Internal quality control (IQC) is a set of procedures a laboratory uses every day to verify that its test results are reliable before releasing them for patient care. It works by running samples with known values alongside real patient samples. If the control sample produces the expected result, the testing system is working correctly. If it doesn’t, patient results are held until the problem is found and fixed.
IQC is the frontline defense against reporting inaccurate lab results. It catches problems in real time, whether they stem from a reagent going bad, an instrument drifting out of calibration, or a preparation error by the technologist running the test.
How Internal QC Works in Practice
The basic concept is straightforward. A laboratory uses control materials: samples manufactured to contain a known concentration of whatever substance is being measured (glucose, cholesterol, hemoglobin, and so on). These controls are processed through the same testing system, at the same time, and under the same conditions as actual patient specimens. The result the instrument produces for the control is then compared against the expected value. If it falls within an acceptable range, the system is considered “in control” and patient results can be reported. If it falls outside that range, something has gone wrong.
Laboratories typically run at least two levels of control material, often a “normal” level and an “abnormal” level, to confirm the instrument performs accurately across the range of values it might encounter in real patients. U.S. federal regulations under CLIA require labs to test at minimum two levels of external QC materials each day they perform nonwaived testing. Some test systems with built-in electronic monitoring can reduce this to weekly or even monthly external control testing, but the internal monitoring checks themselves must still happen at least daily.
Controls are also required with each complete change of reagents, each new lot number or shipment, after major preventive maintenance, and after replacing any critical part that could affect test performance.
The Levey-Jennings Chart
Raw control values on their own are hard to interpret over time. That’s where the Levey-Jennings chart comes in. It’s the visual backbone of any IQC program.
The chart plots each day’s control result on a graph. The y-axis shows the measured value, and the x-axis shows time (typically each run or each day). A green horizontal line marks the expected mean. Yellow lines are drawn at two standard deviations above and below the mean, and red lines sit at three standard deviations. Each new control result gets plotted as a point, and the points are connected with lines so that patterns, shifts, and trends become immediately visible.
A result landing near the green line is reassuring. One drifting toward the yellow or red lines signals that something may be changing. A single point outside three standard deviations is a clear rejection. But the real power of the chart is in revealing gradual drift: if you see five or six consecutive points creeping steadily upward, even if none of them individually breaks a rule, that trend tells you the system is shifting and needs attention before it produces a clinically significant error.
Westgard Rules for Evaluating Control Data
Plotting data on a chart is only useful if you have clear criteria for deciding when results are acceptable and when they aren’t. The most widely used criteria are the Westgard rules, a set of decision rules applied to Levey-Jennings charts.
- 1-2s (warning): A single control result exceeds two standard deviations from the mean. This doesn’t automatically reject the run but triggers closer inspection using the rules below.
- 1-3s (rejection): A single control result exceeds three standard deviations. The run is rejected outright.
- 2-2s (rejection): Two consecutive control results both exceed two standard deviations on the same side of the mean. This pattern suggests a systematic shift rather than random variation.
- R-4s (rejection): Within the same run, one control exceeds two standard deviations above the mean while another exceeds two standard deviations below it. This spread of four standard deviations between controls within a single run points to a random error problem.
- 4-1s (rejection): Four consecutive control results all fall on the same side and beyond one standard deviation from the mean. Even though individually each point looks fine, the persistent pattern signals a developing bias.
The rules work together as a system. The 1-2s rule acts as a tripwire. When it fires, the technologist checks the remaining rules to determine whether the situation is a harmless statistical blip or a genuine problem requiring action.
What Happens When QC Fails
A failed QC event doesn’t mean patient results are automatically wrong, but it does mean they cannot be released until the issue is resolved. The first step is usually the simplest: verify that the control material isn’t expired, was stored properly, and was prepared correctly. These mundane causes account for a surprising number of failures.
If the basics check out, one common approach is to repeat the control. If the repeat falls within two standard deviations, the initial failure is treated as a random occurrence and patient results can be reported. If the repeat also fails, the run is rejected and all patient results are held.
From there, the laboratory characterizes the error. The pattern on the Levey-Jennings chart provides the first clue. If the data suggests random error (points scattered unpredictably in both directions), the investigation focuses on things like bubbles in the sample, inconsistent pipetting, or electrical interference. If the pattern suggests systematic error (a consistent shift or trend in one direction), the likely culprits include degraded reagents, a calibration drift, or a change in room temperature.
Once the cause is identified and corrected, the instrument is recalibrated and controls are retested. If those results come back in range, any patient samples that may have been affected are retested and then reported. If the controls still fail, a deeper root cause analysis begins, and patient results remain held.
Internal QC vs. External Quality Assurance
Internal QC and external quality assurance (EQA) serve different but complementary purposes. IQC is something a lab does for itself, every day, to monitor its own consistency. EQA is an outside check: an external organization sends identical samples to many laboratories, collects their results, and compares them. This reveals whether a lab’s results agree with those of other labs using the same methods.
Think of IQC as checking your bathroom scale every morning to make sure it gives the same reading for the same weight. EQA is like having someone else step on your scale and compare the number to what their doctor’s scale says. IQC catches day-to-day drift within your own system. EQA catches biases you might never notice on your own because they’ve been consistent all along.
EQA programs typically operate on a periodic schedule (monthly or quarterly), so they can’t catch problems in real time the way daily IQC can. Both are required under laboratory accreditation standards, and neither can substitute for the other.
Individualized Quality Control Plans
Not every test system fits neatly into a one-size-fits-all QC schedule. CLIA regulations now allow laboratories to develop an Individualized Quality Control Plan (IQCP) that customizes QC practices based on the specific testing environment, instrument capabilities, and patient population. An IQCP accounts for all the quality practices a lab already uses (not just running control materials at a set frequency) and builds a comprehensive plan around them.
IQCP is available for all nonwaived testing across all CLIA specialties except pathology. It’s particularly useful for newer analyzers that have extensive built-in electronic checks, where running traditional liquid controls at the default frequency may be redundant. The plan must be documented, approved, and reviewed regularly, and the lab remains responsible for demonstrating that its approach effectively monitors the complete testing process.
Why IQC Matters Beyond the Lab
Every clinical decision built on a lab result, from adjusting a medication dose to diagnosing a new condition, depends on that result being accurate. A glucose value that reads 15% too high could lead to unnecessary insulin. A potassium level that drifts low could mask a dangerous electrolyte imbalance. IQC is the mechanism that stands between an instrument malfunction and a patient receiving the wrong treatment.
For laboratory professionals, running and interpreting QC is one of the most consequential parts of the daily routine. It’s not paperwork or a regulatory checkbox. It’s the process that earns the right to report a result.