Quality Assurance / Quality Control

Slides:



Advertisements
Similar presentations
So how good are your results? (An introduction to quantitative QC)
Advertisements

DATA & STATISTICS 101 Presented by Stu Nagourney NJDEP, OQA.
Day to Day Management of Quality Control
Module 6: QC Basic Rules and Charts
1 Manufacturing Process A sequence of activities that is intended to achieve a result (Juran). Quality of Manufacturing Process depends on Entry Criteria.
Statistical Quality Control N.Obeidi Descriptive Statistics Descriptive Statistics include: Descriptive Statistics include: – The Mean- measure of central.
Control of Analytical Variables Dr. Roula Hamid MSc Clin Biochem Central Puplic Health Laboratory QC Chemistry.
Introduction to Summary Statistics
Introduction to Summary Statistics
Dr Samah Kotb Lecturer of Biochemistry 1 CLS 432 Dr. Samah Kotb Nasr El-deen Biochemistry Clinical practice CLS 432 Dr. Samah Kotb Nasr.
Chapter 10 Quality Control McGraw-Hill/Irwin
Laboratory Quality Control
Week 2: Spectrophotometry Spectrophotometry Beer’s law Standard curve Dilution problems Quality control Control samples Levey-Jenning chart Shift and trend.
Survey Research & Understanding Statistics
Quality Assurance.
Internal Quality Control (QC) for Medical Laboratories: An introduction Dr. Otto Panagiotakis and Dr. Alexander Haliassos ESEAP – Greek Proficiency Testing.
Quality Assurance / Quality Control
Short Course on Introduction to Meteorological Instrumentation and Observations Techniques QA and QC Procedures Short Course on Introduction to Meteorological.
This teaching material has been made freely available by the KEMRI-Wellcome Trust (Kilifi, Kenya). You can freely download,
Quality Assurance in the clinical laboratory
Chemometrics Method comparison
Unit #7 - Basic Quality Control for the Clinical Laboratory
Quality Assurance.
Quality Assessment 2 Quality Control.
MLAB 2401: Clinical Chemistry
APCCB AVERAGE OF DELTA – A NEW CONCEPT IN QUALITY CONTROL GRD Jones Department of Chemical Pathology, St Vincents Hospital, Sydney, Australia.
ABC of Quality Control A problem based approach RT ERASMUS NHLS / FACULTY OF HEALTH SCIENCES, UNIVERSITY OF STELLENBOSCH 20th July, 2007, Bela Bela.
Managing Software Projects Analysis and Evaluation of Data - Reliable, Accurate, and Valid Data - Distribution of Data - Centrality and Dispersion - Data.
Biostatistics: Measures of Central Tendency and Variance in Medical Laboratory Settings Module 5 1.
Introduction to Summary Statistics. Statistics The collection, evaluation, and interpretation of data Statistical analysis of measurements can help verify.
Prof. of Clinical Chemistry, Mansoura University.
Automated CBC Parameters
Automated CBC Parameters
Quality Control Lecture 5
Laboratory QA/QC An Overview.
FREQUANCY DISTRIBUTION 8, 24, 18, 5, 6, 12, 4, 3, 3, 2, 3, 23, 9, 18, 16, 1, 2, 3, 5, 11, 13, 15, 9, 11, 11, 7, 10, 6, 5, 16, 20, 4, 3, 3, 3, 10, 3, 2,
Why do we need to do it? What are the basic tools?
QC THE MULTIRULE INTERPRETATION
An Overview. Definitions (1)  Quality Control - QC refers to the measures that must be included during each assay run to verify that the test is working.
Biochemistry Clinical practice CLS 432 Dr. Samah Kotb Lecturer of Biochemistry 2015 Introduction to Quality Control.
Quality control & Statistics. Definition: it is the science of gathering, analyzing, interpreting and representing data. Example: introduction a new test.
QC/QA.
Statistical analysis Why?? (besides making your life difficult …)  Scientists must collect data AND analyze it  Does your data support your hypothesis?
Application of Westgard multi rules in medical laboratories
Quality Control: Analysis Of Data Pawan Angra MS Division of Laboratory Systems Public Health Practice Program Office Centers for Disease Control and.
Wenclawiak, B.: Fit for Purpose – A Customers View© Springer-Verlag Berlin Heidelberg 2003 In: Wenclawiak, Koch, Hadjicostas (eds.) Quality Assurance in.
ERT 207 Analytical Chemistry ERT 207 ANALYTICAL CHEMISTRY Dr. Saleha Shamsudin.
Engineering College of Engineering Engineering Education Innovation Center Analyzing Measurement Data Rev: , MCAnalyzing Data1.
Lecture 8: Measurement Errors 1. Objectives List some sources of measurement errors. Classify measurement errors into systematic and random errors. Study.
Quality Control Internal QC External QC. -Monitors a test's method precision and analytical bias. -Preparation of quality control samples and their interpretation.
Quality Control Copyright © 2015 McGraw-Hill Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill.
L ABORATORY Q UALITY C ONTROL. INTRODUCTION _A major role of the clinical laboratory is the measurement of substances in body fluids or tissues for the.
 Remember Chemistry panel Quality Control:-  In a medical laboratory, it is a statistical process used to monitor and evaluate the analytical process.
Westgard Multirule System
LSM733-PRODUCTION OPERATIONS MANAGEMENT By: OSMAN BIN SAIF LECTURE 30 1.
Diagnostic clinical chemistry
Quality Assessment.
Lesson 1-9 Quality Assessment.
Unit #6 - Basic Quality Control for the Clinical Laboratory
Pakistan Society Of Chemical Pathologists Zoom Series of Lectures ZT 24. Quality Managent 1 Brig Aamir Ijaz MCPS, FCPS, FRCP (Edin), MCPS-HPE HOD and.
Quality Assurance in the clinical laboratory
Laboratory Quality Control
Practical clinical chemistry
Quality Control (2) Lecture 6
Process control in Hematology section
Introduction To Medical Technology
Quality Control Lecture 3
▪Internal quality control:
Quality Assessment The goal of laboratory analysis is to provide the accurate, reliable and timeliness result Quality assurance The overall program that.
Presentation transcript:

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 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: Next slide

Statistical concepts

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

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 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