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]