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Health informatics is the study and implementation of computer structures and algorithms to improve communication, understanding, and management of medical information. It can be viewed as branch of engineering and applied science. The health domain provides an extremely wide variety of problems that can be tackled using computational techniques. Health informatics is a spectrum of multidisciplinary fields that includes study of the design, development and application of computational innovations to improve health care. The disciplines involved combines medicine fields with computing fields, in particular computer engineering, software engineering, information engineering, bioinformatics, bio-inspired computing, theoretical computer science, information systems, data science, information technology, autonomic computing, and behavior informatics. In academic institutions, medical informatics research focus on applications of artificial intelligence in healthcare and designing medical devices based on embedded systems. In some countries term informatics is also used in the context of applying library science to data management in hospitals. In this meaning health informatics aims at developing methods and technologies for the acquisition, processing, and study of patient data, An umbrella term of biomedical informatics has been proposed. There are many variations in the name of the field involved in applying information and communication technologies to healthcare, public health, and personal health, ranging from those focused on the molecular (e.g., genomic), organ system (e.g., imaging), individual (e.g., patient or consumer, care provider, and interaction between them), to population-level of application. A spectrum of activity spans efforts ranging from theory and model development, to empirical research, to implementation and management, to widespread adoption. 'Clinical informaticians' are qualified health and social care professionals and 'clinical informatics' is a subspecialty within several medical specialties. Subject areas within health informatics Jan van Bemmel has described medical informatics as the theoretical and practical aspects of information processing and communication based on knowledge and experience derived from processes in medicine and health care. The Faculty of Clinical Informatics has identified six high level domains of core competency for clinical informaticians: Health and Wellbeing in Practice Information Technologies and Systems Working with Data and Analytical Methods Enabling Human and Organizational Change Decision Making Leading Informatics Teams and projects. Tools to support practitioners Clinical informaticians use their knowledge of patient care combined with their understanding of informatics concepts, methods, and health informatics tools to: assess information and knowledge needs of health care professionals, patients and their families. characterize, evaluate, and refine clinical processes, develop, implement, and refine clinical decision support systems, and lead or participate in the procurement, customization, development, implementation, management, evaluation, and continuous improvement of clinical information systems. Clinicians collaborate with other health care and information technology professionals to develop health informatics tools which promote patient care that is safe, efficient, effective, timely, patient-centered, and equitable. Many clinical informaticists are also computer scientists. Telehealth and telemedicine Telehealth is the distribution of health-related services and information via electronic information and telecommunication technologies. It allows long-distance patient and clinician contact, care, advice, reminders, education, intervention, monitoring, and remote admissions. Telemedicine is sometimes used as a synonym, or is used in a more limited sense to describe remote clinical services, such as diagnosis and monitoring. Remote monitoring, also known as self-monitoring or testing, enables medical professionals to monitor a patient remotely using various technological devices. This method is primarily used for managing chronic diseases or specific conditions, such as heart disease, diabetes mellitus, or asthma. These services can provide comparable health outcomes to traditional in-person patient encounters, supply greater satisfaction to patients, and may be cost-effective. Telerehabilitation (or e-rehabilitation[40][41]) is the delivery of rehabilitation services over telecommunication networks and the Internet. Most types of services fall into two categories: clinical assessment (the patient's functional abilities in his or her environment), and clinical therapy. Some fields of rehabilitation practice that have explored telerehabilitation are: neuropsychology, speech-language pathology, audiology, occupational therapy, and physical therapy. Telerehabilitation can deliver therapy to people who cannot travel to a clinic because the patient has a disability or because of travel time. Telerehabilitation also allows experts in rehabilitation to engage in a clinical consultation at a distance. Decision support, artificial intelligence and machine learning in healthcare A pioneer in the use of artificial intelligence in healthcare was American biomedical informatician Edward H. Shortliffe. This field deals with utilisation of machine-learning algorithms and artificial intelligence, to emulate human cognition in the analysis, interpretation, and comprehension of complicated medical and healthcare data. Specifically, AI is the ability of computer algorithms to approximate conclusions based solely on input data. AI programs are applied to practices such as diagnosis processes, treatment protocol development, drug development, personalized medicine, and patient monitoring and care. A large part of industry focus of implementation of AI in the healthcare sector is in the clinical decision support systems. As more data is collected, machine learning algorithms adapt and allow for more robust responses and solutions. Numerous companies are exploring the possibilities of the incorporation of big data in the healthcare industry. Many companies investigate the market opportunities through the realms of "data assessment, storage, management, and analysis technologies" which are all crucial parts of the healthcare industry. The following are examples of large companies that have contributed to AI algorithms for use in healthcare: IBM's Watson Oncology is in development at Memorial Sloan Kettering Cancer Center and Cleveland Clinic. IBM is also working with CVS Health on AI applications in chronic disease treatment and with Johnson & Johnson on analysis of scientific papers to find new connections for drug development. In May 2017, IBM and Rensselaer Polytechnic Institute began a joint project entitled Health Empowerment by Analytics, Learning and Semantics (HEALS), to explore using AI techn.... Discover the Institute For Clinical Research Nih My popular books. Find the top 100 most popular Institute For Clinical Research Nih My books.