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Now that you understand the definition of variation, explain
how it is measured and the key techniques for identifying
sources of variation. ***I need up to 200 words***
Chapter 3
Measurement, Variation
and CQI Tools
Contents
Introduction
Learning from measurement
The role of variation in quality improvement
Quality improvement tools
Recent trends in CQI tools
Six Sigma
Conclusion
Introduction
Measurement is a central element of CQI
Health care institutions are full of data, plus ‘factoids’,
opinions, and anecdotes masquerading as data
Analytical approaches require the use of data to evaluate current
contexts, analyze and improve processes, and track progress

Introduction
The quality evolution from industry to health care has included
the transfer and adoption of industrial statistical tools to
measure quality improvement
These tools come from biostatistics, economics, epidemiology,
and health services research, but should be considered as an
integrated portfolio
Learning from Measurement
The primary purpose of measurement in any quality
improvement initiative is to make improvements
Risk: the application of statistical methods and a focus on
results without the necessary step of thinking critically and
understanding what our data tell us about the system we are
trying to improve
Learning from Measurement
“Measurement is only a handmaiden to improvement but
improvement cannot act without it. We speak here not of
measurement for the purpose of judgment (for deciding whether
or not to buy, accept or reject) but for the purpose of learning.”
(Berwick,1996 p.621)
THE ROLE OF VARIATION IN QUALITY IMPROVEMENT
What is variation?
Nature of process variation
Measurement and statistical analysis
Process capability
Interpreting process performance
Process requirements

The Role of Variation in CQI
The starting point for any QI is understanding the type and
causes of system variation (Deming 1993; Nolan and Provost
1990)
Statistical control (or statistical process control) of stable or “in
control” processes is the basis of CQI activities (Shewhart,
1931)
If a process exhibited variation, then the cause of that variation
had to be discovered and removed
The Role of Variation in CQI
Determining variation and analyzing its causes in order to
remove them is one primary function of TQM and CQI
Deming’s notion of profound knowledge relates to variation and
how it interacts with other elements to lead to system
improvements (1993).
In recent years the business concepts related to understanding
variation have also been extended specifically to health care
(Nelson, Splaine, Batalden and Plume 1998; Carey and Lloyd
2001).
Discussion Question
From your perspective:
What is the role of variation in quality improvement?
Why are measurement and statistical analysis are vital to quality
improvement efforts?
What is Variation?

Variation is the extent to which a process differs from the norm
It is related to the statistical concepts of variance and standard
deviation
Variation is like a band of output around the central measure of
a process
Average time
to
X-ray
takes
10 mins
Variation
X-ray
takes
15 minutes
Variation
X-ray
takes
8 minutes
What is Variation?
The concept of variation in health care may be viewed from

several different perspectives
From the national perspective variation highlights health care
quality issues relative to access, medical errors, patient
outcomes, and resource allocation
At the organizational management perspective variation
provides insights on the links between variation and
organization effectiveness and results
From the individual perspective may be considered from
practitioner, employee, and customer points of view
Nature of Process Variation
There are two general categories of variation, first described by
Deming (1986), special and common.
Special causes of process variation: unnecessary variation
associated with specific material(s), machine(s), or
individual(s)
Common cause variation is the inherent variance in the process
that is a result of how the process is performed
It is also referred to as systemic or internal variation
Can be addressed by those working directly with the process
Nature of Process Variation
2. Common causes of variation: those associated with aspects of
the system itself such as design, training, materials, machines,
or working conditions.
Special cause (or externally caused) variations can be attributed
to a particular source
Special cause variation may be traced to the source eliminated

but common cause variation can only be reduced by improving
the underlying process or system
Responsibility of management to correct, as management is
responsible for correcting and preventing system problems
MEASUREMENT AND STATISTICAL ANALYSIS
Process Capability
Interpreting Process Performance
Process Requirements
Process Capability
Process capability studies are to understand the expected output
of a process, or the behavior of the process
This involves plotting outputs from the process on a histogram
The aim is to answer the question “Is the process inherently
predictable or dependable?”
The next two slides show just such histograms
The first plots the turnaround time for 223 STAT blood tests
during a 23-hour period at one large hospital
The second shows a histogram of time spent in the dentist’s
waiting room before being led to the dental chair for the exam
to begin.
Figure 3.1: Process Capability
Laboratory Test Turnaround Time
Figure 3.2: Process Capability

