Volume 8 Supplement 1

Proceedings of Advancing the Methods in Health Quality Improvement Research 2012 Conference

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Understanding and managing variation: three different perspectives

  • Michael E Bowen 1 , 2 , 3 &
  • Duncan Neuhauser 4  

Implementation Science volume  8 , Article number:  S1 ( 2013 ) Cite this article

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Managing variation is essential to quality improvement. Quality improvement is primarily concerned with two types of variation – common-cause variation and special-cause variation. Common-cause variation is random variation present in stable healthcare processes. Special-cause variation is an unpredictable deviation resulting from a cause that is not an intrinsic part of a process. By careful and systematic measurement, it is easier to detect changes that are not random variation.

The approach to managing variation depends on the priorities and perspectives of the improvement leader and the intended generalizability of the results of the improvement effort. Clinical researchers, healthcare managers, and individual patients each have different goals, time horizons, and methodological approaches to managing variation; however, in all cases, the research question should drive study design, data collection, and evaluation. To advance the field of quality improvement, greater understanding of these perspectives and methodologies is needed [ 1 ].

Clinical researcher perspective

The primary goal of traditional randomized controlled trials (RCTs) (ie a comparison of treatment A versus placebo) is to determine treatment or intervention efficacy in a specified population when all else is equal. In this approach, researchers seek to maximize internal validity. Through randomization, researchers seek to balance variation in baseline factors by randomizing patients, clinicians, or organizations to experimental and control groups. Researchers may also increase understanding of variation within a specific study using approaches such as stratification to examine for effect modification. Although the generalizability of outcomes in all research designs is limited by the study population and setting, this can be particularly challenging in traditional RCTs. When inclusion criteria are strict, study populations are not representative of “real world” patients, and the applicability of study findings to clinical practice may be unclear. Traditional RCTs are limited in their ability to evaluate complex processes that are purposefully and continually changing over time because they evaluate interventions in rigorously controlled conditions over fixed time frames [ 2 ]. However, using alternative designs such as hybrid, effectiveness studies discussed in these proceedings or pragmatic RCTs, researchers can rigorously answer a broader range of research questions [ 3 ].

Healthcare manager perspective

Healthcare managers seek to understand and reduce variation in patient populations by monitoring process and outcome measures. They utilize real-time data to learn from and manage variation over time. By comparing past, present, and desired performance, they seek to reduce undesired variation and reinforce desired variation. Additionally, managers often implement best practices and benchmark performance against them. In this process, efficient, time-sensitive evaluations are important. Run charts and Statistical Process Control (SPC) methods leverage the power of repeated measures over time to detect small changes in process stability and increase the statistical power and rapidity with which effects can be detected [ 1 ].

Patient perspective

While the clinical researcher and healthcare manager are interested in understanding and managing variation at a population level, the individual patient wants to know if a particular treatment will allow one to achieve health outcomes similar to those observed in study populations. Although the findings of RCTs help form the foundation of evidence-based practice and managers utilize these findings in population management, they provide less guidance about the likelihood of an individual patient achieving the average benefits observed across a population of patients. Even when RCT findings are statistically significant, many trial participants receive no benefit. In order to understand if group RCT results can be achieved with individual patients, a different methodological approach is needed. “N-of-1 trials” and the longitudinal factorial design of experiments allow patients and providers to systematically evaluate the independent and combined effects of multiple disease management variables on individual health outcomes [ 4 ]. This offers patients and providers the opportunity to collect, analyze, and understand data in real time to improve individual patient outcomes.

Advancing the field of improvement science and increasing our ability to understand and manage variation requires an appreciation of the complementary perspectives held and methodologies utilized by clinical researchers, healthcare managers, and patients. To accomplish this, clinical researchers, healthcare managers, and individual patients each face key challenges.

Recommendations

Clinical researchers are challenged to design studies that yield generalizable outcomes across studies and over time. One potential approach is to anchor research questions in theoretical frameworks to better understand the research problem and relationships among key variables. Additionally, researchers should expand methodological and analytical approaches to leverage the statistical power of multiple observations collected over time. SPC is one such approach. Incorporation of qualitative research and mixed methods can also increase our ability to understand context and the key determinants of variation.

Healthcare managers are challenged to identify best practices and benchmark their processes against them. However, the details of best practices and implementation strategies are rarely described in sufficient detail to allow identification of the key drivers of process improvement and adaption of best practices to local context. By advocating for transparency in process improvement and urging publication of improvement and implementation efforts, healthcare managers can enhance the spread of best practices, facilitate improved benchmarking, and drive continuous healthcare improvement.

Individual patients and providers are challenged to develop the skills needed to understand and manage individual processes and outcomes. As an example, patients with hypertension are often advised to take and titrate medications, modify dietary intake, and increase activity levels in a non-systematic manner. The longitudinal factorial design offers an opportunity to rigorously evaluate the impact of these recommendations, both in isolation and in combination, on disease outcomes [ 1 ]. Patients can utilize paper, smart phone applications, or even electronic health record portals to sequentially record their blood pressures. Patients and providers can then apply simple SPC rules to better understand variation in blood pressure readings and manage their disease [ 5 ].

As clinical researchers, healthcare managers, and individual patients strive to improve healthcare processes and outcomes, each stakeholder brings a different perspective and set of methodological tools to the improvement team. These perspectives and methods are often complementary such that it is not which methodological approach is “best” but rather which approach is best suited to answer the specific research question. By combining these perspectives and developing partnerships with organizational managers, improvement leaders can demonstrate process improvement to key decision makers in the healthcare organization. It is through such partnerships that the future of quality improvement research is likely to find financial support and ultimate sustainability.

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VA National Quality Scholars Fellowship, Tennessee Valley Healthcare System, Nashville, Tennessee, 37212, USA

Michael E Bowen

Division of General Internal Medicine, Department of Medicine, University of Texas Southwestern Medical Center, Dallas, Texas, 75390, USA

Division of Outcomes and Health Services Research, Department of Clinical Sciences, University of Texas Southwestern Medical Center, Dallas, Texas, 75390, USA

Department of Epidemiology and Biostatistics, Case Western Reserve University, Cleveland, Ohio, 44106, USA

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assignable variation types

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ASSIGNABLE CAUSES OF VARIATIONS

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Assignable causes of variation are present in most production processes. These causes of variability are also called special causes of variation ( Deming, 1982 ). The sources of assignable variation can usually be identified (assigned to a specific cause) leading to their elimination. Tool wear, equipment that needs adjustment, defective materials, or operator error are typical sources of assignable variation. If assignable causes are present, the process cannot operate at its best. A process that is operating in the presence of assignable causes is said to be “out of statistical control.” Walter A. Shewhart (1931) suggested that assignable causes, or local sources of trouble, must be eliminated before managerial innovations leading to improved productivity can be achieved.

Assignable causes of variability can be detected leading to their correction through the use of control charts.

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Deming, W. Edwards (1982). Out of the Crisis, Center for Advanced Engineering Study, Massachusetts Institute of Technology, Cambridge, Massachusetts.

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Shewhart, W. A. (1939). Statistical Method from the Viewpoint of Quality Control, Graduate School, Department of Agriculture, Washington.

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(2000). ASSIGNABLE CAUSES OF VARIATIONS . In: Swamidass, P.M. (eds) Encyclopedia of Production and Manufacturing Management. Springer, Boston, MA . https://doi.org/10.1007/1-4020-0612-8_57

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Six Sigma Study Guide

Six Sigma Study Guide

Study notes and guides for Six Sigma certification tests

Ted Hessing

Posted by Ted Hessing

Variation is the enemy! It can introduce waste and errors into a process. The more variation, the more errors. The more errors, the more waste.

What is Variation?

Quick answer: it’s a lack of consistency. Imagine that you’re manufacturing an item. Say, a certain-sized screw. Firstly, you want the parameters to be the same in every single screw you produce. Material strength, length, diameter, and thread frequency must be uniform. Secondly, your customers want a level of consistency. They want a certain size of screw all to be the same. Using a screw that’s the wrong size might have serious consequences in a construction environment. So a lack of consistency in our products is bad.

We call the differences between multiple instances of a single product variation .

(Note: in some of Game Change Lean Six Sigma’s videos, they misstate Six Sigma quality levels as 99.999997 where it should be six sigma = 99.99966 % )

Why Measure Variation?

We measure it for a couple of reasons:

  • Reliability: We want our customers to know they’ll always get a certain level of quality from us. Also, we’ll often have a Service Level Agreement or similar in place. Consequently, every product needs to fit specific parameters.
  • Costs: Variation costs money. So, to lower costs, we need to keep levels low.

Measuring Variation vs. Averages

Once, companies tended to measure process performance by average. For example, average tensile strength or average support call length. However, a lot of companies are now moving away from this. Instead, they’re measuring variation. For example, differences in tensile strength or support call lengths.

Average measurements give us some useful data. But they don’t give us information about our product’s consistency . In most industries, focusing on decreasing fluctuations in processes increases performance. It does this by limiting factors that cause outlier results. And it often improves averages by default.

