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.