LabCourses - Laboratory QC Training Manual and Course
Publisher: John Jones
The manual has three jobs. First, it explains what the accompanying course does and does not teach, so training coordinators can decide where it fits in a broader QC curriculum. Second, it walks an LMS administrator through installing and tracking the SCORM package on their own platform. Third, it reproduces the core…
LabCourses — Laboratory QC Training Manual & Course
A reference manual and deployment guide for the downloadable SCORM e-learning course, Laboratory Quality Control Fundamentals — covering why QC is run, how to read a Levey-Jennings chart, Westgard multirule interpretation, out-of-control response, and audit-ready documentation.
Purpose & Scope
This manual is the print-and-reference companion to a self-contained SCORM 1.2 e-learning course, Laboratory Quality Control Fundamentals. It is not a substitute for the course, and it is not a regulatory standard in itself — it is a bridge between the two.
What this manual covers
The manual has three jobs. First, it explains what the accompanying course does and does not teach, so training coordinators can decide where it fits in a broader QC curriculum. Second, it walks an LMS administrator through installing and tracking the SCORM package on their own platform. Third, it reproduces the core reference content of the course — control theory, statistical QC, Westgard rules, out-of-control response, and documentation expectations — in a linear, searchable, printable form that works as a standalone job aid long after the course itself has been completed.
What this manual is not
This manual describes general laboratory QC practice at a working-knowledge level. It does not replace your laboratory's own standard operating procedures (SOPs), your accrediting body's checklist language, or the specific requirements of CLIA, CAP, ISO 15189, or any other applicable framework. Where general practice described here and your lab's own SOP appear to differ, the SOP and the governing regulatory framework are always the authoritative source.
Intended audience
Lab Staff & Trainees
Anyone who runs, reads, or responds to daily control results and wants a reference to revisit after finishing the course.
Training Coordinators
Staff assembling a QC onboarding curriculum who need to know exactly what this course teaches — and what it assumes the learner already knows.
LMS Administrators
Staff responsible for installing the SCORM package, confirming it reports correctly, and reviewing completion data.
QA Managers & Auditors
Reviewers who want a citable reference for what staff training on QC fundamentals actually included.
Relationship to LabCourses
This manual and its accompanying course are published as part of LabCourses, LabLynx's training and certification add-on application. The course itself is a standards-based SCORM 1.2 package, so it can be delivered through LabCourses or imported into any other SCORM 1.2-compliant learning management system your organization already uses — it does not require LabCourses to run.
How to Use This Manual With the Course
The manual and the course cover the same eight topics, but they are built for different moments: the course for first-pass, interactive learning; the manual for lookup, review, and citation afterward.
Recommended workflow
- Assign the course first. The SCORM package is interactive — it includes a clickable Levey-Jennings chart and a scored knowledge check that reports a pass/fail mastery score back to your LMS. That interactivity is best experienced before reading the equivalent reference material.
- Use the manual for review and lookup. Once a learner has completed the course, this manual becomes the thing they actually keep open on a second monitor during their first weeks running QC independently — searchable, linear, and printable.
- Use the manual for onboarding documentation. Because it is a standalone HTML file, it can be attached to an onboarding checklist, linked from an SOP, or printed for a training binder without needing LMS access.
Chapter-to-module correlation
Every content module in the course has a directly corresponding chapter in this manual, so a learner or auditor can move between the two without hunting for equivalent material.
| Course Module | Manual Chapter |
|---|---|
| Welcome & Learning Objectives | Chapter 1 — Purpose & Scope |
| 1. What Is Lab QC? | Chapter 5 — What Is Laboratory QC? |
| 2. Control Materials & Levels | Chapter 6 — Control Materials & Levels |
| 3. QC Frequency & Scheduling | Chapter 7 — QC Frequency & Scheduling |
| 4. Statistical QC Basics | Chapter 8 — Statistical QC Basics |
| 5. Westgard Multirules | Chapter 9 — Westgard Multirules |
| 6. Out-of-Control Troubleshooting | Chapter 10 — Out-of-Control Troubleshooting |
| 7. Documentation & Regulatory Context | Chapter 11 — Documentation & Regulatory Context |
| 8. Knowledge Check (10 questions, 80% to pass) | Chapter 12 — Knowledge Check Question Bank (facilitator answer key) |
Chapter 12 reproduces every knowledge-check question with its correct answer and explanation. Keep that chapter out of learners' hands before they attempt the course — it is meant as a facilitator's answer key and a source for follow-up discussion, not as a study sheet to memorize in place of the course itself.
