Big Data Analytics
Driving Healthcare Transformation
Greg Caressi
SVP Healthcare & Life Sciences
November, 2014
Six Big Themes for the New Healthcare Economy
Themes
Modernizing Care
Delivery
Role of New
Participants
– Clinical practice is moving
– The emergence of IT
based tools and services is
witnessing the rise of a
new breed of competitors.
from intuition based decisions
to more analytics and data
based approaches.
Rethinking the
Customer
– Patients are no longer
going to be passive
participants in the process.
Companies Revamping
Strategies
– Many industry participants
as currently structured can
not maintain viability without
significant changes to their
business model.
Who Pays?
-The spiraling costs of care to
government and private payors is
forcing the launch of new methods
and models for payment of
healthcare services and products.
New Partnerships
– An industry that historically
operated in distinct silos is now
being forced to integrate, and
thus leading to firms seeking
new types of partnerships and
collaborations.
Source: Frost & Sullivan analysis.
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Care Delivery Transformation: Acute Care to Prevention
Track, Predict, Intervene, Manage
•Early identification and prevention
Prevention/Wellness
•New models of care delivery to improve:
• Collaboration among providers
• Patient knowledge, self-help and health
Size of Impacted Population
$$
Goal:
Keep
People
Healthy
Longer
Healthy /
Well
•Increase intervention
• Higher touch at lower cost
Goal:
Manage
or Mitigate
Risk
At Risk
Disease/Care
Management
Goal:
Diagnose
and
Reduce
Treatment
Delay
Undiagnosed
Goal:
Move to
More
Interaction
and SelfMgmt
Chronically Ill
Managed
Continuum of Care
Goal:
Manage
Chronically Ill
Unmanaged
Goal:
Quality of
Life
End of
Intervention
Source: Frost & Sullivan
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Data Creating Value in Healthcare
Improved
patient safety
Tracking of key
metrics
Auditable
Rapid
intervention
Early trend
identification
Collaboration
Flexible
access
Real-time info
Improved
visualization
Innovation
Reduce
response time
Combine info
for insights
Efficient
stakeholder
interaction
Reduce
capital, staff
costs
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Leveraging Big Data to Reduce Healthcare Costs
EHR / EMR
Remote
monitoring
mHealth apps
Genomic data
Email, text
messages
Microbiomic
data
Clinician notes
Metabolomic
data
Claims data
Biometric sensors
Cues to
intervene
Analytics
Published
research
Social media
Natural Language Processing
Imaging files
Patient
engagement
Photos, video
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More Traditional
Healthcare Analytics
New Analytics Tools
Needed By ACOs
Types of Analytics Applied in Healthcare Big Data Solutions
COMPARATIVE
ANALYTICS
PRESCRIPTIVE
ANALYTICS
DESCRIPTIVE
ANALYTICS
PREDICTIVE
ANALYTICS
What Happened?
What Will Happen?
Source: Frost & Sullivan
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Who’s At-Risk? Many Layers of Analytics In Preventive Care
Reduce utilization
by ‘frequent flyers’
Avoid readmission
penalties
Avoid catastrophic events
in diagnosed patients
Better disease management
to reduce long term costs
Reduce the number who
develop chronic conditions
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Hurdles to Reaching the Promise of Digital Health
DATA
We are
creating
millions of
useful data
points,
from a wide
variety of
sources…
INTEGRATION
ANALYTICS
…But the data is
provided in
separate
solutions which
prevent getting a
holistic view of
the patient.
FREEING DATA
AND
INTEGRATING
DATA ARE KEY
Predictive
analytics has
arrived…
Prescriptive
insights from
–omics are next
…Interpretation
needed to make
genomic data
valuable
SUSTAINABLE
CHANGE
Analytics alone
cannot transform
healthcare. What
actions are taken
based on analytics?
- Behavior change
- Process change
- Organizational
culture change
Source: Frost & Sullivan
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IT Transformation Helping To Drive Healthcare Transformation
Cloud
Measure
Outcomes
Define Population
AUTOMATED &
ONGOING
Cloud
Social
Media
Mobility
Manage
Care
•
•
•
•
Data Integration
Analysis
Reporting
Communications
and Alerts
Identify
Care Gaps &
Stratify Risks
Big
Data
Cloud
Big
Data
Engage
Patients
Social
Media
Mobility
Cloud
Source: The Institute for Health Technology Transformation, 2013; and Frost & Sullivan
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Cloud Solutions Are Aligned With Healthcare Shifts
Cloud frees data from
proprietary enterprise
software solutions
•
Cloud allows scale in
storing big data
Collaboration requires
flexible access and
merged data sets
Collaboration in research
means moving partners
to the data
Cloud solutions reduce
impact of IT staff shortages
•
Cloud is a key enabler
of mobilizing data for
use by consumers and
enterprise users
Source: Frost & Sullivan analysis
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Business Model Transformation
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What Will Healthcare Organizations Invest In To Address
Changing Goals?
