On October 13, 2009, Bill Howell’s life changed. He was diagnosed with Type 2 Adult Onset Diabetes. For most people, this diagnosis leads to diet changes, dependence upon prescription medication, and the need to monitor daily blood glucose levels.
Howell handled his diagnosis in a much different way.
In his book I Took Control: Effective Actions for a Diabetes Diagnosis , Howell outlines how he has successfully managed his diabetes with a Six Sigma-centered strategy, data analysis with Minitab Statistical Software, and a lot of self-motivation.
When a co-worker noticed that Howell was suffering from many classic diabetes symptoms—including dry mouth, leg cramps and vision loss—she insisted that he take a blood glucose test. A normal target blood glucose level is around 100 mg/dL. Howell’s test exceeded 600 mg/dL—so high that the meter could not give an exact result, just a warning. Howell scheduled a doctor’s appointment that week.
In the days leading up to his appointment, Howell recorded his blood glucose levels and graphed the data with Minitab.
The time series plot showed that his recent blood glucose levels typically topped 400 mg/dL—a clear indication of uncontrolled diabetes.
“I brought along my test results in graphical form,” says Howell. “And the doctor confirmed my suspicions with a diagnosis of Type 2 Adult Onset Diabetes.”
The doctor gave Howell a recommended diet, medication, and a plan to test his blood glucose several times per day. Howell was determined to follow his doctor’s advice and take complete control of his disease—and he knew just the way to do it.
He treated his situation as a Six Sigma project.
Six Sigma improves processes by using data analysis to identify and remove defects, and Howell looked at his own symptoms as defects that could be eliminated. A self-proclaimed “numbers junkie” and quality professional at Schneider Electric, he took control of his disease with the same statistical methods he uses to improve quality.
“Viewing this as a project allowed me to modify the outcomes so they were more in my favor,” says Howell.
He had managed many Six Sigma projects in his job, but this project was different—it was his health on the line.
Howell divided his diabetes plan into Six Sigma’s five DMAIC phases—define, measure, analyze, improve, and control—and chose to rely on his project “sponsor,” his physician, for guidance at every step.
He began by defining the problem he needed to solve, the impacts he sought to lessen, and crafted a goal statement. Since his symptoms corresponded to high blood glucose, he had to bring his levels below 125 mg/dL. To reduce symptoms naturally and curb dependence upon medication, Howell also wanted to follow his doctor’s recommendations for diet and exercise.
Since his daily blood glucose levels were the key metric in understanding his disease, Howell created a data collection plan to sample his blood three times per day, recording his data and charting his glucose levels over time.
As a Six Sigma practitioner, Howell knew he needed to verify that his blood glucose measurements were reliable. To make sure his glucose meter produced valid results, he followed the manufacturer’s weekly calibration procedure and recorded the calibration results over time. By graphing his results, he confirmed that his values fell within the manufacturer’s calibration limits. Now he was confident his meter made accurate measurements.
But he wondered about the potential effects of drawing blood from different locations on his test results. Howell’s doctor encouraged him to draw blood only from his fingertips to control for any variability due to location of the testing site. But did it matter which finger he drew the blood from?
To find out, Howell used Minitab to create a randomized pattern of numbers from 1 to 10. He assigned each of his fingers a number, then tested his fingers in the randomized order. He recorded the glucose levels for each finger tested and charted the results using a Minitab dotplot. The plot revealed that groupings of test results shared the same random pattern, which suggested that finger selection did not impact the test results.
Howell also performed a One-Way Analysis of Variance (ANOVA) to offer further statistical proof of equality between fingers. The ANOVA analysis aligned with the dotplot findings, and revealed no evidence that a given finger will impact the test outcome.
Now confident his measurement system produced valid results, Howell began assessing the potential causes for his increased blood glucose levels. He created a cause-and-effect (fishbone) diagram in Minitab, which allowed him to organize all of his brainstorming information in one place. Focusing on causes he could control and analyze himself, such as diet and exercise, helped him plan the next step of his project—recording his daily food intake.
