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Ex Vivo Lung Perfusion-
‘Lungs in a Box’ 26
PAGE
08
PAGE
Promising Role of AI in Imaging
Opportunities and Transformations
REZA FORGHANI
MD, PhD, Prof of Radiology & Artificial Intelligence (AI)
and Vice Chair of AI, Director, Radiomics & Augmented Intelligence Laboratory
Sponsor
ISSUE 04 2024 www.americanhhm.com
TRANSFORMING HEALTHCARE
THROUGH ARTIFICIAL INTELLIGENCE
& THE WAY AHEAD
3
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Artificial intelligence (AI) is set to revolutionize the future
of healthcare delivery, promising enhancements in
quality, personalized medicine, and potential cost
efficiency. However, achieving these benefits requires a
transformative approach to hospital health IT, leveraging
advanced third-party platforms to fully harness AI's
potential.
Traditional IT systems are inadequate for the
digital transformation. This transformation is needed
to integrate AI tools into healthcare effectively. The
future of healthcare demands sophisticated third-party
AI deployment and management platforms that enable
seamless, cost-effective, and timely integration of AI
technologies.
These platforms are crucial for several reasons.
They enable optimal selection of AI tools, ensuring that
healthcare providers can implement the most effective
solutions. Additionally, they form the foundation for
adopting more advanced AI tools in the future, while
also ensuring robust safety monitoring.
The transformative potential of AI in healthcare is
already evident, with a growing number of FDA-cleared
or classified medical AI applications, particularly in
diagnostic medical imaging and radiology. These tools
are set to multiply and evolve in complexity over the
next five years, driven by rapid advancements in AI and
technology. While the experimental applications of AI
in medicine often make headlines, the real challenge
lies in overcoming practical adoption and workflow
barriers in clinical practice.
Early experiences with AI tools have highlighted
significant challenges in timely and seamless integration
within clinical settings. As healthcare operations
N D Vijaya Lakshmi
Editor
How AI is Redefining Healthcare with IT Innovations
become increasingly digital and AI-supported, one
of the most fundamental challenges will be the efficient
deployment and management of a growing array of
complex AI tools. This article delves into the limitations
of traditional IT approaches and explores the potential
of third-party AI deployment and orchestration platforms
to support seamless, safe, and effective adoption of
AI in healthcare.
We invite you to explore the future of healthcare with
us in this edition of American Hospital & Healthcare
Management magazine. Join us on a journey where AI
turns possibilities into realities, transforming healthcare
delivery for future generations. Thank you for being
a vital part of the AmericanHHM community. We
eagerly anticipate the continued exchange of ideas
and knowledge as we navigate the ever-evolving global
healthcare landscape together.
If you have a perspective, an idea, or a story to
share, we welcome your contributions in our upcoming
issues. Whether it's an article on emerging trends, an
interview with a thought leader, or a unique insight into
the healthcare ecosystem, your wisdom can guide
others on their healthcare journey. We want to hear
from you via email at editorial@americanhhm.com.
Stay tuned for more in upcoming editions!
