Seven Ways Big Data Improves Healthcare Outcomes for Payers

Includes Forrester Research report, ‘Seven Ways Big Data Improves Healthcare Outcomes’
Empowering the Data-driven Enterprise

Data-driven Healthcare for Payers2
EXECUTIVE SUMMARY
Healthcare data and its impact upon the patient care decision process via accurate, real-time, reliable data
from disparate sources is creating a digital health revolution. Physician groups, nursing facilities, hospitals,
pharmaceutical companies, clinical researchers, and medical equipment manufacturers are all churning out
vast amounts of data during their daily operations. This data has tremendous value and can revolutionize
patient care, diagnosis, real-time decisions and help deliver new, unimagined innovations with quality of
patient care.
Data driven healthcare is beginning to have a huge impact with Payers. The primary drivers include controlling
the spiraling medical costs and building cost optimization and efficiencies into the payer model on the
basis of data and business insights, One of the payer challenges is developing a customer centric approach
coupled with analytics offering actionable insights to help them resolve customer issues and complaints, gain
customer loyalty, help patients manage their own health, and adapt to the dynamic patient care needs. There
is a greater sense of urgency to implement changes within the healthcare payers and delivery systems quickly
and efficiently by developing the right strategy and operating model. Big Data analysis can quickly and easily
provide evidence to fine tune the quality of care for a patient, leading to tremendous healthcare efficiencies,
better, optimal, precise payer services and a healthier world of tomorrow.
The tremendous opportunity of a data-driven strategy is apparent to the payer ecosystem. Data-driven
technology solution such as the Solix Common Data Platform (CDP) provides a next generation data
management platform that not only meets the analytic demands of the data-driven organization but also
addresses the cost, compliance, and governance challenges that come along. The Solix CDP combines
human and computer analysis based on huge volumes of data to produce optimal decisions at every level
of the healthcare business. Providers can take complete advantage of the data-driven healthcare revolution
by adopting such a technology foundation significantly enhancing patient care, and achieve tremendous
efficiencies themselves.
Healthcare Revolution and Challenges En Route
We are witnessing a data-driven healthcare revolution with widespread digitization of electronic health
record systems. But with compelling opportunities, we also see massive data volumes, evolving patient
expectations, and expanding regulations. Data in varying formats from an increasing array of sources
must be integrated to ensure optimal outcomes, whether obtaining a diagnosis, ensuring accurate claims
processing, developing new pharmaceutical treatments, or addressing regulatory challenges.
To accelerate this healthcare revolution, the industry has to manage key challenges such as government
regulations, information security, privacy protocols, changing technology landscape (such as electronic
health records, data analytics), while also containing the cost of rolling out new drugs, plans, and products
into the healthcare market. Government healthcare programs are growing rapidly and cannot be ignored.
The healthcare organization’s need to comply with government requirements such as accountability, the
performance improvement mandate, and evidence-based outcomes, will require considering technology
options to create efficiencies. Payers, providers, the pharmaceutical industry and medical equipment
suppliers have unique challenges that are largely intertwined and require a concerted industry plan crafted
in unison.

Payers are being challenged as the industry shifts from volume-based care to a value-based reimbursement
structure that would benefit the patient, the healthcare provider and the payer. New payment models
including fee-for-service only and pay-for performance creates impetus for payers to acquire, aggregate, and
analyze data, not just within a siloed network, but across healthcare exchanges that include financial and
clinical information about the patient. Payer challenges include compliance with the Patient Protection
and Affordable Care Act (2010), implementing this value-based reimbursement and understanding the
Total Cost of Care, which means working on a strategy for population health management, applying the
right technologies such as analytics, and entering into new types of risk-based contracts, based upon data
analytics, with the ability to identify fraud practices quickly.
Data-driven Healthcare Can Help Overcome Industry Challenges
The healthcare industry has recognized the emerging challenges well, is reconciled to the new versus
traditional business model, and is embracing the technology innovation that will position players for long-term
success. Every healthcare ecosystem partner will need to optimize its business models, grow its customer
base, address regulatory pressures with emerging technologies such as artificial intelligence, machine learning,
block chain, and virtual reality, along with data mining, Big Data, and analytics-based approaches.
Data powered tools can accelerate this healthcare revolution with innovations shaping and improving the
healthcare system to respond better to patient needs via accurate, collated, aggregated, and meaningful data
that provides information and actionable insights for every segment. Payers need tools that can respond on
demand to provide recommendations incorporating all the existing data and the latest medical research,
while reducing the cost of healthcare and optimizing their own balance sheet. Addressing the demand for
accurate, reliable data is key to success. Healthcare data is created at the source by providers such as physician
groups, pharmacies and medical equipment manufacturers. Ultimately, some of this financial, clinical, and
administrative information ends up with the payer, third party vendors, and government agencies.
New payment models
Pay for performance
Compliance with evolving regulations
Value-based reimbursement
Total cost of care
Population health management
Fraud detection
V
TPayers
CHALLENGES

