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<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Fig1 Walker JofPathInformatics2016 7.jpg|240px]]</div>
<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Fig2 Berezin PLoSCompBio23 19-12.png|240px]]</div>
'''"[[Journal:Perceptions of pathology informatics by non-informaticist pathologists and trainees|Perceptions of pathology informatics by non-informaticist pathologists and trainees]]"'''
'''"[[Journal:Ten simple rules for managing laboratory information|Ten simple rules for managing laboratory information]]"'''


Although [[Clinical pathology|pathology]] [[Informatics (academic field)|informatics]] (PI) is essential to modern pathology practice, the field is often poorly understood. Pathologists who have received little to no exposure to [[informatics]], either in training or in practice, may not recognize the roles that informatics serves in pathology. The purpose of this study was to characterize perceptions of PI by noninformatics-oriented pathologists and to do so at two large centers with differing informatics environments. Pathology trainees and staff at Cleveland Clinic (CC) and Massachusetts General Hospital (MGH) were surveyed. At MGH, pathology department leadership has promoted a pervasive informatics presence through practice, training, and research. At CC, PI efforts focus on production systems that serve a multi-site integrated health system and a [[Reference laboratory#Referral and diagnostic|reference laboratory]], and on the development of applications oriented to department operations. The survey assessed perceived definition of PI, interest in PI, and perceived utility of PI. ('''[[Journal:Perceptions of pathology informatics by non-informaticist pathologists and trainees|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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