Improving Health Care Quality

Оглавление

Cecilia Fernanda Martinez. Improving Health Care Quality

Table of Contents

List of Tables

List of Illustrations

Guide

Pages

Improving Health Care Quality: Case Studies with JMP®

Foreword

Preface

Acknowledgments

Acronyms and Synonyms

About the Companion Website

1 Introduction. 1.1 Key Concepts

1.2 Quality Improvement in Healthcare

1.3 Understanding Variability: The Key to QI

1.4 Quality Improvement Frameworks

1.4.1 Define–Measure–Analyze–Improve–Control (DMAIC)

1.4.2 Plan–Do–Check–Act (PDCA)

1.4.3 Choosing a Framework

1.5 Statistical Tools for Quality Improvement

1.5.1 Data Visualization

1.5.2 Subgrouping Data

1.5.3 Control Charts

1.5.4 The Importance of Assumptions

1.6 Using this Casebook

1.7 Summary

1.7.1 Exercises

1.7.2 Discussion Questions

References

2 Improving Patient Satisfaction. 2.1 Key Concepts

2.2 DMAIC

2.3 PDCA

2.4 Background

2.5 The Task

2.6 The Data: ComplaintData.xlsx and PatientFeedback.jmp

2.7 Data Management

2.8 Analysis. 2.8.1 Complaint Data

2.8.2 Patient Satisfaction Data

2.9 Summary. 2.9.1 Statistical Insights

2.9.2 Implications and Next Steps

2.9.3 Summary of Tools and JMP Features

2.9.4 Exercises

2.9.5 Discussion Questions

Reference

3 Length of Stay and Readmission for Hospitalized Diabetes Patients. 3.1 Key Concepts

3.2 DMAIC

3.3 PDCA

3.4 Background

3.5 The Task

3.6 The Data: HospitalReadmission.jmp

3.7 Data Management

3.8 Analysis

3.9 Summary. 3.9.1 Statistical Insights

3.9.2 Implications and Next Steps

3.9.3 Summary of Tools and JMP Features

3.9.4 Exercises

3.9.5 Discussion Questions

4 Identify and Communicate Opportunities for Reducing Hospital Length of Stay Using JMP® Dashboards. 4.1 Key Concepts

4.2 DMAIC

4.3 PDCA

4.4 Background

4.5 The Task

4.6 The Data: HospitalReadmission.jmp

4.7 Data Management

4.8 Analysis. 4.8.1 Creating Dashboards with Combine Windows

4.8.2 Creating Dashboards with Dashboard Builder

4.8.3 Saving and Sharing JMP Dashboards

4.9 Summary. 4.9.1 Statistical Insights

4.9.2 Implications and Next Steps

4.9.3 Summary of Tools and JMP Features

4.9.4 Exercises

4.9.5 Discussion Questions

References

5 Variability in the Cost of Hip Replacement. 5.1 Key Concepts

5.2 DMAIC

5.3 PDCA

5.4 Background

5.5 The Task

5.6 The Data: SouthernTier_HipReplacement.csv

5.7 Data Management

5.7.1 Initial Data Review

5.7.2 Adjusting JMP Column Properties

5.7.3 Deleting Unneeded Columns

5.7.4 Shortening Character Columns

5.8 Analysis

5.8.1 Descriptive Analysis

5.8.2 Assessing Variability

5.9 Summary. 5.9.1 Statistical Insights

5.9.2 Implications and Next Steps

5.9.3 Summary of Tools and JMP Features

5.9.4 Exercises

5.9.5 Discussion Questions

References

6 Benchmarking the Cost of Hip Replacement. 6.1 Key Concepts

6.2 DMAIC

6.3 PDCA

6.4 Background

6.5 The Task

6.6 The Data: HipNYSPARCS_SouthernTier.jmp

6.7 Data Management

6.8 Analysis. 6.8.1 Descriptive Analysis

6.8.2 Statistical Test of Hypothesis

6.8.3 Confidence Interval for Mean Total Cost

6.9 Summary. 6.9.1 Statistical Insights

6.9.2 Implications and Next Steps

6.9.3 Summary of Tools and JMP Features

6.9.4 Exercises

6.9.5 Discussion Questions

References

7 Nursing Survey. 7.1 Key Concepts

7.2 DMAIC

7.3 PDCA

7.4 Background

7.5 The Task

7.6 The Data: NursingResearch_Survey_Responses.jmp

7.7 Data Management

7.7.1 Initial Data Review

7.7.2 Recoding the Primary Role Column

7.8 Analysis

7.8.1 Descriptive Analysis

7.8.2 One‐Sample Test of Proportion

7.8.3 Test for Difference of Two Proportions

7.9 Summary. 7.9.1 Statistical Insights

7.9.2 Implications and Next Steps

7.9.3 Summary of Tools and JMP Features

7.9.4 Exercises

7.9.5 Discussion Questions