But the challenge lies in how payers come to grips with the explosion of data and make it available in a usable
form to practitioners, insurance underwriters designing new health insurance products, and private individuals
trying to pick the right plan to manage their family’s specific healthcare needs.
The current landscape of healthcare payer
systems is complicated and fragmented
across the industry with many data sources.
The cost and complexity of integrating,
managing, and storing data is a constant issue
for everyone within the healthcare ecosystem.
The payer ecosystem has multiple disparate
systems exist that have a variety of data
formats that make integrating, exchanging,
and harnessing data a challenge. Analyzing all
this data from all these sources and staging
them for advanced integration and analytics is
hard but very fruitful. The alternative is missed
financial opportunities and a not so optimized
approach to payers working with patients,
providing customer satisfaction, keeping an
eye on income and revenue.
The industry finds it imperative to capture
and store all sorts of data that will provide
the ability to run a 360-degree analysis of the
patient, with an optimal patient care recommendation. The new definition of data includes free-form text such
as doctors’ notes, radiologists’ reports, medical journal articles with the latest findings and discoveries, emails,
still images such as CAT scans, videos, recorded speech, patient historic data, social media data, genome files,
biometric, and other scientific data from clinical research and drug development. It also includes data from
stand-alone systems - EMR, PACS, RTHS, EMPI, LIS and PMS, and Internet-of-Things (IoT) data from wearables,
medical devices, respirators, blood pressure monitors, and other connected devices. All together the resulting
insights can contribute to the 360-degree view of the patient, making a huge difference in the quality of care.
There is an estimated 50 petabytes of data in the healthcare realm, predicted to grow to 25,000 petabytes
by 2020.2
There are many new systems including wearables and mobile apps rolled out daily, that are adding
velocity to the data growth. But there is value only if we can analyze this data quickly and effectively. The
healthcare industry has realized quickly that extracting more meaningful insights via Big Data can make a
tremendous difference.
1
http://www.zdnet.com/article/solix-launches-healthcare-data-management-platform-based-on-Hadoop/
2
http://www.scribd.com/doc/107279699/Big-Data-in-Healthcare-Hype-and-Hope
Growing
Payer Data
Sources
Claims
Clinical
Eligibility
Patient Info
Provider Info
Billing
Health Plans

Big Data Can Revolutionize all the Healthcare segments
The healthcare world has created a volume, variety and velocity of healthcare data, a unique trifecta that,
once addressed, can make huge strides in healthcare decision-making and patient care. The volume of data in
healthcare, a lack of standardization of healthcare data from various sources such as providers, payers, disease-
management groups, social media, medical laboratories, personalized genetic testing companies, patients’
personal information, along with the need for urgency and real-time analytics that could potentially save lives,
makes Big Data ideally suited to work its magic in healthcare. Big Data can be applied to prevent deaths,
identify medical conflicts, even predict epidemics and cure diseases. It can proactively identify a child’s potential
upcoming health issues and recommend protective measures, and chart out a plan to alleviate the spend in
healthcare disbursements over the child’s lifespan. Big Data and advanced analytics can improve healthcare
decisions on patient care at all levels, from supporting Real-Time Health Systems (RTHS) to all forms of digital
medicine.
Big Data can reduce the cost of healthcare and of insurance significantly, helping to make a huge expansion
of healthcare coverage a reality. Decision algorithms can provide an additional layer of support and
interaction with the patient, in addition to the doctor. Big Data analysis can incorporate patient lab results,
the longitudinal patient record, medical imaging, etc., to make treatment recommendations, providing
better treatment while relieving the busy, overtly stretched medical professional from hours of work,
allowing her to focus on higher value activities.
Providers can optimize their existing offerings by leveraging intelligent data-driven strategies to reduce
soaring healthcare costs. Big Data analysis can optimize provider resources, distributing it among patients
based on their condition and specific need. For the payer, application areas range from fraud detection to
real-time continuous patient monitoring outside the clinical setting using personal/ IoT sensors. Other
verticals have successfully targeted customers with campaigns that have increased business. The healthcare
industry can do the same, but in this case to provide better patient care, to optimize existing resources, and
ultimately increase revenue, providing immense benefit to the patients.
BIG DATA
BENEFITS
Mine Patient data to improve care
Predict adverse outcome
Determine populations at risk
for illness
Pinpoint where education and
prevention is need
Detect medical fraud
Reduce Readmission
Identify procedures likely to succeed
Customer experience
Supply chain optimization;
timely deliveries
Reduce the cost to care for by
predictive maintenance insights
Provide additional insights to
end-user; Upsell
Ensure even utilization and wear
and tear of machines
Accelerated drug discovery
Targeted Marketing
Reduce drug fatalities via
predictive modeling
Patient compliance via IOT devices
Payer
Medical
Equipment
Pharma
Provider