4AMERICAN HOSPITAL & HEALTHCARE MANAGEMENT ISSUE 04 - 2024
CONTENTS
CoverStory
HEALTHCARE MANAGEMENT
08 Promising Role of AI in Imaging
Opportunities and Transformations
W. Alex Campbell, Division of Vascular and Interventional
Radiology, Department of Radiology, University of Virginia
Mina S. Makary, Division of Vascular and Interventional
Radiology, Department of Radiology, The Ohio State
University Wexner Medical Center
MEDICAL SCIENCES
14 Use of Cognitive Enhancers in Patients
with Disorders of Consciousness
Benjamin Wai Yue Lo, MD PhD FRCSC, Queen Mary
Hospital, Hong Kong Hospital Authority
18 Human Hearts Cannot Swim
Thomas N Muziani, President and CEO of HEMO-STAT
Blood Management Consulting
26 Ex Vivo Lung Perfusion- ‘Lungs in a Box’
Anitha Chandrasekhar, Clinical Lead- Lung
Bioengineering & Organ Procurement, Northwestern
Medicine
SURGICAL SPECIALITY
34 Vascular Surgery in the Hybrid Age
Jakob Nowotny, Senior Vascular and Endovascular
Surgeon, Department of Vascular surgery, Sha'are Tzedek
Medical Center
TECHNOLOGY, EQUIPMENT &
DEVICES
46 A New Era in Patient Care
AI Transforms Electronic Health Records
Vijay Adapala, EVP & GM Global Supply Partners,
Doceree
EXPERT TALK
58 Enhancing Patient Care through Point of
Care Ultrasound
Ai Phi Thuy Ho, Cardiology Specialist, Hospital Kalnes
Trust
35
REZA FORGHANI
MD, PhD, Prof of Radiology & Articial
Intelligence (AI) and Vice Chair of AI,
Director, Radiomics & Augmented
Intelligence Laboratory
TRANSFORMING
HEALTHCARE
THROUGH
ARTIFICIAL
INTELLIGENCE &
THE WAY AHEAD
64 Quality Management in Healthcare
Hassan Mostafa, Quality Manager, DEEF Pharmaceutical
72 THROUGH THE HOURGLASS
78 EVENTS LIST
80 EVENT PREVIEW
88 NEWS
DIAGNOSTICS
42 The Evolution of Ultrasound Technology
and the Rise of Point of Care Ultrasound
Ai Phi Thuy Ho, cardiology Specialist, Hospital Kalnes
Trust
52 AMERICAN HOSPITAL & HEALTHCARE MANAGEMENT ISSUE 04 - 2024
Artificial intelligence (AI) is likely to transform healthcare
delivery of the future, increasing quality, personalized
medicine, and potentially cost efficiency. However,
successful deployment, orchestration, and safety
monitoring of this technology will likely require rethinking
of hospital health IT and leveraging of third-party
platforms for AI to achieve its full potential.
Reza Forghani
MD, PhD, Prof of Radiology & Artificial Intelligence (AI) and Vice Chair
of AI, Director, Radiomics & Augmented Intelligence Laboratory
or radiological images. These are furthermore
likely to multiply in the next 5 years. They
are also likely to increase in complexity and
sophistication, fueled by the rapidly evolving
AI landscape and technological advances, most
recently exemplified by large language and
Artificial Intelligence (AI) is likely
to transform the healthcare practice
of the future. There are steadily
increasing FDA cleared or classified medical
AI applications, and currently over half of these
tools are based on diagnostic medical imaging
Artificial Intelligence
Transformation of
Healthcare AI of the Future
Revisiting Approach to Healthcare IT
and Use of Third-party Platforms
CoverStory
53
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other emerging multimodal foundation
models. While there are many interesting
and headline grabbing applications of
AI tools in medicine in experimental
preclinical settings, the practical adoption
and workflow barriers that must be
overcome for successful adoption of
such tools in clinical practice are
frequently overlooked. Notwithstanding
the potential of this technology, early
experience with AI tools has revealed
significant challenges for timely and
seamless AI adoption in the clinical. In
an increasingly digital and AI-supported
healthcare operation, one of the most
basic foundational challenges for
healthcare organizations will be seamless
and timely deployment and management
Traditional or legacy IT approaches for AI
tool adoption are not cost-effective and
unlikely to be able to effectively support
the anticipated digital transformation
and adoption of multiple AI tools for
leading-edge and competitive healthcare
organizations in the future
There are increasingly sophisticated third-
party AI deployment and management
platforms that enable seamless, cost-
effective, and timely AI deployment and
facilitate optimal AI tool selection; these are
also likely to form the foundation for future
more advanced AI tool adoption and safety
monitoring
Healthcare leaders should become
familiar and consider adoption of AI
deployment platforms as part of their digital
transformation roadmap and future AI
infrastructure planning
KEY POINTS
COVER STORY
54 AMERICAN HOSPITAL & HEALTHCARE MANAGEMENT ISSUE 04 - 2024
of an increasing number of complex AI tools.