Waiting Time at the Dentist
Process Capability
The following aspects of a process capability histogram
provide valuable information about how the process is
performing
The shape of the curve formed by a histogram (normal or non-
normal)
Suggests which type of tools should be used for further analysis
A measure of central tendency (mean, median, or mode)
Provides information about the average level of performance
The standard deviation
Shows the range of performance that may be expected.
Process Capability
By plotting the variables over time, patterns or trends in data
emerge which can signal:
a problem with the process
that it is time to identify the source of the problem
to prompt action to resolve the problem, and to monitor the
impact of the solution.
Interpreting Process Performance
Variation exists in every process
The key is to determine if the average level of performance and
amount of common cause variation is acceptable
Acceptable variation depends on understanding the expectations
or requirements for the process
Context (turnaround time (TAT) required by an ED department
vs primary care)

Specific requirements of the task (e.g. differences in time taken
to apply different tests)
Customer requirements
Technical requirements
Process requirements are the criteria from which the
effectiveness of a process is evaluated
They function both as inputs to designing a process and outputs
from executing a process
These requirements may be considered from three perspectives:
the customer
other stakeholders
market in general
The next slide illustrates the sequence of questions that should
be asked in order to interpret process performance.
Figure 3.3: Process Requirements
Process Requirement Determination Process: Sequence of
Questions
Who are health service customers?
A customer is defined as anyone who has expectations regarding
a process operation or outputs (for health care services this
might be the patient, while for the community, it is the public
health agency)
Internal customers are those within the organizations and are
sometimes thought of as those departments or co-workers
‘downstream’ from the process (e.g. patient care units as

customers of radiology departments)
Payers may be considered as external customers, that is, those
outside the provider organization
Stakeholders are groups or individuals with an interest in or
affected by the work health services do (for example regulatory
bodies and professional associations)
What do customers require of your services?
What do patients require? Access? Competent, courteous
providers?
What do payers require? A certain level of clinical results
delivered in a cost-effective manner?
What do regulatory bodies require? Compliance?
What do markets require? Aculturally diverse approach to
delivering services?
Requirements:
Are vital to determining how services should be specified
Shape how the processes comprising the services are designed
and improved
Provide the basis for selecting variables or attributes that will
measure the process performance
Are the measure against outputs of processes are evaluated, in
order to determine if the process performance is acceptable
Are dynamic and change over time, which is why feedback is
important

The following slide illustrates how one health services
organization makes operational the link between customer
requirements, process design, and measurement. This includes:
The requirements from important stakeholder groups (i.e.,
regulatory, accreditation, etc.)
Key organizational processes that address the requirements of
these groups are identified
Attributes or variables that the organization measures to
understand the degree to which their processes are meeting
stakeholder requirements are listed
The related performance goals are identified
If the process capability is not aligned with organizational goals
as derived from the stakeholder requirements, then the process
must be improved.
Table 3.1: Process Requirements
Links Between Customer Requirements, Process Design, and
Measurement
The next slide illustrates the core processes for each phase of
the continuum of care
The patients’ interface with this organization follows the
following path:
Admission
Care delivery-treatment

Discharge
Assessment
Table 3.2: Process Requirements
Links Between Process Stages, Requirements, and Measures
Figure 3.4: Process Requirements
Process Performance v. Process limits: (a) A process that does
not have difficulty maintaining quality will have normally
distributed observations over time. (b) A process that has
difficulty maintaining quality may still have normally
distributed observations over time but may have control limits
outside the natural bounds of the process and a mean that is not
at the center of the normal curve. The shaded areas in this
diagram represent the areas out of specification.

