Lean Six

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A Lean Six Sigma Case Study

VSB 1005

PROJECT DESCRIPTION

 

The following Lean Six Sigma case study will reflect a real-life healthcare problem with Continuous Improvement and Lean Six Sigma Tools to show how some of the tools are put into place in the real world.  You will be required to complete the project along with some analysis at the end of each section.

 

 

 

 

 

 

 

 

 

 

 

Case Study:

 Process Improvement – Reduction in Wait Time for Patients in a Doctor Office

 

 

Executive Summary

Dr. Deasley is a popular Doctor in Tampa, Florida specializing in primary care.  Because Dr. Deasley is so popular, he spends a great deal of time with his patients.  Because he is spending almost one hour per patient, there are may other patients waiting in the waiting room impatiently.  Dr. Deasly is booking 10 patients per day, but due to time limitations, he is overbooking and cannot see all of his patients.  The office is starting to get complaints about the wait time in the office.  They would like to see the Doctor within 10 minutes of arriving and spend no more than 30 minutes in the office total.  If able, the Doctor would like to see 15 patients per day.  The changes need to made within 3 months in order to not lose any patients.

Define

What are key Milestones?

 

Please complete a High-Level Process Map

 

Complete a Project Charter with all of the Information

Conclusion of Define: The DEFINE stage showed the customer’s and their problems along with the goals of the Doctor and the office he works in. A process map was completed in order to better fully understand the steps. The project team now has a baseline to begin the Measure phase through the process steps.

 

Measure

 

Please create a SIPOC of the process based on the information that you know.  Feel free to use your imagination for this.

 

Please create a Critical to Quality Tree utilizing the Voice of the Customer.

 

The Black Belt team did a pareto analysis of the data and determined that five factors were causing over 95% of the problem with wait time.  Those factors are:

Proper Medical Devices not Available

Rooms Available at Doctor’s Office

Staffing of Doctor’s Office

Arrival Time of Patients

Time the Doctor was Spending with Patients

You need to determine the ‘biggest contributors to the problem.  One tool to accomplish this is the Pareto Chart.

You need to know if it is reasonable to assume that these five ‘product parameters’ are normally distributed.

The data is as follows:

Categories # of occurrences
Proper Medical Devices N/A 30
Rooms Available at Dr. Office 22
Staffing at Dr. Office 41
Arrival Time of Patients 52
Time Dr. Spends with Patients 79

 

Please create a Pareto chart with the data and explain what the focus area should be.

 

 

 

  1. Construct FIVE (5) histograms for the below data sets.
  2. Interpret each of the histograms to determine whether the assumption of normality is reasonable.
  3. If the data are not approximately normally distributed, why not?

 

Proper Medical Devices N/A Rooms Available at Dr. Office Staffing at Dr. Office Arrival Time of Patients Time Dr. Spends with Patients
10.82 7.45 0.5502 172 48
10.82 7.55 0.5522 169 34
10.86 7.67 0.546 177 23
10.87 7.65 0.5462 170 32
10.84 7.62 0.5491 174 19
10.85 7.59 0.5486 175 37
10.86 7.6 0.5428 167 20
10.87 7.52 0.5532 171 47
10.89 7.49 0.5472 168 27
10.8 7.54 0.5522 172 31
10.81 7.52 0.5494 168 44
10.89 7.61 0.5519 163 27
10.81 7.52 0.5509 174 61
10.9 7.61 0.5412 169 17
10.87 7.53 0.5518 171 26
10.86 7.57 0.5523 172 50
10.85 7.59 0.5415 172 11
10.85 7.55 0.5477 168 53
10.86 7.61 0.553 169 18
10.86 7.54 0.55 166 75
10.83 7.57 0.5437 172 27
10.89 7.51 0.5463 168 36
10.76 7.63 0.5566 174 40
10.78 7.5 0.541 175 30
10.86 7.58 0.5542 164 23
10.9 7.55 0.5569 173 15
10.83 7.51 0.5432 168 15
10.82 7.5 0.5487 170 35
10.87 7.59 0.5537 173 45
10.88 7.58 0.541 170 25
10.67 7.64 0.5554 173 42
10.72 7.48 0.5521 167 64
10.65 7.57 0.5532 169 23
10.7 7.46 0.5563 172 53
10.67 7.53 0.5508 165 50
10.65 7.6 0.5527 170 16
10.6 7.49 0.5546 169 41
10.66 7.65 0.5478 170 7
10.61 7.55 0.5468 165 31
10.69 7.55 0.5566 172 18
10.71 7.51 0.5531 168 53
10.66 7.49 0.5482 173 34
10.64 7.49 0.5473 172 37
10.62 7.49 0.5442 170 80
10.63 7.56 0.5491 176 19
10.67 7.59 0.5596 175 26
10.62 7.47 0.5491 170 13
10.62 7.58 0.5507 169 18
10.63 7.55 0.556 177 36
10.65 7.47 0.5428 178 7
10.68 7.63 0.5488 172 34
10.68 7.47 0.5531 171 28
10.63 7.68 0.5483 171 44
10.68 7.55 0.5431 171 18
10.58 7.47 0.545 177 23
10.59 7.59 0.5392 172 17
10.64 7.57 0.5512 170 25
10.64 7.53 0.5465 169 15
10.68 7.58 0.5479 164 23
10.6 7.6 0.5452 174 21
Upper Spec 11 7.66 0.56 180 60
Lower Spec 10.5 7.45 0.54 165 0
Target 10.75 7.55 0.55 170 20

