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I would to accept Galen's challenge to moderate well and try to keep us on track. The
first group did a wonderful job of actually giving us a little lead time so that we could get
set up. I'm also going to pose a challenge to you all which is that if a few of you could
just move up and fill in the sea of void that's up here in the front, that would be really,
really cool. So those of you that are brave enough to move up, I think it will help you
see the slides much better. That's the bonus. And I want to pick up on - I really was
excited that Brad started the session with the YouTube video. That's one of my favorite
YouTube videos, and Brad said - and this is how it ends, and he loves how it ends well, I really love how it ends because it ends in that that's a Kelly Clarkson song - What
Doesn't Kill You Will Make You Stronger - it's her hit. She adopts those people, comes
to visit them, embraces them, and brings all sorts of accolades to that facility, that
hospital which renders a lot of big publicity and PR, so just an example of how you think
you're doing one thing when you can do so much more, so thanks, Brad, for starting
with that.
We're going to allow each of the presenters to present the data that drives our
communication. I had the opportunity to look through most of the slides and am really
excited because I'm a data freak, but it's always great to know that there's a reason for
what we do and much of that reason is driven by the data, so I'm going to spare you the
introductions. The bios are provided, and I encourage you to look at the phenomenal
backgrounds that each person brings to us this morning.
We're going to start with Kelly Blake who is a representative and a program director at
the National Cancer Institute, and she's going to talk about some of the work that she
does and the data they use.
Good morning. It's always hard to follow Brad Hesse in a presentation. I'm really happy
to be here and be part of the uniting data around action panel, and the slides that I'm
presenting are not totally my own. They're the collective wisdom of the HINTS
Management Team, the Health Information National Trends Survey Management Team
that's led by Brad Hesse, our project officer, and also includes Rick Moser, Lila Finney
Rutten, and Ellen Beckjord.
So my talk gives an overview of HINTS which is a survey that provides population
estimates that are derived from a nationally representative probability sample, and we
do this because cancer communication surveillance is essential to ensuring equity in
health information access, usage, and comprehension. NCI provides a plethora of
resources to make the data usable and accessible to researchers and practitioners
alike, and we'll go over some of those today as well as highlight ways in which the
HINTS program partners with CDC and others to integrate data and share resources.
1
So just a little background on the Health Information National Trends Survey. I actually
do know that there are a couple people in the audience who haven't heard of it, so this
won't be totally repetitive to everyone although maybe to 98%, it will be, but, again,
HINTS is a nationally representative survey that monitors changing patterns, needs, and
information opportunities in health, changing communication trends, information access
and usage, and cancer-related perceptions, attitudes, and behaviors. We've had about
a decade of HINTS now. We started in 2003, and just last week, HINTS 4 Cycle 1 Data
were publicly released on August 15, so people can go in and access that now and
SPSS data and SAS, and the prevalence estimates will be available on the HINTS
website online in September.
So HINTS was conceived during an NCI-sponsored risk communication conference
back in 1998. Maybe some of you were there. Attendees spanned a range of
disciplines including communication, psychology, public health, journalism, health
education, health behavior and medicine, and prior risk communication research was
reviewed and recommendations for future research were made, and during the
conference, attendees discussed how there was a lack of population level data about
health information and health communication variables and encouraged NCI to develop
a national communication population survey to provide baseline and follow-up data on
the population's access to, need for, and use of cancer information. So HINTS content
includes constructs and measures that help us assess the impact of the new
communication environment, national investments, disease prevention and health
promotion, and health-related knowledge, attitudes, and behaviors. Many of the
constructs and measures are unique to HINTS and are very cancer communication and
health communication focused while others replicate measures that are assessed in
other national surveys such as BRFSS and Pew.
And so in terms of the effects on the new communication environment, HINTS is helping
us to monitor its effect on the population including disparities in health communication
use and the digital device. It helps us to monitor the impact of national investments and
disease prevention and health promotion providing population estimates of levels of
awareness of cancer communication-related constructs as well as confusion, which
Brad alluded to in his presentation, and HINTS, like other national surveys, measures
knowledge, attitudes, and behaviors related to cancer prevention and control and health
communication broadly, and particularly something unique to HINTS I think is that we
measure levels of trust in sources of health information as well as health information
seeking.
So just a little bit of background on HINTS methods and administration. Over a decade
now, we've repeated HINTS cyclically to track trends. Since 2003 - we had a 2003
administration, 2005, and 2007-2008, and HINTS 4, as I mentioned, that was just
released - the data - last week. We entered the field in the Fall of 2011. HINTS 4 is
going to have four multiple cycles that are fielded over four years, and it's a selfadministered male survey only - and did that in hopes of increasing response rates, and
some of our early data from our cycle 1 show that we've had the best response rate yet.
2
So HINTS 4 is being fielded in four cycles over four years with approximately 3500
people per cycle. Again, we just publicly released the Cycle 1 data and Cycle 2 is set to
be fielded in October of this year. And the four cycles of HINTS consist of core items
that make up about 50% of the survey, and those core items will be trended over four
years, and, in addition, there are rotating modules and special emphasis modules that
cover specific topics such as the use of technology, tobacco, numeracy, etc., and here
you get sort of a range of the constructs and measures that we include in HINTS. And
so several of HINTS' resources are publicly available for both data users and cancer
communication practitioners, and all of these resources are available at
HINTS.cancer.gov. In addition to the website, which I'll show you in just a second, we
have resources such as Knowledge Maps that I'm going to preview in a few later slides,
and because soon, our Knowledge Maps by the end of this year are going to be
available on the State Cancer Profile's website which is a joint site between NCI and
CDC and so I'll show you a preview of what some of that's going to look like, and I also
want to say that our HINTS data users meeting is going to happen in March 2013 in
conjunction with the Society for Behavioral Medicine meeting in San Francisco, so we'll
have a pre-conference data users meeting with SBM the day before and then also kind
of a half-day workshop I think during SBM or a session during SBM.
So our main resource and public face for HINTS data users and results users is our
website. In the "what does HINTS tell us about" section, you can get a good feel of the
areas and types of questions that are asked in HINTS if you aren't familiar with the
survey. Topics include cancer communication, patient provider communication, internet
use, numeracy, as well as several other areas of cancer prevention, and our hope is
that the website makes HINTS data more accessible to communication practitioners
and cancer control planners in addition to our current community of data users by
bringing forward the top line survey results that are easy to grab and use for intervention
planning, grant writing, and other uses.
Another resource we make available for HINTS results users primarily are the HINTS
briefs. They're the most popular resource from the HINTS program. They're two-page
fact sheet like summaries of HINTS data that provide a snapshot of noteworthy
research findings, introducing population level estimates for specific questions in the
survey, and summarizing significant research findings that are a result of analyzing how
certain demographic characteristics influence specific cancer control outcomes, and
most briefs summarize research findings from recent peer review journal articles that
use HINTS data, and all of them include a short section called "how can this inform your
work," which is intended to give communication and public health practitioners directives
for how the data can be used in planning and practice.