In this article, the challenges with traditional or
legacy information technology (IT) approaches
and the potential utility of third-party AI
deployment and orchestration platforms will
be discussed for empowering and supporting
seamless deployment, orchestration, and safe
adoption of this technology in the healthcare
environment.
Current state
It is fair to state that traditionally, hospital IT
systems are frequently under-resourced. If the
healthcare of the future is going to be truly
revolutionized and transformed using digital
technologies and AI, the status quo will no
longer be sustainable or acceptable. Under the
current legacy health IT paradigm, AI software
such as those used in radiology are procured
and technically deployed one at a time. This
is repeated over and over every time a new AI
tool is acquired. The process is painstakingly
slow, laborious, and costly to the organization,
both from the perspective of resources utilized
for procurement and implementation of each
AI tool as well as the opportunity cost of
hindering trials to determine the optimal tool
for the organization and delaying the adoption
of potentially transformative technology. Even
in the traditional setting, this legacy approach
has not been optimal and its negative impact
on the organization’s progress and competitive
edge is likely underestimated. However, in a
potential future world where hospitals may
have to adopt and manage tens if not hundreds
of AI tools, the legacy approach will simply
not be sustainable. In addition to resources
required for deployment and management of
these tools, at some point, the complexity may
exceed what can be reasonably expected of a
hospital IT department. Afterall, hospitals are
not IT companies, and the point may come when
they have neither the resources nor the full
complement of necessary technical expertise
in order to effectively manage and increasingly
complex AI landscape. One likely solution
that is increasingly being considered for these
fundamental challenges is the strategic use of
third-party AI deployment and orchestration
platforms that help mitigate these problems.
Platforms for AI deployment and
orchestration
Over the past few years, an increasing number
of platforms have become available that can
facilitate the deployment and orchestration
of AI tools. These platforms are becoming
increasingly sophisticated and can represent
a solution and a fundamental framework
facilitating seamless, timely, and cost-effective
adoption of multiple AI applications in the
healthcare setting. It is neither the intention
nor within the scope of this article to discuss
individual platforms, their advantages, and
pitfalls. Rather, the purpose of this article
is to provide an overview and bring these
platforms and their importance to the attention
of key stakeholders within the healthcare
organization’s leadership as part of their
necessary infrastructure planning for the future
COVER STORY
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Increasingly sophisticated third-
party AI deployment platforms
enable seamless, cost-effective,
and timely AI adoption, providing
one important cornerstone for
digital AI transformation of future
healthcare organizations.
digital transformation and adoption of AI in
their respective organizations.
There are currently several different
vendors providing advanced platforms that
can be used to deploy, manage and orchestrate
AI tools. Currently, the primary focus of
many of these platforms is the deployment of
diagnostic imaging AI applications but these
have the potential, and their functionalities
can be expanded in the future, in order to
integrate other modalities and data types.
These platforms come with different
configurations, but all of the major ones
have the core functionality of being able
to host and effortlessly deploy multiple
AI algorithms from the standpoint of the
healthcare institution. Depending on the
platform, they can also have other key
functionalities that range from image routing
to sophisticated abilities to orchestrate and
integrate various applications that form the
foundation for effective deployment and
use of increasingly sophisticated AI tools,
including multi-modal applications. For the
purposes of this article, the focus will be on
the basic but fundamental advantages of these
platforms in facilitating cost effective and
timely adoption of multiple AI applications
and empowering the organization to try
and select the optimal AI application that
serves its unique needs, followed by a brief
overview of other current and potential future
fundamental advantages of these platforms.
Seamless, cost-effective, and timely
AI deployment using platforms
Timely deployment of AI tools remains a
major challenge under the current legacy
healthcare IT paradigm. It is not unusual for
the process of procuring and making an AI
application available to clinician providers
to take 3 to 6 months, if not more. From an
implementation cost perspective, ballpark
estimates are that the resources required to
get each AI tool through this process, including
security review and integration of the tool, can
cost an organization in the range of 15,000 to
20,000$ or more per application. There are in
addition indirect and opportunity costs for such
delays. One negative downstream effect is the
delay in deploying cutting edge tools that can
improve healthcare processes and patient care.
