LabQMHub Editorial Team · ISO 15189:2022 Quality Management Series
A validated method can still fail on any given day — a reagent lot degrades, an instrument component wears, a calibration silently drifts. Internal quality control, or IQC, is the laboratory’s daily, ongoing safeguard against exactly this kind of undetected failure, run alongside patient specimens specifically to catch problems before they reach a clinician.
For quantitative tests — those producing a numeric result — ISO 15189:2022 expects a statistically grounded IQC program capable of detecting both sudden errors and gradual drift, not merely a control run once per shift and glanced at without genuine statistical interpretation.
Why Quantitative IQC Requires Statistical Rigor
Quantitative results allow for genuinely statistical quality control: control materials with known target values and expected ranges are tested alongside patient specimens, and the results are plotted and evaluated against statistically derived control limits, most commonly using Levey-Jennings charts and Westgard multi-rule interpretation. This statistical foundation is what allows a laboratory to distinguish normal, expected variation from a genuine analytical problem requiring investigation.
A control result that falls outside expected limits is not simply a nuisance to be repeated until it passes — it is a signal requiring investigation into cause, and a decision about whether patient results generated since the last acceptable control must be reviewed and potentially withheld or corrected.
Building an Effective Quantitative IQC Program
An effective program uses control materials at multiple concentration levels, positioned to detect errors across the clinically relevant range rather than only at a single point, and runs controls with a frequency appropriate to test volume, stability, and clinical risk — a high-volume, high-risk test warrants more frequent control testing than a low-volume, stable one.
Selecting appropriate Westgard rules, rather than applying a single rigid rule set to every test regardless of its performance characteristics, allows a laboratory to balance sensitivity to genuine error against an unnecessarily high false-rejection rate that would otherwise disrupt workflow without improving genuine error detection.
Responding to Out-of-Control Results
When a control violates a rule, the laboratory must investigate before reporting affected patient results — reviewing recent maintenance, reagent lot changes, calibration status, and control material integrity as potential causes, rather than simply repeating the control until it happens to pass, which resolves nothing about the underlying cause.
A documented corrective action, addressing the confirmed root cause, should be completed before returning the test to reportable status, connecting directly to the nonconforming work and corrective action principles covered later in this program — an IQC failure is, functionally, an early-detected nonconformity.
Trend Analysis: Catching Problems Before They Become Failures
Beyond individual rule violations, reviewing control data trends over time — a gradual shift or increasing scatter that has not yet triggered a formal rule violation — can reveal a developing problem while it is still manageable, before it produces an outright control failure or, worse, an undetected drift in patient results.
Laboratories with mature IQC programs review Levey-Jennings trends periodically, not only reactively when a rule is violated, treating quality control data as a genuine monitoring tool rather than a pass/fail gate to clear before releasing results.
Frequently Asked Questions
What are Westgard rules?
A set of statistical rules applied to quality control data to distinguish random, acceptable variation from systematic or random errors requiring investigation, commonly used in quantitative laboratory testing.
How often should quality control be run for a quantitative test?
Frequency should be based on test volume, stability, and clinical risk, with high-risk or high-volume tests typically requiring more frequent control testing than stable, low-volume ones.
What should a laboratory do when a control result violates a rule?
Investigate the likely cause before reporting affected patient results, address the confirmed root cause with documented corrective action, and confirm the test is back in control before resuming reporting.
Key Takeaways
- Internal quality control is the laboratory’s daily safeguard against undetected analytical error, run alongside patient specimens.
- Quantitative IQC relies on statistical tools like Levey-Jennings charts and Westgard multi-rule interpretation to distinguish normal variation from genuine problems.
- Control materials should be tested at multiple concentration levels with a frequency matched to test volume, stability, and clinical risk.
- An out-of-control result requires investigation into cause, not simply repeated testing until a control happens to pass.
- Corrective action addressing the confirmed root cause should be completed and documented before returning a test to reportable status.
- Reviewing control data trends proactively can catch a developing problem before it produces an outright rule violation.
Conclusion
Internal quality control is where a validated method’s promised performance is confirmed, or challenged, every single day it is in use. It is one of the most consequential, continuously running safeguards in the entire laboratory quality system.
ISO 15189:2022’s expectations for quantitative IQC ensure this safeguard is statistically grounded and genuinely responsive, not a routine formality performed and forgotten between shifts.
See how internal quality control adapts for qualitative and semi-quantitative testing in the next course of this ISO 15189:2022 training series.
Source note: this article draws on “Quality Management in Clinical Laboratory Demystified” by Dr. Taleb Chalab Cham, ISO 15189:2022, and widely recognized clinical laboratory quality references including CLSI guidelines, CAP accreditation checklists, and WHO laboratory quality guidance.