On some measures in HINTS, we provide GIS isopleths maps so that users can
visualize regional variation and knowledge about cancer prevention. This is one of the
maps that Brad showed in his talk. HINTS doesn't provide state and county level data
but this is a way to sort of smooth the national estimates for broader data visualization
and application. This particular map shows knowledge about smoking and cancer, and
you can kind of see the regional differences wherein fewer people in the Southeast
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responded that smoking increases the chances of cancer a lot, even though there was
sort of broad awareness across the population. We're very excited, as I mentioned
earlier, that our HINTS knowledge maps are going to become part of state cancer
profiles by the end of 2012 - the end of this year. So, as many in this audience know,
the State Cancer Profile is a partnership between CDC and NCI and is the second step
on Cancer Control P.L.A.N.E.T. The objective of the State Cancer Profile's website is to
provide a system to characterize the cancer burden in a standardized manner in order
to motivate action into great surveillance and to cancer control planning and expose
health disparities. The focus is on cancer sites for which there are evidence-based
control interventions and the site does provide interactive graphs and maps for visual
support for deciding where to focus cancer control efforts.
Soon, by the end of 2012, what we'll be able to do in this support data section of the
state cancer profile site is to go to the cancer knowledge piece - just a second, I'm
having trouble navigating here. Cancer knowledge - and under cancer knowledge,
under the support data and cancer knowledge, you'll be taken to a landing page where
you choose your topic, so cervical cancer, colorectal cancer, etc., and then, from those
topic areas, you can choose a HINTS question that assessed knowledge and
awareness on that particular topic, so here I'll show you the example of cervical cancer
where you can choose a map for the HINTS question, "Ever heard of HPV?" or two data
points for "do you think HPV can cause cervical cancer?" And so what those sites will
pull up for you is - so here's a map using HINTS data from 2005 showing that the
Southwest had a higher likelihood of knowing that HPV causes cervical cancer with
some Southwest regions having about 62% agreement. And then, in 2007, the trend
shifted quite a bit showing that the South and Southwest were a bit more aware of the
link between HPV and cervical cancer with the low across the country being at 59%
whereas in 2005, the low was 7%, and so researchers can use these visualizations to
generate hypotheses about why this may be the case, communication campaigns,
vaccine promotion - that sort of thing - and practitioners can use the data to target
communication strategies where we see clear gaps in knowledge and awareness
across the country.
So, of course, the State Cancer Profiles page, in addition to the maps, will also link out
to the individual data years and trend charts for population estimates for each particular
HINTS question, so from these standard bar charts, it's pretty clear that people are
becoming more aware of the HPV cervical cancer link, but we think that the maps
provide a richer layer of interpretation for regional planning and for targeted
communication efforts. And again, those maps will be part of the State Cancer Profile's
website by the end of this year, and many of them are currently available on
HINTS.cancer.gov.
And so, in summary, changes in technology have led to changes in information access
and use and changes in healthcare use and delivery. HINTS was developed to monitor
the effects of the new communication environment on health-related knowledge,
attitudes, and behavior, and our analyses contribute to the evidence base and informed
cancer communication. And so the HINTS program makes resources available for
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researchers and practitioners, and we welcome collaboration with members of the
National Cancer Program. Thank you.
[APPLAUSE]
As we mentioned earlier, we're going to be holding after each panel a discussion
session, so if you're like me, I would encourage you to jot your questions down so that
you won't forget them.
Next, we have Dr. Ed Sondik who is the director of the CDC National Center for Health
Statistics.
Thank you very much. How much time do I have, an hour?
You have a portion of an hour.
Ten minutes, unbelievable. Okay, we're going to go very, very fast then. In a way, I
think this presentation is going to be kind of like an appendix - to what Brad did - and let
me just say that I'm looking at this from the standpoint of the National Center for Health
Statistics or data across the department, data across health, and it's not going to be
particularly cancer specific, which I think - as you'll see - I think it's extremely important
in this.
First of all, there's never been in my many, many, many gray-haired experiences in the
Federal government and outside for that matter, such a tremendous focus on data. This
administration put forth the Open Government Initiative, but the prior one as well was
very, very strong on getting the data out, getting the information out, and this is not a
commercial, but this group of HHS leadership has really been quite extraordinary in this,
and I think you'll see.
What about community data? Well, we're the National Center for Health Statistics, but I
assure you, we're very much interested in the community side. Why is it important?
Well, in a way, it's obvious, but you need this information for setting priority, for
evaluating progress, and this famous statistician, Tip O'Neill, who may be too old for
everybody in this room, said "all politics is local, all data are local." Actually, it's not well
known that he said the latter part of that, but I have said it on many, many occasions,
and I assure you, it's been drummed into my head by I can't tell you how many people
who said "but we need information on Hartford, we need information on the Blue Hill
section in Hartford - why aren't you giving us that?"
Well, we and many, many others are trying. I think the best example of why we need
community data as far as I'm concerned is healthy people. We've got 1100 objectives what does Montgomery County hear? How do they set their priorities? They can't
pursue 1100 - each one with the same zeal, to use an old-fashioned word, as every
other, so they've got to set objectives.
5
There's a wealth of data. We produce a lot of the core national data, but there's a great
deal others. SAMSA, of course, produces the information, AHRQ does, MEPS is built
on - if you don't know what these things mean, we can talk later - is built on top of the
HIS framework, so there's a great deal, but it is national. It's critical. We need that
information, and we publish that data, okay? But when it comes down to where the
rubber meets the road, at the local area, the community area, the state area - the
national is fine, but people don't want to make decisions on the basis of national.
Well, let me just run over a few of the really critical resources. BRFSS, the Behavioral
Risk Factor Surveillance System or Surveys has been mentioned at least a couple of
times so far. It's extremely important. It's a phone survey run by our colleagues in
Atlanta - I should really say enabled by them - and it's a family of state surveys. It's
very, very important because it provides data where really there isn't other data.
There's a little lag in this, have you noticed that? Yeah, it's back.
Another example is the California Health Interview Survey, in some ways an offshoot or
a son or daughter of the National Health Interview Survey which has really pioneered, in
my view, doing surveys on a state basis. It's very impressive. I think the total number
of languages that are used is 30 or thereabouts. It's a very, very large phone survey,
and it's really become part of the health infrastructure in California. It has spawned and
enabled - and the late Rick Brown was the force behind this - a national network of state
and local health surveys. I would hope that we see more of this. The surveys that are
listed here are the ones that are in this now, and there are actually way too few. First of
all, I think we have way too few state health surveys. There's one from Colorado, the
California survey, Houston, and activity in Riverside, California, Iowa, and Ohio, but we
really need more than that. Nancy Breen from NCI has written a very nice paper on
using local data in the fight against cancer, and I refer you to that. You would think that
from healthy people, that everyone would use this and develop their own state plan,
speaking of local data.
