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

From LIMSWiki
Jump to navigationJump to search
(Updated article of the week text.)
(Updated article of the week text)
(135 intermediate revisions by the same user not shown)
Line 1: Line 1:
<!--<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Fig2 Schmidt BMCMedInfoDecMak2020 20.png|240px]]</div>-->
<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Fig1 Karaattuthazhathu NatJLabMed23 12-2.png|260px]]</div>
'''"[[Journal:Utilizing connectivity and data management systems for effective quality management and regulatory compliance in point-of-care testing|Utilizing connectivity and data management systems for effective quality management and regulatory compliance in point-of-care testing]]"'''
'''"[[Journal:Sigma metrics as a valuable tool for effective analytical performance and quality control planning in the clinical laboratory: A retrospective study|Sigma metrics as a valuable tool for effective analytical performance and quality control planning in the clinical laboratory: A retrospective study]]"'''


Point-of-care testing (POCT) is one of the fastest growing disciplines in [[clinical laboratory]] medicine. POCT [[Medical device|devices]] are widely used in both acute and chronic patient management in the [[hospital]] and [[Physician office laboratory|primary care physician office]] settings. As demands for POCT in various healthcare settings increase, managing POCT testing quality and [[regulatory compliance]] are continually challenging. Despite technological advances in applying automatic system checks and built-in [[quality control]] to prevent analytical and operator errors, poor planning for POCT [[Interface (computing)|connectivity]] and [[Informatics (academic field)|informatics]] can limit [[Data sharing|data accessibility]] and [[Information management|management]] efficiency which impedes the utilization of POCT to its full potential. This article will summarize how connectivity and data management systems can improve timely access to POCT results, effective management of POCT programs, and ensure regulatory compliance. ('''[[Journal:Utilizing connectivity and data management systems for effective quality management and regulatory compliance in point-of-care testing|Full article...]]''')<br />
For the release of precise and accurate reports of [[Medical test|routine tests]], its necessary to follow a proper [[quality management system]] (QMS) in the [[clinical laboratory]]. As one of the most popular QMS tools for process improvement, Six Sigma techniques and tools have been accepted widely in the [[laboratory]] testing process. Six Sigma gives an objective assessment of analytical methods and instrumentation, measuring the outcome of a process on a scale of 0 to 6. Poor outcomes are measured in terms of defects per million opportunities (DPMO). To do the performance assessment of each clinical laboratory [[analyte]] by Six Sigma analysis and to plan and chart out a better, customized [[quality control]] (QC) plan for each analyte, according to its own sigma value ... ('''[[Journal:Sigma metrics as a valuable tool for effective analytical performance and quality control planning in the clinical laboratory: A retrospective study|Full article...]]''')<br />
<br />
''Recently featured'':
''Recently featured'':
{{flowlist |
{{flowlist |
* [[Journal:Definitions, components and processes of data harmonization in healthcare: A scoping review|Definitions, components and processes of data harmonization in healthcare: A scoping review]]
* [[Journal:Why do we need food systems informatics? Introduction to this special collection on smart and connected regional food systems|Why do we need food systems informatics? Introduction to this special collection on smart and connected regional food systems]]
* [[Journal:Interoperability challenges in the cybersecurity information sharing ecosystem|Interoperability challenges in the cybersecurity information sharing ecosystem]]
* [[Journal:Data management challenges for artificial intelligence in plant and agricultural research|Data management challenges for artificial intelligence in plant and agricultural research]]
* [[Journal:Data without software are just numbers|Data without software are just numbers]]
* [[Journal:A blockchain-driven IoT-based food quality traceability system for dairy products using a deep learning model|A blockchain-driven IoT-based food quality traceability system for dairy products using a deep learning model]]
}}
}}

Revision as of 16:52, 29 April 2024

Fig1 Karaattuthazhathu NatJLabMed23 12-2.png

"Sigma metrics as a valuable tool for effective analytical performance and quality control planning in the clinical laboratory: A retrospective study"

For the release of precise and accurate reports of routine tests, its necessary to follow a proper quality management system (QMS) in the clinical laboratory. As one of the most popular QMS tools for process improvement, Six Sigma techniques and tools have been accepted widely in the laboratory testing process. Six Sigma gives an objective assessment of analytical methods and instrumentation, measuring the outcome of a process on a scale of 0 to 6. Poor outcomes are measured in terms of defects per million opportunities (DPMO). To do the performance assessment of each clinical laboratory analyte by Six Sigma analysis and to plan and chart out a better, customized quality control (QC) plan for each analyte, according to its own sigma value ... (Full article...)
Recently featured:

Retrieved from "https://www.limswiki.org/index.php?title=Template:Article_of_the_week&oldid=63391"