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This book takes an in-depth look at the emerging technologies that
are transforming the way clinicians manage patients, while at the
same time emphasizing that the best practitioners use both
artificial and human intelligence to make decisions. AI and machine
learning are explored at length, with plain clinical English
explanations of convolutional neural networks, back propagation,
and digital image analysis. Real-world examples of how these tools
are being employed are also discussed, including their value in
diagnosing diabetic retinopathy, melanoma, breast cancer, cancer
metastasis, and colorectal cancer, as well as in managing severe
sepsis. With all the enthusiasm about AI and machine learning, it
was also necessary to outline some of criticisms, obstacles, and
limitations of these new tools. Among the criticisms discussed: the
relative lack of hard scientific evidence supporting some of the
latest algorithms and the so-called black box problem. A chapter on
data analytics takes a deep dive into new ways to conduct subgroup
analysis and how it's forcing healthcare executives to rethink the
way they apply the results of large clinical trials to everyday
medical practice. This re-evaluation is slowly affecting the way
diabetes, heart disease, hypertension, and cancer are treated. The
research discussed also suggests that data analytics will impact
emergency medicine, medication management, and healthcare costs. An
examination of the diagnostic reasoning process itself looks at how
diagnostic errors are measured, what technological and cognitive
errors are to blame, and what solutions are most likely to improve
the process. It explores Type 1 and Type 2 reasoning methods;
cognitive mistakes like availability bias, affective bias, and
anchoring; and potential solutions such as the Human Diagnosis
Project. Finally, the book explores the role of systems biology and
precision medicine in clinical decision support and provides
several case studies of how next generation AI is transforming
patient care.
The complex challenges facing healthcare require innovative
solutions that can make patient care more effective, easily
available, and affordable. One such solution is the digital
reconstruction of medicine that transitions much of patient care
from hospitals, clinics, and offices to a variety of virtual
settings. This reconstruction involves telemedicine,
hospital-at-home services, mobile apps, remote sensing devices,
clinical data analytics, and other cutting-edge technologies. The
Digital Reconstruction of Healthcare: Transitioning from Brick and
Mortar to Virtual Care takes a deep dive into these tools and how
they can transform medicine to meet the unique needs of patients
across the globe. This book enables readers to peer into the very
near future and prepare them for the opportunities afforded by the
digital shift in healthcare. It is also a wake-up call to readers
who are less than enthusiastic about these digital tools and helps
them to realize the cost of ignoring these tools. It is written for
a wide range of medical professionals including: Physicians,
nurses, and entrepreneurs who want to understand how to use or
develop digital products and services IT managers who need to fold
these tools into existing computer networks at hospitals, clinics,
and medical offices Healthcare executives who decide how to invest
in these platforms and products Insurers who need to stay current
on the latest trends and the evidence to support their cost
effectiveness Filled with insights from international experts, this
book also features Dr. John Halamka's lessons learned from years of
international consulting with government officials on digital
health. It also taps into senior research analyst Paul Cerrato's
expertise in AI, data analytics, and machine learning. Combining
these lessons learned with an in-depth analysis of clinical
informatics research, this book aims to separate hyped AI
"solutions" from evidence-based digital tools. Together, these two
pillars support the contention that these technologies can, in
fact, help solve many of the seemingly intractable problems facing
healthcare providers and patients.
The complex challenges facing healthcare require innovative
solutions that can make patient care more effective, easily
available, and affordable. One such solution is the digital
reconstruction of medicine that transitions much of patient care
from hospitals, clinics, and offices to a variety of virtual
settings. This reconstruction involves telemedicine,
hospital-at-home services, mobile apps, remote sensing devices,
clinical data analytics, and other cutting-edge technologies. The
Digital Reconstruction of Healthcare: Transitioning from Brick and
Mortar to Virtual Care takes a deep dive into these tools and how
they can transform medicine to meet the unique needs of patients
across the globe. This book enables readers to peer into the very
near future and prepare them for the opportunities afforded by the
digital shift in healthcare. It is also a wake-up call to readers
who are less than enthusiastic about these digital tools and helps
them to realize the cost of ignoring these tools. It is written for
a wide range of medical professionals including: Physicians,
nurses, and entrepreneurs who want to understand how to use or
develop digital products and services IT managers who need to fold
these tools into existing computer networks at hospitals, clinics,
and medical offices Healthcare executives who decide how to invest
in these platforms and products Insurers who need to stay current
on the latest trends and the evidence to support their cost
effectiveness Filled with insights from international experts, this
book also features Dr. John Halamka's lessons learned from years of
international consulting with government officials on digital
health. It also taps into senior research analyst Paul Cerrato's
expertise in AI, data analytics, and machine learning. Combining
these lessons learned with an in-depth analysis of clinical
informatics research, this book aims to separate hyped AI
"solutions" from evidence-based digital tools. Together, these two
pillars support the contention that these technologies can, in
fact, help solve many of the seemingly intractable problems facing
healthcare providers and patients.
