Carilion Clinic’s Journey on the
Population Health Management
and Big Data Highways
June 5, 2014
Tom Denberg, MD
Chief Strategy Officer
Executive Vice President
Carilion Clinic
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Greetings from Western Virginia
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Tonight’s
Topic
Health IT
And
Population
Health
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Big Data and Healthcare-behind
but catching up
Health Catalyst
Big Data and Healthcare
• Big data is a term used for massive amounts of information
that can be interpreted by analytics to provide an overview of
trends or patterns.
• Organizations leverage big data by gathering records and
information captured and then interpreting it with analytics.
• Common in other industries, big data has only recently begun
to become a factor in healthcare. It has applications range
from provider-specific business intelligence to scouring over
an entire state's health records to pinpoint people who are at
risk for certain ailments.
• Many believe that big data can help target early warning signs
and improve patient safety
Healthcare IT News 2014
Enterprise Data Warehouse
CLAIMS/Plan
Data Sources
EPIC EMR
Operational
Database
(Cache)
Web-based User
Interface
LY
HT
G L
NI ET
Aetna
Employee
Group,
ACO
(Wholehealth)
Claims
Lab
Rx
Eligibility
Cloud-Based/ASP services
CARILION CLINIC
Claims Data
Population
Advisor
Premier/Verisk
CMS
Medicare
Shared
Savings
Temporary
Claims Staging
Database
sk
Ri
s,
p
a
a
/ G Dat
rns ion
e
t
n
Co fica
re trati
a
C
S
Clarity
Relational Database
ETL
TMG
Medicare
Advantage
Claims
EPIC EMR
QNTX
Medicare
HMO
(Majesticare)
Other
Plans - TBD
SAP/
Business
Objects
Enterprise
Enterprise Data
Warehouse
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Healthcare IT and ACOs
The Critical List
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Population identification - attribution
Identification of care gaps – Decision Support
Risk Stratification
Cross Continuum Care management
Quality and Outcomes measurement
Patient engagement
Telemedicine
Mixing claims and clinical data
Predictive modeling
Clinical information exchange
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Excess Cost Domain Estimates
Cost in Billions of $$$
Unnecessary Services ($210 B)
$75
$210
$55
$105
$130
$190
IOM. The Healthcare Imperative, 2010.
Inefficiently Delivered Services
($130 B)
Excess Administrative Costs
($190 B)
Excessive Pricing ($105 B)
Missed Prevention Opportunities
($55 B)
Fraud ($75 B)
Clinician-Driven Sources of Excessive
Health Care Costs
(Population Health Management Focus)
•
Preventable/avoidable hospital (re-)admission
and ED visits
(Case Management, Readmission Reduction)
•
Missed prevention
(Pay-for-performance)
•
Unnecessary care
(Utilization Management)
Key patient populations
Ambulatory Case Management
Sickest and/or highestutilizing 5-10%
Patient engagement, care coordination,
Extensivists, palliative care, transitions of care
protocols
Advanced CHF, COPD, IHD, DM, asthma,
cancer, psychosocial problems
Rising-risk 40-50%
Patients with less severe chronic
illnesses or behaviors that
significant elevate morbidity or
mortality risks; HTN, DM,
hyperlipidemia, tobacco use,
obesity
Ambulatory Quality / Pay for
Performance (P4P)
Cancer screening, BP, lipid,
A1c, etc.; various patient
engagement and contact
components
Low risk 45-55%
Patients without medical
problems; focus on
prevention, wellness, and
connectivity to health system
Behavioral Health / Psychosocial
Key Strategic Initiatives
Pay-for-performance
• Core measures, value-based purchasing (Hospital)
• HCAHPS (Hospital)
• HEDIS, NQF (Ambulatory)
• CGCAHPS (Ambulatory)
CLBSI
CAUTI
CHF Readmission rate…
…
BP control
A1c control
Breast CA screening…
Utilization Management
“Off hand, I’d say
you’re suffering from
an arrow through
your head, but just
to play it safe, let’s
get an echo.”
% CBCs ordered without apparent clinical indication during preventive exams
% CBCs ordered without apparent clinical indication during preventive exams
The Future- Proactive Care
• Identify patients at risk before they
develop symptoms of heart failure
• Maximize treatment of underlying conditions
• Closer follow up
• Delay or prevent the onset of severe heart
failure
• Bend the disease curve
CHF Onset Project
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•
•
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Collaboration ( Carilion, IBM, Epic)
3 years data / 500,000 records reviewed
NLP used to obtain unstructured data (20M)
8500 patients at risk
• 3500 identified with NLP
• Risk score generated based on clinical ,
social and demographic data
• Score available in EMR
• Develop treatment protocols to address at
risk patients.
Big Data – Lessons Learned
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A journey, not a project
Hard work
Expensive
New skill sets
Organizational discipline
Executive support
Dividends can be huge