There are different types of process capabilities
Segment A in the previous slide shows the result of a process
that is highly unpredictable
Segment B shows the QA approach of looking for outliers
Segment 3 shows the CQI or process improvement approach
In the process improvement not only is the average level of
performance improved, but the width of the band has narrowed
This shows a predictable process that will deliver more
consistent results
The graphs are process performance charts, also known as a
statistical process control charts
A process performance chart is the most effective way to
measure, document, analyze, and understand the capability of a
process
The graphs in Figure 3–4 are examples of process performance
charts, also known as a statistical process control charts. A
process performance chart is the most effective way to measure,
document, analyze, and understand the capability of a process.
QUALITY IMPROVEMENT TOOLS
Process Flow Chart
Cause-and-Effect Diagram
Histogram and Pareto Diagram
Regression Analysis
Run Charts and Process Performance Charts
Quality Improvement Tools
A systematic, fact-based approach is required to provide

permanent solutions to root causes of problems in health care
Different tools, techniques, and methods may be used to
accomplish the purpose of each phase of the PDSA cycle
Data and analytical tools may be used throughout the entire
PDSA cycle
Berwick (1996) notes that it is critical at the studying stage of a
PDSA cycle to take the time to reflect and learn about the
impact of improvements that have already been made
This should include evaluation of whether these changes have
actually been improvements, and then decide on what further
improvements to make.
Quality Improvement Tools
Activity network diagrams;
Affinity diagrams;
Brainstorming;
Cause & effect (fishbone) diagrams;
Check sheets;
Concentration diagrams;
Control charts;
Failure mode and effects analysis (FMEA);
Flowcharts (process, deployment, top-down, opportunity);
Force field analysis;
Frequency plots;
Histograms;
Interrelationship digraphs (ID);
Matrix diagrams;
Pareto charts;
Prioritization matrices;
Process capability charts;
Radar charts;
Run charts;

Scatter diagrams;
Suppliers, process steps, inputs, outputs, customers (SPIOC)
diagrams;
Time plots;
Tree diagrams;
Workflow diagrams.
Process Flow Chart
Flowcharts:
Are also known as process flow diagrams, flowcharts pictorial
representations of how a process works
They define, describe, and communicate clinical,
administrative, and operational processes
They trace the steps that the “object” (specimen, piece of paper,
patient) of a process goes through from start to finish
Often used to describe the sequence of actions that must be
carried out in order to complete a particular task
Process Flow Chart
Flow diagrams are constructed by:
Defining the basic stages of a process
Breaking each stage of the process down into specific steps
needed to complete the process
Following the object through the process a number of times to
verify the process by observation
Reviewing the process to clarify the process and include any
steps that might be missing
Figure 3.5: Process Flow Chart
Flowchart Symbols: Arrows are used to connect the symbols

indicating sequencing and interrelationship.
Figure 3.7: Process Flow Chart
Flowchart of Medication Administration Source
Reprinted with permission by VHA and First Consulting Group
from the VHA 2002 Research series publication, Surveillance
for Adverse Drug Events: History, Methods and Current Issues
by Peter Kilbridge, M.D. and David Classen, M.D., First
Consulting Group.
Process Flow Chart
Once an accurate representation of the current process has been
achieved, the following questions will be asked:
How effective is the process in meeting customer requirements?
Are there performance gaps or perceived opportunities for
improvement?
Have the relevant stages of the process been represented? Are
“owners” of each stage represented on the team? If not, what
needs to be done to gather their feedback and ideas?
What are the inputs required for the process and where do they
come from? Are the inputs constraining the process or not?
Which ones?
Are there equipment or regulatory constraints forcing this
approach?
Is this the right problem-process to be working on? To continue
working on?
Cause-and-effect diagrams are also known as Ishikawa or
fishbone diagrams because the shape resembles the skeleton of a
fish
They are most useful in identifying variation once the process

has already been described and document
They are a schematic means of relating the causes of variation
to the effect of variation on the process
They help to organize the contributing causes to a problem in
order to prioritize, select, and improve the source of the
problem
Figure 3.8: Cause-and-Effect Diagram
Multilayered Process of Developing a Fishbone Chart
Step 1: the identified performance gap or problem is put on the
right and an arrow is drawn leading to it that represents the
overall causation
Step 2: spines are drawn from the arrow to represent main
classifications or categories of causes, such as labor, materials,
and equipment
Step 3: each major spine is labeled with specific causes, which
also may occur at multiple levels
It may be necessary to stratify cause-and-effect diagrams
further to achieve finer gradations of error causes and help
identify corrective action
Figure 3-9: Cause-and-Effect Diagram
Cause-and-Effect Diagram of Medication Adverse Event: Root

