The document discusses quality control in clinical laboratories. It defines total quality management and the five Qs of quality management. Quality control refers to technical procedures used in quality assurance programs to control pre-analytical, analytical, and post-analytical variables in order to reduce errors. Key aspects of quality control include establishing standard operating procedures, training staff, collecting and analyzing quality control data using control charts, and implementing corrective actions when quality control rules are violated. The goal of quality control is to ensure high quality healthcare and accurate diagnosis and treatment of diseases.

1. Experiment No. ___
Aim- Use of Quality Control (QC) in clinical
laboratory.

2. Total Quality Management- It is defined as an
integrated organizational effort designed to improve
the quality of laboratory at every level.

3. The 5 Qs in the quality management are:

4. In order to implement a good quality assurance program in the
laboratory, there should be
• Commitment towards work
• Availability of facilities and resources
• Technical competence
Quality control refers to the technical procedures employed in
quality assurance program. These include control of pre
analytical variables, analytical variables including monitoring the
quality of analysis and post analytical variables. Mismanagement
of any of the variables can lead to respective errors.

5. Pre analytical control Analytical control Post analytical control
Patient preparation Personnel training
Sample testing
Record of results
Sample collection and
labeling
Quality control
calibration
Equipment calibration
Interpretation
Personnel competency,
training of staff and test
evaluation
SOP (Standard Operating
Procedure) to be made
Documentation such as
use of reagents, stability
of instruments,
procedure etc.
Less turnaround time (time
taken to complete the test)
Record of patient receipt Control limit charts e.g.
Levey Jenning charts
and Westgard rules
Reporting
Sample transport Biosafety
Sample Storage

6. Objectives of quality control in laboratory
• High quality health care
• Less mortality rate
• Reduce economic loss
• Ensure credibility of laboratory
• Generate confidence in laboratory results
• Ensure correct diagnosis of disease
• Ensure correct treatment for disease

7. Implementation of quality control program
– Establish written policies and procedures
– Assign responsibility for monitoring and reviewing
– Train staff
– Obtain control materials
– Collect data
– Set target values (mean, SD)
– Establish control charts e.g. Levey Jennings charts and
Westguard rules
– Routinely plot control data
– Establish and implement troubleshooting and corrective
action protocols
– Establish and maintain system for documentation

8. Basic quality parameters
• Trueness/true value
• Accuracy
• Precision
• Specificity
• Sensitivity
The ability to maintain both precision and accuracy is
called reliability.

9. Trueness/true value- it is the actual value of the
analyte/parameter in the given sample.
Accuracy- it refers to the closeness of the estimated value of an
analyte to its actual value in the given sample. Value farther away
from the true value are less accurate than those closer.

10. Precision- this refers to the reproducibility of the result. A test is
said to be precise if its repeated estimated values on the same
sample are close to each other and do not vary widely from each
other.

11. Precise and accurate

12. Inaccurate and imprecise

13. Specificity- it denotes that only one substance will answer the
particular test. For e.g. Glucose Oxidase-Peroxidase enzymatic
method is only for the estimation of glucose in serum. Specificity
is determined by the method of the analysis.
Sensitivity- it indicated whether the method could be utilized to
test a very dilute solution or minute fractions. The sensitivity of
an assay is the fraction of those with a disease that the assay
correctly predicts.
A test should be both specific and sensitive.
As the sensitivity is increased, the specificity of the test is
decreased.

14. Types of Quality control
Internal Quality control: IQC
Nature: Concurrent
Performed by: laboratory staff
Objective: Reliable results on a daily basis
Internal QC- daily monitoring of precision & accuracy
External Quality Assessment: EQA
Nature: to evaluate IQC
Performed by: Independent agency
Objective: Ensure inter-laboratory comparability
External QA- long term accuracy of analytical methods

15. Control Materials
• Specimens that are analyzed for quality control purpose are
known as control materials.
• Control materials should be available in the stable form in
aliquots or vials for analysis over an extended period of time
i.e. at least one year.
• Sufficient material from same lot number is provided for use.
• May be frozen, freeze-dried or chemically preserved
according to instructions by the manufacturer.
• Requires very accurate reconstitution if necessary.
• Always stored as recommended by manufacturer.
• Similar to the test specimen, usually protein matrix is used as
control to analyze the serum specimen.

16. Types of Control Materials
• Assayed
– manufactured in laboratories.
– expensive
– must be certified.
• Unassayed
– less expensive.
– must perform data analysis also.
• Homemade or In-house
– pooled serum collected in the laboratory.
– Control serum is prepared from human sera with
chemical additive and tissue extracts of human origin.
Bacteriostatic agents are added. It has known
concentration of the analyte, 2-3 samples of patients are
used as control. It is used to validate reliability of the test
system.
– preserved in small quantities for daily use.

17. Calibrators
• It has a known concentration of the substance (analyte).
• It is used to adjust instrument, kit, test system in order to
standardize the assay.
• Sometimes called a standard, although usually not a true
standard and also not a control.

18. Control Charts
A common method to compare the values observed for control
materials with their known values.
Simple graphical display in which the observed values are plotted
versus the time when the observations are made.
When plotted points falls within the control limit, the method is
performing properly and when the points falls outside control limit,
error may be developing.
Data set of at least 20 points obtained over a 30 day period is
required..
Established control limits are taken by calculating mean and standard
deviation.
Example- Levey-Jennings charts/graph

19. Calculation of Mean
X = Mean
X1 = First result
X2 = Second result
Xn = Last result in series
n = Total number of results
x = x1+x2+x3…….+xn
n

20. Calculation of Standard Deviation

21. Normal Distribution of data
• All values are symmetrically distributed around the
mean.
• Characteristic “bell-shaped” curve is formed.
• Performed for all quality control statistics.

