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E-raamat: Improving Health Care Quality: Case Studies with JMP

(Clarkson University), (Clarkson University), (University of Southern Indiana, Indiana), (University of Massachusetts)
  • Formaat: PDF+DRM
  • Ilmumisaeg: 04-May-2020
  • Kirjastus: John Wiley & Sons Inc
  • Keel: eng
  • ISBN-13: 9781119604648
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Improving Health Care Quality: Case Studies with JMPSuurem pilt
  • Formaat: PDF+DRM
  • Ilmumisaeg: 04-May-2020
  • Kirjastus: John Wiley & Sons Inc
  • Keel: eng
  • ISBN-13: 9781119604648
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Learn how to improve the quality of health care offered by your institution using data you already have

Improving Health Care Quality: Case Studies with JMP® teaches readers how to systematically identify problems, collect and interpret data, and solve issues in the real world. Relying on JMP® software, the authors walk readers through the process of applying quality improvement techniques to real-life health care problems.

The case studies provided in the book vary significantly and provide a wide-ranging view of the application of quality improvement techniques in the health care field. Studies regarding length of stay of diabetes patients to benchmarking the costs of hip replacement all serve to illuminate and explain the underlying concepts of statistical analysis.

The authors break each case study down into several sections, including:

  • Background and Task
  • Data and Data Management
  • Analysis
  • Summary
  • Concepts and Tools
  • Exercises and Discussion Questions

Each section reinforces the lessons learned in each case study and helps the reader learn to apply statistical data to their own health care quality problems.

