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Quality Assurance / Quality Control

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Quality Assurance / Quality Control An Overview for MLAB 2360 Clinical 1 * * * * * * * * * * * Quality Assurance & Quality Control Common Westgard rules R4s One ... – PowerPoint PPT presentation

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Title: Quality Assurance / Quality Control


1
Quality Assurance / Quality Control
  • An Overview for
  • MLAB 2360 Clinical 1

2
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.

3
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?

4
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.

5
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)

6
Quality Assurance Quality Control
  • Quality Assessment and Quality Control measures
    must include a means to identify, classify, and
    limit error.

7
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.

8
Error
  • Error
  • Error is the discrepancy between the result
    obtained in the testing process and its True
    Value / Accepted True Value

9
Error
  • Sources of Error
  • Reagents
  • Standards
  • Technique
  • Environment
  • Specimen collection, handling etc.
  • Many more

10
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.

11
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.

12
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. )

13
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 ?

14
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

15
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 ? )

16
Statistical concepts
  • Another way looking at a Gaussian curve
  • Next slide

17
Statistical concepts
18
Statistical concepts
  • What does the normal pattern look like? what is
    it called? (random dispersion)
  • Levey Jennings chart examples follow

19
Statistical concepts
  • Shift when there are 6 consecutive data results
    on the same side of the mean

20
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.)

21
Introduction to Clinical Chemistry Quality
Control
22
Introduction to Clinical Chemistry Quality
Control
23
Introduction to Clinical Chemistry Quality
Control
24
Introduction to Clinical Chemistry Quality
Control
  • 95 confidence limit ( 2 SD) - 95 of all the
    results in a Gaussian distribution

25
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

26
Statistical concepts
  • Important terms
  • Reference solutions
  • Biological in nature
  • Have an assigned value
  • Used exactly like a standard.
  • Examples

27
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.

28
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.

29
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

30
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

31
Quality Assurance Quality Control
  • Common Westgard rules
  • 13s
  • A single control measurement exceeds three
    standard deviations from the target mean
  • Action - Reject

32
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

33
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

34
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

35
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

36
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)

37
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
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