Well, according to the "Healthy People" people, there are about 54% of states and
territories that use the National Healthy People to plan their program. Every one of
those is going to need a lot of data. This is the front piece to the Maryland plan. The
Secretary of Health, Josh Sharfstein, and the governor I understand - I don't know the
governor personally - are very much focused on management by objectives, and they
have made a major effort to set up a variety of measures which are not coming up on
the screen - maybe it's another one….We should use the clicker? Okay, that gives me
another four minutes. Okay. But a lot of different objectives in this plan, but my point is
every one of these, whether it's having to do with babies or the social environment, safe
physical environment, every one of these needs a considerable amount of data.
Infectious disease, chronic disease, etc., or the one in the middle there which is number
31 - reduce the proportion of children and adolescents who are considered obese - it
doesn't do Maryland much good to know what the national figures are. They really need
to know what it is in Montgomery County let alone national. They're making a major
effort to address this.
6
A very innovative effort that actually in some sense isn't innovative at all is in Camden,
New Jersey that's looking at local data. This received a great deal of attention when
[inaudible] wrote about it in the New Yorker. There's a CNN special on it, and what they
do here - she's pointing out that I have two and a half minutes - is their approach is to
understand the problem, develop interventions to target it, and evaluate the impact of
the solutions. What's innovative in that? Well, what's innovative is that they took data
that is available from the hospitals and they made it available and looked at the highest
cost patients and focused in on interventions having to do with those patients, and it
appears that they're really having an impact on those. This is really using local data and
using the analytic resources from the New Jersey and the Philadelphia area, and they
really seem to be making a difference. It's very impressive. The evaluation side, by the
way, of all of these local efforts is really a large question mark. I don't think - just
because there's an activity going on, people using the data, intervening - doesn't mean
they're really having an impact, and I'll try to say a word about that at the end of this
talk.
The Federal government, again, has really been pushing the use of data and getting it
out of what some people view as somehow when we collect the data, we stick it in a
vault and we don't want anybody to use it. Of course, nothing could be further from the
truth. On the other hand, finding ways to really make it available is a challenge, so this
past June, there was the third of these "data paloozas," and I don't know if you can
make her out, but the woman speaking at this thing was the secretary. Now would you
ever think that the secretary of Health and Human Services would be out there using
the word "data palooza" and supporting this? Well, in fact, she, the Institute of
Medicine, National Academy of Sciences, Robert Wood Johnson Foundation, and any
number of other organizations are behind this effort, and it really seems to be making a
difference. One of the differences is we produce this Cosco of health indicators here,
the Health Indicators Warehouse - we don't charge anything for this - but in it, we have
a variety of data that - and this is really a change for a Federal statistical agency - isn't
ours. We didn't collect this, but we're putting together data that we consider of really
solid quality or reasonable quality at least that we feel can be useful to the community at
large, and this includes the county health rankings, community health status indicators,
the healthy people data, and very important - CMS community indicators.
Let me just run over some of these. I hope you're familiar with some of these. The
Community Health Status Indicators are 200 plus indicators for every county in the US.
Two hundred - again - a wide variety of measures but all having to do with health for
every county in the US. It's easy to go in, compare counties. It's an important source of
information for counties that are not collecting their own information. There's the County
Health Rankings and Road Maps supported recently by the Robert Wood Johnson
Foundation. It was built on an effort in Wisconsin, and it gets a great deal of attention
because it just doesn't produce the data but on 30 different measures, it actually ranks
the counties and at least once a year, it gets that. This relates to the Healthy
Communities Institute which Brad actually showed a slide related to this. This - several
years ago, about five years ago I think or so - started producing dashboards, and it
contracts with counties to produce a dashboard. This is the one for Sonoma County,
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looking at one measure in that dashboard - age adjusted death rates due to breast
cancer. Our local Montgomery County actually is supported by this effort. Brad showed
a slide which was Healthy San Francisco, and there are a number of efforts in Hawaii,
Arizona, and elsewhere that are taking this approach, but I ask you to think about the
question that I will not answer - how effective is it to put all this information out in a
community? I don't know of an alternative, but certainly it seems like it's a necessary
thing to do. Another . . .
[END OF FILE]
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. . . effort. This one is called the Health Equity Alliance in Connecticut that focuses on
comparing in detail data from the Census Bureau put together in a really interesting
geographic way so that you can compare the equity of healthcare, equity of health
status. That in a way leads to this community commons effort which is building a
community of researches, practitioners, policy people, public health people who are
focused on improving health using these tools that they have created that include - as
they put it - 7000 different data sets that can all be put on a variety of different types of
maps.
So where are we going with this? This is an activity called Asmopolis. Asmopolis puts
a device on every one of these inhalers that transmits information to a central area
which then integrates that and produces maps about where people - kids in this case are using their inhalers. It's a very, very innovative example of crowd sourcing that
people are really pretty excited about. There's a good deal more, I think, in the works
on this.
This is Stats of the Union which is an app that takes the community health status
indicators - it's produced by GE - and enables you to look at this data on your iPad. I
found it - to use the term of the moment - cool, but at the same time, I wondered what
do I really do with it? We developed - with the National Academy of Sciences - this
health statistics framework in 2002, but you know, today, I really think it's passe. I think
we have to think about health statistics in a different way. We have to think about not
the usual sources of information, not the usual surveys, but that we've got individuals
and those individuals can communicate information in a variety of ways, whether you
call it crowd sourcing, whether it's Asmopolis or a variety of other things. The same with
providers and the same with providing access. Many, many different ways of providing
access to all of this information. And all of this is going to take new disciplines. The
standard public health curricula that people get is just completely inadequate in my
view. We need to do more for that, and I'm just about through.
I just want to raise a couple of issues that I think are really important in all of this. The
potential is huge, but one of the things we have to keep in mind is the quality of the
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information. How do we know the quality of the information? And what's the strategy
that local areas, particularly states, should use in getting this information? New York
City did its own NHANES. NHANES is probably the most precise information that we
can get on the health status of a population because it's not self-reported. It's clinically
measured. And I think doing that - and I tried to proselytize this for the last decade doing that say once every five years or seven years provides a base where you can use
a variety of other information that is nowhere near as precise. The question is do we
achieve in all of this improved health? And one example - something probably
everybody in here has heard about is how crime has decreased in the United States,
particularly in New York City. In 1990, New York City put in place a data system that
enabled the local precinct captains to evaluate on almost a continual basis where the
crime was, what it was, and then they put marshalled their resources to deal with this.
It's been replicated throughout the United States, and at the same time, there is some
scholarly work by, in fact, the National Institute of Justice which is the research arm of
the Department of Justice which really says that we really don't know whether all this
local information has really played a role in this or whether there are other factors
involved that have been driving crime down.
So what we need to do in all this effort is we need to evaluate as carefully as we can
what we're achieving. And my last slide is we need to watch competition. There’s
competition and data, and we need to be very, very careful about what it is we're
choosing. Thank you for your patience.
. . . is Daniel Davenport, and Daniel is a partner at a company called Think D2C. For
those that are chiming in from virtually, you can always tweet us your questions, and
once we get to the question and answer period, we will make sure that we check those
and engage you in the conversation at that time as well.
[TIMEOUT FOR TECHNICAL ISSUE - MUSIC PLAYING]
We're back. Just a short musical interlude. Thank you for your patience.
mentioned, David Davenport is next on our speaker list from Think D2C.