There are also other negative effects, including
friction within the organization that arises
from such delays and an overall contribution
to staff and provider dissatisfaction. The costs
and challenges are compounded and multiply
COVER STORY
56 AMERICAN HOSPITAL & HEALTHCARE MANAGEMENT ISSUE 04 - 2024
with an increasing number of AI tools, making
the legacy approach unsustainable. All of this
can have a substantially negative impact an
organization’s operations and competitive edge.
When using a platform, on the other hand, the
“heavy lifting” is done once. Once a platform is
reviewed and securely integrated and adopted
by an organization, deploying individual AI
software can be as seamless as tuning on an
App, making a tool immediately available and
avoiding the repetitive, laborious, and costly
approach of legacy one at a time, standalone AI
tool adoption. This will enable the competitive
organization to lead in technology adoption.
Empowering the organization to
select the optimal AI application
AI tool performance can vary at different
hospital sites. Furthermore, for a given tool
performing a similar task, there are unique
aspects both from a diagnostic performance
and operational/workflow standpoint that
can make a given tool more or less desirable
depending on the specific hospital/organization’s
unique needs. One important direct benefit of
enabling effortless AI application deployment
through a platform is that it empowers the
organization and providers to try different
applications, providing them with first-hand
knowledge and exposure to functionality of
a given tool within that organization. Using
legacy approaches, trials (even if provided at
no cost by the vendor) are resource intensive
and costly to the organization, essentially
requiring the same degree resources that is
required for full adoption of a standalone
software discussed earlier. This disincentivizes
trials, especially when multiple applications
are being considered, and ultimately may
lead to procurement of a less-than-optimal
tool. By making application trials effortless
and less resource intensive, AI deployment
platforms enable seamless evaluation of
multiple applications, ultimately enabling
the organization to select the tool that is best
suited to the needs of the organization and its
providers.
Other benefits and a foundation for
AI of the future
Although a detailed discussion is beyond the
scope of this article, the ideal platform can
have many additional advantages that support
technology adoption and transformation of a
healthcare organization. Platforms support
interoperability and have the potential to
make AI applications more effective using
sophisticated image/date routing. They facilitate
management and integration of multiple
applications and can potentially support
multimodal applications that include both image
analysis and language or reporting tasks. Lastly,
with increasingly complex AI applications,
including development of biomarker tools that
perform tasks such as predicting a molecular
phenotype or treatment response, there will be a
requirement for more robust quality monitoring
tools. One can envisage platforms playing a
fundamental role in this essential requirement
of future advanced AI models. These are few
COVER STORY
57
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among a range of potential possible benefits
of using platforms for safe and effective AI
deployment and management.
Conclusion
Digital technologies and AI are likely to
play an essential role in the healthcare of the
future. Although much attention has been
paid to specific applications and clinical or
healthcare process-related tasks they perform,
the essential infrastructure investments and
planning that form the foundation and are
required for successful adoption of these
technologies in clinical practice does not always
get the necessary attention. To ensure that a
healthcare organization is competitive and leads
Dr. Reza Forghani is a
radiologist and researcher with
medical Artificial Intelligence
(AI) expertise. He is Professor
of Radiology & AI and Vice
Chair of AI at the Department of
Radiology, University of Florida
College of Medicine where he is
also the director of Radiomics
& Augmented Intelligence
Laboratory (RAIL).
AUTHOR BIO
in technology adoption, it is imperative that
its leaders make the necessary infrastructure
planning and investments that will facilitate
successful future digital transformation and
AI tool adoption. In that regard, legacy IT
approaches for AI tool deployment may not
be viable or satisfactory for leading clinics
and hospital systems. AI deployment and
orchestration platforms can provide the
necessary infrastructure to make seamless,
effective and timely AI tool adoption possible in
clinical practice. Healthcare organization leaders
would benefit from familiarizing themselves
and considering adoption of such platforms
as part of their digital transformation and AI
roadmap.
COVER STORY