Foreword xv
Preface xvii
Acknowledgments xix
Acronyms and Synonyms xxi
About the Companion Website xxiii
1 Introduction
1(16)
1.1 Key Concepts
1(1)
1.2 Quality Improvement in Healthcare
1(1)
1.3 Understanding Variability: The Key to QI
2(1)
1.4 Quality Improvement Frameworks
3(3)
1.4.1 Define-Measure-Analyze-Improve-Control (DMAIC)
4(1)
1.4.2 Plan-Do-Check-Act (PDCA)
4(1)
1.4.3 Choosing a Framework
5(1)
1.5 Statistical Tools for Quality Improvement
6(5)
1.5.1 Data Visualization
8(1)
1.5.2 Subgrouping Data
8(1)
1.5.3 Control Charts
9(1)
1.5.4 The Importance of Assumptions
10(1)
1.6 Using this Casebook
11(1)
1.7 Summary
12(2)
1.7.1 Exercises
13(1)
1.7.2 Dfscussion Questions
14(1)
References
14(3)
2 Improving Patient Satisfaction
17(14)
2.1 Key Concepts
17(1)
2.2 DMAIC
17(1)
2.3 PDCA
17(1)
2.4 Background
17(1)
2.5 The Task
18(1)
2.6 The Data: ComplaintData.xlsx and PatientFeedback.jmp
18(1)
2.7 Data Management
19(1)
2.8 Analysis
20(6)
2.8.1 Complaint Data
20(1)
2.8.2 Patient Satisfaction Data
21(5)
2.9 Summary
26(3)
2.9.1 Statistical Insights
26(1)
2.9.2 Implications and Next Steps
27(1)
2.9.3 Summary of Tools and JMP Features
27(1)
2.9.4 Exercises
27(1)
2.9.5 Discussion Questions
28(1)
Reference
29(2)
3 Length of Stay and Readmission for Hospitalized Diabetes Patients
31(12)
3.1 Key Concepts
31(1)
3.2 DMAIC
31(1)
3.3 PDCA
31(1)
3.4 Background
31(1)
3.5 The Task
32(1)
3.6 The Data: HospitalReadmission.jmp
32(1)
3.7 Data Management
32(1)
3.8 Analysis
32(7)
3.9 Summary
39(4)
3.9.1 Statistical Insights
39(1)
3.9.2 Implications and Next Steps
39(1)
3.9.3 Summary of Tools and JMP Features
40(1)
3.9.4 Exercises
40(1)
3.9.5 Discussion Questions
41(2)
4 Identify and Communicate Opportunities for Reducing Hospital Length of Stay Using JMP® Dashboards
43(12)
4.1 Key Concepts
43(1)
4.2 DMAIC
43(1)
4.3 PDCA
43(1)
4.4 Background
43(1)
4.5 The Task
44(1)
4.6 The Data: HospitalReadmission.jmp
44(1)
4.7 Data Management
44(1)
4.8 Analysis
44(4)
4.8.1 Creating Dashboards with Combine Windows
44(1)
4.8.2 Creating Dashboards with Dashboard Builder
45(3)
4.8.3 Saving and Sharing JMP Dashboards
48(1)
4.9 Summary
48(5)
4.9.1 Statistical Insights
48(4)
4.9.2 Implications and Next Steps
52(1)
4.9.3 Summary of Tools and JMP Features
52(1)
4.9.4 Exercises
53(1)
4.9.5 Discussion Questions
53(1)
References
53(2)
5 Variability in the Cost of Hip Replacement
55(16)
5.1 Key Concepts
55(1)
5.2 DMAIC
55(1)
5.3 PDCA
55(1)
5.4 Background
55(1)
5.5 The Task
56(1)
5.6 The Data: SouthernTier HipReplacement.csv
56(1)
5.7 Data Management
56(5)
5.7.1 Initial Data Review
57(1)
5.7.2 Adjusting JMP Column Properties
58(1)
5.7.3 Deleting Unneeded Columns
59(1)
5.7.4 Shortening Character Columns
60(1)
5.8 Analysis
61(6)
5.8.1 Descriptive Analysis
62(1)
5.8.2 Assessing Variability
63(4)
5.9 Summary
67(3)
5.9.1 Statistical Insights
67(1)
5.9.2 Implications and Next Steps
67(1)
5.9.3 Summary of Tools and JMP Features
68(1)
5.9.4 Exercises
68(1)
5.9.5 Discussion Questions
69(1)
References
70(1)
6 Benchmarking the Cost of Hip Replacement
71(8)
6.1 Key Concepts
71(1)
6.2 DMAIC
71(1)
6.3 PDCA
71(1)
6.4 Background
71(1)
6.5 The Task
72(1)
6.6 The Data: HipNYSPARCS SouthernTier.jmp
72(1)
6.7 Data Management
72(1)
6.8 Analysis
73(2)
6.8.1 Descriptive Analysis
73(1)
6.8.2 Statistical Test of Hypothesis
73(2)
6.8.3 Confidence Interval for Mean Total Cost
75(1)
6.9 Summary
75(3)
6.9.1 Statistical Insights
75(1)
6.9.2 Implications and Next Steps
76(1)
6.9.3 Summary of Tools and JMP Features
76(1)
6.9.4 Exercises
76(1)
6.9.5 Discussion Questions
77(1)
References
78(1)
7 Nursing Survey
79(16)
7.1 Key Concepts
79(1)
7.2 DMAIC
79(1)
7.3 PDCA
79(1)
7.4 Background
79(1)
7.5 The Task
80(1)
7.6 The Data: Nursing Research Survey Responses.jmp
80(1)
7.7 Data Management
81(4)
7.7.1 Initial Data Review
81(2)
7.7.2 Recoding the Primary Role Column
83(2)
7.8 Analysis
85(5)
7.8.1 Descriptive Analysis
85(2)
7.8.2 One-Sample Test of Proportion
87(1)
7.8.3 Test for Difference of Two Proportions
88(2)
7.9 Summary
90(3)
7.9.1 Statistical Insights
90(1)
7.9.2 Implications and Next Steps
90(1)
7.9.3 Summary of Tools and JMP Features
91(1)
7.9.4 Exercises
91(1)
7.9.5 Discussion Questions
92(1)
References
93(2)
8 Determining the Sample Size for a Nursing Research Study
95(12)
8.1 Key Concepts
95(1)
8.2 DMAIC
95(1)
8.3 PDCA
95(1)
8.4 Background
95(1)
8.5 The Task
96(1)
8.6 The Data
96(1)
8.7 Study Design and Data Collection Methodology
96(1)
8.8 Analysis
97(4)
8.8.1 Analysis Plan
97(1)
8.8.2 The Basics of Sample Size Determination
98(1)
8.8.3 Sample Size Determination for the Bee Sting Study
99(2)
8.9 Summary
101(4)
8.9.1 Statistical Insights
101(1)
8.9.2 Implications and Next Steps
102(1)
8.9.3 Summary of Tools and JMP Features
103(1)
8.9.4 Exercises
104(1)
8.9.5 Discussion Questions
104(1)
References
105(2)
9 Mapping California Ambulance Diversion
107(12)
9.1 Key Concepts
107(1)
9.2 DMAIC
107(1)
9.3 PDCA
107(1)
9.4 Background
107(1)
9.5 The Task
108(1)
9.6 The Data: ED ambulance diversion trend.xlsx and CA healthcare facility locations.xlsx
