LLOYD P. PROVOST is a cofounder of Associates in Process Improvement, the developers of the Model for Improvement roadmap and the Quality as a Business Strategy template for focusing organizations on improvement. Lloyd is a senior fellow at the Institute for Healthcare Improvement, where he supports the use of data for learning in programs.SANDRA K. MURRAY is a principal in Corporate Transformation Concepts, an independent consulting firm. She is faculty for the Institute for Healthcare Improvement’s year-long Improvement Advisor Professional Development Program and their Breakthrough Series College. Sandra has taught numerous programs through the National Association for Healthcare Quality. Her cohort of client organizations encompasses the spectrum of health care delivery.

• Figures, Tables, and Exhibits xiiiPreface xxixAcknowledgments xxxiiiThe Authors xxxvAbout the Companion Website xxxviiPart I Using Data for Improvement 1Chapter 1 Improvement Methodology 3Fundamental Questions for Improvement 4What Are We Trying to Accomplish? 5How Will We Know that a Change is an Improvement? 7What Changes Can We Make That Will Result in Improvement? 8The PDSA Cycle for Improvement 9Tools and Methods to Support the Model for Improvement 13Designing PDSA Cycles for Testing Changes 15Analysis of Data from PDSA Cycles 19Summary26 Key Terms 26Chapter 2 Using Data for Improvement 27What Does the Concept of Data Mean? 27How are Data Used? 29Types of Data 36Using A Family of Measures 43The Importance of Operational Definitions 47Data for Different Types of Studies 51Sampling53 Sampling Strategies 55What About Sample Size? 58Stratification of Data 61What about Case-Mix Adjustment? 63Transforming Data 65Analysis and Presentation of Data 68Summary75 Key Terms 75Chapter 3 Understanding Variation Using Run Charts 77Introduction77 What Is a Run Chart? 77Use of a Run Chart 80Constructing a Run Chart 80Examples of Run Charts for Improvement Projects 84Rules to Aid in Interpreting Run Charts 89Special Issues in Using Run Charts 97Stratification with Run Charts 113Using the Cumulative Sum Statistic with Run Charts 116Summary120 Key Terms 121Chapter 4 Learning from Variation in Data 123The Concept of Variation 123Introduction to Shewhart Charts 129Depicting and Interpreting Variation Using Shewhart Charts 135The Role of Annotation with Shewhart Charts 140Establishing Limits for Shewhart Charts 141Revising Limits for Shewhart Charts 145Stratification with Shewhart Charts 147Shewhart Charts and Targets, Goals, or Other Specifications 152Special Cause: Is It Good or Bad? 155Summary157 Key Terms 158Chapter 5 Understanding Variation Using Shewhart Charts 159Selecting the Type of Shewhart Chart 160Shewhart Charts for Continuous Data 163I Charts 164Examples of Shewhart Charts for Individual Measurements 166Rational Ordering with an I Chart 168Example of I Chart for Deviations from a Target 170Xbar S Shewhart Charts 171Shewhart Charts for Attribute Data 177Subgroup Size for Attribute Charts 178The P Chart for Classification Data 180Examples of P Charts 182Creation of Funnel Limits for a P Chart 186Shewhart Charts for Counts of Nonconformities 188c charts 190U Chart 192Creation of Funnel Limits for a U Chart 195Alternatives for Attribute Charts for Rare Events 197G Chart for Opportunities Between Rare Events 198T Chart for Time Between Rare Events 202Process Capability 206Process Capability from an I Chart 208Capability of a Process from Xbar and S Charts 208Capability of a Process from Attribute Control Charts 210Capability from a P Chart 210Capability from a C or U Chart 210Summary211 Key Terms 212Appendix 5.1 Calculating Shewhart Limits 213I Chart (For Individual Values Of Continuous Data) 213Xbar S Chart (For Continuous Data In Subgroups) 214P Chart (For Classification Data) 217c chart (count Of Incidences) 218U Chart (Incidences Per Area Of Opportunity) 219G Chart (Cases Between Incidences) 220T Chart 221Chapter 6 Additional Tools For Understanding Variation In