Guide to Data-Driven
Healthcare Innovation
Defining and Deploying Data
as a Catalyst for Change

eBook
Overcoming Roadblocks to Transformationand Understanding Two Core Models of Innovation

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Guide to Data-Driven Healthcare Innovation

Table ofContents
ABOUT THIS EBOOKThis eBook will look at the meaning of innovation in thehealthcare industry, the barriers to implementing newprocesses within an organization, and how data can be acatalyst for change. This eBook will also look at the twocore models that drive innovation in healthcare today.
With new insights covered in this eBook, health systems
can better understand how to meet today‘s industry
challenges at a pace needed to impact quality andperformance improvement, patient care, and revenue.

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01

Dissecting the Buzzword: What is Innovation?

02

Roadblocks to Transformation

03

Removing the Bottlenecks: How to Unlock the Power of Data04

Data-Driven Healthcare Innovation Models

05

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Guide to Data-Driven Healthcare Innovation

Overview

Innovation is the engine that powers transformation in
the healthcare industry—improving business models
and patient care, all while boosting revenue. This is
the essence of progress in the industry and what every
healthcare organization is driving towards.
Acute challenges face the industry today. As humanbehavior continues to evolve, health systems must
continually revise the way we approach and implementtransformation of care. With labor shortages, workforce
retention, consumer demands, and market disruption
at the top of healthcare agendas, the time to adopt
innovative solutions to help alleviate these challenges
is now.
A BUZZWORD

Innovation has emerged as one of the industry’s most
overused buzzwords. Despite the ubiquity of the term,
its value is indisputable as healthcare leaders aroundthe globe continue to make it a top business priority forthe foreseeable future.

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As a concept, innovation impacts every aspect of ahealthcare organization and must be leveraged acrossboth internal and external systems. Without innovation
driving transformation, there can be no hope offurthering our industry.

IN ORDER TO MAKE THE BEST INFORMED DECISIONSNECESSARY TO DRIVE THIS LE VEL OF SWEEPINGC H A N G E , H E A LT H C A R E W I L L R E Q U I R E T H E A B I L I T Y T O
Q U I C K LY A C C E S S , A N A LY Z E , A N D U N D E R S TA N D DATA .

Innovation has emerged
as one of the industry’s
most overused buzzwords.
When coupled with the right technology, data is the key toprogress, facilitating the following foundational elements
of innovation:+ Improved information exchange+ Seamless collaboration with external and internal
partners+ Streamlined research processes+ Accelerated healthcare quality and performance
improvements
Processes and efficiencies improve with increased
innovation. By adopting new tools and technologies,
healthcare organizations can begin to realize positivechange for better patient outcomes, more effective
employee retention, and cost savings across theentire organization.

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T H E B E S T- I N F O R M E D D E C I S I O N S

In order to make thebest-informed decisions
necessary to address this levelof sweeping change, healthcare
will require the ability toquickly access, analyze, andunderstand data.

501

Although a seemingly nebulous term withmany definitions, simply put, innovation is
the outcome of creating something newthat brings value to others.1
It is realizing the potential for something
great, then taking action to make it areality, promoting disruption and excelling
beyond the status quo.
When you think about a progressive healthcare future,
data and technology are two key elements that can drive
fast, impactful change. When these two worlds collide, apowerful transformation can be set in motion.
Delving deeper into practical applications–we look at
two core models for healthcare: operational innovationand corporate innovation. Each model presents its own
unique opportunities for transformation and growth within
modern healthcare systems and organizations. Together,
these two models can create a continuous learningorganization and lay the foundation for an innovative
healthcare infrastructure.

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Guide to Data-Driven Healthcare Innovation

DISSECTING THE BUZZWORD:

What isInnovation?

T H E O PP O R T U N IT Y TO D R I V E C H A N G E IS AVA I L A B L E
T O E V E R Y O N E —T H E K E Y I S K N O W I N G H O W T O
I M P L E M E N T I T.

For the purposes of this eBook, innovation(specific to the healthcare industry) is defined as:
The adoption of new products, technology,
services, or processes that improve organizational
performance or patient care
The activation of these novel approaches acrossthe health system
A scalable approach to continued transformation
with desirable outcomes

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A GAME-CHANGER

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Data and technology are two
key elements that can drive
fast, impactful change. Whenthese two worlds collide, apowerful transformation canbe set in motion.

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Guide to Data-Driven Healthcare Innovation

Core Models for
Healthcare Innovation
Below are two core models for healthcare innovation. While one model drives transformationfrom within the organization, the other model seeks to reach outside the organization tofoster partnerships that are mutually beneficial to both parties. Both models can workin-tandem, creating a data-driven healthcare ecosystem.

1

Operational
Innovation+ Driving Internal Excellence+ Process and Performance Improvement+ Team and Individual Empowerment+ Impacting Patient Outcomes
Operational innovation is the adoption, creation, or
deployment of new ways of doing work. It is the strategic
ability to better the current healthcare organizationby employing the latest proven software, technology,
processes, or procedures and to activate them across thesystem. Operational innovation drives better outcomesinternally and works to promote a continuous
learning organization.

