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A '''[[laboratory information system]] (LIS)''' is a software system that records, manages, and stores data for clinical [[laboratory|laboratories]]. A LIS has traditionally been most adept at sending laboratory test orders to lab instruments, tracking those orders, and then recording the results, typically to a searchable database. The standard LIS has supported the operations of public health institutions (like [[hospital|hospitals]] and clinics) and their associated labs by managing and reporting critical data concerning "the status of infection, immunology, and care and treatment status of patients."
'''"[[Journal:Ten simple rules for managing laboratory information|Ten simple rules for managing laboratory information]]"'''


There is often confusion regarding the difference between a laboratory information system (LIS) and a [[laboratory information management system]] (LIMS). While the two laboratory informatics components are related, their purposes diverged early in their existences. Up until recently, LIMS and LIS have exhibited a few key differences, such as a LIS being designed primarily for processing and reporting data related to individual patients in a clinical setting, with a LIMS being traditionally designed to process and report data related to batches of samples from drug trials, water treatment facilities, and other entities that handle complex batches of data. However, distinctions between the two systems have faded somewhat as some LIMS vendors have adopted the case-centric information management normally reserved for a LIS, blurring the lines between the two components further. ('''[[Laboratory information system|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'': [[Denuder]], [[Infectious disease informatics]], [[National Institute for Occupational Safety and Health]]
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Latest 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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