Download Quality Assurance

Quality….
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Quality Assurance
Quality Control
Quality System
Quality Management
Quality assurance
• A well defined, organized program design to
enhance patient care through the ongoing
objective assessment aspects of patient care and
the correction of identified problems
• Planned and systematic activities to provide
adequate confidence that requirements for quality
will be met
• Includes IQC, EQA, pre-analytic phase, test
standardization, post-analytic phase, management,
and organization
The Quality Assurance Cycle
Patient/Client Prep
Sample Collection
Reporting
•Data and Lab
Management
•Safety
•Customer
Service
Personnel Competency
Test Evaluations
Sample Receipt
and Accessioning
Record Keeping
Quality Control
Testing
Sample Transport
Quality Assurance Target
1. Preanalytical Process
1. Anticuagulant,labeling,storage…
2. Postanalytical Process
1. How report,time of report,…
2. *never rely on a single value(out of reference range)
to make a diagnosis
3. *oslers rule: Try to attribute all abnormal findings to a
single case
3. Analytical Process
1. Internal QC
2. External QC
Quality Assurance Programme
• Internal Quality Control (IQC) Procedures
• External Quality Assessment (EQA)
• Quality Management
The ultimate goal of quality system is to
obtain test results that are
Reliable, relevant and reproducible.
Quality Control
• Quantitative and statistical
• Process or system for monitoring the quality of
laboratory testing, and the accuracy and precision
of results
• Routinely collect and analyze data from every test
run or procedure
• Allows for immediate corrective action
• AIM: to reduce both systematic and random error
Internal Quality Control
• set of procedures for continuously
assessing laboratory work and the
emergent results; immediate effect,
should actually control release of
results
External Quality Assessment
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Evaluates past performance
Testing of unknown samples
Compare performance with others
Provides a forum for improvements and
correction of errors
Quality Management
• All activities of the overall management
function that determine quality policy
objectives, implement them by means such
as quality planning, quality control, quality
assurance, and quality improvement within
the system
Quality System
• Organizational structure, resources, processes
and procedures needed to implement quality
management
Quality System
Quality Assurance
Quality Control
and
• Precise and inaccurate
• Precise and accurate
Measures of Central Tendency
• Mean = the calculated average of the
values
• Median = the value at the center
(midpoint) of the observations
• Mode = the value which occurs with the
greatest frequency
Calculation of Mean
X = Mean
X1 = First result
X2 = Second result
185-182-179-183-178-176183-184-177-186 (g/l)
Xn = Last result in series
n – Total number of results mean=1813/10=181.3
‫‪Median‬‬
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‫اگر تعدادی خوانده داشته باشیم و به ترتیب صعودی یا نزولی ردیف کنیم ‪،‬‬
‫عددی که دو طرف آن تعداد خوانده ها برابر باشد‬
‫‪(n+1) / 2‬‬
‫فرد‬
‫‪110-100-130-140-120-140-150‬‬
‫‪110-120-120-130-140-140-150‬‬
‫‪N+1/2=8/2=4‬‬
‫‪}n/2 + (n/2)+1}/2‬‬
‫زوج‬
‫‪120-110-100-130-140-120-140-150‬‬
‫‪100-110-120-120-130-140-140-150‬‬
‫‪)120+130(/ 2 = 125‬‬
‫‪n/2=8/2=4‬‬
‫‪n/2+1=4+1=5‬‬
‫‪Mode‬‬
‫• نما عبارت است از داده ای که بیشترین فراوانی را دارد‬
‫‪143-110-135-110-125-155-140-110-120‬‬
‫• ‪110‬‬
Measures of Dispersion
or Variability
• There are several terms that describe the
dispersion or variability of the data around
the mean:
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Range
Variance
Standard Deviation
Coefficient of Variation
Range
• Range refers to the difference or spread
between the highest and lowest
observations.
• It is the simplest measure of dispersion.
• It makes no assumption about the shape of
the distribution or the central tendency of
the data.
‫‪Range‬‬
‫• دامنه عبارت است از فاصله بیشترین مقدار تا کمترین‬
‫مقدار‬
‫• ‪16-12-9-8-7‬‬
‫• ‪Range=16-7=9‬‬
Normal Distribution
• All values are symmetrically distributed
around the mean
• Characteristic “bell-shaped” curve
• Assumed for all quality control statistics
Normal Distribution Curve
Gaussian Curve
Mean-Mode-Median
–1SD
–2SD
–3SD
Variance
• Variance is a measure of variability about
the mean.
• It is calculated as the average squared
deviation from the mean.
– the sum of the deviations from the mean,
squared, divided by the number of observations
(corrected for degrees of freedom)
Calculation of Variance (S2)
(X1  X )
2
2

