1. Pakistan Society of Chemical Pathologists Zoom Series of Lectures ZT 25. Quality Management 2 Brig Aamir Ijaz MCPS, FCPS, FRCP (Edin), MCPS-HPE HOD and Professor Chemical Pathology AFIP Rawalpindi

2. Specific Learning Outcome
At the end of this lecture the students will be able to describe the processes involved in Quality Management.

3. Laboratory Errors

4. Types of Lab Errors Pre-analytical Random Errors Systematic Errors
Trend
Shift
Analytical
Post-analytical

5. Random Error
Imprecision of the test system causing a scatter or spread of control values around the mean

6. D. Fluctuation in power supply
MCQ No 4
Which of the following is mostly a cause of Random Error?
Change in reagent or calibrator lot numbers
Deterioration of reagents or calibrators
Fluctuation in power supply
Improperly prepared reagents
Wrong calibrator values
D. Fluctuation in power supply

7. Causes of Random Error Air bubbles in reagent
Improperly mixed reagents
Faut in reagent lines, sampling, or reagent syringes
Improperly fitting pipette tips
Clogged or imprecise pipetter
Fluctuations in power supply

8. Systematic Error
Systematic change in the test system resulting in a displacement of the mean from the original value
Systematic error of an analytic system is predictable and causes shifts or trends on control charts that are consistently low or high

9. Causes of Systematic Error
Change in reagent or calibrator lot numbers
Wrong calibrator values
Improperly prepared reagents
Deterioration of reagents or calibrators
Inappropriate storage of reagents or calibrators
Variation in sample or reagent volumes due to pipettor misalignments
Variation in temperature or reaction chambers
Deterioration of photometric light source
Variation in procedure between technologists

10. Accuracy and Precision
The degree of fluctuation in the measurements is indicative of the precision of the assay.
Precision-refers to the ability to get the same (but not necessarily ‘true’) result time after time.
The closeness of measurements to the true value is indicative of the accuracy of the assay.
Accuracy - An accurate result is one that is the ‘true’ result.

11. What this Diagram Indicates?

12. Accuracy and Precision
Precise and inaccurate
Imprecise and inaccurate
Random Error
Systematic Error

13. Shifts and Trends Shift
QC data results are distributed on one side of the mean for 6-7 consecutive days
Trend
Consistent increase or decrease of QC data points over a period of 6-7 days

15. Bias
Bias – the amount by which an analysis varies from the correct result.
Example, If the Expected Value is 50 units, and the result of an analysis is 47, the bias is 3 units.

16. Westgard Multirule System
Multi-rule system developed by Dr. James O. Westgard based on statistical concepts
Combination of decision criteria or rules to assess if a system is in control
Used when at least 2 levels of control are run with the examination run
Cannot be used with only one control
Dr. Westgard
8-5 16

17. 12s Rule
12s refers to the control rule that is commonly used with a Levey-Jennings chart when the control limits are set as the mean plus/minus 2s. In the original Westgard multirule QC procedure, this rule is used as a warning rule to trigger careful inspection of the control data by the following rejection rules.

18. 13s Rule
13s refers to a control rule that is commonly used with a Levey-Jennings chart when the control limits are set as the mean plus 3s and the mean minus 3s. A run is rejected when a single control measurement exceeds the mean plus 3s or the mean minus 3s control limit.

19. 22s Rule
22s - reject when 2 consecutive control measurements exceed the same mean plus 2s or the same mean minus 2s control limit.

20. R4s Rule
R4s - reject when 1 control measurement in a group exceeds the mean plus 2s and another exceeds the mean minus 2s.

21. 41s Rule
41s - reject when 4 consecutive control measurements exceed the same mean plus 1s or the same mean minus 1s control limit. 

22. 10X Rule
10x - reject when 10 consecutive control measurements fall on one side of the mean.

23. Task No 2 Identify the rule at: 3,4,7,9,10,11,12,14,20
Name type of error
High: mean=250 and s=5)
Low: mean=200 and s=4

24. Run 3
Both control results exceed their respective +2s limits, therefore there is a 22s rule violation across materials.
A systematic error is most likely occurring and is affecting the results throughout the critical analytical range from at least 200 to 250 mg/dL.

25. Run 4
The high control result is below its -2s limit, which is a warning of a possible problem. Inspection with the 13s, 22s, and R4s rejection rules that can be applied within the run do not confirm a problem.
Note that the across-runs rules would not be applied because the previous run was rejected.

26. Run 7
a. The high control result exceeds its +3s limit, therefore there is a 13s control rule violation.
b. This most likely indicates random error.

27. Run 9 The high control result is below its -2s limit.
Inspection of the control results by the rejection rules does not confirm a problem.