How Do Discrepancies Creep into Processes?

Discrepancies occur when:

  • There is wear and tear in a machine.
  • Someone changes a process.
  • A measurement mistake is made.
  • The material quality or makeup varies.
  • The environment changes.
  • A person’s work quality is unpredictable.

There are six elements in any process:

  • Mother Nature, or Environmental
  • Man or People
  • Measurement

In Six Sigma, these elements are often displayed like this:

6M's of Six Sigma

Discrepancies can creep into any or all elements of a process.

To read more about these six elements, see 5 Ms and one P (or 6Ms) .

For an example of changing processes contrarily causing variation, see the Quincunx Demonstration .

The process spread vs. centering

Process spread vs centering

Types of Variation

There are two basic types that can occur in a process:

  • common cause
  • special cause

Common Cause

Common cause variation happens in standard operating conditions. Think about the factory we mentioned before. Fluctuations might occur due to the following:

  • temperature
  • metal quality
  • machine wear and tear.

Common cause variation has a trend that you can chart. In the factory mentioned before, product differences might be caused by air humidity. You can chart those differences over time. Then, you can compare that chart to Weather Bureau’s humidity data.

Special Cause

Conversely, special cause variation occurs in nonstandard operating conditions. Let’s go back to the example factory mentioned before. Disparities could occur if:

  • A substandard metal was delivered.
  • One of the machines broke down.
  • A worker forgot the process and made a lot of unusual mistakes.

This type of variation does not have a trend that can be charted. Imagine a supplier delivers a substandard material once in a three-month period. Subsequently, you won’t see a trend in a chart. Instead, you’ll see a departure from a trend.

Why is it Important to Differentiate?

It’s important to separate a common cause and a special cause because:

  • Different factors affect them.
  • We should use different methods to counter each.

Treating common causes as special causes leads to inefficient changes. So, too, does treating a special cause like a common cause. The wrong changes can cause even more discrepancies.

How to Identify

Use run charts to look for common cause variation.

  • Mark your median measurement.
  • Chart the measurements from your process over time.
  • Identify runs . These are consecutive data points that don’t cross the median marked earlier. They show common cause variation.

Control Charts

Meanwhile, use control charts to look for special cause variation.

  • Mark your average measurement.
  • Mark your control limits. These are three standard deviations above and below the average.
  • Identify data points that fall outside the limits marked earlier. In other words, it is above the upper control limit or below the lower control limit. These show special cause variation.

Calculating

Variation is the square of a sample’s standard deviation .

How to Find the Cause of Variation

So far, you’ve found no significant variation in your process. However, you haven’t found what its cause might be. Hence, you need to find the source.

You can use a formal methodology like Six Sigma DMAIC or a multi-vari chart to identify the source of variation.

How to Find and Reduce Hidden Causes of Variation

DMAIC methodology is the Six Sigma standard for identifying a process’s variation, analyzing the root cause, prioritizing the most advantageous way to remove a given variation, and testing the fix. The tools you would use depend on the kind of variation and the situation. Typically, we see either a “data door” or a “process door” and the most appropriate use techniques.

You could try Lean tools like Kaizen or GE’s WorkOut for a smaller, shorter cycle methodology.

How to Counter Variation

Once you identify its source, you need to counter it. As we implied earlier, the method you use depends on its type.

Counter common cause variation using long-term process changes.

Counter special cause variation using exigency plans.

Let’s look at two examples from earlier in the article.

  • Product differences due to changes in air humidity. This is a common cause of variation.
  • Product differences due to a shipment of faulty metal. This is a special cause variation.

Countering common cause variation

As stated earlier, to counter common cause variation, we use long-term process changes. Air humidity is a common cause. Therefore, a process change is appropriate.

We might subsequently introduce a check for air humidity. We would also introduce the following step. If the check finds certain humidity levels, change the machine’s temperature to compensate. The new check would be run several times a day. Whenever needed, staff would change the temperature of the machine. These changes then lengthen the manufacturing process slightly. However, they also decrease product differences in the long term.

Countering special cause variation

As mentioned earlier, we need exigency plans to counter special cause variation. These are extra or replacement processes. We only use them if a special cause is present, though. A large change in metal quality is unusual. So we don’t want to change any of our manufacturing processes.

Instead, we implement a random quality check after every shipment. Then, an extra process to follow if a shipment fails its quality check. The new process involves requesting a new shipment. These changes don’t lengthen the manufacturing process. They do add occasional extra work. But extra work happens only if the cause is present. Then, the extra process eliminates the cause.

Combining Variation

Rather than finding variation in a single sample, you might need to figure out a combined variance in a data set. For example, a set of two different products. For this, you’ll need the variance sum law .

Firstly, look at whether the products have any common production processes.

Secondly, calculate the combined variance using one of the formulas below.

No shared processes

What if the two products don’t share any production processes? Great! Then, you can use the simplest version of the variance sum law.

Shared processes

What if the two processes do share some or all production processes? That’s OK. You’ll need the dependent form of the variance sum law instead.

Calculate covariance using the following formula.

  • μ is the mean value of X.
  • ν is the mean value of Y.
  • n = the number of items in the data set.

https://www.youtube.com/watch?v=0nZT9fqr2MU

Additional Resources

ANOVA Analysis of Variation

What You Need to Know for Your Six Sigma Exam

Combating variation is integral to Six Sigma. Therefore, all major certifying organizations require that you have substantial knowledge of it. So, let’s walk through how each represents what they expect.

Green Belts

Asq six sigma green belt.

ASQ requires Green Belts to understand the topic as it relates to:

Exploratory data analysis Create multi vari studies . Then, interpret the difference between positional, cyclical, and temporal variation. Apply sampling plans to investigate the largest sources. (Create)

IASSC Six Sigma Green Belt

IASSC requires Green Belts to understand patterns of variation. Find this in the Analyze Phase section.

Black Belts

Villanova six sigma black belt.

Villanova requires Black Belts to understand the topic as it relates to:

Six Sigma’s basic premise

Describe how Six Sigma has fundamentally two focuses– variation reduction and waste reduction that ultimately lead to fewer defects and increased efficiency. Understand the concept of variation and how the six Ms have an influence on the process . Understand the difference between assignable cause and common cause variation along with how to deal with each type.

Multi vari studies

Create and interpret multi vari studies to interpret the difference between within piece, piece to piece, and time to time variation.

Measurement system analysis

Calculate, analyze, and interpret measurement system capability using repeatability and reproducibility , measurement correlation, bias, linearity, percent agreement, precision/tolerance (P/T), precision/total variation (P/TV), and use both ANOVA and c ontrol chart methods for non-destructive, destructive, and attribute systems.

ASQ Six Sigma Black Belt

ASQ requires Black Belts to understand the topic as it relates to:

Multivariate tools

Use and interpret multivariate tools such as principal components, factor analysis, discriminant analysis, multiple analysis of variance, etc to investigate sources of variation.
Use and interpret charts of these studies and determine the difference between positional, cyclical, and temporal variation.

Attributes data analysis

Analyze attributes data using logit, probit, logistic regression , etc to investigate sources of variation.

Statistical process control (SPC)

Define and describe the objectives of SPC, including monitoring and controlling process performance, tracking trends, runs, etc, and reducing variation in a process.

IASSC Six Sigma Black Belt

IASSC requires Black Belts to understand patterns of variation in the Analyze Phase section. It includes the following:

  • Multi vari analysis .
  • Classes of distributions .
  • Inferential statistics .
  • Understanding inference.
  • Sampling techniques and uses .

Candidates also need to understand its impact on statistical process control.

ASQ Six Sigma Black Belt Exam Questions

Question: A bottled product must contain at least the volume printed on the label. This is chiefly a legal requirement. Conversely, a bottling company wants to reduce the amount of overfilled bottles. But it needs to keep volume above that on the label.

variation question

Look at the data above. What is the most effective strategy to accomplish this task?

(A) Decrease the target fill volume only. (B) Decrease the target fill variation only. (C) Firstly, decrease the target fill volume. Then decrease the target fill variation. (D) Firstly, decrease the target fill variation. Then decrease the target fill volume.

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D: Reduce variation in your process first, then try to make improvements. Otherwise, your results from a change can be worse. For example, think of the quincunx demonstration . It shows that just changing your puck placement doesn’t help. In fact, it makes your results worse. This is because you didn’t shrink the dispersion. In other words, you didn’t reduce variation, so your results varied even more.

When you’re ready, there are a few ways I can help:

First, join 30,000+ other Six Sigma professionals by subscribing to my email newsletter . A short read every Monday to start your work week off correctly. Always free.

If you’re looking to pass your Six Sigma Green Belt or Black Belt exams , I’d recommend starting with my affordable study guide:

1)→ 🟢 Pass Your Six Sigma Green Belt​ ​

2)→ ⚫ Pass Your Six Sigma Black Belt  ​​ ​

You’ve spent so much effort learning Lean Six Sigma. Why leave passing your certification exam up to chance? This comprehensive study guide offers 1,000+ exam-like questions for Green Belts (2,000+ for Black Belts) with full answer walkthroughs, access to instructors, detailed study material, and more.

