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Move confidently into the future of healthcare with a clear understanding of new technology and the growing field of health informatics! The following classifications, code sets, and terminologies are discussed: ICD, CPT, NDC, CDT, MEDCIN, DSM, HCPCS, SNOMED, and LOINC. Drug terminology systems, terminologies used in nursing practice, specialty international classifications, and other emerging vocabulary, terminology, and classification systems are included.This book covers multiple terminologies, vocabularies, code sets, and classification systems. It clearly explains key systems to prepare you for the adoption of the electronic health record (EHR). Discover how the various data sets can be created, accessed, combined, manipulted, and shared.Develop and understanding of the components making up the infrastructure of electronic health records, how standard diagnosis and procedure code sets interact with emerging code sets and data standards, how new terminologies, vocabularies, and classifications systems work together with HIPAA standard code sets in the identification and organization of clinical data.
Discover How Electronic Health Records Are Built to Drive the Next Generation of Healthcare Delivery The increased role of IT in the healthcare sector has led to the coining of a new phrase "health informatics," which deals with the use of IT for better healthcare services. Health informatics applications often involve maintaining the health records of individuals, in digital form, which is referred to as an Electronic Health Record (EHR). Building and implementing an EHR infrastructure requires an understanding of healthcare standards, coding systems, and frameworks. This book provides an overview of different health informatics resources and artifacts that underlie the design and development of interoperable healthcare systems and applications. Electronic Health Record: Standards, Coding Systems, Frameworks, and Infrastructures compiles, for the first time, study and analysis results that EHR professionals previously had to gather from multiple sources. It benefits readers by giving them an understanding of what roles a particular healthcare standard, code, or framework plays in EHR design and overall IT-enabled healthcare services along with the issues involved. This book on Electronic Health Record: Offers the most comprehensive coverage of available EHR Standards including ISO, European Union Standards, and national initiatives by Sweden, the Netherlands, Canada, Australia, and many others Provides assessment of existing standards Includes a glossary of frequently used terms in the area of EHR Contains numerous diagrams and illustrations to facilitate comprehension Discusses security and reliability of data
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 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 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.
eHealth has revolutionized health care and the practice of medicine. Internet technologies have given the most rural communities access to healthcare services, and automated computer algorithms are improving medical diagnoses and speeding up the delivery of care. Handheld apps, wearable devices, and artificial intelligence lead the way, creating a global healthcare solution that is smarter and more accessible. Read what leaders in the field are doing to advance the use of electronic technology to improve global health.
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.