ü The document discusses Information-as-a-Service (IaaS) in the context of commercial health insurance. ü It provides an introduction to key concepts like federated data access, common data models, and treating information as an asset. This allows for a consistent and contextual view of information across systems. ü Current healthcare payer systems have evolved organically and operate largely as silos. IaaS provides a path forward to establish a unified approach to information management that can support future needs around interoperability, data sharing, and regulatory standards.

1. Perspectives in
Commercial Health
Insurance:
Leveraging
Information-as-a-Service
An introduction to the primary component of modern payer
information architecture strategy
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2. All information in this guide is subject to change without notice. This document is provided
for informational and knowledge purposes only and SOE, Inc. makes no guarantees,
representations or warranties, either expressed or implied, about the information
contained within the document or about the document itself. Salus One, Inc.™ is a
trademark of Salus One, Ltd.
Copyright © 2017 Salus One, Ltd. All rights reserved.
Created in the United States of America.
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3. |
Guide Content
Section One: Introduction to IaaS in the Health Insurance Industry
▪ Section Two: Industry Perspectives
▪ Section Three: Common Perspectives
▪ Section Four: Strategic Performances
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4. Section One:
An Introduction to Information-as-a-Service
in the Health Insurance Industry
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5. |
Introduction to Information-as-a-Service
Multiple industries have been struggling to
establish cohesive view, definition, and utilization
of enterprise information across a multitude of
transactional and analytical data silos.
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6. |
Introduction to Information-as-a-Service
These verticals have invested
billions of dollars sourcing and
managing transactional data….
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7. |
Introduction to Information-as-a-Service
… establishing a
consistent view of the
information across all
customers and
partners…
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8. |
Introduction to Information-as-a-Service
…while
leveraging data
to develop
business insights,
and identify
emerging trends
in an attempt to
provide effective
compliance
operational
reporting.
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9. |
Introduction to Information-as-a-Service
Addressing information management
complexity requires:
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10. |
Introduction to Information-as-a-Service
• Separation of service
channels from operational
and analytical
environments
• Consistent business
definition and
management of
information assets
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11. |
Introduction to Information-as-a-Service
• Consolidated and
cohesive resources
are utilized to source,
manage, and access
information
• Acquisition and building
common tools, methods,
and procedures to
manage enterprise data
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12. |
Introduction to Information-as-a-Service
Recently, commercial and government health insurance
leaders have initiated similar efforts due to:
• Health care reform
• Consumerism needs
• Increased competition and the need to reduce costs
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13. |
Introduction to Information-as-a-Service
Information-as-a-Service (IaaS) incorporates the aspects
of enterprise modeling and separate, tightly-linked
service capabilities (e.g., customer service
representatives, portals, mobile) from core platforms
while establishing a loosely-coupled approach to access
required information.
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14. |
Introduction to Information-as-a-Service
To this end, this guide will cover core aspects of the use
of the Federated Data Access (FDA) pattern and its
impact on a healthcare payer organization’s
information technology portfolio.
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15. |
Before we move forward, here are a few key terms to remember:
Federated Data Access – ability
to access multiple heterogeneous
and distributed repositories via a
single request using common
enterprise data model (e.g.,
virtualized database).
This capability manages semantic
definitions across multiple sources
of operational and analytical data.
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16. |
Before we move forward, here are a few key terms to remember:
Customer – The entity (group/
individual) that procures a service
from a healthcare payor and is
maintained at the contract level.
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17. |
Before we move forward, here are a few key terms to remember:
Person – The view of an individual that
is generated upon obtaining coverage
and is maintained regardless of the year
of coverage or the plan. The Person
creates the view necessary to promote a
payor organization’s goal of creating a
“Customer for Life”.
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18. |
Before we move forward, here are a few key terms to remember:
Member –The current view of the
individual that defines the active coverage
amounts and benefits, their location and
customer identifier (either as an individual
or group).
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19. |
Before we move forward, here are a few key terms to remember:
Core Systems – Distinct
membership systems used
within a healthcare payer
organization. These systems
are considered the basis of
all systems within the
respective insurer.
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20. |
Before we move forward, here are a few key terms to remember:
Channel Solutions –
Solutions that enable
customer / partner
operational support
services via different
channels (e.g., call
center, portals, IVR,
mobile.
