Recent technological innovations - influenced primarily by the development of more sophisticated, faster and cheaper computer systems - permitted also the evolution of more affordable systems for Patient Data Management, so called PDM-Systems. The experience of the authors, on one of the first PDMS installation sites in Europe, shows that the purchase of such a system is not an easy task, since accurate data are not available in a comparable format. Therefore the first part of the book is devoted to a comparison of already installed, commercially distributed bedside based PDMS with regard to their specifications, functions and performance. The methods included a questionnaire with detailed questions for the vendors to answer and a "table of functions” comparing the most important functions which should be included in a PDMS. With that list the different systems (which were all in clinical use) were checked for the availability and the way of use of these functions. To evaluate variations in the systems performance an "information retrieval test” was designed and executed. In the second part the different vendors, whose systems were included in the study, were to describe the systems from their viewpoints. The third part contains papers describing the users’ experiences. The fourth and last part shows how to use PDMS-data for scientific and therapeutic purposes including two papers on clinical expert systems. Thus, this book provides valuable information for clinicians and hospital managers who have to decide on the purchase of a Patient Data Management System.

This User’s Guide is intended to support the design, implementation, analysis, interpretation, and quality evaluation of registries created to increase understanding of patient outcomes. For the purposes of this guide, a patient registry is an organized system that uses observational study methods to collect uniform data (clinical and other) to evaluate specified outcomes for a population defined by a particular disease, condition, or exposure, and that serves one or more predetermined scientific, clinical, or policy purposes. A registry database is a file (or files) derived from the registry. Although registries can serve many purposes, this guide focuses on registries created for one or more of the following purposes: to describe the natural history of disease, to determine clinical effectiveness or cost-effectiveness of health care products and services, to measure or monitor safety and harm, and/or to measure quality of care. Registries are classified according to how their populations are defined. For example, product registries include patients who have been exposed to biopharmaceutical products or medical devices. Health services registries consist of patients who have had a common procedure, clinical encounter, or hospitalization. Disease or condition registries are defined by patients having the same diagnosis, such as cystic fibrosis or heart failure. The User’s Guide was created by researchers affiliated with AHRQ’s Effective Health Care Program, particularly those who participated in AHRQ’s DEcIDE (Developing Evidence to Inform Decisions About Effectiveness) program. Chapters were subject to multiple internal and external independent reviews.

"The database and simulator were developed in C and implemented under the OS/2 operating system environment. The database is based on the OS/2 Extended Edition relational Database Manager." --

In Medical Informatics three types of processes play a central role: organizational, patient-related and decision making related processes. The first type deals with settings, such as a hospital care setting or a primary care setting; the second is related to health and disease (i.e. to patients); the third type of process aims at assisting in decision making and therapy and evolves in the brains of health care professionals. Hence, in all domains data, information and knowledge play a key role. As these three processes evolve, dealing with individuals - patients, doctors and nurses - because.

Most industries have plunged into data automation, but health care organizations have lagged in moving patients' medical records from paper to computers. In its first edition, this book presented a blueprint for introducing the computer-based patient record (CPR). The revised edition adds new information to the original book. One section describes recent developments, including the creation of a computer-based patient record institute. An international chapter highlights what is new in this still-emerging technology. An expert committee explores the potential of machine-readable CPRs to improve diagnostic and care decisions, provide a database for policymaking, and much more, addressing these key questions: Who uses patient records? What technology is available and what further research is necessary to meet users' needs? What should government, medical organizations, and others do to make the transition to CPRs? The volume also explores such issues as privacy and confidentiality, costs, the need for training, legal barriers to CPRs, and other key topics.

Healthcare is an industry that has seen great advancements in personalized services through big data analytics. Despite the application of smart devices in the medical field, the mass volume of data that is being generated makes it challenging to correctly diagnose patients. This has led to the implementation of precise algorithms that can manage large amounts of information and successfully use smart living in medical environments. Professionals worldwide need relevant research on how to successfully implement these smart technologies within their own personalized healthcare processes. Applications of Deep Learning and Big IoT on Personalized Healthcare Services is a pivotal reference source that provides a collection of innovative research on the analytical methods and applications of smart algorithms for the personalized treatment of patients. While highlighting topics including cognitive computing, natural language processing, and supply chain optimization, this book is ideally designed for network designers, analysts, technology specialists, medical professionals, developers, researchers, academicians, and post-graduate students seeking relevant information on smart developments within individualized healthcare.