As I
Hi there. Thank you very much. That was exciting, wasn't it? It's nice to have PCs up
in front. So thank you guys very much. It's very nice to be here. I'm not exactly - you
know, which one of these things is not like the other - so I've been trying to decide what
I was going to be able to present to all of you very smart people this morning. I decided
that I would just do my best to give you a POV of kind of where I come from, which is I
come from the internet, so here we go. And I'm going to tell you a little bit about
horizontal technologies to try, if I can, to demystify social because I think people have
interesting ideas of what social is. So in the very beginning, way back, way back in
1990, we had the internet, and the internet was a horizontal technology, but publishing
material on the internet was kind of hard to do. Everybody wanted to be on the internet,
but it took some combinations of new skills that didn't exactly all exist in one place. So
without doing any bio stuff, I made these two sites on the left. We used to do a lot of
work for a lot of different companies. Everybody had a reason to be online on the
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internet, right? It was great. If you squint really hard, you can see those little flags
waving on the White House site. No, they're not.
So anyway, everybody can use the internet, that's great. So now here we have social
media. So my theory here is that social media, like the internet, is a horizontal
technology, and it's really just providing an additional level of capability for us,
everybody here, to be able to use a content management system and publish
information. We saw a great YouTube video - there's how many days of video uploaded
every minute - that was not possible back in 1990.
Video back in 1990 was very hard. You had to have a lot of expensive equipment. You
had to have a lot of technical capabilities. Now, you can - with your cell phone, iPhone run out there and take a video and hit a few buttons and it goes up to your Facebook
page and the whole world can see it. It's a very different situation. It's very exciting, but
it had some limitations.
So where we are now is you can do a high bandwidth, low bandwidth, quick messages
all the way up to video. Tons of people on line. I think the earlier number was two
billion. I know that there's about a billion - 1.2 billion - that are usually referred to as the
socially enabled crowd, and what do those folks do? This is a PDF because I didn't
want to have any foibles up here with technology or I would have dramatically built this
slide out for you. But here's what it looks like for me. So this is me, I'm just a little guy,
right? I've got some hardware stuff, some tools, and then I've got some social network
things that I use tools for content management and the distribution, so there are all
these people that can now not read my blog posts or not look at the pictures of my
garden on Pinterest. Sometimes, people take pictures of me at conferences and I wind
up on Facebook but usually not much.
Most of what I produce is baby pictures and baby videos, and that's what the one
person out there on the social networks - my father - is real interested in. Other than
that, somebody - just a normal person like me doesn't really produce very much
interesting information. You guys, of course, produce a lot of interesting information, so
you're a special case, but little people like me - yeah, baby pictures. So in thinking a
little bit more about how to approach social, this is a chart by David Edelman.
Edelman's a big fancy PR agency, and so they've got this way of looking at social that's
beyond like oh, you're going to do a Twitter profile thing or I've got a Facebook page wow, I'm social. So they take a little bit more structured approach to it. This came out
in McKenzie Quarterly in the spring.
So if you look along the top, you may or may not be able to read that. Monitor, respond,
amplify, and lead are the categories at the top, and Edelman's a PR agency. They are
squarely in the respond category, and from this big long paper, you can really tell that
once they got into the amplify and the lead, they had no idea what they were doing.
They were just guessing at things. The one thing that everybody needs to do is
monitor, and we'll talk about that in a little bit. So another way to think about this top
row would be research, PR, advertising, and marketing. So that's a little bit more of the
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world that I come from. When you look on the left-hand side, that's a funnel. It's just a
straight-up funnel. You guys may not be as aware of what funnels are, but at the top of
the funnel - it's awareness. The bottom of the funnel used to be was buy. I'd sell a lot
of dog food for clients, so you know, we're trying to say hey, look at our dog food and
then buy our dog food. That's a funnel - marketing funnel.
Now that we have social, everybody makes their own - what happens after buy, you
know, experience - advocate, bond, ambassador, all these fancy words, but it's really
just like loyalty-based stuff. So if you think about the funnel on the left and the
categories across the top, this could be anything. Instead of social media, it could be
digital media, it could be traditional media. It's all the same thing. You're just
approaching it in a little bit more structured way, and I think a lot of companies that I
deal with have a hard time approaching social media for a variety of reasons.
One is that they're not as structured as they possibly could be. Another reason is that
not every topic or product naturally lends itself to social media. For instance, I do a lot
of work for Orkin, the pest control people, and Orkin is always trying to figure out how
do we get social? And at the end of the day, not that many people want to post on their
Facebook well, gosh, the Orkin man was just here and he killed a rat. It's not one of
those things, so social is great but it's good for different reasons for different people,
and one of the things that I think is the most important and potentially interesting to you
guys is the monitoring, the social listening.
So social listening used to be pretty pedestrian but now we're into a second generation,
third generation kind of natural language processing type situation. So instead of doing
surveys or phone surveys, what I like to talk to people about in this regard is these are
like ambient focus groups. You don't recruit people or anything, it's just conversation
that's going on. It's going on right now. It's going on whether or not you monitor it. You
can tap into it with some fairly sophisticated things, like this one is a program called
Crimson Hexagon out of Boston, and you can really train it to look for things that are
much more granular than sentiment. If you've ever used any of these things like
Radian6, you know that sentiment analysis is - 10% positive, 5% negative, and 85%
neutral. You don't get a lot of valuable information on that.
With the new stuff, you can and so you've got this great ability to tap into all of this
information, and there is tons and tons of information. I think there's a zetta byte
created each year in the social realm, so wow, a lot of information. The problem with
that is like my little blog post - nobody cares about that. You guys are already out there
going if we just had another baby picture on the internet, we might be able to cure
cancer. Nobody's saying that, so there's another thing that's been happening that
people call the internet of things just because they didn't get very creative that morning.
They hadn't had very much coffee and neither have I, but basically, what the internet of
things is that everything is going to have the internet embedded in it. So you can think
refrigerator, you know, car of course, whatever it is, toaster oven - whatever - and the
problem this is bridging is that people aren't very good about reporting events in the real
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world. They're just not very reliable. So what this allows us to do is get down into a
device layer, another horizontal layer, for collecting much more accurate data.
For you guys, the interesting thing is that all of this internet of things stuff kind of is
coalescing around health and wellness. So wow, this is a great trend that you guys are
standing right in front of. Super! So just with the cell phone device, the smart phones
that we have, now there are little Bluetooth device guides that can take your blood
pressure, that can weigh you, and do your heart monitor stuff, so there's all this
information that's now about an individual - and remember, we've got the internet layer,
then we've got the social layer, and now we've got the device layer, so we've got all
these little layers hooked up for personal data creation and the hip kids out in San
Francisco at Wired Magazine called this the quantified self movement. So now all of a
sudden, you're creating all this great data about yourself and that's awesome.
So here's what it looks like together. On one side, you have brand marketers like me
that are trying to figure out what dog food you like. On the other side, you have
healthcare providers that are trying to figure out how to keep us all healthy. So this
should be a really interesting situation coming on. I'll probably get slaughtered for this if
anybody reads the slide very carefully, but the problem is you guys don't do a very good
job of really either the social or the device or the personal, and so you've got all these
people creating all this data and it's just going - you might as well just be throwing it out
the window because you don't really have a good way to organize and use it.
