Quality control

QUALITY CONTROL
- Dr. Pavalaveelzi C.M

Joseph Juran
Edward Deming
 Quality is “uniformity and
dependability”
 Focus on SPC and statistical
tools
 “14 Points” for
management
PDCA method
 Quality is “fitness for use”
 Pareto Principle (the 80-20
law))
Cost of Quality
 General management approach
as well as statistics

The Absolutes of Quality Management
Crosby defined Four Absolutes of Quality Management, which are,
1. The First Absolute: The definition of quality is conformance to
requirements
2. The Next Absolute: The system of quality is prevention
3. The Third Absolute: The performance standard is zero defects
4. The Final Absolute: The measurement of quality is the price of non-
conformance
The Quality Vaccine
Crosby explained that this vaccination was the medicine for organizations
to prevent poor quality which includes,
Integrity, Systems, Communication, Operations, Policies.
Philip Crosby

Total Quality Management (TQM) process is also referred to
as total quality control (QC), total quality leadership,
continuous quality improvement, quality management
science, or, more generally industrial quality management.
 Many healthcare organizations have adopted the concepts
and principles o QM.
QUALITY is defined as conformance with the requirements of
users or customers and the satisfaction to their needs and
expectations.

Total quality management (TQM) view of an organization as a
system of processes.
1. Empowerment of
front line
employees
2. Direct involvement

Total quality management (TQM) framework for managing
quality in a healthcare laboratory

Analytical processes,
general policies,
practices & and
procedures that
define how all aspects
of the work are done.

general policies,
practices & and
procedures that
statistical control
procedures, nonstatistical
check procedures, such
as linearity checks,
reagent and standard
checks, and temperature
monitors.

general policies,
practices & and
procedures that
statistical control
monitors.
laboratory
performance, such as
TAT, specimen
identification, patient
identification, & test
utility.

general policies,
practices & and
procedures that
statistical control
monitors.
laboratory
TAT, specimen
utility.
structured problem-
solving process to
help identify the root
cause of a problem
and a remedy for that
problem

general policies,
practices & and
procedures that
statistical control
monitors.
laboratory
TAT, specimen
utility.
structured problem-
solving process to
help identify the root
cause of a problem
and a remedy for that
problem
standardize the remedy, establish measures for performance
monitoring, ensure that the performance achieved satisfies quality
requirements, and document the new QLP.

PDCA cycle / the Shewhart Cycle/ the Deming Cycle

Need of quality control in clinical laboratory
1. Support provision of high quality healthcare
- reduce morbidity
- reduce mortality
- reduce economic loss
2. Ensure credibility of lab
3. Generate confidence in lab results.

Consequences of poor quality
1. Inappropriate action
- over-investigation
- over treatment
- mistreatment
2. Inappropriate inaction
- lack of investigation
- no treatment
3. Delayed action
4. Loss of credibility of lab
5. Legal action

Quality assurance represent practices that are
generally recommended for ensuring that desired
quality goals are achieved.
• It is a broad spectrum of plans, policies, and
procedures that together provide an
administrative structure for a laboratory’s efforts
to achieve quality goals.
Quality control is often used to represent those
techniques and procedures that monitor
performance parameters.

• Quality Assurance includes all phases of pre-analytical,
analytical and post analytical
• Quality control is a part of quality assurance
• It is a continual process.

 Continuous is
without checks
 Continual
includes check
points to assess
the efficiency

 Continuous is
without checks
 Continual
includes check
points to assess
the efficiency
(BEST)

 Fundamental requirement for all objective quality control
systems are clearly defined quality goals.
 Each assay and each clinical application of each assay
logically should have its own optimal and its own acceptable
performance limits.
 Error - Deviation from the truth or from an accepted,
expected true or reference value
 Bias - A systematic difference or systematic error between
an observed value and some measure of the truth.
Generally used to describe the inaccuracy of a method
relative to a comparative method in a method comparison
experiment

 Random error, RE - An error that can be either positive or negative,
the direction and exact magnitude of which cannot be exactly predicted.
• Examples – errors in pipetting, changes in incubation period
• Can be minimized by training, supervision and adherence to standard
operating procedures.
 Systematic error, SE - An error that is always in one direction and is
predictable
• Examples – changes in reagent batch, modifications in testing
procedures.
 Allowable total error, Tea - An analytical quality
requirement that sets a limit for both the imprecision
(random error) and inaccuracy (systematic error) that are
tolerable in a single measurement or single test result.