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2

Corporate
Innovation+ Driving Outside Partnerships and Investments+ Accelerator and Incubator Programs+ Collaborations with External Organizations
At a broader scale, corporate innovation is driven by thedesire to collaborate with external organizations and create
new opportunities for the existing business. Driven by dataand performance indicators, the health system seeks new
partnerships to develop new tools, patents, and technologies
with third parties, or to create a larger impact on healthcare asa whole. Health systems also have the ability to impact revenue
with successes driven by these life science, startup, entrepreneur,or digital health company collaborations.

EXAMPLE:

EXAMPLE:A health system may identify the readmission rate
in a specific cohort of heart failure patients andpredict future clinical events as part of a quality
improvement initiative.

A health system develops an innovation acceleration
hub that encourages a digital health startup to build
research connections in exchange for partnership.

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Guide to Data-Driven Healthcare Innovation

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Innovation calls for
disruptive methodologies:
Driving economic growth andincreasing efficiency requires
healthcare systems to adapt during
times of recession and crisis. When
unexpected shifts occur, innovation
can be the determining factor of anorganization’s success.

Clearly outline their challenges andpain points

To continue moving innovation
forward, health systems must:

Continue learning from shortcomings

Remain open-minded to new ideas
Collaborate with other organizations

Maintain a willingness to take risks

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The Ulimate Guide to Synthetic Data in Healthcare

ADAPTING TO CHANGE

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When unexpected shifts
occur, innovation can be
the determining factor ofan organization’s success.

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02

Roadblocks toTransformation
When adopting and deploying innovation,an organization will undoubtedly facenumerous challenges along the way.

Guide to Data-Driven Healthcare Innovation

Single Data ProjectC O M P L I C AT E D DATA E X P L O R AT I O NApprovals
IRB
Extraction
Modeling
Adjust / Fix
Visualize

Other Health
Systems

Startup
Innovators

Communicating change, addressing fears, and convincing
others that they should adapt new processes are all crucial
to seamlessly implementing novel ideologies.
Supporting the highest levels of management is alsoessential to instigating a cultural shift. Making thesefundamental changes to the fabric of any company canbe onerous, and large organizations may face significant
pushback along the chain of command during approvals.
Strategic misalignment between innovation and businessstrategy can lead to reduced productivity and costs,
decreased efficiency, and lost profitability. The underlying
challenge, however, is that of data. When data is big,disparate, inaccessible, or unshareable, innovation can stall.

How can data fast-track innovation? With data-backed
decision-making supporting innovative changes,
overcoming the above cultural roadblocks and fears
can be more easily attainable.

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Research
Academia

Life Sciences
PharmaMultiple Data Projects
C O M P L I C AT E D E X T E R N A L C O L L A B O R AT I O N SABOVE: When singular data projects are complex within ahealthcare system, working with outside organizations becomeseven more complicated, sometimes nearly impossible.

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Guide to Data-Driven Healthcare Innovation

Data Changes Everything

At the heart of innovation, data changes
everything.
Data is a critical component of innovation, as it can
provide both new information about what was previously
unknown and back decisions.
Data enforces choices, reveals patterns and outliers,
determines cause and effect, and validates results. It
lays the foundation for an organization to make theright decisions at the right times. And when data is
inaccessible, unreliable, or disconnected, it makes it very
difficult to affirm conclusions and promise success.
In fact, analyst firm IDC estimates that in 2023, 70
percent of healthcare organizations’ attempts to scalevalue-based care models will fail unless they invest in
better data management and governance.2
However, it’s not just the volume and infrastructure—it’s
the quality and granularity of data.

70%of healthcare organizations’ attempts
to scale value-based care models will
fail in 2023 unless they invest in betterdata management and governance.2

Discover how to unlock healthcare data:
LEARN MORE 

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T H E H E A R T O F I N N O VAT I O N

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At the heart of innovation,
data changes everything.

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Guide to Data-Driven Healthcare Innovation

D I S PA R AT E D ATA

One of the overarching challenges faced by ourindustry is the inability to use information fromdisparate sources. Every second, an exponential
amount of healthcare data is generated, butleveraging it in a cohesive, meaningful way has
proven nearly impossible.
Among respondents to a Performance
Management Trends and Priorities in Healthcare
survey, 90 percent of healthcare Chief Financial
Officers (CFOs) and finance executives believe
their organization should be doing more toleverage financial and operational data to inform
strategic decisions. In that same survey, morethan 50 percent wanted access to trustworthy
data and better dashboards.3
With new trends emerging, such as big data
analytics backed by data science technology,
the potential to connect and centralize thesecomplex sources can unlock answers for bothmanagement and frontline staff. It’s a criticalinnovation opportunity that could dramatically
impact healthcare organizations.