So I need to get some of my data. I'm going to get my line data from my Nike thing, and
Nike's got a nice little website built for me so that I can track all that. Well, what about
my doctor? Now my doctor has 20 patients or a hundred patients let's say that all have
the Nike Fuel thing. Do you think Nike's putting out a portal for doctors to view all their
individual patients? No, they're not. Maybe in the future if it makes business sense for
them, but there's no aggregate system for all these little doo-dads that are producing all
this data all the time so the data just gets flushed much less how do we [inaudible] the
data and get it out to researchers or farmers or biotech and then get huge amounts of
data that we want to push to decision support platforms like Watson, the really high end
AI stuff.
So there's problems. There's opportunities. There's not much time left in my
presentation, so hold on - do not be afraid of this slide. I'm going to explain it in detail,
so just stay where you are. Social media, great personal - people can create and
distribute content like never before to a very broad audience. Device media, very
exciting. Very exciting for the health industry because it just seems like the first wave of
a lot of this stuff is focused on personal health and wellness data. The big problem is
that, at least from my standpoint, is that oh, electronic medical records. What are we
doing there? I mean it's been 40 years since everybody’s going, oh….Everybody's
going to have their own - what if I took away everybody's Facebook password right
now? It probably wouldn't cause too much trauma in this group, but in some groups, it
may. So all of a sudden, you can't look at my baby pictures, your baby pictures - no cat
photos. What’s going to happen? Well, that's kind of the entre that electronic medical
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records is. I've never seen any of my medical records. I have no idea. I wouldn't know
who to call to get them, so I'm creating all this new media device and my heart and
blood pressure and all this stuff. It doesn't land anywhere. It doesn't go anywhere, and
that's just a real pedestrian example. If somebody gets flooded out of their city and has
to go to another city and get a new profile for their cancer treatment and they have to
start their cancer treatment over, that's real, and so if I could ask you guys in the big
government world that you live in, maybe you could get that medical record thing
hooked up. Thank you!
Our last presentation before we go into the discussion is Durado Brooks, and Durado is
the director of the Prostate and Colorectal Cancer Program in the American Cancer
Society.
Good morning! Welcome everybody to this wonderful conference. It's been great so
far. I'm going to kind of follow on the heels of a lot of what's been said so far and, in
particular, talk about what Daniel just talked about, and that is taking all of this data that
we've all discussed and bringing it down to what it means at the individual level because
that's a piece that frankly is missing from a lot of what we currently have available, and
in particular, I'm going to talk about when the data doesn't give us an answer or it gives
us multiple answers, so when evidence conflicts, how do we talk about that uncertainty
with our patients?
Uncertainty in medical care is actually huge, and I was startled when I [END OF AUDIO FILE]
diskone00004
. . . of the interventions that we recommend, both for prevention and treatment, have
some level of uncertainty about them. For many of them, we have very little evidence of
efficacy yet we keep on doing them, and it's also very clear from a growing body of
empirical evidence that clinicians very infrequently will discuss uncertainties with their
patients even in areas where the uncertainty is very well documented.
Polity - I'm not sure of the pronunciation - did a very extensive review of the literature
around medical uncertainty a few years ago, and categorized it into five main types or
five main ways that we deal with uncertainty in the medical arena. One, just the whole
area of risk and how risk is calculated and what risks we're talking about. Second is
ambiguity or uncertainty about the strength or the validity of the evidence and the data
that we're dealing with to ascertain these risks. There's also uncertainty about the
personal significance of whatever the particular risk factors are and whether those are
going to impact an individual this year, five years from now, 10 years from now.
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There's uncertainty because of the complexity of the information. The idea of risk is a
nice one but we're usually dealing with multiple risks and how do they play off against
each other. Obesity has an impact on cancer risks. Physical activity has an impact on
cancer risks. How do we balance where those things fit in? And then there's
uncertainty resulting from ignorance, ignorance at the individual level of a patient's
family history. They don't know it or they haven't shared with the clinician - ignorance
about risk factors, occupational exposures, a variety of other things. So a lot of
uncertainty in medical care in general.
So the potential impact obviously of all these different types of uncertainty is severe,
and when you start trying to explain some of these uncertainties to patients, it can get
very overwhelming and confusing. It overwhelms and confuses me, and I deal with it
every day. You also can exceed the patient's capacity to deal with the information, and
this can lead to decision avoidance. It has been shown that the more information that
you share with patients about uncertainty, the lower the likelihood that they're going to
take advantage of whatever that activity is, at least as it comes to screening and
preventive services.
Discussing uncertainty also can lead to distrust of the information. If you tell patients
that it's not clear whether intervention X works, then they begin to think that maybe your
information is not right or maybe you as the clinician don't know what you're talking
about. It can heighten patient vigilance and worry and anxiety, and for individuals who
take advantage or don't take advantage of a particular intervention, if they have a
health-related outcome - say they chose not to be screened and somewhere down the
line, they end up with a cancer diagnosis, then there are issues of regret related to the
decision they made in the face of that uncertainty.
So how do you deal with uncertainty in communicating with patients? You can deal with
it verbally and give them general terminology - "it's unlikely that this particular
intervention is going to be beneficial in your case." You can talk about it numerically
from a variety of different directions. You can talk about relative risk, the number
needed to treat, but all of these can be very challenging in part because of the very low
numeracy that we have across this country certainly where people simply have - many
people have difficulty comprehending complex quantitative information. You can also
present the information visually using pie charts and histograms and other kinds of
charts and graphs. You must also identify and address patient values as part of
whatever this discussion is and tailored messaging so all of these have been looked at
and explored, but for researchers in the room, please perk your ears up. We have very
little data supporting any of these. There's a little bit of information that supports all of
these, but there's very little data that looks at this in detail, so I think the research
opportunities for exploring how to optimize behavior change in the face of uncertainty is
wide open.
So let's shift now to a specific example, and let's talk about uncertainty in prostate
cancer. Now most of you have probably heard about the questions about screening or
not screening, but prostate cancer is an area ripe with uncertainty because if an
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individual decides to screen, then when they get their PSA level back, well, what do you
do? When do you biopsy? Do you biopsy for a PSA of 2.5, for a PSA of 4? Do you
look at other factors and make that determination? And then suppose they have the
biopsy and the biopsy shows cancer. Well, now do we observe the cancer or do we
treat the cancer? Many of you are probably aware of the PIVOT study, a long-term
study of strict observation versus treatment for prostate cancer was published just in the
last few weeks and showed that for men with certain varieties of low-grade cancer, after
10 years of follow up, less than 10% of men who chose to be treated and less than 10%
of men who chose strictly observation died from prostate cancer. And then if they
decide that they want to be treated, well, so they want surgery? Do they want
radiation? Maybe we'll throw in some hormone treatment or cryotherapy. So anyway, a
lot of different uncertainties around prostate cancer. I'm going to focus for the
remainder of time I have specifically on prostate cancer screening and talk about the
ACS perspective.