 Accuracy - Closeness of the agreement between the result of a
measurement and a true value of the measurand.
 Precision - Closeness of agreement between quantity values
obtained by replicate measurements of a quantity, under specified
conditions

Variables that may cause imprecision
1. Equipment
• multiple instruments
• Pipettes
• Sporadic maintenance
2. Reagents
• Lot to lot variation
3. Staff
• Difference in training, competencies

CONTROL OF ANALYTICAL VARIABLES
 Reference Materials and Methods
They are major factors in determining the quality and reliability of the
analytical values produced by a clinical laboratory.
Definitive method, DM - An analytical method that has been subjected
to thorough investigation and evaluation for sources of inaccuracy, including
nonspecificity.
Primary reference materials are those of the highest quality
that are used in the
1. development and validation of reference methods,
2. calibration of definitive and reference methods and
3. production of secondary reference materials.

Secondary reference materials are used to provide
working calibrators for field methods and to assign values to
control materials.
Control materials are used only to monitor field methods.
Calibration - The process of testing and adjustment of an
instrument, kit, or test system, to provide a known relationship
between the measurement response and the value of the
substance being measured by the test procedure
Standard - Material or solution with which the sample is
compared in order to determine the concentration or other
quantity.

IMPLEMENTING A QC PROGRAM
- Establish written policies and procedures
- Assign responsibility for monitoring and reviewing
- Train staff
- Obtain control materials
- Collect data
- Set target values (mean, SD)
- Establish control charts
- Plot control data
- Establish and implement trouble shooting and corrective
action protocols
- Establish and maintain system for documentation

 STANDARD OPERATING PROCEDURES (SOP)
 It is a comprehensively
written document that
describes the
Laboratory procedure
and all related issues.
 Essential for ensuring
uniformity in
laboratory procedures.
Documentation of Analytical Protocols

• Method procedures and manuals
should be reviewed annually and
revised whenever changes occur.
• It is also a good practice to retain
outdated procedures in an archival
file.

Inventory Control of Materials
• Process of ensuring that appropriate amount of stocks
are maintained so as to be able to meet demands
without delay.
• Stable operation of laboratory methods is critically
dependent on the materials used with the methods.
• Storage temperatures for stability should be defined
and monitored.
• Initiating orders when stock reaches a certain
predetermined level reduces required storage

 The quality of materials should be
monitored when they are received.
 Patient results from new lots of reagents
and calibrators be compared with those
obtained with previous lots
 Establishing pretesting procedures is costly,
in terms of both time and money.

 Control limits –
• Limits on a control chart which are used as criteria for
indicating the need for action, or for judging whether a
set of data does or does not indicate a state of control
• refer to the defined limits or ranges of results expected
 Control materials –
• Specimens that are analyzed for QC purposes
• It is available, often commercially, liquid or lyophilized,
and packaged in aliquots that can be prepared and
used individually.

Control material
Assayed
- manufacturer
has measured
the values
- control limits
too wide for
use in an
individual
laboratory.
Unassayed
- less expensive
- Must perform
data analysis
Homemade or
in-house
- Pooled sera
collected in
the lab

• Little vial-to-vial variation should occur, so that
differences between repeated measurements
are attributed to the analytical method alone.
• The concentration of analyte should be in
normal and abnormal intervals, corresponding
to concentrations that are critical in the
medical interpretation of test results.

