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Intended for family physicians and others in primary care delivery. Compatible with International classification of diseases, 9th ed.
In response to a request by the Health Care Financing Administration (HCFA), the Institute of Medicine proposed a study to examine definitions of serious or complex medical conditions and related issues. A seven-member committee was appointed to address these issues. Throughout the course of this study, the committee has been aware of the fact that the topic addressed by this report concerns one of the most critical issues confronting HCFA, health care plans and providers, and patients today. The Medicare+Choice regulations focus on the most vulnerable populations in need of medical care and other services-those with serious or complex medical conditions. Caring for these highly vulnerable populations poses a number of challenges. The committee believes, however, that the current state of clinical and research literature does not adequately address all of the challenges and issues relevant to the identification and care of these patients.
This open access book describes the results of natural language processing and machine learning methods applied to clinical text from electronic patient records. It is divided into twelve chapters. Chapters 1-4 discuss the history and background of the original paper-based patient records, their purpose, and how they are written and structured. These initial chapters do not require any technical or medical background knowledge. The remaining eight chapters are more technical in nature and describe various medical classifications and terminologies such as ICD diagnosis codes, SNOMED CT, MeSH, UMLS, and ATC. Chapters 5-10 cover basic tools for natural language processing and information retrieval, and how to apply them to clinical text. The difference between rule-based and machine learning-based methods, as well as between supervised and unsupervised machine learning methods, are also explained. Next, ethical concerns regarding the use of sensitive patient records for research purposes are discussed, including methods for de-identifying electronic patient records and safely storing patient records. The book’s closing chapters present a number of applications in clinical text mining and summarise the lessons learned from the previous chapters. The book provides a comprehensive overview of technical issues arising in clinical text mining, and offers a valuable guide for advanced students in health informatics, computational linguistics, and information retrieval, and for researchers entering these fields.
This edition of ICD-O, the standard tool for coding diagnoses of neoplasms in tumour and cancer registrars and in pathology laboratories, has been developed by a working party convened by the International Agency for Research on Cancer / WHO. ICD-O is a dual classification with coding systems for both topography and morphology. The book has five main sections. The first provides general instructions for using the coding systems and gives rules for their implementation in tumour registries and pathology laboratories. Section two includes the numerical list of topography codes, which remain unchanged from the previous edition. The numerical list of morphology codes is presented in the next section, which introduces several new terms and includes considerable revisions of the non-Hodgkin lymphoma and leukaemia sections, based on the WHO Classification of Hematopoietic and Lympoid Diseases. The five-digit morphology codes allow identification of a tumour or cell type by histology, behaviour, and grade. Revisions in the morphology section were made in consultation with a large number of experts and were finalised after field-testing in cancer registries around the world. The alphabetical index gives codes for both topography and morphology and includes selected tumour-like lesions and conditions. A guide to differences in morphology codes between the second and third editions is provided in the final section, which includes lists of all new code numbers, new terms and synonyms added to existing code definitions, terms that changed morphology code, terms for conditions now considered malignant, deleted terms, and terms that changed behaviour code.
This 12-chapter book is an attempt to present the current best practices in this specialty of HIM to Students, educators, and practitioners of HIM. This will help to ward off the frontier of academic and practical inadequacies present both in the training and practice areas. Therefore, the book promises to provide the much needed companionship for both students Educators, and Practitioners, as they cannot afford not to be compliant in the present world that has become a "global village", particularly in the field of HIM. Every chapter in the book is very interesting as they examine fresh and emerging facts in the practice of Disease Classification and Clinical coding. Some of these include chapters on emerging classification; classification, nomenclature, and terminology; Introduction to Procedural Coding System; an introductory chapter on Billing and Reimbursement Methodologies; ethical issues in coding; future consideration and many more interesting topics.
Part of the highly respected Requisites series, Radiology Noninterpretive Skills, by Drs. Hani H. Abujudeh and Michael A. Bruno, is a single-volume source of timely information on all of the non-imaging aspects of radiology such as quality and safety, ethics and professionalism, and error management in radiology. Residents and radiologists preparing for the boards and recertification will find this book invaluable, as well as those practitioners wanting to broaden their knowledge and skills in this increasingly important area. - Offers a readable and concise introduction to the essential noninterpretive skills as defined by the IOM, ACR, and other national organizations. - Covers what you need to know about quality and safety; leadership and management; health economics; legal, business, ethics and professionalism; statistical tools; error reporting and prevention; evidence-based imaging; health IT and internet applications; "Image Wisely" and "Imaging 3.0" ACR initiatives; legal issues and malpractice; current and future payment models in radiology; and much more. - Summarizes key information with numerous outlines, tables, ''pearls,'' and boxed material for easy reference. - Provides comprehensive coverage of key "milestones" in training identified by the Accreditation Council for Graduate Medical Education (ACGME). - Fills an important gap for those preparing for the current MOC and ABR exams, covering the many topics touched upon in a major section of the examinations. - Brings together in one source the experience of leading national experts and a select team of expert contributors. - Expert ConsultTM eBook version included with purchase. This enhanced eBook experience allows you to search all of the text, figures, Q&As, and references from the book on a variety of devices.
Most women who die from cervical cancer, particularly in developing countries, are in the prime of their life. They may be raising children, caring for their family, and contributing to the social and economic life of their town or village. Their death is both a personal tragedy, and a sad and unnecessary loss to their family and their community. Unnecessary, because there is compelling evidence, as this Guide makes clear, that cervical cancer is one of the most preventable and treatable forms of cancer, as long as it is detected early and managed effectively. Unfortunately, the majority of women in developing countries still do not have access to cervical cancer prevention programmes. The consequence is that, often, cervical cancer is not detected until it is too late to be cured. An urgent effort is required if this situation is to be corrected. This Guide is intended to help those responsible for providing services aimed at reducing the burden posed by cervical cancer for women, communities and health systems. It focuses on the knowledge and skills needed by health care providers, at different levels of care.