Report
OPEN HEALTH DATA MOVEMENT:
Adolescence as Transformation
and Disruption
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Open Health Data Movement
Health DataPalooza brought together thousands of people
from academia, government, and the public sector, all of whom
are radically committed to one goal: promoting access to and
the innovative use of health data to increase the overall health
of Americans and to improve the delivery and quality of health
care.
RowdMap was smack in the center, sponsoring the event,
organizing the technical track, moderating sessions across
analytics, visualization and business applications of the new
data and receiving a shout out from NPR about our take on the
adolescence of the Open Health Data movement.
But the real power of the event was around how others are
jumping on the train towards using government data to run
government businesses in order to create market value and
improve public health. This means transformation for some,
but disruption for others.
The Open Health Data Movement is gaining steam. Not only
have a host of new technologies, products, services and
business started using open data, businesses have found
unexpectedly powerful uses of this data, transforming those
with business models nimble enough to accept and adopt, while
disrupting others unable to escape their own gravity.
Read on to see what's going on with all this and how it will
The Open Health Data
Movement is transforming
businesses with models
nimble enough to accept
and adopt it, while
disrupting others unable to
escape their own gravity.
affect you.
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Adolescence, A Time of Change
The open data movement is coming of age and it turns out that
maturity means outperforming a number of traditional Fee for
Service models.
For example, David Wennberg was on hand to showcase new
research using longstanding, well-known and widely accessible
data sets with new impact. Wennberg, CTO of the Dartmouth
Institute for Health Policy and Clinical Practice, recently
published an article in the British Medical Journal entitled, "A
population health approach to reducing observational intensity
bias in health risk adjustment."
He demonstrated how a traditional public data set, from the
Behavioral Risk Factor Surveillance System, actually
outperforms claims-based risk models for government
programs such as Medicare Advantage.
Wennberg delivered a presentation uncovering "a fundamental
This public data set
outperforms claims-based
risk models for government
programs such as Medicare
Advantage.
flaw" in observation bias, something that affects data from both
claims and Electronic Health Records. Rather than leaving us
high and dry, he went on to demonstrate a better approach
using public government data.
In some ways this is even more groundbreaking than the work
on supply driving demand with the Dartmouth Atlas for
Unwarranted Variation or the cost and experience work around
End of Life care.
Claims and EHR based
models have a fundamental
flaw around observational
intensity bias. This public
data set opens up a better
approach, one grounded in
population health.
For those who can apply a population health approach, this data
has potential to transform risk modeling, opportunity
assessment, resource allocation and intervention prioritization.
One notable application comes in the form of minimizing the
"data delay" of traditional claims or EHR lag when beginning to
work with a new population of members of patients.
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Such a use of this data has far reaching potential to disrupt with
staggering impact as claims-based models were the traditional
default of the Legacy Fee for Service system and the base on
which virtually all traditional industry leaders have built their
methods, systems and platforms.
Transformative Disruption from New Data
Transformation and disruption from traditional public data is
only part of the story. Perhaps the greatest potential for either
comes from newly released data.
This year's Health DataPalooza came on the heels of two major
data releases. After 30 years of litigation from physician groups,
CMS released two massive data sets covering millions of
providers and naming names spanning payments, practices and
prescriptions.
CMS released data
covering millions of
providers, naming names
spanning payments,
practices and
prescriptions.
Of course, this made typical headlines in mainstream media
around things we already know - cf. The New York Times and
Wall Street Journal explaining how some doctors prescribe
more than others. But that's just the tip of the iceberg.
Using this data, combined with additional sets like the
Dartmouth Atlas, you can see actual practice and patterns. You
can tell which doctors are operating within geographic norms
You can see actual practice
patterns and tell which
providers are within
geographic norms and
which are outliers.
and which are outliers, either positive or negative.
This data shows a picture of the natural topography of the
landscape of care and how the patients flow between providers.
This gives all parties visibility not only into the contracted
networks, but what's actually happening on the ground as
individuals follow geographic, topological paths, often the
routes of least resistance.
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Finally, this data allows you to start with population
benchmarks, working top-down from the patterns of care,
looking at how some geographic areas have large pockets of
unwarranted variation and much higher associated costs, then
drilling into the details against which you can compare any
provider in the nation.