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21. Section One Summary:
ü Federated Data Access – ability to access multiple heterogeneous and distributed
repositories via a single request using common enterprise data model (e.g., virtualized
database) that manages semantic definitions across multiple sources of operational and
analytical data
ü Person – The view of an individual that is generated upon obtaining coverage and is
maintained regardless of the year of coverage or the plan.
ü Channel Solutions – Solutions that enable customer / partner operational support
services via different channels
ü Core Systems – Distinct membership systems used within a healthcare payor
organization.
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22. |
Guide Content
▪ Section Three: Common Perspectives
▪ Section Four: Strategic “Go Forward” Perspectives
Section Two: Industry Perspectives
▪ Section One: Introduction to IaaS in the Health Insurance Industry
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23. Section Two:
Industry Perspectives
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24. |
Business’ demand for faster and smarter
information management capabilities is ever-growing:
Real-time
access
"We recently launched credit cards for small businesses.
Customer behavior data will not be available for 3-4
months"
– Retail Risk
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25. |
Efficient
information
architecture
"We can avoid the annual spending on duplicate CIFs
and data warehouses as well as multiple interfaces
between these data systems"
– Architecture/Integration
Business’ demand for faster and smarter
information management capabilities is ever-growing:
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26. |
Restrain
data
proliferation
"We had 11 incidents last year compared with 1 or 2 by
our competitors. Many times we do not have enough
information needed to identify and monitor risk"
– Operational risk
Business’ demand for faster and smarter
information management capabilities is ever-growing:
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27. |
Last mile
information
access
"Because of poor data quality, we spend a lot of
time on investigations. We can reduce this and still
keep the high levels of risk control"
– Compliance
Business’ demand for faster and smarter
information management capabilities is ever-growing:
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28. |
Many tech-enabled trends are directly connected with information
- but getting the basics right first is a key factor to success…
New CLM: Leverage customer experience
with new touch points and manage all
channels and products through extended
periods
Advanced semantic data analysis:
Help organizations reconcile and
normalize data’s meaning for various
sources and content
Predictive product management:
Develop products and services with
advanced use of analysis, simulation,
and virtualization
Next level of Business Intelligence:
Real-time decision making and active
prediction of business events based on
complex analytical processing
Innovative data-driven business
models: Leverage breadth and depth of
available information to enable realization
and delivery of value in the enterprise
Enabling advanced data capabilities
means getting the foundations right
▪ Well-structured, easily accessible, and fully
integrated information in the enterprise
▪ Common understanding of main business
objects and relevant unstructured data for the
business purpose
▪ Advanced capabilities to analyze and
aggregate data
▪ Cost-effective and future-proof information
architecture
▪ Dedicated data ownership to organize and
maintain data quality and to ensure sustainability
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29. |
Why are payers, providers, and regulatory organizations
taking on the Information-as-a-Service challenge now?
Drivers of change Examples (partial list)
Need for increased agility/faster time
to market
▪ Executives require detailed information to make strategic decisions
▪ Leading competitors are reducing time to market with new products
▪ Change in regulation driving change in business models (e.g., Mergers
and Acquisitions)
▪ New versions of HIPAA and FDA guidance
▪ Changing healthcare delivery models – focus on prevention and
disease management
▪ New payment models - multi-provider, HSA, and potentially multi-payor
▪ Focus on individual vs. population, requiring new level of introspection
▪ Wellness and individualized medicine require integrated view of
member data shared across multiple channels
▪ Changes from encounter to episode and from fee-for-service to
evidence-based care
▪ Telemedicine and remote healthcare delivery models create new sets of
structured and unstructured data. New partnership models between
payors and providers
▪ Customers and stakeholders require a consistent experience and up-to-
date information accessible across multiple channels
Regulatory and compliance
requirements
Greater awareness of the value at
stake/higher on priority list
Technology to manage and deliver
data more efficiently and effectively
Cost of not improving Enterprise
Information Management capabilities
continuously increasing
Greater complexity and operational
cost of serving multiple stakeholders
through various channels
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30. |
What is Information-as-a-Service (IaaS)?