108(1)
9.7 Data Management
108(4)
9.7.1 Merging the Data Tables
109(1)
9.7.2 Reviewing the Merged File
109(3)
9.7.3 Extracting General Acute Care Hospital Data
112(1)
9.8 Analysis
112(4)
9.8.1 Descriptive Analysis
112(1)
9.8.2 Geographic Distribution of Total Diversion Hours
113(3)
9.9 Summary
116(2)
9.9.1 Statistical Insights
116(1)
9.9.2 Implications and Next Steps
116(1)
9.9.3 Summary of Tools and JMP Features
117(1)
9.9.4 Exfrcises
117(1)
9.9.5 Discussion Questions
118(1)
References
118(1)
10 Monitoring Ambulance Diversion Hours
119(14)
10.1 Key Concepts
119(1)
10.2 DMAIC
119(1)
10.3 PDCA
119(1)
10.4 Background
119(1)
10.5 The Task
120(1)
10.6 The Data: CedarsSinai Diversion Hours.jmp
120(1)
10.7 Data Management
121(1)
10.8 Analysis
121(9)
10.8.1 Descriptive Analysis
121(1)
10.8.2 Control Chart Basics
122(1)
10.8.3 Ambulance Diversion Process
123(1)
10.8.4 Setting the Control Limits
123(3)
10.8.5 Monitoring Ambulance Diversion with IR Charts
126(4)
10.9 Summary
130(2)
10.9.1 Statistical Insights
130(1)
10.9.2 Implications and Next Steps
130(1)
10.9.3 Summary of Tools and JMP Features
131(1)
10.9.4 Exercises
131(1)
10.9.5 Discussion Questions
132(1)
References
132(1)
11 Ambulatory Surgery Start Times
133(14)
11.1 Key Concepts
133(1)
11.2 DMAIC
133(1)
11.3 PDCA
133(1)
11.4 Background
133(1)
11.5 The Task
134(1)
11.6 The Data: ASU.jmp
134(1)
11.7 Data Management
134(1)
11.8 Analysis
135(6)
11.8.1 Case 1 Analysis
138(2)
11.8.2 Case 2 Analysis
140(1)
11.9 Summary
141(4)
11.9.1 Statistical Insights
141(2)
11.9.2 Implications and Next Steps
143(1)
11.9.3 Summary of Tools and JMP Features
144(1)
11.9.4 Exercises
144(1)
11.9.5 Discussion Questions
145(1)
Reference
145(2)
12 Pre-Op TJR Process Improvement - Part 1
147(18)
12.1 Key Concepts
147(1)
12.2 DMAIC
147(1)
12.3 PDCA
147(1)
12.4 Background
147(1)
12.5 The Task
148(1)
12.6 The Data: TJR.xlsx
148(2)
12.7 Data Management
150(3)
12.8 Analysis
153(6)
12.9 Summary
159(4)
12.9.1 Statistical Insights
159(2)
12.9.2 Implications and Next Steps
161(1)
12.9.3 Summary of Tools and JMP Features
161(1)
12.9.4 Exercises
161(1)
12.9.5 Discussion Questions
162(1)
Reference
163(2)
13 Pre-Op TJR Process Improvement - Part 2
165(12)
13.1 Key Concepts
165(1)
13.2 DMAIC
165(1)
13.3 PDCA
165(1)
13.4 Background
165(1)
13.5 The Task
166(1)
13.6 The Data: TJR.jmp
166(1)
13.7 Data Management
166(1)
13.8 Analysis
167(6)
13.9 Summary
173(2)
13.9.1 Statistical Insights
173(1)
13.9.2 Implications and Next Steps
174(1)
13.9.3 Summary of Tools and JMP Features
174(1)
13.9.4 Exercises
174(1)
13.9.5 Discussion Questions
175(1)
References
175(2)
14 Pre-Op TJR Process Improvement - Part 3
177(16)
14.1 Key Concepts
177(1)
14.2 DMAIC
177(1)
14.3 PDCA
177(1)
14.4 Background
177(1)
14.5 The Task
177(1)
14.6 The Data: TJR.jmp
178(1)
14.7 Data Management
179(1)
14.8 Analysis
179(8)
14.9 Summary
187(4)
14.9.1 Statistical Insights
187(1)
14.9.2 Implications and Next Steps
188(2)
14.9.3 Summary of Tools and JMP Features
190(1)
14.9.4 Exercises
190(1)
14.9.5 Discussion Questions
191(1)
References
191(2)
Index 193
Mary Ann Shifflet is an Assistant Professor in the Romain College of Business at the University of Southern Indiana in Evansville, Indiana. Dr. Shifflet received her undergraduate degree in Statistics from Oneonta State in New York and Master's Degree and PhD in Statistics from Virginia Tech in Blacksburg, Virginia. Prior to joining USI she worked in industry as a statistical consultant for General Foods, Merck Pharmaceuticals and for Spencer Research.

Cecilia Martinez is an Associate Professor of Engineering and Management in the Reh School of Business at Clarkson University. She received her Ph.D. in Engineering Management from Texas Tech University and her M.S. in Manufacturing Systems and B.S. in Industrial Engineering from Monterrey Tech. She has taught courses in Quality Management and Lean Enterprise, Operations and Supply Chain Management and Interdisciplinary Engineering Design.

Jane Oppenlander is an Assistant Professor in the Reh School of Business and The Bioethics Program at Clarkson University where she teaches statistics in both classroom and online formats. She received her Ph.D. in Administrative and Engineering Systems from Union College and an M.S. in statistics, a B.A. in mathematics, and a B.S. in education, all from the University of Vermont. Jane is a certified Six Sigma Master Black Belt.

Shirley Shmerling is a full-time faculty of the Isenberg School of Management at the University of Massachusetts. She teaches Operations and Information Management courses, has several publications in journals such as Physician Leadership Journal and is a board member at the American College of Healthcare Trustees. Shirley holds a B.Sc. (Computer Science) and M.Sc. (Operations Research) from the Israel Institute of Technology and a Ph.D. in Management Science from the University of Massachusetts.
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