This sort of approach was a hallmark of research in university
settings decades ago and proved remarkably effective. But it
was limited by the nature and scope of the available data,
usually coming from select hospital or provider groups
participating in specific studies often with conflicting
methodology.
Now, thanks to HHS, you can apply this type of approach to the
entire US population using publicly available data sets.
Specifically, you can use the new Part B and Part D data
releases, informed by the Dartmouth Atlas, to determine the
key markers of care along a funnel. You can then determine
how a given geography, network or provider is doing compared
to how they should be doing - whether a provider is habitually
practicing or avoiding unwarranted surgery or over prescription.
The kicker is you don't have to come up with a fancy
methodology, but simply use standard CMS definitions and you
don't have to dig through claims or embark on a massive EHR
project, but simply use the new data sets that CMS publically
You don't have to dig
through claims or embark
on a massive EHR project.
released. That allows you to score providers out of the gate
using CMS benchmarks and indexes.
On one hand, in less than savvy hands, this information could
paint incomplete or inaccurate pictures, which was what the
AMA stated as a concern, and indeed some of that has
happened. But the data is so powerful that as of late the AMA
has become much more positive about using government data
for transparency in government programs.
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In some ways, this newly released data has the greatest
potential to transform or disrupt. In any case, it has the
potential to radically transform the business model paradigm of
plans interested in intelligent growth, purpose-built products
and curated networks. For providers it means opportunities for
immediate visibility into a network, group or individual provider
for selecting risk and payment models, defining and optimizing
a network from general practitioner to specialist to skilled
nursing facility and identifying and managing leakage. For
organizations committed to preserving legacy infrastructure,
whether grounded in technology or personnel, this data has
For organizations
committed to preserving
legacy infrastructure, this
data has unprecedented
power to disrupt.
unprecedented power to disrupt.
Innovate or die; embrace and transform or resist and disrupt
are classic tropes. In perversely incentivized markets, such as
healthcare, resistance and preservation sometimes win. At this
point in the Open Health Data movement's maturation,
however, the train may already be out of the gate.
#Hdpalooza Technical Track
The technical track of HDI was held against the backdrop of a
steady stream of newly public data releases. Then Health and
Human Services chief Kathleen Sebelius announced another
major breakthrough in the health data movement at this year's
Health DataPalooza , the FDA's newest health data
initiative, openFDA. According to the organization's press
release, "openFDA will encourage the innovative use of the
agency’s publicly available data by highlighting potential data
applications and providing a place for community interaction
with each other and with FDA domain experts."
With this context of a rolling train of data releases, the technical
track focused on using this new data to transform both extant
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and new technologies and the products, services and business
models that they are supporting.
One of our esteemed co-founders, Joshua Rosenthal (did we
mention Chief Technology Officer of the US, Todd Park, called
Josh a "Visionary Genius" in his key note speech), organized the
event's Technical Track. The goal of the tech track was not to be
all about Apps, but about how government, research,
academics, policy, and the public market are coming together to
use health data in a meaningful way.
Health data is really, really "big", but that's not enough; the key
is making it understandable and actionable through
interpretation, visualization, and comparison then directly
applying it to business models through practice.
Not only did Josh organize the technical track, but he
moderated four of the sessions: one on the importance of
visualization, one on analytics, an entrepreneur's shark tank,
It's about making open
health data understandable
and actionable through
interpretation,
visualization, and
comparison then directly
applying it to business
models through practice.
and finally the healthcare entrepreneurs boot camp (a favorite
of event- goers).
Practice- The Sessions
The content was pretty awesome!
(Shout out to all who presented/participated)
"When KISS Isn't Simple, or Stupid, Enough"
A Session on Data Visualization
Hilary Wall and Linda Roesch of the CDC discussed the role data
visualization has played in measuring alignment, reporting, and
performance in the organization's Million Hearts initiative. Also
on the panel were Christine Carmichael and Ben Jones of
Tableau. They discussed the value visualization brings to open
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data and shared some of the best health data visualizations
from Tableau Public.
What we learned:
• The number of health data sources available to
researches, innovators, businesses, and consumers have
seen exponential growth, but how do you make sense
of it all?
• Never underestimate the power of visualization in
storytelling.