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90%

50%

of healthcare CFOs believe theirorganization should be doing moreto leverage financial and operational
data to inform strategic decisions.3

of healthcare CFOs want better
access to trustworthy data andbetter dashboards.3

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142Guide to Data-Driven Healthcare Innovation

D ATA I N F R A S T R U C T U R E

According to investment bank RBC Capital Markets, thehealthcare industry generates around 30 percent of theworld’s data today, and by 2026, the compound annual
growth rate of healthcare data will reach 36 percent.4
With a potential market reach of $44.53B by that time,
the big data healthcare market is expected to grow at an
astronomical pace.5
With such explosive growth, collecting data (and withenough detail) to conduct valid research has provendifficult. The disparity of sources, IRB and approvalboards, IT and data teams needed, and additional
barriers to discovery create roadblocks to obtain thevolume of data needed to be effective.

3

D ATA A C C E S S

Looking back, 2022 was a historically difficult year
regarding cybersecurity at heath systems. Throughout
the year, cyber attacks forced several large healthsystems to suspend patient operations and take systems
offline to combat the attacks, resulting in lost revenue andmassive investment in rebuilding systems and networks.
Now, more than ever, the privacy of patient information
is vital. Even with patient privacy safeguards in place,healthcare data privacy is an extremely sensitive subject.
Accessing it for research, patient care, population studies,or performance reports requires long timelines, external
resources, and cumbersome processes because a breachin patient data security can be debilitating for a healthsystem. To be effective, data needs to be accessible to theusers of an organization while still maintaining privacy.
Without this access, healthcare organizations can’t expect
to uncover new insights, therapies, or processes withefficiency. With patient privacy as a top priority, it’s also
critical that data utility is maximized in order to promote
positive system-wide impacts.

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4

G R A N U L A R - L E V E L , D E TA I L E D D ATA

Along with access to healthcare data, health systems mustalso have high-quality data in a structured format that can beeffectively leveraged. Often, aforementioned privacy restrictions
remove information from datasets that could impact research.
For example, a name field can either be a single field or
subdivided into first name, middle name, and last name. By
subdividing the name and specifying its parts, the data is
considered to be more granular.
In order to make the best decisions possible, organizations needcomprehensive, detailed information regarding patients without
any characteristics or descriptors removed. De-identified andaggregated data are common methods for sharing detailed
patient information while mitigating risk, but these options often
lack the necessary granularity and hold the risk of potential
re-identification.

W H AT I S G R A N U L A R D ATA?

Granular data is detailed data, or the lowest level that data canbe in a target set. It refers to the size that data fields are divided
into, in short, how detail-oriented a single field is.6
D I S C O V E R M D C LO N E G R A N U L A R D ATA 15

M A K I N G S E N S E O F D ATA

20With so much data at hand, deriving meaningful insightsfrom such vast amounts of information remains anotherkey challenge for healthcare. The industry needs asingle, intuitive platform to proactively generate easy
to understand insights that will enable physicians andclinicians to enact change. To truly make sense of data,
the industry needs a scalable solution that incentivizes
innovation within, and across, service and business lines.

10

In a survey conducted by eHealth Initiative and theCollege of Healthcare Information Executives analyzing
clinicians at 98 different healthcare systems, 39 percent
said they use between 11 and 50 electronic platforms
to collect and analyze data. In spite of the array oftechnology, Healthcare Information and Management
Systems Society (HIMSS) analytics still indicate the “useof data analytics by providers remains immature.”7

0

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Guide to Data-Driven Healthcare Innovation

39% of clinicians surveyed
said they use between

30

11-50
electronic platforms tocollect and analyze data7

R A P I D D ATA T R A N S F E R

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60

Institutions with streamlined processes or tools can share
their data with third parties quickly and easily leading to
new insights and new revenue streams. When healthsystems give external entities the power to access data,
innovation can advance at a faster pace than ever before.

50

The process of sharing health data with third parties cantake years of long, drawn-out conversations, contracts,
and processes. Without a streamlined way of providing
access to healthcare data, health systems are unable tocollaborate quickly or efficiently, delaying timelines andeven hindering collaboration altogether.

40

6

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Guide to Data-Driven Healthcare Innovation

03
REMOVING THE BOTTLENECKS

How toUnlock thePower of Data
With data challenges at hand, innovation
can stall or even be stopped in its tracks
completely.
The challenges in place are immense, but health systems
need to realize they are sitting on a goldmineof information.
If bottlenecks can be removed and data can be effectively
leveraged, the possibilities are limitless. The desire to
be more data-driven isn‘t standing in the way; but theuncertainty of how to achieve that level of insight efficiently
and effectively.
A study from IDC indicates that 83 percent of CEOs
want their organization to be more data-driven, and 87
percent of CXOs said being an intelligent enterprise istheir top priority.8

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87%

of CXOs top priority is to be
an intelligent enterprise 8

83%

of CEOs want their organization
to be more data-driven 8

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Guide to Data-Driven Healthcare Innovation

Unlocking the Power of Data

When it comes down to thedetails, there are three main
components necessary to
unlock the power of data in
any healthcare organization– connection, empowerment,
and accessibility.

