How to manage your lab

How to Manage
your Lab ?
BY
Dr. Nashwa Elsayed
Clinical Pathologist
KFH lab Quality Coordinator
MB.BCH
M.Sc. of Clinical & Chemical Pathology
Diploma in Health Care Quality Management
Diploma in Infection Prevention & Control

The principles of Quality
management, Quality Assurance
and Control have become the
foundation by which clinical labs
are managed and operated.
How to Manage Your Lab? Dr. Nashwa Elsayed

Laboratory cost reduction and
quality improvement require
(Total Quality Management).

What is The Quality ?
• Quality is Conformance to the requirements
of users or customers and the satisfaction of
their needs and expectations
• The universal principles of total quality management
are:
o Customer focus
o Management commitments
o Training
o Process capability and control
o Measurement through quality improvement tools.

Quality Cost
Cost of Conformance
( Good Quality )
Appraisal Cost
Ex.:
* Training
* Calibration
* Maintenance
Prevention
Costs
Ex.:
* Inspection
* Quality Control
Cost of Nonconformance
( Bad Quality )
Internal failure
Costs
Ex.:
* Scrap
* Rework
* Repeat runs
External failure
Costs
Ex.:
* Complaints
* Service
* Repeat request

Quality & Cost
understanding of quality and cost leads
to a new prospective of the relationship
between these two concepts.
** Improvements in quality lead to
reduction in cost.
• For example, with better analytical
quality a laboratory eliminates repeat
runs and repeat requests of the tests
Elimination of waste

Process Of Quality Improvement
• Quality Improvement Occurs when
problems are eliminated permanently.
• Problems arise primarily form imperfect
processes, not from imperfect individuals.
• Thus quality problems are primarily
management problem.
Only Management has the power to change
work processes.

To solve a problem
1. Careful definition of the problem.
2. Establishment of baseline measure of process performance
3. Identification of root causes of the problem
4. Identification of a remedy for the problem
5. Verification that the remedy actually work
6. Standardization or generalization of the solution for routine
implementation of an improved process.
7. Establishment of ongoing measures for monitoring and
control of the process.

Total Quality Management
Framework

Total Testing Process
• Accurate and timely test reports are the
responsibility of the laboratory. However
many problems arise before and after
submitted specimens are analyzed.
• Therefore the total testing process must
be managed properly in the
• Pre-analytical
• Analytical
• Post analytical phases.

Control of Pre-analytical Variables
Establishing effective methods for the
monitoring and control of pre-
analytical variables is difficult because
many of the variables are outside the
traditional lab area.

Pre-analytical Variables
➢ Patient identification
➢ Turn around time
➢ Laboratory log
➢ Transcription errors
➢ Patient preparation
➢ Specimen collection
➢ Specimen transport
➢ Specimen separation and aliquoting

Control of Analytical Variables
Many analytical variables must
controlled carefully to ensure
accurate measurements by
analytical methods.

Analytical Variables
➢ Water quality
➢ Calibration of analytical balances
➢ Calibration of volumetric glassware and
automatic pipettes
➢ Stability of electric power and the
temperature of heating baths, refrigerators,
freezers and centrifuges should be
monitored on a laboratory wide basis
because they affect many laboratory
methods.

Documentation of Analytical
Protocols
Step by step procedures of
analytical methods should be
written and documented and
should be kept available for all
operators.
Policy & Procedure

Policy & Procedure Outlines
➢ Procedure name
➢ Clinical significance
➢ Principle of method
➢ Specimen
➢ Reagents & equipment
➢ Procedure
➢ Reference values
➢ Comments
➢ References

Monitoring Technical Competency
( Training of Lab Personnel )
1. List of Objectives that outline the critical
tasks and knowledge
It is a helpful tool in training of personnel on new analytical
method.
2. Continuing Education Programs
help maintain and improve competence.
3. Employee Conferences
help uncover non-technical problems that may affect work quality.

Statistical Control
of Analytical Methods
• Done to monitor the Analytical Quality of the
measurement during stable operation.
• QC, like safety, are recognized as important if
something bad happened, but they seem to be a
waste of time and effort when thing are going OK.
• From the technologist’s standpoint, the objectives
of the control procedure are simply to “alert” me
when the method has a problem and “don’t alert”
me when method is working OK.

Control Materials
• Analytical methods are monitored by
analyzing a specimen of known
concentration ( Control Material )
then comparison of the observed values
with the known values.
• Control Solution or Material is a “specimen”
analyzed solely for Quality Control
Purposes NOT for Calibration.

Matrix
• It the substance or base from which
the control material is prepared.
• Ideally, control materials should
have the same matrix as the
specimen being tested.

