Chat with us, powered by LiveChat 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. ?VSB10 | WriteDen

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. ?VSB10

 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.  

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.

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