​  Join 10,000+ students here. 

Comments (6)

Ijust wanted to thank you Ive been calling and searching reading etc never could find one source to stay focused on to study. Thanks to you now I have found that course and plan to stay on track any recommendations Thanks for helping and taking the time to help people I really appreciate this really thanks any suggestioins you have for me I appreciate.

May God bless you and thanks

Again, you’re welcome, Anthony. I have a write up on how to approach any Six Sigma exam here.

If during Analyze phase of DMAIC the team undersands that the process has many common causes of variation and the process should be redesigned, can the team switch to DMADV?

Absolutely. Pivoting is essential in many cases as new information is discovered.

I would caution that clear communication with your stakeholders is essential here. You want to ensure that the cost to redesign & deploy the new process doesn’t exceed the benefit you’d achieve.

Hi, the link above under 6-M-pictures does lead to nowhere: “5 Ms and one P (or 6Ms)”.

Thank you, Tatjana! Updated!

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Table of Contents

Types of variance, common cause variation, common cause variation examples, special cause variation, special cause variation example, choose the right program, common cause variation vs. special cause variation.

Common Cause Variation Vs. Special Cause Variation

Every piece of data which is measured will show some degree of variation: no matter how much we try, we could never attain identical results for two different situations - each result will be different, even if the difference is slight. Variation may be defined as “the numerical value used to indicate how widely individuals in a group vary.” 

In other words, variance gives us an idea of how data is distributed about an expected value or the mean. If you attain a variance of zero, it indicates that your results are identical - an uncommon condition. A high variance shows that the data points are spread out from each other—and the mean, while a smaller variation indicates that the data points are closer to the mean. Variance is always nonnegative.

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Change is inevitable, even in statistics. You’ll need to know what kind of variation affects your process because the course of action you take will depend on the type of variance. There are two types of Variance: Common Cause Variation and Special Cause Variation. You’ll need to know about Common Causes Variation vs Special Causes Variation because they are two subjects that are tested on the PMP Certification  and CAPM Certification exams. 

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Common Cause Variation, also referred to as “Natural Problems, “Noise,” and “Random Cause” was a term coined by Harry Alpert in 1947. Common causes of variance are the usual quantifiable and historical variations in a system that are natural. Though variance is a problem, it is an inherent part of a process—variance will eventually creep in, and it is not much you can do about it. Specific actions cannot be taken to prevent this failure from occurring. It is ongoing, consistent, and predictable.

Characteristics of common causes variation are:

  • Variation predictable probabilistically
  • Phenomena that are active within the system
  • Variation within a historical experience base which is not regular
  • Lack of significance in individual high and low values

This variation usually lies within three standard deviations from the mean where 99.73% of values are expected to be found. On a control chart, they are indicated by a few random points that are within the control limit. These kinds of variations will require management action since there can be no immediate process to rectify it. You will have to make a fundamental change to reduce the number of common causes of variation. If there are only common causes of variation on your chart, your process is said to be “statistically stable.”

When this term is applied to your chart, the chart itself becomes fairly stable. Your project will have no major changes, and you will be able to continue process execution hassle-free.

How to Successfully Ace the PMP Exam?

How to Successfully Ace the PMP Exam?

Consider an employee who takes a little longer than usual to complete a specific task. He is given two days to do a task, and instead, he takes two and a half days; this is considered a common cause variation. His completion time would not have deviated very much from the mean since you would have had to consider the fact that he could submit it a little late.

Here’s another example: you estimate 20 minutes to get ready and ten minutes to get to work. Instead, you take five minutes extra to get ready because you had to pack lunch and 15 additional minutes to get to work because of traffic. 

Other examples that relate to projects are inappropriate procedures, which can include the lack of clearly defined standard procedures, poor working conditions, measurement errors, normal wear and tear, computer response times, etc. These are all common cause variation.

Special Cause Variation, on the other hand, refers to unexpected glitches that affect a process. The term Special Cause Variation was coined by W. Edwards Deming and is also known as an “Assignable Cause.” These are variations that were not observed previously and are unusual, non-quantifiable variations.

These causes are sporadic, and they are a result of a specific change that is brought about in a process resulting in a chaotic problem. It is not usually part of your normal process and occurs out of the blue. Causes are usually related to some defect in the system or method. However, this failure can be corrected by making changes to affected methods, components, or processes.

Characteristics of special cause variation are:

  • New and unanticipated or previously neglected episode within the system
  • This kind of variation is usually unpredictable and even problematic
  • The variation has never happened before and is thus outside the historical experience base

On a control chart, the points lie beyond the preferred control limit or even as random points within the control limit. Once identified on a chart, this type of problem needs to be found and addressed immediately you can help prevent it from recurring.

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Earn 60 PDUs: Pick from 6 Courses

Let’s say you are driving to work, and you estimate arrival in 10 minutes every day. One day, it took you 20 minutes to arrive at work because you were caught in the traffic from an accident zone and were held up.

Examples relating to project management are if machine malfunctions, computer crashes, there is a power cut, etc. These kinds of random things that can happen during a project are examples of special cause variation.

One way to evaluate a project’s health is to track the difference between the original project plan and what is happening. The use of control charts helps to differentiate between the common cause variation and the special cause variation, making the process of making changes and amends easier.

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assignable variation types

Identifying and Managing Special Cause Variations

Updated: June 14, 2023 by Lori Kinney

assignable variation types

Common cause variations are those variations that happen as part of natural patterns. These kinds of variances are inherent in the process , consistent, and normal. Special cause variations are quite different than these sorts of variances.

Overview: What is Variation (Special Cause)?

These are the variances that are unexpected, non-quantifiable, and unusual. Special cause variations are those that in a process have not been encountered before. They are caused by unpredictable factors. Examples of special cause variations include machine faults, power surges, operator absences, and computer faults.

3 drawbacks of special cause variations

There are some drawbacks to special cause variations that should be acknowledged:

1. They can be difficult to prepare for

Special cause variances can be so random that it can be extremely difficult to adequately prepare for them.

2. They may cause serious problems

Some special cause variations can be eliminated due to them being a technical issue. If preventable, they should be as they can cause major problems for the business.

3. The source can be difficult to find on a control chart

The source of special cause variations can be difficult to spot on a control chart if you are not plotting in real-time. To find the source of the special cause, you would likely have to have annotated data or an exceptionally good memory.

Why are special cause variations important to understand?

For the following reasons, special case variations are important to understand:

Statistical instability

If you have a chart that only has common cause variations, it means that your process is likely “statistically stable.” Understanding special cause variations on your chart, helps you recognize the inverse. It means that your processes are “statistically unstable” and that modifications may need to occur.

You can spot them on a control chart

By understanding special cause variations, you can probably pretty easily spot them on a control chart. Should the measurements of a process be distributed normally, it is almost guaranteed that a measurement will fall within plus/minus three deviations. Measurements that fall outside these limits are likely to be special cause variations.

Understanding them makes them easier to investigate

If you get a measurement on a control chart that appears to be a special cause variation, the expectation is that it will be investigated, there will be a root cause analysis, and appropriate measures will be taken.

An industry example of a special cause variation

A project manager has been leading the test-drilling for a new site where it is believed that there is likely a significant amount of untapped crude oil. The test-drilling was supposed to last for a week, but one of the drills malfunctioned, which caused a delay. Instead, the test-drilling took a total of 30 days once the faulty drill issue was addressed. The malfunction is an example of a special cause variation.

3 best practices when thinking about special cause variations

Here are a few practices to bear in mind when it comes to special cause variations:

1. Countering special cause variations

Contingency plans can be used to counter special cause variations. With this strategy, additional processes are incorporated into operations that prevent or counter a special cause variation.

2. Recognize them on a control chart

Remember that if it falls plus/minus three deviation limits of a measurement on a control chart, it is probably a special cause variation.

3. You can avoid over-tampering

If you do not understand how variation works, you could feasibly over-adjust, which can then lead to even more variation every time there is a process change.

Frequently Asked Questions (FAQ) about Variation (Special Cause)

What is assignable cause.

It is another term for special cause variation.

Who introduced the idea of special cause variations?

M. Edwards Deming was an engineer and statistician who is considered a founding father of Total Quality Management. He is credited with being the person that came up with the concept of special cause variation.

What percentage of issues relate to special cause variations?

Special cause variations account for less than 10%. Common cause accounts for closer to 90% of variances that may occur.

Eliminating special cause variations

While it can be difficult to predict an initial special cause variation, steps can be taken to help ensure that the same problem does not arise again. Technical improvements, proper training, and other strategies can all help protect your company from repeat incidents.

About the Author

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Lori Kinney

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SOURCES OF VARIATION: COMMON AND ASSIGNABLE CAUSES

If you look at bottles of a soft drink in a grocery store, you will notice that no two bottles are filled to exactly the same level. Some are filled slightly higher and some slightly lower. Similarly, if you look at blueberry muffins in a bakery, you will notice that some are slightly larger than others and some have more blueberries than others. These types of differences are completely normal. No two products are exactly alike because of slight differences in materials, workers, machines, tools, and other factors. These are called common , or random, causes of variation . Common causes of variation are based on random causes that we cannot identify. These types of variation are unavoidable and are due to slight differences in processing.

