The survey polled over 50 health systems to understand AI/ML adoption, challenges, and outlook. Key findings include: - AI/ML adoption is higher among large health systems (> $1B revenue), with 71% of systems over $4B having adopted it - Lack of clear use cases and ROI, skills shortage, and technology selection are top challenges for CIOs - Clinical performance and operational improvements are top priority domains for seeing ROI from AI/ML - While few health systems have large dedicated AI/ML teams now, 75% of large systems plan significant team growth in 3 years

1. The 2019 CitiusTech ‘AI in Healthcare’ Readiness
Survey polled over 50 health systems to develop
an industry viewpoint and key insights on the
state of AI/ML adoption, key challenges and the
near-term outlook for healthcare providers.
Survey hosted by
Are Healthcare Providers
Ready for AI/ML?
2019 CitiusTech Survey
CHIME Foundation

2. AI/ML: The next wave of
healthcare disruption
CitiusTech’s 2019 ‘AI in Healthcare’ Readiness Survey of
CHIME CIOs received responses from 50+ key IT
decision makers of health systems. 35% of respondents
were from large health systems ($1bn or more in
revenues) and had a nearly equal distribution of urban
versus semi-urban coverage.
The survey results indicate that there is significant
optimism among CIOs and healthcare technology
executives around AI/ML. As adoption becomes
mainstream, getting the right clinical / operational use
cases, model development, technology selection and
finding / retaining talent top the CIO’s priority list.
While challenges remain, the next few years will see
health systems derive real ROI from AI/ML initiatives,
through smarter, faster processes, enhanced clinical
quality, lower costs and better care.
12019 CitiusTech ‘AI in Healthcare’ Readiness Survey
Survey Indicators
of health systems with
revenue of $4B+ have been
early adopters of AI/ML
felt that lack of use case /
ROI clarity is a major
challenge
of large health systems
(revenue of $1B+) plan to
use third party software
vendors for AI/ML needs
of health systems with $2B
or more in revenue are
likely to make significant
investments in AI/ML teams
over the next 3 years
71%
58%
59%
75%

3. AI in healthcare is starting to take off, and large
health system CIOs are fast getting onboard
Healthcare IT leaders have significant challenges in
AI/ML e.g., ROI clarity, access to skill
Healthcare organizations are flexible with their
AI/ML strategies and execution options
Clinical and operational performance are high on
the healthcare AI/ML priority list
Healthcare organizations have not yet invested
significantly in AI/ML teams
Healthcare organizations will grow their AI/ML
budgets and teams over the next 3 years
1
2
3
4
5
6
Key Survey Insights
2 2019 CitiusTech ‘AI in Healthcare’ Readiness Survey

4. What we heard
▪ Overall, only 40% of participants say that they have begun their
AI/ML journey, and only 4% believe they are past the initial phase
and are now cruising
▪ In health systems with $1B+ in revenue, 62% have started with
AI/ML, as compared to 28% in companies under $1B
▪ 71% of health systems larger than $4B in revenue have already
taken off with AI/ML, which is significantly higher adoption than
smaller health systems (revenue less than $4bn)
Our perspective
AI/ML in healthcare is starting to take off in large health systems.
There seems to be a direct correlation between company size and
early AI/ML adoption. Over the next few years, we can expect large
health systems to pave the way in terms of best practices, clinical
models and development of key use cases.
Where are you on the AI/ML journey?
AI in healthcare is starting to take off, and large health
system CIOs are fast getting onboard1
All health systems
60%
not taken
off yet
36%
just took off
4%
took off and
cruising
Taken off – by size ($B)
<1 <1-2 <2-4 >4
28%
50%
60%
71%
32019 CitiusTech ‘AI in Healthcare’ Readiness Survey

5. Healthcare IT leaders have significant challenges in
AI/ML e.g., ROI clarity, access to skill2
What we heard
▪ 58% of respondents stated that use case / ROI clarity was a key
challenge in AI/ML adoption amongst companies
▪ The ability to develop an AI/ML team was the next key area that
42% of respondents felt would affect their AI/ML adoption plans
▪ Technology and skill concerns are more acute with the large
health systems (more than $4B in revenue) – 57% felt that these
were key challenges
Our perspective
Lack of clarity around use cases and availability of skills are the
primary roadblocks for adoption of AI/ML by health systems.
Availability of experienced AI/ML professionals is a significant issue
across all industries and unlikely to be resolved soon. Large health
systems can collaborate with strategic technology partners with
deep clinical knowledge – to develop use cases, validate ROI,
choose the right technology / tools and provide AI/ML skills.
What are the biggest challenges in
your AI/ML journey?
Use case /
ROI clarity
58%
Tools /
technology
Build / retain
team
Quantity /
quality of data
Leadership
buy-in
42%
36%
29%
22%
Health system challenges
around AI/ML
4 2019 CitiusTech ‘AI in Healthcare’ Readiness Survey