 Control chart –
 A graphical method for evaluating whether a process is
or is not in a 'state of statistical control.'
 The determinations are made through comparison of
the values of some statistical measure(s) for an ordered
series of samples, or subgroups, with control limits.
 In healthcare laboratories, the Levey-Jennings chart is
commonly used to plot the result observed for a stable
control material versus time, usually the day or run
number.

 Control limits are usually calculated from the mean (x)
and standard deviation(s) obtained from repeated
measurements on known specimens by the particular
analytical method that is to be controlled.
 Mean –
- The arithmetic average of a set of values.
- A measure of central tendency of the distribution of a set
of replicate results.
- Often abbreviated by an x with a bar over it.

 Standard deviation –
- A statistic that describes the dispersion or spread of a set of
measurements about the mean value of a gaussian or normal
distribution.
- The standard deviation is not a valid statistic if a distribution is not
Gaussian.
 The control limits are set
to include most of the
control values, usually 95
to 99.7%, which
correspond to the mean
±2 or 3 SDs.

• The initial estimate should be based on measurements
obtained over a period of at least 20 days
 shift in the mean – accuracy problem = SE
 increase in the SD – precision problem = RE

• It is common practice to plot 1 month’s data on a chart,
usually only one or two points a day.
• Interpretation of the control data is guided by certain
decision criteria or control rules, which define when an
analytical run is judged “in control” (acceptable) or “out of
control” (unacceptable).
• Analytical run - segment of data for which a decision on
acceptability is to be made.
• The different control procedures have different
performance capabilities

Performance characteristic - A property of a test that is
used to describe it quality.
For a control procedure, the performance characteristics are
the probabilities for error detection and false rejection, or the
average run lengths for rejectable and acceptable quality.
For a measurement procedure, the performance
characteristics include analytical range, precision, accuracy,
interference, recovery, and also the frequency and duration
of analytical errors.

Probability refers to the likelihood that an event will occur
 Probability for false rejection (pfr) –
• no analytical errors are present except for the inherent
imprecision or inherent random error of the analytical
method.
• The probability for false rejection should be zero.
 Probability for error detection (ped) –
• there is an analytical error, in addition to the inherent or
background random error.
• The probability for error detection should be high (near 1)
Critical performance characteristics

 Different QC procedures have different
sensitivities or capabilities for detecting
analytical errors.
 Practical goals are to achieve a probability of
error detection of 0.90 (i.e., a 90% chance of
detecting the critically sized systematic error),
while keeping the probability of false rejection
at 0.05 or less

 Power function graph - describe the statistical
power of the control procedure
 Two power
function graphs are
necessary to
describe,
1. The performance
for random error
(RE)
2. The performance
for systematic
error (SE).

Power functions are determined by mathematical
calculations or by computer simulation studies.
USES –
1. evaluating the performance capabilities of individual
control procedures,
2. for comparing the performance of different control
procedures, and
3. for designing new procedures with improved
performance characteristics.
 Best control procedure – one with the lowest probability
for false rejection and the highest probability for detecting
errors.

 Operating specifications chart (OPSpecs chart)
Relationship between the quality requirement for a
test, the imprecision and inaccuracy that are allowable, and
the QC that is necessary
This OPSpecs chart is derived from the analytical quality
planning model
Z – chance of exceeding the quality requirement. When z is 1.65, a
maximum defect rate of 5% may occur before an analytical run is
rejected.

This OPSpecs chart is derived from the analytical quality planning model
shown earlier, setting ΔRE to 1.0, then rearranging as follows:
analytical quality requirement of 10% (Tea) and an analytical quality
assurance for systematic error of 90%.