• Hypothesis generating data > hypothesis driven data
• On putting something out there (direct to consumer
style), "It was messy, but if I waited for it to be perfect,
I'd be here forever" - Hilary Wall.
"Strong Correlation... Close Enough?"
A Session on Data Analytics
We heard about analytics from the clinical end to scientific
applications to the high level business model. Ronald
Ozminkowski of Optum, Inc. began the discussion with his view
that data is not big enough. Gurjeet Singh, PhD, Sujata Bhatia,
MD, PhD, PE, and Suchi Saria, PhD followed by explaining how
understanding and visualizing big data can generate new
hypotheses that could not be predicted by humans.
What we learned:
• You can never have too many PhD's on one panel (or
can you?).
• Big data is great, but it's what you do with it that
really matters.
• New discoveries in the data will increase the
importance of balancing the efforts of research with the
business or clinical needs of the industry.
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• We need to ask ourselves if the data is really the
question we're trying to answer? Is the solution valid
outside the context of the analysis? Will it have a
meaningful impact?
• ACA is a whole new business paradigm - requires
"new data".
• Don't underestimate the importance of a business
model and ROI, or your idea will never get absorbed.
And finally....
• Accidental deaths and Nick Cage movies: causal or
merely corollary?
Entrepreneurs - "The Shark Tank"
We had a board of top-notch sharks who included Krishana
Yeshwant of Google Ventures to Adam Goulburn of Lux Capital
and Christina White of Thrive Capital. The products the brave
men and women serving as "bait" pitched ranged from a
nutrition - tracker, to activity and health monitors for children
and one for dogs (think FitBit for your pet).
What we learned:
• Disintermediation of funding and lower capital
requirements means adding value is key to survival.
• "Never underestimate the [process of the]
consumerization of a product [this] never comes from a
doctor office" - your product may have the science, but
will people use it?
• "America we're fat, and so are our dogs!"
• Capital must not be commodity: only enter the
trenches with people you want to be there with. The
largest returns often come on those [startups] that are
boot-strapped. Raising lots of money doesn't guarantee
success.
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• Capital as intervention is changing in post ACA world
where talent trumps technology. The market is ripe for
disruption: how will industry icons react?
And the Main Event...
Healthcare Entrepreneur's BootCamp "Strategy, Practice, & Games for Using Public Data to
Build, Scale, and Deliver Value"
Healthcare start-ups fail at astounding, disproportionate rates.
This is no surprise due to the complexity of this dynamic
market. How do you go from a data-based tech product to a
company with a meaningful value proposition? It takes
practice. The BootCamp brought together a talented,
experienced, and good-looking, group of industry leaders with
expertise in using data to drive business strategy. Participants
worked in teams to come up with a mock business pitch that
asswers these four questions (i.e. key questions any
entreprenuer, or seasoned busniess vet. should be asking):

What market is your product reaching?

What business need does it address?

What data will it use?

What is your competitve edge over potential barriers?
Given the new data we're all entrepreneurs, using new stuff
The healthcare business is drastically changing, and the key is
who can create real value - this isn't just for entrepreneurs, how
industry icons react will be very telling. Business to business is
the way of the past; it's all direct - to -consumer from here. Data
is just the start, tying action to data is the key. Those that do
may experience wonderfully positive transformation; those
that can't will face disruption.
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Report
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Open Health Data Movement: Adolescence as Transformation and Disruption
RowdMap’s Board of Advisors include Dave Dickey (Second
Story Sales, previously co-founder RedBrick Health), Kyle Rolfing
(Principal at Savvysherpa, Inc., previously co-founder Definity
Health), Abir Sen (Co-founder & CEO Gravie, previously cofounder Bloom Health), Marshall Votta (Leverage Health
Solutions, previously NaviNet) and David Wennberg (CEO
Northern New England Accountable Care Collaborative & CTO
Dartmouth Institute for Health Policy and Clinical Practice,
previously co-founder Health Dialog Analytic Solutions).
RowdMap, Inc. provides this report for information purposes only. It is not
intended to be advice for a particular situation or legal advice. Consult with an
appropriate professional for your situation.
The information in this document is subject to change without notice. This
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© 2014 RowdMap, Inc.
www.RowdMap.com
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