Laboratory medicine and pathology workflow · BEGINNER LESSON
Quality control
Quality control is the set of specific checks and tests used during or after a process to find defects, deviations, or unreliable results. In laboratories and digital pathology, it helps confirm that measurements, images, and reports meet predefined standards.
What quality control is
Quality control, or QC, is not the whole quality program; it is the specific checking step. Sources describe QC as tests and checks done during or after a process to identify defects, deviations, or out-of-specification results. In medical laboratories, QC supports reliable patient testing. In digital pathology, QC can check slide images for focus, stain quality, resolution, tissue detection, and image integrity.
[1] Digital pathology imaging artificial intelligence in cancer research ... [2] Refining quality control strategies in highly automated laboratories [3] Development and Implementation of an Internal Quality Control and ...
How QC works in practice
QC uses predefined rules or limits. In laboratory medicine, internal quality control (IQC) monitors the analytical process continuously by using control materials, control limits, QC charts, and responses to out-of-control signals. External quality assessment (EQA) checks whether results are comparable across laboratories. In digital pathology, QC may be performed before or after scanning to verify section thickness, image resolution, color fidelity, and whether the image matches model requirements before artificial intelligence (AI) is used.
[1] Digital pathology imaging artificial intelligence in cancer research ... [3] Development and Implementation of an Internal Quality Control and ...
Mechanisms and planning
QC plans can be risk-based. One source describes using statistical ideas such as sigma metric, imprecision, bias, and total allowable error to choose QC rules and sample run size. Sigma metric summarizes analytical performance using the difference between allowable error and observed systematic error, divided by imprecision. Another source notes that QC design may aim to keep the expected number of unreliable final patient results below one between QC events. In digital pathology, human review can be combined with AI to flag slides needing rescanning, reducing manual workload.
[2] Refining quality control strategies in highly automated laboratories [1] Digital pathology imaging artificial intelligence in cancer research ...
Limitations
QC depends on good measurements, good limits, and stable procedures. One source notes that sigma-based plans rely on accurate estimates of imprecision, bias, and allowable error, and that those values can change. Another source reports practical limits such as the need for training, workflow adaptation, and lack of automated tools. QC also cannot replace broader quality assurance systems that set standards, training, accreditation, and traceability.
[2] Refining quality control strategies in highly automated laboratories [3] Development and Implementation of an Internal Quality Control and ... [1] Digital pathology imaging artificial intelligence in cancer research ...
SEE THE IDEA
How quality control fits into a workflow
A lab result or scanned pathology image is produced.
Rules, limits, or visual inspection look for defects or deviations.
Pass, repeat, rescan, or investigate if the item fails QC.
Only items meeting standards move forward for reporting or analysis.
QC sits between production and final use, acting as a check that can stop or redirect poor-quality outputs.
QC is a filter-like step that detects problems before results are released or analyzed further.
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Sources and further reading
This general lesson was generated from public source material. It explains a concept; it does not interpret a private paper or provide medical advice.