Statistical Process Control Tools Used to Monitor Healthcare Practices

7/29/2019 Statistical Process Control Tools Used to Monitor Healthcare Practices

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SPC used to track dailycalorie contentCOURSE PROJECT SPC-721

A case study using SPC tools to monitor the daily calorie intake of an individual

2013

Submitted by:

RaghavaKashyap

Jyothi Swaroop Reddy Yerasi


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Contents1. Introduction .......................... .......................... .......................... ......................... .......................... ... 3

2. Process characteristic ......................... .......................... .......................... ......................... ................ 4

2.1 Process Characteristic Measurement ............................................................................................. 4

2.2 Estimated Costs to implement the plan .......................................................................................... 6

2.3 Conditions of the subject for experimentation ............................................................................... 6

3. Data Collection .......................... .......................... .......................... ......................... ......................... 7

4. Characteristics of the data collection point ....................... .......................... .......................... ........... 8

5. Control Charts ....................... .......................... .......................... ......................... ........................... .. 9

5.1 Process Capability analysis ........................................................................................................... 10

6. Conclusions ....................... ......................... .......................... ......................... ........................... ..... 11

7. References ........................................................................................................................................ 12


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Executive Summary

According to Centers for Disease Control and Prevention (CDC) 33.3% of adults, aged 20 years

and over are overweight and 35.9% are obese. Overweight is the most common phenomenon and it

occurs when either the body metabolism is slow or when the calorie content is more than needed.There are several other factors such as age, hormonal imbalances, lack of exercise, etc. that make

people over weight. Of these factors, the major factor contributing to overweight is the lack of balance

between the amount of calories taken in and the amount of calories burned. The scope of this project is

limited to track the amount of calories in take by an individual over a period of 30 days and thereby

understanding the dietary process.

With the level of technological development and competition in the world, there is no much

time left for people to think and bother about the calories consumed. While there is no direct cure or

shortcut to reduce weight, a calculated approach using statistical tools can help in reducing or maintain

weight. Every extra pound gained by a person is a weight on his own legs. There are many side effects of

being overweight. Some of them being accelerated ageing, asthma, fatigue, high blood pressure, joint

and muscle pain, infertility etc.

Statistical process control involves tools to monitor whether the process is under control and

reduce the variability. Nowadays SPC is used not only to monitor manufacturing processes but also

improving health care practices. It is proven that diseases like diabetics, high blood pressure,

hypothyroidism etc., can also be monitored using SPC. Using these approaches doesnt cost a lot of

money but a few minutes every day to track down the data.

In this case study, we are analyzing a subject who weighs 200 pounds and wants to maintain his

weight over a period of 30 days. For this, we averaged the number of calories he burned over a day andthen tracked the number of calories he consumed. We used an online calorie calculator to identify the

ideal number of calories he needs to take in order to maintain his weight. The minimum and maximum

number of calories to be consumed obtained from this tool are used as the upper and lower

specification limits to monitor his dietary process. Individual and MR control charts are plotted and

analyzed to understand his process performance.


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1. IntroductionAccording to American Diabetes Association, approximately 26 million children and adults in US

have diabetes and there are many reasons for this disease like genetic defects, pancreatic defects,

hypothyroidism etc. While diabetes can be controlled with proper medication, one has to keep an eye

on the calorie consumption on daily basis and maintain proper blood sugar by eating well, exercising

and sleeping in proper time. Managing calories intake is very important to control weight and thereby to

control all the diseases having a root cause of excessive weight.

Figure 1: Tracking Total calories per day

Statistical tools like Statistical process control (SPC) are being used now days in health care and

direct patient care applications. There is a great misconception that SPC is used only in manufacturing

industries but can be used anywhere is you want to control delay, deviation and defects. The main aim

of the SPC is to reduce the variation and it is often used to identify assignable causes of variation. SPC is

very important tool for process improvement and guides us in making decisions about where ourimprovement efforts have to be made. Statistically derived conclusions motivate us to change our

behavior and improve our performance especially if it is related to health care. SPC in particular to

control charts helps us to identify whether our process is in control, if not build up a case to brainstorm

the assignable cause responsible for the process to be out of control.


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This project uses SPC tools like control charts, histograms etc. to track the daily calories in diet

and to understand the process of dietary intake. Control charts here in this case acts as a visual control

in understanding and tracking our daily calorie content which is otherwise difficult to keep an eye onto.

They are used to understand whether the process of dietary intake is in control and can be used as an

alarm if few values fall out of designed control limits. They also identifies whether the process is drifting

to the either side of the control limits. Histograms with reference to the lines on the other hand are

used to track daily calories, fats and carbohydrates intake. The goal of this project is to improve the

process of dietary intake thereby maintaining proper weight and reduce the chances of getting attacked

by various diseases related to excessive weight.

2. Process characteristicSince daily dietary intake is the process that is going to be studied in this project, checking the

calories intake on daily basis seems to be appropriate process characteristic. Calories are the units of

energy contained in the food and drink we consume. If the amount of calorie intake is more than

calories we spend the excess to requirements is stored as fat and thus we gain weight. On the other

hand if the amount of calories we spend is more than the amount of calories we take we lose weight. To

track the dietary intake process on a daily scale, the calorie intake has to be measured on a daily scale.

