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<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Tanzania hospital information mgt system.jpg|220px]]</div>
<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Fig2 Berezin PLoSCompBio23 19-12.png|240px]]</div>
A '''[[clinical laboratory]]''', (sometimes referred to as a '''medical laboratory''') is a laboratory where tests are done on clinical specimens in order to get information about the health of a patient as pertaining to the diagnosis, treatment, and prevention of disease. The [[Clinical Laboratory Improvement Amendments]] (CLIA) program defines a clinical (medical) laboratory as "a facility that performs testing on materials derived from the human body for the purpose of providing information for the diagnosis, prevention, or treatment of any disease or impairment of, or assessment of the health of, human beings."
'''"[[Journal:Ten simple rules for managing laboratory information|Ten simple rules for managing laboratory information]]"'''


The clinical laboratory at one level, whether chemistry or pathology, operates like many other testing laboratories. However, there are a number of operational differences between the clinical laboratory and the many other laboratories. One of these differences is the need to have a specific unidirectional workflow. This is intended to both minimize the risk of biohazard contamination, and to establish assurance that samples cross contamination is minimized. Another difference involves the regulations governing the management of patient data. This creates a significant challenge not generally experienced by other types of laboratories. ('''[[Clinical laboratory|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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''Recently featured'': [[Hospital information system]], [[Imaging informatics]], [[Reference laboratory]]
''Recently featured'':
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{{flowlist |
* [[Journal:Hierarchical AI enables global interpretation of culture plates in the era of digital microbiology|Hierarchical AI enables global interpretation of culture plates in the era of digital microbiology]]
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* [[Journal:Judgements of research co-created by generative AI: Experimental evidence|Judgements of research co-created by generative AI: Experimental evidence]]
}}

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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