Difference between revisions of "Template:Article of the week"

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<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Fig1 Riley JOfBioEng2017 11.gif|240px]]</div>
<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Fig1 Dagliati FrontInDigiHum2018 5.jpg|240px]]</div>
'''"[[Implementation and use of cloud-based electronic lab notebook in a bioprocess engineering teaching laboratory|Implementation and use of cloud-based electronic lab notebook in a bioprocess engineering teaching laboratory]]"'''
'''"[[Journal:Big data as a driver for clinical decision support systems: A learning health systems perspective|Big data as a driver for clinical decision support systems: A learning health systems perspective]]"'''


[[Electronic laboratory notebook]]s (ELNs) are better equipped than paper [[laboratory notebook]]s (PLNs) to handle present-day life science and engineering experiments that generate large data sets and require high levels of data integrity. But limited training and a lack of workforce with ELN knowledge have restricted the use of ELN in academic and industry research [[Laboratory|laboratories]], which still rely on cumbersome PLNs for record keeping. We used [[LabArchives, LLC|LabArchives]], a cloud-based ELN in our bioprocess engineering lab course to train students in electronic record keeping, good documentation practices (GDPs), and [[data integrity]].
Big data technologies are nowadays providing health care with powerful instruments to gather and analyze large volumes of heterogeneous data collected for different purposes, including clinical care, administration, and research. This makes possible to design IT infrastructures that favor the implementation of the so-called “Learning Healthcare System Cycle,” where healthcare practice and research are part of a unique and synergistic process. In this paper we highlight how "big-data-enabled” integrated data collections may support clinical decision-making together with biomedical research. Two effective implementations are reported, concerning decision support in diabetes and in inherited arrhythmogenic diseases. ('''[[Journal:Big data as a driver for clinical decision support systems: A learning health systems perspective|Full article...]]''')<br />
 
Implementation of ELN in the bioprocess engineering lab course, an analysis of user experiences, and our development actions to improve ELN training are presented here. ELN improved pedagogy and learning outcomes of the lab course through streamlined workflow, quick data recording and archiving, and enhanced data sharing and collaboration. It also enabled superior data integrity, simplified information exchange, and allowed real-time and remote monitoring of experiments. ('''[[Journal:Implementation and use of cloud-based electronic lab notebook in a bioprocess engineering teaching laboratory|Full article...]]''')<br />
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Revision as of 15:17, 18 June 2018

Fig1 Dagliati FrontInDigiHum2018 5.jpg

"Big data as a driver for clinical decision support systems: A learning health systems perspective"

Big data technologies are nowadays providing health care with powerful instruments to gather and analyze large volumes of heterogeneous data collected for different purposes, including clinical care, administration, and research. This makes possible to design IT infrastructures that favor the implementation of the so-called “Learning Healthcare System Cycle,” where healthcare practice and research are part of a unique and synergistic process. In this paper we highlight how "big-data-enabled” integrated data collections may support clinical decision-making together with biomedical research. Two effective implementations are reported, concerning decision support in diabetes and in inherited arrhythmogenic diseases. (Full article...)

Recently featured:

▪ Implementation and use of cloud-based electronic lab notebook in a bioprocess engineering teaching laboratory
▪ An open experimental database for exploring inorganic materials
▪ Broad-scale genetic diversity of Cannabis for forensic applications
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