Knowledge Engineering In Health Informatics
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Knowledge Engineering in Health Informatics
Author: Homer R. Warner
language: en
Publisher: Springer Science & Business Media
Release Date: 2012-12-06
This monograph series is intended to provide medical information scien tists, health care administrators, physicians, nurses, other health care pro viders, and computer science professionals with successful examples and experiences of computer applications in health care settings. Through these computer applications, we attempt to show what is effective and efficient, and hope to provide guidance on the acquisition or design of medical information systems so that costly mistakes can be avoided. Health care provider organizations such as hospitals and clinics are experiencing large demands for clinical information because of a transition from a "fee-for-service" to a "capitation-based" health care economy. This transition changes the way health care services are being paid for. Previ ously, nearly all health care services were paid for by insurance companies after the services were performed. Today, many procedures need to be pre approved and many charges for clinical services must be justified to the insurance plans. Ultimately, in a totally capitated system, the more patient care services are provided per patient, the less profitable the health care provider organization will be. Clearly, the financial risks have shifted from the insurance carriers to the health care provider organizations. For hospitals and clinics to assess these financial risks, management needs to know what services are to be provided and how to reduce them without impacting the quality of care. The balancing act of reducing costs but maintaining health care quality and patient satisfaction requires accurate information about the clinical services.
Healthcare Knowledge Management
Author: Rajeev Bali
language: en
Publisher: Springer Science & Business Media
Release Date: 2010-05-30
Healthcare practitioners and managers increasingly find themselves in clinical situations where they have to think fast and process myriad diagnostic test results, medications and past treatment responses in order to make decisions. Effective problem solving in the clinical environment or classroom simulated lab depends on a healthcare professional's immediate access to fresh information. Unable to consult a library for information, the healthcare practitioner must learn to effectively manage knowledge while thinking on their toes. Knowledge Management (KM) holds the key to this dilemma in the healthcare environment. KM places value on the tacit knowledge that individuals hold within an institution and often makes use of IT to free up the collective wisdom of individuals within an organization. Healthcare Knowledge Management: Issues, Advances and Successes will explore the nature of KM within contemporary healthcare institutions and associated organizations. It will provide readers with an understanding of approaches to the critical nature and use of knowledge by investigating healthcare-based KM systems. Designed to demystify the KM process and demonstrate its applicability in healthcare, this text offers contemporary and clinically-relevant lessons for future organizational implementations. The editors of this book have assembled a group of international contributors that reflects the diversity of KM applications in the healthcare sector. While many KM texts suffer from pitching theoretical issues at too technical a level, Healthcare Knowledge Management approaches the topic from the more versatile "twin" perspectives of both academia and commerce. This unique text is integrative in nature – a practical guide to managing and developing KM that is underpinned by theory and research.
Machine Learning for Health Informatics
Machine learning (ML) is the fastest growing field in computer science, and Health Informatics (HI) is amongst the greatest application challenges, providing future benefits in improved medical diagnoses, disease analyses, and pharmaceutical development. However, successful ML for HI needs a concerted effort, fostering integrative research between experts ranging from diverse disciplines from data science to visualization. Tackling complex challenges needs both disciplinary excellence and cross-disciplinary networking without any boundaries. Following the HCI-KDD approach, in combining the best of two worlds, it is aimed to support human intelligence with machine intelligence. This state-of-the-art survey is an output of the international HCI-KDD expert network and features 22 carefully selected and peer-reviewed chapters on hot topics in machine learning for health informatics; they discuss open problems and future challenges in order to stimulate further research and international progress in this field.