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Patient-Based Real-Time QC (PBRTQC): Detect Errors Earlier & Improve Lab Quality

In this must-watch tutorial, we dive deep into Patient-Based Real-Time QC (PBRTQC) — exploring how patient-based QC and real-time laboratory QC algorithms are transforming laboratory quality control and clinical lab QC workflows. Discover how your lab can use patient data QC, moving average QC, moving median QC, and real-time error detection systems to move beyond conventional QC and adopt a future-facing quality control using sigma metrics and automated QC alerts approach.

You’ll learn how to implement PBRTQC implementation step-by-step, understand what makes patient-based QC algorithms different from traditional controls, and why this shift to patient-based real-time quality control for high-volume assays is gaining rapid traction in advanced labs. With real-time monitoring of test results and analytical quality control informed by live patient streams, labs are improving sensitivity for error detection, reducing waste from synthetic controls, and optimizing overall laboratory quality assurance.

Whether you’re a medical laboratory scientist, clinical chemist, QA officer, or advanced student, this video covers everything from algorithm selection (EWMA QC, moving average QC) and block-size design to integrating PBRTQC into your LIS/HIS, balancing hybrid models, and ensuring compliance with ISO/CAP standards. We also compare real-time laboratory QC vs traditional QC, show how automated QC alerts tie into hospital information systems, and discuss the potential for machine-learning QC in next-gen labs.

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