Data 223Depicting Variation 223Additional Tools for Learning from Variation 225Frequency Plots 225Frequency Plot Construction 226Frequency Plots Used with Shewhart Charts 228Frequency Plots and Stratification 232Pareto Charts 236Pareto Chart Construction 238Pareto Charts Used with Shewhart Charts 239Pareto Chart and Stratification 244Scatterplots250 Scatterplot Construction 251Scatterplots Used with Shewhart Charts 254Scatterplots and Stratification 258Radar Charts 260Constructing a Radar Chart 261Radar Charts Used with Shewhart Charts 261Radar Charts and Stratification 263Summary265 Key Terms 265Chapter 7 Shewhart Chart Savvy: Dealing with Common Issues 267Creating Effective Shewhart Charts 267Tip 1: Type of Data and Subgroup Size 267Tip 2: Rounding Data 268Tip 3: Formatting Charts 268Tip 4. Decisions for Recalculating limits, or Rephasing, on a Shewhart Chart 274Extending Centerline and Limits Backward 277Typical Problems with Software for Calculating Shewhart Charts 279Characteristics to Consider When Purchasing SPC Software 282Another Caution with I Charts and Chart Selection 285Guidelines for Shewhart Charts in Research Studies and Publications 287Use of Shewhart Charts in Research Studies 288Shewhart Charts in Publications 290Shewhart’s Theory versus Statistical Inference 292Summary296 Key Terms 296Part II Advanced Theory and Methods with Data For Improvement 297Chapter 8 More Shewhart-Type Charts 299Other Shewhart-Type Charts 301The NP Chart 301Xbar Range (Xbar R) Chart 302Median Chart 304Attribute Charts with Large Subgroup Sizes (P’ and U’) 306Prime Charts (P’ and U’) 307Negative Binomial Chart 313Some Adaptations to Shewhart Charts 316MA Chart 317CUSUM Chart 320Exponentially Weighted Moving Average (EWMA) Chart 328Standardized Shewhart Charts 331Multivariate Shewhart-Type Charts 334Summary338 Key Terms 339Chapter 9 Special Uses for Shewhart Charts 341Shewhart Charts with a Changing Centerline 341Shewhart Charts with a Sloping Centerline 342Shewhart Charts with Seasonal Effects 344Adjusting Shewhart Charts for Confounders 349Transformation of Data with Shewhart Charts 355Shewhart Charts for Autocorrelated Data 361Risk-Adjusted or Case-Mix Adjusted Shewhart Charts 366Comparison Charts 368Confidence Intervals and Confidence Limits 369Summary373 Key Terms 373Chapter 10 Drilling Down Into Aggregate Data for Improvement Ii 375What are Aggregate Data? 375What is the Challenge Presented by Aggregate Data? 376Introduction to the Drill Down Pathway 381Stratification 381Sequencing 382 Rational Subgrouping 383An Illustration of the Drill Down Pathway: Adverse Drug Events384 Drill Down Pathway Step One 385Drill Down Pathway Step Two 385Drill Down Pathway Step Three 387Drill Down Pathway Step Three, Continued 389Drill Down Pathway Step Four 393Drill Down Pathway Step Five 397Drill Down Pathway Step Six 400Summary400 Key Terms 401Part III Applications of Shewhart Charts in Health Care 403Chapter 11 Learning from Individual Patient Data 405Examples of Shewhart Charts for Individual Patients 407Example 1: Asthma Patient Use of Shewhart Charts 408Example 2: Prostate-Specific Antigen (PSA) Screening for Prostate Cancer 409Example 3: Monitoring Patient Measures in the Hospital 411Example 4: Bone Density for a Patient Diagnosed with Osteoporosis 412Example 5: Temperature Readings for a Hospitalized Patient 415Example 6: Shewhart Charts for Continuous Monitoring of Patients 418Example 7: Monitoring Weight 420Example 8: Monitoring Blood Sugar Control for Patients with Diabetes 421Example 9: Using Shewhart Charts in Pain Management 422Summary423Chapter 12 Learning from Patient Feedback to Improve Care 425Summarizing Patient Feedback Data 429Presentation of Patient Satisfaction Data 437Using Patient Feedback for Improvement 438The PDSA Cycle for Testing and Implementing Changes 438Improvement Team Working on Clinic Satisfaction 438Improvement Team Working on Pain 442Feedback from Employees 444Using Patient Satisfaction Data in Planning for Improvement 445Special Issues with Patient Feedback Data 447Are There Challenges When Summarizing and Using Patient Satisfaction Survey Data? 447Does Survey Scale Matter? 