
To understand assumption of Xbar chart
To be able to construct an Xbar chart using Excel.
To be able to interpret the meaning of findings from Xbar chart
There are two reasons why to construct a control chart.
To discipline intuitions. Data on human judgment show that we, meaning all of us including you, have a tendency to attribute system improvements to our own effort and skill and system failure to chance events. In essence, we tend to fool ourselves. Control charts help see through these gimmicks. It helps us see if the improvement is real or we have just been lucky.
An example may help you understand my point. In an air force, a program was put in place to motivate more accurate bombing of targets during practice run. The pilot who most accurately bombed a target site, was given a $2,000 bonus for that month. After a year of continuing the program, we found an unusual relationship. Every pilot who received a reward did worse the month after. How is this possible? Rewards are supposed to encourage and not discourage positive behavior. Why would pilots who did well before do worse now just because they received a reward? The explanation is simple. Each month, the pilot who won did so not because he/she was better than the others but because he/she was lucky. We were rewarding luck; thus, it was difficult to repeat the performance next month. Control chart helps us focus on patterns of changes and go beyond a focus on luck.
In a field like medicine, over time a poor outcome occurs the natural tendency is to think of it as poor care. But such rash judgments are misleading. In an uncertain field such as medicine, from time to time there will be unusual outcomes. If you focus on these occasional outcomes, you would be punishing good clinicians whose efforts have failed by chance. Instead, focus on patterns of good or bad outcomes. Then you know that the outcomes are associated with the skills of the people and the underlying features of the process and not just chance events. Control charts help you see if there is a pattern in the data and move you away from making judgments about quality through case by case review.
To tell a story. Pcharts display the change over time. These charts tell how the system was performing before and after we changed it. They are testimonials to the success of our improvement efforts. Telling these types of stories helps the organization to:
celebrate small scale successes, an important step in keeping the momentum for continuous improvement.
communicate to others not part of the cross functional team. Such communications help pave the way for eventual organization wide implementation of the change.
You can of course present the data without plotting it and showing it. But without charts and plots, people not be able to see the data. Numbers makes people understand the data but plots and charts, especially those drawn over time, make people connect a story with the data, they end up feeling what they have understood. For many people seeing the data is believing it. When these charts are posted publicly in the organization, control charts prepare the organization for change. They transfer and explain the experience of one unit of the organization to other units.
Pcharts were designed for monitoring the performance of manufacturing firms. These charts assume that the input to the system is the same at each time period. In manufacturing this makes sense. The metal needed for making a car changes little over time. But in health care this makes no sense. People are different. People are different in their severity of illness, in their ability and will to recover from their illness and in their attitudes toward heroic interventions to save their lives. These differences affect the outcomes of care. If these differences are not accounted for, we may mistakenly blame the process when poor outcomes were inevitable and praise the process when good outcomes were due to the type of patients arriving at our unit.
Some institutions receive many severely ill patients. These institutions would be unfairly judged if their outcomes are not adjusted for their case mix before comparing them to other institutions. Similarly, in some months of the year, there are many more severely ill patients. For example, seasonal variations affect the severity of asthma. Holidays affect both the frequency and the severity of trauma cases.
But even more significant source of change in the severity of illness of our patients is our own actions. Many process changes lead to changes in the kinds of patients attracted to our unit. Consider for example, if we aggressively try to educate patients for the need for avoiding Csection, we may get a reputation for normal birth delivery and we may attract patients who have less pregnancy complications and wish for normal birth delivery. In the end, our report that we have reduced csections in our unit is not true, all we have done is to attract a new kind of patients. Nothing fundamentally has changed in our processes, except for the input to the process.
Risk adjustment of control charts is one method of making sure that the observed improvement in the process are not due to changes in the kind of patients that we are attracting to our unit.
Figure 1: Xbar chart is best when there
are multiple observations 
When tracking data over time, you have a number of options. You could use a Pchart, designed specifically to track mortality or adverse health events over time. You could use an Xbar chart designed for tracking health status and satisfaction surveys of a group of patients over time. You could use a moving average chart to help you construct control chart for an individual patient's data over time. This section helps you decide which of these various charts are appropriate for your application. If you do not have a specific application in mind or if you wish to learn more about each of the various different charts, skip this section. In the following, we ask you 47 questions and based on your answers advise you which chart is right for the application that you have in mind.
