Improving Health Care Quality

Case Studies with JMP

Inbunden, Engelska, 2020

Av Mary Ann Shifflet, Cecilia Martinez, Jane Oppenlander, Shirley Shmerling, Indiana) Shifflet, Mary Ann (University of Southern Indiana, Cecilia (Clarkson University) Martinez, Jane (Clarkson University) Oppenlander, Shirley (University of Massachusetts) Shmerling

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Learn how to improve the quality of health care offered by your institution using data you already haveImproving 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 TaskData and Data ManagementAnalysisSummaryConcepts and ToolsExercises and Discussion QuestionsEach 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.

Produktinformation

Utgivningsdatum2020-06-05
Mått10 x 10 x 10 mm
Vikt454 g
FormatInbunden
SpråkEngelska
Antal sidor224
FörlagJohn Wiley & Sons Inc
ISBN9781119604617

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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.

Foreword xvPreface xviiAcknowledgments xixAcronyms and Synonyms xxiAbout the Companion Website xxiii1 Introduction 11.1 Key Concepts 11.2 Quality Improvement in Healthcare 11.3 Understanding Variability: The Key to QI 21.4 Quality Improvement Frameworks 31.4.1 Define–Measure–Analyze–Improve–Control (DMAIC) 41.4.2 Plan–Do–Check–Act (PDCA) 41.4.3 Choosing a Framework 51.5 Statistical Tools for Quality Improvement 61.5.1 Data Visualization 81.5.2 Subgrouping Data 81.5.3 Control Charts 91.5.4 The Importance of Assumptions 101.6 Using this Casebook 111.7 Summary 121.7.1 Exercises 131.7.2 Discussion Questions 14References 142 Improving Patient Satisfaction 172.1 Key Concepts 172.2 DMAIC 172.3 PDCA 172.4 Background 172.5 The Task 182.6 The Data: ComplaintData.xlsx and PatientFeedback.jmp 182.7 Data Management 192.8 Analysis 202.8.1 Complaint Data 202.8.2 Patient Satisfaction Data 212.9 Summary 262.9.1 Statistical Insights 262.9.2 Implications and Next Steps 272.9.3 Summary of Tools and JMP Features 272.9.4 Exercises 272.9.5 Discussion Questions 28Reference 293 Length of Stay and Readmission for Hospitalized Diabetes Patients 313.1 Key Concepts 313.2 DMAIC 313.3 PDCA 313.4 Background 313.5 The Task 323.6 The Data: HospitalReadmission.jmp 323.7 Data Management 323.8 Analysis 323.9 Summary 393.9.1 Statistical Insights 393.9.2 Implications and Next Steps 393.9.3 Summary of Tools and JMP Features 403.9.4 Exercises 403.9.5 Discussion Questions 414 Identify and Communicate Opportunities for Reducing Hospital Length of Stay Using JMP® Dashboards 434.1 Key Concepts 434.2 DMAIC 434.3 PDCA 434.4 Background 434.5 The Task 444.6 The Data: HospitalReadmission.jmp 444.7 Data Management 444.8 Analysis 444.8.1 Creating Dashboards with Combine Windows 444.8.2 Creating Dashboards with Dashboard Builder 454.8.3 Saving and Sharing JMP Dashboards 484.9 Summary 484.9.1 Statistical Insights 484.9.2 Implications and Next Steps 524.9.3 Summary of Tools and JMP Features 524.9.4 Exercises 534.9.5 Discussion Questions 53References 535 Variability in the Cost of Hip Replacement 555.1 Key Concepts 555.2 DMAIC 555.3 PDCA 555.4 Background 555.5 The Task 565.6 The Data: SouthernTier_HipReplacement.csv 565.7 Data Management 565.7.1 Initial Data Review 575.7.2 Adjusting JMP Column Properties 585.7.3 Deleting Unneeded Columns 595.7.4 Shortening Character Columns 605.8 Analysis 615.8.1 Descriptive Analysis 625.8.2 Assessing Variability 635.9 Summary 675.9.1 Statistical Insights 675.9.2 Implications and Next Steps 675.9.3 Summary of Tools and JMP Features 685.9.4 Exercises 685.9.5 Discussion Questions 69References 706 Benchmarking the Cost of Hip Replacement 716.1 Key Concepts 716.2 DMAIC 716.3 PDCA 716.4 Background 716.5 The Task 726.6 The Data: HipNYSPARCS_SouthernTier.jmp 726.7 Data Management 726.8 Analysis 736.8.1 Descriptive Analysis 736.8.2 Statistical Test of Hypothesis 736.8.3 Confidence Interval for Mean Total Cost 756.9 Summary 756.9.1 Statistical Insights 756.9.2 Implications and Next Steps 766.9.3 Summary of Tools