Laboratory Quality Systems
From Quality Control to Clinical Decision Integrity
An advanced training platform that transforms how laboratory professionals understand quality — from analytical metrics to clinical consequence. Built around decision scenarios, not passive content delivery.
Course Details
Alignment
CLIP-Aligned Certificate
Accreditation Standards
Quality systems prevent patient harm
Laboratory results drive 70% of clinical decisions. When quality fails, decisions fail.
of clinical decisions are laboratory-driven
Treatment plans, diagnoses, and monitoring protocols all depend on laboratory data accuracy.
of lab errors occur pre-analytically
Before the sample reaches the analyser — collection, transport, and identification failures dominate.
performance target for clinical reliability
Methods operating below 3 Sigma are considered analytically deficient and clinically unreliable.
Diagnostic errors impact patient care
Missed diagnoses, delayed treatment, and inappropriate therapy — all traceable to laboratory failures.
Quality systems prevent harm
Robust QC, validated methods, and intelligent delta checks are patient safety infrastructure, not regulatory bureaucracy.
Labs are decision engines
A laboratory is not a test center. It is a clinical decision engine. Every result carries responsibility.
The Total Testing Process
Every laboratory result passes through three phases. Error at any point propagates forward — often invisibly.
Pre-Analytical
46–68% of errors
Quality Checkpoints
Error Points
Analytical
7–13% of errors
Quality Checkpoints
Error Points
Post-Analytical
18–47% of errors
Quality Checkpoints
Error Points
Clinical Decision
Patient Impact
Quality Checkpoints
Error Points
Key insight: Most laboratories focus quality resources on the analytical phase — where only 7–13% of errors occur. The pre-analytical phase, responsible for the majority of errors, often receives minimal systematic attention.
Five modules. One integrated system.
Each module builds on the last — from foundational quality principles through to clinical decision integrity. Click any module to explore topics and the core question driving that module.
Decision scenarios from real laboratory practice
Read the situation, study the data, formulate your decision — then reveal the rationale and teaching insight. Each case is drawn from real-world laboratory intelligence challenges.
Silent QC Failure in Glucose Reporting
Situation
Morning IQC run for glucose: Level 1 passes (mean +1.2 SD), Level 2 passes (mean +1.8 SD). No Westgard rule triggers. Results reported. Four hours later, a clinician queries a glucose of 2.1 mmol/L in an alert, asymptomatic patient.
IQC Level 1
+1.2 SD
Within limits
IQC Level 2
+1.8 SD
Within limits
Patient Glucose
2.1 mmol/L
Flagged by clinician
4-day trend
Systematic shift +
Not triggered by rules
Decision Challenge
No individual Westgard rule was violated. The lab passed QC. What went wrong?
ELISA Variability in Biomarker Research
Situation
An ELISA for serum HMGB1 is run on 24 samples across two separate plates on the same day using the same kit lot. Researcher reports a 28% difference in mean OD between plates. The intra-assay CV is acceptable (8%). The inter-assay CV exceeds 20%.
Intra-assay CV
8%
Acceptable
Inter-assay CV
22%
Exceeds target (15%)
Plate 1 OD (mean)
0.81
Reference plate
Plate 2 OD (mean)
1.04
Same kit lot
Decision Challenge
Results look consistent within each plate but differ between plates. Can you report both plates together?
Delta Check Abnormality in Sodium
Situation
An inpatient sodium result of 156 mmol/L is generated. The previous result 18 hours ago was 138 mmol/L. The laboratory system flags a delta check alert (change >12 mmol/L per day). The lab technologist is about to call the result. The sample was collected from an IV arm.
Current Sodium
156 mmol/L
Flagged
Previous Sodium
138 mmol/L
18h ago
Delta
+18 mmol/L
Exceeds threshold
Collection site
IV arm
Saline infusion noted
Decision Challenge
The delta check is positive. Should this result be reported, queried, or cancelled?
Learn by doing, not just reading
These course modules are directly integrated with laboratory intelligence tools. Use them while learning, not after.
ELISA Intelligence System
Applied in Modules 01, 03, 04
Enter OD readings from ELISA plates and receive full QC analysis: intra-assay CV, inter-assay comparison, curve fitting, and quality flag generation. Apply directly to Case 02.
Sigma Metrics Calculator
Applied in Module 03
Calculate sigma performance from CV, bias, and total allowable error. Visualise performance on the sigma scale and determine appropriate QC rule selection based on analytical risk.
Viva preparation & practical intelligence
Structured questions for examination readiness, combined with practical insights and a guide to the most common conceptual mistakes.
Viva Questions
Common Mistakes to Avoid
Treating all Westgard rule violations the same
Distinguish warning rules (1-2s) from rejection rules (1-3s, 2-2s). Each has different clinical implications.
Assuming a passed IQC run means all patient results are accurate
IQC only monitors the analytical process. Pre-analytical errors are invisible to QC charts.
Reporting ELISA results from separate plates without inter-assay validation
Always validate inter-assay CV before combining data from different runs.
Ignoring delta check alerts under time pressure
Delta check positivity requires a structured decision pathway — never bypass it routinely.
Confusing total allowable error with expanded uncertainty
TEa is a performance goal; uncertainty is a measured property of a result. Both must be understood.
Intelligence you can apply immediately
A 1-2s Westgard warning does not require run rejection. It should trigger surveillance of the next result.
Module 03Sigma > 6 allows minimal QC (1 control per run). Sigma < 3 requires multi-level, multi-rule approaches.
Module 03Haemolysis raises potassium by ~0.5 mmol/L per 1 g/dL haemoglobin — an important pre-analytical correction factor.
Module 01Measurement uncertainty is not the same as imprecision. It includes all sources of variability in the measurement chain.
Module 04A positive delta check should never be reflexively cancelled. It requires a structured decision pathway.
Module 05Inter-assay CV >15% for low-abundance ELISA biomarkers means results from separate runs cannot be directly compared.
Module 04Start Learning Laboratory Intelligence
Move beyond quality control compliance. Build the decision intelligence that protects patients and elevates laboratory medicine.
5
Modules
3
Case Scenarios
6
Viva Questions