 

The team also believed there was a Motorola shift during the process.  Please describe the Motorola Shift and potential causes that they could have experienced the shift.

Conclusion of Measure: Data was taken of as many parameters as possible before changing any variables.  It was found that Dr. Deasley was spending more time with his patients than necessary.  The process needs to be analyzed based on the data.

 

 

Analyze

Please create a Stem and Leaf Plot for the downtimes that we captured from the patient wait times in the waiting rooms.

The data is as follows:

Downtime (minutes) for the last 70 patient wait times:
 
16 21 11 16 16 17 6 48 47 20  
16 18 47 26 44 22 49 47 20 64  
17 75 38 17 48 10 48 20 50 16  
37 15 17 65 45 18 47 71 35 44  
47 17 20 15 50 51 48 47 21 82  
32 13 49 17 49 14 52 50 46 51  
48 47 19 48 63 80 46 95 48 58  

 

Two different staff members were being used for the Doctor office so we wanted to see if they were acting identically.  25 random samples were taken for each line.  We want to see if Assistant 2 performs better than Assistant 1 since she is a new employee.  The data for Assistant 2 is as follows:

0.009
0.010
0.011
0.011
0.010
0.011
0.011
0.013
0.008
0.012
0.010
0.013
0.014
0.012
0.009
0.014
0.011
0.015
0.011
0.012
0.015
0.011
0.011
0.012
0.008

 

The historical mean for Line 1 was .0126. 

Please state the following:

Line 2 Average

Line 2 Standard Deviation

Null Hypothesis

Alternative Hypothesis

T-Test Statistic

Critical Value

Statistical Conclusion for the null and alternative hypothesis.

 

Conclusion of Analyze: Data was analyzed to review if different staff members were performing similarly or not.  We also wanted to plot the data of the wait times in different methods.

 

IMPROVE

A team member has been saying since day one that there is a correlation between the Room Availability and the Patient arrival time.  Should the team have listened?  Construct a scatter diagram and calculate the correlation coefficient to see if she is correct.

The Data is as follows:

Data: Temp Thickness      
  154 0.554      
  153 0.553      
  152 0.552      
  152 0.551      
  151 0.549      
  151 0.549      
  151 0.548      
  151 0.548      
  151 0.548      
  151 0.547      
  151 0.547      
  151 0.547      
  151 0.547      
  151 0.547      
  151 0.547      
  151 0.546      
  150 0.546      
  150 0.546      
  150 0.546      
  150 0.546      
  150 0.546      
  150 0.545      
  150 0.545      
  150 0.545      
  149 0.545      
  149 0.545      
  149 0.545      
  148 0.545      
  148 0.543      
  148 0.543      
  147 0.542      
  147 0.542      
  146 0.541      
  146 0.540      
  145 0.538      

 

Is there strong correlation between temperature and thickness?

IF there is strong correlation, is it positive or negative?  (Answer with positive, negative or N/A)

What is the correlation coefficient between the two variables?  (Use 6 decimal places)

Discuss the 8 Deadly Wastes (MUDA) of the process.

Create a Fishbone Diagram explaining some of the key Root causes.

Discuss Improvements that you would suggest.

 

Conclusion of Improve: Optimal settings were also found and a Scatter Plot was created to see correlation. Many improvement suggestions were made.

 

CONTROL

An I-MR chart was plotted for the Doctor’s office to ensure the specifications were performing as planned and the patients and Doctor’s were satisfied.

Please indicate if the control chart is stable and if any Shewhart Rules have occurred.

A normality test was conducted.  Please advise if the data is normal.

A capability study was completed.  Please advise if the process is stable and any analysis you find is relevant.

Please complete a Control Plan for the project.

Conclusion of Control: We have taken all data after making many improvements to see if the process is now stable.  We will continue to monitor our progress and follow the control plan.

 

Please make final conclusions of the project.