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Books > Science & Mathematics > Mathematics > General
Using Predictive Analytics to Improve Healthcare Outcomes Winner of
the American Journal of Nursing (AJN) Informatics Book of the Year
Award 2021! Discover a comprehensive overview, from established
leaders in the field, of how to use predictive analytics and other
analytic methods for healthcare quality improvement. Using
Predictive Analytics to Improve Healthcare Outcomes delivers a
16-step process to use predictive analytics to improve operations
in the complex industry of healthcare. The book includes numerous
case studies that make use of predictive analytics and other
mathematical methodologies to save money and improve patient
outcomes. The book is organized as a "how-to" manual, showing how
to use existing theory and tools to achieve desired positive
outcomes. You will learn how your organization can use predictive
analytics to identify the most impactful operational interventions
before changing operations. This includes: A thorough introduction
to data, caring theory, Relationship-Based Care(R), the Caring
Behaviors Assurance System(c), and healthcare operations, including
how to build a measurement model and improve organizational
outcomes. An exploration of analytics in action, including
comprehensive case studies on patient falls, palliative care,
infection reduction, reducing rates of readmission for heart
failure, and more--all resulting in action plans allowing
clinicians to make changes that have been proven in advance to
result in positive outcomes. Discussions of how to refine quality
improvement initiatives, including the use of "comfort" as a
construct to illustrate the importance of solid theory and good
measurement in adequate pain management. An examination of
international organizations using analytics to improve operations
within cultural context. Using Predictive Analytics to Improve
Healthcare Outcomes is perfect for executives, researchers, and
quality improvement staff at healthcare organizations, as well as
educators teaching mathematics, data science, or quality
improvement. Employ this valuable resource that walks you through
the steps of managing and optimizing outcomes in your clinical care
operations.
Most of our everyday life experiences are multisensory in nature;
that is, they consist of what we see, hear, feel, taste, smell, and
much more. Almost any experience you can think of, such as eating a
meal or going to the cinema, involves a magnificent sensory world.
In recent years, many of these experiences have been increasingly
transformed and capitalised on through advancements that adapt the
world around us - through technology, products, and services - to
suit our ever more computerised environment. Multisensory
Experiences: Where the senses meet technology looks at this trend
and offers a comprehensive introduction to the dynamic world of
multisensory experiences and design. It takes the reader from the
fundamentals of multisensory experiences, through the relationship
between the senses and technology, to finally what the future of
those experiences may look like, and our responsibility in it. This
book empowers you to shape your own and other people's experiences
by considering the multisensory worlds that we live in through a
journey that marries science and practice. It also shows how we can
take advantage of the senses and how they shape our experiences
through intelligent technological design.
From Euclidian to Hilbert Spaces analyzes the transition from
finite dimensional Euclidian spaces to infinite-dimensional Hilbert
spaces, a notion that can sometimes be difficult for
non-specialists to grasp. The focus is on the parallels and
differences between the properties of the finite and infinite
dimensions, noting the fundamental importance of coherence between
the algebraic and topological structure, which makes Hilbert spaces
the infinite-dimensional objects most closely related to Euclidian
spaces. The common thread of this book is the Fourier transform,
which is examined starting from the discrete Fourier transform
(DFT), along with its applications in signal and image processing,
passing through the Fourier series and finishing with the use of
the Fourier transform to solve differential equations. The
geometric structure of Hilbert spaces and the most significant
properties of bounded linear operators in these spaces are also
covered extensively. The theorems are presented with detailed
proofs as well as meticulously explained exercises and solutions,
with the aim of illustrating the variety of applications of the
theoretical results.
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