449Summary450 Key Terms 450Chapter 13 Using Shewhart Charts in Health Care Leadership 451A Health Care Organization’s Vector of Measures 452Developing a VOM 453So How do We Best Display a VOM? 461Administrative Issues with a VOM 464Some Examples of Measures for Other VOMs 467Emergency Department 468Primary Care Center 468System Flow Measures 469Health Authority 469Large Urban Hospital 471IHI Whole System Measures 471Summary473 Key Terms 474Chapter 14 Shewhart Charts for Epidemic Data 475Shewhart Charts in Epidemiology 476Development of Shewhart Charts for Epidemic Data 479c charts (Epoch 1) 479Charts of Epoch 2 481Charts for Epoch 3 485Charts for Epoch 4 486Some Issues with the Hybrid Chart for COVID-19 Deaths 487Data Quality 487Day-of-the-Week Adjustment 487Application of the Hybrid Charts to Cases, Hospitalizations, and Intensive Care Unit Admissions 489Summary492 Key Term 492Chapter 15 Case Studies 493Case Study A: Improving Access to a Specialty Care Clinic 495Case Study B: Radiology Improvement Projects 504Case Study C: Reducing Post-Cabg Infections 514Case Study D: Drilling Down into Percentage of C-Sections 526Case Study E: Reducing Length of Stay After Surgery 537Case Study F: Reducing Hospital admissions 551Case Study G: Accidental Puncture/Laceration Rate 558Case Study H: Improving Telemedicine Failed Calls and No Shows 568Case Study I: Variation in Financial Data 583Index 595Shewhart Chart Selection Guide 609

LLOYD P. PROVOST is a cofounder of Associates in Process Improvement, the developers of the Model for Improvement roadmap and the Quality as a Business Strategy template for focusing organizations on improvement. Lloyd is a senior fellow at the Institute for Healthcare Improvement, where he supports the use of data for learning in programs.SANDRA K. MURRAY is a principal in Corporate Transformation Concepts, an independent consulting firm. She is faculty for the Institute for Healthcare Improvement’s year-long Improvement Advisor Professional Development Program and their Breakthrough Series College. Sandra has taught numerous programs through the National Association for Healthcare Quality. Her cohort of client organizations encompasses the spectrum of health care delivery.

• Figures, Tables, and Exhibits xiiiPreface xxixAcknowledgments xxxiiiThe Authors xxxvAbout the Companion Website xxxviiPart I Using Data for Improvement 1Chapter 1 Improvement Methodology 3Fundamental Questions for Improvement 4What Are We Trying to Accomplish? 5How Will We Know that a Change is an Improvement? 7What Changes Can We Make That Will Result in Improvement? 8The PDSA Cycle for Improvement 9Tools and Methods to Support the Model for Improvement 13Designing PDSA Cycles for Testing Changes 15Analysis of Data from PDSA Cycles 19Summary26 Key Terms 26Chapter 2 Using Data for Improvement 27What Does the Concept of Data Mean? 27How are Data Used? 29Types of Data 36Using A Family of Measures 43The Importance of Operational Definitions 47Data for Different Types of Studies 51Sampling53 Sampling Strategies 55What About Sample Size? 58Stratification of Data 61What about Case-Mix Adjustment? 63Transforming Data 65Analysis and Presentation of Data 68Summary75 Key Terms 75Chapter 3 Understanding Variation Using Run Charts 77Introduction77 What Is a Run Chart? 