6. Healthcare organizations are flexible with their AI/ML
strategies and execution options3
What we heard
▪ 69% of companies with $1B+ in revenue said that they would
prefer to build their own AI/ML models and pipeline
▪ The inclination to use third party software vendors for AI/ML
needs is significant (59%) for health systems with $1BN+ in
revenue. This goes up to 86% for companies which are $4B or
more in revenue
Our perspective
As AI/ML budgets start to grow and ROI-positive use cases evolve,
large health systems will focus their energies on developing custom
AI/ML models, and will require teams with strong skills around
statistical modeling, predictive analytics and machine learning.
However, for effective operationalization of data models, it would
be more effective to leverage technology companies that can
provide pre-built AI/ML components and tools for standard
healthcare use cases.
How would you best describe your
AI/ML strategy?
Own AI/ML
models
Software
vendors
3rd party
models
Only validated
models
45%
69%
59% 56%
34%
44%
10%
All health systems
Software
vendors
3rd party
models
Own AI/ML
models
86%
57%
57%
Health systems with $4B+ revenue
Revenue <$1B
Revenue >$1B
52019 CitiusTech ‘AI in Healthcare’ Readiness Survey

7. Clinical and operational performance are high on the
healthcare AI/ML priority list4
What we heard
▪ Health systems see clinical performance (80%) as the domain
that is likely to provide the best ROI from AI/ML initiatives
▪ For companies greater than $2B in revenue, 92% of respondents
said that clinical performance would provide the best ROI
▪ Operational performance is the next priority at 64% across all
health systems
Our perspective
Most healthcare organizations that have already begun working on
AI/ML pilots have taken up common use cases under clinical and
operational performance - such as decision support, clinical
documentation, population health, gaps-in-care, utilization, denials,
etc. To derive strong ROI, health systems will need to have
significant availability of high quality data (i.e., strong data
management capabilities), and the ability to integrate AI/ML results
into their clinical and operational workflows
Which domain is likely to provide the
best ROI in AI/ML?
All health systems Revenue > $2B
Clinical
Performance
Operational
Performance
Financial
Performance
80%
92%
64% 67%
44% 42%
6 2019 CitiusTech ‘AI in Healthcare’ Readiness Survey

8. Healthcare organizations have not yet invested
significantly in AI/ML teams5
What we heard
▪ Only 31% of respondents said that their organizations have at
least a core AI/ML team (5 or more members) in place today
▪ The investment in AI/ML teams is much higher in larger health
systems. 50% of health systems with revenue of $2B and above
have 5 or more members in their AI/ML teams
▪ 33% of companies greater than $2B in revenue have larger teams
(between 10 and 50 members)
Our perspective
Most organizations have not yet begun setting up AI/ML teams.
Investments in AI/ML teams are limited to pilot initiatives. As
expected, larger health systems are ahead when it comes to scaling
up their AI/ML talent pool.
High turnover of AI/ML professionals means that small team sizes
carry higher execution risks. Partnering with third party AI/ML
vendors is an alternate option for health systems.
All health systems
69%
less than 5
18%
5 to 10
11%
10 to 20
2%
20 to 50
Health systems with $2B+ revenue
50%
less than 5 17%
5 to 10
25%
10 to 20
8%
20 to 50
72019 CitiusTech ‘AI in Healthcare’ Readiness Survey
How large is your AI/ML team?

9. Healthcare organizations will grow their AI/ML budgets &
teams over the next 3 years6
How large will your AI/ML team likely
be in the next 3 years?
What we heard
▪ 58% of healthcare organizations are expected to have AI/ML
team sizes of 5 or more members, as compared to 31% today
▪ The largest shift in adoption will be among health systems with
$2B or more in revenue. Within this group 50% of companies
would have AI/ML teams with 20 to 50 members
▪ 25% of these companies with $2B+ revenue would have very
large teams (more than 50 members)
Our perspective
Over the next 3 years, mainstream adoption of AI/ML technology by
health systems will lead to greater spends and demand for data
scientists. Finding skills around AI/ML tools, statistical modelling,
predictive analytics, Big Data processing, etc. will become a key
priority. We can expect larger spends on acquiring top talent.
All health systems
42%
less than 5
22%
5 to 10
9%
10 to 20
20%
20 to 50
7%
over 50
Health systems with $2B+ revenue
50%
20 to 50
8%
less than 5
8%
5 to 10
8%
10 to 20
25%
over 50
8 2019 CitiusTech ‘AI in Healthcare’ Readiness Survey

10. Conclusion: AI in healthcare – organizations have not
taken off completely, but are well on their way
Overall, the survey results show that AI/ML is an area
that will see significant investments in the healthcare
industry over the next 3 to 5 years.
A key imperative for healthcare organizations as well as
healthcare technology vendors is to demonstrate strong
ROI-driven use cases that leverage AI techniques to
solve real-world problems. CIOs and IT decision makers
will also need to make a number of strategic choices
around tools and technologies that are best suited for
their unique business challenges.
Success of AI/ML initiatives will depend significantly on
data quality (i.e., strong data management capabilities),
and how effectively AI/ML tools and models are
integrated with clinical and operational workflows.
Consequently, as AI/ML initiatives grow within
healthcare organizations, CIOs need to rethink their
approach to managing data and workflows.
CitiusTech’s Data Science & Consulting Proficiency is
uniquely positioned to help clients drive their AI/ML
strategy across multiple dimensions, including:
Data science consulting
▪ Understanding business problem and workflows
▪ Assessment of current technology and data readiness
Model development
▪ Data cleansing and quality scorecard
▪ Exploratory data analysis (EDA)
▪ Data mining dashboards for actionable insights
▪ Development of custom AI/ML models
Model operationalization
▪ External Model Validation and Model Refinement
▪ Model integration with existing infrastructure
▪ Model output integration with existing workflows &
client applications
92019 CitiusTech ‘AI in Healthcare’ Readiness Survey