Causes of Medication Errors
Once the cause-and-effect diagram is generated, data is
collected to quantify how often the different causes occur
The simplest way to display this is a histogram (a vertical bar
chart representing the frequency distribution of set of data),
which visualizes the nature of underlying statistical
distribution.
The bars are arrayed on the X-axis representing equal or
adjacent data intervals or discrete events
The length of the bar against the Y-axis shows the number of
observations falling on that interval or event classification
Successive histograms can be used to indicate whether or not
there has been a change in the variability of a process.
Figure 3.10: Histogram and Pareto Diagram
Histogram of Linen and Discard Causes
A Pareto diagram is a vertical bar chart with the bars arranged
from the longest first on the left and moving successively
toward the shortest
The vertical bars give an visual indication of the relative
frequency of the contributing causes of the problem with each
bar representing one cause
Concentrating on the vital few causes are likely to constitute the
areas of highest payback
Concentrating on the high-volume, useful many causes should
have the largest potential for reducing process variation

Figure 3.11: Histogram and Pareto Diagram
Pareto Chart: Root Causes of Adverse Drug Events
Regression Analysis
Regression analysis tests the hypothesis that one event is
temporally or causally related to another by some form of
correlational modeling
Negative findings about cause-and-effect relationships are not a
bad outcome in CQI
They reduce the complexity of the set of cause-and-effect
hypotheses to be studied by reducing the number of possible
causes
Regression analysis is used to test what may turn out to be
erroneous impressions about the causes of poor performance
It can also provide a way of looking for unknown or underrated
associations and to verify and support any improvement
programs and processes
Performance data need to be monitored on an ongoing basis to
identify:
What the temporal behavior of the process is;
Establish the time of process performance changes so that they
can be linked to the time of other possibly related events
Figure 3–13 shows a series of run charts and some diagnostic
interpretations of those data. Since the effects of health care
errors tend to be asymmetrical, it is best to look at one-sided
rules of thumb for process control. A process is considered
under control if most of the observations are near the centerline,
if there are few points near the extreme values (above the mean
plus or minus three standard deviations), and there are no runs

(more than eight consecutive observations to one side of the
mean). Run charts are very easy to generate using spreadsheet
software.
Run charts are frequently used in the quality improvement
process to answer the questions, “How are we doing?” and “Are
we doing better since implementing the improvement
intervention?”
Performance data needs to be monitored on an ongoing basis to:
Identify what the temporal behavior of the process is (does it
change over time);
Establish the time of process performance changes so that they
can be linked to the time of other possibly related events
Figure 3.13: Run Charts and Process Performance Charts
Three Examples of Run Charts. In (a), the data is considered to
be under control—the points are apparently randomly
distributed on either side of the mean, and do not go outside of
the control limits. In (b), there are extreme values (outside the
control limits), and thus the process is not in control. Another
thing to be cautious of is too many observations on one side of
the mean. In (c), there are too many values in a row (>8) below
the mean.
A process is considered under control if:
Most of the observations are near the centerline
There are few points near the extreme values (above the mean
plus or minus three standard deviations)
There are no runs (more than eight consecutive observations to
one side of the mean)

There are two types of measures that can be used to develop run
charts and control charts: attributes or variables
Attribute data arise from (1) classification of items, such as
products or services, into categories; from (2) counts of the
number of items or the proportion in a given category; and from
(3) counts of the number of occurrences per unit . . . Important
attributes (are): fraction defective, number of defects, number
of defects per unit (Gitlow et al. 1989, pp. 78, 79, 144).
There are two types of measures that can be used to develop run
charts and control charts: attributes or variables
Attribute data arises from (1) classification of items, such as
products or services, into categories; from (2) counts of the
number of items or the proportion in a given category; and from
(3) counts of the number of occurrences per unit . . . Important
attributes (are): fraction defective, number of defects, number
of defects per unit (Gitlow et al. 1989, pp. 78, 79, 144).
Variables are either measured directly or based on direct
measures only and do not result from a classification scheme
Charts often present the:
variable mean (X-bar)
process range (R), and/or
standard deviation (s) for a specific process parameter
A run chart that includes notations indicating the control limits
of plus or minus three standard deviations may be referred to as
a control chart or a process performance chart.

