Abc Of Health Informatics
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ABC of Health Informatics
New addition to the ABC series looking at how technology can aidhealth care This ABC focuses on how patient data, health knowledge, andlocal service information are managed during the routine tasks thatmake up clinical work. It looks at medical record keeping, how touse the information that records contain for clinical, qualityimprovement and research activities, how to use new media tocommunicate with clinical colleagues and patients, and theavailability and uses of clinical knowledge resources. After a short introduction to health informatics, each chapteris organised around a typical patient scenario that illustratesinformation dilemmas arising in clinical consultations. These casestudies help make the link between prescribing and treatment. A final chapter considers the implications of informatics andeHealth for the future of the health professions and their work. Italso includes a glossary of health informatics terms. Click on the sample chapter above for a look at what is healthinformation.
Guide to Health Informatics
This essential text provides a readable yet sophisticated overview of the basic concepts of information technologies as they apply in healthcare. Spanning areas as diverse as the electronic medical record, searching, protocols, and communications as well as the Internet, Enrico Coiera has succeeded in making this vast and complex area accessible and understandable to the non-specialist, while providing everything that students of medical informatics need to know to accompany their course.
Health Informatics: A Computational Perspective in Healthcare
This book presents innovative research works to demonstrate the potential and the advancements of computing approaches to utilize healthcare centric and medical datasets in solving complex healthcare problems. Computing technique is one of the key technologies that are being currently used to perform medical diagnostics in the healthcare domain, thanks to the abundance of medical data being generated and collected. Nowadays, medical data is available in many different forms like MRI images, CT scan images, EHR data, test reports, histopathological data and doctor patient conversation data. This opens up huge opportunities for the application of computing techniques, to derive data-driven models that can be of very high utility, in terms of providing effective treatment to patients. Moreover, machine learning algorithms can uncover hidden patterns and relationships present in medical datasets, which are too complex to uncover, if a data-driven approach is not taken. With the help of computing systems, today, it is possible for researchers to predict an accurate medical diagnosis for new patients, using models built from previous patient data. Apart from automatic diagnostic tasks, computing techniques have also been applied in the process of drug discovery, by which a lot of time and money can be saved. Utilization of genomic data using various computing techniques is another emerging area, which may in fact be the key to fulfilling the dream of personalized medications. Medical prognostics is another area in which machine learning has shown great promise recently, where automatic prognostic models are being built that can predict the progress of the disease, as well as can suggest the potential treatment paths to get ahead of the disease progression.