Austin and Boxerman's Information Systems For Healthcare Management, Seventh Edition Review

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(More customer reviews)Brand new book. No hassles. Came in a few days. I got it before classes began. Easy purchase. Good price.

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This bestselling text provides managers with the knowledge, skills, and competencies necessary to effectively manage healthcare information systems. Key Features: New and revised material includes: Expanded discussion of strategic planning, including the importance of system integration and IM/IT governance Extensive updates to the project management chapter, including information on establishing a centralized IM/IT portfolio management office (PMO) to improve project success rates A new chapter on the government s role in IM/IT, including the impact of HIPAA and other legislation A new chapter that explains how IM/IT investments are evaluated and provides a framework for conducting these analyses Updated information on the electronic health record and other clinical and administrative applications used in healthcare enterprises A comprehensive profile of hospital IM/IT leadership, including the role of the Chief Information Officer (CIO) Each chapter features learning objectives, web resources, and discussion questions Includes a case that illustrates the design of an EMR in a multi-specialty group practice Includes a glossary that clarifies technical terms

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Biosignal and Medical Image Processing (Signal Processing and Communications) Review

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(More customer reviews)This book assumes that you have both a prior knowledge of Matlab and of signal processing concepts. It spends the first three chapters going over measurement and transducer systems, basic signals and systems, and classical methods of spectral analysis. Even though these chapters are meant to be a quick review, there are some Matlab implementations of basic algorithms in each chapter. Chapter four introduces digital filters and shows Matlab implementations of both IIR and FIR filters. A special treat of chapter four is that some time is spent introducing the reader to the Matab signal processing toolkit. Now that the basics of digital filtering have been introduced, more advanced signal processing techniques are tackled. These include modern methods of spectral analysis and also time-frequency analysis using such methods as the Wigner-Ville distribution. Again, in all cases, the equations are concise, the prose is very accessible, and all concepts are demonstrated using Matlab programs. There is a separate chapter devoted to the wavelet transform and to its use in filter banks, denoising, and feature detection. Quite frankly, I found this chapter far more accessible than entire books that have been devoted to the subject, especially if you are interested in getting to the heart of the matter and using wavelets to perform a task. Two more chapters round out the section of the book on general signal processing techniques- one is about adaptive filters and another on principal component analysis. The final four chapters of the book concern themselves with image processing and Matlab, and the Matlab image processing toolkit in particular. You should already be familiar with the basic concepts behind image processing, and as with the signal processing portion of this book, the point is to have a single text with all of the relevant signal processing techniques briefly described along with Matlab code for the purpose of biosignal processing. However, even if you are not a biomedical engineer, and I am not, you should find this book helpful in the general sense of producing implementations of signal processing concepts. This book would also be helpful for biometric professionals since it goes into great detail on how to turn biological features into measurements that can be processed and compared.

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