77Use of a Run Chart 80Constructing a Run Chart 80Examples of Run Charts for Improvement Projects 84Rules to Aid in Interpreting Run Charts 89Special Issues in Using Run Charts 97Stratification with Run Charts 113Using the Cumulative Sum Statistic with Run Charts 116Summary120 Key Terms 121Chapter 4 Learning from Variation in Data 123The Concept of Variation 123Introduction to Shewhart Charts 129Depicting and Interpreting Variation Using Shewhart Charts 135The Role of Annotation with Shewhart Charts 140Establishing Limits for Shewhart Charts 141Revising Limits for Shewhart Charts 145Stratification with Shewhart Charts 147Shewhart Charts and Targets, Goals, or Other Specifications 152Special Cause: Is It Good or Bad? 155Summary157 Key Terms 158Chapter 5 Understanding Variation Using Shewhart Charts 159Selecting the Type of Shewhart Chart 160Shewhart Charts for Continuous Data 163I Charts 164Examples of Shewhart Charts for Individual Measurements 166Rational Ordering with an I Chart 168Example of I Chart for Deviations from a Target 170Xbar S Shewhart Charts 171Shewhart Charts for Attribute Data 177Subgroup Size for Attribute Charts 178The P Chart for Classification Data 180Examples of P Charts 182Creation of Funnel Limits for a P Chart 186Shewhart Charts for Counts of Nonconformities 188c charts 190U Chart 192Creation of Funnel Limits for a U Chart 195Alternatives for Attribute Charts for Rare Events 197G Chart for Opportunities Between Rare Events 198T Chart for Time Between Rare Events 202Process Capability 206Process Capability from an I Chart 208Capability of a Process from Xbar and S Charts 208Capability of a Process from Attribute Control Charts 210Capability from a P Chart 210Capability from a C or U Chart 210Summary211 Key Terms 212Appendix 5.1 Calculating Shewhart Limits 213I Chart (For Individual Values Of Continuous Data) 213Xbar S Chart (For Continuous Data In Subgroups) 214P Chart (For Classification Data) 217c chart (count Of Incidences) 218U Chart (Incidences Per Area Of Opportunity) 219G Chart (Cases Between Incidences) 220T Chart 221Chapter 6 Additional Tools For Understanding Variation In Data 223Depicting Variation 223Additional Tools for Learning from Variation 225Frequency Plots 225Frequency Plot Construction 226Frequency Plots Used with Shewhart Charts 228Frequency Plots and Stratification 232Pareto Charts 236Pareto Chart Construction 238Pareto Charts Used with Shewhart Charts 239Pareto Chart and Stratification 244Scatterplots250 Scatterplot Construction 251Scatterplots Used with Shewhart Charts 254Scatterplots and Stratification 258Radar Charts 260Constructing a Radar Chart 261Radar Charts Used with Shewhart Charts 261Radar Charts and Stratification 263Summary265 Key Terms 265Chapter 7 Shewhart Chart Savvy: Dealing with Common Issues 267Creating Effective Shewhart Charts 267Tip 1: Type of Data and Subgroup Size 267Tip 2: Rounding Data 268Tip 3: Formatting Charts 268Tip 4. Decisions for Recalculating limits, or Rephasing, on a Shewhart Chart 274Extending Centerline and Limits Backward 277Typical Problems with Software for Calculating Shewhart Charts 279Characteristics to Consider When Purchasing SPC Software 282Another Caution with I Charts and Chart Selection 285Guidelines for Shewhart Charts in Research Studies and Publications 287Use of Shewhart Charts in Research Studies 288Shewhart Charts in Publications 290Shewhart’s Theory versus Statistical Inference 292Summary296 Key Terms 296Part II Advanced Theory and Methods with Data For Improvement 297Chapter 8 More Shewhart-Type Charts 299Other Shewhart-Type Charts 301The NP Chart 301Xbar Range (Xbar R) Chart 302Median Chart 304Attribute Charts with Large Subgroup Sizes (P’ and U’) 306Prime Charts (P’ and U’) 307Negative Binomial Chart 313Some Adaptations to Shewhart Charts 316MA Chart 317CUSUM Chart 320Exponentially Weighted Moving Average (EWMA) Chart 328Standardized Shewhart Charts 331Multivariate Shewhart-Type Charts 334Summary338 Key Terms 339Chapter 9 Special Uses for Shewhart Charts 341Shewhart Charts with a Changing Centerline 341Shewhart Charts with a Sloping Centerline 342Shewhart Charts with Seasonal Effects 344Adjusting Shewhart Charts for Confounders 349Transformation of Data with Shewhart Charts 355Shewhart Charts for Autocorrelated Data 361Risk-Adjusted or Case-Mix Adjusted Shewhart Charts 366Comparison Charts 368Confidence Intervals and Confidence Limits 369Summary373 Key Terms 373Chapter 10 Drilling Down Into Aggregate Data for Improvement Ii 375What are Aggregate Data? 