1
2
3

T H R E E M A I N C O M P O N E N T S T O U N L O C K D ATA :A Connected Ecosystem
Centralized, Connected, Accessible Data

Empowered Users
Using Intuitive Technology to Ask Questions

Accessible Healthcare Insights
Synthetic Data and Third-Party Collaboration
Opportunities

Empowered Users

A Connected Ecosystem
Accessible
Healthcare
Insights

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Guide to Data-Driven Healthcare Innovation

A Connected Ecosystem
Centralized, Connected, Accessible Data
Now, more than ever, the privacy of patient information
is vital. Even with patient privacy safeguards in place,healthcare data privacy is an extremely sensitive subject.
Accessing it for research, patient care, population
studies, or performance reports requires long timelines,
external resources, and cumbersome processes because
a breach in patient data security can be debilitating
for a health system. To be effective, data needs to beaccessible to the users of an organization while still
maintaining strict patient privacy.

W I T H E T H I C A L C O N S I D E R AT I O N S O V E R A R C H I N G

Typical (Unconnected) Data Sources
within a Health System
These (often) disjointed connections between healthdata create barriers to optimal and fast discovery.
EMR (demographics, diagnoses, procedures, labs,
medications)Physician Notes
Ancillary Lab Systems ( often disconnected fromthe main EMR because labs can be independent of
hospital)

D ATA C O N N E C T I V I T Y, T H E R E I S S T I L L A
CRITICAL NEED TO REMOVE THE BARRIERS

Oncology EMR (often separate)T H AT P R E V E N T E N D -T O - E N D D ATA D I S C O V E R Y
A N D C O L L A B O R AT I O N .

Within most health systems today, data sources are often
disconnected, requiring multiple systems to source data,
multiple resources to pull data, and lengthy turn-aroundtimes to retrieve data. Without this access, healthcareorganizations can’t expect to uncover new insights,
therapies, or processes with efficiency. With patient
privacy as a top priority, it’s also critical that datautility is maximized in order to promote positive
system-wide impacts.
S O LV I N G T H E C H A L L E N G E

Today‘s technology has the ability to consolidate andsecurely store these data from different sources into acentralized big data warehouse or lake. From this datalake, information can be organized into one, cohesive,
understandable story and easily accessible by those whoneed it.
The technology is here; we just need to utilize it.

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Patient Device Data (i.e. glucose monitors, heart
monitors)
Financial Systems (billing, cost reporting)
Clinical Support Software (scheduling)
Radiology, Imaging, Pathology Reports

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The Ulimate Guide to Synthetic Data in Healthcare

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A C R I T I C A L N E E D I N H E A LT H C A R E

With ethical considerations
overarching data connectivity,
there is still a critical need toremove the barriers that prevent
end-to-end data discovery
and collaboration.

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Guide to Data-Driven Healthcare Innovation

Empowered Users
Using Self-Service Platforms to Ask Questions
Self-service data analytics platforms empower both technical and non-technical users to ask questions and gain insights at rapid
speeds at any time. Users no longer need to wait in departmental queues for staff to query a specific data set. Without these longwait times, practitioners and clinicians can quickly and efficiently get answers to healthcare-related questions.
By accessing the capabilities to access data individually, health systems can begin to deploy innovation from the bottom up,meaning clinicians and frontline staff can be empowered to use data in their everyday work. This simple addition to the process
encourages every user to become more engaged with the data as they find meaningful insights that propel better patient
outcomes, overall operations, and performance improvements across the healthcare ecosystem.

FINANCIAL EMPOWERMENT

Healthcare workers can understand the fluctuation in medical costs
for top use cases, noting how even the smallest changes in supply
and device costs might lead to significant savings with no negative
impacts on operations. They can also inform value-based caregoals within and outside the system by understanding patient total
cost of care and care utilization.
CLINICAL EMPOWERMENTProviders can use data to forecast length of stay, identify negative
patient outcomes proactively, and prescribe medications for certain
comorbidities. They can also develop care algorithms to improve
care model design, track impact of care management and patient
support programs on specific populations, andmedical presentations.
O P E R AT I O N A L E M P O W E R M E N T

Managers can forecast admissions to optimize resources, oversee
supply changes more effectively, and compare performance
between providers for more streamlined care. To help solve fortoday‘s workforce challenges, managers can also identify drivers ofretention and operational efficiencies that drive employee wellbeing.

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Guide to Data-Driven Healthcare Innovation

Accessible Healthcare Insights
Synthetic Data and Third-Party Collaboration
Opportunities
Synthetic data is a powerful force beginning to open the doors
between healthcare organizations and third-party entities. This
type of data is the key to enabling health systems to overcome
data-sharing challenges while maintaining patient privacy.

E A R LY
INTERVENTION

PREDICTIVE
MODELS

CARE
MODELSR E A L-T I M E
PERFORMANCE
VIEWS

Derived from original patient information collected from actual
patient populations, synthetic data provides information about
patients’ health statuses and healthcare delivery. Based ondata routinely collected from sources such as electronic healthrecords (EHRs), claims and billing activities, and productand disease registries, a completely anonymized, brand newparallel of insights is created.
Users working with synthetic data can glean insights anddraw conclusions they would otherwise not have been able
to reach due to privacy boundaries. Synthetic data cannot be
traced back to individuals in the original patient population
like other forms of data (such as de-identified or limited use).
This anonymity allows users with access to the information
to avoid the stringent compliance regulations that come withsharing real patient data.
With synthetic data, health systems can:+ Facilitate research+ Aid in the discovery of health patterns+ Assist clinicians in treating patients

To learn more about synthetic data for healthcare,
D O W N LO A D T H E S Y N T H E T I C D ATA E B O O K With a connected data ecosystem, an intuitive technology
platform, and the power of synthetic data, innovation canbecome a reality.
Health systems and life science organizations are able
to seamlessly collaborate—testing hypotheses, building
models, validating studies, developing clinical trials, improving
care optimization, and creating life-saving therapies at anaccelerated pace.