Hospitals are starting to apply Big Data to sift through complex variables such as lab tests, family history,
and diagnosis, taking into account a variety of disparate data elements, in some cases to provide proactive
intervention with a patient to head off a long-term costly health challenge. Payers are leveraging Big Data
analysis to identify and prevent medical fraud early, saving billions annually. Pharmaceutical companies are
leveraging Big Data to streamline and reduce the cost of screening compounds in drugs discovery research.
Predictive analysis models working on massive virtual databases of molecular and clinical data can
accelerate the process and reduce cost, identify risk factors and can optimize yield from the drug
manufacturing process. Big Data’s impact upon order management for medical equipment manufacturers
can improve demand planning, identify customer behaviors, and provide insights to deliver goods in a
timely fashion. These are only some of the many use cases that benefit from applying Big Data.
Big Data Needs a Big Technology Shift
Traditionally, organizations depended on the Enterprise Data Warehouses (EDW) for all their analytic and
business intelligence requirements. However, with the rapidly evolving analytics landscape and the adoption
of Big Data, traditional EDWs are falling short of the capabilities needed. Not only are EDWs prohibitively
expensive, they lack the ability to store and process unstructured data, and the healthcare industry has
more unstructured than structured data. Additionally, due to its schema-on-write requirement, EDWs cannot
support the ad-hoc rapid exploration of data which is now become a key requirement of every data driven
organization.
Big Data in Action
Power clinical recommendation engines using electronic medical record data. The University of Michigan
Medical School harnesses intensive care signals and integrates them with their ICU patient charts. It mines data
and creates tools that combine bedside real-time facts with clinical rules to signal potential dangers within the
ICU. This solution flags risk and recommends diagnostic and treatment options for the critically ill patients. Like
most of these types of development initiatives, the school uses its own institution as its spearhead client. It
is developing the business programs necessary to bring these insights to market once it feels confident of the
efficacy of the solution.
Create an institutional benchmark for cancer treatment. Memorial Sloan Kettering Cancer Center built a
longitudinal repository of individuals with cancer with great fidelity. It combined publicly available Centers
for Medicare and Medicaid Services (CMS) data’s administrative facts such as diagnosis, procedure codes, and
provider IDs with clinical facts, such as what cancer stage, from the National Program of Cancer Registries. This
greatly enhances the meaning of the administrative data allowing the center to compare one institution’s results
for similar cancers to another. The melding of two public data sources to gain insight about the efficacy of cancer
treatment across the US is a significant achievement.
Ref: Forrester Research report, ‘Seven Ways Big Data Improves Healthcare Outcomes’ (included)

A Big Data technology platform such as Apache Hadoop provides in-built advantages to help realize the
data-driven healthcare vision by ingesting a wide variety of healthcare data, whether structured, semi-
structured, or unstructured, in a single repository in low cost bulk storage, eliminating costly and slow ETL
processes. The data is stored “as-is” and applies a schema on read. This allows ad-hoc analytic query and
in-memory processing in real-time as and when needed. Apache Hadoop also provides massively scalable
distributed processing, which is required for complex machine learning and analytic use cases. Finally,
Hadoop enables advanced text and voice search, structured queries and advanced analysis tools working
seamlessly against multiple data types and formats. Hadoop provides the ability to ask ad-hoc questions
to get quick responses, along with the ability to drill down to precise information based upon a natural
language search.
However, Apache Hadoop does not provide enterprise grade capabilities such as codeless data ingestion,
metadata management, Information Lifecycle Management (ILM), data governance and security.
Additionally, the constantly evolving Hadoop ecosystem makes it a daunting task for enterprises to identify
which newer Hadoop technologies are worth incorporating as part of their Hadoop cluster. What exacerbates
the problem is that Apache open source technologies are not designed to work together and have no
industry standard interfaces, making building a full technology stack a daunting task requiring scarce skills.
Organizations need an enterprise grade Big Data management system built on Apache Hadoop such as the
Solix Common Data Platform (CDP) for Healthcare.
Introducing Solix Common Data Platform (CDP) for Healthcare
The Solix Common Data Platform (CDP) is a highly scalable and robust next-generation Big Data
management platform that features uniform data collection, metadata management, data governance,
ILM, data security, data discovery, and a full set of interfaces to support plug-and-play stack creation and
modernization. It leverages the high-performance and low-cost characteristics of the open source Apache
Hadoop framework to allow economical storage and real-time processing of petabytes of structured and
unstructured healthcare data.

