Quality Management in Healthcare
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Chapter04.pptxChapter 4: Understanding Variation, Tools and Data Sources for CQI in Health Care
Outline
Introduction
Health Care Systems and Processes
Gaining Knowledge Through Measurement
The Critical Role of Understanding Variation
Control Charts/Process Behavior Charts
Process behavior case study
Quality Improvement Tools
Sources of Data for CQI
Conclusions
Introduction
CQI requires knowledge about the behavior of systems, which is obtained from:
Data and other sources of information
Application of tools and techniques that have been proven to be effective throughout the evolution of CQI
Robust and effective methods
Understanding variation
The goal is to gain knowledge by utilizing data to understand processes, sources of variation, and the impact of improvements.
Most important in CQI applications is the requirement to understand variability and the predictability of health care processes.
Introduction
The quality evolution from industry to health care has included the transfer and adoption of industrial statistical tools to measure quality improvement
As with CQI itself, these tools have undergone a continuous improvement process that enhances our learning about how to apply them
Our goal is not simply to learn how to use tools – but to understand the role of variation in quality improvement, and why measurement and statistical thinking are vital to quality improvement efforts
Health Care Systems and Processes
Process Capability
Interpreting Process Requirements and Performance
Process Capability
Process capability studies are to understand the expected output of a process, or the behavior of the process
The aim is to answer the question “Is the process inherently predictable or dependable?”
To answer this question we need to know if the process is stable
There are several clear benefits of a stable process
Benefits of a Stable Process
The process has an identity (capability); it is predictable. Therefore, there is a rational basis for planning.
Costs and quality are predictable.
Productivity is at a maximum and costs at a minimum under the present system.
The effect of changes in the process can be assessed with greater speed and reliability. In an unstable process it is difficult to separate changes to the process from special causes of variation. Therefore, it is more difficult to know when a change results in improvement.
(Source: Deming , 1986)
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
that it is time to take action to resolve the problem, and to monitor the impact of the solution.
A process behavior chart is the most effective way to measure, document, analyze, and understand the capability of a process.
These will be discussed in detail later
Interpreting Process Requirements and 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
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 which 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
Process 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
Health Service Customers
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)
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 (e.g., regulatory bodies and professional associations)
Process Requirements
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?
A culturally diverse approach to delivering services?
Process Requirements
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.
Process Requirements
Links Between Customer Requirements, Process Design and Measurement
Process Requirements
This slide and the next (Table 4.2) illustrates the core processes for each phase of the continuum of care and the links between process stages, requirements and measures
The patients’ interface with this organization follows the following path:
Admission
Care delivery-treatment
Discharge
Assessment
Process Requirements
Links Between Process Stages, Requirements, and Measures
Process Requirements
Links Between Process Stages, Requirements, and Measures
Gaining Knowledge Through Measurement
Data are required to measure process performance
Critical and statistical thinking are required to convert data into knowledge and make appropriate decisions about process performance and improvements
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)
Understanding Variation
What is variation?
Nature of process variation
Measurement and statistical analysis
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)
Listening to the voice of the process with knowledge of variation will lead to the appropriate actions to take for improvement
Fundamental to CQI
The Role of Variation in CQI
Determining variation and analyzing its causes is one of the primary functions of 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).
What is Variation ?