Inside the SCORM Package
The course is packaged to the SCORM 1.2 standard, the most widely supported e-learning interoperability standard across commercial and open-source learning management systems.
Package Contents & Manifest
The download in Chapter 4 is a single .zip archive built as a standard SCORM 1.2 content package. Its manifest (imsmanifest.xml) declares one organization containing one SCO (shareable content object):
├─ imsmanifest.xml
├─ index.html ← SCO entry point
├─ css/style.css
└─ js/
├─ scorm-api.js ← LMS communication
├─ quiz-data.js ← knowledge-check bank
└─ course.js ← course logic & content
| Manifest Property | Value |
|---|---|
| SCORM version | 1.2 (ADL SCORM) |
| Organization title | Laboratory Quality Control Fundamentals |
| Mastery score | 80% |
| Time limit action | Continue, no message |
| SCO entry point | index.html |
Installing in Your LMS
Because it follows the SCORM 1.2 standard, the package installs the same way any SCORM 1.2 course does in a compliant LMS. Exact menu names vary by platform, but the sequence is consistent:
- Download the .zip package from Chapter 4 — do not unzip it. Most LMS platforms expect the packaged .zip as-is.
- In your LMS, locate the course or content import function (commonly labeled "Add Content," "Import Package," "Upload SCORM Package," or similar).
- Select SCORM 1.2 as the package type if your LMS asks you to specify a standard rather than auto-detecting it from the manifest.
- Upload the .zip file directly. The LMS will read
imsmanifest.xmlto register the course title, organization, and mastery score automatically. - Assign the resulting course to the appropriate learners, groups, or roles as you would any other course in your catalog.
This package has not been validated against every SCORM 1.2-compliant LMS on the market. If your platform's import step behaves differently from the steps above, consult your LMS vendor's own SCORM import documentation.
Tracking & Completion Data
The course reports standard SCORM 1.2 CMI data elements back to the LMS as the learner progresses:
- cmi.core.lesson_status — set to incomplete on first launch, and to passed or failed once the knowledge check is submitted, based on the 80% mastery threshold.
- cmi.core.score.raw / min / max — the learner's knowledge-check score, reported on a 0–100 scale.
- cmi.core.lesson_location — the last section the learner viewed, so progress resumes correctly if the learner exits and re-launches later.
- cmi.core.session_time — time spent in the current session, recorded on exit and before the browser unloads the page.
Because progress is tracked at the section level and committed continuously, a learner who exits mid-course and relaunches later returns to the same section rather than starting over.
Download the Course Package
The button below downloads the complete SCORM 1.2 package as a single .zip file, ready to import into your learning management system exactly as described in Chapter 3.
Laboratory Quality Control Fundamentals — SCORM 1.2 Package
⬇ Download lab-qc-scorm12.zipLeave the file zipped and follow the import steps in Installing in Your LMS. If your browser renames the file on download, rename it back to end in .zip before importing — some LMS import forms check the file extension.
What Is Laboratory Quality Control?
Every result a laboratory reports rests on one quiet assumption: that the test system producing it was working correctly at the time. Quality control is how a lab earns the right to make that assumption.
Quality control is the practice of testing a material with a known, expected result alongside patient samples, to confirm the test system is performing within acceptable limits before those patient results are released.
Three related but distinct activities
It is easy to blur quality control together with two neighboring concepts. They serve different purposes and run on different schedules.
Quality Control (QC)
Internal. Run by the lab itself, typically every shift, day, or per analytical run. Uses control material with an established mean and acceptable range.
Quality Assurance (QA)
The broader system — policies, training, maintenance schedules, and monitoring that keep the whole testing process reliable. QC is one piece of QA, not the whole of it.
Proficiency Testing (PT)
External. A few times a year, the lab tests unknown samples sent by an outside program and compares its results against peer labs or a reference value.
QC answers a narrow, urgent question: right now, can I trust what this instrument is telling me? The rest of this manual is really about answering that question well.
Control Materials & Levels
A control is a sample manufactured to behave like a patient specimen but with a value the lab already knows, within a defined range.
Running a control tells you whether the instrument, reagents, and technique are behaving as expected — independent of any single patient's result.
Why multiple levels?
Most tests run at least two levels of control — commonly a normal and an abnormal (high or low) level, and often a third mid-range level for wider assays. A method can be accurate at a normal concentration and still drift at the high or low end; a single level would miss that entirely.
Matrix matters
Control material should behave like the specimen type it is standing in for — serum, whole blood, urine — so it responds to the same analytical conditions patient samples do. A mismatch in matrix can mask or exaggerate a problem that would otherwise be caught.