Solix CDP stores data “as-is” to eliminate costly ETL operations during data ingestion and provides an ability
to transform data post-ingestion to feed the unique needs of downstream NoSQL and analytic applications.
It includes modern Big Data processing engines like Apache Spark, Impala and Hive, to meet the machine
learning and advanced analytic needs of today’s real-time Data-driven organizations.
With a built-in enterprise data lake, enterprise archiving, application retirement, and eDiscovery solutions,
Solix CDP provides organizations with an unparalleled enterprise data management and analytic tools and
framework. This makes it possible for organizations to leverage data for effective medical diagnosis, clinical
trials, drug discovery, and fraud prevention, while saving on storage costs and complying with complex
healthcare regulations (including HIPPA, HITECH, CFR etc.).
Solix CDP is certified to operate with both the Cloudera and Hortonworks Hadoop distributions. Additionally,
it can be deployed on-prem or on the cloud (supports AWS, Azure, Oracle and Google cloud).
Solutions Overview:
Enterprise Data Lake for Machine Learning and Advanced Analytics
The Solix CDP-enabled healthcare data lake is a self-contained enterprise data hub that provides robust
data collection, data governance and data preparation tools with self-service visualization and business
intelligence. It provides authorized data consumers with a singular repository of structured and unstructured
healthcare data from a wide range of data sources including EHR, PACS, health trackers, diagnostic
equipment, published research, and more. This data is captured into the repository by Solix CDP in an “as
is” form along with its associated metadata. This eliminates the need for costly ETL during the ingestion
process, while making it easy to discover, understand, and consume data. It would be nearly impossible and
extremely expensive for any traditional EDW to incorporate such variety and large volume of information at
such velocity.
DATA MART
SONOGRAPHY PATHOLOGY
PHARMACOLOGY
SCANS
MEDICAL RECORDS
UNSTRUCTURED DATA
SEMI STRUCTURED DATA
FITNESS TRACKERS
IOT SENSORS
EHR/EMR DATA
IMAGER/PACS DATA RESEARCH DATA
STRUCTURED DATA
HISTORIC PATIENT DATA BIOMETRIC
CRM DATATRANSACTION/DATABASE DISCOVERY
SEARCH
STAGE
TRANSFORM
ARCHIVE
DATA LAKE
HIVEHIVE
ANALYTICS REPORTINGDATA MINING

The metadata captured during data ingestion coupled with the strong data governance and data security
features of the Solix CDP ensure the data in the healthcare data lake is made securely available to the
right people with little or no support from IT. Additionally, the in-depth data preparation features and the
inclusion of advanced open source data processing engines, like Apache Spark and Impala, make the
healthcare data lake an ideal platform for machine learning and advanced healthcare analytics.
Owing to its advanced data storage and data processing capabilities, the healthcare data lake can enable
a wide range of predictive and prescriptive analytics necessary to support delivery of quality healthcare
services leading to better patient outcomes, cost reduction, identification of abuse and fraud, better clinical
research, and more.
Enterprise Archiving and Application Retirement
In a typical enterprise, up to 80 percent
of data in core production applications
is inactive and up to 40 percent of
enterprise applications are rarely used.
This holds true even in the healthcare
industry with large volumes of unused
data in EHR, PACS, ERP systems, and the
many legacy applications occupying the
IT environment.
At a time when organizations are looking
to reduce costs, reallocate resources to
high ROI driven IT activities, enterprise
archiving and application retirement are a boon. As part of enterprise archiving and application retirement,
application data running online is first moved into Tier 2 or Hadoop infrastructure, and then purged from
its source location, according to data retention policies defined as part of the ILM strategy. Archived data
is further classified for security and compliance requirements such as legal hold, and universal access is
provided for business users through role-based structured reports and full text search.
SOLIX ENTERPRISE ARCHIVING
Information Lifecycle Management (ILM)
Data Archiving Application Retirement
• Manage data growth
• Improve application performance
• Improve availability
• Reduce infrastructure costs
• Structured, unstructured data
• Print stream archiving
• Eliminate maintenance cost
• Meet compliance & governance
objectivities
• Data center consolidation
• Print stream retirement
Semi/Unstructured Data
Universal Access
Native
Access
BI Reporting
Analytics
Solix Big Data
Suite
Archiving
Solix EDMS
Database
Archiving
Archive Database
DB
ActiveData
Structured Data
MOVE & COPY
MOVE, COPY, PRINT
Enterprise Business Record
Print Stream Capture
Search & Query Access
Retention Management and Legal Hold
SOLIXCOMMONDATAPLATFORM
Semi-ActiveData
(RDBMS)
InActiveData
(Hadoop ) Reporting/BITools
Solix
BigData
Suite
Solix APM
(Repository, Query, Search)
HISTORIC PATIENT DATA BIOMETRIC
CRM DATA
TRANSACTION/
DATABASE
FITNESS TRACKERS
IOT SENSORS
EHR/EMR DATA
IMAGER/PACS DATA
RESEARCH DATA