…. Information involves the attribution of data with meaning around context,
applicable to business process and function. The meaning of the same data
evolves with the context it is used in, varying based on user, process,
channels of use, etc.
.... For data to be used as an asset it needs the contextual metadata and
operational controls to manage it. For data to become an asset it must be
consistently managed through a common set of processes
... Data Quality enables reliability, traceability and usability of the of data in a
consumer context, and establishes trust in the data. It is a critical success
factor for the use of information as an asset
... Data is trustable and compliant from a regulatory perspective, irrespective
of whether it comes from internal service operations or from external
partners
For organizations to treat information as an Asset...
Information is an
asset enabling
the agility …
… and
accuracy
… of
organizational
decisions
IaaS is a Data Virtualization architecture pattern that leverages common data model (e.g.,
canonical) to enable consistent and contextual delivery of the information
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31. |
What is Information-as-a-Service (IaaS)?
IaaS provides a unified view and access to internal / external information
by using common data services invoked by consumers.
The consumer will always receive trusted data in its context,
from an authentic source.
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32. |
Data virtualization enables access to disparate data sources
without building a physical infrastructure for consolidation:
Data Virtualization combines disparate data sources into a single
“virtual” data layer that provides unified access and integrated
data services to consuming applications in real-time.
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33. |
Data Virtualization combines disparate data sources into a single
“virtual” data layer that provides unified access and integrated data
services to consuming applications in real-time
Data
reposi-
tories
Data
sources
BI/
analytics
From
Service
Ops
BI
Analytics /
Reporting
New sources
To
Service
Ops
IMS
Internal
…
External
BI
Data marts and cubes
CRM Finance …
Data warehouses and ODS
IMS
Internal
…
External
CRM Finance …
Data virtualization layer
Data warehouses and ODS
Channels Channels
Data virtualization enables access to disparate data sources
without building a physical infrastructure for consolidation:
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34. |
Cross-industry business issues are often caused by problems
with data consistency and integrity:
Examples:
Global logistics provider with 20% invoicing
errors and significant under billing
High error rate in sales and
service delivery
European telecom operator with 15% of
inactive and blocked last mile connections
from cancelled contracts
Low utilization of corporate
assets or infrastructure
Tactical
drivers
Ineffective cross-selling
convergence lost revenue riskStrategic
drivers
Inability to introspect and have a consistent
view of data is costing healthcare payors
millions in lost revenue
Large North American retailer had more than
583 terabytes (TB) of unutilized sales and
inventory data
Low utilization of own data
repositories
Data as an
enabler
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35. |
Cross-industry business issues are often caused by problems
with data consistency and integrity:
▪ Diverged view on data models and main attributes
▪ Localized and inconsistent data models
▪ Fragmented data architecture with most data stored in local
applications
▪ Limited data integration capabilities
▪ No common data governance and ownership
▪ Limited data quality control
▪ Complexities in integrating structured and unstructured data
Behind
the
scenes
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36. |
Current healthcare payer data management capabilities limit the
options for businesses to innovate and improve process
efficiency:
▪ Complex and long IT demand processes to request
new data features (e.g., integration of new data
sources, new reports/KPIs)
▪ No governance rules/ mechanisms to enforce,
maintain, or change a corporate data model
▪ No minimal requirements for data interoperability
between domains specified
▪ No accountability for data quality based on data
owners/stewards
Data design
Data
governance
Data
technologies
▪ Heterogeneous data schemes (objects,
attributes, relations)
▪ Critical data objects across core business
processes not identified
▪ Non-compliance with relevant external data
exchange standards, e.g., vertical schemes
▪ Unconsolidated or federated data stores with
redundant data
▪ Data aggregation mechanisms, like data
warehouses, data marts, operational data marts,
lacking, insufficient, or not-harmonized
▪ No multidimensional data exploration based on
OLAP server
▪ No reference / master data management to link
data across multiple sources
Complication
▪ Slow time-to-market for new reporting features
and new analytical procedures
– Small changes (even in reports) in data
model require substantial efforts
– Even for simple extensions the help of IT
is needed
– Software stack often not capable of
fulfilling new business requirements
(higher granularity, real-time), requiring
substantial investments
▪ No processes and tools for business to
explore existing data assets and quickly
generate value
– Business limited to a static toolset for
reporting and basic OLAP
– Experimentation usually done outside the
official environment in Excel and Access
– Large amount of „unofficial data“ sitting on
local drives
▪ Limited possibilities for integrating new
internal data sources or use new external
sources (requires complex IT demand
process)
▪ No plan how to integrate semi-structured or
unstructured data into existing environment
Situation
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37. Section Two Summary
ü Information-as-a-Service or IaaS is a Data Virtualization architecture pattern that
leverages common data model (e.g., canonical) to enable consistent and contextual
delivery of the information
ü Next level of Business Intelligence drives Real-time decision-making and active
prediction of business events based on complex analytical processing
ü Data Quality enables reliability, traceability, and usability of the data in a consumer
context, and establishes trust in the data.
