1. Nursing qualitymanagement
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I. Contents of nursing quality management
Abstract
In Australia, the traditional Quality Assurance approach used in the hospital setting has played
an important role in nursing practice. During the past decade, nurses have begun making a
paradigm shift from Quality Assurance to Total Quality Management but scant attention has
been paid to quality management practices in nursing in the higher education sector. This paper
reports on a quantitative study examining the perceptions of nurse academics to the applicability
of TQM to nursing in universities. The findings identified how TQM could be applied to suit the
nursing culture in the higher education sector.
Quality Assurance in health care
Until recently, the most popular approach to monitoring standards and productivity in both the
manufacturing and health care industries was Quality Assurance (QA). In the health care
industry, the evaluation of health care is a process used to determine the quality of services
provided to clients. An historical overview of QA showed that ‘the earliest records reveal
concern for the quality of medical care and as might be expected they also reveal concern for the
quality of manufactured products’ (Ellis & Whittington 1993, p36). Thus, Quality Assurance is
not a new concept. According to Schmele (1996, p510), ‘it is the traditional program used by
organisations to assess, monitor, and improve quality’.
Nurses have participated in the monitoring of quality of client care for many years, and Quality
Assurance has long been an institution within nursing in the hospital setting. The evolving nature
of Quality Assurance is evident in the literature, with over one thousand QA papers published in
the last ten years (Ellis & Whittington 1993). The plethora of published literature on Quality

2. Assurance and the fact that the majority of papers are written by nurses confirms that nurses
view QA as an important aspect of nursing practice.
However, QA evaluation did not always give a true indication of the delivery of client care.
According to Potter and Perry (1993), early Quality Assurance programs were centralised;
nursing units throughout a health care facility were monitored using the same clinical criteria.
‘Measurement was often performed with agency surveys or by QA staff members who collected
data about nursing units’ (Potter & Perry 1993, p226). While it is acknowledged that attempts
were made to collect data, nursing procedures were often performed differently across units, thus
QA often ‘failed to provide meaningful information about the delivery of quality care on a
specific unit’ (Potter & Perry 1993, p226). These authors stated that ‘as a result, few nurses felt
that the problems encountered were defined, and thus nursing practice infrequently changed’
(p226).
In more recent years, criticisms have been made of the traditional QA approach adopted by
nurses in health care facilities (Masters & Schmele 1991; Bull 1994; Gillies 1994; Larrabee
1995; Schmele 1996). A major limitation of QA programs is that they direct staff to inspect and
repair rather than prevent, innovate, and develop personnel (Schroeder 1988). According to
Schmele (1996, p142), efforts in QA have ‘reflected professional values, and focused on
inspection and identifying deficiencies rather than on continuous improvement and preventing
problems’. In addition, the development of measurable standards has been viewed as a
critical201 component of QA programs but as Ellis and Whittington (1993, p61) pointed out that
‘increasingly, the development of measurable standards and clearly documented procedures is
seen to be a necessary but by no means sufficient part of assuring quality. Of greater importance
in maintaining and indeed exceeding predetermined standards of excellence are the attitudes and
perceptions of everyone associated with the organisation’.
Criticisms of the traditional QA approach coupled with changes in economic, political and
societal forces have led health care leaders in the 1990s to reassess the ways they have viewed
the concept of quality as it relates to quality care in the hospital setting. This has brought a
paradigm shift ‘from reacting to deficiencies to proacting to prevent problems, with consumer
input the driving force in the new paradigm’ (Schmele 1996, p142). Thus, changing from
detection to prevention has required a change in management style and way of thinking. Changes
in quality management practices in the health care industry have largely evolved from health
professionals examining and adopting quality management practices from the manufacturing
industry
==================
III. Quality management tools
1. Check sheet

3. 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
the chart indicates that the monitored process is
not in control, analysis of the chart can help

4. 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
common sources of defects, the highest occurring type
of defect, or the most frequent reasons for customer

5. 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
equation for the correlation between the variables can be
determined by established best-fit procedures. For a linear

6. 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
6. Histogram method

7. 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]
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