Variation is everywhere:
A diabetic’s glucose level
Waiting time in a physician’s office
Cancer incidence
All of the above vary all the time
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
From the organizational management perspective variation provides insights on the links between variation and organizational effectiveness and results
From the individual perspective variation may be considered from practitioner, employee, and customer (patient) points of view
Importance of Understanding Variation
The appropriate action for making process improvement depends on the correct interpretation of patterns of variation
Special vs. common causes of variation (Deming, 1986)
Also referred to as signals vs. noise, respectively (Wheeler, 2000)
The importance of understanding this classification of causes directly relates to predictability of the outcomes of a process, and where special causes of variation are detected, outcomes are not predictable with any reasonable degree of belief
Special Causes of Variation
Special causes of variation are not a part of the process all of the time and do not affect every outcome but arise because of specific circumstances
Special cause variations can be attributed to a particular source
Special cause variation may be traced to the source and eliminated but common cause variation can only be reduced by improving the underlying process or system
Common Causes of Variation
Common causes of variation are the causes and conditions inherently present that impact every outcome of a process
Common causes of variation are those associated with aspects of the system itself such as design, training, materials, machines, or working conditions
the inherent variance in the process that is a result of how the process is performed
Nature of Process Variation
A process is stable and in statistical control
If only common causes are affecting outcomes over time
Outcomes are predictable within statistically determined limits
A process is unstable when outcomes are affected by both common and special causes of variation
Appropriate action for improvement is to investigate and prevent special causes from happening again
Once the process is stable the appropriate action for improvement is to make a change which reduces or removes some of the common causes
Using various tools
Starting with control/process behavior chart
How to Understand Causes of Variation
Understanding causes of variation and using that knowledge to determine when and how to make process improvements involves the use of tools and procedures designed for these purposes.
A key point to remember is that these tools can be used together in a complementary cyclical manner to identify the need for process improvement, decide on what improvements are needed, measure the impact of those changes and assess what further improvements are needed
A critical starting point is the use of control / process behavior charts
Definition of Process Behavior Charts
Process behavior chart
Also known as control charts
Both names are used interchangeably
A methodology for analyzing and understanding process variability and predictability
First developed by Shewhart, 1931
The most powerful tool for distinguishing between common and special causes of variation and predicting future performance of a stable process within a range (Langley, et al., 2009)
Process Behavior Charts
A plot of process measurements over time overlaid by three statistically computed lines
Center line (often the mean or median)
Upper control limit
Lower control limit
Horizontal and vertical axes
Horizontal: time scale (hours, days, weeks…)
Vertical: actual value of the measure being studied to assess process performance and variability
Process Behavior Chart Types
Type depends on measurement scale:
Classification data/nominal data: p chart or np chart
Count data: c chart or u chart
Continuous data: I chart (X chart/XmR chart), Xbar chart
Also depends on number of observations per time point; for example, for continuous data the Xbar chart is useful when there are multiple observations per time point and individual or XmR charts are used when there is one observation per time point.
Process Behavior Charts
Use of a process behavior 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 are again creeping in;
whether the underlying processes has changed
The most common form of process behavior chart used is the X-bar chart
A plot of the sample mean (X-bar) of the multiple observations at each time point
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
Three Examples of Control Charts
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.
Three Examples of Control Charts
Three Examples of Control Charts
In (c), there are too many values in a row below the mean. Merits further investigation, using other tools.
XmR Charts
Useful in many health care applications
when n=1observation per time point
Examples:
Number of patients waiting to see a clinician at any point in time (e.g., hourly)
Length of time to start up a clinic at beginning of day
See case study
Can be used when measure at each time point is a proportion in place of a p chart (does not require assumptions about underlying distributions)
XmR Chart Characteristics
XmR charts are actually two charts:
Plot of the individual process outcomes, or X values, over time(X Chart);
Plot of the absolute differences from one X to the next X over time, or the Moving Ranges – hence, MR.
The center line of the X chart is typically the Average (mean) of the individual X values and the Upper Control Limit or UCL is the: Average + 2.66 times the average of the Moving Ranges. The Lower Control Limit or LCL is the: Average – 2.66 times the average of the Moving Ranges.
The center line of the MR chart is the average of the Moving Ranges and the UCL is 3.27 times the average MR. There is no LCL for the MR chart since the absolute values are used.
Note: When skewness in data is observed, XmR chart should be based on median instead of mean - requires different multipliers to compute control limits (2.66 changes to 3.14; 3.27 changes to 3.87)
(Source: Wheeler, 2000).
Clinic Preparation Time Case Study Illustrating XmR Chart
(CQI in an primary care clinic: Amount of time in minutes to prepare the clinic each morning for the arrival of patients; note: n=1 observation per day)
Examples of Questions to Ask to Understand Processes and Sources of Variation in a Primary Care Clinic
What would be some examples of process measures to collect?
How to effectively analyze these measures to understand what the level of quality is?