Enterprise archiving and application retirement frees up valuable resources in production environment
and eliminates unnecessary license and maintenance costs. This could translate into millions in potential
savings for a healthcare organization.
Enterprise Business Records (EBRs)
By modeling, ingesting, and managing all types of data into a single Hadoop repository, the Solix CDP
enables the creation of an Enterprise Business Record (EBR). An EBR is a denormalized, point-in-time
snapshot of a business transaction, which may include structured, semi-structured, or unstructured data
elements.
EBRs support both the regulatory and analytic use cases by providing a quick and well-structured access to
complete transactional data along with a history of changes. EBRs are accessible via text or voice search
and Restful APIs.
Data Governance, Security and Compliance
Proper data governance requires that compliance and security measures be in place, and nowhere is data
governance more vital than in the highly regulated healthcare industry. One key question in any patient
privacy audit is who has the access to sensitive information. Each time someone on the payer staff needs
to access a patient record, proper authentication must occur to ensure that only those with permission to
access records can do so.

The Solix CDP provides a robust, multi-layered security model:
• Perimeter: Kerberos and AD/LDAP protect the Hadoop cluster with authentication and network
isolation.
• Access Control: Apache Sentry manages what the data users and applications can access by roles
based permissions and authorizations.
• Encryption/Masking: End-to-end encryption for data when in motion and at rest, tokenization and
data masking to restrict unauthorized usage
• Audit: Audit trail and reporting on the complete data lifecycle including security classification,
lineage, access, retention, legal hold, etc.
Additionally, the Information Lifecycle Management (ILM) capability discovers and classifies enterprise
data and then establishes rules and retention policies to manage the data throughout its lifecycle.
Comprehensive retention policies with exception handling such as legal hold and GDPR help further in
meeting complex regulatory and compliance requirements.

Data-driven Finance - Emagia Receivables Management Suite
The ready-to-deploy Emagia Receivables Management Suite (ERMS) is about finding the most cost efficient
resources to accelerate cash flow. EMRS ensures the most effective receivables, credit policy management,
and automation of credit-to-cash (CTC) and order-to-cash (OTC) processes.
EMRS is a leading data-driven solution helping customers improve their return on cash. With the
introduction of new reimbursement plans (MACRA rules, QPP, MIPS, ACO) a huge amount of data needs
to be analyzed to arrive at an appropriate reimbursement formula to maximize incentives. Emagia Cash
provides enterprise OTC and CTC solutions to transform, automate, and optimize receivables, credit, and
collections.
Furthermore, hospital networks have decentralized silos of financial information, each with separate cash
management systems. By consolidating disparate cash systems with the Solix CDP, EMRS delivers dramatic
credit risk reduction, DSO improvement and cash flow maximization.
Conclusion
Payers now have an impetus to optimize their plans, and build performance based metrics with the access
of data and business insights. With the resultant access to vast amounts of structured, semi-structured,
and unstructured data, they can potentially identify patterns to build a healthcare system that is not only
efficient, but proactively addressing the patient needs, while avoiding data breaches and fraud.
To be able to draw meaningful correlations from these patterns, payer organizations need to embrace the
best of Big Data technologies. Unfortunately, these technologies can be quite complex and daunting. The
good news is Solix CDP is an enterprise grade Big Data management platform that leverages the best
of open source technologies combined with enterprise class data collection, governance, and discovery
features. In a world where data analysis is the key to success and data is measured in exabytes, the Solix
CDP is vital.
Access ShareMeasure PredictAnalyze
Data-driven Finance
TM

