Data-driven quality assurance to prevent erroneous test results.
Increasing laboratory automation and efficiency requires quality assurance (QA) approaches to ensure that reported results are precise and accurate. Prerequisites for designing optimal QA strategies include an in-depth understanding of the laboratory processes, the expected results, and of the mecha...
| Published in: | Critical Reviews in Clinical Laboratory Sciences Vol. 57; no. 3; pp. 146 - 161 |
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| Main Authors: | , , |
| Format: | review tables/charts Journal Article |
| Published: |
Taylor & Francis Ltd
May2020
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| Online Access: | View this record in EBSCOhost |