Event Details
HHS has so much data! Medicare, substance abuse and mental health, social services and disease prevention are only some of the MANY topical domains where HHS provides huge amounts of free data for public consumption. It’s all there on HealthData.gov! Don’t know how the data might be useful for you? In the DataLab you’ll meet the people who collect and curate this trove of data assets as they serve up their data for your use. But if you still want inspiration, many of the data owners will co-present with creative, insightful, innovative users of their data to truly demonstrate its alternative value for positive disruptions in health, health care, and social services.
Moderator: Damon Davis, U.S. Department of Health & Human Services
Panelists: Natasha Alexeeva, Caretalia; Christina Bethell, PhD, MBA, MPH, Johns Hopkins; Lily Chen, PhD, National Center for Health Statistics; Steve Cohen, Agency for Healthcare Research & Quality; Manuel Figallo, Sas; Reem Ghandour, DrPH, MPA, Maternal and Child Health Bureau; Jennifer King, U.S. Department of Health & Human Services; Jennie Larkin, PhD, National Institutes of Health; Brooklyn Lupari, Substance Abuse & Mental Health Services Administration; Rick Moser, PhD, National Cancer Institute; David Portnoy, MBA, U.S. Department of Health & Human Services; Chris Powers, PharmD, Centers for Medicare and Medicaid Services; Elizabeth Young, RowdMap
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RowdMap at DATALAB at Health Datapalooza 2015
1.
2.
3. PhDs can use open health data
But the goal is to open it to the masses
and let 1000 flowers bloom.
Inother words, can these guysuse it?
Let’s give it a shot
Working with open
health data at
RowdMap, Inc.
for about a year
4. Government is releasing lots of data….
And it’s been hard work….
But now you don’t
need a PhD to use
this data in a
meaningful way …
For mechanics of how to do this:
http://goo.gl/Y64Fa2
Have an Idea? Attend Bootcamp:
HealthCare Entrepreneurs’ BootCamp
Tomorrow , 4:15pm
Lincoln 2-3-4
6. Government
performance
data
Government
provider etc.
data
Government
socio-demo
data
Consumer
web / social
data
Analysis-based derived
data
Sentiment as a Key Driver (psychographic) - measured by Index scores for:
- Domains (chronic, wellness, quality of care, customer satisfaction, customer
service);
- Brands (parent org and you individually)
Market Growth; Census;
Healthy Food; County
Health Rankings &
Indicators; Behavioral
Health Factors; etc.*
Dartmouth Atlas; STAR;
Hospital Compare; Actual,
Expected & Predicted
Readmissions; Part B & D,
etc.*
STAR; Price, Bid,
Rebate;
Hospitals, Nursing
Homes; Market, etc.*
* Dozens of Primary Data Sets, updated at various frequencies
When we say a lot…we mean a lot.
8. And it’s powerful, disruptive, game changing
David Wennberg,
RowdMap Advisory
Board
9. New Government
Released Referral Data
(Patient flows between
PCPS, specialists,
hospitals and post acute
centers)
Dartmouth Atlas for
Unwarranted Variation
(Decades of research and data on
unwarranted variation by condition
and geography to keep things
apples-to-apples for comparisons,
hence “Unwarranted” in the name)
New Government
Released Performance Data
(Individual providers, groups,
hospitals and post acute
centers including the new part
B&D)
Provider Pattern Intensity Profiles
and Risk Readiness for every
provider, hospital, post acute
center in the US. All preloaded
with no IT.
OPEN DATA –
Particularly powerful when pulled together
Affordable Care Act data to determine
Risk-Readiness of Providers / Networks
10. CMS: 50% of FFS
will be gone by 2018
The business context has changed- health plans,
government payers, providers, and hospital
systems need to develop Risk-Readiness SM
strategies to excel as they transition from fee-for-
service to pay-for value.
12. What you can do [without a PhD]
With mashups of gov’t data (CMS HHS, Gov, CDC)
Chronic prevalence & physician supply
Population Health Report
Population Report Card
Match practice patterns to the right
risk arrangements – PFV Readiness
Group Risk-Readiness SM Report
Physician Risk-Readiness SM Report
Hospital Risk-Readiness SM Report
Post Acute Center Risk-Readiness SM Report
Risk-Readiness SM Arrangement Match-Maker
Manage clinical care and costs –
Remove No Value Care
Group Unnecessary Cost Report
Physician Unnecessary Cost Report
Hospital Unnecessary Cost Report
Post Acute Center Unnecessary Cost Report
Unnecessary Cost Referral and Value Chain Report
13. What you can do [without a PhD]
With mashups of gov’t data (CMS HHS, Gov, CDC)
Chronic Prevalence &
Physician Supply
Match Practice Patterns to the right
Risk Arrangements – PFV Readiness
Manage Clinical Care and Costs –
Remove No Value Care
14. Diabetes Prevalence -
Westchester
Use this data to allocate providers and care management
resources around condition-specific population needs by zip.