How to decide if and when changes in processes are needed?
What specific changes should be made and who decides?
How to determine if the changes made were improvements?
Note: Case study will focus on questions 2 and 3 to illustrate use of process behavior charts
Table 4.3a: Clinic Preparation Time in Minutes (June 5th-18th)
Date Time
5-Jun 19
6-Jun 15
7-Jun 13
8-Jun 14
9-Jun 15
10-Jun 16
11-Jun 17
12-Jun 19
13-Jun 16
14-Jun 14
15-Jun 14
16-Jun 16
17-Jun 22
18-Jun 18
Average=16.3 minutes;
LCL=10.4 minutes; UCL=22.2 minutes
Process Behavior Chart for Clinic Preparation Time: June 5-18
Clinic Preparation Time in Minutes (June 5–July 7)
Process Behavior Chart for Clinic Preparation Time June 5 – July 7
Case Study Discussion Questions
1. Why was it important to use a process behavior chart to analyze clinic preparation times?
2. What unique characteristic(s) of these data made an XmR chart the appropriate type of process behavior chart for studying clinic preparation time in this case study?
3. What are the most important conclusions that can be made about clinic preparation time based on the process behavior charts presented here?
Case Study Discussion Questions
4. What is unusual about clinic preparation time on July 4th?
a) What action, if any, needed to be taken after observing the value on July 4th?
5. Why was it important to base the UCL and LCL on the data from Table 4.3a (June 5th to June 18th)?
6. How long would you recommend that the process behavior chart continue to be used to study clinic preparation time?
7. Can you think of a few other measures of a typical clinic process that could be analyzed using process behavior charts?
Quality Improvement Tools
A systematic, fact-based approach is required to evaluate system performance and improvement in health care
Different tools, techniques, and methods may be used to accomplish the purpose of each phase of an improvement process, e.g., a 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.
Examples of Quality Improvement Tools
Activity network diagrams;
Affinity diagrams;
Brainstorming;
Cause & effect (fishbone) diagrams;
Check sheets;
Concentration diagrams;
Control/Process behavior charts;
Failure mode and effects analysis (FMEA);
Flowcharts (process, deployment, top-down, opportunity);
Force field analysis;
Frequency charts;
Examples of Quality Improvement Tools
Run charts;
Scatter diagrams;
Suppliers, process steps, inputs, outputs, customers (SPIOC) diagrams;
Time plots;
Tree diagrams;
Workflow diagrams.
Interrelationship digraphs (ID);
Matrix diagrams;
Pareto charts;
Prioritization matrices;
Process capability charts;
Process Checklist
Radar charts;
QUALITY IMPROVEMENT TOOLS Discussed in Chapter 4
Run Charts
Process Flow Chart
Process Checklist
Cause-and-Effect Diagram
Frequency Chart and Pareto Diagram
Run Chart
Run charts are graphical time series displays of a process measurement along with the median value of that measure
Similar to control charts the vertical axis represents actual values of some process measure of interest and the horizontal axis presents a series of time points at which process measures have been collected;
However unlike control charts they do not include upper and lower control limits.
Run Chart
Some important uses of the run chart (which it also shares with the control chart) are:
Displaying data to make process performance visible
Determining if changes tested result in improvement
Determining if we are holding the gains made by our improvement
Allowing for a temporal (analytic) view of data versus a static (enumerative) view
(Source: Perla, Provost and Murray, 2011, p. 47)
Examples of Run Charts
Examples of Run charts to answer the questions:
“How are we doing?”(See next slide – case study example) and,
“Are we doing better since implementing the improvement intervention?” (See Figure 4.3 in text, illustrated here)
Run Chart Example Using Case Study Data: From Table 4.3a
Median=16
Run Chart for Clinic Preparation Time
Time
42891 42892 42893 42894 42895 42896 42897 42898 42899 42900 42901 42902 42903 42904 19 15 13 14 15 16 17 19 16 14 14 16 22 18 Median
42891 42892 42893 42894 42895 42896 42897 42898 42899 42900 42901 42902 42903 42904 16 16 16 16 16 16 16 16 16 16 16 16 16 16
Clinic Preparation Time in Minutes
Run Chart Example of Process Improvement
Run Chart of Number of Medication Errors
Run Chart Interpretation
A Run chart provides evidence that a process is under control if:
Most of the observations are near the centerline
There are few extreme values - high or low
Especially “astronomical values”, i.e., blatantly different from the rest of the points (see Perla, Provost and Murray, 2011)
Run Chart Interpretation
There are no runs
A run is defined as “a consecutive sequence of data points all on the same side of the median”
Balestracci provides guidance about how many values in a row constitute a run, and in particular “the guideline” that a run of 8 or more may indicate concern that there are other than common causes of variation in the data (Balestracci, 2009, p.147).