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Random causes that cannot be identified.

An important task in quality control is to find out the range of natural random variation in a process. For example, if the average bottle of a soft drink called Cocoa Fizz contains 16 ounces of liquid, we may determine that the amount of natural variation is between 15.8 and 16.2 ounces. If this were the case, we would monitor the production process to make sure that the amount stays within this range. If production goes out of this range—bottles are found to contain on average 15.6 ounces—this would lead us to believe that there ...

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The meaning of variation to healthcare managers, clinical and health-services researchers, and individual patients

Duncan neuhauser.

1 Department of Epidemiology and Biostatistics, Case Western Reserve University, Cleveland, Ohio, USA

Lloyd Provost

2 Associates in Process Improvement, Austin, Texas, USA

3 Centre for Health Improvement, Chalmers University of Technology, Gothenburg, Sweden

Healthcare managers, clinical researchers and individual patients (and their physicians) manage variation differently to achieve different ends. First, managers are primarily concerned with the performance of care processes over time. Their time horizon is relatively short, and the improvements they are concerned with are pragmatic and ‘holistic.’ Their goal is to create processes that are stable and effective. The analytical techniques of statistical process control effectively reflect these concerns. Second, clinical and health-services researchers are interested in the effectiveness of care and the generalisability of findings. They seek to control variation by their study design methods. Their primary question is: ‘Does A cause B, everything else being equal?’ Consequently, randomised controlled trials and regression models are the research methods of choice. The focus of this reductionist approach is on the ‘average patient’ in the group being observed rather than the individual patient working with the individual care provider. Third, individual patients are primarily concerned with the nature and quality of their own care and clinical outcomes. They and their care providers are not primarily seeking to generalise beyond the unique individual. We propose that the gold standard for helping individual patients with chronic conditions should be longitudinal factorial design of trials with individual patients. Understanding how these three groups deal differently with variation can help appreciate these three approaches.

Introduction

Health managers, clinical researchers, and individual patients need to understand and manage variation in healthcare processes in different time frames and in different ways. In short, they ask different questions about why and how healthcare processes and outcomes change ( table 1 ). Confusing the needs of these three stakeholders results in misunderstanding.

Meaning of variation to managers, researchers and individual patients: questions, methods and time frames

Health managers

Our extensive experience in working with healthcare managers has taught us that their primary goal is to maintain and improve the quality of care processes and outcomes for groups of patients. Ongoing care and its improvement are temporal, so in their situation, learning from variation over time is essential. Data are organised over time to answer the fundamental management question: is care today as good as or better than it was in the past, and how likely is it to be better tomorrow? In answering that question, it becomes crucial to understand the difference between common-cause and special-cause variation (as will be discussed later). Common-cause variation appears as random variation in all measures from healthcare processes. 1 Special-cause variation appears as the effect of causes outside the core processes of the work. Management can reduce this variation by enabling the easy recognition of special-cause variation and by changing healthcare processes—by supporting the use of clinical practice guidelines, for example—but common-cause variation can never be eliminated.

The magnitude of common-cause variation creates the upper and lower control limits in Shewhart control charts. 2–5 Such charts summarise the work of health managers well. Figure 1 shows a Shewhart control chart (p-chart) developed by a quality-improvement team whose aim was to increase compliance with a new care protocol. The clinical records of eligible patients discharged (45–75 patients) were evaluated each week by the team, and records indicating that the complete protocol was followed were identified. The baseline control chart showed a stable process with a centre line (average performance) of 38% compliance. The team analysed the aspects of the protocol that were not followed and developed process changes to make it easier to complete these particular tasks. After successfully adapting the changes to the local environment (indicated by weekly points above the upper control limit in the ‘Implementing Changes’ period), the team formally implemented the changes in each unit. The team continued to monitor the process and eventually developed updated limits for the chart. The updated chart indicated a stable process averaging 83%.

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Annotated Shewhart control chart—using protocol.

This control chart makes it clear that a stable but inferior process was operating for the first 11 weeks and, by inference, probably before that. The annotated changes (testing, adapting and implementing new processes of care) are linked to designed tests of change which are special (assignable) causes of variation, in this case, to improvement after week 15, after which a new better stable process has taken hold. Note that there is common-cause (random) variation in both the old and improved processes.

After updating the control limits, the chart reveals a new stable process with no special-cause variation, which is to say, no points above or below the control limits (the dotted lines). Note that the change after week 15 cannot easily be explained by chance (random, or common-cause, variation), since the probability of 13 points in a row occurring by chance above the baseline control limit is one divided by 2 to the 13th power. This is the same likelihood that in flipping a coin 13 times, it will come up heads every time. This level of statistical power to exclude randomness as an explanation is not to be found in randomised controlled trials (RCTs). Although there is no hard-and-fast rule about the number of observations over time needed to demonstrate process stability and establish change, we believe a persuasive control chart requires 20–30 or more observations.

The manager's task demonstrates several important characteristics. First is the need to define the key quality characteristics, and choose among them for focused improvement efforts. The choice should be made based on the needs of patients and families. The importance of these quality characteristics to those being served means that speed in learning and improvement is important. Indeed, for the healthcare manager, information for improvement must be as rapid as possible (in real time). Year-old research data are not very helpful here; just-in-time performance data in the hands of the decision-makers provide a potent opportunity for rapid improvement. 6

Second, managerial change is holistic; that is, every element of an intervention that might help to improve and can be done is put to use, sometimes incrementally, but simultaneously if need be. Healthcare managers are actively working to promote measurement of process and clinical outcomes, take problems in organisational performance seriously, consider the root causes of those problems, encourage the formation of problem solving clinical micro-system teams and promote the use of multiple, evolving Plan–Do–Study–Act (PDSA) tests of change.

This kind of improvement reasoning can be applied to a wide range of care processes, large and small. For example, good surgery is the appropriate combination of hundreds of individual tasks, many of which could be improved in small ways. Aggregating these many smaller changes may result in important, observable improvement over time. The protocol-driven, randomised trial research approach is a powerful tool for establishing efficacy but has limitations for evaluating and improving such complex processes as surgery, which are continually and purposefully changing over time. The realities of clinical improvement call for a move from after-the-fact quality inspection to building quality measures into medical information systems, thereby creating real-time quality data for providers to act upon. Caring for populations of similar patients in similar ways (economies of scale) can be of particular value, because the resulting large numbers and process stability can help rapidly demonstrate variation in care processes 7 ; very tight control limits (minimal common-cause variation) allow special-cause variation to be detected more quickly.

Clinical and health-services researchers

While quality-management thinking tends towards the use of data plotted over time in control-chart format, clinical researchers think in terms of true experimental methods, such as RCTs. Health-services researchers, in contrast, think in terms of regression analysis as their principal tool for discovering explainable variation in processes and outcomes of care. The data that both communities of researchers use are generally collected during fixed periods of time, or combined across time periods; neither is usually concerned with the analysis of data over time.

Take, for example, the question of whether age and sex are associated with the ability to undertake early ambulation after hip surgery. Clinical researchers try to control for such variables through the use of entry criteria into a trial, and random assignment of patients to experimental or control group. The usual health-services research approach would be to use a regression model to predict the outcome (early ambulation), over hundreds of patients using age and sex as independent variables. Such research could show that age and sex predict outcomes and are statistically significant, and that perhaps 10% of the variance is explained by these two independent variables. In contrast, quality-improvement thinking is likely to conclude that 90% of the variance is unexplained and could be common-cause variation. The health-services researcher is therefore likely to conclude that if we measured more variables, we could explain more of this variance, while improvement scientists are more likely to conclude that this unexplained variance is a reflection of common-cause variation in a good process that is under control.

The entry criteria into RCTs are carefully defined, which makes it a challenge to generalise the results beyond the kinds of patients included in such studies. Restricted patient entry criteria are imposed to reduce variation in outcomes unrelated to the experimental intervention. RCTs focus on the difference between point estimates of outcomes for entire groups (control and experimental), using statistical tests of significance to show that differences between the two arms of a trial are not likely to be due to chance.

Individual patients and their healthcare providers

The question an individual patient asks is different from those asked by manager and researcher, namely ‘How can I get better?’ The answer is unique to each patient; the question does not focus on generalising results beyond this person. At the same time, the question the patient's physician is asking is whether the group results from the best clinical trials will apply in this patient's case. This question calls for a different inferential approach. 8–10 The cost of projecting general findings to individual patients could be substantial, as described below.