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04

Guide to Data-Driven Healthcare Innovation

The Two Models of
Healthcare InnovationO P E R AT I O N A L I N N O VAT I O N

Data-Driven
Healthcare
Innovation
ModelsSince the onset of the pandemic, health systems havebecome more creative in applying practical solutions
to solve real-world problems. These modern healthcare
solutions increasingly evolved in recent years to meet
new and growing challenges. For example, healthsystems have created new ways of monitoring the
spread of disease via centralized data analytics andvisualization hubs.
Pharmaceutical companies have developed new vaccines
to mitigate the spread of disease and shorten the periodof illness. And technologists have built novel web-based
applications and software to better treat patients virtually.
In regards to innovation, two core models can help spark
data-driven change inside and outside the organization:
operational innovation and corporate innovation. With
these two models in place, health systems can lay thefoundation for an infrastructure of continuous learning
centered around improvement.

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C O R P O R AT E I N N O VAT I O NABOVE: By working in tandem, the two models can improve
operational, quality, and performance functions at a rapid pace,setting the infrastructure for a continuous learning organization.

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Guide to Data-Driven Healthcare Innovation

Operational Innovation
When operational innovation is a corebusiness objective for a health system,
the organization seeks to elevate clinical,
financial, and operational systems bydriving internal excellence, process andperformance improvement, andemployee empowerment.
With today‘s healthcare challenges demanding answers,
health systems will need to align primary businessobjectives with the strategic ability to better the current
healthcare organization. And do so quickly.
CHALLENGES OF CHANGE

Health system will need to identify challenges in
implementing quality and performance improvement (QI/
PI) initiatives. These include fear of change or failure,grief over loss of familiar processes, lack of expertise,and more.
Along with this, the organization must also identify
factors that can facilitate the improvement process. To
aid in the adoption of new processes, organizations canencourage social interaction—like evaluations from early
adopters—and internal communication of successes.

Ultimately, by proactively and openly informing an organization’s
staff about innovative, new practices and implementing themaccordingly, the health system can raise the standard of
expectations and encourage disruptive, positive change.
D ATA H O L D S T H E A N S W E R S

Innovation can be a driving force for quality improvement—robust
analytics, artificial intelligence, and intuitive reporting can uncoveranswers at a faster pace than today‘s cumbersome, resourceheavy, disjointed data extraction processes. If data were better
leveraged, streamlined, and made accessible (even fragmented
internal and external data sources), health systems would be able
to better understand the inefficiencies impacting the organization.
Data holds the answers to these complex operational healthcare
challenges. With access to real-time, detailed, and reliable
data, health systems can improve decision-making and elevate
operational innovation with concrete evidence to reinforce
these decisions.
With data unlocked, health systems are able to activate providers
to adopt self-service analytics. With data at their fingertips,
providers can better understand the impacts of value-basedcare and population health management. With bottom-up data
activists asking questions and seeking change, health systems
are able to impact operational, financial, and clinical systems at agreater scale.

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24

QUALITY IMPROVEMENT AND
PERFORMANCE IMPROVEMENT

One of the most critical ways health systems caninnovate internally is through quality improvement andperformance improvement (QI/PI).
According to the Institute of Medicine, QI involves carethat is safe, timely, effective, efficient, equitable, andpatient-centered while PI entails improving operational
efficiency of a healthcare system.9
Innovation in QI/PI moves an organization forward,
easing the burden of delivering care and improving theprocesses required to treat patients. Effective quality andperformance improvement innovations can span theentire organization, impacting advances in data analysis,
EHR improvements, upgrades to financial systems, andmore efficient storage system developments.
Examples of such improvements include:+ Patient Flow and Throughput+ Monitor Clinical-Related Outcomes+ Revenue Cycle Management+ Supply Chain Analysis+ New Clinical Guidelines+ Procedural Changes+ Inventory Management+ Care Model Redesign+ Workforce Planning and Efficiency
Driving excellence internally (improving quality andprocesses, solving for inefficiencies, and streamlining
systems for the betterment of the organization) will be a
critical component of strategic initiatives in the
coming years.
With the help of the latest proven software andtechnology, health systems have the opportunity toharness data to scale operational innovation at a fasterpace than ever before.

