Data analytics have taken on a new importance in today’s healthcare delivery system.

1. Accelerating Your Move to Value-Based Care
Achieving Information Management Maturity for Faster Results
1
Dan Schultz – Information Builders
Rahul Ghate – Prosperata

2. Moving from Volume to Value Based Care…
2

3. The Industry is Reacting to These Pressures
Consolidation, Mergers and Acquisitions
Ecosystem Convergence
Shared Risk/Savings
Evolution of Patient to Consumer
3

4. Today, It’s All About Facing Data Challenges…
Clinical Data Challenges
4

5. These Challenges Aren’t Small, Either…
 Patient Matching
 Difficult to identify across continuum of care
 No common identification number for a person
 IT Resource Staffing in Small Physician Groups
 Lack dedicated staff
 Little knowledge of IT requirements for data
sharing
 Data Volumes
 Overwhelming amount of data in healthcare
 Vital to identify data relevant to clinical
measures that improve cost & quality of care
5

6. And Sometimes, It’s Process and Technology…
 Incorrect Data
 More harmful than a lack of
information
 Leads to inaccurate or incomplete
treatment
 Data Quality & Terminology Gaps
 Provider systems struggle with
compatibility
 Numerous standards and clinical
terminologies
 Local proprietary codes need to map
standard codes
6

7. Information Builders Data Management Platform
7
Introducing Omni-HealthData™

8. Omni-HealthData
A Person-based Information Management
solution for Health Insurers and Providers:
 Pre-built data models for mastered and
transactional domains
 Pre-built processing, quality, mastering,
and remediation rules
 360 Degree View on Members/Patients
and Providers through Data and Analytics
8
What is Omni-HealthData?

9. Omni-HealthData
 Programs & Applications
 Quality Reporting Programs – HEDIS & STAR
 Care coordination and Transition of Care in PCMH setting
 Value based reimbursement models
 Risk Stratification/Adjustment
 Greatest details about patient health and risks
 Validate risk assumptions and predictions
 Optimize Utilization
 Reduce/avoid redundant testing and variability in care
 Address fraud or medically unnecessary utilization
 Optimize Costs
 Real time integration with HIEs and EMRs
 Reduce manual chart chase
 Member Outreach
 Faster and more targeted campaigns
(High Risk Patients with multiple Chronic Conditions)
9
Business Value
 Improved Patient and Provider Experience
 Total cost of care and 360 view of patient
 Timely intervention
 Build trust – single version of truth across a
spectrum of care

10. Omni-HealthData
 Richer Data Set
 Vital Signs, Lab Results
 Social History, Family History
 More Complete Set of Diagnoses
 More than just what physician bills in EMR
 Clinical Data can be used to impute diagnosis
 Timeliness
 Clinical data is available near real-time
 Claim data could be delayed by weeks
 Longitudinal View
 Patient history vs. particular Visit/Encounter
10
Clinical Data
Claims Data
Diagnoses
Family History
Social History
Vital Signs
Lab Results

11. Omni-HealthData
Map, Master, and Steward
Downstream apps
Provider relations
Claims adjudication
Analytics
Data warehouses
Data marts
External
Provider & member portals
Reimbursement
Onramps:
CCD,relational,XML,etc.
Consumption:
HEDISGrouper,CCD,views,etc.
Integrate, Cleanse,
Correlate, Steward
Reference
data
Code sets:
HLI
Internal data
Member (e.g., Initiate)
Claims
Eligibility
External data
Member
Administrative
Clinical (CCD, HL7, etc.)
Facility
Provider info

12. Omni-HealthData
 Built from the Omni Repository
 Consumption Views: De-normalized
for easy consumption in BI and
analytics
 Metrics Views:
 Pre-analyzed, materialized views
 Supports standard volume and
quality metrics
 Healthcare analytics and regulatory
metrics
HealthViews
Omni Repository
HV - Consumption Views
HealthViews - Metrics Views
Customer Queries / Presentation Views
Custom
Omni-HealthData Insights, WebFOCUS, Cohort Builder