Now prostate cancer screening is an area of great controversy in part because of
relatively limited data about the value of screening. The two largest trials that we have
were published just a little over two years ago in the same issue of the New England
Journal back to back. The first trial, the European study of prostate cancer said
screening is associated with a 20% lower risk of prostate cancer death. Everybody
went yay and then you turned the page and the next study, the prostate, lung cancer,
and ovarian study from here in the US said screening had no impact on the risk of dying
from prostate cancer. So many of us in this field have been waiting for 10-12 years for
the results of these studies, and needless to say, there was a little disappointment, but
this has then contributed to a continuing debate regarding what to do about screening
for prostate cancer, and this chart shows the large physicians - there are a group of
organizations - the American Urologic Association, the National Conference in Cancer
Network, and other organizations that still recommend that all men should be screened
for prostate cancer. That's their lead. All men should be screened.
The American Cancer Society - we fall on the side of informed decision making. Men
should understand the benefits and limitations and then make a decision about whether
or not to be screened based on their own values and preferences. And then the US
Preventative Services Task Force, as many of you all probably heard, recently came out
with a recommendation against routine screening. No men should be routinely
screened, and that obviously makes for a lot of conflict, confusion, controversy. In
terms of discussing this information, though, the way that we at ACS and I personally
choose to frame it is yes, there's disagreement among these groups about exactly what
to do in this, but look for areas where there is knowledge, where we do know some
things, and share that information, and look for areas of agreement, and if you look at
the guidelines of all of these organizations, in spite of the headline, all of the
organizations within the body of their recommendations say that men should be
informed. They should understand the benefits and limitations and that men should be
free to make a choice as to whether or not to be screened, so trying to draw those areas
of agreement into sharper focus as opposed to telling the public that well, the experts
can't agree on anything.
15
Simplifying messages is also very important in a very complex topic, but the ACS tries
to lead this discussion with men with a few very basic messages. Prostate cancer is
common. It affects a lot of men. There are tests that can find the disease early, but we
don't know if the benefits of being tested outweigh the risks of being tested, and men
should learn about these, so simple basic messages - it's been shown very clearly that
information overload can lead to, again, avoidance of decision making or lack of clarity
and a lot of confusion for men. It's also important to discuss benefits and risks. There
have been a number of studies looking at physician discussions around prostate cancer
screening with men, and in many instances, while the benefits are extolled and men are
encouraged to be screened, many of those conversations include little or no information
about the potential risks, so providing information on both risks and benefits is very
important, and we try to do that in all of our materials. This particular piece was
developed for clinicians to help them begin to have their conversation with patients.
It's also important to recognize that understanding should not be assumed. The gray
and yellow bars here indicate that - this is a study of men's comprehension of terms
related to prostate cancer - and gray and yellow indicate either they weren't familiar with
the term or they misinterpreted the term. Some terms that are very commonly used in
the discussion of prostate cancer include things like erection and impotence, and as you
can see, less than half of men in this group had any understanding of what those terms
are - and incontinence, another major side effect. Less than 10% of men understood,
so they may be having a conversation with their clinician, but their clinician is talking
about one thing and they have no idea what's going on. So making sure that messages
are tailored to address the particular audience, both from the standpoint of literacy, from
a cultural standpoint, to talk about prostate cancer screening to African-American men
and failed to mention that African-American men are much more likely to be diagnosed
with the disease and die from the disease - is doing those men a disservice. Not that
you necessarily want to encourage them to be screened, but you want to make sure
that men have as much of the information as possible but present it in a fashion that is
accessible to them, and this is basically the same information that I showed you on that
clinician-directed chart but we've taken out the impotence and incontinence and instead
talk about things like problems controlling your urine, problems with sex, terms that are
simply more accessible to men.
Also, the use of pictographs - Dr. Hesse pointed that out earlier, but numeracy is a huge
issue and actually not just among uneducated and undereducated men. There are
many educated people who still - when you tell them you have a 30% chance of
developing X and a 5% chance of Y really don't have that, so it's been shown in some
work that pictographs like this can be very helpful at helping people to have a clearer
impression of what you're actually discussing with regard to their risks.
And then, finally, the issues of values and preferences must be taken into account.
Realize that when we're talking risk statistics, those statistics are developed at the
population level, but the decision about what to do is an individual one, and each
individual varies in terms of how they feel about their own personal health, their own
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personal risk status, what's important to them, and we need to be very careful not to
superimpose our values and preferences on the patients that we're interacting with and
caring for, so there's, again, a great deal of work going on in this area, but for any of you
young researchers, I think that exploring uncertainty and communicating around
uncertainty is an area ripe for research and hopefully with some of the funding agencies
in here, ripe for grant dollars. Thank you all very much.
It's my pleasure to introduce as our repertoire, Bill Novelli. I've sort of tracked his career
over time and am really excited that he's with us today to kind of share his thoughts on
the presentations that we've heard thus far.
Thanks very much. Good morning everybody. I'm still Bill Novelli. Our panel, as you
know, is titled "Uniting Action Around Data," an extremely topical subject. In the
evolution of health and healthcare, digital and social media and now a new term for me,
device media, are an increasing source of value in health systems for individual health
consumers and for social impact, initiatives, and campaigns. But we're still in early
pioneering stages of development. It reminds me of the gold rush of 1849. There is
gold out there, but how much and where is it and how do you access it? And I think that
there are at least listening this morning three things that are needed for all this to evolve
effectively.
First of all, to be evidence and science based, and that hasn't changed. That's always
been the case. It always will be the case. It is the case today. But we need to get
much better at communicating this to the public.
Secondly, to use an old term, to empower patients and their families and other health
consumers, to utilize the information to engage more fully in the management of their
own health.
And third, to understand and to exploit this new media environment. This echo system
as it's been called that's still developing to increase exposure and increase persuasion
to public and professional audiences.
And we mustn't forget professional audiences as we just heard. Communication to
health professionals and especially clinicians is very, very important. To help
consumers arrive at good decisions in partnership with their medical team. I'm working
now on an IOM study that's going to be released soon, and it shows how much room for
improvement there is in clinician/patient communication. The uncertainty, as we heard
Dr. Brooks describe it, is actually huge.
I've also been working in reforming advanced illness and end-of-life care, and in
advanced illness and end-of-life care, the opportunities for improved quality of care and
resultant cost savings are enormous, and much of the challenge here is communication.
So uniting action around data. There was an NCI project many years ago that I had the
privilege of working on, and the project was about trying to reduce smoking among
physicians and nurses, and they were smoking at a much higher level than the national
17
population. So that was the challenge, and since then, much has changed including
cancer communication and the science and the media and the communication
environment.
But one thing, in my mind, has not changed, and that is the need for effective strategies
that will motivate and persuade target audiences. And I think, as I listen this morning, to
develop these strategies, the HINTS surveys that Kelly Blake described seem especially
important. To be able to go to those data and look at disease prevention and health
promotion and monitoring the effects of communication, examining confusion in
consumer perceptions, look at knowledge and attitudes and behavior among different
populations, and then, of course, source credibility.