Consider the implications of a drug trial in which 100 patients taking a new drug and 100 patients taking a placebo are reported as successful because 25 drug takers improved compared with 10 controls. This difference is shown as not likely to be due to chance. (The drug company undertakes a multimillion dollar advertising campaign to promote this breakthrough.) However, on closer examination, the meaning of these results for individual patients is not so clear. To begin with, 75 of the patients who took the drug did not benefit. And among those 25 who benefited, some, perhaps 15, responded extremely well, while the size of the benefit in the other 10 was much smaller. To have only the 15 ‘maximum responders’ take this drug instead of all 100 could save the healthcare system 85% of the drug's costs (as well as reduce the chance of unnecessary adverse drug effects); those ‘savings’ would, of course, also reduce the drug company's sales proportionally. These considerations make it clear that looking at more than group results could potentially make an enormous difference in the value of research studies, particularly from the point of view of individual patients and their providers.

In light of the above concerns, we propose that the longitudinal factorial study design should be the gold standard of evidence for efficacy, particularly for assessing whether interventions whose efficacy has been established through controlled trials are effective in individual patients for whom they might be appropriate ( box 1 ). Take the case of a patient with hypertension who measures her blood pressure at least twice every day and plots these numbers on a run chart. Through this informal observation, she has learnt about several factors that result in the variation in her blood pressure readings: time of day, the three different hypertension medicines she takes (not always regularly), her stress level, eating salty French fries, exercise, meditation (and, in her case, saying the rosary), and whether she slept well the night before. Some of these factors she can control; some are out of her control.

Longitudinal factorial design of experiments for individual patients

The six individual components of this approach are not new, but in combination they are new 8 9

  • One patient with a chronic health condition; sometimes referred to as an ‘N-of-1 trial.’
  • Care processes and health status are measured over time. These could include daily measures over 20 or more days, with the patient day as the unit of analysis.
  • Whenever possible, data are numerical rather than simple clinical observation and classification.
  • The patient is directly involved in making therapeutic changes and collecting data.
  • Two or more inputs (factors) are experimentally and concurrently changed in a predetermined fashion.
  • Therapeutic inputs are added or deleted in a predetermined, systematic way. For example: on day 1, drug A is taken; on day 2, drug B; on day 3, drug A and B; day 4, neither. For the next 4 days, this sequence could be randomly reordered.

Since she is accustomed to monitoring her blood pressure over time, she is in an excellent position to carry out an experiment that would help her optimise the effects of these various influences on her hypertension. Working with her primary care provider, she could, for example, set up a table of randomly chosen dates to make each of several of these changes each day, thereby creating a systematically predetermined mix of these controllable factors over time. This factorial design allows her to measure the effects of individual inputs on her blood pressure, and even interactions among them. After an appropriate number of days (perhaps 30 days, depending on the trade-off between urgency and statistical power), she might conclude that one of her three medications has no effect on her hypertension, and she can stop using it. She might also find that the combination of exercise and consistently low salt intake is as effective as either of the other two drugs. Her answers could well be unique to her. Planned experimental interventions involving single patients are known as ‘N-of-1’ trials, and hundreds have been reported. 10 Although longitudinal factorial design of experiments has long been used in quality engineering, as of 2005 there appears to have been only one published example of its use for an individual patient. 8 9 This method of investigation could potentially become widely used in the future to establish the efficacy of specific drugs for individual patients, 11 and perhaps even required, particularly for very expensive drug therapies for chronic conditions. Such individual trial results could be combined to obtain generalised knowledge.

This method can be used to show (1) the independent effect of each input on the outcome, (2) the interaction effect between the inputs (perhaps neither drug A or B is effective on its own, but in combination they work well), (3) the effect of different drug dosages and (4) the lag time between treatment and outcome. This approach will not be practical if the outcome of interest occurs years later. This method will be more practical with patient access to their medical record where they could monitor all five of Bergman's core health processes. 12

Understanding variation is one of the cornerstones of the science of improvement

This broad understanding of variation, which is based on the work of Walter Shewart in the 1920s, goes well beyond such simple issues as making an intended departure from a guideline or recognising a meaningful change in the outcome of care. It encompasses more than good or bad variation (meeting a target). It is concerned with more than the variation found by researchers in random samples from large populations.

Everything we observe or measure varies. Some variation in healthcare is desirable, even essential, since each patient is different and should be cared for uniquely. New and better treatments, and improvements in care processes result in beneficial variation. Special-cause variation should lead to learning. The ‘Plan–Do–Study’ portion of the Shewhart PDSA cycle can promote valuable change.

The ‘act’ step in the PDSA cycle represents the arrival of stability after a successful improvement has been made. Reducing unintended, and particularly harmful, variation is therefore a key improvement strategy. The more variation is controlled, the easier it is to detect changes that are not explained by chance. Stated differently, narrow limits on a Shewhart control chart make it easier and quicker to detect, and therefore respond to, special-cause variation.

The goal of statistical thinking in quality improvement is to make the available statistical tools as simple and useful as possible in meeting the primary goal, which is not mathematical correctness, but improvement in both the processes and outcomes of care. It is not fruitful to ask whether statistical process control, RCTs, regression equations or longitudinal factorial design of experiments is best in some absolute sense. Each is appropriate for answering different questions.

Forces driving this new way of thinking

The idea of reducing unwanted variation in healthcare represents a major shift in thinking, and it will take time to be accepted. Forces for this change include the computerisation of medical records leading to public reporting of care and outcome comparisons between providers and around the world. This in turn will promote pay for performance, and preferred provider contracting based on guideline use and good outcomes. This way of thinking about variation could spread across all five core systems of health, 12 including self-care and processes of healthy living.

Competing interests: None.

Provenance and peer review: Not commissioned; externally peer reviewed.