28. Run 10
The control chart for the high control material shows that the last two measurements have both exceeded the -2s limit, therefore a 22s rule violation has occurred within material and across runs.
This situation would be consistent with a loss of linearity that is beginning to affect the high end of the analytical range.

29. Run 11
There is a 12s warning on the high level control material, but inspection doesn't show any other rule violations, therefore, the patient test results in this run can be reported.

30. Run 12
The control charts for the high and low materials show that the last four control observations have exceeded their respective +1s limits, therefore a 41s rule violation appears to have occurred across materials and across runs.

31. Run 14
The control results for the high material exceeds its +2s limit and the control result for the low material exceeds its -2s limit, therefore an R4s rule violation has occurred.
This most likely indicates a random error.

32. Run 20
The last five control results on the high material and the last five results on the low material all are lower than their respective means, giving a total of ten consecutive control results on one side of the mean. There is a 10x rule violation across runs and across materials
It indicates that a systematic error most likely has occurred.

33. Summary of Westgard System

35. MCQ No 3
In a tertiary care hospital a Consultant Chemical Pathologist has joined the department after getting training from Japan. He launches a new programme for a marked reduction of the lab errors and sets a target of < 3.4 errors per million.
Which of the following QM procedure he is trying to introduce in his lab:
Delta Check
External Quality Assurance
Internal Quality Control
Quality Planning
Six Sigma
E. Six Sigma

36. MCQ No 4
Six Sigma Matrix are commonly used in Medical Field to assess overall quality programme. Following is a list of error logs of various laboratories in terms of Six Sigma. Which lab is performing the best: 
Lab No. 1 is 3.5
Lab No. 2 is 5.8
Lab No. 3 is 2.2
Lab No. 4 is 3.9
Lab No. 5 is 4.7
B. Lab No. 2 is 5.8

37. MCQ No 5 D. Lean Management
Which of the following quality processes is integrated with Six Sigma to enhance efficiency and quality of lab work:
Delta Check
Human Resource Management
Laboratory Certification
Lean Management
Proficiency Testing
D. Lean Management

38. Six Sigma Companies
1980
First developed by Motorola Company - Adapted six sigma strategy
After 4 years saved $2.2 billion
Improved elimination of wastes and reruns
Jack Welch made it a central focus of his business strategy at General Electric – 1995
Sony
American Express
Today it is used in different sectors of industry

39. Why It Is Called So?
Sigma is the Greek letter representing the standard deviation of a population of data
Sigma is a measure of variation
(the data spread) σ μ

40. What Does Variation Mean?
Variation means that a process does not produce the same result every time
Some variation will exist in all processes
Variation directly affects customer experiences

41. The pizza delivery example . .
Customers want their pizza delivered fast!
Guarantee = “30 minutes or less”
We measured performance and found an average delivery time of 23.5 minutes?
On-time performance is great, right?
Our customers must be happy with us, right?

42. how often are we delivering on time? answer: look at the variation!
30 min. or less
Managing by the average doesn’t tell the whole story. The average and the variation together show what’s happening. s x 10 20 30 40 50

43. Introduction to six sigma
Statistical measure of quality
Based on rigorous process based performance
Process for continuous improvement
To improve process in any business
Changes ways of thinking
Creates a special infrastructure of people within the organization
Defects per million
1 million =1000,000

44. Relating sigma to defect levels
DPMO
(Defects per million opportunities)
Error free rate
Six sigma
3.4 %
Five sigma
233
99.977%
Four sigma
6210
99.4%
Three sigma
66810
93%
Two sigma
308500
69%
One sigma
691500
31%

45. In short
Processes that operate with "six sigma quality" over the short term are assumed to produce long-term defect levels below 3.4 defects per million opportunities

46. Six Sigma And Clinical Laboratories
Higher sigma values indicate better performance
Lower values indicate a greater number of defects per unit
Quality is assessed on the σ scale with a criterion of 3 σ as the minimum allowable sigma for routine performance and a sigma of 6 being the goal for world-class quality

47. Six Sigma And Clinical Laboratories
Laboratory performance can be appraised with the application of six sigma in laboratory functions
When the method sigma is ≥6, stringent internal QC rules need not be adopted
In such cases, false rejections can be minimized by relaxing control limits up to 3s
A method sigma below 3 calls for the adoption of a newer and better method as quality of the test cannot be assured even after repeated QC runs

48. Six Sigma And Clinical Laboratories
Any lab process can be evaluated in terms of a sigma metric
Describes how many sigma fit within the tolerance limits
For laboratory measurements, the sigma performance of a method can be formulated

49. Thanks and Best of Luck