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25

Guide to Data-Driven Healthcare Innovation

Corporate Innovation
When corporate innovation is a strategic
driver for a health system, the organization
seeks partnerships with external third parties
to collaborate on new technologies, products,
and services.
These types of partnerships and programs are sometimes
described as accelerator programs or innovation hubs.
The co-creation of these projects is beneficial to both the healthsystem and the third party as the health system benefits fromthe new innovations (be it a medical device, software, or novel
therapy) or by diversifying revenue streams with investments
in promising companies. At the same time, the third party
benefits from access to expert teams, information and data,advisors, and industry connections.
Oftentimes, these third parties need access to data whencollaborating with health systems to validate results, test
hypotheses, and find populations for study. As noted in chapter
2, accessing and sharing that data is difficult unless the righttechnology is in place to alleviate those challenges. Whendata sharing is effective, secure, and easy, third parties canthrive within the environment, moving new ideas forward withconfidence and speed.
The demand for accessible healthcare data is vital in thisdynamic, as higher education institutions, government
agencies, pharmaceutical companies, and life scienceorganizations strive to understand how healthcare systems
are operating and how patients are responding to specifictherapies. Data must be shareable to generate economic,
clinical, socioeconomic, and commercial value.

T H E P O W E R O F S Y N T H E T I C D ATA

To overcome external data-sharing challenges, one solution
lies in the power of synthetic healthcare data. Synthetic data
enables third-party data-sharing with security and patient
privacy preserved.
With synthetic data, healthcare organizations can not only
let members of their internal teams explore freely, but they
can also share discoveries and research with external teams
worldwide without undue constraints.

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Guide to Data-Driven Healthcare Innovation

Real-World Evidence and Clinical Trials
for Life Sciences
There is an important need to speed up the clinical trial
process for life science companies. Complex processes,
complicated protocols, high-risk investments, fragmented
data sets, and long timelines hinder life scienceorganizations from discovering life-saving therapies.
Health systems have the opportunity to partner with life
science organizations to help facilitate and accelerate
the research process. Through synthetic data sharing,
health systems can provide access to specific, granularpatient data so life science organizations can test
hypotheses, build models, validate studies, develop
clinical trials, improve care optimization—all while
exploring information securely. With access to thisdata, life science organizations can determine if subject
populations are sufficient before committing to traditional
data access processes for clinical trials. Being able toquery, adjust, and analyze populations of interest allows
life science organizations to build out projects withconfidence and at an accelerated pace.
Synthetic data provides life science organizations withreal-world evidence at their fingertips. Once analyzed, thisdata helps to:+ Gain insights into specific patient populations+ Understand which competitor products, medical
devices, or pharmaceuticals are being used andin what capacity+ Get a grasp on the entire pharmaceutical landscapefor a specific therapeutic area
+ Identify which clinical trials to pursue and prioritize
Additionally, the health system does not need to assign
staff to these external projects because the life scienceorganization has the ability to do the work themselves.
With new insights unlocked, life science organizations
have an opportunity to refine their vision and goals, enact
more efficient product development processes, andsave time and money. It‘s a win-win for health systems
and life science organizations with costs saved for eachparticipating entity.

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Incubators and Accelerators
Startup incubators and accelerators are two additional examples
of corporate innovation. Healthcare incubators aid external
entrepreneurs in solidifying their ideas and building theircompanies while accelerators provide educational resources andtools that startups can use to accelerate growth.
When health systems engage in building incubator or accelerator
programs, they build a network of startups, entrepreneurs, andvendors to work together on new technology and new digital
health tools while mutually benefiting from the partnership.
Because health systems store an abundance of data, enabling
synthetic data sharing can offer a powerful benefit for incubatorand accelerator programs, streamlining research and testing
hypotheses to get to answers faster.
The successes of these partnerships cultivate a portfolio ofpotentially ground-breaking technology, co-development ofinnovative new therapies, and provide possible passive revenue
streams. Revenue diversification increases financial stability,
provides exposure to new audiences, and decreases dependence
upon a single source of revenue, thereby reducing risk. In theincreasingly competitive startup landscape, the type of insightsthat synthetic data provides could easily make theseburgeoning companies more competitive and more attractive
to venture capitalists.

27

THE NEW CURRENCY

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Data, and effective useof data, will be the new
currency of successful
healthcare performancein the coming years.

The Ulimate Guide to Synthetic Data in Healthcare

28

Guide to Data-Driven Healthcare Innovation

05

The MDClone
Difference

MDClone offers health systems the ability
to maximize use of healthcare data while
maintaining complete patient privacy.
The ability to activate healthcare teams
to utilize data to its fullest extent can bea major catalyst for innovation within ahealthcare ecosystem.
Designed as a big data system for healthcare, the
MDClone ADAMS Platform is a powerful, intuitiveenvironment created for workers within a healthcareorganization to ask questions on-demand, find insights
instantly, measure performance, set KPIs, and implement
action for better patient care or improved processes.
Along with a robust semantics engine, longitudinal
data organization, and a natural language processing
application, the ADAMS Platform also includes the ability
to analyze both original or synthetic data. With synthetic
data capabilities enabled, users can share information
broadly with internal or external teams, opening the door
for healthcare organizations to collaborate seamlessly
with third parties.

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With uncomplicated access to real-world data, health systems
can implement an innovation infrastructure to boost revenue,
partnerships, and digital transformation.
Health systems are in the midst of rapid transformations in key
areas including business models, care delivery models, andmarket positioning. With continued margin pressure in 2023,
we expect to see acceleration in the use of healthcare data to
optimize clinical capacity and service lines, to find new revenue
streams, and to discover new breakthroughs in treatment.
Data, and effective use of data, will be the new currency of
successful healthcare performance in the coming years.

