Thomas-Denberg - Virginia Chamber of Commerce

Report
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
1
Greetings from Western Virginia
2
3
Tonight’s
Topic
Health IT
And
Population
Health
4
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
7
Healthcare IT and ACOs
The Critical List
•
•
•
•
•
•
•
•
•
•
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
8
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
•
•
•
•
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
•
•
•
•
•
•
•
A journey, not a project
Hard work
Expensive
New skill sets
Organizational discipline
Executive support
Dividends can be huge

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