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

From LIMSWiki
Jump to navigationJump to search
m (Shortened a touch)
(Updated article of the week text)
 
(446 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:HL7 Reference Information Model.jpg|260px]]</div>
<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Fig1 Karaattuthazhathu NatJLabMed23 12-2.png|260px]]</div>
'''[[Health Level Seven]]''' ('''HL7''') is an international non-profit volunteer-based organization involved with the development of international health care informatics interoperability standards. The HL7 community consists of health care experts and information scientists collaborating to create standards for the exchange, management, and integration of electronic health care information. The term "HL7" is also used to refer to some of the specific standards created by the organization (e.g., HL7 v2.x, v3.0, HL7 RIM). HL7 and its members provide a framework (and related standards) for the exchange, integration, sharing, and retrieval of electronic health information. v2.x of the standards, which support clinical practice and the management, delivery, and evaluation of health services, are the most commonly used in the world.
'''"[[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]]"'''


In total HL7 develops conceptual standards (e.g., HL7 RIM), document standards (e.g., HL7 CDA), application standards (e.g., HL7 CCOW), and messaging standards (e.g., HL7 v2.x and v3.0). The HL7 messaging standards v2.x and 3.0 are the primary standards from the organization. They provide a framework for data exchange among clinical and healthcare systems in an ideal format. The 2.x standards are flexible, with several implementation options, loosely geared towards "clinical interface specialists" working to move clinical data in the application space. The 3.0 standards are designed to be more fixed, precise, and international, geared towards governments and end users of clinical applications. ('''[[Health Level Seven|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'': [[ISO/IEC 17025]], [[Good Automated Manufacturing Practice]], [[End-stage renal disease facility]]
{{flowlist |
* [[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:Data management challenges for artificial intelligence in plant and agricultural research|Data management challenges for artificial intelligence in plant and agricultural research]]
* [[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]]
}}

Latest 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: