Is your organization at the analytics crossroads? Have you made strides collecting and sharing massive amounts of data from electronic health records, insurance claims, and health information exchanges but found these efforts made little impact on efficiency, patient outcomes, or costs?

1. © 2017 MapR TechnologiesMapR Confidential 1
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Jack Norris, SVP, Data & Applications, MapR
Thomas Kelly, Practice Director, Analytics & Information Management, Cognizant
Joe Blue, Director of Data Science, MapR
Best Practices for Data
Convergence in Healthcare

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Intro
Jack Norris
SVP Data & Applications, MapR

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$
$
$
TransformingCustomerExperience
Driving Operational Excellence

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$
TransformingCustomerExperience
Driving Operational Excellence

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A Once-in-30-year Re-platforming of The Enterprise
New Applications Existing Applications
Open Source Analytic Innovations Legacy
CONVERGED DATA PLATFORM
On Premise Private Cloud Public Cloud
Heterogeneous Hardware
• Critical infrastructure for next-gen applications
• Data platform enabler for required Speed, Scale, & Reliability

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Three Dimensions to Agility
Data Agility
• Unified Files, Tables, Streams
• Support for schemas that change
• Multi-modal support in a DBMS
Application Agility
• Microservices-based development
• Streaming applications in real time
• Machine learning
Infrastructure Agility
• Multi-dimensional elasticity
• Global multi-cloud
• Container apps with data persistence
DATABASE EVENT STREAMING
High Availability
WEB-SCALE STORAGE
Real Time Unified Security Multi-tenancy Disaster Recovery Global Namespace

7. © 2017 Cognizant
Thomas Kelly
Practice Director, Analytics & Information Management,
Cognizant

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The Healthcare Industry is being Transformed by Market Forces

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Driving Change in Healthcare through Data Science and Analytics

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Payers – Member Churn Analysis
Customer
Summary
Identify factors in which
members who visit out of
network tend to leave the plan
Business Objective
Improved Customer
Experience
• Optimized services to
reduce members’ out-
of-pocket expenses
• Identified service gaps
to reduce visits out of
network
Cost Management
Maximized ROI while
reducing member
acquisition and
retention costs
Improved Member
Retention
Customer behavior
insights to drive
member retention
strategies
Business Impact Delivered
Data Sciences Solution Approach
• EXTRACT membership and
claims data into Sandbox
• INTEGRATE member and
provider unstructured data
Leading US based
health plan
Health Plan
• DEFINE metrics and
descriptive statistics
• SELECT features for machine
learning algorithms
CONVERGED DATA PLATFORM
• CLUSTER / ASSOCIATE
member and claims patterns
• SCORE scenarios
• TEST models
• CREATE dashboards and
reports for multiple
audiences

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Payers – Incident Root Cause Assessment and Inflow Prediction
Customer
Summary
• Reduce operational costs
through optimized resource
planning for incident tickets
• Improve customer
satisfaction through insights
from ticket analysis
Business Objective
Customer Satisfaction
Identified issue symptoms and
possible root causes of incidents
to build robust solution and
improve customer satisfaction
Timely Resolution
Accurately predicted ticket
volumes to ensure optimal
resource planning and timely
resolution, enabling cost
reduction
Business Impact Delivered
Data Sciences Solution Approach
• SELECT dimension variables
for extraction
• EXTRACT weekly data from
Incident Management
database
Health Plan
• DEFINE metrics and
predictive statistics
• SELECT features for machine
learning algorithms
CONVERGED DATA PLATFORM
• CLUSTER incidents using
unsupervised ML
• PREDICT incidents using
supervised ML
• MEASURE predicted incident
volume and issue
classification
• VERIFY prediction and
fine-tune
US based health
plan

12. © 2017 Cognizant12
Providers – Proactively Identify High Risk Drug-Seeking Patients
• Identify patients becoming
addicted to prescription drugs
• Identify factors supporting
drug-seeking behaviors
• Determine how drug seeking
behaviors lead to increased risk
and decreased health outcomes
Business Objective
Improved Recognition of
Drug-Seeking Behavior
Factors behind drug
abuse/addiction to
prescription drugs were
highlighted
Better Health
Outcomes
Identified patients are
being educated and
treated for their drug-
seeking behaviors
Savings Realized
Potential annual
saving of $12M per
1000 correctly
identified drug
seekers
Business Impact Delivered
• EXTRACT EMR data for
known drug-seeking
patients
• INTEGRATE unstructured
data from physicians’
notes on these patients
Leading US based
healthcare
provider network
Provider Network
• DEFINE metrics and
descriptive statistics
• SELECT features for
machine learning
algorithms, including
disease state and reported
conditions
• CLUSTER patients by
disease state, reported
conditions, and other
factors
• CREATE prediction models
• TEST prediction models
• SINGLE CLICK for patient
drug-seeking behavior
identification
Customer
Summary
Data Sciences Solution Approach

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Providers – Identifying High Readmission Risk Patients
• Identify patient cases with high
risk of readmission
• Identify discharge protocols that
are most effective in reducing
readmissions
Business Objective
Reduced
Readmission Risk
Analytical model correctly
classifies over 90% of
patient cases
Lower Cost
of Care
Steadily declining
care costs due to
readmission over
long term
Improved Quality
of Care
Personalized care
plans for discharge
and preventive
interventions
Business Impact Delivered
• EXTRACT data from
disparate data sources,
including EMR/EHR, payer,
research, patient forums,
social forms, compliance,
clinical, care, and case
management systems
Leading US based
healthcare
provider network
Provider Network
• DEFINE metrics and
descriptive statistics
• DEFINE integrated data
model
• SELECT features for
statistical and ML
algorithms
• CREATE readmission
propensity model,
stratifying patients by
disease state and risk level
• TRAIN the model on
historical data
• IDENTIFY risk mitigation
factors
• DEFINE disposition / post-
discharge protocols
• FEEDBACK efficacy results
into model
Customer
Summary
Data Sciences Solution Approach