S  N 1
 mg /dl
2
2
Calculation of Standard
Deviation
S
(x  x )
N 1
1
2
 mg/dl
Standard Deviation and Probability
X
Frequency
• For a set of data with a
normal distribution, a value
will fall within a range of:
– +/- 1 SD 68.2% of the
time
– +/- 2 SD 95.5% of the
time
– +/- 3 SD 99.7% of the
time
68.2%
95.5%
99.7%
-3s-
2s
-1s
Mean
+1s
+2s
+3s
Calculation of
Coefficient of Variation
• The coefficient of
variation (CV) is the
standard deviation
(SD) expressed as a
percentage of the
mean
SD
CV 
x 100
mean
Quality assurance programme
a)At all time:
*Correlation system
-Cumulative report forms
-Blood film with blood count
-Blood count with clinical data
Correlation system
True leucocyte count=
Total count*100/nrbc+100
MCV/micro,macr
o
WBC/obj40*2000=
WBC count
Mch/hhpo,hyper
Plt/obj100*20000=
Plt count
Rule of Three
Rules of Three for normal Hematology
• Rule #1
– Hgb XNot
3 = Hct
for +2
calibration
• Rule #2
– RBC x 3.3 = Hgb + 1.5
• Rule #3
– RBC x 9 = Hct +3
….QA prog.
b) Daily
1. Test on control specimen
Levey jenning control chart
2. Duplicate test on patents
specimen
3. Check test
4. Delta test
5. Daily mean
Control Chart : example
X
X-X
(X-X)2
12.44
0.14
0.02
12.5
0.2
0.04
12.2
-0.1
11.7
-0.6
( X  X )2
0.01

sd 
n 1
12
-0.3
0.09
12.6
0.3
0.09
12.6
0.3
0.09
12.7
(x  x) 2
0.4
n 1
0.16
sd 
0.36
12.3
0
0
12
-0.3
0.09
Sum=
123
Mean=
12.3
sd 
0.95
sd 
2
(
X

X
)

n 1
0.95
 0.106  0.33
9
2sd=0.66
Example
13.5
12.96
13
12.5
12.3
12
11.5
11
12
…
…
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11
10
9
8
7
6
5
4
3
2
1
10.5
0
11.64
Westgard Rules :
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1 2s warning
1 3s Reject
2 2s
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4 1s
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R 4s
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6 or 10 warning
• Drift
/Trend
• Shift
• Dispersion
values drift(Trend), problem progressively
developing
Trend
shift: abrupt change, values oscillate around new
mean
shift
Dispersion
• Look for widely scatter data points.
• 1] fluctuating electrical voltage (stability problem)
[2] poor mixing of control specimens
(inconsistency in technique).
Westgard Multirule QC
Control Chart
4 1S
l+3SD
240
+2SD
220
+1SD
Mean
-1SD
-2SD
-3SD
200
180
160
140
1
2
3
4
5
6
7
8
9
10
Day
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1 2s
1 3s
2 2s
4 1s
R 4s
6 or 10
X
RE
RE
SE shift
SE
RE shift
SE
Error
• Random error:
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variance
Increase scatter of value about the true value
Results of chance(eg.sampling error)
Don’t affect an entire batch of specimens
Are not be detected by control samples
• Systematic error:
– not due to chance
– eg.deteriorating reagents
bias
Random Error
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Incomplete mixing
Bubble or particle in reagent
Probe and syringe variation
Optical problem
Sample line problem
……….
Systematic Error
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Inaccurate standard
Poor calibration
Inadequate blank
Improperly prepared reagents
Degradation of reagent
Drift of detector
Degradation of instrument components
Improper setting of temperature bath
………..
Duplicate Test
d2
First
count
Second
count
d
5.4
8.3
5.8
10.5
-0.4
-2.2
0.16
4.84
17.2
5.4
12.2
18
5.4
11.8
-0.8
0
0.4
0.64
0
0.16
sd 
=5.8
d>2sdrandom error
sd 
2
d

