Lecture Notes on Quality Control Protocols

Introduction

• Topic: Protocols for Quality Controls
• Progress: Extensive journey from basic concepts to advanced applications

Recap of Discussions

Distributions and Central Tendencies

• Types of distributions
• Central Tendencies (mean, mode, median)
• Focus on Gaussian (Normal) Distribution
  • Mean = Mode = Median

Gaussian Application in Laboratories

• Creating Levey-Jennings (LJ) charts for lab use

Quality Control Rules

• Importance and applications of rules
• Rule violations
• Error detection in labs
• Error control strategies

Chart Accuracy

• Importance of making accurate charts
• Impact of wrong charts

New Quality Control Lot

• Setting right numbers through parallel testing

Bias and Total Error

• Concept of bias
• Calculating bias
• Total error and its importance
• Quality specifications and tolerance limits
• Using databases for allowable error

Internal Quality Control (ISO 15189 Clause 5.6)

• Ensuring quality of examinations
• Additional concepts: Bias, Total Error, Total Allowable Error, Sigma Metrics

Sigma Metrics

Concept and Importance

• 19th-century mathematical theory used in modern business (Motorola, 1980s)
• Goal: Define tolerance limits and describe intended use
• Six Sigma: Less than 3.4 defects per million opportunities (DPMO)

Steps to Achieve Six Sigma

• Identify and eliminate errors systematically
• Define quality specifications (Sigma levels)
• Allowable error and tolerance limits

Examples of Sigma Metrics

• Airline safety (6 Sigma)
• Airline baggage handling (4.15 Sigma)
• Lab examples: Pre-analytical sample (5.1 Sigma), Controlled lab (3.4 Sigma)

Calculating Sigma for Labs

• Importance of controlling bias and imprecision
• Sigma Calculation: (Total Allowable Error - Bias) / Standard Deviation
• Practical usage with peer group data for bias calculation
• Tools and software for calculations

Rule Selection Based on Sigma

• Power function graphs for QC rule selection
  • Critical for identifying appropriate QC procedures
  • Performance levels and required QC rules
• Examples: Different Sigma levels and corresponding rules

Effective QC Design

• Ensure high error detection (>90%) and low false rejection (<5%)
• Minimize QC runs, meet regulatory requirements
• Use of single vs multiple rules based on performance

Best Practices

• Systematic daily and periodic monitoring
• Use of multi-rule QC procedures to balance false rejection and error detection
• Application of statistical and non-statistical methods
• Define quality for each test, calculate Sigma, choose appropriate rules

Conclusion

• Stable internal QC ensures analytical stability
• Training and protocol adherence are crucial

Summary

• Detailed exploration of quality control protocols
• Advanced concepts like Sigma metrics and their application
• Practical steps to ensure lab quality and minimize errors
• Importance of accurate measurements, bias control, and systematic monitoring.