Stability of QM
• When possible, it is desirable to
purchase at least a one year supply
of the same lot or patch number.
• Many products are now available
with expiration date exceeding two
years.

Vial to vial variability
• The variation observed when monitoring a
method is almost entirely due to measurement
imprecision and vial to vial variability.
• Lyophilized control materials must be
reconstituted with water or special diluent.
• A class A volumetric pipettes, de-ionized type I
water should be used in reconstituting control
material.
• Liquid control materials are also available, but
they are expensive and contain some additives
or preservatives.

Assayed Versus Un-Assayed
• Assayed control materials generally
come with an assay sheet of
expected values for analytes assayed
by various methods and
instruments.
• These ranges are provided only as
guidelines until the laboratory has
established his own statistical limits.

Analyte levels
• Analyte levels of quality control materials should
be chosen at medical decision levels and/or at
critical method performance limits such as upper
and lower linearity limits.
• Choosing control material at critical
concentrations (medical and/or performance) will
allow the analyst to estimate the random error at
critical levels of the method during stable
operation.

The Levey-Jennings Control Chart

Calculations
➢ Are any calculations necessary if the control
material has an assay sheet the lists the range of
acceptable values for my method?
• Yes you still need to collect your own control
limits.
➢ What statistics need to be calculated to
establish my own control limits?
• You need to calculate the mean and standard
deviation from the control results.

➢ How many control values needed for the
calculations?
• At least 20 measurements over at least 2 weeks.
➢ How many significant figures are needed in the
control results?
• Control results should have at least one more
significant figure than the value reported to the
patient results.

➢ What is the equation of the mean?
➢ What does the mean tell me about
method performance?
• The mean provides an estimate of the
central tendency.

➢ What is the equation for the standard deviation
(SD)?
➢ What does the SD tell about method
performance?
• SD is related to the spread of control results
about the expected mean.

Control limits
• Given the mean and standard deviation for a
control material, control limits are calculated as
the mean ± a certain multiple of the standard
deviation, such as 2s or 3s.
• For example, a control material has a mean for
cholesterol of 200 mg/dl and a SD of 4 mg/dl,
the 2s limit would be 192 and 208 mg/dl and
the 3s limits would be 188 and 212 mg/dl

Preparation of Control Chart
• Control chart is constructed manually using
standard graph paper.
• Label the chart.
• Scale and label x- axis and y-axis.

Preparation of Control Chart
• Draw lines for mean and control limits for our
example of cholesterol value of 200 mg/dl and
a SD of 4 mg/dl

The chances for rejection
• The room or building you’re in most likely has
a fire alarm or a whole system of fire detectors.
• What is the chance that a fire will detected by
your system if the source of fire is:
• One match?
• A whole matchbook?
• A wastebasket?
• Your whole desk?

QC Error Alarm
• The fire we want to detect in an analytical testing
process is any analytical error that would burn a
physician or patient.
• The alarm is supposed to detect situations of unstable
method performance with 100% certainty, and ideally,
shouldn’t give any false alarms (0.00% chance).
• You would expect that the chance of detecting an
analytical problem will depend on:
• the size of the error occurring
• the number of controls used
• and the sensitivity of the statistical rules being used

• The stable imprecision of the measurement is
the random variation observed when the same
patient or control materials is analyzed
repeatedly, such as in the replication
experiment that is part of the initial evaluation
studies performed on a method.

• An accuracy problem, or increase in
Systematic Error, would changes the
mean of the histogram, therefore shifting
all the control values in one direction,
higher or lower.

• A precision problem, or increase in
Random Error, would cause the standard
deviation to increase therefore, widening
the expected distribution of control
values, causing some values to be higher
and some to be lower.

CONTROL RULES
Westgard Rules

Westgard Rules
• Control rule means a decision criterion for
judging whether an analytical run is in-control or
out-of-control.

13s Rule
• 13s refer to a control rule that is commonly
used with a Levey-Jennings chart when the
control limits are set as the mean plus 3s and
the mean minus 3s.

12s Rule
• 12srefers to the control rule that is commonly used with a
Levey-Jennings chart when the control limits are set as the
mean plus/minus 2s. In the original Westgard multirule QC
procedure, this rule is used as a warning rule to trigger
careful inspection of the control data by the following
rejection rules.

22s Rule
• 22s rejected when 2 consecutive control
measurements exceed the same mean plus
2s or the same mean minus 2s control limit.

R4s Rule
• R4s reject when 1 control measurement in a group
exceeds the mean plus 2s and another exceeds the mean
minus 2s. This rule should only be interpreted within-run,
not between-run. The graphic below should really imply
that points 5 and 6 are within the same run.

41s Rule
• 41s - reject when 4 consecutive control
measurements exceed the same mean plus
1s or the same mean minus 1s control limit.

