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    이동 평균법을 활용한 환자기반 실시간 정도관리의 구현 및 적용 = Implementation and Application of Patient-based Real-time Quality Control Using Moving Average Method

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    https://www.riss.kr/link?id=A109770077

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    Internal quality control (IQC) is an essential component for monitoring the performance of analytical systems in clinical laboratories. Nevertheless, traditional IQC has several limitations, including discontinuous characteristics and non-commutability of control materials. Moving average quality control (MA QC) has been suggested as a tool for analytical quality control for decades. In addition, despite being proven for its advantages, its complexity and lack of evidence-based guidelines make it difficult to apply in practice. Previous studies have reported that the MA method can rapidly detect and correct errors in the testing system by applying the MA method in creatinine, routine chemistry tests, international normalized ratio and activated partial thromboplastin time, and serum Na, and emphasized the importance of MA QC as a quality control that complements the existing IQC. Therefore, MA QC can be added when IQC is limited. In conclusion, using an optimized MA method can increase the reliability of test results and minimize significant errors in patient treatment. The MA method is expected to expand with the advances in artificial intelligence and machine learning technologies.
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    Internal quality control (IQC) is an essential component for monitoring the performance of analytical systems in clinical laboratories. Nevertheless, traditional IQC has several limitations, including discontinuous characteristics and non-commutabilit...

    Internal quality control (IQC) is an essential component for monitoring the performance of analytical systems in clinical laboratories. Nevertheless, traditional IQC has several limitations, including discontinuous characteristics and non-commutability of control materials. Moving average quality control (MA QC) has been suggested as a tool for analytical quality control for decades. In addition, despite being proven for its advantages, its complexity and lack of evidence-based guidelines make it difficult to apply in practice. Previous studies have reported that the MA method can rapidly detect and correct errors in the testing system by applying the MA method in creatinine, routine chemistry tests, international normalized ratio and activated partial thromboplastin time, and serum Na, and emphasized the importance of MA QC as a quality control that complements the existing IQC. Therefore, MA QC can be added when IQC is limited. In conclusion, using an optimized MA method can increase the reliability of test results and minimize significant errors in patient treatment. The MA method is expected to expand with the advances in artificial intelligence and machine learning technologies.

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