375What is the Challenge Presented by Aggregate Data? 376Introduction to the Drill Down Pathway 381Stratification 381Sequencing 382 Rational Subgrouping 383An Illustration of the Drill Down Pathway: Adverse Drug Events384 Drill Down Pathway Step One 385Drill Down Pathway Step Two 385Drill Down Pathway Step Three 387Drill Down Pathway Step Three, Continued 389Drill Down Pathway Step Four 393Drill Down Pathway Step Five 397Drill Down Pathway Step Six 400Summary400 Key Terms 401Part III Applications of Shewhart Charts in Health Care 403Chapter 11 Learning from Individual Patient Data 405Examples of Shewhart Charts for Individual Patients 407Example 1: Asthma Patient Use of Shewhart Charts 408Example 2: Prostate-Specific Antigen (PSA) Screening for Prostate Cancer 409Example 3: Monitoring Patient Measures in the Hospital 411Example 4: Bone Density for a Patient Diagnosed with Osteoporosis 412Example 5: Temperature Readings for a Hospitalized Patient 415Example 6: Shewhart Charts for Continuous Monitoring of Patients 418Example 7: Monitoring Weight 420Example 8: Monitoring Blood Sugar Control for Patients with Diabetes 421Example 9: Using Shewhart Charts in Pain Management 422Summary423Chapter 12 Learning from Patient Feedback to Improve Care 425Summarizing Patient Feedback Data 429Presentation of Patient Satisfaction Data 437Using Patient Feedback for Improvement 438The PDSA Cycle for Testing and Implementing Changes 438Improvement Team Working on Clinic Satisfaction 438Improvement Team Working on Pain 442Feedback from Employees 444Using Patient Satisfaction Data in Planning for Improvement 445Special Issues with Patient Feedback Data 447Are There Challenges When Summarizing and Using Patient Satisfaction Survey Data? 447Does Survey Scale Matter? 449Summary450 Key Terms 450Chapter 13 Using Shewhart Charts in Health Care Leadership 451A Health Care Organization’s Vector of Measures 452Developing a VOM 453So How do We Best Display a VOM? 461Administrative Issues with a VOM 464Some Examples of Measures for Other VOMs 467Emergency Department 468Primary Care Center 468System Flow Measures 469Health Authority 469Large Urban Hospital 471IHI Whole System Measures 471Summary473 Key Terms 474Chapter 14 Shewhart Charts for Epidemic Data 475Shewhart Charts in Epidemiology 476Development of Shewhart Charts for Epidemic Data 479c charts (Epoch 1) 479Charts of Epoch 2 481Charts for Epoch 3 485Charts for Epoch 4 486Some Issues with the Hybrid Chart for COVID-19 Deaths 487Data Quality 487Day-of-the-Week Adjustment 487Application of the Hybrid Charts to Cases, Hospitalizations, and Intensive Care Unit Admissions 489Summary492 Key Term 492Chapter 15 Case Studies 493Case Study A: Improving Access to a Specialty Care Clinic 495Case Study B: Radiology Improvement Projects 504Case Study C: Reducing Post-Cabg Infections 514Case Study D: Drilling Down into Percentage of C-Sections 526Case Study E: Reducing Length of Stay After Surgery 537Case Study F: Reducing Hospital admissions 551Case Study G: Accidental Puncture/Laceration Rate 558Case Study H: Improving Telemedicine Failed Calls and No Shows 568Case Study I: Variation in Financial Data 583Index 595Shewhart Chart Selection Guide 609

LLOYD P. PROVOST is a cofounder of Associates in Process Improvement, the developers of the Model for Improvement roadmap and the Quality as a Business Strategy template for focusing organizations on improvement. Lloyd is a senior fellow at the Institute for Healthcare Improvement, where he supports the use of data for learning in programs.SANDRA K. MURRAY is a principal in Corporate Transformation Concepts, an independent consulting firm. She is faculty for the Institute for Healthcare Improvement’s year-long Improvement Advisor Professional Development Program and their Breakthrough Series College. Sandra has taught numerous programs through the National Association for Healthcare Quality. Her cohort of client organizations encompasses the spectrum of health care delivery.

