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'''"[[Journal:Laboratory information system – Where are we today?|Laboratory information system – Where are we today?]]"'''
<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Fig2 Berezin PLoSCompBio23 19-12.png|240px]]</div>
'''"[[Journal:Ten simple rules for managing laboratory information|Ten simple rules for managing laboratory information]]"'''


Wider implementation of [[laboratory information system]]s (LIS) in [[Clinical laboratory|clinical laboratories]] in Serbia was initiated 10 years ago. The first LIS in the Railway Health Care Institute was implemented nine years ago. Before the LIS was initiated, manual admission procedures limited daily output of patients. Moreover, manual entering of patient data and ordering tests on analyzers was problematic and time-consuming. After completing tests, [[laboratory]] personnel had to write results in a patient register (with potential errors) and provide invoices for health insurance organizations. The first LIS brought forward some advantages with regards to these obstacles, but it also showed various weaknesses. These can be summarized as rigidity of the system and inability to fulfill user expectation. After four years of use, we replaced this system with another LIS. Hence, the main aim of this paper is to evaluate the advantages of using LIS in the Railway Health Care Institute's laboratory and also to discuss further possibilities for its application. ('''[[Journal:Laboratory information system – Where are we today?|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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