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Variations in Care

Figure 16-1 . County-level risk-standardized 30-day heart failure readmission rates (%) in Medicare patients by performance quintile for July 2009 to June 2012. (Data from Centers for Medicare & Medicaid Services; available at https://data.medicare.gov/data/hospital-compare .) HISTORY AND DEFINITIONS Variation in clinical care, and what it reveals about that care, is a topic of great interest to researchers and clinicians. It can be divided broadly into outcome variation , which occurs when the same process produces different results in different patients , and process variation , which refers to different usage of a therapeutic or diagnostic procedure among organizations, geographic areas, or other groupings of health care providers . Studies of outcome variation can provide insight into patient characteristics and care delivery that predispose patients to either a successful or an adverse outcome and help identify patients for whom a particular treatment is likely to be effective (or ineffective). Process variation, in contrast, can provide insight into such things as the underuse of effective therapies or procedures and the overuse of ineffective therapies or procedures.   Study of the variation in clinical care dates back to 1938, when Dr. J. Allison Glover published a study revealing geographic variation in the incidence of tonsillectomy in school children in England and Wales that could not be explained by anything other than variation in medical opinion on the indications for surgery. Since then, research has revealed variation among countries and across a range of medical conditions and procedures, including prostatectomy, knee replacement, arteriovenous fistula dialysis, and invasive cardiac procedures. Actual rates of use of procedures, different variability in supply of health care services, and the system of health care organization and financing (health maintenance organizations [HMOs], fee-for-service [FFS], and national universal health care) do not necessarily determine or even greatly affect the degree of variation in a particular clinical practice. Rather, the degree of variation in use relates more to the characteristics of the procedure. Important characteristics include: •  The degree of professional uncertainty about the diagnosis and treatment of the condition the procedure addresses •  The availability of alternative treatments •  Controversy versus consensus regarding the appropriate use of the procedure •  Differences among physicians in diagnosis style and in belief in the efficacy of a treatment   When studying variation in medical practice—or interpreting the results of someone else’s study of variation—it is important to distinguish between warranted variation , which is based on differences in patient preference, disease prevalence, or other patient- or population-related factors ; and unwarranted variation , which cannot be explained by patient preference or condition or the practice of evidence-based medicine . Whereas warranted variation is the product of providing appropriate and personalized evidence-based patient care, unwarranted variation typically indicates an opportunity to improve some aspect of the quality of care provided, including inefficiencies and disparities in care.   John E. Wennberg, MD, MPH, founding editor of the Dartmouth Atlas of Health Care and a leading scholar in clinical practice variation, defines three categories of care and the implications of unwarranted variation within each of them:      1. Effective care is that for which the evidence establishes that the benefits outweigh the risks and the “right rate” of use is 100% of the patients defined by evidence-based guidelines as needing such treatment. In this category, variation in the rate of use within that patient population indicates underuse.      2. Preference-sensitive care consists of those areas of care in which there is more than one generally accepted diagnostic or therapeutic option available, so the “right rate” of each depends on patient preference.      3. Supply-sensitive care is care for which the frequency of use relates to the capacity of the local health care system. Typically, this is viewed in the context of the delivery of care to patients who are unlikely to benefit from it or whose benefit is uncertain; in areas with high capacity for that care (e.g., high numbers of hospital beds per capita) more of these patients receive the care than in areas with low capacity, where the resources have to be reserved for (and are operating at full capacity with) patients whose benefits are more certain. Because studies have repeatedly shown that regions with high use of supply sensitive care do not perform better on mortality rates or quality of life indicators than regions with low use, variation in such care may indicate overuse. Local health care system capacity can influence frequency of use in other ways, too. For example, the county-level association between fewer primary care physicians and higher 30-day hospital readmission rates suggests that inadequate primary care capacity may result in preventable hospitalizations.   Table 16-1 provides examples of warranted and unwarranted variation in each of these categories of care. Table 16-1. Examples of warranted and unwarranted variations in heart failure care.   A second important distinction that must be made when considering variation in care is between common cause and special cause variation . Common cause variation ( also referred to as “expected” or “random” variation) cannot be traced to a root cause and as such may not be worth studying in detail. Special cause variation ( or “assignable” variation) arises from a single or small set of causes that can be traced and identified and then implemented or eliminated through targeted quality improvement initiatives ). Statisticians have a broad range of tests and criteria to determine whether variation is assignable or random and with the increasing sensitivity and power of numerical analysis can measure assignable variation relatively easily. The need for statistical expertise in such endeavors must be emphasized, however; the complexity of the study designs and interpretation of results (particularly in distinguishing true variation from artifact or statistical error) carries a high risk of misinterpretation in its absence. LOCAL VARIATION Although variation in care processes and outcomes frequently is examined and discussed in terms of large-scale geography (among countries, states, or hospital referral regions, as, for example, was shown in the heart failure readmissions national map in Figure 16-1 ), it can be examined and provide equally useful information on a much smaller scale. For example, Figure 16-2 shows variation in 30-day risk-adjusted heart failure readmission rates for hospitals within a single county (Dallas, Texas), ranging from 20% below to 25% above the national average and with three hospitals showing readmission rates that were statistically significantly lower than the national average. Although no hospitals had readmission rates that were statistically significantly higher than the national rate, the poorer performing hospitals might nevertheless be interested in improving. Cooperation among the quality and clinical leaders of the hospitals within Dallas County would enable investigation of differences in practices and resources among the hospitals, which might identify areas to be targeted for improvement for those hospitals with higher readmission rates. Figure 16-2 . Forest plot showing variation in heart failure 30-day risk-standardized readmission rates (HF 30-day RSRR, %) in Medicare patients for hospitals in Dallas County, Texas for July 2009 to June 2012. Hospitals were assigned random number identifiers in place of using names. (Data from Centers for Medicare & Medicaid Services; available at https://data.medicare.gov/data/hospital-compare .)   Local between-provider variation is often encountered in the form of quality reports or scorecards. Such tools seek to identify high versus low performers among hospitals, practices, or physicians to create incentives for high performance either by invoking providers’ competitive spirit or by placing a portion of their compensation at risk according to their performance through value-based purchasing or pay-for performance programs. In other words, they show unwarranted variation in the delivery of care. Care must be taken in presenting and interpreting such variation data, however. For example, league tables (or their graphical equivalent, caterpillar charts), which order providers from the lowest to highest performers on a chosen measure and use CIs to identify providers with performance that is statistically significantly different from the overall average, are both commonly used to compare provider performance on quality measures and easily misinterpreted. One’s instinct on encountering such tables or figures is to focus on the numeric ordering of the providers and assume, for example, that a provider ranked in the 75th percentile provides much higher quality care than one in the 25th percentile. This, however, is not necessarily the case: league tables do not capture the degree of uncertainty around each provider’s point estimate, so much of the ordering in the league table reflects random variation, and the order may vary substantially from one measurement period to another, without providers making any meaningful changes in the quality of care they provide. As such, there may not be any statistically significant or meaningful clinical difference among providers even widely separated in the ranking.   Forest plots, such as Figure 16-2 , for hospitals in Dallas County are a better, although still imperfect, way of comparing provider performance. Forest plots show both the point estimate for the measure of interest (e.g., risk-adjusted heart failure 30-day readmission rates) and its CI (represented by a horizontal line) for each provider, as well as a preselected norm or standard (e.g., national average; represented by a vertical line). By looking for providers for whom not only the point estimate but the entire CI falls to either the left or right of the vertical line, readers can identify those whose performance was either significantly better or significantly worse than the preselected standard. Although Forest plots may be ordered so that hospitals are ranked according to the point estimates, that ranking is vulnerable to the same misinterpretation as in league tables. An easy way to avoid this problem is to order the providers according to something other than the point estimate—for example, alphabetically by name. Because Forest plots are easy to produce without extensive statistical knowledge or programming skills, such an approach can be very useful in situations in which experienced statisticians are not available to assist with the performance comparisons.   The funnel plot is probably the best approach for presenting comparative performance data, but it does require more sophisticated statistical knowledge to produce. In a funnel plot, the rate or measure of interest is plotted on the y axis against the number of patients treated on the x axis; close to the origin, the CI bands drawn on the plot are wide (where the numbers of patients are small) and narrow as the numbers of patients increase. The resulting funnel shape gives its name to the plot. Providers with performance falling outside the CI bands are outliers, with performance that may be statistically significantly better or worse than the overall average. Those that excel can be examined as role models to guide others’ improvement. Those that lag behind their peers can be considered as opportunities for improvement, which might benefit from targeted interventions. And because the funnel plot does not attempt to rank providers (beyond identifying the outliers), it is less open to misinterpretation by readers who fail to consider the influence of random variation.   Control charts (discussed later in detail in the context of examining variation over time) can be used in a manner similar to funnel plots to compare provider performance. In such control charts, the CI bands of the funnel plot are replaced with upper and lower control limits (typically calculated as ±3 standard deviations [SDs] from the mean [or other measure of central tendency]), and providers need not be ordered according to decreasing number of patients in the denominator of the measure of interest. As in the funnel plot, however, the providers whose performance is statistically significantly higher (or lower) than the mean are identified as those for whom the point estimate falls above the upper (or below the lower) control limit. Figure 16-3 shows an example of such a control chart for the risk-adjusted 30-day heart failure readmission rates for the hospitals in Dallas County, Texas. Unlike the forest plot in Figure 16-2 , which compares each hospital’s performance with the national average, Figure 16-3 considers only the variation among the hospitals located in Dallas County. As can be seen, no data points fall outside the control limits. Interpretation of control charts is discussed in greater detail later, but this suggests that all the variation in the readmission rates among these hospitals is explained by common cause variation (not attributable to any specific cause) rather than by any specific difference in the hospitals’ characteristics or practices. This is interesting in light of the Figure 16-2 results, which show that three hospitals’ readmission rates differed significantly from the national average. However, it should be kept in mind, first, that the CIs used to make this determination in Figure 16-2 are set at 95% compared with the control limits in Figure 16-3 which are set at 3 SDs (corresponding to 99.73%) for reasons explained in the following section. Second, Figure 16-3 draws only on the data for 18 hospitals, which is a much smaller sample than the national data, and the smaller number of observations results in relatively wide control limits. Figure 16-3 . Control chart showing variation in heart failure 30-day risk-standardized readmission rates (HF 30-day RSRR, %) in Medicare patients for hospitals in Dallas County for July 2009 to June 2012). Hospitals were assigned random number identifiers in place of using names. LCL, lower control limit; UCL, upper control limit. (Data from Centers for Medicare & Medicaid Services; available at https://data.medicare.gov/data/hospital-compare .)   Finally, variation can be studied at the most local level: within a provider—even within a single physician—over time. Such variation is best examined using control charts, discussed in detail in the next section. QUANTITATIVE METHODS OF STUDYING VARIATION Data-driven practice-variation research is an important diagnostic tool for health care policymakers and clinicians, revealing areas of care where best practices may need to be identified or—if already identified—implemented. It compares utilization rates in a given setting or by a given provider with an average utilization rate; in this it differs from appropriateness of use and patient safety studies, which compare utilization rates with an identified “right rate” and serve as ongoing performance management tools.   A good framework to investigate unwarranted variation should provide:      1. A scientific basis for including or excluding each influencing factor and to determine when the factor is applicable or not applicable      2. A clear definition and explanation of each factor suggested as a cause      3. An explanation of how the factor is operationalized, measured, and integrated with other factors Statistical Process Control and Control Charts Statistical process control (SPC), similar to continuous quality improvement, is an approach originally developed in the context of industrial manufacturing for the improvement of systems processes and outcomes and was adopted into health care contexts only relatively recently. The basic principles of SPC are summarized in Table 16-2 . Particularly in the United States, SPC has been enthusiastically embraced for quality improvement and applied in a wide range of health care settings and specialties and at all levels of health care delivery, from individual patients and providers to entire hospitals and health care systems. Its appeal and value lie in its integration of the power of statistical significance tests with chronological analyses of graphs of summary data as the data are produced. This enables similar insights into the data that classical tests of significance provide but with the time sensitivity so important to pragmatic improvement. Moreover, the relatively simple formulae and graphical displays used in SPC are generally easily understood and applied by nonstatistician decision makers, making this a powerful tool in communicating with patients, other clinicians, and administrative leaders and policymakers. Table 16-3 summarizes important benefits and limitations of SPC in health care contexts. Table 16-2. Basic principles of statistical process control.    1. Individual measurements of any process or outcome will show variation.    2. If the process or outcome is stable (i.e., subject only to common cause variation), the variation is predictable and will be described by one of several statistical distributions (e.g., normal [or bell-shaped], exponential, or Poisson distribution).    3. Special cause variation will result in measured values that deviate from these models in some observable way (e.g., fall outside the predicted range of variation).    4. When the process or outcome is in control, statistical limits and tests for values that deviate from predictions can be established, providing statistical evidence of change. Table 16-3. Benefits and limitations of statistical process control in health care.   Tools used in SPC include control charts, run charts, frequency plots, histograms, Pareto analysis, scatter diagrams, and flow diagrams, but control charts are the primary and dominant tools.   Control charts are time series plots that show not only the plotted values but also upper and lower reference thresholds (calculated using historical data) that define the range of the common cause variation for the process or outcome of interest. When all the data points fall between these thresholds (i.e., only common cause variation is present), the process is said to be “in control.” Points that fall outside the reference thresholds may indicate special cause variation due to events or changes in circumstances that were not typical before. Such events or changes may be positive or negative, making control charts useful both as a warning tool in a system that usually performs well and as a tool to test or verify the effectiveness of a quality improvement intervention deliberately introduced in a system with historically poor performance.   The specific type of control chart needed for a particular measure depends on the type of data being analyzed, as well as the behavior and assumed underlying statistical distribution. The choice of the correct control chart is essential to obtaining meaningful results. Table 16-4 matches the most common data types and characteristics for the appropriate control chart(s). Table 16-4. Appropriate control charts according to data type and distribution.   After the appropriate control chart has been determined, further issues include (1) how the upper and lower control limit thresholds will be set, (2) what statistical rules will be applied to separate special cause variation from common cause variation, and (3) how many data points need to be plotted and at what time intervals.   Broadly speaking, the width of the control limit interval must balance the risk between falsely identifying special cause variation where it does not exist (type I statistical error) and missing it where it does (type II statistical error). Typically, the upper and lower control limits are set at ±3 SDs from the estimated mean of the measure of interest. This range is expected to capture 99.73% of all plotted data compared with the 95% captured by the 2 SDs criterion typically used in traditional hypothesis testing techniques. This difference is important because, unlike in the traditional hypothesis test in which the risk of type I error (false positive) applies only once, in a control chart, the risk applies to each plotted point. Thus, in a control chart with 25 plotted points, the cumulative risk of a false positive is 1 – (0.9973) 25 = 6.5% when 3 SD control limits are used compared with 1 – (0.95) 25 = 72.3% when 2 SD limits are used.   The primary test for special cause variation, then, is a data point that falls outside the upper or lower control limit. Other common tests are listed in Table 16-5 . Although applying these additional tests does slightly increase the false-positive rate from that inherent in the control limit settings, they greatly increase the control chart’s sensitivity to improvements or deteriorations in the measure. The statistical “trick” here lies in observing special cause patterns and accumulating information while waiting for the total sample size to increase to the point where it has the power to detect a statistically significant difference. The volume of data needed for a control chart depends on: Table 16-5. Common control chart tests for special cause variation.

