More Related Content Similar to Best Practices for Data Convergence in Healthcare (20) More from MapR Technologies (13) Best Practices for Data Convergence in Healthcare1. © 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
2. © 2017 MapR TechnologiesMapR Confidential 2
Intro
Jack Norris
SVP Data & Applications, MapR
3. © 2017 MapR TechnologiesMapR Confidential 3
$
$
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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
6. © 2017 MapR TechnologiesMapR Confidential 6
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
10. © 2017 Cognizant10
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
11. © 2017 Cognizant11
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
13. © 2017 Cognizant13
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
14. © 2017 Cognizant14
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
15. © 2017 MapR TechnologiesMapR Confidential 15
Leveraging Healthcare Data
via the MapR Platform
Joe Blue, Director of Data Science
16. © 2017 MapR TechnologiesMapR Confidential 16
Healthcare Data Ecosystem
PROVIDERS PAYERS
PATIENTS
17. © 2017 MapR TechnologiesMapR Confidential 17
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…
18. © 2017 MapR TechnologiesMapR Confidential 18
Stage 1: Delivery
PROVIDERS
PATIENTS
Predict outcomes
from live patient
data, including 50%
sepsis mortality
decrease
19. © 2017 MapR TechnologiesMapR Confidential 19
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.)
22. © 2017 MapR TechnologiesMapR Confidential 22
Stage 2: After Care
PROVIDERS
PATIENTS
Use follow-up info
from EMR – improve
30day readmit by 12-
15% over LACE model
23. © 2017 MapR TechnologiesMapR Confidential 23
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%
26. © 2017 MapR TechnologiesMapR Confidential 26
Stage 3: Beyond the Claim
MEMBERS
PAYERS
Deeper and more
efficient work with
claims delivered a
$22:1 ROI in new
overpayments
27. © 2017 MapR TechnologiesMapR Confidential 27
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
28. © 2017 MapR TechnologiesMapR Confidential 28
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
30. © 2017 MapR Technologies 30
Q&A
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technologies
ENGAGE WITH US
Contact us at:
855-NOW-MAPR
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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/
Editor's Notes Why…Organizations are pursuing digital transformation…..Transforming the customer experience
Customer acquisition and retention is being threatened by competitors who can exploit data to transform the customer experience.
The competitiveness of an organization’s product or service is also being threatened by companies who can leverage data to improve operational efficiencies and agility
And the disruption of digital transformation is driven by data. Why…Organizations are pursuing digital transformation…..Transforming the customer experience
Customer acquisition and retention is being threatened by competitors who can exploit data to transform the customer experience.
The competitiveness of an organization’s product or service is also being threatened by companies who can leverage data to improve operational efficiencies and agility
And the disruption of digital transformation is driven by data. And Data is the critical leverage point….it’s independent of the hardware choice..it’s independent of the infrastructure whether that’s on-premise or cloud...
In fact, the underlying data later is critical to support the next-gen applications to provide speed and reliability at scale...
Pursuing business agility..only as agile as their weakest point...Convergence Requires data agility......
Infrastructure agility how do you move beyond the rigid infrastructures that are in place today invariably involves cloud and containers....
Requires Application agility – enable real-time streaming, microservices