Category: Practice & Regulation | Estimated reading time: 8 minutes
Where Does QC Fit in the Bigger Picture?
Many medical laboratory technologists (ATLM) view QC as a standalone activity — something done in the morning before processing samples, and then it is finished. However, QC is one point in a longer workflow, and understanding its position in that flow will change how you view every step taken daily.
The QC Workflow from Upstream to Downstream
There are seven sequential points in a complete decision cycle:
Control Material → Measured → Data → Control Chart → Evaluation → Decision → Action
Every point in this flow has a role that cannot be skipped.
Control Material is the starting point. The selection of control material is not trivial — the levels chosen must be clinically relevant, covering meaningful value ranges for decision-making. Control materials with two levels (normal and abnormal) provide much richer information than a single level alone, as instrument performance can differ across different value ranges. Storage conditions, expiration dates, and preparation consistency also influence the validity of the entire QC system.
Measured — measurements must be performed under normal operational conditions, by the operator who usually operates the instrument, at a representative time. Measuring QC under "special" conditions — for example, just after service or calibration, by a senior technologist who is not the daily operator — produces data that does not represent the actual laboratory condition.
Data — The Mean, SD, and CV calculated from the preliminary testing period are the foundation of the entire system. These figures are not taken from the manufacturer's kit insert — they must be established by the laboratory itself, based on its specific conditions and instruments.
Control Chart — data that already has value is only half the story if it is not visualized. Levey-Jennings charts turn rows of numbers into visual patterns that can be read, interpreted, and communicated. One point out of range might be a random error. Three points moving consistently in one direction is something else — and a trained eye will catch that before any official rule is violated.
Evaluation — the core of the entire system. Evaluation uses Westgard rules and compares the actual TE with the TEa. This is where intellectual decisions are made: whether what is seen on the chart is normal variation or a signal that requires follow-up.
Decision — Accept or Reject. Two choices that seem simple but have major consequences. Accept means we declare that today's analytical process is reliable enough to produce patient results. Reject means we declare otherwise — and are responsible for the consequences of that delay.
Action — a rejection without action is a wasted rejection. Action includes investigation of the cause, correction, re-verification, and documentation. Documentation is not a formality — it is an institutional memory that helps us recognize recurring patterns of problems before they become crises.
IQC vs. EQA: Two Complementary Components
IQC (Internal) EQA (External) Who The lab itself External parties (EQAS, Prolab) When Daily Periodically (2x/year) Purpose Internal problem detection Compare with other labs Assessment Imprecision (CV) Inaccuracy (d%) This Assessment row is the one most often misunderstood in the field.
Why does IQC assess imprecision (CV)? IQC is run with control materials whose values we set ourselves through preliminary testing. We know the target value — we set it. Therefore, what can be evaluated from IQC data is how consistently our instrument produces values around that target. This consistency is called precision, and CV is the way to measure it.
Good IQC will show a small CV — the instrument produces consistent values from run to run. Poor IQC will show a large CV — the instrument is not precise, and the results cannot be relied upon.
Why does EQA assess inaccuracy (d%)? EQA uses samples sent by the organizer — the values are unknown to us beforehand. After we report our results, the organizer compares our results with the average results of all participating laboratories using the same methods and instruments (peer group). From this comparison, what can be evaluated is how far our results are from the consensus — namely bias or inaccuracy, expressed as d%.
Good EQA will show a small d% — our results are consistent with the results of other comparable laboratories. Poor EQA will show a large d% — there is a systematic bias in our method or calibration that is not detected by IQC alone.
This is why IQC and EQA cannot replace each other. IQC can detect precision problems but cannot detect calibration bias that occurs evenly across all measurements. EQA can detect bias but cannot provide real-time information about the instrument's condition today. Both are needed — and together they provide a much more complete picture of laboratory performance than either one alone.
Why Understanding This System Is Important
Westgard & Westgard (2016) emphasize that rational QC planning begins with an understanding of the position of each component in the larger system. Laboratories that understand this flow will know:
- When a problem detected in IQC is likely a precision problem — and when it is likely an accuracy problem.
- Why poor EQA results can happen even if daily IQC looks good — because the two measure different things.
- How data from both programs can be integrated to make better decisions about method performance.
This understanding also forms the basis of the Sigma Metric concept — where CV (from IQC) and d% (from EQA) together with TEa are used to calculate the sigma metric that describes the overall quality of the examination method. An evaluation framework based on quality indicators of this kind is in line with what was proposed by Plebani, Sciacovelli, and Aita (2017) in their study on quality indicators for the entire laboratory testing process (total testing process) — covering the pre-analytical, analytical, and post-analytical phases.
The obligation to conduct IQC and EQA is not merely a best practice, but is mandated directly by Indonesian regulation through Minister of Health Regulation No. 43 of 2013 concerning Good Clinical Laboratory Practices, which establishes quality criteria, including quality assurance, as part of the implementation of a responsible clinical laboratory.
Conclusion
QC is not an island. It is a point in a longer cycle — from the selection of control materials to the documentation of corrective actions. And it is part of a larger system consisting of IQC and EQA — two programs that measure different things and together provide a complete picture.
Understanding the position of QC in this system is not just an academic matter. It determines how you read data, how you investigate problems, and how you make decisions that truly protect the patient.