10x Rule
• 10x - reject when 10 consecutive
control measurements fall on one side
of the mean.

• In addition, you will sometimes
see some modifications of this
last rule to make it fit more
easily with Ns of 4

8x Rule
• 8x - reject when 8 consecutive control
measurements fall on one side of the
mean.

12x Rule
• 12x - reject when 12 consecutive control
measurements fall on one side of the mean.

• The preceding control rules are
usually used with N's of 2or 4, which
means they are appropriate when two
different control materials are
measured 1 or 2 times per material.

Rules & Errors
TypeofError Controlrulethatdetectsit
Randomerror 12.5s,13s,13.5s R4s,R0.05,R0.01
Systematicerror22s,41s,2of32s,31s 6x,8x,9x,10x,12x,x0.05,x0.01,cusum
• There are two types of errors, random and systematic.
Also coincidently, there are control rules which detect
random errors better than systematic errors, and control
rules that pick up systematic errors better than random
errors. So the multirule combines the use of those two
types of rules to help detect those two types of errors.

The Out-of-Control
Problem

Bad habit # 1
• Change the old bad habits:
• The first response to an “out-of-control”
situation is to automatically repeat the control.
While repeating the control will often give us a
value that may be “within the limits”, careful
inspection of the actual repeat result will often
show that “we may have just squeaked by” and
what we really have done is to delay the
troubleshooting and problem solving until future
run.

Example of repeating control

Bad Habit # 2
• Another myth is that the control is “bad”. It is
true that sometimes controls are short sampled,
are used beyond the stability date, have been
stored improperly, or were prepared incorrectly.
Why does this happen?
• Written instructions for the careful
reconstitution, mixing, handling, storage and
stability of controls should be included with the
QC procedure and also included in the QC
procedure training and implementation.

Development Good Habit -
Solve Problems
• Problem-solving or trouble-shooting is both a
skill and an attitude. It’s a skill because it depends
on your knowledge and experience.
• It is an attitude because it depends on having
confidence to investigate the unknown, often
under pressure of delaying the reports of critical
test results and the stress of having others looking
over your shoulder while they wait for you to get
the analytical system running again.

Good Habit # 1
Inspect the control charts or rules violated to
determine type of error.
• In order to solve a control problem, it is useful to identify
the type of error (random or systematic) that is causing the
QC failure. Different control rules have different
sensitivities for detecting different types of errors.
• Rules that test the tails of a distribution or the width of a
distribution, such as 13s and R4s rules, usually indicate
random error.
• Rules that look for consecutive control values exceeding
the same control limit, such as 22s, 41s, and 10 rules,
usually indicate systematic error.

For example:
• 1st event 41s is violated.
• 2nd event again, 41s is violated.
• 3rd event again shows 41s .
• 4th and 5th events both show 22s.

Good Habit # 2
• The type of error observed provide a clue about
the source of error
• because random and systematic errors have
different causes.
Relate the type of error to potential causes

➢ Systematic errors
May be caused by factors such as a
• Change in reagent lot or in calibrator lot,
• Wrong calibrator values,
• Badly prepared reagents,
• Deterioration of reagents or calibrator,
• Inadequate storage of reagent or calibrator,
• Change in sample or reagent volumes,
• Change in temp. of incubators,
• Deterioration of photometric lamp and change from one
operator to another.

➢ Random errors
May be caused by factors such as
• Bubbles in reagents and reagent lines,
• Improperly mixed reagents,
• Unstable temp and incubation,
• Unstable electrical supply,
• Individual operator variation in pipetting,
timing etc.

Good Habit # 3
• When many tests within an instrument are
displaying QC problems, troubleshooting steps
should be aimed at those things that the tests
have in common.
• Examples are tests using same filter, same lamp
or same mode of detection (endpoint or kinetic).
Consider factors in common

Good Habit # 4
Relate causes to recent changes
• A sudden shift (systematic error) is usually due to a recent
event such as replacement of reagent, recent calibrator
etc.
• A systematic trend can be more difficult to resolve than a
shift simply because the problem is occurring over a long
period of time.
• In contrast, random error problems are more difficult to
identify and resolve, mostly due to the nature of the error,
which cannot be predicted or quantified as can systematic
error.

Good Habit # 5
Verify the solution and document the remedy
• After the cause of the problem has been
identified, it must be corrected and the solution
verified by re-testing all of the controls.
• The out-of-control event must be documented
along with the corrective action.
• Troubleshooting reports should be completed
for unusual problems to aid in future problem-
solving.

‫الجودة‬=‫اإلحسان‬‫و‬ ‫ان‬‫اإلتق‬
Dr. Nashwa Elsayed

How to manage your lab

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