Quality Assurance / Quality Control
An Overview for
MLAB 2360 – Clinical 1
Quality Assurance & Quality Control
• Quality assurance (aka QA)
• refers to planned and systematic processes
that provide confidence of a product's or
service's effectiveness. – Wikipedia
• It makes ‘quality’ a main goal of a production.
• From the lab perspective, it is the all of the
procedures, actions and activities that take
place to be sure the results given to the
physician are accurate.
Quality Assurance & Quality Control
• Quality Control (QC)
• A procedure or set of procedures intended
to ensure that a manufactured product or
performed service adheres to a defined
set of quality criteria or meets the
requirements of the client or customer.
• In the laboratory that means ....…
• What do you think that means?
Quality Assurance & Quality Control
•
At the very basic level in the laboratory, Quality
Control - QC refers to the measures that must be
included during each assay run to verify that the
test is working properly.
• This requires the routine gathering & processing
of data obtained by testing controls along with
patient samples.
• The processing of the data very often requires
use of statistical procedures.
Quality Assurance & Quality Control
• An important tool in the statistical
analysis is determining:
• Standard Deviation (SD) - a measure of
the scatter around the arithmetic average
(mean) in a Gaussian distribution (Bell
curve, or normal frequency distribution)
Quality Assurance & Quality Control
• Quality Assessment and Quality Control
measures must include a means to identify,
classify, and limit error.
True Value
• True value – an ideal concept, which cannot
be achieved
• Accepted True value – The value
approximating the ‘True Value’; the
difference between the two values is
negligible.
Error
• Error
• Error is the discrepancy between the
result obtained in the testing process and
its ‘True Value’ / ‘Accepted True Value’
Error
• Sources of Error
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Reagents
Standards
Technique
Environment
Specimen collection, handling etc.
Many more
Error
• Types of Error
• Pre-Analytical error
• Includes clerical error, wrong patient, wrong
specimen drawn, specimen mis-handled, etc.
• Through Quality Assurance measures, the
laboratory tries to maintain control over these
factors
• Well trained phlebotomy staff
• Use of easy patient & specimen identification
methods, such as bar code identification.
• Willingness to be information resource and / or
trainers for physicians and floor personnel often
involved with specimen collection.
Error
• Types of Error
• Analytical error
• Random or indeterminate
• Hard or impossible to trace, ie fluctuations in
elect. temp, effects of light, etc
• Systematic or determinant
• Have a definite cause, ie piece of equipment that
fails to function properly, poorly trained personnel,
contaminated reagent
• Through Quality Control measures, such as
always running controls, the laboratory limits
these errors.
Error
• Types of Error
• Post-Analytical error
• Errors that occur after the testing process is
complete.
• Clerical errors very possible here as well.
• Test result fails to get to the physician in a timely
manner
• Quality Assurance measures must be
implemented if problems identified.
• (My opinion – these seem to be the hardest to
control. )
Statistical concepts
• Gathering data
• For some procedures, control results are
positive or negative (yes it worked, or no it
did not)
• Examples?
• For other procedures, such as those that
produce a data result, you must tabulate
the data over a period of time and perform
statistical analysis
• Examples ?
Statistical concepts
• When there are data results, they can
be laid out and evaluated.
• Measures of Central tendency
( how numerical values can be expressed as
a central value )
• Mean - Average value
• Median - Middle observation
• Mode - Most frequent observation
Statistical concepts
• Another way of reviewing data
• Dispersal / or how the individual data points are
distributed about the central value
( how spread out are the numbers ? )
Statistical concepts
•
Another way looking at a Gaussian curve:
• This slide and the next
Introduction to Clinical Chemistry – Quality Control
Statistical concepts
• What does the normal pattern look
like? & what is it called? (random
dispersion)
Levey Jennings chart examples follow
Statistical concepts
• Shift – when there are 6 consecutive data results on
the same side of the mean
Statistical concepts
• Trend – when there is a consistent increase OR
decrease in the data points over a period of 6 days.
(A line connecting the dots will cross the mean.)
Introduction to Clinical Chemistry – Quality Control
Introduction to Clinical Chemistry – Quality Control
Introduction to Clinical Chemistry – Quality Control
• 95% confidence limit (± 2 SD) - 95% of all the
results in a Gaussian distribution
Statistical concepts
• Important terms:
• Standard
• Highly purified substance, whose exact
composition is known.
• Non- biological in nature
• Uses
• Control or patient results can be compared to a
standard to determine their concentration
• Can be used to calibrate an instrument so control and
patient samples run in the instrument will produce
valid results
• Examples:
Statistical concepts
• Important terms:
• Reference solutions
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Biological in nature
Have an ‘assigned’ value
Used exactly like a standard.
Examples:
Statistical concepts
• Important terms:
• Controls
• Resemble the patient sample
• Have same characteristics as patient sample, color
viscosity etc.
• Can be purchased as
• ‘assayed’ – come with range of established values
• ‘un-assayed’ - your lab must use statistical measures
to establish their range of values.
• The results of any run / analysis must be
compare to the ‘range of expected’ results to
determine acceptability of the analysis.
Statistical concepts
• Important terms:
• Controls, cont. – using 1 control level
• Again – the result of an individual testing of the
control value is compared ONLY to its
established range of values.
• If it is in control, the patient results can be
accepted and reports released.
• If it is not in the range, results must be held
until problem is resolved – meaning testing must
be repeated.
Statistical concepts
•
Comparing / Contrasting Controls and Patients
• Controls and patient samples similar in composition
• Control results - compared to their own range of
expected results
• Patient values – compared to published normal
values… as found in reputable literature or as
established by the laboratory
Statistical concepts
• James O. Westgard, PhD
• University of Wisconsin
• Teaches in CLS program
• Director of Quality Management Services at
the U of W Hospital
• Westgard rules
• http://www.westgard.com/mltirule.htm
Quality Assurance & Quality Control
• Common Westgard rules
• 13s
• A single control measurement exceeds three
standard deviations from the target mean
• Action - Reject
Quality Assurance & Quality Control
• Common Westgard rules
• 12s
• A single control measurement exceeds two
standard deviations from the target mean
• Action – must consider other rule
violations
• This is a warning
Quality Assurance & Quality Control
• Common Westgard rules
• 22s
• Two consecutive control measurements exceed
the same mean plus 2S or the same mean minus
2S control limit.
• Action – Reject
Quality Assurance & Quality Control
• Common Westgard rules
• R4s
• One control measurement in a group exceeds
the mean plus 2S and another exceeds the
mean minus 2S.
• Action – Reject
Quality Assurance & Quality Control
• Common Westgard rules
• 41s
• Four consecutive control measurements
exceed the same mean plus 1S or the same
mean minus 1S control limit.
• Action – Reject
Quality Assurance & Quality Control
• Other QC checks
• Delta checks
• Compares a current test result on a patient
to last run patient test, flagging results
outside expected physiological variation.
• A 1981 study concluded delta checks are
useful, despite a high false-positive rate.
• But another study suggests looking at delta
checks with tests that have a high clinical
correlation (e.g., ALT and AST)
Quality Assurance & Quality Control
• Other QC checks
• Common quality indicator calculations
• MCHC
• Hgb / Hct * 100 (expect 32-36)
• Hemoglobin x3 = hematocrit
• Chemistry
• Compare patient BUN / creatinine (10/1 – 20/1)
• Calculate electrolyte anion gap
• Na – (Cl + CO2) expect 12 ± 4 mEq/L