Locate clinics, health fairs, etc. based on chronic needs.
Income
Obesity
Depression
Health Opportunity Index
Demand and Supply
Lots of diabetics
but few PCPs
Lots of diabetics
and lots of PCPs
What type of populations?
Medicare FFS Geo. Variation: http://go.cms.gov/1D8j7LE
CDC Behavioral Risk Factor Surveillance: http://1.usa.gov/1PzcisT
Medicare FFS Part B: http://go.cms.gov/OCmyoy
Medicare FFS Part D: http://bit.ly/1mGyBxk
PCP Density –
Westchester
15. 15
Demand and Supply
County Profiles
Largest Counties In Ohio
People use this data to calibrate expectations for profitability
by incorporating population health and provider performance
into product strategy. Use excess to subsidize operations in
counties with fewer high-performing resources
Risk
Scores
Total
Cost
PMPM
Reimbursement
Overall
Star
Chronic
Star
Health
Rank
MA Profit
Opportunity
- MA
Profit
Opportunity
- Exchange
MA
Eligibles
MA
Enrolled
Exchange
Subsidize
d
Exchange
Enrolled
Compare to National
and Regional
Benchmarks
Medicare FFS Geo. Variation: http://go.cms.gov/1D8j7LE
CDC Behavioral Risk Factor Surveillance: http://1.usa.gov/1PzcisT
Medicare FFS Part B: http://go.cms.gov/OCmyoy
Medicare FFS Part D: http://bit.ly/1mGyBxk
16. What you can do [without a PhD]
With mashups of gov’t data (CMS HHS, Gov, CDC)
Chronic Prevalence &
Physician Supply
Match Practice Patterns to the right
Risk Arrangements – PFV Readiness
Manage Clinical Care and Costs –
Remove No Value Care
17. At the core of Risk-Readiness SM is
Unwarranted Variation:
RowdMap applies the Dartmouth Atlas for Unwarranted
Variation methodologies to data on Medicare Parts B & D.
This research has been repeatedly validated over the last 30
years and we now have a national data set to apply the
methodologies at a large scale.
The estimated 30% of medical expense that
goes to unnecessary care. This unnecessary
spend drives billing in a fee-for-serve economic
model, but success in pay-for-value comes
from managing and mitigating these pockets
of variation.
Every provider has a unique
practice pattern that informs Risk-
Readiness SM
Pay for Value Readiness
18. Los Angeles, CA
Compare to National
or Regional
Benchmarks
Pay for Value Readiness
Provider Profiles
Identify highly efficient, Risk-Ready practices and
physicians to profitably grow into. Improve profitability of
lower performing practices with large panel sizes through
modified arrangements or performance improvement plans.
Medicare FFS Part B: http://go.cms.gov/OCmyoy
Medicare FFS Part D: http://bit.ly/1mGyBxk
Referrals: http://1.usa.gov/1FzoEOV
19. Identify high and low performing hospitals
and post-acute facilities— are there post
acute facilities that hospitals with poor
chronic readmits are routing members to?
Pay for Value Readiness
EOL Hosp Days: Which hospitals fewer end-of-life
days than their peers?
Chronic Admits: Which hospitals see their most
chronic population repeatedly/ with the most
frequency?
Cardiac Imaging: Which hospitals are more likely to
over-utilize cardiac imaging compared to their peers?
Dartmouth Atlas: http://bit.ly/1GXvlJp
CMS Hospital Compare: https://goo.gl/p8MtoI
CMS Hospital Readmissions: http://goo.gl/02KnQd
CMS Nursing Home Compare: https://goo.gl/3DpT8m
20. Pay for Value Readiness
Great profile for
aggressive risk
Tread carefully for
some risk
Match appropriate risk arrangements based on
provider practice patterns and
Population characteristics within a geography.