ü IaaS provides a unified view and access to internal/external information by using
common data services invoked by consumers; and the consumer will always get
trusted data in its context, from an authentic source
ü Data Virtualization combines disparate data sources into a single “virtual” data layer
that provides unified access and integrated data services to consuming applications in
real-time
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38. |
Guide Content
▪ Section One: Introduction to IaaS in the Health Insurance Industry
Section Three: Common Perspectives
▪ Section Four: Strategic “Go Forward” Perspectives
▪ Section Two: Industry Perspectives
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39. Section Three:
Common Perspectives
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40. |
What is the status of prototypical commercial healthcare
payers in the United States?
Most commercial healthcare payers have systems and processes that have evolved
organically in response to tactical drivers and currently operate as silos with point-to-
point integration of business process model of the past.
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41. |
In the current business environment - with major drivers such as the Affordable Care Act,
HCV and individualized care driving initiatives - legacy architecture environments will
have difficulties supporting future information management and integration needs
(e.g., interoperability, trust, data sharing and standardization). Additionally, existing
tactical, point-to-point integration approaches limit enterprise agility and asset re-use
while increasing IT portfolio complexity, time-to-market delays and overhead costs.
What is the status of prototypical commercial healthcare
payers in the United States?
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42. |
These payer organizations have made
technology investments, such as initial
Service Oriented Architecture (SOA) and
master data management (MDM)
technology bases, that provide initial
technical capabilities in order to enable
treatment of information as an asset.
What is the status of prototypical commercial healthcare
payers in the United States?
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43. |
An enterprise canonical model and
well-established governance
framework are missing. Both are
foundational in realization of the
“Information is an Enterprise Asset’
business priority. Canonical models
need to be acquired and adopted before
the real value behind these investments
is realized.
What is the status of prototypical commercial healthcare
payers in the United States?
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44. |
Observed current state limitations include:
Absence of an enterprise
canonical model - without its
adoption, the value of information as
an asset will not be realized, for both
operational and analytic information
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45. |
Observed current state limitations include:
Current SOA solution architecture and design patterns promote implementation of
‘heavy’ services that are difficult to re-use
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46. |
Observed current state limitations include:
Inconsistent implementation and support of uniquely identifiable entities, such
as person or member, inhibiting key enterprise goals such as "Customer for Life”
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47. |
Observed current state limitations include:
Limited, tactically focused enterprise governance, (e.g., data, enterprise
architecture, program/projects) leading to opportunistic, unplanned investment,
preventing establishing enterprise-wide capabilities.
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48. |
Observed current state limitations include:
Limited service and
operational data
ownership and
governance – Significant
variations in its definition
and use exist in many
environments.
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49. |
Observed current state limitations include:
Inability to securely share and access information in a traceable manner
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50. |
Observed current state limitations include:
Limited ability to carry out cross-functional reporting and analytics
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51. |
Guide Content
▪ Section One: Introduction to IaaS in the Insurance Industry
▪ Section Two: Industry Perspectives
Section Four: Strategic “Go Forward” Perspectives
▪ Section Three: Common Perspectives
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52. Section Four:
Strategic “Go Forward” Perspectives
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53. |
To be competitive in the marketplace, healthcare payers
require the following capabilities:
▪ Provide, share and integrate information seamlessly across customer / partner
channels, business domains and operational processes in a seamless, secure and
compliant manner.