Have you collected observations over different time periods?
This section covers a new type of control charts specially set up for analysis of average satisfaction rating or average health status data. If you do not know how to measure patients' satisfaction or health status, please review these two lectures before proceeding.
For example, in analyzing satisfaction ratings, Xbar charts are used to track changes in average satisfaction ratings over time. In analysis of health status of a group of patients, Xbar charts are used to trace the group's health status over time.
Learn by doing
In this section, we introduce Xcharts one step at a time. It is important that you take each step and complete the assignment in the step before proceeding. To help you understand the steps, we provide you with data and ask you to analyze this data as we proceed. The lecture ends when you have completed an Xbar chart and faxed it to me.
The only way you can learn about control charts is to do some. You learn by doing. You gain confidence in your own ability to create control charts when you do some. Do not just read these pages. Get a piece of paper and do the exercises as you proceed.
Here is the data we need to analyze. Over several months, we tracked the satisfaction with our unit. The following Table shows the data for these months.
Satisfaction Ratings
Time Period 
Ratings of 1st Subject 
Ratings of 2nd Subject 
Ratings of 3rd Subject 
Ratings of 4th Subject 
Average Rating 
1 
80 
84 
82 
80 
? 
2 
70 
72 
74 
70 
? 
3 
76 
78 
76 
78 
? 
4 
80 
78 
78 
80 
? 
The question we have is whether the unit has improved over time.
How would you answer this question? Look at the data. There are wide variations in the data. Could these variations be due to chance? The first step is to calculate the average for each time period. Calculate average rates by summing the ratings in each time period and dividing by the number of cases in that period. Thus, for the first time period, the average will be 80+84+82+80 divided by 4, which is 81.5.
Do not proceed until you have calculated the averages for all time periods. Then, you need to plot the data. Numbers are deceiving. They hide much. To understand numbers you must see them. The next step is to create a xy plot; where the xaxis is time and the yaxis is the average satisfaction rating. Create a xy plot of the data before proceeding further.
Before we go further, let me do what you have done so far. Let me plot the data so that you can compare your work against mine. In the picture below, you see the data for the four time periods.
Figure 2: Average Satisfaction Rating Over Time
The xy plot you have created already tells you a lot. But it will tell you more, if you add to the plot the upper and lower control limits, between which one expects 95% of the data. The upper and lower control limits in an Xbar chart is based on the assumption that data are normally distributed. So before we calculate these limits, we need to check and see if the assumptions are met.
Continuous Interval Scale. The variable being averaged must be a
continuous variable on an interval scale, where the differences between the
scores are meaningful. An ordinal scale cannot be averaged. Satisfaction
rating and health status ratings are generally assumed to be interval
scales.
Independent events. The observations over each period of time are not
affected by the previous observations. In our example, the satisfaction
ratings in time period two should not be affected by ratings in the first
time period. This assumption will be violated in an example where the same
patient is rating the unit in every time period. It is likely that this
patient's first impression affects subsequent evaluations. The assumption
seems reasonable when different patients are rating in different time
periods.
Normal distribution. If we were to stack all the ratings, most will fall
on the average rating, some on each side. A normal distribution suggests
that the stack will peek on the average, slowly decline on both sides of the
average, and the shape of the curve will be symmetrical. The
law of
large numbers says that no matter what the distribution of a
variable is, the average of the variable will tend to have a Normal
distribution. As the number of cases for calculation of the average
increases, the average is more likely to be Normal. A minimum of four cases
is needed for applying the law of large numbers. The law of large numbers
tells us that the average of any distribution, no matter how strange, has a
Normal distribution.
Constant variance. This assumption can be verified on a control chart. It states that deviations from the average should not consistently increase or decrease over time.
When the assumptions of Normal distribution are met, we can proceed to the next step of calculating upper and lower control limits.
Calculate and plot the grand average. The grand average is the average of all ratings across all time periods. Do not calculate this by averaging the mean of each time period. The correct way to do this is to sum all the ratings for all time periods and divide the sum by the number of ratings. In the example provided here, it makes no difference how you calculate the grand average, but in situations where the number of cases in each time period is changing, it does make a difference.