22. LEVEY- JENNINGS GRAPH
A graphical method for displaying control results and evaluating
whether a procedure is in-control (unacceptable) or out-of-
control (unacceptable).
• Control values (Y-axis) are plotted versus time (X-axis).
• Used for simple data analysis and display.
• Easy adaptation and integration into existing control
practices.
• Low chances of false rejection or false alarms.

23. • Red color is used for data points that lie outside.
• Use to evaluate the precision and accuracy of repeated
measurements.
• Used to give a visual indication whether a laboratory test is
working well.
• Should be reviewed at defined intervals and take necessary
action and documentation.
• Use to detect the type of error, whether the error is
systematic (due to calibration) or a random (can occur at any
step) error.
 Systematic error shows that process is precise but
inaccurate.
 Random error shows that procedure is imprecise as
well as inaccurate.

24. Plotted LJ Chart e.g. cholesterol (mg/dl)

25. Westgard Rules
• Also known as Multirule Quality Control
• Uses a combination of decision criteria or control rules
• Allows determination of whether an analytical run is in-
control or out-of-control.
• Generally used where 2 levels of control material are
analyzed per run.
• 12S rule
• 13S rule
• 22S rule
• R4S rule
• 41S rule
• 10X rule

26. Westgard – 12S Rule
• Warning rule
• One of two control results falls outside ±2SD
• Gives alert to possible problems
• Not cause for rejecting a run
• Must then evaluate the 13S rule

27. 12S Rule = A warning to trigger careful inspection
of the control data
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
Mean
Day
+1SD
+2SD
+3SD
-1SD
-2SD
-3SD
12S rule
violation

28. Westgard – 13S Rule
• If either of the two control results falls outside of
±3SD, rule is violated
• Primarily sensitive to random error
• Run must be rejected
• If 13S not violated, check 22S

29. 13S Rule = Reject the run when a single control
measurement exceeds the +3SD or -3SD control limit
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
Mean
Day
+1SD
+2SD
+3SD
-1SD
-2SD
-3SD
13S rule
violation

30. Westgard – 22S Rule
• 2 consecutive control values for the same level fall
outside of ±2SD in the same direction
• Sensitive to systematic error
• Patient results cannot be reported
• Requires corrective action

31. 22S Rule = Reject the run when 2 consecutive control
measurements exceed the same
+2SD or -2SD control limit
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
Mean
Day
+1SD
+2SD
+3SD
-1SD
-2SD
-3SD
22S rule
violation

32. Westgard – R4S Rule
• One control exceeds the mean by –2SD, and the
other control exceeds the mean by +2SD
• The range between the two results will therefore
exceed 4 SD
• Random error has occurred, test run must be
rejected

33. R4S Rule = Reject the run when 1 control
measurement exceed the +2SD and the other
exceeds the -2SD control limit
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
Mean
Day
+1SD
+2SD
+3SD
-1SD
-2SD
-3SD
R4S rule
violation

34. Westgard – 41S Rule
• Requires control data from previous runs
• Four consecutive QC results for one level of control
are outside ±1SD
• Sensitive to systematic error

35. Westgard – 10X Rule
• Requires control data from previous runs
• Ten consecutive QC results for one level of control
are on one side of the mean (above or below, with
no other requirement on the size of deviation)
• Sensitive to systematic error

36. 10x Rule = Reject the run when 10 consecutive control
measurements fall on one side of the mean
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
Mean
Day
+1SD
+2SD
+3SD
-1SD
-2SD
-3SD
10x rule
violation

37. Westgard Multirule QC

38. When a rule is violated
• Warning rule = use other rules to inspect the control points.
• Rejection rule = “out of control”
– Stop testing
– Identify and correct problem
– Repeat testing on patient samples and controls
– Do not report patient results until problem is solved and
controls indicate proper performance

39. Solving “out-of-control” problems
Do Careful inspection of
(i) analytical method
(ii) equipment
(iii) reagents
(iv) specimen
Follow policies and procedures for remedial action.
Find alternatives to run rejection.

40. External Quality Assessment
The basic operation of these programs involves all the
participating laboratories analyzing the same lot of control
material usually daily as a part of internal quality activities. The
results are sent to the sponsoring group for data analysis. The
overall mean of all laboratories in the program is taken as a true
or correct value and is used for comparison with individual
mean.
SDI= Lab mean – Group mean
Overall group SD
Standard Deviation Index >2 indicates that a laboratory is not in
agreement with the rest of laboratories in program.
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41. Six sigma in Quality Control
Six sigma is a disciplined, data driven approach and methodology
for eliminating defects in a systematic way to achieve high levels
of test results. It is based on principles set up by quality experts.
The term sigma refers to the standard deviation of the data
where six standard deviations are taken between the mean and
the nearest control limit. It is a set of techniques and tools used
for process improvement.
It is statistical term that measures how far a given data deviates
from the normal value.
It has two key methodologies: DMAIC and DMADV
41

42. DMAIC is a five step approach in six sigma that focuses on
improving the quality and to reduce the defects in an existing
process.
D- Define- define the problem that needs to be addressed.
M- Measure- measure the problem from where it is produced.
A- Analyze- analyze data and process to determine root cause of
defects.
I- Improve- improve the process by finding solutions
C- Control- implement control to keep the process going.

43. DMADV is a five step approach in six sigma that measures and
identify critical to quality characteristics in a process.
D- Define
M- Measure
A- Analyze
D- Design
V- Verify