Presentation on theme: "Quality Assurance / Quality Control"— Presentation transcript:
1 Quality Assurance / Quality Control An Overview forMLAB 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. – WikipediaIt 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 ErrorErrorError 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 factorsWell trained phlebotomy staffUse 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, etcSystematic or determinantHave a definite cause, ie piece of equipment that fails to function properly, poorly trained personnel, contaminated reagentThrough 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 mannerQuality 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 analysisExamples ?
14 Statistical conceptsWhen 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 valueMedian - Middle observationMode - 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
18 Statistical conceptsWhat does the normal pattern look like? & what is it called? (random dispersion)Levey Jennings chart examples follow
19 Statistical conceptsShift – when there are 6 consecutive data results on the same side of the mean
20 Statistical conceptsTrend – 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 natureUsesControl or patient results can be compared to a standard to determine their concentrationCan be used to calibrate an instrument so control and patient samples run in the instrument will produce valid resultsExamples:
26 Statistical concepts Important terms: Reference solutions Biological in natureHave an ‘assigned’ valueUsed exactly like a standard.Examples:
27 Statistical concepts Important terms: Controls Resemble the patient sampleHave 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 levelAgain – 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 conceptsComparing / Contrasting Controls and PatientsControls and patient samples similar in compositionControl results - compared to their own range of expected resultsPatient 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 programDirector of Quality Management Services at the U of W HospitalWestgard rules
31 Quality Assurance & Quality Control Common Westgard rules13sA single control measurement exceeds three standard deviations from the target meanAction - Reject
32 Quality Assurance & Quality Control Common Westgard rules12sA single control measurement exceeds two standard deviations from the target meanAction – must consider other rule violationsThis is a warning
33 Quality Assurance & Quality Control Common Westgard rules22sTwo 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 rulesR4sOne 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 rules41sFour 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 checksDelta checksCompares 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)