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Using control charts to detect common-cause variation and special-cause variation

In this topic, what are common-cause variation and special-cause variation, what special-cause variation looks like on a control chart, using brainstorming to investigate special-cause variation, don't overcorrect your process for common-cause variation.

Some degree of variation will naturally occur in any process. Common-cause variation is the natural or expected variation in a process. Special-cause variation is unexpected variation that results from unusual occurrences. It is important to identify and try to eliminate special-cause variation. Out-of-control points and nonrandom patterns on a control chart indicate the presence of special-cause variation.

Examples of common-cause and special-cause variation

A process is stable if it does not contain any special-cause variation; only common-cause variation is present. Control charts and run charts provide good illustrations of process stability or instability. A process must be stable before its capability is assessed or improvements are initiated.

assignable variation types

This process is stable because the data appear to be distributed randomly and do not violate any of the 8 control chart tests.

assignable variation types

This process is not stable; several of the control chart tests are violated.

A good starting point in investigating special-cause variation is to gather several process experts together. Using the control chart, encourage the process operators, the process engineers, and the quality testers to brainstorm why particular samples were out of control. Depending on your process, you may also want to include the suppliers in this meeting.

  • Which samples were out of control?
  • Which tests for special causes did the samples fail?
  • What does each failed test mean?
  • What are all the possible reasons for the failed test?

A common method for brainstorming is to ask questions about why a particular failure occurred to determine the root cause (the 5 why method). You could also use a cause-and-effect diagram (also called fishbone diagram).

While it's important to avoid special-cause variation, trying to eliminate common-cause variation can make matters worse. Consider a bread baking process. Slight drifts in temperature that are caused by the oven's thermostat are part of the natural common-cause variation for the process. If you try to reduce this natural process variation by manually adjusting the temperature setting up and down, you will probably increase variability rather than decrease it. This is called overcorrection.

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Chance & assignable causes of variation.

Links to all courses Variation in quality of manufactured product in the respective process in industry is inherent & evitable. These variations are broadly classified as- i) Chance / Non assignable causes ii) Assignable causes i) Chance Causes: In any manufacturing process, it is not possible to produce goods of exactly the same quality. Variation is inevitable. Certain small variation is natural to the process, being due to chance causes and cannot be prevented. This variation is therefore called allowable . ii) Assignable Causes: This type of variation attributed to any production process is due to non-random or so called assignable causes and is termed as preventable variation . Assignable causes may creep in at any stage of the process, right from the arrival of the raw materials to the final delivery of goods. Some of the important factors of assignable causes of variation are - i) Substandard or defective raw materials ii) New techniques or operation iii) Negligence of the operators iv) Wrong or improper handling of machines v) Faulty equipment vi) Unskilled or inexperienced technical staff and so on. These causes can be identified and eliminated and are to discovered in a production process before the production becomes defective. SQC is a productivity enhancing & regulating technique ( PERT ) with three factors- i) Management ii) Methods iii) Mathematics Here, control is two-fold- controlling the process ( process control ) & controlling the finished products (products control). 

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Assignable causes of variation have an advantage (high proportion, domination) in many known causes of routine variability. For this reason, it is worth trying to identify the assignable cause of variation , in such a way that its impact on the process can be eliminated, of course, assuming that project managers or members are fully aware of the assignable cause of variation. Assignable causes of variation are the result of events that are not part of the normal process. Examples of assignable causes for variability are (T. Kasse, p. 237):

  • incorrectly trained people
  • broken tools
  • failure to comply with the process
  • 1 Identify data of assignable causes
  • 2 Types of data for assignable causes
  • 3 Determining the source of assignable causes of variation in an unstable process
  • 4 Examples of Assignable cause
  • 5 Advantages of Assignable cause
  • 6 Limitations of Assignable cause
  • 7 Other approaches related to Assignable cause
  • 8 References

Identify data of assignable causes

The first step you need to take when planning data collection for assignable causes is to identify them and explain your goals . This step is to ensure that the assignable causes data that the project team gathers provides the answers that are needed to carry out the ' process improvement ' project efficiently and successfully. The characteristics that are desirable and most relevant for an assignable causes are for example: relevant, representative, sufficient. In the planning process for collecting data on assignable causes, the project team should draw and mark a chart that will provide the findings before actual data collection begins. This step gives the project team an indication of what data that can be assigned is needed (A. van Aartsengel, S Kurtoglu, p. 464).

Types of data for assignable causes

There are two types of data for assignable causes, qualitative and quantitative . Qualitative data is obtained from deseriography resulting from observations or measures of different types of characteristics of the results of the process in terms of narrative words and statements. However, the next group of data, which are quantitative data on assignable causes, are derived from the description of observations or measures of process result characteristics in terms of measurable quantity in which numerical values are used (A. van Aartsengel, S. Kurtoglu, p. 464).

Determining the source of assignable causes of variation in an unstable process

If an unstable process occurs then the analyst must identify the sources of assignable cause variation. The source and the cause itself must be investigated and, in most cases, unfortunately also eliminated. Until all such causes are removed, then the actual capacity of the process cannot be determined and the process itself will not work as planned. In some cases, however, assignable cause variability can improve the result, then the process must be redesigned (W. S. Davis, D. C. Yen, p. 76). There are two possibilities for making the wrong decision, which concerns the appearance of assignable cause variations: there is no such reason (or it is incorrectly assessed) or it is not detected (N. Möller, S. O. Hansson, J. E. Holmberg, C. Rollenhagen, p. 339).

Examples of Assignable cause

  • Poorly designed process : A poorly designed process can lead to variation due to the inconsistency in the way the process is operated. For example, if a process requires a certain step to be done in a specific order, but that order is not followed, this can lead to variation in the results of the process.
  • Human error : Human error is another common cause of variation. Examples include incorrect data entry, incorrect calculations, incorrect measurements, incorrect assembly, and incorrect operation of machinery.
  • Poor quality materials : Poor quality materials can also lead to variation. For example, if a process requires a certain grade of material that is not provided, this can lead to variation in the results of the process.
  • Changes in external conditions : Changes in external conditions, such as temperature or humidity, can also cause variation in the results of a process.
  • Equipment malfunctions : Equipment malfunctions can also lead to variation. Examples include mechanical problems, electrical problems, and computer software problems.