21. What you can do [without a PhD]
With mashups of gov’t data (CMS HHS, Gov, CDC)
Chronic Prevalence &
Physician Supply
Match Practice Patterns to the right
Risk Arrangements – PFV Readiness
Manage Clinical Care and Costs –
Remove No Value Care
22. Remove no-value Care
Manage Unnecessary Spend
Risk-Readiness℠ looks at a different
category of spending
Shift focus from clinical edits, audits, and recovery efforts
to identifying care that is clinically appropriate, but
unnecessary. Historical efforts have shown returns, but
they only look at a fraction of total spending. Unnecessary
care can account for up to 30% of total spending and
provides significantly larger opportunities for cost
containment and quality improvement.
Clinically Appropriate,
but Unnecessary Care
(30% of spend)
Claims Spend for a Health Plan
Necessary Utilization
(70%)
“It’s generally agreed that about
30 percent of what we spend on
health care is unnecessary. If we
eliminate the unneeded care, there
are more than enough resources in
our system to cover everybody.”
-Dr. Elliott Fisher,
Dartmouth Institute for Health Policy
23. Remove no-value Care
Manage Unnecessary Spend
RowdMap tackles the 30% of the U.S. health care spend
that goes to clinically appropriate, but unnecessary care
Over $9B in
Orange County, CA
How much unnecessary spend is in your market?
Over $66B in Florida
$850 Billion Unnecessary Spend* in 2014
Least Unnecessary
Spend
Most
Unnecessary Spend
RowdMap tackles the 30% of U.S. health care
spend that goes to clinically appropriate, but
unnecessary care. RowdMap’s models
identify the cost-savings opportunities in a
geography based on the collective intensity
of care delivered by doctors in that area.
* Unnecessary Spend =
(Dartmouth Avg cost) * (Population) *
(RowdMap Network Opportunity Index)
24. Remove no-value Care
Manage Unnecessary Spend
Unnecessary Spend in Florida
In Broward Co. alone,
there is over $7.6B in
unnecessary spend.
Let’s look at which hospitals, groups and physicians
account for this and for what conditions
25. Physician Marketshare
by Major Clinical Categories
Remove no-value Care
Manage Unnecessary Spend
Match appropriate risk arrangements based on provider practice
patterns and Population characteristics within a geography.
Hospital Marketshare
by Major Clinical Categories
Provider Group Marketshare
by Major Clinical Categories
Unnecessary Spend in Broward
By condition across hospitals,
groups and physicians
This Physician.
Let’s start here
This GroupThis Hospital
Circulatory
Muscular-
skeletal
Respiratory
26. Remove no-value Care
Manage Unnecessary Spend
All contents are proprietary to RowdMap, Inc. and are being provided on a confidential basis.
Any use, reproduction or distribution of this information, in whole or in part, or the disclosure of any of its contents
without the prior written consent of the Company, is prohibited.
Physicians Driving Unnecessary Care in Broward
Musculoskeletal care is major contributor to unnecessary spend in Broward. Let’s
take a physician who is not an outlier but in the middle of the pack such as Dr.
Spend*. Let’s walk through what his clinically acceptable, but medically
unnecessary, practice pattern creates in unnecessary spend.
27. Remove no-value Care
Manage Unnecessary Spend
Referral Patterns and Physician Value Chains
Identify high performing providers and downstream
referral patterns. Encourage referrals to
high-performing specialists.
28. Remove no-value Care
Manage Unnecessary Spend
Least Unnecessary
Spend
Most
Unnecessary Spend
Option 2: Reinforce
highest-performing referral
and care pathways.
Increase the number of patient interactions
with green dot doctors.
Option 1: Change provider behavior.
Requires lots of provider education. Requires
payer to make up a significant portion of a
provider’s revenue. Increase the number of
green dot doctors.
Zoom to zip
29. Remove no-value Care
Manage Unnecessary Spend
If had same ratio as :
• His decompression rate would drop from
6.01 to 0.436 per patient.
• Which translates to 2,608 fewer
decompressions per year.
• At an average cost of $332 per
decompression, this represents potential
savings of over $850K
If decompression to fusion rate were
average for orthopedic surgeons:
• He would have 1629 fewer decompressions
for a potential savings of $540K.
*Actual physician names have been changed.
For every 10 back fusions, does
103 decompressions
For every 10 back fusions,
does 2 decompressions.
Dr. Save*
Dr. Spend’s
Dr. Spend*
Dr. Save*
That’s one physician, with one procedure, in one clinical condition.
This savings would not be picked up in unit cost or utilization analysis,
but cumulatively dwarfs fraud, waste and abuse outliers.
Intense practice patterns like this power FFS arrangements
but success in Pay for Value comes from identifying Risk-Ready providers.
Dr. Spend*
30. Start with Data for Business Context then add Tech.
The ACA at your finger tips
For Payers & Providers