11. CitiusTech data science &
consulting proficiency
The Data Science & Consulting Proficiency is a multi-
disciplinary team of 200+ professionals – data scientists,
business analysts, statisticians, data architects, business
process consultants and clinical informatics
professionals. The team has strong expertise across AI
and machine learning applications - NLP, chatbots,
image analytics and Big Data analytics.
Medictiv
Medictiv is an end-to-end suite of analytics tools and
services to assist healthcare organizations in leveraging
their data assets to derive actionable insights.
Medictiv offers strong capabilities for statistical mining,
predictive modeling, machine learning, model lifecycle
management and AI techniques. It enables healthcare
organizations to integrate these techniques into their
day-to-day operations to enhance clinical performance,
derive powerful insights and augment decision making
at the point of care.
Recent Work
Clinical Text Analysis
Developed an NLP system (based on Apache cTAKES)
that extracts and processes clinical notes for structured
output and auto-mapping to clinical codes / standards.
Patient Risk Stratification
Designed and developed a predictive analytics
dashboard to identify high-risk Chronic Kidney Disease
(CKD) patients progressing towards ESRD.
Readmission Management
Developed regression-based predictive model to
improve interventions and reduce readmissions.
Denials Management
Developed statistical models to predict status of claims
and denials, optimize workflows and lower the
probability of denials.
10 2019 CitiusTech ‘AI in Healthcare’ Readiness Survey

12. CitiusTech enables healthcare organizations to drive
clinical value chain excellence, across integration &
interoperability, data management (EDW, Big Data),
performance management (BI / analytics), AI/ML
(predictive analytics, Machine Learning, AI) and digital
engagement (mobile, IoT).
CitiusTech helps customers accelerate innovation in
healthcare through specialized solutions, healthcare
technology platforms, proficiencies and accelerators.
With cutting-edge technology expertise, world-class
service quality and a global resource base, CitiusTech
consistently delivers best-in-class solutions and an
unmatched cost advantage to healthcare
organizations worldwide.
To know more about CitiusTech, visit
www.citiustech.com
CitiusTech: Accelerating
innovation in healthcare
3,500+
healthcare technology
professionals worldwide
200+
data Science &
consulting professionals
700+
performance management
professionals
800+
data management
professionals
1,500+
product engineers
112019 CitiusTech ‘AI in Healthcare’ Readiness Survey

13. 12 2019 CitiusTech ‘AI in Healthcare’ Readiness Survey
CitiusTech Key Contacts
Fernando has 20 years of experience
in Data Science and Mathematics.
He has spent 15+ years in academia,
including Tenured Professorship at
the University of Tennessee. He has
extensive AI/ML experience across
provider, payer, life sciences,
diagnostics and digital advertising.
Prior to CitiusTech, Fernando was
the Chief Data Scientist and Head of
AI at Prognos.ai, a NYC-based
Healthcare startup. He holds a PhD
in Mathematics from Cornell
University and an Engineering
Degree in Applied Mathematics.
Email Fernando at:
fernando.schwartz@citiustech.com
Fernando Schwartz
Vice President
Data Science & Consulting
CitiusTech
Abhay has over 20 years of
professional experience in
healthcare professional services,
population health, ACO, emerging
technologies (Big Data, Cloud,
Mobile Enablement), integration
(EMR, PMS, Claims, Labs, Orders,
etc.), revenue cycle, analytics, supply
chain, enterprise architecture. At
CitiusTech, Abhay heads Account
Management, Sales and Partner
Channels for the Provider market.
He holds a Bachelors degree in
Technology and Masters in Business
Administration.
Email Abhay at:
abhay.singhal@citiustech.com
Abhay Singhal
Vice President
Provider Market
CitiusTech
Dhaval has over 20 years of
experience in healthcare
technology, spanning various
domains including healthcare
interoperability and enterprise
application architecture. At
CitiusTech, Dhaval heads strategic
partnership management for large
healthcare organizations.
Prior to CitiusTech, Dhaval worked
with leading healthcare
organizations and contributed in
different roles such as Research
Engineer, Lead Engineer and Chief
Architect.
Email Dhaval at:
dhaval.shah@citiustech.com
Dhaval Shah
Sr. Vice President
Healthcare Technology
CitiusTech

14. This document is confidential and contains proprietary information, including trade secrets of CitiusTech. Neither the document
nor any of the information contained in it may be reproduced or disclosed to any unauthorized person under any circumstances
without the express written permission of CitiusTech.
www.citiustech.comCopyright 2019 CitiusTech. All Rights Reserved.