the
studies that they’re doing in these centers across
the country and their collaborative ventures, that
that data can be available from their studies
to each other. And that will help a great
deal with the issue of the significance of the findings
when the populations tend to be small. And so we’re
very optimistic. This is a group that
is working very hard. The results of their
discussions in our meetings are being published. And so the recent
publication will — you’ll be seeing those coming out. And the next publication
will be more specific about the common data elements. But we are extremely
optimistic that that will help us move the science
forward in a much more robust and meaningful way. So again, we have,
actually, Sue Bakken, one of our speakers, is one
of those — is involved in one of those centers. But others you’ll see,
there’s been a fair amount of focus on this. The work of Sharron
Docherty at Duke, Bruce Rapkin at Albert
Einstein, Sue at Columbia, and also Connie Delaney at
University of Minnesota. And so they have — the work
that they’ve done so far has been getting a fair
amount of acclaim in the literature, and also our
joint publications as well. So, I do want to bring to
your attention a number of funding opportunities
that are available. These are NIH-wide
opportunities, but most of these,
if you notice, as you look at the
opportunities that are coming out, more and
more of these provide the requirement or the
usage for big data. And so I think that’s
something too that identifies a trend that’s
very obvious from our point of view. So, looking at
moving forward, there are number of seeds
of opportunities that we’ve talked about this morning,
but there are a number of ways to move forward and
so there are really more opportunities than we
even have people to take advantage of these. But looking at some of
the predictive areas, we know that nursing
informatics has been around for a long time, been very
active, very interactive, very trans-disciplinary. And now it is really setting
the stage through a number of workshops and through a
number of their efforts. Setting the stage to be
able to move forward, identify opportunities, and
to create opportunities for sharing that data. So, as we move forward,
the big areas of focus and promise, there’s such
an enormous ability, and a capacity to
generate new knowledge. Each time – we have had a
number of speakers point that out to us – that each
time that you are collecting any data for a clinical
trial, any of your studies, and you think about the
amount of information that you’re getting. The demographic information. The phenotypic information. The symptom science
information. The genomic information. Think of all of that
information you’re getting. And much of that is
collected and not heretofore not systematized or
organized in a fashion that that can be retrieved. So that needs to change. How we disseminate
the knowledge? How do we get people to
understand that this is a good thing? Big data, even with
the privacy issues, etc. This is a good thing. So we want to get people
tuned into these systems, and to be ready and
willing to enter into it. The system’s biology and the
electronic health record data – the health record is
still a work in progress. There are a lot of groups
that are pulling in different directions to put
things in, take things out, etc. One of the big groups
that is pushing to be involved in it, that has
struggled to be included, is the patient group. And so the citizen input
into this is something that we really need to make
certain is there, and that it is valued. As well as the other
observations that are — that have previously been
entered disqualitatively, but not entered in a
way that could be used. The other piece of this
is that potentially, as all of this
data is available, particularly with the
electronic health record, that is then
available to patients. Or healthy people walking
around who are being judicious about
their prevention. And so that information
potentially then can help people be much healthier
because they’re going to be informed as to where they
are on the spectrum of health or disease. So the, again, not too
many quotes this morning, but we really are at the
place where we are raising a lot of new questions. There are a lot of
possibilities around this, and we do have
the opportunity, starting today as we start
with the early notes on infection control – that we
do have an opportunity to look at what we
do in a new way, and to learn much more from
the way that we do it and the data that is available. So that we can move
forward in a safer, more effective way. So, in closing, we do have a
few minutes for questions, but I wanted to – I wanted
to take advantage of one advertisement to have
you save the date. We are celebrating our
30th anniversary upcoming. It’s hard to
imagine, but it is. And so we will be starting
to celebrate that year-long celebration on October 13,
2015 here in Washington. And many of you will already
be here for the State of the Science meetings, and
other meetings as well. So we want to make sure
that you put this on your calendar. It will be the scientific
symposium will – promises to be extremely interesting. All the data is
on the website, so I don’t have the
agenda for you here, but that is available. And we invite all of you to
come, in person if possible, and check the archives
out if you can’t. So with that, I will close,
and open the floor for questions before we hear
from our next speaker. [applause] Any questions
from the audience? Yes.>>Female Speaker: How
many of your centers of excellence in [inaudible]
science are focusing on the pediatric population?>>Patricia Grady: Oh,
that’s a good question. We do have a focus on
pediatric populations and I think about two or three
of those centers have some focus on pediatrics, but
not an exclusive focus. So we do not have a large
focus on pediatrics in the centers program itself,
although that is changing.>>Female Speaker: Thank you.>>Patricia Grady:
Good question. Other questions
from the audience? I know it’s still early. Yes, I see some over
on the right side.>>Female Speaker: Is there
a focus on a [inaudible].>>Patricia Grady: I
couldn’t quite hear you. A focus on…?>>Female Speaker: I said, is there a focus on a
low literate population, just as a whole?>>Patricia Grady:
Actually, yes. One of the centers in
particular is focusing on literacy as a part of
their center focus, yes. We — Health disparities is
a really important thread throughout all
of the centers, and so we are focusing on
disparate populations. Also focusing on
rural populations, as well as ethnically
diverse populations. Other questions? I saw one or
two other hands. Okay, well if you have
additional questions throughout the week, we have
plenty of people here to answer them, and I’m – and I
may see you throughout the week as well. So I’m going to turn the
podium back to Mary Engler who will introduce
our next speaker. We’re delighted
that he’s here, he actually is one of the
early experts in this area. And he’s a bit modest, so he
may not underscore his area of expertise as well, so I
want to make sure that I do. Mary? [applause] [music playing]

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