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Medical Devices – Predictive Maintenance Model
Customer
Summary
• Identify root causes of
device failures
• Enable predictive
maintenance for cost
reduction
• Assess medical device
product quality
Business Objective
Improved Customer
Experience
High customer
satisfaction through
reduced equipment
downtime
Device
Improvements
Leveraged data-driven
insights to build new
sophisticated devices
Optimized
Maintenance Costs
Failure event prediction
and key driver
identification leading to
lower operational costs
Business Impact Delivered
Data Sciences Solution Approach
• PROCESS large volumes of
machine log data
• CONVERT encoded binary
data to ASCII
• EXTRACT attributes/values
• SELECT features for
predictive modeling
CONVERGED DATA PLATFORM
• TRAIN models on sample
data
• TEST models to verify
achievement of prediction
targets
• PROCESS inventory against
predictive models
• CREATE dashboards and
reports for multiple
audiences
Life Sciences
Leading US based
medical device
manufacturer

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Leveraging Healthcare Data
via the MapR Platform
Joe Blue, Director of Data Science

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Healthcare Data Ecosystem
PROVIDERS PAYERS
PATIENTS

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Healthcare Data Ecosystem
PROVIDERS
PAYERS
PATIENTS
Claims
Labs, Rx,
Genomics
EMR
Doctor’s
Notes
Images,
Voice, etc.
Monitors
Mobile,
Wearables
The MapR platform
derives incremental
value from leveraging
more data…

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Stage 1: Delivery
PROVIDERS
PATIENTS
Predict outcomes
from live patient
data, including 50%
sepsis mortality
decrease

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Stage 1: Delivery
PROVIDERS
PATIENTS
Predict outcomes
from live patient
data, including 50%
sepsis mortality
decrease
Deep learning can
categorize images
(e.g. lung cancer)
more accurately
than humans

20. © 2017 MapR TechnologiesMapR Confidential 20
Stage 1: Delivery
PROVIDERS
PATIENTS
Predict outcomes
from live patient
data, including 50%
sepsis mortality
decrease
Deep learning can
categorize images
(e.g. lung cancer)
more accurately
than humans
Genomic analysis
leads to more
effective cancer
treatments,
precision medicine

21. © 2017 MapR TechnologiesMapR Confidential 21
Stage 1: Delivery
PROVIDERS
PATIENTS
Predict outcomes
from live patient
data, including 50%
sepsis mortality
decrease
Deep learning can
categorize images
(e.g. lung cancer)
more accurately
than humans
Genomic analysis
leads to more
effective cancer
treatments,
precision medicine
Analysis of EMR
trends aids
logistics (e.g.
beds, staffing,
etc.)

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Stage 2: After Care
PROVIDERS
PATIENTS
Use follow-up info
from EMR – improve
30day readmit by 12-
15% over LACE model

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Stage 2: After Care
PROVIDERS
PATIENTS
Use follow-up info
from EMR – improve
30day readmit by 12-
15% over LACE model
Scanned doctors
notes prevent
omission of procs
from claim before
submittal

24. © 2017 MapR TechnologiesMapR Confidential 24
Stage 2: After Care
PROVIDERS
PATIENTS
Use follow-up info
from EMR – improve
30day readmit by 12-
15% over LACE model
Scanned doctors
notes prevent
omission of procs
from claim before
submittal
Combine EMR with
labs & rx to find
co-morbidities
and suggest
treatment changes

25. © 2017 MapR TechnologiesMapR Confidential 25
Stage 2: After Care
PROVIDERS
PATIENTS
Use follow-up info
from EMR – improve
30day readmit by 12-
15% over LACE model
Scanned doctors
notes prevent
omission of procs
from claim before
submittal
Combine EMR with
labs & rx to find
co-morbidities
and suggest
treatment changes
With remote
monitoring, reduce
costs 75% & improve
pat. satisfaction
to 90%

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Stage 3: Beyond the Claim
MEMBERS
PAYERS
Deeper and more
efficient work with
claims delivered a
$22:1 ROI in new
overpayments

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Stage 3: Beyond the Claim
MEMBERS
PAYERS
Deeper and more
efficient work with
claims delivered a
$22:1 ROI in new
overpayments
Identify needed
screens for
preventable
cond. through
graph analytics

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Stage 3: Beyond the Claim
MEMBERS
PAYERS
Deeper and more
efficient work with
claims delivered a
$22:1 ROI in new
overpayments
Identify needed
screens for
preventable
cond. through
graph analytics
With deep learning,
diagnose more
conditions through
risk scoring

29. © 2017 MapR TechnologiesMapR Confidential 29
Stage 3: Beyond the Claim
MEMBERS
PAYERS
Deeper and more
efficient work with
claims delivered a
$22:1 ROI in new
overpayments
Identify needed
screens for
preventable
cond. through
graph analytics
Complex ensembles
yield PMPY
forecasts more
accurate than
regression models
With deep learning,
diagnose more
conditions through
risk scoring

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Q&A
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Additional Resources
MapR Guide to Big Data in Healthcare
Download today at:
https://mapr.com/mapr-guide-big-data-healthcare/
Healthcare Solutions Page on MapR.com
https://mapr.com/solutions/industry/healthcare-
and-life-science-use-cases/
For the latest books, whitepapers, webinars, etc.
visit:
https://mapr.com/resources/