Please calculate the central tendency of the satisfaction ratings and plot it on your xy plot before proceeding further. This is how it will look like now:
Figure 3: Averaged Ratings Are Better Understood When Central Tendency Is Plotted
With the central tendency line in, we have a visual line to compare the data to. It already tells us a lot, it gives us a sense of which time periods are closer to our central tendency line.
If you notice, in some time periods you have larger differences from the central tendency line than in others. The question arises whether these differences are so large that they are beyond what could be expected from chance events. To answer this question we need to add two other lines to our plot. These are upper and lower control limits. Then points beyond these limits are differences that could not be due to chance. They indicate real changes in the patients' satisfaction with our services.
To calculate the upper control limit follow these steps:
Calculate the standard deviation for the observed ratings. I calculated this to be 4.1. More►
Estimate the standard deviation for observations within each time period by dividing the standard deviation of all of the observations by the square root of the number of cases in the time period. Thus, for our first time period this will be 4.1/square root of 4 = 2.05.
Calculate the upper lower limit for each time period as the grand
average plus
1.96
times the standard
deviations of the time. In a normal distribution, the mean
plus and minus 1.96 times the standard deviation of the distribution
contains 95% of the data. For our first time period this will be
77.25+1.96*2.05
When the number of observations is small, the use of this number makes sense
only if the assumption of Normal distribution of the data can be verified.
Otherwise, we suggest you use the
tstudent value corresponding to the sample size.
More►
Calculate the lower limit for each time period as the grand average minus 1.96 times the standard deviation of that time period.
On your chart, plot the upper and lower control limits. Note that the control limits are the same at each time period and the control limits are symmetrical around the grand average.
Calculate and plot the upper and lower control limits for each time period. This is how the control chart will look like when you complete your work.
Figure 4: Adding in Control Limits
Note that the control limits are straight lines in this example because in every time period we sampled the same numbers of cases. If this were not the case, the control limits would be tighter when the sample size was larger. If observations fall within the control limits, then the change in the observed rates may be due to chance.
Note also that the second time period is lower than the lower control limit. Therefore, patients rated our services worst in this time period and the change in the ratings were not due to chance events. It marks a real change in satisfaction with our services. We would not have known this until we created a control chart.
Examine the plot that you created. Remember that we are trying to answer the question of whether there has been improvements in the process. Look at the plot. What does it tell you. Is it easier to understand the data now that you have plotted it and put limits around the data?
Now that you have learn how to create an xbar chart, you can move on to creating a riskadjusted xbar chart. In these charts, outcomes of care are compared to expected outcomes given the patients case mix, severity or prognosis. For more see continuation of lecture on risk adjusted xbar charts. Continue►
There are three sets of presentations for this lecture:
Use Excel to create Xbar chart Excel 2003► Video► SWF►
Use Excel to create risk adjusted Xbar chart Excel 2003►
Narrated slides and videos require Flash.
Alemi and Sullivan provide an example of application of risk adjusted xbar chart to diabetes control. More►
Advanced learners like you, often need different ways of understanding a topic. Reading is just one way of understanding. Another way is through analyzing data. The enclosed questions are designed to get you to think more about the concepts taught in this session.
Download data for this assignment Download►
Information on calculation of standard deviations Google►
John Flaig discusses when should you use mean of observations as opposed to individual observations. More►
Annotated bibliography of using control charts to improve health care. PubMed►
Studentt distribution More►
Question: Does Xbar charts, required the measurement intervals to be the same length of time? Answer: Good question, one that requires a lot of thought. The time intervals are generally based on equal intervals and the effect of unequal intervals has not been explored. At issue is whether unequal intervals cause a violation of independence of observations. The answer depends on why intervals are unequal. If you select the intervals based on reports from the front line that there is a problem, then the resulting unequal equal intervals cannot be analyzed using Xbar charts. You have to ask yourself does the sampling procedure lead to samples on certain days being more likely to report out of control cases. For example consider adaptive sampling. In this approach, additional samples are drawn at first sign of problem. In this case Xbar chart is not appropriate. This question was asked on 3/27/2008 7:43:18 AM and answered on 3/27/2008 7:59:17 AM.
Question: The calculated result for standard deviation of the differences is different in the lecture vs the "create X Bar" exercise and I am confused about how you got the 151 as the SD of the differences. Answer: I see the discrepancy and will investigate and correct it. The slides seem to be off and the correct answer seems to be in the video. I will update this. Thanks for pointing it out. This question was asked on 3/25/2008 8:48:10 PM and answered on 3/26/2008 7:32:27 AM.
