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

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(Updated article of the week text)
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'''"[[Journal:Advanced engineering informatics: Philosophical and methodological foundations with examples from civil and construction engineering|Advanced engineering informatics: Philosophical and methodological foundations with examples from civil and construction engineering]]"'''
'''"[[Journal:Timely delivery of laboratory efficiency information, Part I: Developing an interactive turnaround time dashboard at a high-volume laboratory|Timely delivery of laboratory efficiency information, Part I: Developing an interactive turnaround time dashboard at a high-volume laboratory]]"'''


We argue that the representation and formalization of complex engineering knowledge is the main aim of inquiries in the scientific field of [[Wikipedia:Engineering informatics|advanced engineering informatics]]. We introduce [[Ontology (information science)|ontology]] and logic as underlying methods to formalize [[Information#As an influence which leads to a transformation|knowledge]]. We also suggest that it is important to account for the purpose of engineers and the context they work in while representing and formalizing knowledge. Based on the concepts of ontology, logic, purpose, and context, we discuss different possible research methods and approaches that scholars can use to formalize complex engineering knowledge and to validate whether a specific formalization can support engineers with their complex tasks. On the grounds of this discussion, we suggest that research efforts in advanced engineering should be conducted in a bottom-up manner, closely involving engineering practitioners. We also suggest that researchers make use of social science methods while both eliciting knowledge to formalize and validating that formalized knowledge. ('''[[Journal:Advanced engineering informatics: Philosophical and methodological foundations with examples from civil and construction engineering|Full article...]]''')<br />
Mean [[wikipedia:Turnaround time|turnaround time]] (TAT) [[reporting]] for testing [[Laboratory|laboratories]] in a national network is typically static and not immediately available for meaningful corrective action and does not allow for test-by-test or site-by-site interrogation of individual laboratory performance. The aim of this study was to develop an easy-to-use, visual dashboard to report interactive graphical TAT data to provide a weekly snapshot of TAT efficiency. An interactive dashboard was developed by staff from the National Priority Programme and Central Data Warehouse of the National Health Laboratory Service in Johannesburg, South Africa, during 2018. Steps required to develop the dashboard were summarized in a flowchart. To illustrate the dashboard, one week of data from a busy laboratory for a specific set of tests was analyzed using annual performance plan TAT cutoffs. Data were extracted and prepared to deliver an aggregate extract, with statistical measures provided, including test volumes, global percentage of tests that were within TAT cutoffs, and percentile statistics. ('''[[Journal:Timely delivery of laboratory efficiency information, Part I: Developing an interactive turnaround time dashboard at a high-volume laboratory|Full article...]]''')<br />
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Revision as of 16:24, 7 December 2021

Fig1 Cassim AfricanJLabMed2020 9-2.jpg

"Timely delivery of laboratory efficiency information, Part I: Developing an interactive turnaround time dashboard at a high-volume laboratory"

Mean turnaround time (TAT) reporting for testing laboratories in a national network is typically static and not immediately available for meaningful corrective action and does not allow for test-by-test or site-by-site interrogation of individual laboratory performance. The aim of this study was to develop an easy-to-use, visual dashboard to report interactive graphical TAT data to provide a weekly snapshot of TAT efficiency. An interactive dashboard was developed by staff from the National Priority Programme and Central Data Warehouse of the National Health Laboratory Service in Johannesburg, South Africa, during 2018. Steps required to develop the dashboard were summarized in a flowchart. To illustrate the dashboard, one week of data from a busy laboratory for a specific set of tests was analyzed using annual performance plan TAT cutoffs. Data were extracted and prepared to deliver an aggregate extract, with statistical measures provided, including test volumes, global percentage of tests that were within TAT cutoffs, and percentile statistics. (Full article...)

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