• Figures, Tables, and Exhibits xiiiPreface xxixAcknowledgments xxxiiiThe Authors xxxvAbout the Companion Website xxxviiPart I Using Data for Improvement 1Chapter 1 Improvement Methodology 3Fundamental Questions for Improvement 4What Are We Trying to Accomplish? 5How Will We Know that a Change is an Improvement? 7What Changes Can We Make That Will Result in Improvement? 8The PDSA Cycle for Improvement 9Tools and Methods to Support the Model for Improvement 13Designing PDSA Cycles for Testing Changes 15Analysis of Data from PDSA Cycles 19Summary26 Key Terms 26Chapter 2 Using Data for Improvement 27What Does the Concept of Data Mean? 27How are Data Used? 29Types of Data 36Using A Family of Measures 43The Importance of Operational Definitions 47Data for Different Types of Studies 51Sampling53 Sampling Strategies 55What About Sample Size? 58Stratification of Data 61What about Case-Mix Adjustment? 63Transforming Data 65Analysis and Presentation of Data 68Summary75 Key Terms 75Chapter 3 Understanding Variation Using Run Charts 77Introduction77 What Is a Run Chart? 77Use of a Run Chart 80Constructing a Run Chart 80Examples of Run Charts for Improvement Projects 84Rules to Aid in Interpreting Run Charts 89Special Issues in Using Run Charts 97Stratification with Run Charts 113Using the Cumulative Sum Statistic with Run Charts 116Summary120 Key Terms 121Chapter 4 Learning from Variation in Data 123The Concept of Variation 123Introduction to Shewhart Charts 129Depicting and Interpreting Variation Using Shewhart Charts 135The Role of Annotation with Shewhart Charts 140Establishing Limits for Shewhart Charts 141Revising Limits for Shewhart Charts 145Stratification with Shewhart Charts 147Shewhart Charts and Targets, Goals, or Other Specifications 152Special Cause: Is It Good or Bad? 155Summary157 Key Terms 158Chapter 5 Understanding Variation Using Shewhart Charts 159Selecting the Type of Shewhart Chart 160Shewhart Charts for Continuous Data 163I Charts 164Examples of Shewhart Charts for Individual Measurements 166Rational Ordering with an I Chart 168Example of I Chart for Deviations from a Target 170Xbar S Shewhart Charts 171Shewhart Charts for Attribute Data 177Subgroup Size for Attribute Charts 178The P Chart for Classification Data 180Examples of P Charts 182Creation of Funnel Limits for a P Chart 186Shewhart Charts for Counts of Nonconformities 188c charts 190U Chart 192Creation of Funnel Limits for a U Chart 195Alternatives for Attribute Charts for Rare Events 197G Chart for Opportunities Between Rare Events 198T Chart for Time Between Rare Events 202Process Capability 206Process Capability from an I Chart 208Capability of a Process from Xbar and S Charts 208Capability of a Process from Attribute Control Charts 210Capability from a P Chart 210Capability from a C or U Chart 210Summary211 Key Terms 212Appendix 5.1 Calculating Shewhart Limits 213I Chart (For Individual Values Of Continuous Data) 213Xbar S Chart (For Continuous Data In Subgroups) 214P Chart (For Classification Data) 217c chart (count Of Incidences) 218U Chart (Incidences Per Area Of Opportunity) 219G Chart (Cases Between Incidences) 220T Chart 221Chapter 6 Additional Tools For Understanding Variation In Data 223Depicting Variation 223Additional Tools for Learning from Variation 225Frequency Plots 225Frequency Plot Construction 226Frequency Plots Used with Shewhart Charts 228Frequency Plots and Stratification 232Pareto Charts 236Pareto Chart Construction 238Pareto Charts Used with Shewhart Charts 239Pareto Chart and Stratification 244Scatterplots250 Scatterplot Construction 251Scatterplots Used with Shewhart Charts 254Scatterplots and Stratification 258Radar Charts 260Constructing a Radar Chart 261Radar Charts Used with Shewhart Charts 261Radar Charts and Stratification 263Summary265 Key Terms 265Chapter 7 Shewhart Chart Savvy: Dealing with Common Issues 267Creating Effective Shewhart Charts 267Tip 1: Type of Data and Subgroup Size 267Tip 2: Rounding Data 268Tip 3: Formatting Charts 268Tip 4. Decisions for Recalculating limits, or Rephasing, on a Shewhart Chart 274Extending Centerline and Limits Backward 277Typical Problems with Software for Calculating Shewhart Charts 279Characteristics to Consider When Purchasing SPC Software 282Another Caution with I Charts and Chart Selection 285Guidelines for Shewhart Charts in Research Studies and Publications 287Use of Shewhart Charts in