When there is evidence from a run chart that a process is not in control, then further investigation is warranted
starting with the construction of Control/Process Behavior Charts, and other tools, such as Cause and Effect diagrams
Run Charts vs. Process Behavior Charts
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
Both Run Charts and Process Behavior Charts accomplish 1 and 2
Process Behavior Charts add the capability of identifying common vs. special causes of variation
Run Charts vs. Process Behavior Charts
A run chart is a useful tool with broad applications in health care, to gain knowledge about process performance with minimal mathematical complexity. “Because of its utility and simplicity, the run chart has wide potential application in health care for practitioners and decision-makers. Run charts also provide the foundation for more sophisticated methods of analysis such as Shewhart (control) charts and planned experiments.” (Perla, Provost and Murray, 2011, p.46).
Case Study Discussion Question
Referring to the Clinic Preparation Time Case Study and its Run Chart, please consider the following questions:
Using any standard statistical text or referring to Balestracci, 2009, p.147, please review the computation of the median in the case study run chart and verify that it is correct.
If we had not also computed process behavior charts for these data, what general conclusions could be reached about clinic preparation time based on the run chart alone for the period June 5th to June 18th?
Can you think of a few other measures of a typical clinic process that could be analyzed using run charts?
Process Flow Chart
Process Flow Charts:
Are also known as process flow diagrams
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
Are often used to describe the sequence of actions that must be carried out in order to complete a particular task
Process Flow Chart
Process Flow Charts 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
e.g., review by CQI team members who are closest to the process steps is invaluable for carrying out this step (see Chapter 6)
Process Flow Chart
Formal flowchart symbols are sometimes used as descriptive devices to better illustrate the steps and objects in a flow chart. Arrows are used to connect the symbols indicating sequencing and interrelationship (For an example see Figure 4.4).
Simple Flowchart of Medication Administration Process
Process Flow Chart (Using rectangles and arrows to describe steps and objects)
Flowchart of Medication Management
Modified from VHA and First Consulting Group: VHA 2002 Research Series Publication, Surveillance for Adverse Drug Events: History Methods and Current Issues by Killbridge, P. & Classen, D. First Consulting Group
Process Flow Chart Interpretation
Once an accurate representation of the current process has been described in a flow chart, 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?
Process 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)
Process Checklist Example CQI Summary Report Production Checklist
__1) Report Outline and Analysis Plan Completed
__2) Internal (CQI Team) Review of Analysis Plan
__3) Sponsor (Senior Management) Review of Analysis Plan.
__4) Analysis Programs Completed
__5) Internal Review of Analysis Database
__6) Sponsor Review of Analysis Database
__7) Internal Review of Draft Report
__8) Sponsor Review of Draft Report
__9) Final Report Completed
__10) Final Report Sent to Sponsor
Note: For each step, initials of responsible team member and /or/ date of step completion may be entered
Cause-and-Effect Diagram
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 documented
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
Cause-and-Effect Diagram
Multilayered Process of Developing a Fishbone Diagram
Development Steps: Cause-and-Effect Diagram
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
Note: It may be necessary to stratify cause-and-effect diagrams further to achieve finer gradations of error causes and help identify corrective action
Cause-and-Effect Diagram
Cause-and-Effect Diagram of Adverse Drug Events
Frequency Chart
May be used alone to describe steps in processes / or together with cause and effect diagram, eg, once the cause-and-effect diagram is generated, data are collected to quantify how often the different causes occur
The simplest way to display this is a frequency chart
a vertical bar chart representing the frequency distribution of set of data
The bars are arrayed on the X-axis representing discrete events
The length of the bar against the Y-axis shows the number of observations falling on each event classification
Successive frequency charts over time can be used to indicate whether or not there has been a change in the frequency of occurrence of specific causes.