29

Guide to Data-Driven Healthcare Innovation

A MODEL FOR CONTINUOUS LEARNING

MDClone’s Approach toOperational Innovation
For Healthcare Performance
Improvement and Accelerated Discovery
With the platform‘s unique underlying technology,
health systems can leverage ideas from across theentire ecosystem, overcoming common obstacles
and long timelines hindering research, innovation, andcollaboration. The ADAMS Platform’s powerful, selfservice data analytics environment enables healthcare
teams to quickly organize and access information,
explore data, and find new ways to optimize business
functions, leading to impactful healthcare change, better
outcomes, and secure collaboration.
K E Y P L AT F O R M F E AT U R E S I N C L U D E :+ Unstructured or Structured Longitudinal
Data Organization+ Powerful Natural Language Processing+ Robust Semantics Engine+ Synthetic Data Generation+ User-friendly Interface
Data from any source is collected into a data lake
hosted by the healthcare organization, and any user isable to access that data—for patient care, research, orprocess improvement. With data compiled from EHRs,
financial claims, genomics, medical devices, and more,
information forms a cohesive and intuitive story forclinicians and leadership to identify insights and patterns
quickly in order to innovate at a faster pace and withsolid, data-driven decisions.

EXAMPLEO P E R AT I O N A L I N N O VAT I O N I N A C T I O NDetermining risk stratification andtreatment options for patients withpulmonary embolism.
Intermountain Healthcare needed to be able to accessand properly review data to determine risk stratificationand treatment options for patients with pulmonary
embolism. The team required a tool that would analyze
the levels of clinical biomarkers in patients withpulmonary embolism, provide information about themortality rates for these patients, and generate insights
into how different treatment options affect outcomes.
Leveraging the MDClone platform to develop a
pre-discharge model for venous thromboembolism
treatment could avert approximately 1,180
venous thromboembolism events annually acrossIntermountain. This process opened visibility to a stateof care that had never previously been realized with dataat the organization.
The insight will lead to a standardized care processmodel for all patient locations. It also leads to a request
for capital funding for a point-of-care solution to use
this risk stratification for patients on admission, a
quality improvement approach to continuously evaluate
thrombectomy and new lysis procedure outcomes, andan abstract for submission to a journal.
READ THE STORY

MDClone for Operational Innovation
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30

Guide to Data-Driven Healthcare InnovationA M O D E L F O R O U T S I D E C O L L A B O R AT I O N

MDClone’s Approach to
Corporate Innovation
Fueling Innovation For Life Scienceand Programs
For collaboration with life science and externalorganizations, MDClone offers a ground-breaking
approach for health systems to deploy. With a secure,controlled environment in place, health systems canwork with outside organizations to allow accessto granular patient data so researchers can test
hypotheses, build models, validate studies, and create
life-saving therapies for improved patient outcomes.

EXAMPLEWith synthetic data technology at the forefrontof this approach, external third parties are able toanalyze structured or unstructured real-world data inincredible detail—all within an environment that offers
unprecedented flexibility and independence. With instantaccess to detailed data, third parties can reduce the time
to discovery and accelerate real-world evidence projects.

Performing research while using synthetic data makes
it easier to locate any event along a patient‘s journey
without having to worry about patients‘ privacy issues.

Health systems can completely control the process of
sharing data for external collaboration, enabling newrevenue streams while paving the way for innovation.
When health systems and life science organizations
collaborate, data sets are pooled into the secureenvironment and analyzed based on project needand intended users. When analysis is complete, thecontrolled environment is switched off and data is
no longer accessible. The ability to control accessbased on project needs ensures another level of dataprotection and peace of mind for both health systems
and life science organizations so they can focus on theimportant lifesaving discoveries needed.

MDClone for Corporate Innovation
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C O R P O R AT E I N N O VAT I O N I N A C T I O NJanssen conducts research usingsynthetic data provided by MDClone
and Sheba Medical Center.

Sheba‘s ARC Center (Accelerate, Redesign, Collaborate)
brings together key players in digital medicine physicians, researchers, start-ups, investors, and top-tier
medical centers with the goal of redesigning healthcare.
As the pharmaceutical division of Johnson & Johnson,
Janssen focuses on developing innovative molecules forpatients in different therapeutic areas, to serve unmet
medical needs. This aligns well with Sheba‘s position as
one of the worlds most digitally advanced hospitals.10
READ THE ARTICLE