Inpatient
Care
Ambulatory
Care
Home Care
Wellness
Services
CDS at point
of care
Data
Analytics
Imaging
Diagnostics
Patient
Engagement
Chronic
Disease Mgmt
mHealth
Conditions of focus
Diabetes
Cardiovascular conditions, e.g., Congestive Heart Failure, Chronic Heart Disease
Hypertension
Chronic Obstructive Pulmonary Disease (COPD)
Source: Frost & Sullivan
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Competitive Success Factors Change Who Will Win
Last Decade
Current Decade
Next Decade
Integrated platforms to
provide comprehensive
continuum of care. Focus on
quality of life.
Companies that understand
consumer behavior, needs,
and pain points.
Success
Drivers
Technologies geared
towards extending life
Technology to improve
outcomes and mitigate
risk
Types of
Companies
Who Excel
Companies that
advanced standard of
care.
Companies that could
make treatments safer
and easier to perform.
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Business Models Must Adapt
Yesterday
Tomorrow?
Frugal innovation, tiered product
markets
Product focus only (vs solution,
service, software, support)
Start with the need, not with the
technology
All innovation is technologybased (vs process, efficiency)
Look beyond your core customer
base
Individual clinician as customer
Know when to fold
Creates new data stream in
isolated silo
Applying techniques from other
industries to healthcare
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New Ecosystems In Development
S Health Platform
Value added through
• Data integration platform
• New apps, analytics built on
integrated data
• Holistic view of individual health
and fitness
• Business model = drive
smartphone / tablet sales or
health / wellness services?
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Moving to the Consumer
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Direct To Consumer Models
Regulatory issues to address
• Will consumers make treatment decisions based on the information provided?
• Is a clinician interface needed to present information to the consumer?
• Genetic profile only versus risk analysis
• Research to support interpretation of risk
assessment
• Next step toward addressing consumer
questions – opportunity or risk?
• Content or services needed beyond the data?
• Moving from “Dr Google” to Dr
Watson as a consumer resource
• How is the solution delivered to the
consumer?
• Clinician or service interface to assist
with next steps and interpretation?
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Leveraging mHealth to Reduce Avoidable Medical Costs
Key Pain Points Addressed by Current mHealth Technologies
Annual Cost to US
Healthcare System
mHealth
Technology
~ $300 billion
• Help patients track the timing & dosage of their medicines
• Help providers gain more insight into patients’ conditions &
behavior to offer the right treatment & advice
• Provide information on side effects, benefits, etc.
Patients don’t
give doctors
enough
information
~ $45 billion
• Collect data through an application which keeps consumers
engaged with the help of games, content and interaction
• Flag potential health problems and figure out exactly which
people on the medical team they need to see
Unnecessary
visits to doctors
~ $125 billion
• Provide information to patients by allowing them to pose
health related questions to a network of physicians
• Video teleconsultations
Missing critical
warning signals
~ $7 billion
• Allow doctors to remotely monitor the vital signs of hospital
patients and at home through sensors/devices
~ $100 billion
• Allow people to track calories through weight-control
applications
• Prompt people to set reasonable goals, exercise, and count
calories
Patients don’t
follow their
prescriptions
Unhealthy
diet & lack of
exercise
•
Source: mHealth report published by PwC,
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Mobile Platforms Offer Many Ways To Interact With Patients
Medical mobile apps enable patients to continuously monitor their health and share data with
providers, thus encouraging an ongoing focus on wellness that extends beyond the office and live
visits to the doctor.
Diagnostic Tools
Data Exchange/
Secure
Messaging
Biometric
Monitoring
“You can take the phone and
EHRs/PHRs
make it a lab on a chip. You
can do blood tests, saliva
tests, urine tests—all kinds
of things—sweat tests,
through your phone. This is
a powerful device.”*
Decision
Support
Lab & Imaging
Results
Social Networks
Educational
Content
*Eric J. Topol, M.D., West Endowed Chair of Innovative Medicine at Scripps Health and Professor of Genomics at the Scripps Research Institute
and author of The Creative Destruction of Medicine: How The Digital Revolution Will Create Better Health Care (Basic Books, 2012)
Source: Frost & Sullivan
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Wearable Technology Driving Current mHealth Market
Market Size (billion $)
Market Potential and Growth Projection
(Global, 2010 – 2018)
• Sensor technology will
continue to advance and
create better biometric data
• Winners based on brand vs
sensor capabilities vs style,
fashion?
• Will all devices be
commoditized over time?
• Nike decision to exit devices
• Fitbit decision to not integrate
with HealthKit
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Thank You
Greg Caressi
Senior Vice President
Healthcare & Life Sciences
(+1) 650 475-4555
[email protected]
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