• ΔSEcrit is the critical systematic error that would shift
the distribution enough to cause defined percentage
of the test results to exceed TE.
• detected to maintain a defined quality requirement.
Calculated as [(TEa - biasmeas)/smeas] - 1.65
Critical Systematic Error

Step-by-Step Process for Selecting QC Procedures
1. Define the analytical quality requirement in the form of
an allowable total error (TEa).
2. Evaluate method performance to obtain estimates of
imprecision and inaccuracy
3. Obtain power function graphs for the control rules and
n’s of interest, or OPSpecs charts for the defined TEa.
4. Calculate the critical systematic error
5. Assess the probabilities for error detection and false
rejection.

6. Select control rules and n’s that provide 90% detection
of the critical systematic error and less than 5% false
rejections.
7. Select a total QC strategy to provide an appropriate
balance between statistical and nonstatistical process
improvement and preventive maintenance QC
procedures.
8. Reassess for changes in method performance as
necessary.Cost-effective QC depends on doing the right
QC to ensure that the desired quality is achieved at
minimum cost

These control rules and n’s are
usually implemented by setting up
 a Levey-Jennings control chart or
 a Westgard multirule chart,

 Levey-Jennings Control Chart
 Otherwise called a single-value chart
 To use a Levey-Jennings control chart the steps below are to
be followed.
1. Analyze samples of the control material by the analytical
method to be controlled on at least 20 different days
during stable method performance.
-The mean and standard deviation are to be Calculated.

2. Construct a control chart
• Label x axis – time (day, run number)
y-axis – control values
• Set the control limit , ± 3 s, n >= 2
± 2 s, n = 1
3. Introduce control specimens into each analytical
run, record the values, and plot each value on the
control chart.

Eg. Levey-Jennings control chart having control limits set
as the mean ± 3 s

• False rejections are in effect an inherent
property of the control procedure.
• They occur because of the control limits
that have been selected—not because of
any problems with the analytical method.

Advantages of LJ chart
• Simple data analysis and display
• Easy adaptation and integration into existing
control practices
• A low level of false rejections

Disadvantages
• The use of 2 s control limits are not generally
recommended.
• With the use of 3 s control limits, the false rejection
problem is eliminated, but unfortunately error
detection is also reduced.

Westgard Multirule Chart
• A series of control rules for interpreting control data
are used.
• The probability of false rejections is kept low by
selecting only those rules whose individual
probabilities for false rejection are very low (0.01 or
less).
• The probability for error detection is improved by
selecting those rules that are particularly sensitive to
random and systematic errors.

12s Rule
• One control observation exceeding the mean ± 2 s
• “warning” rule
• initiates testing of the control data by the other control
rules.

13s Rule
• One control observation exceeding the mean ± 3 s
• It is a rejection rule
• Primarily sensitive to random error.

• Two consecutive control observations exceeding the
same mean plus 2 s or mean minus 2 s limit
• It is a rejection
• sensitive to systematic error.
22S Rule

R4S Rule
• One observation exceeding the mean plus 2 s and another
exceeding the mean minus 2 s
• sensitive to random error.

• Four consecutive observations exceeding the mean plus 1 s
or the mean minus 1 s
41S Rule

100 Rule
• Ten consecutive control observations falling on one side of
the mean (above or below, with no other requirement on
size of the deviations)

• R4S rule is applied only within a run, so that
between run systematic errors are not
wrongly interpreted as random errors.
• 22S, 41S, and 100 rules can be applied across
runs and materials.
• However, the false rejections do increase
as n increases, limiting n to a maximum of
4 to 6.

• Comparison of the probability for error detection
between the multirule procedure and the Levey-
Jennings chart having 3 s limits shows improved error
detection for the multirule procedure.
• The R4S rule improves the detection of random error
• The 22S, 41S, and 100 rules improve the detection of
systematic error.

Control of analytical quality using patient data
1. Time consuming
2. Less sensitive
3. However, many of the control problems detected with
these techniques may not be evident with conventional
QC systems.