Howell’s doctor recommended a daily 1,800-calorie diet, which included 50 grams of fats and 200 carbohydrates. Using bar charts with reference lines showing daily limits, he tracked each day’s total calories, fats, and carbohydrates. The charts helped him keep his diet in check, and showed him where making diet changes might help him meet other project goals, such as keeping his cholesterol down.
“I found that even a simple chart or graph can do wonders,” says Howell. “Bring charts and your set of data with you to your doctor’s visit, and together you can make an informed decision.”
After recording and graphing several months of daily blood glucose levels and diet information, Howell analyzed his data to identify sources of variation. To determine if his three daily blood tests produced the same average level, he ran an ANOVA.
The results revealed that the evening blood sample average, taken before dinner, was statistically lower than the morning and night average readings. The analysis also suggested that the evening reading was more uniform, because it had a lower standard deviation than the other times of day.
Howell also wanted to identify how process inputs (calories, fats, carbohydrates, and pills consumed) affected his process output (blood glucose levels). A four-panel scatterplot in Minitab revealed a clear relationship between the number of blood glucose-lowering pills consumed and blood glucose levels. The plot shows that it took about 30 pills for Howell to reach target levels of 100 mg/dL.
To identify gaps between current performance and goal performance, Howell used Control Charts to graph his diet, pill intake, and glucose levels in relation to predetermined upper and lower bounds. If his data fell outside of the bounds, Howell knew that his process changed, and he could adjust accordingly.
For example, the Individuals Control Chart below shows the total calories Howell consumed in a two-month span. The chart reveals a stable process and shows that he met his caloric intake requirements the majority of the time, with the exception of one data point falling above the upper control limit (UCL). On this day, Howell consumed more calories than his target caloric intake of 1,800 calories, so he ate fewer calories the following day.
Howell also used Xbar-R Charts, like the one below, to evaluate the spread between the three daily blood glucose test results (lower chart) and the average of test results for each day (upper chart). Both charts show medication level (either 1 or 2 pills per day) in relation to time.
Control charts like these motivated Howell and helped him keep his diet on track. As a quality practitioner, he was very reluctant to let any internal desire he had to eat too much (or too much of the wrong thing) break the symmetry of his charts.
“I am happiest when I look back at my results for a month, quarter or longer and see a stable process,” he says. “There is beauty in symmetry and an outlier breaks the symmetry and indicates a process change.”
Howell’s approach to managing his disease was very thorough, but how well did he meet his key performance objectives? By tracking his dietary intake and following his doctor’s prescribed diet, exercise, medication, and blood testing plan, Howell brought his daily blood glucose level down to the 125 mg/dL target level. Just two months after starting the project, his long-term process level average in December was several points below the target, at 116.3 mg/dL.
To make sure his blood test results fell into the predefined specification limits (70 mg/dL – 150 mg/dL), Howell used Minitab’s Process Capability Analysis. He found that 97.85% of his test results met the criteria for success.
By continuing to follow his process, Howell eventually weaned himself completely from the medication he initially took to lower blood glucose levels. By August 2010, he was able to maintain stable blood glucose levels without any pills. He attributes this change to controlling his diet and recording and charting everything he ate.
Howell says he is healthier than he’s been in years. He’s dropped nearly 45 pounds and has seen an almost complete reduction in all of his symptoms, including dry mouth, blurry vision and inability to sleep.
Howell credits much of his success to changes in his diet and following his physician’s medical recommendations, but says he wouldn’t have been able to control his disease as quickly and as effectively without Six Sigma and the statistical tools found in Minitab software.
“I’m a big believer in using data to make informed decisions in everything I do,” says Howell. “With the use of simple statistical methods and simple graphs, diabetes and many other diseases can be controlled.”
Howell’s complete strategy for managing diabetes is detailed in his book, “ I Took Control: Effective Actions for a Diabetes Diagnosis .”
Diabetes image used under Creative Commons Attribution ShareAlike 3.0 license.