Converting “BIG” Data into Value
Disclosure Statement
•
I have no financial COI issues to disclose•
Neither myself nor Mayo Clinic endorse any ofthe sponsors of this meeting
Mayo Clinic Health System
One System – Four Regions
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Moving from volume to value, but differentapproaches to contracting (commercial ACOs, employer contracts, no contracts)
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Focused on proactive patient management, butvaried priorities and resourcing (PCMH, disease-specific outreach, etc.)
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Previously limited view of population anddisparate access to claims data, but all looking
for more sophisticated clinical analytics
Goals of Analytics in the MCHS
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Aggregate clinical data from EMR•
Easy Access of Standardized Reports•
Minimal Training for the End User•
Just in Time Reporting•
Use of High Level Analytics and PredictiveModeling to Improve care
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Comparative Data with Other Similar SystemsWhat is “Informatics”
Informatics:
The science of organizing and analyzing data into useful information, providing easier access to more knowledge for wiser decisionsData
Information
Knowledge
Wisdom
© 2013 Humedica, Inc., All Rights Reserved 7
Clinical Data Are Essential
Healthy/Lo w-Risk At-Risk High- Risk Symptomatic Active Illness 80% of Costs Clinical Interventional Opportunity
Corollary for Healthcare
:
To know how to improve
we must
measure
it!
Alice’s
Paradox
“If you don’t know where you are going any road
will get you there!”
Humedica MinedShare
®•
Implemented in October 2012 to bring togetherclinical and cost data
•
Governance and delivery focused on:1.
Education•
Weekly region-specific training sessions toanalyze and discuss data trends
2.
Adoption•
Formal request/review process that asks:“What are you going to DO with the data?”
Adding the Clinical Dimension
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Patients missing BMIscreening ©2011 MFMER | slide-11
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Patients w/ BMI > 35•
DM patients missing A1c test•
DM patients w/ A1c > 9•
DM patients in control on A1c, LDL and BP•
Coded HF patients•
Patients w/ EF < 40 but noHF code
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HF patients not on ACE/ARBExamples of Humedica MinedShare
Reports in Use
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Preventive Services (E&Ms, mammograms,colonoscopies, BMI screenings, etc.)
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High Utilizers (ED frequent fliers, readmits,patients missing PCP follow-up visits, etc.)
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Chronic Disease Management (Diabetes,Hypertension and Heart Failure screenings, risk stratification and clinical outcomes, etc.)
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Panel Management (risk adjusted panel sizing,RVUs, control rates, E&M utilization, etc.)
Additional Humedica Opportunities
•
Uncoded chronic diease patients•
CHF patients missing EF reading•
Patients with > 5 ED visits (12 months)•
Mean RVUs by Risk Score (by PCP)•
CHF at-risk for admissions (MinedSharepredictive model)
Key Questions Prior to Release (Clinical Data)
•
Is the data easy to understand-or is trainingrequired?
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Has the data been “vetted”? Are there potentialinaccuracies in the data?
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What levels of access do you authorize to howmany people?
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How much training and understanding isneeded to be released as a “superuser”
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Controlling “read access” vs. “write access”CHF Predictive Model Categories
High Risk CHF Panels by PCP
Idealized High Risk Patient Management
Flow
Use Humedica monthly report to identify Pts. most likely to be
admitted by disease state
P.t list given to each site care coordinator
CC updates info from EMR and from payer and internal billing databases CC addresses issues of pharmacy or therapeutic non-compliance and motivates pt. or
arranges for PCP visit
If CC motivation is result-recheck by CC 1 month
If PCP visit-PCP
addresses issues and arranges for either return visit of CC phone call
What is WHIO?
•
WHIO is the Wisconsin all payer database•
Incorporated in late 2005•
Organization of Organizations•
Providers•
Payers•
Purchasers•
State of Wisconsin•
WHIO uses Ingenix as its vended datamartKey Questions Prior to Release (Claims Data)
•
Is the data easy to understand-or is trainingrequired?
•
Has the data been “vetted”? Are there potentialinaccuracies in the data? Is vetting an option?
•
Who has access to the database?•
How much understanding is needed prior to therelease of the data?
•
What are the limitations of a claims basedreporting system?
©2011 MFMER | slide-26
Site ER Hosp Svc Lab Pharmacy PCP Radiology Specialty Overall Cost Overall Quality
A 0.80 0.86 0.72 1.00 1.04 0.98 0.91 0.97 1.05 B 0.47 0.60 0.66 1.12 1.09 0.92 1.02 1.00 1.07 C 1.21 1.50 0.90 1.18 0.95 1.18 1.54 1.20 1.00 D 0.73 1.00 0.81 0.92 0.99 0.92 1.07 0.98 0.98 E 0.51 0.70 0.83 0.82 1.37 0.73 0.86 1.00 1.03 F 0.53 1.31 0.58 0.98 1.07 0.93 1.14 1.04 1.01 G 0.84 0.92 0.82 0.75 0.90 1.37 1.34 1.05 0.96 Competitor 0.98 1.07
services driven Rad-MRI driven
encounter driven
both
p<0.05
©2011 MFMER | slide-27
Clinic A Providers ER Hosp Svc Lab Pharmacy PCP Radiology Specialty Cost Quality Case Mix
A
0.67 1.1 0.75 0.98 1.07 1.04
1.78
1.21 0.99 1.05
B
0.83 0.82 0.75 0.83 0.96 0.45 0.8 0.81 1.04 1.16
C
1.22
1.74
0.83
1.54
0.97 1.08
1.83
1.33 0.97 1.09
D
0.48 0.72 0.68 0.92 1.09
1.38
1.17
1.08
1
1.07
Services driven
Encounter driven
Both
The Value of Big Data From Large
Collaborative Databases
•
Understanding how you are performingcompared to other similar organizations
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Accurate risk adjusting models•
Ends the our patients are sicker response•
Allows for normalization of local chargevariations
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Allows for predictive modeling toolsKey Takeaways
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Learn your data before using it•
Examine: Find the trends in your population•
Diagnose: Focus on the actionable opportunities•
Treat: Design evidence-based interventions•
Choose opportunities that are sized to currentresources
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Balance centralized standards with customizedapplications
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Design initiatives with measurement in mindKey Takeaways
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Governance is critical•
Maintain control of data requests•
Require use plan before data mining•
Ensure end user understanding of data prior torelease
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Validate that data provided is being used toimprove processes and …
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Measure outcomes-did results improve?