This book takes an in-depth look at the emerging technologies that
are transforming the way clinicians manage patients, while at the
same time emphasizing that the best practitioners use both
artificial and human intelligence to make decisions. AI and machine
learning are explored at length, with plain clinical English
explanations of convolutional neural networks, back propagation,
and digital image analysis. Real-world examples of how these tools
are being employed are also discussed, including their value in
diagnosing diabetic retinopathy, melanoma, breast cancer, cancer
metastasis, and colorectal cancer, as well as in managing severe
sepsis. With all the enthusiasm about AI and machine learning, it
was also necessary to outline some of criticisms, obstacles, and
limitations of these new tools. Among the criticisms discussed: the
relative lack of hard scientific evidence supporting some of the
latest algorithms and the so-called black box problem. A chapter on
data analytics takes a deep dive into new ways to conduct subgroup
analysis and how it's forcing healthcare executives to rethink the
way they apply the results of large clinical trials to everyday
medical practice. This re-evaluation is slowly affecting the way
diabetes, heart disease, hypertension, and cancer are treated. The
research discussed also suggests that data analytics will impact
emergency medicine, medication management, and healthcare costs. An
examination of the diagnostic reasoning process itself looks at how
diagnostic errors are measured, what technological and cognitive
errors are to blame, and what solutions are most likely to improve
the process. It explores Type 1 and Type 2 reasoning methods;
cognitive mistakes like availability bias, affective bias, and
anchoring; and potential solutions such as the Human Diagnosis
Project. Finally, the book explores the role of systems biology and
precision medicine in clinical decision support and provides
several case studies of how next generation AI is transforming
patient care.
The Transformative Power of Mobile Medicine: Leveraging Innovation,
Seizing Opportunities, and Overcoming Obstacles of mHealth
addresses the rapid advances taking place in mHealth and their
impact on clinicians and patients. It provides guidance on reliable
mobile health apps that are based on sound scientific evidence,
while also offering advice on how to stay clear of junk science.
The book explores the latest developments, including the value of
blockchain, the emerging growth of remote sensors in chronic
patient care, the potential use of Amazon Alexa and Google
Assistant as patient bedside assistants, the use of Amazon's IoT
button, and much more. This book enables physicians and nurses to
gain a deep understanding of the strengths and weaknesses of mobile
health and helps them choose evidence-based mobile medicine tools
to improve patient care.
Protecting Patient Information: A Decision-Maker's Guide to Risk,
Prevention, and Damage Control provides the concrete steps needed
to tighten the information security of any healthcare IT system and
reduce the risk of exposing patient health information (PHI) to the
public. The book offers a systematic, 3-pronged approach for
addressing the IT security deficits present in healthcare
organizations of all sizes. Healthcare decision-makers are shown
how to conduct an in-depth analysis of their organization's
information risk level. After this assessment is complete, the book
offers specific measures for lowering the risk of a data breach,
taking into account federal and state regulations governing the use
of patient data. Finally, the book outlines the steps necessary
when an organization experiences a data breach, even when it has
taken all the right precautions.
Realizing the Promise of Precision Medicine: The Role of Patient
Data, Mobile Technology, and Consumer Engagement explains the
potential of personalized medicine and the value of those
approaches in making that potential a reality. The book helps
transform one-size-fits-all healthcare into a system that focuses
on individual needs and the unique needs of each family member,
discussing topics such as U.S. sponsored precision medicine
initiative, genomics, the role of electronic health records and
mobile medicine, patient engagement and empowerment, health
information exchange and patient data protection. In addition, the
book discusses the barriers and limitations of precision medicine
and how to overcome them. Readers will find valuable insights into
how big data, patient engagement, mobile technology, and genomics
help individualize medical care and offer a pathway to help detect
many undiscovered causes of diseases.
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