13. But, It Takes Work to Get to an Omni-HealthData…
13

14. Success in Value-Based Care with Mature
Information Management
3 KEY STEPS

15. 15WHY WE EXIST
• Help healthcare organizations use their data
assets and technology to:
 Thrive in the world of value-based care
 Meet revenue and profitability targets
 Achieve your version of the triple-aim objectives
Copyright © Prosperata, LLC. All Rights Reserved.
Prosperata wants healthcare to prosper with its data

16. 16
Tough Questions Require Better Analytics for Better
Decisions
We need to manage
diabetes populations. How
can I identify the population
and develop a strategy that
improves outcomes?
How can we maximize our
in-network referrals to
better accommodate
Veterans needs?
Healthcare
Executive
Our project portfolio is over
budget. How can I get to
the root-cause and turn this
around?
We need to reduce the
number of redundant MRIs
how can I identify the
outliers and prevent future
outliers?
The “tough” questions in healthcare are fundamentally enterprise data challenges and require a
comprehensive enterprise approach.
“Predicting and preparing for the world of tomorrow is no easy task. Reliably forecasting
outcomes, events, and patterns will in most cases require not only substantial data, but clean and
correct data, along with sophisticated models and analysis.”
HFMA - Healthcare Financial Management

17. 17Understanding Data 3.0
Data 1.0
Data related to specific
business processes
• E.g. Departmental data
marts
Data 2.0
Data focused on
enterprise wide
initiatives
• E.g. EDW, SCM, ERP
Data 3.0
Data is actionable,
explainable, trusted
and contextualized
Data front and center,
used to transform
businesses and support
new business models
Copyright © Prosperata, LLC. All Rights Reserved.

18. 18DATA MATURITY in 3 STEPS
•Develop strategic
plan to differentiate
with Data
Information
Management
Strategy
•Deploy better Data
Governance
•Evaluate and
improve Data Quality
Data Governance,
Data Quality
•Modernize existing
BI/Data investments
Modernization to
Data 3.0
Copyright © Prosperata, LLC. All Rights Reserved.
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2
3
Current Status: 35% Current Status: 23%