Advantages of Assignable cause

One advantage of identifying the assignable causes of variation is that it can help to eliminate their impact on the process. Some of these advantages include:

  • Improved product quality : By identifying and eliminating the assignable cause of variation, product quality will be improved, as it eliminates the source of variability.
  • Increased process efficiency : When the assignable cause of variation is identified and removed, the process will run more efficiently, as it will no longer be hampered by the source of variability.
  • Reduced costs : By eliminating the assignable cause of variation, the cost associated with the process can be reduced, as it eliminates the need for additional resources and labour.
  • Reduced waste : When the assignable cause of variation is identified and removed, the amount of waste produced in the process can be reduced, as there will be less variability in the output.
  • Improved customer satisfaction : By improving product quality and reducing waste, customer satisfaction will be increased, as they will receive a higher quality product with less waste.

Limitations of Assignable cause

Despite the advantages of assigning causes of variation, there are also a number of limitations that should be taken into account. These limitations include:

  • The difficulty of identifying the exact cause of variation, as there are often multiple potential causes and it is not always clear which is the most significant.
  • The fact that some assignable causes of variation are difficult to eliminate or control, such as machine malfunction or human error.
  • The costs associated with implementing changes to eliminate assignable causes of variation, such as purchasing new equipment or hiring more personnel.
  • The fact that some assignable causes of variation may be outside the scope of the project, such as economic or political factors.

Other approaches related to Assignable cause

One of the approaches related to assignable cause is to identify the sources of variability that could potentially affect the process. These can include changes in the raw material, the process parameters, the environment , the equipment, and the operators.

  • Process improvement : By improving the process, the variability caused by the assignable cause can be reduced.
  • Control charts : Using control charts to monitor the process performance can help in identifying the assignable causes of variation.
  • Design of experiments : Design of experiments (DOE) can be used to identify and quantify the impact of certain parameters on the process performance.
  • Statistical Process Control (SPC) : Statistical Process Control (SPC) is a tool used to identify, analyze and control process variation.

In summary, there are several approaches related to assignable cause that can be used to reduce variability in a process. These include process improvement, control charts, design of experiments and Statistical Process Control (SPC). By utilizing these approaches, project managers and members can identify and eliminate the assignable cause of variation in a process.

  • Davis W. S., Yen D. C. (2019)., The Information System Consultant's Handbook: Systems Analysis and Design , CRC Press, New York
  • Kasse T. (2004)., Practical Insight Into CMMI , Artech House, London
  • Möller N., Hansson S. O., Holmberg J. E., Rollenhagen C. (2018)., Handbook of Safety Principles , John Wiley & Sons, Hoboken
  • Van Aartsengel A., Kurtoglu S. (2013)., Handbook on Continuous Improvement Transformation: The Lean Six Sigma Framework and Systematic Methodology for Implementation , Springer Science & Business Media, New York

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  1. Assignable Cause

    Concepts Assignable Cause Published: November 7, 2018 by Ken Feldman Assignable cause, also known as a special cause, is one of the two types of variation a control chart is designed to identify. Let's define what an assignable cause variation is and contrast it with common cause variation.

  2. Common cause and special cause (statistics)

    Definitions Common-cause variations Common-cause variation is characterised by: Phenomena constantly active within the system; Variation predictable probabilistically; Irregular variation within a historical experience base; and Lack of significance in individual high or low values.

  3. Understanding and managing variation: three different perspectives

    Quality improvement is primarily concerned with two types of variation - common-cause variation and special-cause variation. Common-cause variation is random variation present in stable healthcare processes. Special-cause variation is an unpredictable deviation resulting from a cause that is not an intrinsic part of a process.

  4. ASSIGNABLE CAUSES OF VARIATIONS

    1 Citations Download reference work entry PDF Assignable causes of variation are present in most production processes. These causes of variability are also called special causes of variation ( Deming, 1982 ). The sources of assignable variation can usually be identified (assigned to a specific cause) leading to their elimination.

  5. Assignable Cause

    A control chart can identify one of two types of variation: assignable cause (also known as a special cause) and common cause. Let's look at what assignable cause variation looks like and compare it to common cause variation. This article will explain how to determine your control signals, and how to respond if it does.

  6. Variation

    What is Variation? Quick answer: it's a lack of consistency. Imagine that you're manufacturing an item. Say, a certain-sized screw. Firstly, you want the parameters to be the same in every single screw you produce. Material strength, length, diameter, and thread frequency must be uniform. Secondly, your customers want a level of consistency.

  7. The Power of Special Cause Variation: Learning from Process Changes

    A cycle or repeating pattern. A run: 8 or more points on either side of the average. A special cause of variation is assignable to a defect, fault, mistake, delay, breakdown, accident, and/or shortage in the process. When special causes are present, process quality is unpredictable. Special causes are a signal for you to act to make the process ...

  8. Special Causes of Variation

    Special Causes of Variation | Assignable causes | Types of variations April 8, 2020 / SPC / By TQP Special Causes of Variation are also known as Assignable Causes (un natural) of variation.

  9. Common Cause Variation Vs. Special Cause Variation

    Common Cause Variation. Common Cause Variation, also referred to as "Natural Problems, "Noise," and "Random Cause" was a term coined by Harry Alpert in 1947. Common causes of variance are the usual quantifiable and historical variations in a system that are natural. Though variance is a problem, it is an inherent part of a process ...

  10. Distinguishing between Common Cause Variation and Special Cause

    Controlling variation is an important aspect of quality improvement. Deming distinguishes between common cause variation and special cause variation and argues that both types of variation frequently result from people participating in the process. Confusing common cause and special cause variation can lead to incorrect decisions.

  11. Assignable causes of variation and statistical models: another approach

    We consider two different types of assignable causes of variation. One—called type I—affects only the parameters of a model of the underlying distribution. The other—called type X—impacts the type of the original distribution.

  12. Identifying and Managing Special Cause Variations

    Here are a few practices to bear in mind when it comes to special cause variations: 1. Countering special cause variations. Contingency plans can be used to counter special cause variations. With this strategy, additional processes are incorporated into operations that prevent or counter a special cause variation. 2.

  13. Sources of Variation: Common and Assignable Causes

    Common causes of variation are based on random causes that we cannot identify. These types of variation are unavoidable and are due to slight differences in processing. Common causes of variation. Random causes that cannot be identified. An important task in quality control is to find out the range of natural random variation in a process.

  14. Assignable Cause: Learn More From Our Online Lean Guide

    An assignable cause is a type of variation in which a specific activity or event can be linked to inconsistency in a system. In effect, it is a special cause that has been identified.. As a refresher, common cause variation is the natural fluctuation within a system. It comes from the inherent randomness in the world. The impact of this form of variation can be predicted by statistical means.

  15. The meaning of variation to healthcare managers, clinical and health

    Introduction Health managers, clinical researchers, and individual patients need to understand and manage variation in healthcare processes in different time frames and in different ways. In short, they ask different questions about why and how healthcare processes and outcomes change ( table 1 ).

  16. Chapter 10 Quality Control

    Chapter 10 Quality Control. - appraisal of goods or services. - a product or service conforms to specifications. - statistical evaluation of the output of a process during production. - natural variation in the output of a process, created by countless minor factors. - in process output, a variation whose cause can be identified. - a time ...

  17. Variations in Care

    Special cause variation (or "assignable" variation) ... The specific type of control chart needed for a particular measure depends on the type of data being analyzed, as well as the behavior and assumed underlying statistical distribution. The choice of the correct control chart is essential to obtaining meaningful results.

  18. Using control charts to detect common-cause variation and ...

    Control charts are used to monitor two types of process variation, common-cause variation and special-cause variation. In This Topic What are common-cause variation and special-cause variation? What special-cause variation looks like on a control chart Using brainstorming to investigate special-cause variation

  19. Chance & assignable causes of variation

    This type of variation attributed to any production process is due to non-random or so called assignable causes and is termed as preventable variation. Assignable causes may creep in at any stage of the process, right from the arrival of the raw materials to the final delivery of goods.

  20. Assignable cause

    Assignable causes of variation have an advantage (high proportion, domination) in many known causes of routine variability. For this reason, it is worth trying to identify the assignable cause of variation, in such a way that its impact on the process can be eliminated, of course, assuming that project managers or members are fully aware of the assignable cause of variation.

  21. Operations Management CH.6 Flashcards

    Study with Quizlet and memorize flashcards containing terms like If a sample of items is taken and the mean of the sample is outside the control limits, the process is: A. likely out of control and the cause should be investigated. B. monitored closely to see if the next sample mean will also fall outside the control limits. C. within the established control limits with only natural causes of ...

  22. Solved Up to three standard deviations above or below the

    Up to three standard deviations above or below the centerline is the amount of variation that statistical process control allows for. a. Type I errors. b. about 95.5% variation. c. natural variation. d. all types of variation. e. assignable variation. There are 2 steps to solve this one.