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'''Health informatics''' (also called '''health care informatics''', '''healthcare informatics''', '''medical informatics''', '''nursing informatics''',  '''clinical informatics''', or '''biomedical informatics''') is a discipline at the intersection of [[information science]], computer science, and health care. It deals with the resources, devices, and methods required to optimize the "collection, storage, retrieval, [and] communication ... of health-related data, [[information]], and knowledge." Health informatics is applied to the areas of nursing, clinical care, dentistry, pharmacy, public health, occupational therapy, and biomedical research. Health informatics resources include not only computers but also clinical guidelines, formal medical terminologies, and information and communication systems.
'''"[[Journal:Ten simple rules for managing laboratory information|Ten simple rules for managing laboratory information]]"'''


Worldwide use of technology in medicine began in the early 1950s with the rise of computers. Medical informatics research units began to appear during the 1970s in Poland and in the U.S., with medical informatics conferences springing up as early as 1974. Since then the development of high-quality health informatics research, education, and infrastructure has been the goal of the U.S. and the European Union. Hundreds of datasets, publications, guidelines, specifications, meetings, conferences, and organizations around the world continue to shape what health informatics is today. ('''[[Health informatics |Full article...]]''')<br />
[[Information]] is the cornerstone of [[research]], from experimental data/[[metadata]] and computational processes to complex inventories of reagents and equipment. These 10 simple rules discuss best practices for leveraging [[laboratory information management system]]s (LIMS) to transform this large information load into useful scientific findings. The development of [[mathematical model]]s that can predict the properties of biological systems is the holy grail of [[computational biology]]. Such models can be used to test biological hypotheses, guide the development of biomanufactured products, engineer new systems meeting user-defined specifications, and much more ... ('''[[Journal:Ten simple rules for managing laboratory information|Full article...]]''')<br />
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Revision as of 18:03, 10 June 2024

Fig2 Berezin PLoSCompBio23 19-12.png

"Ten simple rules for managing laboratory information"

Information is the cornerstone of research, from experimental data/metadata and computational processes to complex inventories of reagents and equipment. These 10 simple rules discuss best practices for leveraging laboratory information management systems (LIMS) to transform this large information load into useful scientific findings. The development of mathematical models that can predict the properties of biological systems is the holy grail of computational biology. Such models can be used to test biological hypotheses, guide the development of biomanufactured products, engineer new systems meeting user-defined specifications, and much more ... (Full article...)

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