(Note: When the events being described are continuous measures (interval scale) similar concepts can be described using histograms, which visualizes the nature of an underlying statistical distribution.)
Frequency Chart
Frequency Chart of Linen Discard Causes
Pareto Diagram
A Pareto diagram is a frequency (bar) chart with the bars arranged from the longest first on the left and moving successively toward the shortest on the right
The vertical bars give a visual indication of the relative frequency of the contributing causes of the problem with each bar representing one cause
Concentrating on the high-volume causes should have the largest potential for reducing process variation
100%
0%
Threadbare Soiled Small Tear Small Holes Discolored
Discard Reasons
% discarded
Cumulative
Reasons for the Discarding of Linens
% D i s c a r d e d
Pareto Diagram Example Illustrating Conversion of Frequency Chart to Pareto Diagram
Pareto Diagram Example
Pareto Chart: Five Rights of Medication Administration: Cause of Adverse Drug Events
Tools: Case Study Discussion Questions
Referring to the Clinic Preparation Time Case Study please consider the following questions:
In addition to process behavior charts and run charts what other tools described in this presentation might be useful for understanding the clinic preparation time process?
Give examples of how you would use each and what you might hope to learn.
Can you think of a few other measures of a typical primary care clinic process that could be analyzed using the tools presented here?
Sources of Data for CQI
Key Concepts
Timeliness
Accessibility
Data Quality
Data standards
Data management
Quality assurance
Sources of Data for CQI
Classification of Data Source
Primary
Collected for the specific purpose of measuring performance and improvement
e.g. Primary Care Clinic Case study data illustrated in chapter
Secondary
Collected for other purposes
But useful in measuring performance and improvement
Primary Data
Population Data
Utilizing all data collected for a specific CQI initiative
Sample Data
Selecting a representative subset of data from a larger population
Sampling strategy
Clearly defined measures and time frame
Sampling methodology
Sampling Methodology
Perla, Provost and Murray present two important concepts, based on the previous work of Deming and Shewhart, which provide guidance for sampling in improvement projects:
“Obtaining just enough data based on past experience to guide our learning in the future.
Making full use of subject matter expertise in selecting the most appropriate samples.”(2013, p.37)
Sampling Methodology
Broad Classification of Methodologies
Random Sample
Probability sampling
Judgement Sample
Purposive sampling
Using subject matter experts
Wide range of methods for both of the above
See references in text
Sampling Considerations
Sampling Frame
Careful definition of population and time period
Sample Size
Number of observations (n) to be selected for each time point
And frequency (how often)
Be cautious of non-sampling errors
Most important / most common in patient surveys
Non response rate
e.g., low response rates to patient satisfaction surveys
See text for references
Secondary Data
Data collected for purposes other than specific process performance or improvement initiative
Examples
Claims data, such as data available from CMS (Centers for Medicare and Medicaid Services)
See QIO example in text
Electronic health records (EHR)
Commonly available in large hospital systems and physician practices
Very useful, but exercise caution
Since data were collected for other purposes may lack specificity for process measurement and improvement
Conclusions
Understanding variation, CQI tools and sources of data are central to effectively carrying out continuous quality improvement in health care
Most important is understanding:
Variation/the causes of variation
The distinction between special and common causes of variation
Critical to define cause of variation before determining improvement strategies
Control/process behavior charts are robust methods for displaying patterns of variation over time and identifying special causes of variation
Conclusions
There are a wide set of tools and data sources that are typically used to identify, measure and interpret process performance and improvement
These should be carefully chosen, and analyzed critically to understand fully how and when to apply improvement strategies
The ultimate goal of measurement and CQI tools for health care organizations is to gain knowledge in an orderly manner about system performance and motivate further improvements.