New lot verification
Every control lot has its own manufacturer-assigned range, but that range must be verified against the lab's own instrument and reagents before use. Standard practice is to run the new lot in parallel with the current lot — typically over about 20 data points — to establish the lab's own mean and standard deviation before the old lot expires.
Skipping lot verification means QC limits that don't actually reflect how this lab's own system performs — a manufacturer's published range describes their reference conditions, not necessarily yours.
QC Frequency & Scheduling
How often QC runs depends on the test, the instrument, and applicable regulatory or accreditation requirements — there is no single universal schedule.
| Pattern | Typical Use |
|---|---|
| Per run or per shift | Many chemistry and hematology analyzers run QC at the start of each shift or testing session |
| Daily | A baseline for lower-volume or more stable methods |
| Per new reagent lot or calibration | Regardless of the normal schedule, QC is run whenever something changes that could affect results |
| Per patient batch | Some workflows bracket a batch of patient samples with controls before and after |
The schedule is usually documented in the test's standard operating procedure (SOP), and any deviation — a missed QC run, a late start — should itself be documented, since gaps in QC coverage are exactly what auditors look for.
Statistical QC Basics
Once enough control results accumulate, the lab can describe how that control material actually behaves on its own system — not just whether a single result looks right.
Mean & Standard Deviation
The mean is the expected center value for a control, established from accumulated results. The standard deviation (SD) describes how much individual results normally scatter around that center. Together they define the boundaries used to judge whether a new result looks routine or unusual.
Coefficient of Variation
The coefficient of variation (CV%) expresses that scatter as a percentage of the mean, which makes it possible to compare precision across analytes measured on very different scales:
CV% = (SD ÷ Mean) × 100
The Levey-Jennings Chart
A Levey-Jennings chart plots each control result, run after run, against horizontal lines marking the mean and the ±1SD, ±2SD, and ±3SD boundaries. It turns a column of numbers into a shape you can read at a glance — scatter, drift, and sudden jumps all become visible immediately.
Static illustration for reference. The course's version of this chart is interactive — clicking any point shows how it reads against the control limits and which Westgard rule, if any, it would trigger.
Westgard Multirules
A single control result outside ±2SD happens by chance around 1 in 20 runs, purely from normal scatter. Rejecting a run every time that happens would be needlessly disruptive — Westgard multirules combine several observations to tell routine scatter apart from a real problem.
| Rule | Pattern | Typically Flags |
|---|---|---|
| 1_2s | One control beyond ±2SD | Warning only — check the other rules before acting |
| 1_3s | One control beyond ±3SD | Rejection — usually random error |
| 2_2s | Two consecutive controls beyond the same ±2SD line | Rejection — usually systematic error (e.g. calibration, reagent lot) |
| R_4s | One control >+2SD and another <−2SD within the same run | Rejection — random error, often precision-related |
| 4_1s | Four consecutive controls beyond the same ±1SD line | Rejection — systematic error emerging gradually |
| 10x | Ten consecutive controls on the same side of the mean | Rejection — a small but persistent shift |
In practice, the 1_2s rule is used as a trigger to check the other, stricter rules — not as a rejection rule on its own. This layered approach keeps QC sensitive to real problems without constantly crying wolf over ordinary scatter.
Responding to an Out-of-Control Result
When a control result is rejected, the response is the same regardless of which rule fired.
- Hold patient results. Nothing from the affected run is released until the cause is understood.
- Look for an obvious cause first. Expired reagent, an air bubble, a maintenance step that was skipped, a new lot that wasn't verified, an environmental issue.
- Repeat the control. If it passes and a clear, correctable cause was found and fixed, that is often enough to resume.
- If it fails again, escalate. That may mean recalibrating, contacting the manufacturer, or pulling the instrument from service until resolved.
- Only resume patient testing once QC passes. A repeat pass without an identified cause is a weaker resolution than one with a clear explanation, and some labs require the latter before reporting resumes.
Reporting patient results from a rejected run "to be safe" while the investigation continues, or quietly re-running only the control until it happens to pass. Both defeat the purpose of running QC at all.
Documentation & Regulatory Context
A QC corrective-action record is what turns "we fixed it" into something an inspector, auditor, or your own future self can verify.
What a complete record includes
- What was observed — which rule fired, which analyte, which level
- The investigation into root cause
- The corrective action taken
- Verification that QC passed afterward, before reporting resumed
- Who performed and who reviewed the correction
Governing frameworks
Several frameworks shape how labs are expected to run and document QC, including CLIA regulations in the U.S., CAP accreditation checklists, and the ISO 15189 standard internationally. Requirements differ in their specifics — minimum control levels, frequency, documentation retention — so the governing framework and the test's own SOP are always the authoritative source, not the general practice described in this manual.