31

Guide to Data-Driven Healthcare Innovation

U N L I M I T E D C O L L A B O R AT I O N P O T E N T I A L

One Powerful Solution
With today‘s healthcare challenges at the forefront ofagendas in 2023, health systems will need to find ways
to efficiently understand operational performance, solve
workforce challenges, and find innovative strategies to
impact the bottom line.
Healthcare data is an often underutilized asset of manyorganizations today and unlocking its potential for the
betterment of healthcare can impact patients, business,and the industry as a whole. With easier access to data,by more users within a health system, answers to today‘s
challenges can be discovered at a much faster pacethan before.
With MDClone‘s solution in place, data from any source(both structured and unstructured) is collected into
a data lake (hosted by the health system), organizedlongitudinally, and made accessible to any authorized
user. The user-friendly interface allows healthcare
workers to ask questions and get answers instantly—
visualizing patterns and outliers, and solving for both
immediate patient and larger organizational challenges.
Once discoveries are made, users can create KPIs tomeasure and act on changes, and share discoveries to bothinternal and external teams with synthetic data.
Alongside the powerful platform, MDClone offers a novel
approach to optimizing a high-performing healthcare system—
the ADAMS Center. The ADAMS Center methodology essentiallyaligns business units and service lines within the health system
to create a streamlined approach to understanding bestpractices, projects, and research before deploying those ideas
out to the greater organization. With a birds eye view of the
ADAMS Center initiatives, leadership can quickly understand key
initiatives and progress.
Whether striving to innovate from within the health system or
to build partnerships outside of the organization, the MDClone
platform gives health systems the ability to build a truly
innovative infrastructure with data at the heart of decisions.
From delivering better patient care to partnering with world-classlife science organizations, innovation is a key component forbuilding an unshakeable healthcare foundation in 2023
and beyond.

MDClone enables fast
data exploration for rapid
discovery and transformation.

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32

Guide to Data-Driven Healthcare InnovationA B O U T T H E M D C L O N E A D A M S P L AT F O R M

A B O U T T H E C O N N E C T P L AT F O R M

The MDClone ADAMS Platform is an intuitive data
analytics environment that empowers users toquickly organize and access information, accelerate
research, drive better patient outcomes, empower
healthcare teams to action, and create impactfulhealthcare innovation.

Introducing a new approach for life science organizations
to access real-world data. The MDClone Connect Platform
enables external or cross-organizational collaboration with
unfettered access to data within a secure environment.

Put your organization on a continuous learning pathwith real-time access to insights with our pioneering
healthcare data platform that breaks down barriers indata exploration.

Within the platform, take advantage of key tools andfunctionality to streamline projects. Dynamically explore robust,detailed data needed to answer complex healthcare questions.
With over 50M synthetic patient records available, MDClone
offers both health systems and life science companies
unlimited collaboration opportunities.

mdclone.com/adams-platform 

mdclone.com/connect-platform 

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33

The Ulimate Guide to Synthetic Data in Healthcare

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A T R U LY I N N O VAT I V E I N F R A S T R U C T U R E

Whether striving to innovate fromwithin the health system or to build
external partnerships, the MDCloneADAMS Platform gives healthsystems the ability to build a truly
innovative infrastructure with dataat the heart of decisions.

34

Partner With Us

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Unlock healthcare data. Transform care.

MDClone is a technology firm focused on
unlocking healthcare data and empowering
exploration, discovery, and collaboration.
MDClone democratizes data and empowers
clinicians, researchers, and executives to
explore, drive action, and work together toimprove patients’ health.
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Guide to Data-Driven Healthcare Innovation

35

REFERENCES

1. Hunter, Shawn G. “Out Think: How Innovative Leaders
Drive Exceptional Outcomes.“ Hoboken, NJ: Wiley, 2013.
2. Dunbrack, Lynne. IDC Market Glance: ValueBased Health Services, 2022. International Data
Corporation. April 2022. https://www.idc.com/getdoc.
jsp?containerId=US49058422.
3. Dulac, Jennie D. and Morrissey, Walter W. “FromDisparate Data to Informed Strategies: Using
Technology to Transform Quality, Costs, and thePatient Experience.” hfm Featured, February 6, 2018.
https://www.hfma.org/topics/hfm/2018/
february/59345.html.
4. “The Healthcare Data Explosion.” RBC CapitalMarkets.
https://www.rbccm.com/en/gib/healthcare/episode/the_healthcare_data_explosion.
5. “Big Data Healthcare Global Market Report 2022“.
The Business Research Company. September 2022.
https://www.reportlinker.com/p06320104/BigData-Healthcare-Global-Market-Report.html?utm_
source=GNW.
6. “What does data granularity mean?“ Techopedia.
February 2017. https://www.techopedia.com/
definition/31722/granular-data#:~:text=A%20good%20
example%20of%20data,is%20also%20considered%20
more%20granular.
7. Zieger, Anne. “Providers Use Many Disparate Data
Sources.” Healthcare Dive. June 5, 2014. https://www.healthcaredive.com/news/providers-use-manydisparate-data-sources/270928/.
8. “Value-Based Healthcare and Knowledge-Based
Medicine.“ International Data Corporation. https://www.
idc.com/prodserv/insights/#health.
9. “Crossing the Quality Chasm: A New Health System
for the 21st Century.“ Institute of Medicine (IOM).
Washington, DC: National Academy Press, 2001.
https://pubmed.ncbi.nlm.nih.gov/25057539/.
10. “Janssen Israel and Sheba ARC on digital healthsolutions.“ Healthcare Digital. March 2022. https://
healthcare-digital.com/technology-and-ai/janssen-israeland-sheba-arc.

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Guide to Data-Driven Healthcare Innovation

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