The control limits are referred to as the:
upper control limit (UCL), three standard deviations above the
mean
lower control limit (LCL), three standard deviations below the
mean
Use of a control chart depends on the process being free of
special causes of variation at the time the control limits were
set
Processes have to be reviewed to see:
whether special causes were again creeping in;
whether the underlying processes has changed
The most common form of control chart used is the X-bar chart
A plot of the sample mean (X-bar) of the observations
The sample size per observation for health care is usually 1
This sample size assumes that there is no sampling error and
that all observations are accurate
Control charts often use simple statistics mean, standard
deviation, and range
For variable data (such as time or distance) X-bar charts and R-
charts are used
R-chart is created in the same manner as the X-chart
Both plot the UCL, the center line, the LCL, and the range of
each group of observations
For attribute data (such as mortality rates) p-charts are used
P-charts shows the proportion of cases in which a given defect
or set of defects occurs

3.4: Run Charts and Process Performance Charts
Acute Myocardial Infarction: Door-to-Needle Time
Figure 3.14: Run Charts and Process Performance Charts
X-Bar Chart—Door to Needle Time
© S.P. Johnson, F. Alemi et al: “Rapid Improvement Teams.”
Joint Commission Journal on Quality Improvement, Vol. 24,
No. 3, pp. 119-129, 1998. Reprinted with permission.
Table 3.5: Run Charts and Process Performance Charts
Factors to Determine Control Chart Limits
Recent Trends in CQI Tools
Measuring Medical Errors
Checklists
Measuring Medical Errors
Global Trigger Tool for Measuring Adverse Events was
developed by the Institute for Health Care Improvement
It is used for tracking rates of adverse events and medical errors
in hospital records over time (Griffin and Resar, 2007)
“Triggers” are defined as clues in patient records that indicate
adverse events or medically induced harms
The trigger tool was found to have very high specificity,
reliability and sensitivity (Office of the Inspector General,

2010; Sharek, Parry, Goldmann et al, 2010, Landrigan, Parry,
Bones et al, 2010)
Checklists
Checklists are not statistical tools nor completely new tool in
quality improvement, having been used extensively in aviation
They are considered to be part of an accelerated evolution into
medical care which has had a greater focus on safety issues in
the early part of the 21st century (Gawande 2009; Pronovost
2009).
Checklists have been found to be an effective safety tool in
surgery (Haynes et al, 2009; deVries et. al 2010) and other
medical specialties (Gawande 2009; Pronovost 2006)
Six Sigma
Six sigma (sigma being the Greek symbol used in statistics to
measure variation) utilizes statistical methods to identify and
remove errors and minimize variability in processes
It is a set of practices or strategies which start with process
mapping to identify elements critical to quality and then focuses
on the changes to these via the DMAIC model (Duffy et al.
2009)
P
Process mapping

C
Control
D
M
A
I
Define
Measure
Analyze
Improve

Six Sigma
Lean methodology is often used with six sigma
It is defined as a systematic approach to identifying and
eliminating waste (defined as any non-valued tasks)
Lean six sigma is used in a wide range of health care
applications, and provides a synergistic methodology for
analyzing, and reducing or eliminating waste in health care
processes
Lean and Six Sigma methods are parallel to and can be used
with approaches like PDSA (e.g., as part of the “D” stage, in
PDSA), as well as in conjunction with collaborative
improvement strategies
Conclusion
Measurement concepts are important in continuous quality
improvement
Most important is understanding:
variation and the causes of variability
the distinction between special and common causes of variation
There are a wide set of tools that are typically used to identify,
measure and interpret the results of data collection in CQI
The ultimate goal of measurement and CQI tools for health care
organizations is that the people within them learn how to make
system improvements, to assess the impact of improvements
that have been made to, in order to increase the knowledge

gained and motivate further improvements.

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