A LIMS that logs QC runs, flags rule violations automatically, and timestamps corrective actions turns this record-keeping from a manual burden into something that happens as a byproduct of normal workflow.
Knowledge Check Question Bank
This chapter reproduces all ten questions from the course's scored knowledge check, along with the correct answer and explanation for each. It is intended for trainers and QA reviewers — not as a study sheet for learners in place of the course itself.
The knowledge check requires 80% (8 of 10 questions correct) to pass. A learner who does not pass may retry the knowledge check from within the course.
What is the main reason a lab runs control materials alongside patient samples?
- To calibrate the instrument's software
- To verify the test system is working within expected limits before patient results are released ✓
- To satisfy a billing requirement
- To reduce the number of patient samples needed
Why: QC material has a known, expected value. If it doesn't land where expected, the run's patient results can't be trusted yet — that's the whole point of running it first.
How does Quality Control (QC) differ from Proficiency Testing (PT)?
- They are two names for the same activity
- QC is internal and run frequently by the lab itself; PT is external, periodic, and compares the lab against peer labs ✓
- PT is run every shift; QC is run twice a year
- QC only applies to chemistry analyzers, PT only applies to microbiology
Why: QC is the lab's own ongoing check on its test systems. PT is an outside comparison, usually a few times a year, that verifies the lab's results agree with the broader field.
On a Levey-Jennings chart, what do the horizontal lines above and below the mean typically represent?
- Reportable range limits set by the manufacturer
- Standard deviation (SD) intervals — commonly ±1SD, ±2SD, ±3SD ✓
- The lab's turnaround time targets
- Patient reference ranges
Why: Levey-Jennings charts plot control values over time against the mean and SD bands, so drift or scatter is visible at a glance.
A single control result falls beyond the ±3SD line. Which Westgard rule does this violate?
- 1_2s
- 1_3s ✓
- 2_2s
- 4_1s
Why: 1_3s means one control observation exceeds the mean by more than 3 standard deviations — a classic rejection rule signaling random error.
Two consecutive control results both fall beyond the same ±2SD line (same side). This describes which rule?
- 1_2s (warning only)
- 2_2s ✓
- R_4s
- 10x
Why: 2_2s fires when two consecutive points on the same side exceed 2SD — it usually points to a systematic error, such as a shift in calibration or reagent lot.
Why do most labs run at least two levels of control material (e.g., normal and abnormal) rather than just one?
- It's only done to use up extra control material
- A single level can miss errors that only show up at a different concentration, like nonlinearity at the high or low end ✓
- Regulatory bodies require exactly two levels for every test, with no exceptions
- Two levels are needed so the analyzer can calibrate itself automatically
Why: A method can perform fine at a normal concentration and still be off at a high or low one. Multiple levels catch problems a single level would miss.
A control result is rejected during a run. What is the appropriate immediate action?
- Report the patient results and investigate the control failure later
- Withhold affected patient results until the cause is identified and resolved, then repeat QC before resuming reporting ✓
- Re-run only the control, and if it passes the second time, ignore the first failure
- Switch to a different control lot without documentation
Why: An out-of-control result means the run can't be trusted yet. Patient results are held, the cause is investigated and corrected, and QC must pass again before reporting resumes.
A complete corrective action record for a QC failure should include which of the following?
- Only the date of the failure
- The problem identified, root cause investigation, corrective action taken, and verification that QC passed afterward ✓
- Just a note that the instrument was restarted
- The name of the control manufacturer only
Why: Documentation should tell the full story: what went wrong, why, what was done about it, and proof the fix worked — this is what an auditor or inspector will look for.
What does the coefficient of variation (CV%) describe?
- The absolute number of QC failures per month
- The relative spread (imprecision) of repeated control results, expressed as a percentage of the mean ✓
- The cost variation between control lots
- The percentage of patient results that were abnormal
Why: CV% normalizes the standard deviation against the mean, making it possible to compare precision across analytes or concentrations that have very different scales.
Before a new control lot is put into routine use, what should happen?
- Nothing — new lots can be used immediately once opened
- The new lot should be run in parallel with the current lot to establish its own mean/SD and confirm agreement before the old lot expires ✓
- The new lot should replace the old lot's target values automatically
- Only the lab director needs to be informed, no testing is required
Why: Every control lot has its own expected mean and range. Parallel testing against the outgoing lot establishes valid limits before you depend on the new one.