This document provides an overview of quality management and resources for further information. It discusses proper management of quality including carefully planning, operating, controlling, measuring and continuously improving work processes. Standard processes and quality standards are important to provide consistent quality. Quality management tools are presented such as check sheets, control charts, Pareto charts, scatter plots, Ishikawa diagrams and histograms to analyze processes and identify sources of variation. Additional topics in quality management systems, courses, techniques and standards are also listed.

1. Management of quality
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I. Contents of management of quality
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Quality doesn't happen by itself! Quality must be achieved by work processes that are carefully
planned, properly operated, optimally controlled, appropriately measured, and continuously
improved, i.e., by proper management of quality. This lesson emphasizes the need for standard
laboratory processes to provide consistent quality, as well as standards of quality to guide the
management of those processes. (Preview)
 Quality must be assured, not assumed! As illustrated inmyths of quality, ideas about the current
state of quality in healthcare and laboratory testing may be influenced by mistaken yarns,
theories, and hypotheses, i.e.,myths that are not supported by fact or data. Quality doesn't happen
by itself! Quality must be achieved by work processes that are carefully planned, properly
operated, optimally controlled, appropriately measured, and continuously improved, i.e., by
proper management of quality. This lesson emphasizes the need for standard laboratory
processes to provide consistent quality, as well as standards of quality to guide the management
of those processes.Standard, standard process, standard of quality
 A standard process for managing quality
 Priorities for developing standard quality management processes
 Getting started with quality standards
 Convenient sources of quality standards
 What's the point?
 References

2. Standard, standard process, standard of quality
Standard has many meanings in the laboratory. In analytical terms, a standard solution provides
a known value for calibration of a testing process and represents purity, truth, and correctness. In
management terms, a standard process provides a consistent way of doing things, e.g.,
a standard testing process provides a well-defined protocol for performing a laboratory test.
Likewise, a standard method validation process provides a regular and systematic way for
evaluating the performance of a laboratory testing process. [Note that the idea of "process" can
be applied to any repeated activity, whether physical or mental; both physical work processes
and management decision processes need to be standardized or systematized to assure consistent
and reliable results.]
Another important term is standard of quality which is a criterion or statement that describes the
acceptable level of something. For an analytical test, we need to know how quickly a test result
needs to be reported, as well as how close the result must be to the true or correct value. A
standard for turnaround time is more easily understood than a standard for truth or correctness.
For example, it is obvious that turnaround time should be stated in units of time, usually minutes.
These units are understood by both the party requesting the test and the party providing the
service. The party ordering the test defines the requirement on the basis of the medical service
being provided. Both parties can measure whether the observed performance satisfies the
requirement.
Analytical quality is more difficult because it involves technical concepts such as imprecision
and inaccuracy, which are not always understood by laboratorians and are certainly even less
well understood by the physicians who order the tests or the patients who are the ultimate
consumers of the test results. Customers and consumers of laboratory services can not easily
define the analytical quality that is required (at least not in the analytical terms desired by the
laboratory), nor can they measure or assess analytical quality. The laboratory, therefore, must
take full responsibility for managing the analytical quality of its services.
We invite you to read the rest of this article.
This article, plus many more important, updated, and expanded chapters are available in
the Basic Method Validation manual, 3rd Edition which is available at our online store. You
can download the Table of Contents and other chapters here. You can also enroll in the Basic
Method Validation course and access the new materials online.
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III. Quality management tools

3. 1. Check sheet
The check sheet is a form (document) used to collect data
in real time at the location where the data is generated.
The data it captures can be quantitative or qualitative.
When the information is quantitative, the check sheet is
sometimes called a tally sheet.
The defining characteristic of a check sheet is that data
are recorded by making marks ("checks") on it. A typical
check sheet is divided into regions, and marks made in
different regions have different significance. Data are
read by observing the location and number of marks on
the sheet.
Check sheets typically employ a heading that answers the
Five Ws:
 Who filled out the check sheet
 What was collected (what each check represents,
an identifying batch or lot number)
 Where the collection took place (facility, room,
apparatus)
 When the collection took place (hour, shift, day
of the week)
 Why the data were collected
2. Control chart
Control charts, also known as Shewhart charts
(after Walter A. Shewhart) or process-behavior
charts, in statistical process control are tools used
to determine if a manufacturing or business
process is in a state of statistical control.
If analysis of the control chart indicates that the
process is currently under control (i.e., is stable,
with variation only coming from sources common
to the process), then no corrections or changes to
process control parameters are needed or desired.
In addition, data from the process can be used to
predict the future performance of the process. If