Question: In the video and on the lecture, you say to take the value ex. Sj = S/[(nj)(0.5)] The 0.5, should that be nj^0.5 or nj*0.5? I am confused on that and on the calculation for S, because it does not say ^0.5 Answer: It is to the power of 0.5, which is shown by ^ key. This question was asked on 3/25/2008 1:16:00 AM and answered on 3/25/2008 2:01:32 PM.
Question: For our Homework problems is it necessary for us to include the central tendancy on or charts? Answer: No this is not necessary. Central limits are included so that one can notice patterns around them (e.g. 7 points above the limit). But these are seldom used. So we do not emphasize it in our methods of analysis. This question was asked on 3/24/2008 10:47:05 AM and answered on 3/24/2008 1:56:31 PM.
Question: In calculating the UCL & LCL for question 1 of homework, which do we use 1.96(from slides) or 1.98(from video)? And, what does this number represent? Answer: You should use 1.96 as your constant. This question was asked on 3/23/2008 10:16:38 PM and answered on 3/23/2008 10:59:39 PM.
Question: In calculating the UCL & LCL for question 1 of homework, which do we use 1.96(from slides) or 1.98(from video)? And, what does this number represent? Answer: You should use 1.96 as your constant. This question was asked on 3/23/2008 10:16:38 PM and answered on 3/23/2008 10:53:00 PM.
Question: In the lecture you use 1.96 as the constant for calculating the control limits, in the example you use 1.98. Is this because you wanted a 99% confidence level in the example? Does it make a difference which one to use? Answer: I have not been able to find where you see 1.98. The correct constant is 1.96. This question was asked on 3/23/2008 9:07:19 PM and answered on 3/23/2008 10:52:30 PM.
Question: In what circumstance would you need to use a confidence interval greater than 95%? Answer: When the false alarm error (i.e. saying something is out of control when it is in control) is likely to have large irreversible implications. For example, if someone's life might be affected. This question was asked on 3/20/2008 4:04:26 PM and answered on 3/21/2008 1:25:15 PM.
Question: i have a question about the Xbar homework for the second question about creating a time chart is that the same thing as a timebetween chart? Answer: Yes it is, I will correct the error shortly. This question was asked on 3/19/2008 5:36:24 PM and answered on 3/19/2008 7:52:08 PM.
Question: I am trying to access the tutorial on risk adjusted xbar charts. I am unable to access the information as it appears to be password protected. When I enter my first and last name it is not recognized. Is there another way to access this information? Thanks! Answer: Please use the instructor's first and last name This question was asked on 11/28/2007 9:02:00 AM and answered on 11/28/2007 10:48:33 AM.
Add your own suggestions or read below suggestions made by others regarding how to improve this session:
Suggestion: It was simply stated!! This comment was left on 4/14/2008 9:23:46 PM.
Suggestion: More explanation could be given on how xbar charts are different from time to dissatified customers. This comment was left on 3/27/2008 8:13:13 AM.
Suggestion: The examples are very helpful in trying to determine the different type of charts needed for the different exercises. This comment was left on 3/26/2008 12:44:48 AM.
Suggestion: Lectures & video on how to construct chart were easy to follow. The homeworks were explained well. The walk through process for creating the chart was clear. This comment was left on 3/25/2008 7:46:53 PM.
Suggestion: i liked the teaching stratigy This comment was left on 3/25/2008 5:04:10 PM.
Suggestion: I liked how the lecture had students follow steps in completing the Xbar chart. This exercise was helpful when completing the What Do You Know assignment. This comment was left on 3/25/2008 1:47:50 PM.
Suggestion: Very nice how it was explained stepbystep multiple times. This comment was left on 3/25/2008 1:39:28 AM.
Suggestion: It would be helpful if it was explained more clearly that the instructors name is the password for assignments that require a pw to be viewed. This comment was left on 3/24/2008 10:24:14 PM.
Suggestion: Lectures & video on how to construct chart were easy to follow. This comment was left on 3/24/2008 5:40:21 PM.
Suggestion: The homeworks were explained well. However, you should change the wording in question #2 from time chart to timebetween chart. Just remain consistant. This comment was left on 3/24/2008 10:50:10 AM.