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Santa Clara, CA 95054
Toll Free: +1.888.GO.SOLIX (+1.888.467.6549)
Telephone: +1.408.654.6400
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URL: http://www.solix.com
Copyright ©2017, Solix Technologies and/or its affiliates. All rights reserved.
This document is provided for information purposes only and the contents hereof are subject to change
without notice.
This document is not warranted to be error-free, nor subject to any other warranties or conditions, whether
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We specially disclaim any liability with respect to this document and no contractual obligations are formed
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Seven Ways Big Data Improves
Healthcare Outcomes
by Skip Snow, March 25, 2015
For: CIOs
KEY TAKEAWAYS
Mining Genetic Data Reveals New Treatment Approaches
Research scientists crunch big data to discover how gene expression interacts with
the omics environment, which includes our genes and all of the interactions between
molecules, bacterium, and genes that constitute microphysiology. Insights gained from
this data allow researchers to propose new therapies that ameliorate diseases by altering
the genetic environment.
Drug Companies Harvest Social Media Streams To Find Victims Of Rare
Diseases
A great problem in fighting rare disease is diagnosing it. When a vendor can mine social
media to understand whom to rule out as potentially having a rare disease, big data
becomes a powerful clinical tool, shepherding victims of rare disease through a door of
social triage and into a consultation with the correct specialist.
Big Data Fuels A Possible Paradigm Switch For Epidemiology
Google has all but single handedly changed how we do disease surveillance. In the past
six years, it has determined where flu is based on search queries that users enter into
their phones and computers. It is now tackling new diseases, and health ministries
around the world are starting to depend on these results.
New Business Models Emerge As Big Data Fuels Solutions Offered By
Major Healthcare Providers
By harvesting internal and third-party data, tier 1 hospital systems embed insight from
data into software solutions. They seek to monetize it by licensing solutions to other
hospitals. Monetizing data drives partnerships with tech vendors, creating compelling
solutions and accelerating the globalization of care delivery.

© 2015, Forrester Research, Inc. All rights reserved. Unauthorized reproduction is strictly prohibited. Information is based on best available
resources. Opinions reflect judgment at the time and are subject to change. Forrester®
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, Forrester Wave, RoleView, TechRadar,
and Total Economic Impact are trademarks of Forrester Research, Inc. All other trademarks are the property of their respective companies. To
purchase reprints of this document, please email clientsupport@forrester.com. For additional information, go to www.forrester.com.
FOR CIOS
WHY READ THIS REPORT
In the healthcare industry, knowledge driven by big data is changing the shape of research, clinical, and
administrative operations; standards of care; and even fundamental business models. It is providing new
revenue opportunities previously unattainable in healthcare. Healthcare CIOs are at the center of these
groundbreaking initiatives as they struggle to build the business cases for their own programs. However,
they often ask Forrester for examples of big data in practice and the resulting ROI behind successful big data
initiatives. This report catalogs some major applications of big data Forrester has observed in its research.
Table Of Contents
Big Data Insight Feeds A New Data Economy
Individual And Population Data Combine,
Improving Clinical Outcomes
Big Data Powers Breakthroughs In Research
And Epidemiology
Healthcare Payers Add Value-Based Products
Based On Their Unique Data Access
WHAT IT MEANS
Big Data To Provide Revenue For Large
Healthcare Companies
Supplemental Material
Notes & Resources
Forrester interviewed 42 vendor and user
companies for this report.
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Seven Ways Big Data Improves Healthcare
Outcomes
Compelling Business Cases For Big Data
by Skip Snow
with Patti Freeman Evans, Brian Hopkins, Abigail Komlenic, and Shaun McGovern
2
7
8
MARCH 25, 2015

FOR CIOS
Seven Ways Big Data Improves Healthcare Outcomes 2
© 2015, Forrester Research, Inc. Reproduction Prohibited March 25, 2015
BIG DATA INSIGHT FEEDS A NEW DATA ECONOMY
The healthcare industry has realized data is one of its most valuable assets. Tier 1 institutions
across the healthcare ecosystem innovate by combining unlikely data streams to generate new
insights. Often this process can be turned into new products (e.g., software solutions) that other
healthcare organizations will buy. CIOs are struggling to understand the compelling business cases
that underlie a great deal of these activities. They are often heads-down responding to requests for
data analytics from their workforce without the capacity to respond to the paradigm switch as a
new economy segment within healthcare emerges. Enterprises can win a competitive advantage
by focusing their teams, developing big data initiatives to harness their organization’s unique data
assets. Below we enumerate seven important use cases to stimulate conversations about these
switches that are taking place.
The insights gained facilitate value-based care, inform payers of their reimbursement policies
efficiently, forge new fraud and waste capabilities, help in the discovery of gene interactions, and
change the shape of epidemiology (see Figure 1).
Figure 1 The Three Main Environments Of The Healthcare Ecosystem Seek Common Data Entities
Source: Forrester Research, Inc. Unauthorized reproduction or distribution prohibited.117433
• Finding patients with
rare disease
• Creating clinical test
beds
• Population management
• Health optimization
• Fraud and waste
detection
• Employee behavior
benchmark
Problems
Clinical
Administrative
Care domain
• Finding new drug
therapies
• Finding clinical
care paths
Research
• Claims data
• Clinical data
• Social data
• Epidemiological data
• Consumer behavior data
• Location data
• Criminal history data
• Credit data
• Consumer behavior data
• Omics data
• Molecule pathway data
• Corpus of knowledge