4. the chart indicates that the monitored process is
not in control, analysis of the chart can help
determine the sources of variation, as this will
result in degraded process performance.[1] A
process that is stable but operating outside of
desired (specification) limits (e.g., scrap rates
may be in statistical control but above desired
limits) needs to be improved through a deliberate
effort to understand the causes of current
performance and fundamentally improve the
process.
The control chart is one of the seven basic tools of
quality control.[3] Typically control charts are
used for time-series data, though they can be used
for data that have logical comparability (i.e. you
want to compare samples that were taken all at
the same time, or the performance of different
individuals), however the type of chart used to do
this requires consideration.
3. Pareto chart
A Pareto chart, named after Vilfredo Pareto, is a type
of chart that contains both bars and a line graph, where
individual values are represented in descending order
by bars, and the cumulative total is represented by the
line.
The left vertical axis is the frequency of occurrence,
but it can alternatively represent cost or another
important unit of measure. The right vertical axis is
the cumulative percentage of the total number of
occurrences, total cost, or total of the particular unit of
measure. Because the reasons are in decreasing order,
the cumulative function is a concave function. To take
the example above, in order to lower the amount of
late arrivals by 78%, it is sufficient to solve the first
three issues.
The purpose of the Pareto chart is to highlight the
most important among a (typically large) set of
factors. In quality control, it often represents the most

5. common sources of defects, the highest occurring type
of defect, or the most frequent reasons for customer
complaints, and so on. Wilkinson (2006) devised an
algorithm for producing statistically based acceptance
limits (similar to confidence intervals) for each bar in
the Pareto chart.
4. Scatter plot Method
A scatter plot, scatterplot, or scattergraph is a type of
mathematical diagram using Cartesian coordinates to
display values for two variables for a set of data.
The data is displayed as a collection of points, each
having the value of one variable determining the position
on the horizontal axis and the value of the other variable
determining the position on the vertical axis.[2] This kind
of plot is also called a scatter chart, scattergram, scatter
diagram,[3] or scatter graph.
A scatter plot is used when a variable exists that is under
the control of the experimenter. If a parameter exists that
is systematically incremented and/or decremented by the
other, it is called the control parameter or independent
variable and is customarily plotted along the horizontal
axis. The measured or dependent variable is customarily
plotted along the vertical axis. If no dependent variable
exists, either type of variable can be plotted on either axis
and a scatter plot will illustrate only the degree of
correlation (not causation) between two variables.
A scatter plot can suggest various kinds of correlations
between variables with a certain confidence interval. For
example, weight and height, weight would be on x axis
and height would be on the y axis. Correlations may be
positive (rising), negative (falling), or null (uncorrelated).
If the pattern of dots slopes from lower left to upper right,
it suggests a positive correlation between the variables
being studied. If the pattern of dots slopes from upper left
to lower right, it suggests a negative correlation. A line of
best fit (alternatively called 'trendline') can be drawn in
order to study the correlation between the variables. An

6. equation for the correlation between the variables can be
determined by established best-fit procedures. For a linear
correlation, the best-fit procedure is known as linear
regression and is guaranteed to generate a correct solution
in a finite time. No universal best-fit procedure is
guaranteed to generate a correct solution for arbitrary
relationships. A scatter plot is also very useful when we
wish to see how two comparable data sets agree with each
other. In this case, an identity line, i.e., a y=x line, or an
1:1 line, is often drawn as a reference. The more the two
data sets agree, the more the scatters tend to concentrate in
the vicinity of the identity line; if the two data sets are
numerically identical, the scatters fall on the identity line
exactly.
5.Ishikawa diagram
Ishikawa diagrams (also called fishbone diagrams,
herringbone diagrams, cause-and-effect diagrams, or
Fishikawa) are causal diagrams created by Kaoru
Ishikawa (1968) that show the causes of a specific
event.[1][2] Common uses of the Ishikawa diagram are
product design and quality defect prevention, to identify
potential factors causing an overall effect. Each cause or
reason for imperfection is a source of variation. Causes
are usually grouped into major categories to identify these
sources of variation. The categories typically include
 People: Anyone involved with the process
 Methods: How the process is performed and the
specific requirements for doing it, such as policies,
procedures, rules, regulations and laws
 Machines: Any equipment, computers, tools, etc.
required to accomplish the job
 Materials: Raw materials, parts, pens, paper, etc.
used to produce the final product
 Measurements: Data generated from the process
that are used to evaluate its quality
 Environment: The conditions, such as location,
time, temperature, and culture in which the process
operates

7. 6. Histogram method
A histogram is a graphical representation of the
distribution of data. It is an estimate of the probability
distribution of a continuous variable (quantitative
variable) and was first introduced by Karl Pearson.[1] To
construct a histogram, the first step is to "bin" the range of
values -- that is, divide the entire range of values into a
series of small intervals -- and then count how many
values fall into each interval. A rectangle is drawn with
height proportional to the count and width equal to the bin
size, so that rectangles abut each other. A histogram may
also be normalized displaying relative frequencies. It then
shows the proportion of cases that fall into each of several
categories, with the sum of the heights equaling 1. The
bins are usually specified as consecutive, non-overlapping
intervals of a variable. The bins (intervals) must be
adjacent, and usually equal size.[2] The rectangles of a
histogram are drawn so that they touch each other to
indicate that the original variable is continuous.[3]
III. Other topics related to Management of quality (pdf download)
quality management systems
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quality system management
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