2.1 Process Characteristic Measurement

The ideal calorie intake on daily basis of a person depends on gender, height, body frame, age

and many other factors. There are many online resources which gives tells us the ideal calorie intake

that we need take on daily basis depending on all these factors. The calorie intake on daily basis in

measured through an iphone app called myfitnessspal which is shown in the below figure 2.

Figure 2: myfitnesspal app

This app has inbuilt database of all foods and respective calorie contents. The food and drinks

we take in all courses of time (breakfast, lunch,supper, dinner) is entered into our daily summary as

shown in below figure 3,


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Figure 3: Adding food to myfitnesspal app

After adding each and every food and drinks including water into the daily summary of the app,

it gives us the net calorie intake on a particular day as shown in below figure 4,

Figure 4: Total calorie intake in a day

The rational subgroup of one is chosen in this case because daily total calorie content in a day

can be checked only once. The time of interval between the collection of two data points is 24 hours.

The data is collected for a period of 30 days.


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2.2 Estimated Costs to implement the plan

There are no costs involved in the implementation of the plan. The mobile application used in this

project was a free app available for download both on the iOs market and Android market. The weighing

machine was available at the University Gym. Since there was no major change in diet plans or quantity

of food taken by the individual, there was no drastic change in the food cost. All it took wasonly a couple

of minutes every day to input the data into the mobile application.

2.3Conditions of the subject for experimentation

For this project, the following conditions of our subject are taken into consideration for

determining the ideal number of calories required to maintain weight:

Table 1:Conditions of the subject

Current Weight 200 lbs

Goal 200 lbs

Sex Male

Duration of

experimentation

1 month

Height 5 feet. 10 inches

Age 23

Activity Lightly Active

Workouts 3 times*1hr/week

Here, Daily calorie intake calculator is used to calculate the specification limits of our process and the

following figure 5 shows its output. According to the calculator, our subject must take 2627 calories/day

in order to maintain his weight. Given his exercise and physical activity, our subject starts losing weight

if he consumes less than 2101 calories/day over a period of 30 days. Extreme fat loss may happen if he

consumes 1600 cal/day. Therefore we had chosen 2627 cal as our upper specification limit and 2101cal

as our lower specification limit for monitoring our process. We are assuming our subject to be free from

all medical illness which may result in unnecessary weight gain even if he takes proper diet (for example

Hypothyroidism).


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Figure 5: Output of the calculator

3. Data CollectionData is collected over a period of 1 month and calories are counted every breakfast, lunch,

dinner and snacks. The following histogram shows his daily consumption for this period,


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Figure 6: Graphical representation of calorie consumed

From the above figure we could clearly see, our subject met the minimum requirement for

maintaining his weight almost every day. We could also see the minimum calories in took for this period

is 2000 and the maximum is 2700 cal. This bar chart with reference lines would be very helpful in

identifying his diet changes.

To verify the normality assumption of our data collected, we plotted normal probability plot as

shown in below figure 7. From the plot we can see that most of our data points seem to be falling on the

straight line except for 2 points. Therefore the normality assumption of our data seems to be valid and

we can use normal Shewart control charts to monitor our process performance.

Figure 7: Normal probability plot of Total calories consumed

4. Characteristics of the data collection pointThe selection of control limits is very important in designing control charts. The wider the control

limits the less is the type 1 error, which the risk of a false alarm when actually the process is in control

,but on other hand it might increase type 2 error which is the risk of not detecting an out of control

point when actually there is one. For control charts we have chosen three sigma control limits and since

flow width of data is normally distributed, we find our type 1 error to be 0.0027 (From the standard

normal table). That is an incorrect out of control signal or a false alarm would be generated 27 times outof 10,000 points.

ARL (Average run length) is the average number of points that must be plotted before a point

indicates out of control condition. Since our process observations are uncorrelated, we can calculate ARL

by the following equation,

= 1/

2900280027002600250024002300220021002000

99

95

90

80

70

60

50

40

30

20

10

5

1

Total calories consumed

Percent

Mean 2504

StDev 161.9

N 30

AD 0.384

P-Value 0.374

Probability Plot of Total calories consumedNormal


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Here p, is the probability that any point exceeds out of control limits. Since the value of p in our case

is 0.0027, ARL is calculated to be 370. This means an out of control point is expected after 370 points.

=1

0.0027= 370

ATS (Average time to signal) on the other hand is the average time in which we can expect a falsealarm. It can be estimated by the following equation,

=

Here, h is the fixed time interval between collection two consecutive data points. In our case, since we

are collecting data once in a day, we have our average time to signal to be 370 days.