FOR CIOS
Individual And Population Data Combine, Improving Clinical Outcomes
The perfect storm is brewing. Technology has learned how to find insight from within both
structured and unstructured data, and, because clinical records are now mostly digital, combining
clinical, administrative, and publicly available data often yields unanticipated insight (see Figure
2). Complex pattern-matching algorithms, the need to create a value-based environment, and fast,
inexpensive clusters of commodity computers running open source software have changed what is
possible. Forrester has found examples where big data combined with various other data sources to:
■ Power clinical recommendation engines using electronic medical record data. The University
of Michigan Medical School harnesses intensive care signals and integrates them with their
ICU patient charts. With its partners with IBM and AirStrip Technologies, it mines data and
creates tools that combine bedside real-time facts with clinical rules to signal potential dangers
within the ICU. This solution flags risk and recommends diagnostic and treatment options for
the critically ill patients. Like most of these types of development initiatives, the school uses its
own institution as its spearhead client. It is developing the business programs necessary to bring
these insights to market once it feels confident of the efficacy of the solution.
■ Create an institutional benchmark for cancer treatment.1
Memorial Sloan Kettering Cancer
Center built a longitudinal repository of individuals with cancer with great fidelity. It combined
publicly available Centers for Medicare and Medicaid Services (CMS) data’s administrative facts
such as diagnosis, procedure codes, and provider IDs with clinical facts, such as what cancer
stage, from the National Program of Cancer Registries. This greatly enhances the meaning
of the administrative data allowing the center to compare one institution’s results for similar
cancers to another. The melding of two public data sources to gain insight about the efficacy of
cancer treatment across the US is a significant achievement.
■ Diagnose rare disease by marrying big data and social media communities. Often, clinicians
cannot diagnose people with rare diseases correctly because they have never seen a case in
their practices. Corcept Therapeutics, a niche pharmaceutical company, partners with Liquid
Grid to mine social media for synonyms and semantic equivalents to the clinical descriptions
of Cushing syndrome to promote its therapy Mifepristone.2
According to Liquid Grid’s CEO
Malcolm Bohm:
“We start with our own ontology of medical terms sentiment. We mine Facebook, Twitter,
Tumblr, WordPress, and the metadata of YouTube. This takes no more than a matter of weeks,
and we are ready to use the insight we have on how a lay community describes a condition.”3

FOR CIOS
Figure 2 Facebook Page Used By Concept Therapeutics To Steer Potential Patients To Doctors
Source: Corcept Therapeutics’ Cushing’s Connection Facebook page

FOR CIOS
Big Data Powers Breakthroughs In Research And Epidemiology
Big data can speed time-to-market for therapies, and novel ways of doing disease surveillance
foretell a new paradigm in epidemiology:
■ Labs use big data to disrupt traditional research models and methods. Mount Sinai Hospital
has invested heavily in data scientists and equipment, creating a “dry lab” infrastructure.4
They use
computer and data science to uncover networks of interactions, revealing new targets for genetic
interventions. Clinical trials are already underway as the output of several dry lab discoveries.
Unlike traditional genetic research using computers to sequence genes and human intellect to
interpret the meaning of these sequences, computers with knowledge of what drugs do to target
gene expression suggest possible therapies to the clinicians based on gene network pathology.
Mount Sinai plans to monetize its best-of-breed ability to find patterns in the genetic data.5
■ Google’s disease surveillance invigorates epidemiology. Google parses its search stream to
detect disease instances, e.g., number of dengue fever and flu cases in many nations of the world.
Google works with major academic research institutions and public health officials to curate and
validate its epidemiology algorithms. The company also uses national epidemiology databases
to benchmark and validate its results. Over the six-year span of the project, Google’s results
have become quite accurate.6
The health departments of nations that do not have surveillance
infrastructures seem to depend on Google’s weekly updates on dengue fever.7
The potential to
change the game in epidemiology is real, and we have seen at least one startup that is trying to
capitalize on these business ideas (see Figure 3).