and JMP Features 766.9.4 Exercises 766.9.5 Discussion Questions 77References 787 Nursing Survey 797.1 Key Concepts 797.2 DMAIC 797.3 PDCA 797.4 Background 797.5 The Task 807.6 The Data: NursingResearch_Survey_Responses.jmp 807.7 Data Management 817.7.1 Initial Data Review 817.7.2 Recoding the Primary Role Column 837.8 Analysis 857.8.1 Descriptive Analysis 857.8.2 One-Sample Test of Proportion 877.8.3 Test for Difference of Two Proportions 887.9 Summary 907.9.1 Statistical Insights 907.9.2 Implications and Next Steps 907.9.3 Summary of Tools and JMP Features 917.9.4 Exercises 917.9.5 Discussion Questions 92References 938 Determining the Sample Size for a Nursing Research Study 958.1 Key Concepts 958.2 DMAIC 958.3 PDCA 958.4 Background 958.5 The Task 968.6 The Data 968.7 Study Design and Data Collection Methodology 968.8 Analysis 978.8.1 Analysis Plan 978.8.2 The Basics of Sample Size Determination 988.8.3 Sample Size Determination for the Bee Sting Study 998.9 Summary 1018.9.1 Statistical Insights 1018.9.2 Implications and Next Steps 1028.9.3 Summary of Tools and JMP Features 1038.9.4 Exercises 1048.9.5 Discussion Questions 104References 1059 Mapping California Ambulance Diversion 1079.1 Key Concepts 1079.2 DMAIC 1079.3 PDCA 1079.4 Background 1079.5 The Task 1089.6 The Data: ED_ambulance_diversion_trend.xlsx and CA_healthcare_facility_locations.xlsx 1089.7 Data Management 1089.7.1 Merging the Data Tables 1099.7.2 Reviewing the Merged File 1099.7.3 Extracting General Acute Care Hospital Data 1129.8 Analysis 1129.8.1 Descriptive Analysis 1129.8.2 Geographic Distribution of Total Diversion Hours 1139.9 Summary 1169.9.1 Statistical Insights 1169.9.2 Implications and Next Steps 1169.9.3 Summary of Tools and JMP Features 1179.9.4 Exercises 1179.9.5 Discussion Questions 118References 11810 Monitoring Ambulance Diversion Hours 11910.1 Key Concepts 11910.2 DMAIC 11910.3 PDCA 11910.4 Background 11910.5 The Task 12010.6 The Data: CedarsSinai_Diversion_Hours.jmp 12010.7 Data Management 12110.8 Analysis 12110.8.1 Descriptive Analysis 12110.8.2 Control Chart Basics 12210.8.3 Ambulance Diversion Process 12310.8.4 Setting the Control Limits 12310.8.5 Monitoring Ambulance Diversion with IR Charts 12610.9 Summary 13010.9.1 Statistical Insights 13010.9.2 Implications and Next Steps 13010.9.3 Summary of Tools and JMP Features 13110.9.4 Exercises 13110.9.5 Discussion Questions 132References 13211 Ambulatory Surgery Start Times 13311.1 Key Concepts 13311.2 DMAIC 13311.3 PDCA 13311.4 Background 13311.5 The Task 13411.6 The Data: ASU.jmp 13411.7 Data Management 13411.8 Analysis 13511.8.1 Case 1 Analysis 13811.8.2 Case 2 Analysis 14011.9 Summary 14111.9.1 Statistical Insights 14111.9.2 Implications and Next Steps 14311.9.3 Summary of Tools and JMP Features 14411.9.4 Exercises 14411.9.5 Discussion Questions 145Reference 14512 Pre-Op TJR Process Improvement – Part 1 14712.1 Key Concepts 14712.2 DMAIC 14712.3 PDCA 14712.4 Background 14712.5 The Task 14812.6 The Data: TJR.xlsx 14812.7 Data Management 15012.8 Analysis 15312.9 Summary 15912.9.1 Statistical Insights 15912.9.2 Implications and Next Steps 16112.9.3 Summary of Tools and JMP Features 16112.9.4 Exercises 16112.9.5 Discussion Questions 162Reference 16313 Pre-Op TJR Process Improvement – Part 2 16513.1 Key Concepts 16513.2 DMAIC 16513.3 PDCA 16513.4 Background 16513.5 The Task 16613.6 The Data: TJR.jmp 16613.7 Data Management 16613.8 Analysis 16713.9 Summary 17313.9.1 Statistical Insights 17313.9.2 Implications and Next Steps 17413.9.3 Summary of Tools and JMP Features 17413.9.4 Exercises 17413.9.5 Discussion Questions 175References 17514 Pre-Op TJR Process Improvement – Part 3 177 14.1 Key Concepts 17714.2 DMAIC 17714.3 PDCA 17714.4 Background 17714.5 The Task 17814.6 The Data: TJR.jmp 17814.7 Data Management 17914.8 Analysis 17914.9 Summary 18714.9.1 Statistical Insights 18714.9.2 Implications and Next Steps 18814.9.3 Summary of Tools and JMP Features 19014.9.4 Exercises 19014.9.5 Discussion Questions 191References 191Index 193