References

8 Determining the Sample Size for a Nursing Research Study. 8.1 Key Concepts

8.2 DMAIC

8.3 PDCA

8.4 Background

8.5 The Task

8.6 The Data

8.7 Study Design and Data Collection Methodology

8.8 Analysis. 8.8.1 Analysis Plan

8.8.2 The Basics of Sample Size Determination

8.8.3 Sample Size Determination for the Bee Sting Study

8.9 Summary. 8.9.1 Statistical Insights

8.9.2 Implications and Next Steps

8.9.3 Summary of Tools and JMP Features

8.9.4 Exercises

8.9.5 Discussion Questions

References

9 Mapping California Ambulance Diversion. 9.1 Key Concepts

9.2 DMAIC

9.3 PDCA

9.4 Background

9.5 The Task

9.6 The Data: ED_ambulance:diversion_trend.xlsx and CA_healthcare_facility_locations.xlsx

9.7 Data Management

9.7.1 Merging the Data Tables

9.7.2 Reviewing the Merged File

9.7.3 Extracting General Acute Care Hospital Data

9.8 Analysis. 9.8.1 Descriptive Analysis

9.8.2 Geographic Distribution of Total Diversion Hours

9.9 Summary. 9.9.1 Statistical Insights

9.9.2 Implications and Next Steps

9.9.3 Summary of Tools and JMP Features

9.9.4 Exercises

9.9.5 Discussion Questions

References

10 Monitoring Ambulance Diversion Hours. 10.1 Key Concepts

10.2 DMAIC

10.3 PDCA

10.4 Background

10.5 The Task

10.6 The Data: CedarsSinai_Diversion_Hours.jmp

10.7 Data Management

10.8 Analysis. 10.8.1 Descriptive Analysis

10.8.2 Control Chart Basics

10.8.3 Ambulance Diversion Process

10.8.4 Setting the Control Limits

10.8.5 Monitoring Ambulance Diversion with IR Charts

10.9 Summary. 10.9.1 Statistical Insights

10.9.2 Implications and Next Steps

10.9.3 Summary of Tools and JMP Features

10.9.4 Exercises

10.9.5 Discussion Questions

References

11 Ambulatory Surgery Start Times. 11.1 Key Concepts

11.2 DMAIC

11.3 PDCA

11.4 Background

11.5 The Task

11.6 The Data: ASU.jmp

11.7 Data Management

11.8 Analysis

11.8.1 Case 1 Analysis

11.8.2 Case 2 Analysis

11.9 Summary. 11.9.1 Statistical Insights

11.9.2 Implications and Next Steps

11.9.3 Summary of Tools and JMP Features

11.9.4 Exercises

11.9.5 Discussion Questions

Reference

12 Pre‐Op TJR Process Improvement – Part 1. 12.1 Key Concepts

12.2 DMAIC

12.3 PDCA

12.4 Background

12.5 The Task

12.6 The Data: TJR.xlsx

12.7 Data Management

12.8 Analysis

12.9 Summary. 12.9.1 Statistical Insights

12.9.2 Implications and Next Steps

12.9.3 Summary of Tools and JMP Features

12.9.4 Exercises

12.9.5 Discussion Questions

Reference

13 Pre‐Op TJR Process Improvement – Part 2. 13.1 Key Concepts

13.2 DMAIC

13.3 PDCA

13.4 Background

13.5 The Task

13.6 The Data: TJR.jmp

13.7 Data Management

13.8 Analysis

13.9 Summary. 13.9.1 Statistical Insights

13.9.2 Implications and Next Steps

13.9.3 Summary of Tools and JMP Features

13.9.4 Exercises

13.9.5 Discussion Questions

References

14 Pre‐Op TJR Process Improvement – Part 3. 14.1 Key Concepts

14.2 DMAIC

14.3 PDCA

14.4 Background

14.5 The Task

14.6 The Data: TJR.jmp

14.7 Data Management

14.8 Analysis

14.9 Summary. 14.9.1 Statistical Insights

14.9.2 Implications and Next Steps

14.9.3 Summary of Tools and JMP Features

14.9.4 Exercises

14.9.5 Discussion Questions

References

Index

WILEY END USER LICENSE AGREEMENT

Отрывок из книги

Mary Ann Shifflet

University of Southern Indiana

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Figure 1.3 Statistical analysis process in QI framework.

Data visualization plays an important role in quality improvement, as can be seen in Figure 1.3. Once data has been collected, visualizations are useful in the data cleaning process, for assessing variation, in understanding relationships between variables, and for monitoring key process indicators.

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