19. Copyright © Prosperata, LLC. All Rights Reserved.
STEP 1 – Information
Management Strategy

20. 2014-POINT DATA/ANALYTICS MATURITY MODEL
Copyright © Prosperata, LLC. All Rights
Reserved.
Maturity: Informal Incipient Organized Operational Transformative
ORGANIZATION
Technical
Expertise
No experience managing formal
repository and workflow systems
Struggling 1.0 implementations of
some systems
More advanced version 2.0+
implementations of systems with
focus on business-critical content
Managing repository &
workflow systems is a core IT
skill, with mature systems in
place
Pro-active experimentation &
learning about emerging content
technologies
Business
Experience
Ignorance about value and role
of EIM
Growing sense of need for EIM,
supported by fragmented
initiatives
Departmental ownership of EIM
initiatives; analytical teams built
independently
Executive ownership of EIM as
a practice; process & data
analysis are core skills
Information management is a
required employee skill & part of
their HR reviews
Process Few or no standardized
procedures
Basic process analysis leads to
some ad-hoc information
workflows
Identification of interdepartmental
information dependencies, with
partial automation
Automated information
dissemination processes span
systems & departments
Robust processes to cover
exception-handling &
experimentation
Alignment Key business drivers are not well
understood by IT strategists,
resulting in EIM gaps in IT
portfolio
Improved IT-business
communication, but IT mostly
disconnected from business
outcomes
Sustained efforts for IT-business
collaboration, results still
dependent on negotiation
Execution of IT & business
strategies is cohesive, with
fewer instances of “push pull”
model
IT and business are true partners,
performance metrics fully aligned
with strategic business objectives
INFORMATION
Metadata No formal inventory or
classification
Departmental inventories and
initial content tagging
Enterprise inventory underway;
controlled vocabularies initiated
All new repositories & content
types registered; global
taxonomies created
Ongoing metadata reviews are
standard practice
Quality Data quality is an afterthought Ad-hoc initiatives and manual
interventions
Data quality criteria developed,
partially implemented
Data quality process
implemented and automated
Routine quality reviews and
proactive monitoring of data
processes
Lifecycle No lifecycle management Most content archived
haphazardly; some loose records
management (RM) initiatives
Development of formal electronic &
paper-based RM process;
implementation initiated
Implementation of electronic &
paper-based RM across the
enterprise
All content types go through formal
lifecycle management
Governance No policies & procedures Scattered policies; few or no
formal procedures
Development of information
governance structure & codification
of procedures
Policies & procedures widely
disseminated; Enterprise
ownership in place
Active review & adaptation;
executive support at highest levels
Re-use Content routinely duplicated Some informal consolidation
initiatives
Structured content analysis &
creation of mitigation plan
Information repurposed across
systems & channels
Checks in place to prevent future
duplication
Findability Information is hard to find,
requiring manual effort and
dependency on select few
Systems support search capability
with basic metadata applied
Controlled vocabulary terms
leveraged for search
Consolidation of search
capabilities across key systems
Implementation of enterprise &/or
federated search applications
APPLICATIONS
Analytics Focus on operational reporting Historical data analysis;
dashboards & scorecards
Ad-hoc analysis, information
delivery; what-if modeling;
forecasting
Pervasive self-service
capability; predictive analytics
for selected use cases
Deep predictive & prescriptive
analytics; routine experimentation
with new technologies
Architecture No architectural consistency
across systems
Initial attempts at reference
architecture; Documentation for
key areas
Reference architecture used for key
projects; Logical data model
available; Thorough documentation
Pervasive self-service
capability; predictive analytics
for selected use cases
Enterprise architecture adopted;
Architectural governance in place
Security No security regime in place Security dependent on capability
of individual systems
Formal projects initiated to address
gaps & redundancies due to
multiple solutions
Standardized policies &
procedures exist & are system
enabled
Security is a centralized shared
service; Proactive monitoring of
threats
Usability Lack of systems make end user Employee adoption rates Some initiatives use Scenario User-centered design underpins Usability is a guiding principle in all
OngoingImprovementthroughMeasurement&Monitoring

21. 21OUR PROVEN MATURITY MODEL
Copyright © Prosperata, LLC. All Rights Reserved.
Maturity: Informal Incipient Organized Operational Transformative
ORGANIZATION
Technical Expertise
Business
Experience
Process
Alignment
INFORMATION
Metadata
Quality
Lifecycle
Governance
Re-use
Findability
APPLICATIONS
Analytics
Architecture
Security
Usability
14 areas of maturity, organized into
3 broad categories and 5 levels of
progression

22. 22
• Exhaustive list of enterprise data assets
organized by subject area, data quality and
ownership
Data Asset
Inventory
• Clear understanding of priorities of individual
business units and their dependence on
data/analytics
Business
Workload
Analysis
• Selection of the most viable architectural
components to solve business workloads
Architectural
Component
Mapping
• 3-5 year strategic yet practical roadmap,
ready for execution
3-5 Year
Execution
Roadmap
TYPICAL COMPONENTS OF IM STRATEGY INITIATIVE

23. 23SAMPLE TIMELINE
Copyright © Prosperata, LLC. All Rights Reserved.
Analysis of business
objectives & workloads
W10W9W8W7W6W5W3W2W1 W4
 Project kickoff
meeting
 Biz stakeholder
meetings begin
 C-level buy in
 IM strategy
ready for
execution
Info
architecture
review
3-year maturity
targets
 Access current IM
maturity state and set
transformation targets
 Deep-dive
technical
reviews
Tech capability
discussions
W11 W12
Current state IM
assessment
Prioritiz
ation
Assemble
Roadmap,
Iterate
 Review draft
roadmap
 Iterate to
develop final
version
 VP-level
prioritization
and buy in
C-level
buy-In
PPT
component
mapping