1. Monitoring individual patient results with clinical
correlation
2. correlation with other laboratory tests
3. Intra laboratory duplicates
• simple quality control procedure that does not
require stable control materials
• differences between duplicates are plotted

• When duplicates are obtained from the same
method, this range chart monitors only random error
method.
• When duplicates are obtained from two different
laboratory methods, then the range chart actually
monitors both random and systematic errors
• very effective in identifying biases between methods
that may indicate the need to recalibrate a method or
instrument.

 Delta checks
• Errors detected by comparing laboratory test
results with values obtained on previous
specimens from the same patient.
• Particularly errors in specimen identification
• The expected variability of test results depends
on both the analyte and the time interval
between determinations.

 LIMIT CHECKS
• Patient’s test results should be reviewed to check that
they are within the physiologic ranges compatible with
life.
• Detecting clerical errors, such as transposed digits or
misplaced decimal points.

EXTERNAL QUALITY ASSESSMENT
AND PROFICIENCY TESTING PROGRAMS
• To compare the performance of different laboratories
• internal QC being necessary for the daily monitoring of
the precision and accuracy of the analytical method and
• external quality assessment being important for
maintaining long-term accuracy of the analytical
methods.

• Several external QA programs are offered by professional
societies or by manufacturers of control materials.
• Provided by CMC Vellore, AIIMS Delhi
• Increases the lab confidence
• May be needed for accreditation

• The basic operation of these programs involves having
all participating laboratories analyze the same lot of
control material, usually daily as part of the internal
QC activities.
• The mean of all results or the mean of results from
peer laboratories (those performing the test with
similar methods) is taken as the target value and is
used for comparison with the individual laboratory’s
result.
• Results are sent to the sponsoring group for the data
analysis.

• Summary reports are prepared by the program sponsor
and are distributed to all participating laboratories.
• Time consuming.
• Thus the data analysis is not available in real time
• Useful only for monthly reviews and periodic problem-
solving activities.
• However, with advances in telecommunications and the
arrival of the World Wide Web, real-time external QC is a
possibility

 Proficiency testing (PT) programs are a type of
external quality assessment
 Simulated patient specimens made from a common pool
are analyzed by the laboratories enrolled in the program.
Results are returned to a central facility and are
evaluated to determine the “quality” of each laboratory’s
performance.
 Government and licensing agencies are increasingly using
PT as an objective method for accrediting laboratories,
thereby giving them official authorization to operate.

 Six Sigma Principles and Metrics
 Adopted as the universal measure of process
performance
 GOAL - 6 sigma’s or 6 standard deviations of
process variation should fit within the
tolerance limits for the process

 For laboratory measurements sigma
performance can be as,
Sigma = (TEa − bias)/SD
 Sigma metrics from 6.0 to 3.0 represent the
range from “best case” to “worst case.”
 Methods with Six Sigma performance are
considered “world class”
 Methods with sigma performance less than 3
are not considered acceptable for production.

ANOREXIC QC –
Limits are too tight, so more outliers more false
rejection
GAMBLER QC –
Repeating the quality control again and again until the value
falls with in range
BLIND MAN QC –
Taking SD or limits from outside. Poor quality

ANALYTICAL TRACEABILITY
In 2002, the Joint Committee for Traceability in
Laboratory Medicine (JCTLM) was created.
It provides a,
• Worldwide platform to promote and give guidance
on internationally recognized and accepted
equivalence of measurements in laboratory
medicine and traceability to appropriate
measurement standards and,
• To review and list reference methods, materials, and
laboratories that provide higher order measurement
methods.

International Organization for Standardization (ISO)
 ISO 9000 is a set of standards for ensuring quality management
and quality assurance in manufacturing and service industries
ISO 9001 : 2015 – set out requirements for quality management
ISO 9001 : 2015 – covers basic concepts and language services
ISO 13485 : 2016 – medical devices (2016 - revised every 5 years)
ISO 19011 : 2011 – guidance on international audits of QMS.
ISO 15189 : 2012 – accreditation bodies and autoverification

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