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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.

Construct FIVE (5) histograms for the below data sets.
Interpret each of the histograms to determine whether the assumption of normality is reasonable.
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

0.010

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

0.012

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.