Research Studies 288Shewhart Charts in Publications 290Shewhart’s Theory versus Statistical Inference 292Summary296 Key Terms 296Part II Advanced Theory and Methods with Data For Improvement 297Chapter 8 More Shewhart-Type Charts 299Other Shewhart-Type Charts 301The NP Chart 301Xbar Range (Xbar R) Chart 302Median Chart 304Attribute Charts with Large Subgroup Sizes (P’ and U’) 306Prime Charts (P’ and U’) 307Negative Binomial Chart 313Some Adaptations to Shewhart Charts 316MA Chart 317CUSUM Chart 320Exponentially Weighted Moving Average (EWMA) Chart 328Standardized Shewhart Charts 331Multivariate Shewhart-Type Charts 334Summary338 Key Terms 339Chapter 9 Special Uses for Shewhart Charts 341Shewhart Charts with a Changing Centerline 341Shewhart Charts with a Sloping Centerline 342Shewhart Charts with Seasonal Effects 344Adjusting Shewhart Charts for Confounders 349Transformation of Data with Shewhart Charts 355Shewhart Charts for Autocorrelated Data 361Risk-Adjusted or Case-Mix Adjusted Shewhart Charts 366Comparison Charts 368Confidence Intervals and Confidence Limits 369Summary373 Key Terms 373Chapter 10 Drilling Down Into Aggregate Data for Improvement Ii 375What are Aggregate Data? 375What is the Challenge Presented by Aggregate Data? 376Introduction to the Drill Down Pathway 381Stratification 381Sequencing 382 Rational Subgrouping 383An Illustration of the Drill Down Pathway: Adverse Drug Events384 Drill Down Pathway Step One 385Drill Down Pathway Step Two 385Drill Down Pathway Step Three 387Drill Down Pathway Step Three, Continued 389Drill Down Pathway Step Four 393Drill Down Pathway Step Five 397Drill Down Pathway Step Six 400Summary400 Key Terms 401Part III Applications of Shewhart Charts in Health Care 403Chapter 11 Learning from Individual Patient Data 405Examples of Shewhart Charts for Individual Patients 407Example 1: Asthma Patient Use of Shewhart Charts 408Example 2: Prostate-Specific Antigen (PSA) Screening for Prostate Cancer 409Example 3: Monitoring Patient Measures in the Hospital 411Example 4: Bone Density for a Patient Diagnosed with Osteoporosis 412Example 5: Temperature Readings for a Hospitalized Patient 415Example 6: Shewhart Charts for Continuous Monitoring of Patients 418Example 7: Monitoring Weight 420Example 8: Monitoring Blood Sugar Control for Patients with Diabetes 421Example 9: Using Shewhart Charts in Pain Management 422Summary423Chapter 12 Learning from Patient Feedback to Improve Care 425Summarizing Patient Feedback Data 429Presentation of Patient Satisfaction Data 437Using Patient Feedback for Improvement 438The PDSA Cycle for Testing and Implementing Changes 438Improvement Team Working on Clinic Satisfaction 438Improvement Team Working on Pain 442Feedback from Employees 444Using Patient Satisfaction Data in Planning for Improvement 445Special Issues with Patient Feedback Data 447Are There Challenges When Summarizing and Using Patient Satisfaction Survey Data? 447Does Survey Scale Matter? 449Summary450 Key Terms 450Chapter 13 Using Shewhart Charts in Health Care Leadership 451A Health Care Organization’s Vector of Measures 452Developing a VOM 453So How do We Best Display a VOM? 461Administrative Issues with a VOM 464Some Examples of Measures for Other VOMs 467Emergency Department 468Primary Care Center 468System Flow Measures 469Health Authority 469Large Urban Hospital 471IHI Whole System Measures 471Summary473 Key Terms 474Chapter 14 Shewhart Charts for Epidemic Data 475Shewhart Charts in Epidemiology 476Development of Shewhart Charts for Epidemic Data 479c charts (Epoch 1) 479Charts of Epoch 2 481Charts for Epoch 3 485Charts for Epoch 4 486Some Issues with the Hybrid Chart for COVID-19 Deaths 487Data Quality 487Day-of-the-Week Adjustment 487Application of the Hybrid Charts to Cases, Hospitalizations, and Intensive Care Unit Admissions 489Summary492 Key Term 492Chapter 15 Case Studies 493Case Study A: Improving Access to a Specialty Care Clinic 495Case Study B: Radiology Improvement Projects 504Case Study C: Reducing Post-Cabg Infections 514Case Study D: Drilling Down into Percentage of C-Sections 526Case Study E: Reducing Length of Stay After Surgery 537Case Study F: Reducing Hospital admissions 551Case Study G: Accidental Puncture/Laceration Rate 558Case Study H: Improving Telemedicine Failed Calls and No Shows 568Case Study I: Variation in Financial Data 583Index 595Shewhart Chart Selection Guide 609