FOR CIOS
Figure 3 Google Has Hit The Mark For US Flu Prediction As Compared With CDC Data
Note: Google and the Google logo are registered trademarks of Google, Inc., used with permission.
Google flu
Data source: Google Flu Trends (http://www.google.org/flutrends)

FOR CIOS
Healthcare Payers Add Value-Based Products Based On Their Unique Data Access
As CIOs become more embedded in their organizations’ customer-facing initiatives, they will
find many opportunities to drive customer value, and thus revenue, via big data. Whether it is in
population management or fraud detection, big data initiatives provide new value and ways to
reduce costs in providing care:
■ Health insurance companies target waste and fraud with big data solutions. The insurance
industry is a leader in fraud detection. It rivals the credit card industry in detecting patterns in
data that indicates fraud. Harvard Pilgrim Health Care uses LexisNexis’ Intelligent Investigator
to ferret out fraud. It links unexpected relationships between a provider billing address and risky
individuals associated with that address to uncover more “bad guys.” The program has helped
the organization initiate many new criminal and civil investigations.
■ Pacific Blue Cross preserves privacy and provides aggregated reports to large employers.
Privacy concerns make it impossible for employers to gain access to their staff’s health records.
Pacific Blue Cross creates reports for large companies showing patterns of opportunities to
optimize staff health without exposing employees’ individual health data.8
Such advances in
data-driven, value-based products will differentiate insurance companies for the next decade as
less competent competitors strive to catch up.
W H AT I T M E A N S
BIG DATA TO PROVIDE REVENUE FOR LARGE HEALTHCARE COMPANIES
Big data will be a major driver altering the operational models of healthcare. Developing these
solutions is expensive. New forms to monetize these solutions evolve to fund the necessary
investments. New organizational models are emerging; for example, we see health companies that
traditionally only sell hospital beds to consumers hiring business-to-business (B2B) marketing
staffs. Technology vendors with global reach are looking to the major centers of excellence in care
management to create cobranded products that embed insight locked up in data. Over the next 15
years, the increased globalization of care delivery tools fueled by big data will accelerate. As trends
like retail medicine continue to mature, the data itself will allow clinical standards to evolve. These
standards will be available for economically and geographically challenged communities consuming
these solutions. The successful CIO in large healthcare companies increasingly will run business
units selling solutions. The hiring practices for BT executives is evolving; former software and
consulting executives increasingly are being recruited by traditional technology buyers now turning
into solution vendors.

FOR CIOS
SUPPLEMENTAL MATERIAL
Companies Interviewed For This Report
Accenture
Aetna
Anthem Insurance
Ayasdi
Blue Shield of California
CitiusTech
Cognizant
Corcept Therapeutics
Dell
Elsevier
Epic Systems
Explorys
Google
Harvard Pilgrim Health Care
HCL Technologies
Health Integrated
IBM
Informatica
InterSystems
Kaiser Permanente
Koninklijke Philips
KPMG
McKesson
Memorial Sloan Kettering Cancer Center
Mercy Hospital
Mount Sinai Hospital
MVP Health Care
Nuance Communications
Optum
Orion Health
Pacific Blue Cross Health Benefits Society
Practice Fusion
PwC
SAS
United Healthcare
United States Department of Health and Human
Services
University of Michigan Medical School
Verisk Health
Virtual Radiologic
Webtrends
West
ZirMed

FOR CIOS
ENDNOTES
1
Memorial Sloan Kettering Cancer Center combined data from the national cancer registry and
administrative claims data for Medicare patients from the CMS. Source: Forrester interview with John
Gunn, COO of Memorial Sloan Kettering Cancer Center, September 2014.
2
Check out Medscape for epidemiology on almost any disease. Source: David S. Liebeskind, MD,
“Hemorrhagic Stroke,” Medscape, January 8, 2015 (http://emedicine.medscape.com/article/1916662-
overview#a0156) and Gail K. Adler, MD, “Cushing Syndrome,” Medscape, April 4, 2014 (http://emedicine.
medscape.com/article/117365-overview#aw2aab6b2b4aa).
3
Source: Forrester interview with Liquid Grid, Q3 2014.
4
A dry lab is an increasingly important term of art in the biomedical research world. It refers to a lab that
consists of computers and computer models that pour over clinical and other data to find patterns of insight.
5
For more information on cognitive computing, see the February 13, 2015, “Healthcare Meets Cognitive
Computing” report.
6
For more information on Google’s work with the flu, see the June 20, 2014, “Google Flu Trends — A Big
Data Fail? Not Exactly” report.
7
Google is working with new data sources in the lab to refine its results. It is collaborating in the academic
and public health spheres to refine its results. Yet it has no current plans to monetize these innovations. In
an interview with Steve Crossan, Google’s product manager for disease surveillance, told Forrester: “We are
lucky enough not to need a business model to measure the work we are doing. We are not trying to build
a business around public health surveillance.” He went on to say that when its cycle runs slowly, it does get
feedback from the countries that need the data because it does not have public surveillance for diseases.
8
This information was gathered in an interview with Cindy Bratkowski, senior vice president, information
technology and client services, and Akiko Campbell, director, innovation center and security officer at
Pacific Blue Cross in September 2014.

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