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As a general introduction to the SNOMED CT clinical terminology code system, the book explains in simple terms a wealth of key aspects, including the fundamentals of SNOMED CT, the various ways in which it can be used, and the methods by which it may quickly be deployed for use within an electronic documentation system that deals with clinical and clinics-related data. Further considerations include how end users can employ the system, how healthcare IT designers and developers can build highly ergonomic systems, and how health informatics experts and clinical analysts can successfully harness the various features that the clinical terminology code system provides in order to unleash the hidden potentials of clinical data. The book brings together material from various sources, presenting it in an easy-to-follow manner and supplemented by analyses of a number of different (imaginary) scenarios including case summaries from the author’s experience and knowledge. The book will greatly benefit all stakeholders involved: clinicians, nurses, paramedics, dentists, public health professionals, health informatics professionals and healthcare IT engineers involved in the design and development of information systems for healthcare. Students at both the undergraduate and postgraduate levels seeking a practical introduction to SNOMED CT will find this book to be a valuable guide.
This book provides an introduction to health interoperability and the main standards used. Health interoperability delivers health information where and when it is needed. Everybody stands to gain from safer more soundly based decisions and less duplication, delays, waste and errors. The third edition of Principles of Health Interoperability includes a new part on FHIR (Fast Health Interoperability Resources), the most important new health interoperability standard for a generation. FHIR combines the best features of HL7’s v2, v3 and CDA while leveraging the latest web standards and a tight focus on implementability. FHIR can be implemented at a fraction of the price of existing alternatives and is well suited for use in mobile phone apps, cloud communications and EHRs. The book is organised into four parts. The first part covers the principles of health interoperability, why it matters, why it is hard and why models are an important part of the solution. The second part covers clinical terminology and SNOMED CT. The third part covers the main HL7 standards: v2, v3, CDA and IHE XDS. The new fourth part covers FHIR and has been contributed by Grahame Grieve, the original FHIR chief.
This open access book comprehensively covers the fundamentals of clinical data science, focusing on data collection, modelling and clinical applications. Topics covered in the first section on data collection include: data sources, data at scale (big data), data stewardship (FAIR data) and related privacy concerns. Aspects of predictive modelling using techniques such as classification, regression or clustering, and prediction model validation will be covered in the second section. The third section covers aspects of (mobile) clinical decision support systems, operational excellence and value-based healthcare. Fundamentals of Clinical Data Science is an essential resource for healthcare professionals and IT consultants intending to develop and refine their skills in personalized medicine, using solutions based on large datasets from electronic health records or telemonitoring programmes. The book’s promise is “no math, no code”and will explain the topics in a style that is optimized for a healthcare audience.
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.
Health Informatics (HI) focuses on the application of Information Technology (IT) to the field of medicine to improve individual and population healthcare delivery, education and research. This extensively updated fifth edition reflects the current knowledge in Health Informatics and provides learning objectives, key points, case studies and references.
The second edition of this reference features more than 300 high-quality color illustrations to assist practicing veterinarians and veterinary students in identifying small animal ear diseases. It begins with a review of the science involved in diagnosing and treating ear disease, including the anatomy of the ear, examination techniques, and pathophysiology. Coverage also includes discussions of specific ear disease conditions, based on the standard ear disease classification scheme of predisposing factors, primary causes, and perpetuating factors. The consistent presentation of each disorder includes an introduction, color illustrations of the condition, description of diagnostic techniques, treatment options, suggested readings, and updated references. More than 300 high-quality images illustrate a variety of ear conditions to assist practitioners in practical diagnosis. A comprehensive chapter on marketing ear care and otitis therapy includes strategies for successfully integrating these services into practice to offer expanded patient services and increase profits. A chapter on diagnostic imaging provides the latest information on using imaging to diagnose small animal ear disease. An Ear Product Formulary in the appendix serves as a complete guide to products available for treating small animal ear diseases. 6 new chapters covering: The microbiology of the ear of the dog and cat Laser ear surgery Cytology of the ear in health and disease Adverse food reactions Diseases that affect the pinna Otitis interna and vestibular disease Expanded coverage of otic cytology and a photographic manual of ear cytology In-depth discussions of video otoscopic diagnostics New photos of interesting cases contributed by practitioners