5. Control ChartsWe used control charts to identify any gaps between his current performance and goal

performance. Since we are not having any sub groups and data is collected is only once a day, I/MRcharts are helpful in our case. The limits chosen to monitor his performance are 2627 cal on higher end

and 2100 cal on lower end. These limits are obtained from daily calorie calculator described above. The

following charts are I/MR charts obtained from Minitab,

Figure 8: I chart of Total calorie consumed over 1 month

28252219161310741

3200

3000

2800

2600

2400

2200

2000

Observation

IndividualValue

_X=2504

UCL=3066

LCL=1943

2627

2100

I Chart of Total Calories intake


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Figure 9: Moving range chart of Total calorie intook over 1 month

From the above charts we can see that process seems to be in statistical control with upper and

lower control limits of I chart being 3066 cal and 1943 cal respectively. However with our chosen

specification limits (2100 2627 cal), the process appears to be falling out of limits many times. We

could clearly see many of the points are falling out of our upper target i.e. 2627 cal and only one point

falling below lower target i.e, 2100 cal. The red points in the individual chart indicates the points that lie

outside the limits.

5.1 Process Capability analysis

In order to understand more about our process, we performed process capability study. The

process capability analysis shows that the even though the process seems to be in statistical control. The

process is not capable with our chosen lower specification limit. It shows that 3.33% of the data lies

below the lower specification limit and 26.67% of the data lies above the upper specification limit. This

is around 30% of the data is outside the specification limits. The Cp of the process is 0.47. The value of

which takes process centering into account is merely 0.22. Overall, this shows that the dietary

process needs to be improved and there is a need for developing new strategies to maintain the desired

weight.

28252219161310741

700

600

500

400

300

200

100

0

Observation

MovingRange

__MR=211.2

UCL=690.0

LCL=0

Moving Range Chart of Total Calories intake


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Figure 10: Process Capability study of Daily Dietary Process

6. ConclusionsFrom the analysis it is clear that the dietary process is not capable in achieving the desired

output which in our case is maintaining weight. To validate our results we checked the weight of an

individual at the end of 30 days and we found it to be 201.2 pounds. Although this slight weight gain

cant trigger a sense of problem, desired results will not be obtained in a longer run if he continues tohave such imbalanced diet approach. One recommendation that can be suggested is to increase the

number of calories burnt by exercises. It is very difficult to maintain constant calorie consumption

throughout life time but the easy way to maintain weight is by increasing the calories spent. To decrease

weight, one misconception that people have is to restrict the calories as low as possible but this can lead

to other health problems like malnutrition. A balanced diet approach with proper carbohydrates,

vitamins, proteins with regular exercise, sauna, massages, swimming etc can not only contribute in

maintaining weight but also improves overall health. The limitations of this study are restricted to

accuracy of university weighing machines, myfitnesspal app and daily calorie calculator.

We feel that this Statistical process control tools can be used not only to maintain weight but also tocontrol other health problems like diabetes, blood pressure etc. The effects of number of pills consumed

to control sugar levels and blood pressure levels can also be monitored.

28002600240022002000

LSL USL

LSL 2100

Target *

USL 2627

Sample Mean 2504.38

S ample N 30

StDev(Within) 187.232

StDev(Ov erall) 161.855

Process Data

C p 0.47

CPL 0 .72

CPU 0.22

Cpk 0 .22

P p 0.54

PPL 0 .83

PPU 0.25

Ppk 0 .25

Cpm *

Ov erall C apability

Potential (Within) Capability

% < LSL 3 .33

% > USL 26.67

% Tota l 30.00

Observed Performance

% < LSL 1 .54

% > USL 25.63

% Tota l 27.17

Exp. Within Performance

% < LSL 0 .62

% > USL 22.43

% Tota l 23.06

Exp. O verall Performance

Within

Overall

Process Capability of Total Calories intake


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7. References

1) http://en.wikipedia.org/wiki/Diabetes_mellitus2) http://blog.minitab.com/blog/real-world-quality-improvement/managing-diabetes-with-six-

sigma-and-statistics-part-i

3) Application of statistical process control in healthcare improvement: systematic review by JohanThor et.al.

4) http://www.qualitydigest.com/june08/articles/03_article.shtml
http://en.wikipedia.org/wiki/Diabetes_mellitushttp://en.wikipedia.org/wiki/Diabetes_mellitushttp://blog.minitab.com/blog/real-world-quality-improvement/managing-diabetes-with-six-sigma-and-statistics-part-ihttp://blog.minitab.com/blog/real-world-quality-improvement/managing-diabetes-with-six-sigma-and-statistics-part-ihttp://blog.minitab.com/blog/real-world-quality-improvement/managing-diabetes-with-six-sigma-and-statistics-part-ihttp://blog.minitab.com/blog/real-world-quality-improvement/managing-diabetes-with-six-sigma-and-statistics-part-ihttp://blog.minitab.com/blog/real-world-quality-improvement/managing-diabetes-with-six-sigma-and-statistics-part-ihttp://blog.minitab.com/blog/real-world-quality-improvement/managing-diabetes-with-six-sigma-and-statistics-part-ihttp://blog.minitab.com/blog/real-world-quality-improvement/managing-diabetes-with-six-sigma-and-statistics-part-ihttp://en.wikipedia.org/wiki/Diabetes_mellitus

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Statistical Process Control Tools Used to Monitor Healthcare Practices