Now in addition to this strategic thinking, another thing that I believe remains constant is
the need to create broad environmental change so that appropriate behaviors are seen
as normative behaviors. An individual behavior change often flows from that broad
environmental change, and as Ed Sondik said, community data matters. To create
community change, we need community level data, and we need it broken down by
audiences and sub-audiences, and Ed also pointed out what a big challenge this is. He
and others are tackling it, and that brings us back to the need to convert the data into
action.
Now if some populations in some parts of the country do not connect smoking to
disease as Brad Hesse pointed out, what strategies are needed? Does that call for
more information? Are they communication strategies or smoke-free workplace
strategies or what? So uniting action around the data is truly the goal.
Monitor, respond, amplify, and lead. Those are all I think good parts of a kind of map.
And when Daniel Davenport talked about this, and he talked about the idea of a funnel,
what he was really talking about is the strategic thinking that goes down into that funnel.
Whether digital, social, traditional media, it's a strategic funnel that is key, and the
problem as he described it or as he quoted it from one person is that people have
limited time, limited attention, and accuracy so they're not very good at capturing data
about things in the real world. But I think we have to ask ourselves is this really what
we want them to do? Do we want people to capture data or do we want them to act
positively on the basis of persuasive credible messages? Oftentimes, messages that
appeal both to the head and the heart like Brad Hesse's YouTube video to start the
conference.
Now we all know that none of this is easy. Complexity, ambiguity, the understanding of
risk, the lack of knowledge - these are all the uncertainties that we heard about. And at
the same time that we try to do these things, many risk factors are actually increasing.
Sedentary lifestyle, poor nutrition, and some risk factors like tobacco use are at a
plateau. But this is a promising time. Better data, new media, and decades of learning
about what works in communication and social change.
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So I'd like to conclude by telling you a story about many years ago. I was at a
conference, and I ran into a guy who was the head of research for the advertising
agency that had the Philip Morris account, and we got to talking over lunch, and I said,
"You know, I want to ask you how do you justify working for Philip Morris and selling
tobacco?", and he said, "Well, I just like to hope that our research and our advertising is
not too effective."
Well, I want to reverse that and I want to say to you today that I hope that our research
and our communication in uniting action around data is very effective. Thank you.
Now we have not as many minutes as we wanted but a few minutes for the folks to
engage the panel in a brief discussion. The microphones are standing in the middle
aisle, so if you do have a question, you need to come to the mike because of the
recording of the session. I would like to start as Vish is coming to the mic - actually, I'll
yield. Go ahead.
Q: This is to Dr. Sondik I think. So it's quite impressive that we have all these data sets
and an effort is being made to disseminate these data sets for local consumption. I'm
just curious to what extent - what do we know about the extent of which local entities
such as community-based organizations, local governments, or local institutions - to
what extent are they using it to - what capacity do they have to use this data? How are
we structuring this data so that it can be really taken up by these organizations at the
local level but it could be used effectively?
You know, I don't think we know. Bill's analogy to the gold rush is so apt here. If you
went to this Data Palooza conference that was held in June, tremendous excitement,
and I'm excited about it. I don't think you can't be, but at the same time, I think we have
to ask what is the impact, how can it be structured in such a way or evaluated with the
information fed back so that we can be sure that it's really having an impact? What I'm
very impressed with is the number of organizations that are signing on to these various
efforts, whether it's the community comments or using the community health status
indicators or buying the technology and support for the dashboards, there's tremendous
interest in it, but there's also I think - it's almost imperative that an organization be
tracking this and doing what you said in your third question - really taking a hard look at
this, not a critical look, but saying what kind of impact is it having, who's involved, who
isn't involved, what technologies are they using or not using.
Daniel pointed out to me that the smart phone is really being used in even the poorest
communities so this is really an important - your questions I think are very important,
and I think it's incumbent on us to do the kind of thing that you're suggesting here and
really get a better handle on who is using this technology and for what.
Yes, hi. Okay, let's stick with the gold rush. There's a lot of gold out there. What
should we be applying it to. There are two big choices. One is to use the information
we can get to give people information, to persuade them, to educate them, to give them
better knowledge, to empower them as Bill says with science-based information - that's
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sort of one way to do it - and the other is to use all of this information to persuade the
people to create the environments around people to change those environments, the
policies, the opportunities, the options that they have. If you had to say where's the
gold, where's more gold, change people through their minds and their hearts or change
the world around them that actually has a great influence around their behavior.
Thanks.
[END OF FILE]
diskone00005
. . . and Rob Hornik can talk about this, too. Rob, you know, and I think everybody
agrees - most people would agree I think that we really truly can't do one without the
other. I mean it is imperative that we change social norms, the big picture, and we need
to do that through the media and through policy advocacy and in many other ways. If
everybody in America has permission to be overweight and obese, we're not going to
get to individual behavior change. And at the same time, while we're doing that, we've
got to work on individual behaviors as well. And here, the social media can be helpful,
face-to-face communication, using the old strategies that we all know about, so I would
say that we have to do both and they have to be done in synergy.
Q:
Good morning. Great presentations. I think what was interesting is it was
Professor Novelli who I think was the first person this morning who said anything about
source credibility, and I think the issue is it's not the message, it's the messenger. We
produce all this data. We think we've got it, everybody should use it, but we don't ever
emphasize the messenger part of it. We think we just want to create all this data and
send it out there. Can you all sort of comment on what's the balance between the
message versus the messenger?
I can take a shot at that from an outsider's point of view. There is a reason why people
use celebrities to promote their services, and that's because everybody has an
immediate awareness of and a potential attraction to that personality, so I think that your
point is very interesting, that there should be a real emphasis placed on the messenger
and probably the messaging vehicle because I know that you guys have a ton of great
information but the messenger is very important in my opinion. And I'll just say in the
Health Information National Trends Survey, we do have items related to source
credibility, both in the channel of information delivery, so the sources that people most
use and trust for their health information, whether it's an online newspaper or print
newspaper, broadcast news, that sort of thing and then also, the sources of health
information that don't have to do with a channel, but if it's a government representative,
industry, science, that sort of thing, we do sort of break down different channels and
different sources and we get a gauge on people's trust in those sources and channels
for health information.
20
Q:
This is a terrific panel, so thank you for putting this group of professionals
together to share their insights and their data. My question is really looking at the
translational pieces, communicating facts, data - between national datasets and local
datasets because there is this chasm, and we do work with ethnic media, and we're
looking at where are the messengers, where's the journalists, where's the journalist at
the community-based level. When we bring in subject matter experts at the national
level into brief local journalists combined with local experts, there's conflicts in data, and
it's a power struggle between national and local at times, and I'm wondering if someone
on the panel might be able to address and/or comment on the way in which we're going
to really build this bridge between local-national datasets as well as local experts,
national expertise, and really looking at the diversity of data that we have as well as the
ethnic and racial data that we may or may not have at the local level as well.
