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This monograph series is intended to provide medical information
scien tists, health care administrators, health care providers, and
computer sci ence professionals with successful examples and
experiences of computer applications in health care settings.
Through the exposition of these com puter applications, we attempt
to show what is effective and efficient and hopefully provide some
guidance on the acquisition or design of informa tion systems so
that costly mistakes can be avoided. The health care industry is
currently being pushed and pulled from all directions - from the
clinical side to increase quality of care, from the busi ness side
to improve financial stability, from the legal and regulatory sides
to provide more detailed documentation, and, in a university
environment, to provide more data for research and improved
opportunities for educa tion. Medical information systems sit in
the middle of all these demands. They are not only asked to provide
more, better, and more timely informa tion but also to interact
with and monitor the process of health care itself by providing
clinical reminders, warnings about adverse drug interactions,
alerts to questionable treatment, alarms for security breaches,
mail mes sages, workload schedules, etc. Clearly, medical
information systems are functionally very rich and demand quick
response time and a high level of security. They can be classified
as very complex systems and, from a devel oper's perspective, as
'risky' systems."
Modern critical care is characterized by the collection of large
volumes of data and the making of urgent patient care decisions.
The two do not necessarily go together easily. For many years the
hope has been that ICU data management systems could play a
meaningful role in ICU decision support. These hopes now have a
basis in fact, and this book details the history, methodology,
current status, and future prospects for critical care decision
support systems. By focusing on real and operational systems, the
book demonstrates the importance of integrating data from diverse
clinical data sources; the keys to implementing clinically usable
systems; the pitfalls to avoid in implementation; and the
development of effective evaluation methods.
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