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 textbook begins with an introduction to the US healthcare delivery system, its many systemic challenges and the prior efforts to develop and deploy informatics tools to help overcome those problems. It goes on to discuss health informatics from an historical perspective, its current state and its likely future state now that electronic health record systems are widely deployed, the HL7 Fast Healthcare Interoperability standard is being rapidly accepted as the means to access the data stored in those systems and analytics is increasing being used to gain new knowledge from that aggregated clinical data. It then turns to some of the important and evolving areas of informatics including population and public health, mHealth and big data and analytics. Use cases and case studies are used in all of these discussions to help readers connect the technologies to real world challenges. Effective use of informatics systems and tools by providers and their patients is key to improving the quality, safety and cost of healthcare. With health records now digital, no effective means has existed for sharing them with patients, among the multiple providers who may care for them and for important secondary uses such as public/population health and research. This problem is a topic of congressional discussion and is addressed by the 21st Century Cures Act of 2016 that mandates that electronic health record (EHR) systems offer a patient-facing API. HL7’s Fast Healthcare Interoperability Resources (FHIR) is that API and this is the first comprehensive treatment of the technology and the many ways it is already being used. FHIR is based on web technologies and is thus a far more facile, easy to implement approach that is rapidly gaining acceptance. It is also the basis for a ‘universal health app platform’ that literally has the potential to foster innovation around the data in patient records similar to the app ecosystems smartphones created around the data they store. FHIR app stores have already been opened by Epic and Cerner, the two largest enterprise EHR vendors. Provider facing apps are already being explored to improve EHR usability and support personalized medicine. Medicare and the Veteran’s Administration have announced FHIR app platforms for their patients. Apple’s new IOS 11.3 features the ability for consumers to aggregate their health records on their iPhone using FHIR. Health insurance companies are exploring applications of FHIR to improve service and communication with their providers and patients. SureScripts, the national e-Prescribing network, is using FHIR to help doctors know if their patients are complying with prescriptions. This textbook is for introductory health informatics courses for computer science and health sciences students (e.g. doctors, nurses, PhDs), the current health informatics community, IT professionals interested in learning about the field and practicing healthcare providers. Though this textbook covers an important new technology, it is accessible to non-technical readers including healthcare providers, their patients or anyone interested in the use of healthcare data for improved care, public/population health or research.
Introduction to Clinical Informatics fills a void in the Computer in Health Care series. With this volume, Patrice Degoulet and Marius Fieschi provide a comprehensive view of medical informatics and carry that concept forward into the realm of clinical informatics. The authors draw upon their experi ences as medical school faculty members in France, where informatics has long been integrated into the curriculum and where the French version of this very book has been used, tested, and revised. In intent and content, this volume stands as the companion volume to Introduction to Nursing Informatics, one of the series' best selling titles. For practitioners and students of medicine, pharmacy, and other health profes sions, Introduction to Clinical Informatics offers an essential understanding how computing can support patient care, clarifying practical uses and critical issues. Today medical schools in the United States are making informatics a part of their curriculum, with required medical informatics blocks at the onset of training serving as the base for problem-based learning throughout the course of study. In an increasingly networked and computerized environ ment, health-care providers are having to alter how they practice. Whether in the office, the clinic, or the hospital, health-care professionals have access to a growing array of capabilities and tools as they deliver care. Learning to use these becomes a top priority, and this volume becomes a valuable resource.
The field of machine learning has matured to the point where many sophisticated learning approaches can be applied to practical applications. Thus it is of critical importance that researchers have the proper tools to evaluate learning approaches and understand the underlying issues. This book examines various aspects of the evaluation process with an emphasis on classification algorithms. The authors describe several techniques for classifier performance assessment, error estimation and resampling, obtaining statistical significance as well as selecting appropriate domains for evaluation. They also present a unified evaluation framework and highlight how different components of evaluation are both significantly interrelated and interdependent. The techniques presented in the book are illustrated using R and WEKA, facilitating better practical insight as well as implementation. Aimed at researchers in the theory and applications of machine learning, this book offers a solid basis for conducting performance evaluations of algorithms in practical settings.