24. 24
OngoingImprovementthroughMeasurement&Monitoring
Maturity: Informal Incipient Organized Operational Transformative
ORGANIZATION
Technical
Expertise
No experience managing formal
repository and workflow systems
Struggling 1.0 implementations of
some systems
More advanced version 2.0+
implementations of systems with
focus on business-critical content
Managing repository &
workflow systems is a core IT
skill, with mature systems in
place
Pro-active experimentation &
learning about emerging content
technologies
Business
Experience
Ignorance about value and role
of EIM
Growing sense of need for EIM,
supported by fragmented
initiatives
Departmental ownership of EIM
initiatives; analytical teams built
independently
Executive ownership of EIM as
a practice; process & data
analysis are core skills
Information management is a
required employee skill & part of
their HR reviews
Process Few or no standardized
procedures
Basic process analysis leads to
some ad-hoc information
workflows
Identification of interdepartmental
information dependencies, with
partial automation
Automated information
dissemination processes span
systems & departments
Robust processes to cover
exception-handling &
experimentation
Alignment Key business drivers are not well
understood by IT strategists,
resulting in EIM gaps in IT
portfolio
Improved IT-business
communication, but IT mostly
disconnected from business
outcomes
Sustained efforts for IT-business
collaboration, results still
dependent on negotiation
Execution of IT & business
strategies is cohesive, with
fewer instances of “push pull”
model
IT and business are true partners,
performance metrics fully aligned
with strategic business objectives
INFORMATION
Metadata No formal inventory or
classification
Departmental inventories and
initial content tagging
Enterprise inventory underway;
controlled vocabularies initiated
All new repositories & content
types registered; global
taxonomies created
Ongoing metadata reviews are
standard practice
Quality Data quality is an afterthought Ad-hoc initiatives and manual
interventions
Data quality criteria developed,
partially implemented
Data quality process
implemented and automated
Routine quality reviews and
proactive monitoring of data
processes
Lifecycle No lifecycle management Most content archived
haphazardly; some loose records
management (RM) initiatives
Development of formal electronic &
paper-based RM process;
implementation initiated
Implementation of electronic &
paper-based RM across the
enterprise
All content types go through formal
lifecycle management
Governance No policies & procedures Scattered policies; few or no
formal procedures
Development of information
governance structure & codification
of procedures
Policies & procedures widely
disseminated; Enterprise
ownership in place
Active review & adaptation;
executive support at highest levels
Re-use Content routinely duplicated Some informal consolidation
initiatives
Structured content analysis &
creation of mitigation plan
Information repurposed across
systems & channels
Checks in place to prevent future
duplication
Findability Information is hard to find,
requiring manual effort and
dependency on select few
Systems support search capability
with basic metadata applied
Controlled vocabulary terms
leveraged for search
Consolidation of search
capabilities across key systems
Implementation of enterprise &/or
federated search applications
APPLICATIONS
Analytics Focus on operational reporting Historical data analysis;
dashboards & scorecards
Ad-hoc analysis, information
delivery; what-if modeling;
forecasting
Pervasive self-service
capability; predictive analytics
for selected use cases
Deep predictive & prescriptive
analytics; routine experimentation
with new technologies
Architecture No architectural consistency
across systems
Initial attempts at reference
architecture; Documentation for
key areas
Reference architecture used for key
projects; Logical data model
available; Thorough documentation
Pervasive self-service
capability; predictive analytics
for selected use cases
Enterprise architecture adopted;
Architectural governance in place
Security No security regime in place Security dependent on capability
of individual systems
Formal projects initiated to address
gaps & redundancies due to
multiple solutions
Standardized policies &
procedures exist & are system
enabled
Security is a centralized shared
service; Proactive monitoring of
threats
Usability Lack of systems make end user Employee adoption rates Some initiatives use Scenario User-centered design underpins Usability is a guiding principle in all
YOUR IM JOURNEY: CURRENT STATE AND FUTURE TARGETS

25. 25TYPICAL OUTPUTS of STEP 1
• Data asset inventory
• Current state data maturity assessment
• 3-5 year strategic Information Management roadmap
• Prioritized and time-lined list of initiatives
• ROI/TCO analysis
• High-level execution plan
Copyright © Prosperata, LLC. All Rights Reserved.