Are you from Wash U by any chance because Matt Kreuter is sitting right behind you
and has a great program called Ozioma which is actually, healthdata.org - is that what
it's called now, Matt? Localhealthdata.org - which combines local and national
estimates, and it's a program for journalists to use in their reporting, and he can tell you
much more about it, but that's one way that technology has enabled sort of data
translation for journalists.
Q: I, too, want to thank the organizers for the panel. I really enjoyed hearing the
presentations. I have a comment and then perhaps a question to Durado and perhaps
Daniel to sort of reconcile the issue to some degree perhaps. It's really about the
translation of evidence, and I think Durado, your point is extremely important, that
there's tremendous ambiguity. Even in the areas where we have the best science,
there's still ambiguity, and so the question is how do we handle that and how do we
address that in such a way that we can empower patients to be able to sort of do the
balancing act that has to be done around the probabilities of the benefit of a certain
treatment outweighing the risks involved. And then how do we sort of reconcile this
growth of new data streams so the quantified self all of these information flows. Is that
going to lead to more clarity or certainty or is it going to create greater ambiguity? And
it's an interesting question because if, in fact, there's greater opportunity for us to find
more evidence in those data streams, it might actually lead to greater clarity, but I'm
concerned about that, and so how would you mash up those two? You've got these
huge data streams coming in and you've got the ambiguity and the nature - how do you
translate the evidence?
How much time we got? Well, the ambiguity issue is actually huge. There's a
phenomenon that is described in the literature - ambiguity of version - and that is people
hate ambiguity and they don't want to accept the fact that science doesn't know
everything about everything, and particularly when they're talking with their doctors
about their health, but that I think needs to be taken head on and clinicians - one, must
begin to discuss the fact that they don't know everything and it's not them individually that the scientific evidence - because it is growing at such a fast pace, it's like the more
we learn, the more questions that are opened up. However, after framing it in that
fashion, then making clear - but this is what we do know. We do know that X number of
21
men are going to die from this disease. We do know that if you get screened, you may
lower your risk by this much, and then the whole issue of the quantified self frankly I
think is frightening and where that might go in terms of feeding us so many additional
streams of new information. It'll be like trying to take a drink out of a fire hose. Daniel?
And I was speaking with a gentleman last night that had a very similar reaction to the
concept of quantified self, and his comment was that we don't need more data, we need
to know what to do with the data that we already have, which is very logical, and I can
totally understand your point of view on not having people overloaded with particularly
conflicting data, but on the other side of the equation, the fact of the matter is that the
data that is going to be coming from new data sources is going to be coming hard and
fast and is going to dwarf the amount of social data that's being created which is already
a tremendous amount. So there is a huge amount of data headed you guys' way, and
you're probably going to have to figure out how to police it the best so that you prevent
information overload at the patient level.
I just want to inject another dimension into it to which is values. You know, someone
can very rationally look at the information - the prostate information for example related
to screening and a very rational person can come to one conclusion. Another rational
person using a different base can come to a different conclusion, so we can't expect I
think in this that everybody is really going to go in locked step to what the broader
community feels is the thing to do, and how we incorporate that into all of this I think is a
challenge. Values are extremely important in decision making, and it may be quite
appropriate actually for individuals to adopt a different posture if they've really thought it
through, so I think part of the challenge is to find ways to help people to really think it
through and arrive at their own decision. The prostate example is a really good one
because it is so difficult at this point given the information we have.
Q: Hi, this question is directed to Daniel, and I was sort of interested in your take on like now with all the data that's available, especially with smart phones where you can
collect information from people sort of without their knowledge about where they are,
what they're doing, what sites they're visiting. How do we balance the potential that's
there to collect more accurate information about what people are doing with the whole
issue of privacy and what potential dangers or risks there are with having all this
information out there and what may be bad uses the data could be used for?
Sure, and particularly for this crowd, privacy and security are I'm sure of the utmost
importance. I'm imagining that's why the medical health records has been so difficult to
get out. On the other side of the equation, you can pretty much forget privacy and
security. You're tagged and bagged already from my industry's point of view. So then
you've got to go oh, well, where's the happy medium? We want to get this information,
we want to be able to provide good information - it's a hard situation because there's so
many regulations around you guys. I think that you will find that some of these newer
devices that have specific purposes can be segmented in such a way that the additional
data sets around the primary data so you're just getting the data you're supposed to be
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getting unlike people like me that look at all the data around that and then do things with
that. So it's a big scary issue. I don't know how you're going to resolve it.
Q: Hi, I'm Ellen Beckjord from the University of Pittsburgh, and I think an emerging
theme across all of the talks relates to a tweet that I saw a few weeks ago, and I can't
remember who tweeted it, but the point was that information curation is as or more
important than information creation, and I'd be interested in what folks have to say about
that, particularly where the quantified self piece and uncertainty piece maybe intersect,
so one of the ways I can imagine quantified self decreasing uncertainty for a consumer
is by sort of making their day-to-day or moment-to-moment experience to curate that in
a way that helps them incorporate that into a way that they make a decision about
whether or not to stay on a medication or whether or not to pursue a new treatment,
things like that.
Yeah, that's a great point, and with the huge amount of information, the curation
becomes an essential way to filter way too much information down to specific categories
for people that they can then consume that. I think it also gets back to the messenger
question earlier which is if you've got a knowledgeable curator, then they serve as the
messenger to that community and an authority so that the information that you're
receiving is prescreened and is the good stuff. So I think curation is going to be
incredibly important - the messenger that provides that curation is very important,
especially in this area.
Q: My name is Mike Wilkes, I'm from the University of California, and I'm a clinician and
I don't know how many others in the room are clinicians and have more than 30 or so
gray hairs in your head, but those of us who spent our time as interns and residents and
medical school a couple of decades ago, were exposed to a catheter, a new catheter
that we used for heart failure patients developed by Swan and Ganz, and I spent
probably four or five years of my life copying down data from the catheter for my
attendings and fellows, filling pressures and atrial pressures and pages and pages and
pages of data. It was a decade later that we found out that all of that data was
meaningless. Patients who had these catheters - and we knew everything about their
pressures - survived not a day longer than people who didn't have that data. The same
argument can be made for the past two decades with diabetic patients, looking at
patients with type 2 diabetes and how we've hounded people to do their home finger
sticks. My point is clear I'm sure which is that sometimes lots of data doesn't end up
benefiting patients or society and so we have to be very careful not only with how we
collect data but with making sure that we have the end result, that the data's really going
to make a difference, because otherwise, we're going to burn out people and they're
going to not trust us the next time we ask them to collect data or hear some information.
Thank you. I'll just conclude by saying for those of you that are aware of the
administrative push, I would encourage you to look at the new digital strategy of the
administration that was just released probably a couple of weeks ago, but the point is
that we're being encouraged and I think that encouragement gets stronger every day
and will soon lead to almost a demand that we free the data and make that data
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available for others to grab and go, grab and package, grab and create, and so that's a
shift and a movement that is upon us, and as we work to challenge ourselves in how to
do that more effectively, we encourage the continued dialogue and appreciate it, so with
that note, I'm going to pause and break this panel and end this panel session. Thank
you.
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