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Quality Assurance / Quality Control

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Presentation on theme: "Quality Assurance / Quality Control"— Presentation transcript:

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

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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