▪ Provide information in a channel and source independent manner
▪ Monitor information delivery by meeting varying service levels and
requirements
▪ Enable, monitor and manage secure, auditable and compliant access
▪ Provide a shared, trusted source of enterprise information via a common (e.g.,
canonical) information model
▪ Create and deliver descriptive information to enable optimal sharing and
common understanding
▪ Integrate varied sources with different forms of data into the enterprise
representation, consume information in canonical form
▪ Provide historical storage of operational data acquired from multiple systems,
and including auditing and resilience to structural changes.
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54. |
Example of a Conceptual Target State IaaS Reference Architecture:
§ Enterprise Data Services layer - catalogues of
reusable data services that provides context-
specific information by aggregating desired data
from multiple enterprise data sources via a single
request. Canonical model / Semantic layer –
data supplied by data providers and its consumers
– incorporating a generic and uniform (canonical)
model for the related mappings.
3
4§ Data Provider layer – supplies requested data;
including access interfaces. These systems
may be internal or external (e.g., cloud or
partner/3rd-party provided data), operational or
analytical, data or master or metadata, etc.
1§ Information Consumer layer - includes
Applications, Systems, Processes, UI, partner
interfaces
Reference Architecture Layer overview
- guide focus
§ Enterprise Business Services / Process
Orchestration layer - catalogues of simple or
complex business services and processes that
require uniform access to distributed enterprise
data managed in heterogeneous environments
2
Information Consumer
Data Provider
Enterprise Data Services
Canonical Model / Semantic
1
4
3
Enterprise Business Services / Process
Orchestration
Service 1 Service 2 Service n
2
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55. |
The main characteristics of a successful IaaS implementation in
the healthcare payer environment:
Complements and gradually evolves
existing data architecture rather than
replacing it
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56. |
Can be used for specific use cases
or in dedicated functional areas to
solve business problems rather than
focus of adoption of the entire
enterprise data model – accelerates
value delivery
The main characteristics of a successful IaaS implementation in
the healthcare payer environment:
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57. |
Requires relatively mature
transactional data architecture
landscape to work effectively (at
least with a broader use)
The main characteristics of a successful IaaS implementation in
the healthcare payer environment:
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58. |
Will require additional
investments (e.g., in
operational data stores) to
enable transactional systems to
work in a data virtualization
environment
The main characteristics of a successful IaaS implementation in
the healthcare payer environment:
www.salusoneed.com

59. |
Requires a technology-savvy team
to adapt and extend semantic data
models and data mappings if additional
sources and deeper levels of data
granularity are required
The main characteristics of a successful IaaS implementation in
the healthcare payer environment:
www.salusoneed.com

60. |
Is predominantly used as
a read-only technology to
enable information delivery
to service channels and
business processes. The
IaaS solution supports
reporting, analytics, and
business intelligence
(some first vendors are
introducing read-write)
The main characteristics of a successful IaaS implementation in
the healthcare payer environment:
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61. |
Trend towards integration of
structured with unstructured
data, with data virtualization
technologies offering flexibility
in their semantic model to also
process non-SQL structures
The main characteristics of a successful IaaS implementation in
the healthcare payer environment:
www.salusoneed.com

62. |
Guide Content
▪ Section One: Introduction to IaaS in the Insurance Industry
▪ Section Two: Industry Perspectives
▪ Section Three: Common Perspectives
In Summary: Leveraging IaaS
▪ Section Four: Strategic “Go Forward” Perspectives
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63. In Summary
Perspectives in Commercial Health Insurance:
Leveraging Information-as-a-Service
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64. In Summary: Leveraging Information-as-a-Service
q Information-as-a-Service (IaaS) is a data
virtualization architecture that leverages a common
data model to enable consistent and contextual
delivery of select information.
q Data Quality enables reliability, traceability, and
usability of data in a consumer-trusted context.
q In today’s business environment - with new drivers
such as the Affordable Care Act, Health Care Value,
and individualized care driving new information needs
– legacy environments will have difficulties supporting
information management and integration requirements.
q Multiple current state limitations exist in today’s
infrastructure to meet the goals of IaaS efficiency in the
payor enterprise.
q Successful leveraging of IaaS requires business,
clinical, and information technology in order to be
cohesive in its definition, design, and overall
usefulness
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66. 65
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