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  • [music playing]

  • >> Mary Engler: Well, welcome back from break and

  • I'm delighted to introduce -- after such an incredible

  • morning with such great speakers -- I'm delighted to

  • introduce our next speaker Dr. Bonnie Westra, who'll be

  • presenting Big Data Analytics for Healthcare.

  • Dr. Westra is director for the Center of Nursing

  • Informatics and associate professor in the

  • School of Nursing at the University of Minnesota.

  • She works to improve the exchange and use of

  • electronic health data.

  • Her important work aims to help older adults remain in

  • their community and live healthy lives.

  • Dr. Westra is committed to using nursing and health

  • data to support improved and better patient outcomes as

  • well as developing the next generation of nurse

  • informaticists -- informatistatcians.

  • [laughter]

  • Okay.

  • Please, join me in a warm welcome for Dr. Westra.

  • [applause]

  • >> Bonnie Westra: Is it potato or potato [laughs]?

  • [laughter]

  • So, I am just absolutely thrilled to be here and this

  • is an amazing audience.

  • It's grown since last year, so this is great.

  • So, today what I'd like to do is to relate the

  • importance of big data in healthcare to what we're

  • talking about today, identify some of the

  • critical steps to make data useful so when you think of

  • electronic health record data or secondary use of

  • existing data, there is a lot that has to be done to

  • make it useable for purposes of research.

  • Look at some of the principles of big data

  • analytics and then talk about some examples of some

  • of the science, and you'll hear a lot more about that

  • during the week in terms of more in depth on that.

  • So, when we think about big data science, it's really

  • the application of mathematical algorithms to

  • large data sets to infer probabilities

  • for prediction.

  • That's the very simple definition.

  • You'll hear a number of other definitions as you go

  • through the week as well.

  • And the purpose is really to find novel patterns in data

  • to enable data driven decisions.

  • I think as we continue to progress with big data

  • science, we won't only find novel patterns but in fact

  • we'll be able to do much more of being able to

  • demonstrate hypothesis.

  • One of my students was at a big data conference that

  • Mayo University in Minnesota was putting on, and one of

  • the things that they're starting to do now is to

  • replicate clinical trials using big data, and they're

  • in some cases able to come up with results that are 95

  • percent similar to having done the clinical

  • trials themselves.

  • So we're going to be seeing a real shift in the use of

  • big data in the future.

  • So when I think about big data analytics, what this

  • picture's really portraying is big data analytics exists

  • on a continuum for clinical translational science from

  • T1 to T4 where there's foundational types of work

  • that need to be done but we actually need to apply the

  • results in clinical practice and to learn from clinical

  • practice that it then informs foundational

  • science again.

  • When you look at the middle of this picture, what this

  • is really showing is that this is really what nursing

  • is about.

  • If you look at the ANA's scope and standards of

  • practice on the social policy statements, nursing

  • is really about protecting, promoting health and then to

  • alleviate suffering.

  • So when we focus on -- when we think about big data

  • science in nursing, that's really kind of our area

  • of expertise.

  • And what you see on the bottom of this graph is it's

  • really about when we move from data, you know, we

  • don't lack data.

  • We lack information and knowledge and so it's really

  • about how we transform data into information into

  • knowledge, and then the wise use of that information

  • within practice itself.

  • This was, I -- we were doing a conference back in

  • Minnesota on big data and I happened to run into this

  • graphic that just, you know, it's like how fast is data

  • growing nowadays?

  • And so what you can see is data flows so fast that the

  • total accumulation in the past two years is a zeta byte.

  • And I'm like, "Well, what is a zeta byte?"

  • A zeta byte is a one with 21 zeroes after it.

  • And that what you can see is the amount of data that

  • we've accumulated in the last two years equals all

  • the total information in the last century.

  • So the rate of growth of data is getting to be huge.

  • Data by itself though, isn't sufficient.

  • It really needs to be able to be transferred or

  • transformed into information and knowledge.

  • Well, when we think about healthcare, what we can see

  • is that the definition is that it's a large volume,

  • but it might not be large volume.

  • So when you think about genomics sometimes it's not

  • a large volume, but it's very complex data, and that

  • as we think about getting beyond genomics and we think

  • about where we're at, it's really looking at where are

  • all the variety of data sources and, it's the

  • integration of multiple datasets that we're really

  • running into now.

  • And it's data that accumulates over time, so

  • it's ever changing and the speed of it is

  • ever changing.

  • What you can see in the right-hand corner here is

  • that there -- as we think about the new health

  • sciences and data sources, genomics is a really

  • critical piece, but the electronic health record,

  • patient portals, social media, the drug research

  • test results, all the monitoring and censoring

  • technology and more recently adding in geocoding.

  • So as we think about geocoding, it's really the

  • ability to pinpoint the latitude and longitude of

  • where patients exist.

  • It's a more precise way of looking at the geographical

  • setting in which patients exist, and that there's a

  • lot of secondary data then around geocodes that can

  • give us background information about

  • neighborhoods that include such things as, you know,

  • looking at financial class, education.

  • Now it doesn't mean that it always applies to me,

  • because I might be an odd person in a neighborhood,

  • but it gives us more background information that

  • we may not be able to get from other resources.

  • So, big data is really about volume, velocity, voracity

  • as Dr. Grady pointed out earlier today.

  • Now as we think about big data, 10 years ago when I

  • went to the University of Minnesota and my Dean,

  • Connie Delaney [phonetic sp] had talked about doing data

  • mining and I thought, "Oh, that sounds

  • really interesting."

  • Because I was in the software business before and

  • our whole goal was to collect data in a

  • standardized way that can be reused for purposes of

  • research and quality improvement.

  • I just didn't know what to do with it once I got it.

  • And so I've had the fortune to work with data miners.

  • We have a large computer science department that does

  • internationally known for its data mining, and a lot

  • of that work was funded primarily by the National

  • Science Foundation at that time because it was really

  • about methodologies.

  • Well now we're starting to see big data science being

  • funded much more mainstream in addition now, NIH, CTSA,

  • et cetera, are all working on how do we fund the

  • knowledge, the new methodologies that we need

  • in terms of big data science?

  • So, an example of some of the big data science that

  • really is funded already today is that if we look at

  • our CTSAs.

  • So, there's 61-plus CTSA clinical translational

  • science awards across the country and the goal is to

  • be able to share methodologies, to have

  • clinical data repositories and clinical data

  • warehouses, and then to begin to start to say, "How

  • do we do some research that goes across these CTSAs?

  • How do we collaborate together?"

  • Or as we look at PCORnet.

  • PCORnet is another example.

  • So as we think about, there are 11 clinical data

  • research networks -- this may have increased by now --

  • as well as 18 patient powered research networks.

  • We happen to participate in one that has 10 different

  • academic of healthcare systems working together,

  • and it means that for our data warehouse we have to

  • have a common data model with common data standards

  • with common data queries in order to be able to look at