26. Copyright © Prosperata, LLC. All Rights Reserved.
STEP 2 – Data
Governance, Data Quality

27. 27
The organization
gets so complex
that traditional
management of
data assets is
not sufficient
Data security,
privacy and
quality concerns
Complex
regulatory,
compliance or
contractual
requirements
Weak alignment
between IT and
Business
lowering ability
to use data
assets
WHEN DO ORGANIZATIONS NEED DATA GOVERNANCE?

28. 28TRUST METHODOLOGY
Copyright © Prosperata, LLC. All Rights Reserved.
TAILOR Data Governance
RATE Data Quality
UNLEASH Data Stewardship
STANDARDIZE Business Rules
TRANSFORM Data Quality
• Review current Data Governance constructs
• Design improved Data Governance program
• Train and assist internal team for deployment
• Conduct subjective analysis of data quality
• Use profiling for objective analysis of data quality
• Design customized data quality initiative
• Identify key data activities that need stewardship
• Develop data stewardship processes
• Train data stewards
• Develop data quality rules for key subject areas
• Deploy data quality rules using leading tool
• Develop enterprise data quality dashboards
• Implement data stewardship tool
• Train data stewards for remediation activities
• Implement automated data quality transforms

29. 29SAMPLE TIMELINE for TRUST
Copyright © Prosperata, LLC. All Rights Reserved.
Data
Governance
Optimization
M10M9M8M7M6M5M3M2M1 M4
 Data Governance
discussions with
business & IT
leadership
 Remediation
process for
addressable
DQ issues in
place
Business Rules for
Selected Subject
Areas
Subjective/O
bjective
Analysis
M11 M12
Deploy DQ Tool & Dashboards
Data Stewards Meaningfully Engaged in Data Quality Improvements
 DQ dashboards
deployed to
business & IT
stakeholders
 Business rules for
top subject areas
developed
Deploy Remediation Portal
 Data quality
evaluation
complete

30. Copyright © Prosperata, LLC. All Rights Reserved.
STEP 3 – Modernization
to Data 3.0

31. 31TRADITIONAL APPROACH TO MODERNIZATION
Copyright © Prosperata, LLC. All Rights Reserved.
Data Model Design
Reporting, Advanced
Analytics,
Visualization
Performance &
Scalability
Security Architecture
Master Data
Management
Data Quality
Data Lifecycle
Management
ETL Architecture
Data Governance
e.g.
Payment
Integrity
Financial
Data
Clinical
Data
e.g.
Revenue
Cycle
Data
Mart 1
Data
Mart 2
Alerts, Dashboards, Reports,
Visualization, Discovery, Predictive
Operation
al Data
Other Data
Sources
External Vendor BI Applications
Land &
Transform
Data Governance Program
ODS EDW
Deep
History
Data Extract Staging
Data
Lake
InboundEDI
OutboundEDI
Data
Mart 3
Areas of Review
EDI Operations
Cloud Adoption
Big Data & DW
Offloading

32. 32“NO REFERENCE” ARCHITECTURE APPROACH
Copyright © Prosperata, LLC. All Rights Reserved.
Anticipated Business Workloads
(Payer Example)
New ACO initiative needing intake of
clinical data
New insurance product roll out
Direct access to data for brokers
Improved process for Appeals &
Grievances
Quality checks for data files
submitted to CMS
Architectural Components
Traditional Data
Warehouse
MDB/Cubes
Data Appliance Data Warehouse
Appliance
Columnar Database Data Lake, Hadoop,
etc.
NoSQL Data Quality Tools
Data Virtualization Graph Database
MDM Tools Data Stream
Processing
BI & Data Visualization
Tools
Predictive Analytics
Tools
Healthcare Cloud Mobile Technologies
BPM Tools ILM Tools
Natural Language
Processing
B2B/HIE Tools
Map Business Workloads to Suitable Architectural Components

33. 33SUMMARY
•Develop strategic
plan to differentiate
with Data
•4-20 weeks
Information
Management
Strategy
•Deploy better Data
Governance
•Evaluate and
improve Data Quality
Data Governance,
Data Quality
•Modernize existing
BI/Data investments
Modernization to
Data 3.0
Copyright © Prosperata, LLC. All Rights Reserved.
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