5.8
 0.76
10
2n
5.8
sd 
 0.76
10
2sd=1.5
Check Test
• Similar to Duplicate
Test
But for samples of same
day
• Detection deterioration of
apparatus and reagent
between tests
• Suitable for Hg & Rbc (
4-5 samples)
Delta Test
Hg > 10%
RBC > 10%
WBC > 20-25%
Plat > 50%
Moving Average
(Bull`s Method)
• Assume the population sampled each
day remains constant
• If has minimum 100 sample/day
• Cell counter / mean of mcv-mch-mchc
– Mean > + 2sd  systemic error
– 20 sample batch
– No more than 7 come from one clinical
source
Daily Mean
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Use of normal data
Small laboratories
5 or more healthy people each day
MCV-MCH-MCHC
+2sd
Example
External Quality Assessment
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Consensus method:
Sd , 2sd , mean
Deletion results > mean + 2sd
Again sd , 2sd , mean
DI (deviation index) 
actual result  weighted mean
adjusted sd
Deviation index
• <0.5
• <1
• 1-2
excellent
satisfactory
satisfactory but borderline careful
watch)
• 2-3
requires review of techniques check
on calibration
• >3
require urgent investigation
…..QA prog.
D) Monthly
 Reagent & Kits
check(storage,expire date)
 Sample
collection,anticuagulant,st
orage
 Presicion of cell counter
 Blood film( distribution
staining)
….QA prog.
E) Every six month:
(Photometer &
spectrophotometer,…)
calibration
(Sampler & pipette)
calibration
Other instruments
Standard Operating Procedure
• SOP is an important part of QA
• It is instruction protocol that include all
aspects of laboratory practice
• SOP helps prevent mistakes rather than
detecting them
SOP have the following features:
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Accordance with a standard format
In simple language, readily understood by employees
Contain sufficient details to perform
Sop are written by qualified & experienced lab officer
It must be followed exactly by all staff
It must be given a title, identification number and date
Sop reviewed and update on a regular basis
SOP include:
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Title & id number & date
Staff able to perform test
Principle of the test method
Clinical significance of the test
Specimen
Equipment requirements
Reagent & Stain requirement
Test procedure instruction( step by step)
Calculation & Expected value
Reporting and interpretation of results
Internal quality control procedures and Sources of error
Reference
EDTA
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Mechanism
Increase / decrease effect
Storage & transport
Kinds of :
• EDTA-Na2,2H2o  1.4-2 mg/ml solid
• EDTA-K2,2H2o  1.5-2.2 mg/ml “
• EDTA-K3
 1.5-2.2 mg/ml liquid
Calculation
• Concentration 1.5 mg/1cc blood 3 mg / 2 cc
• EDTA solution 3% 
3gr/100cc or
30gr/1000cc or
30000mg/1000cc
1000cc
30000mg
X=3000/30000=0.1
X
3mg
blood
• EDTA solution 1%
or
10000mg/1000cc
1000
10000mg

=100 l for 2cc
1gr/100cc or 10g/1000cc
X=3000/10000=0.3cc
…EDTA
• NEVER add EDTA
powder directly to the
sample bottle
• NEVER add the blood
before EDTA solution
completely dried
Shrinkage of RBC
Destroy WBC& plt
Dilution blood and
destroy RBC
‫با تشکر‬
Quality assurance programmes
Non-analytical QC
• control of procedures
not directly associated
with the measuring of
a parameter
Analytical QC
• control of procedures
directly associated
with the measurement
of a parameter
Types of Control Materials
• Assayed
– mean calculated by the manufacturer
– must verify in the laboratory
• Unassayed
– less expensive
– must perform data analysis
• “Homemade” or “In-house”
– pooled sera collected in the laboratory
– characterized
– preserved in small quantities for daily use
Implementing a QC Program –
Quantitative Tests
• Select high quality controls
• Collect at least 20 control values over a period of
20-30 days for each level of control
• Perform statistical analysis
• Develop Levey-Jennings chart
• Monitor control values using the Levey-Jennings
chart and/or Westgard rules
• Take immediate corrective action, if needed
– Record actions taken
Quality Assurance
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Internal Quality Control
External Quality Control
Proficiency Surveillance
Standard & Standardization
Statistics in Quality Measures
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Normal Distribution Curve
28/08/1438
78
Cusum
RBC
Diff from mean
Cumulative diff
5.1
+0.1
+0.1
5
0
+0.1
5.1
+0.1
+0.2
4.7
-0.3
-0.1
4.8
-0.2
-0.3
5
0
-0.3
5.1
+0.1
-0.2
5.2
+0.2
0
5.1
+0.1
+0.1
5.2
+0.2
+0.3
5.1
+0.1
+0.4
5.2
+0.2
+0.6
1.5
1
0.5
0
-0.5
-1
-1.5
1
2
day
3
4
5
6
7
8
9
10
11
12
13
Statistics in Quality Measures
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Diagnostic Sensitivity
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The proportion of patients with a disease
who will have positive test results
Defined by the ratio (a /(a + b)) * 100
a is the number of true positive results and b
is the number of false negative results
Statistics in Quality Measures
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Diagnostic Specificity
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The proportion of patients who are correctly
identified by the test as not having the disease
Defined by the ratio (c /(c + d)) * 100
c is the number of false positive results and d
is the number of true negative results
Highly sensitive tests are used to screen
for a condition, highly specific tests are
used to confirm the screen.