a framework for the next generation of advanced analytics ...population health analytics is the...
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Forecast Health Confidential | Copyright 2015
A framework for the next generation of advanced analytics:
www.forecasthealth.com/
Thoughts for hospitals and payers
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Introductions
Jason BurkeSenior Advisor, UNC Innovation Center
Michael Cousins, PhDPresident and CAO, Forecast Health
2Confidential | Copyright 2015
Health Analytics:
Gaining the Insights to Transform Health CareJason Burke, Wiley Publishing, 2013
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Agenda
1)Advanced analytics framework
2)Predictive performance with claims vs EHR/consumer data and
white box analytics
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Where are your analytics looking?Where are your analytics looking?
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The Promise of Population Health Analytics
Population health analytics
is
the domain of advanced analytics
focused on providing strategic
insights into the inter‐
dependencies
in health
outcomes, profitability, and
preferences and behaviors. *
* Burke, J (2011) “The Next Era is Here”, A Shot in the Arm, SAS Institute
Operational
Individual
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Example:
National Collaborative for Bio‐Preparedness
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Do we care about Do we care about ““big databig data””
or or ““big insightsbig insights””??
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Data IssuesStorageStructureTimelinessSemantics &
LanguageValidityReliabilityTriagePedigree
Insight IssuesInnovation
Health OutcomesProfitabilityProductivityTranslational
ScienceCustomer Intimacy
RiskValue
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In an industry with more than
1,000 measures, how will we
know which ones actually matter?
PQRI, Meaningful Use, ACO, Medicare, NQF, NCQA, AHRQ…
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Example: Health Outcomes Analysis
What Happens with “Patients Like This One”
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Claims vs
EHR/consumer‐based models …
and white box analytics
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Claims EHR & Census
C‐Stat IDI
EHR and consumer‐based models as compared to claims:•C‐statistic 18% higher•Integrated Discrimination Improvement 721% higher
Finding
patients “like this one”
then …
predicting
patients like this one:•Use claims data for in/out of system
utilization•Use EHR and person‐level consumer
data for detailed profiling and more
accurate predictions Claims EHR & Consumer
Then …
knowing why
patients like this
one are at risk:•Use white box analytics, not black box
analytics, to understand a patients’
risk
drivers and take action•Use black box analytics for exploratory
data mining
Readmission Prediction
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Summary
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1)
Need forward‐looking analytic perspectives
2)
It’s time to converge multiple sources of data to obtain insights
3)
Just because data is available doesn’t mean it’s important – we
need to be skeptical and focus on what’s actionable
4)
Success is not just about “big data”
‐‐
use better data and better
analytics to get better insights
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Thank you!!
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Jason BurkeWeb: http://jasonburke.usTwitter: @jaburke
Contact Info
Michael Cousins, PhDWeb: http://www.forecasthealth.comEmail: [email protected]
Confidential | Copyright 2015